From c8513f108f8d7761348367b1b9cc2ab1639f3726 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 25 Jan 2024 14:17:35 +0800 Subject: [PATCH 001/154] dbi scheduling first commit: added feature 3 options of scheduling methods. --- examples/dbi/dbi_scheduling.ipynb | 275 ++++++++++++++++++++++++++ src/qibo/models/dbi/double_bracket.py | 139 ++++++++++++- 2 files changed, 411 insertions(+), 3 deletions(-) create mode 100644 examples/dbi/dbi_scheduling.ipynb diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb new file mode 100644 index 0000000000..066d56640d --- /dev/null +++ b/examples/dbi/dbi_scheduling.ipynb @@ -0,0 +1,275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Double-bracket Iteration Scheduling Strategies\n", + "\n", + "This notebook presents the different strategies for scheduling the step durations for the double-bracket iteration algorithm and their resepctive accuracies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", + "from qibo.models.dbi.utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canonical\n", + "Set up the basic test case with the transverse field ising model hamiltonian and the canonical bracket as the generator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.canonical)\n", + "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first generate the relationship between the step duration and the off-diagoanl norm (loss function) for the first step of the iteration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate data for plotting sigma decrease of the first step\n", + "s_space = np.linspace(1e-5, 0.6, 100)\n", + "off_diagonal_norm_diff = []\n", + "for s in s_space:\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s)\n", + " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The default scheduling strategy is grid search: `DoubleBracketScheduling.use_grid_serach`. This strategy specifies a list of step durations to test one by one and finds the one that maximizes the cost function (off-digonal norm of Hamiltonian)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, max_evals=100, step_max=0.6)\n", + "print('hyperopt_search step:', step_hyperopt)\n", + "# polynomial expansion\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.use_polynomial_approximation, n=5)\n", + "print('polynomial_approximation step:', step_poly)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('hyperopt first step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Specified diagonal operator\n", + "\n", + "While for the cannonical case, all the scheduling methods are accurate, it is important to realize that the global minimum of the loss function is not always so obvious. It is thus necessary to show whether the 3 converges to an agreeable step duration using different iteration generators, such as the Pauli 'ZZ..Z' operator and 'ZZ..I' operator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate the digaonal operators\n", + "Z_op = SymbolicHamiltonian(str_to_symbolic(\"Z\"*nqubits)).dense.matrix\n", + "ZI_op = SymbolicHamiltonian(str_to_symbolic(\"Z\"*(nqubits-1)+\"I\")).dense.matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator)\n", + "d = Z_op\n", + "# generate data for plotting sigma decrease of the first step\n", + "s_space = np.linspace(1e-5, 0.6, 100)\n", + "off_diagonal_norm_diff = []\n", + "for s in s_space:\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s,d=d)\n", + " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search, step_max=0.6, d=d)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, d=d, max_evals=100, step_max=0.6)\n", + "print('hyperopt_search step:', step_hyperopt)\n", + "# polynomial expansion\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.use_polynomial_approximation, d=d, n=5)\n", + "print('polynomial_approximation step:', step_poly)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('hyperopt first step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that there are two similar \"minimal point\" at 0.03 and 0.22, with the latter being the absolute minimum by an insignificant advantage. However, for practical reasons, we prefer taking the first close-minimum calculated by polynomial approximation. Hence, we can use the polynomial approximation to restrict the search area and obtain better results. For example, we define a search range of 0.1 around the polynomial step." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use polynomial expansion as an restriction of hyperopt/grid range" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "search_range = 0.1\n", + "if step_poly < search_range/2:\n", + " step_min = 0\n", + " step_max = search_range\n", + "else:\n", + " step_min = step_poly - search_range/2\n", + " step_max = step_poly + search_range/2\n", + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search, step_min=step_min, step_max=step_max, d=d)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, step_min=step_min, step_max=step_max, max_evals=100, d=d,)\n", + "print('hyperopt_search step:', step_hyperopt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('hyperopt first step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "DBF_qibo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 15ffdb007e..bb002d3f90 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -1,6 +1,8 @@ +import math from copy import deepcopy from enum import Enum, auto from functools import partial +from typing import Optional import hyperopt import numpy as np @@ -21,6 +23,17 @@ class DoubleBracketGeneratorType(Enum): # TODO: add double commutator (does it converge?) +class DoubleBracketScheduling(Enum): + """Define the DBI scheduling strategies.""" + + use_hyperopt = auto() + """Use hyperopt package.""" + use_grid_search = auto() + """Use greedy grid search.""" + use_polynomial_approximation = auto() + """Use polynomial expansion (analytical) of the loss function.""" + + class DoubleBracketIteration: """ Class implementing the Double Bracket iteration algorithm. @@ -49,10 +62,12 @@ def __init__( self, hamiltonian: Hamiltonian, mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.canonical, + scheduling: DoubleBracketScheduling = DoubleBracketScheduling.use_grid_search, ): self.h = hamiltonian self.h0 = deepcopy(self.h) self.mode = mode + self.scheduling = scheduling def __call__( self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None @@ -115,6 +130,36 @@ def backend(self): """Get Hamiltonian's backend.""" return self.h0.backend + def grid_search_step( + self, + step_min: float = 1e-5, + step_max: float = 1, + num_evals: int = 100, + space: Optional[np.array] = None, + d: Optional[np.array] = None, + ): + """ + Greedy optimization of the iteration step. + + Args: + step_min: lower bound of the search grid; + step_max: upper bound of the search grid; + mnum_evals: number of iterations between step_min and step_max; + d: diagonal operator for generating double-bracket iterations. + + Returns: + (float): optimized best iteration step (minimizing off-diagonal norm). + """ + if space is None: + space = np.linspace(step_min, step_max, num_evals) + + if d is None: + d = self.diagonal_h_matrix + + loss_list = [self.loss(step, d=d) for step in space] + idx_max_loss = loss_list.index(min(loss_list)) + return space[idx_max_loss] + def hyperopt_step( self, step_min: float = 1e-5, @@ -124,10 +169,10 @@ def hyperopt_step( optimizer: callable = None, look_ahead: int = 1, verbose: bool = False, - d: np.array = None, + d: Optional[np.array] = None, ): """ - Optimize iteration step. + Optimize iteration step using hyperopt. Args: step_min: lower bound of the search grid; @@ -140,12 +185,14 @@ def hyperopt_step( d: diagonal operator for generating double-bracket iterations. Returns: - (float): optimized best iteration step. + (float): optimized best iteration step (minimizing off-diagonal norm). """ if space is None: space = hyperopt.hp.uniform if optimizer is None: optimizer = hyperopt.tpe + if d is None: + d = self.diagonal_h_matrix space = space("step", step_min, step_max) best = hyperopt.fmin( @@ -157,6 +204,92 @@ def hyperopt_step( ) return best["step"] + def polynomial_step( + self, + n: int = 3, + n_max: int = 5, + d: np.array = None, + backup_scheduling: DoubleBracketScheduling = DoubleBracketScheduling.use_grid_search, + ): + r""" + Optimizes iteration step by solving the n_th order polynomial expansion of the loss function. + e.g. $n=2$: $2\Trace(\sigma(\Gamma_1 + s\Gamma_2 + s^2/2\Gamma_3)\sigma(\Gamma_0 + s\Gamma_1 + s^2/2\Gamma_2)) + Args: + n (int, optional): The order to which the loss function is expanded. Defaults to 3. + n_max (int, optional): The maximum order allowed for recurring calls of `polynomial_step`. Defaults to 5. + d (np.array, optional): The diagonal operator, default as $\delta(H)$. + """ + + if d is None: + d = self.diagonal_h_matrix + + def sigma(h: np.array): + return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) + + def Gamma(k: int): + r"""Computes the k_th Gamma function i.e $\Gamma_k=[W,...,[W,[W,H]]...]$, where we take k nested commutators with $W = [D, H]$""" + if k == 0: + return self.h.matrix + else: + W = self.commutator(d, sigma(self.h.matrix)) + result = self.h.matrix + for _ in range(k): + result = self.commutator(W, result) + return result + + # list starting from s^n highest order to s^0 + sigma_gamma_list = np.array([sigma(Gamma(k)) for k in range(n + 2)]) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients for rotation with [W,H] and H + c1 = [ + exp_coef * delta_gamma + for exp_coef, delta_gamma in zip(exp_list, sigma_gamma_list[1:]) + ] + c2 = [ + exp_coef * delta_gamma + for exp_coef, delta_gamma in zip(exp_list, sigma_gamma_list[:-1]) + ] + # product coefficient + trace_coefficients = [0] * (2 * n + 1) + for k in range(n + 1): + for j in range(n + 1): + power = k + j + product_matrix = c1[k] @ c2[j] + trace_coefficients[power] += 2 * np.trace(product_matrix) + roots = np.roots(list(reversed(trace_coefficients[: n + 1]))) + error = 1e-3 + real_positive_roots = [ + np.real(root) + for root in roots + if np.imag(root) < error and np.real(root) > 0 + ] + # solution exists, return minimum s + if len(real_positive_roots) > 0: + return min(real_positive_roots) + # solution does not exist, resort to backup scheduling + elif ( + backup_scheduling == DoubleBracketScheduling.use_polynomial_approximation + and n < n_max + 1 + ): + return self.polynomial_step(d, n=n + 1, backup_scheduling=backup_scheduling) + else: + return self.choose_step(d, backup_scheduling) + + def choose_step( + self, + d: Optional[np.array] = None, + scheduling: Optional[DoubleBracketScheduling] = None, + **kwargs, + ): + if scheduling is None: + scheduling = self.scheduling + if scheduling is DoubleBracketScheduling.use_grid_search: + return self.grid_search_step(d=d, **kwargs) + if scheduling is DoubleBracketScheduling.use_hyperopt: + return self.hyperopt_step(d=d, **kwargs) + if scheduling is DoubleBracketScheduling.use_polynomial_approximation: + return self.polynomial_step(d=d, **kwargs) + def loss(self, step: float, d: np.array = None, look_ahead: int = 1): """ Compute loss function distance between `look_ahead` steps. From 5567114a50519c477569d4a2c6da6fea4e45aeef Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 29 Jan 2024 18:15:22 +0800 Subject: [PATCH 002/154] Tests for double_bracket.py scheduling: test_double_bracket_iteration_scheduling_polynomial; test_double_bracket_iteration_scheduling_grid_hyperopt --- examples/dbi/dbi_scheduling.ipynb | 11 +++- src/qibo/models/dbi/double_bracket.py | 11 +++- tests/test_models_dbi.py | 92 ++++++++++++++++++++------- 3 files changed, 87 insertions(+), 27 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 066d56640d..15f4de2339 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -65,7 +65,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We first generate the relationship between the step duration and the off-diagoanl norm (loss function) for the first step of the iteration." + "We first run a sweep of step duration to map the off-diagonal norm in this range." ] }, { @@ -152,7 +152,7 @@ "outputs": [], "source": [ "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator)\n", - "d = Z_op\n", + "d = ZI_op\n", "# generate data for plotting sigma decrease of the first step\n", "s_space = np.linspace(1e-5, 0.6, 100)\n", "off_diagonal_norm_diff = []\n", @@ -249,6 +249,13 @@ "plt.legend()\n", "print('The minimum for cost function in the tested range is:', step_grid)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hence, we see that the strategy is indeed effective for finding the first minimum of the loss funciton for both the Z operator and the ZI operator." + ] } ], "metadata": { diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index bb002d3f90..6749c378a8 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -209,7 +209,7 @@ def polynomial_step( n: int = 3, n_max: int = 5, d: np.array = None, - backup_scheduling: DoubleBracketScheduling = DoubleBracketScheduling.use_grid_search, + backup_scheduling: DoubleBracketScheduling = None, ): r""" Optimizes iteration step by solving the n_th order polynomial expansion of the loss function. @@ -223,6 +223,9 @@ def polynomial_step( if d is None: d = self.diagonal_h_matrix + if backup_scheduling is None: + backup_scheduling = DoubleBracketScheduling.use_grid_search + def sigma(h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) @@ -271,9 +274,11 @@ def Gamma(k: int): backup_scheduling == DoubleBracketScheduling.use_polynomial_approximation and n < n_max + 1 ): - return self.polynomial_step(d, n=n + 1, backup_scheduling=backup_scheduling) + return self.polynomial_step( + n=n + 1, d=d, backup_scheduling=backup_scheduling + ) else: - return self.choose_step(d, backup_scheduling) + return self.choose_step(d=d, scheduling=backup_scheduling) def choose_step( self, diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 90c0c1804b..6ee130f71e 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -6,74 +6,75 @@ from qibo.models.dbi.double_bracket import ( DoubleBracketGeneratorType, DoubleBracketIteration, + DoubleBracketScheduling, ) from qibo.quantum_info import random_hermitian -NSTEPS = 50 +NSTEPS = 1 """Number of steps for evolution.""" @pytest.mark.parametrize("nqubits", [3, 4, 5]) def test_double_bracket_iteration_canonical(backend, nqubits): h0 = random_hermitian(2**nqubits, backend=backend) - dbf = DoubleBracketIteration( + dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.canonical, ) - initial_off_diagonal_norm = dbf.off_diagonal_norm + initial_off_diagonal_norm = dbi.off_diagonal_norm for _ in range(NSTEPS): - dbf(step=np.sqrt(0.001)) + dbi(step=np.sqrt(0.001)) - assert initial_off_diagonal_norm > dbf.off_diagonal_norm + assert initial_off_diagonal_norm > dbi.off_diagonal_norm @pytest.mark.parametrize("nqubits", [3, 4, 5]) def test_double_bracket_iteration_group_commutator(backend, nqubits): h0 = random_hermitian(2**nqubits, backend=backend) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) - dbf = DoubleBracketIteration( + dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.group_commutator, ) - initial_off_diagonal_norm = dbf.off_diagonal_norm + initial_off_diagonal_norm = dbi.off_diagonal_norm with pytest.raises(ValueError): - dbf(mode=DoubleBracketGeneratorType.group_commutator, step=0.01) + dbi(mode=DoubleBracketGeneratorType.group_commutator, step=0.01) for _ in range(NSTEPS): - dbf(step=0.01, d=d) + dbi(step=0.01, d=d) - assert initial_off_diagonal_norm > dbf.off_diagonal_norm + assert initial_off_diagonal_norm > dbi.off_diagonal_norm @pytest.mark.parametrize("nqubits", [3, 4, 5]) def test_double_bracket_iteration_single_commutator(backend, nqubits): h0 = random_hermitian(2**nqubits, backend=backend) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) - dbf = DoubleBracketIteration( + dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.single_commutator, ) - initial_off_diagonal_norm = dbf.off_diagonal_norm + initial_off_diagonal_norm = dbi.off_diagonal_norm for _ in range(NSTEPS): - dbf(step=0.01, d=d) - dbf(step=0.01) + dbi(step=0.01, d=d) + dbi(step=0.01) - assert initial_off_diagonal_norm > dbf.off_diagonal_norm + assert initial_off_diagonal_norm > dbi.off_diagonal_norm @pytest.mark.parametrize("nqubits", [3, 4, 5]) def test_hyperopt_step(backend, nqubits): h0 = random_hermitian(2**nqubits, backend=backend) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) - dbf = DoubleBracketIteration(Hamiltonian(nqubits, h0, backend=backend)) + dbi = DoubleBracketIteration(Hamiltonian(nqubits, h0, backend=backend)) # find initial best step with look_ahead = 1 initial_step = 0.01 delta = 0.02 - step = dbf.hyperopt_step( + step = dbi.hyperopt_step( step_min=initial_step - delta, step_max=initial_step + delta, max_evals=100 ) @@ -81,12 +82,12 @@ def test_hyperopt_step(backend, nqubits): # evolve following the optimized first step for generator in DoubleBracketGeneratorType: - dbf(mode=generator, step=step, d=d) + dbi(mode=generator, step=step, d=d) # find the following step size with look_ahead look_ahead = 3 - step = dbf.hyperopt_step( + step = dbi.hyperopt_step( step_min=initial_step - delta, step_max=initial_step + delta, max_evals=100, @@ -95,12 +96,59 @@ def test_hyperopt_step(backend, nqubits): # evolve following the optimized first step for gentype in range(look_ahead): - dbf(mode=DoubleBracketGeneratorType(gentype + 1), step=step, d=d) + dbi(mode=DoubleBracketGeneratorType(gentype + 1), step=step, d=d) def test_energy_fluctuations(backend): h0 = np.array([[1, 0], [0, -1]]) state = np.array([1, 0]) - dbf = DoubleBracketIteration(Hamiltonian(1, matrix=h0, backend=backend)) - energy_fluctuation = dbf.energy_fluctuation(state=state) + dbi = DoubleBracketIteration(Hamiltonian(1, matrix=h0, backend=backend)) + energy_fluctuation = dbi.energy_fluctuation(state=state) assert energy_fluctuation == 0 + + +@pytest.mark.parametrize( + "scheduling", + [DoubleBracketScheduling.use_grid_search, DoubleBracketScheduling.use_hyperopt], +) +@pytest.mark.parametrize("nqubits", [3, 4, 5]) +def test_double_bracket_iteration_scheduling_grid_hyperopt( + backend, nqubits, scheduling +): + h0 = random_hermitian(2**nqubits, backend=backend) + d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + for _ in range(NSTEPS): + step1 = dbi.choose_step(d=d, scheduling=scheduling) + dbi(d=d, step=step1) + step2 = dbi.choose_step(scheduling=scheduling) + dbi(step=step2) + assert initial_off_diagonal_norm > dbi.off_diagonal_norm + + +@pytest.mark.parametrize("nqubits", [3, 4, 5]) +@pytest.mark.parametrize("n", [2, 3]) +@pytest.mark.parametrize( + "backup_scheduling", [None, DoubleBracketScheduling.use_polynomial_approximation] +) +def test_double_bracket_iteration_scheduling_polynomial( + backend, nqubits, n, backup_scheduling +): + h0 = random_hermitian(2**nqubits, backend=backend) + d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + scheduling=DoubleBracketScheduling.use_polynomial_approximation, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + for _ in range(NSTEPS): + step1 = dbi.polynomial_step(n=n, d=d, backup_scheduling=backup_scheduling) + dbi(d=d, step=step1) + step2 = dbi.polynomial_step(n=n) + dbi(step=step2) + assert initial_off_diagonal_norm > dbi.off_diagonal_norm From abddfaee79de8e68d2b2794d608937762b1de2ad Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 29 Jan 2024 23:58:15 +0800 Subject: [PATCH 003/154] Updated utils and Pauli-Z notebook for scheduling --- .../dbi/DBI_strategy_Pauli-Z_products.ipynb | 50 +++++-------------- src/qibo/models/dbi/utils.py | 27 +++------- 2 files changed, 20 insertions(+), 57 deletions(-) diff --git a/examples/dbi/DBI_strategy_Pauli-Z_products.ipynb b/examples/dbi/DBI_strategy_Pauli-Z_products.ipynb index 0f76a36245..d89fdd5e74 100644 --- a/examples/dbi/DBI_strategy_Pauli-Z_products.ipynb +++ b/examples/dbi/DBI_strategy_Pauli-Z_products.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -122,31 +122,16 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.4|INFO|2024-01-24 19:59:31]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 8.48528137423857\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# set the qibo backend (we suggest qibojit if N >= 20)\n", "# alternatives: tensorflow (not optimized), numpy (when CPU not supported by jit)\n", "set_backend(\"qibojit\", \"numba\")\n", "\n", "# hamiltonian parameters\n", - "nqubits = 2\n", + "nqubits = 5\n", "h = 3\n", "\n", "# define the hamiltonian\n", @@ -160,20 +145,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-2.-0.j -0.-0.j -0.-0.j -0.-0.j]\n", - " [-0.-0.j 2.-0.j -0.-0.j -0.-0.j]\n", - " [-0.-0.j -0.-0.j 2.-0.j -0.-0.j]\n", - " [-0.-0.j -0.-0.j -0.-0.j -2.-0.j]]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(H_TFIM.matrix)" ] @@ -219,8 +193,9 @@ "# add in initial values for plotting\n", "off_diagonal_norm_history = [dbi.off_diagonal_norm]\n", "steps = [0]\n", + "scheduling = DoubleBracketScheduling.use_hyperopt\n", "for _ in range(NSTEPS):\n", - " dbi, idx, step, flip_sign = select_best_dbr_generator(dbi, Z_ops, compare_canonical=False, max_evals=max_evals, step_max=step_max)\n", + " dbi, idx, step, flip_sign = select_best_dbr_generator(dbi, Z_ops, scheduling=scheduling, compare_canonical=False, max_evals=max_evals, step_max=step_max)\n", " off_diagonal_norm_history.append(dbi.off_diagonal_norm)\n", " steps.append(steps[-1]+step)\n", " if flip_sign < 0:\n", @@ -294,7 +269,6 @@ " step_max = 1,\n", " space = hp.uniform,\n", " optimizer = tpe,\n", - " max_evals = max_evals,\n", " )\n", " dbi_canonical(step=step)\n", " print(f\"New optimized step at iteration {s+1}/{NSTEPS}: {step}, loss {dbi_canonical.off_diagonal_norm}\")\n", @@ -389,7 +363,7 @@ "off_diagonal_norm_history_mixed = [dbi_mixed.off_diagonal_norm]\n", "steps = [0]\n", "for _ in range(NSTEPS):\n", - " dbi_mixed, idx, step, flip_sign = select_best_dbr_generator(dbi_mixed, Z_ops, compare_canonical=True, max_evals=max_evals)\n", + " dbi_mixed, idx, step, flip_sign = select_best_dbr_generator(dbi_mixed, Z_ops, scheduling=scheduling, compare_canonical=True, max_evals=max_evals, step_max=step_max)\n", " off_diagonal_norm_history_mixed.append(dbi_mixed.off_diagonal_norm)\n", " steps.append(steps[-1]+step)\n", " if idx == len(Z_ops):\n", @@ -479,7 +453,7 @@ "remaining_NSTEPS = NSTEPS - cannonical_NSTEPS\n", "dbi_mixed_can.mode = DoubleBracketGeneratorType.single_commutator\n", "for _ in range(remaining_NSTEPS):\n", - " dbi_mixed_can, idx, step, flip_sign = select_best_dbr_generator(dbi_mixed_can, Z_ops, compare_canonical=False)\n", + " dbi_mixed_can, idx, step, flip_sign = select_best_dbr_generator(dbi_mixed_can, Z_ops, scheduling=scheduling, compare_canonical=False, max_evals=max_evals, step_max=step_max)\n", " off_diagonal_norm_history_mixed_can.append(dbi_mixed_can.off_diagonal_norm)\n", " steps_mixed_can.append(step)\n", " if idx == len(Z_ops):\n", diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 5969637b62..f354a9398b 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -11,6 +11,7 @@ from qibo.models.dbi.double_bracket import ( DoubleBracketGeneratorType, DoubleBracketIteration, + DoubleBracketScheduling, ) @@ -71,11 +72,9 @@ def select_best_dbr_generator( dbi_object: DoubleBracketIteration, d_list: list, step: Optional[float] = None, - step_min: float = 1e-5, - step_max: float = 1, - max_evals: int = 200, compare_canonical: bool = True, - mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.single_commutator, + scheduling: DoubleBracketScheduling = None, + **kwargs, ): """Selects the best double bracket rotation generator from a list and runs the @@ -88,11 +87,12 @@ def select_best_dbr_generator( step_max (float): Maximally allowed iteration duration. max_evals (int): Maximally allowed number of evaluation in hyperopt. compare_canonical (bool): If `True`, the optimal diagonal operator chosen from "d_list" is compared with the canonical bracket. - mode (_DoubleBracketGeneratorType): DBI generator type used for the selection. Returns: The updated dbi_object, index of the optimal diagonal operator, respective step duration, and evolution direction. """ + if scheduling is None: + scheduling = dbi_object.scheduling norms_off_diagonal_restriction = [ dbi_object.off_diagonal_norm for _ in range(len(d_list)) ] @@ -104,13 +104,8 @@ def select_best_dbr_generator( flip_list[i] = CS_angle_sgn(dbi_eval, d) if flip_list[i] != 0: if step is None: - step_best = dbi_eval.hyperopt_step( - d=flip_list[i] * d, - step_min=step_min, - step_max=step_max, - space=hp.uniform, - optimizer=tpe, - max_evals=max_evals, + step_best = dbi_eval.choose_step( + d=flip_list[i] * d, scheduling=scheduling, **kwargs ) else: step_best = step @@ -123,13 +118,7 @@ def select_best_dbr_generator( dbi_eval = deepcopy(dbi_object) dbi_eval.mode = DoubleBracketGeneratorType.canonical if step is None: - step_best = dbi_eval.hyperopt_step( - step_min=step_min, - step_max=step_max, - space=hp.uniform, - optimizer=tpe, - max_evals=max_evals, - ) + step_best = dbi_eval.choose_step(scheduling=scheduling, **kwargs) else: step_best = step dbi_eval(step=step_best) From 1948c012166fec7c92e3bc6307c79b8ece389646 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 30 Jan 2024 14:34:55 +0800 Subject: [PATCH 004/154] Notebook section shows difference of scheduling techniques in Pauli-Z strategies --- examples/dbi/dbi_scheduling.ipynb | 110 ++++++++++++++++++++++++++++-- 1 file changed, 106 insertions(+), 4 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 15f4de2339..275d2dea73 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -170,13 +170,16 @@ "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search, step_max=0.6, d=d)\n", - "print('grid_search step:', step_grid)\n", + "grid_min = dbi.loss(step=step_grid, d=d)-dbi.off_diagonal_norm\n", + "print('grid_search step:', step_grid, 'loss', grid_min)\n", "# hyperopt\n", "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, d=d, max_evals=100, step_max=0.6)\n", - "print('hyperopt_search step:', step_hyperopt)\n", + "hyperopt_min = dbi.loss(step=step_hyperopt, d=d)-dbi.off_diagonal_norm\n", + "print('hyperopt_search step:', step_hyperopt, 'loss', hyperopt_min)\n", "# polynomial expansion\n", "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.use_polynomial_approximation, d=d, n=5)\n", - "print('polynomial_approximation step:', step_poly)" + "poly_min = dbi.loss(step=step_poly, d=d)-dbi.off_diagonal_norm\n", + "print('polynomial_approximation step:', step_poly, 'loss', poly_min)" ] }, { @@ -188,6 +191,8 @@ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.text(x=step_grid, y=grid_min, s=f'grid min \\n{round(grid_min,3)}')\n", + "plt.text(x=step_poly, y=poly_min, s=f'grid min \\n{round(poly_min,3)}')\n", "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", @@ -208,7 +213,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Use polynomial expansion as an restriction of hyperopt/grid range" + "## Use polynomial expansion as an restriction for hyperopt/grid range" ] }, { @@ -256,6 +261,103 @@ "source": [ "Hence, we see that the strategy is indeed effective for finding the first minimum of the loss funciton for both the Z operator and the ZI operator." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare in Pauli-Z strategy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from qibo.quantum_info import random_hermitian\n", + "from qibo.hamiltonians import Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "nqubits = 4\n", + "h0 = random_hermitian(2**nqubits)\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(Hamiltonian(nqubits=nqubits, matrix=h0)),mode=DoubleBracketGeneratorType.single_commutator)\n", + "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generate_local_Z = generate_Z_operators(nqubits)\n", + "Z_ops = list(generate_local_Z.values())\n", + "Z_names = list(generate_local_Z.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NSTEPS = 8\n", + "scheduling_list = [DoubleBracketScheduling.use_grid_search,\n", + " DoubleBracketScheduling.use_hyperopt,\n", + " DoubleBracketScheduling.use_polynomial_approximation,]\n", + "scheduling_labels = ['grid search',\n", + " 'hyperopt',\n", + " 'polynomial',]\n", + "Z_optimal_scheduling = []\n", + "s_scheduling = []\n", + "off_norm_scheduling =[]\n", + "for i,scheduling in enumerate(scheduling_list):\n", + " # reinitialize\n", + " dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=deepcopy(h0)), mode=DoubleBracketGeneratorType.single_commutator)\n", + " Z_optimal = []\n", + " # add in initial values for plotting\n", + " off_diagonal_norm_history = [dbi.off_diagonal_norm]\n", + " steps = [0]\n", + " print(f'----------Scheduling {scheduling_labels[i]}----------')\n", + " for _ in range(NSTEPS):\n", + " dbi, idx, step, flip_sign = select_best_dbr_generator(dbi, Z_ops, scheduling=scheduling, compare_canonical=False)\n", + " off_diagonal_norm_history.append(dbi.off_diagonal_norm)\n", + " steps.append(steps[-1]+step)\n", + " if flip_sign < 0:\n", + " Z_optimal.append('-' + Z_names[idx])\n", + " else:\n", + " Z_optimal.append(Z_names[idx])\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with operator {Z_optimal[-1]}, loss {dbi.off_diagonal_norm}\")\n", + " Z_optimal_scheduling.append(Z_optimal)\n", + " s_scheduling.append(steps)\n", + " off_norm_scheduling.append(off_diagonal_norm_history)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "for i, scheduling in enumerate(scheduling_labels):\n", + " plt.plot(s_scheduling[i], off_norm_scheduling[i], '-o', label=scheduling)\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Norm off-diagonal restriction\")\n", + "plt.title(\"Compare Variational Pauli-Z using different scheduling strategies\")\n", + "plt.legend()" + ] } ], "metadata": { From 2754cd22ebe08ad3c261d6a00387fde63bc95c81 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 13 Feb 2024 08:06:30 +0800 Subject: [PATCH 005/154] New branch for implementing magnetic field strategy for double bracket iterations --- ...strategy_Pauli-Z_products.ipynb => dbi_strategy_Pauli-Z.ipynb} | 0 examples/dbi/dbi_strategy_magnetic_field.ipynb | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename examples/dbi/{DBI_strategy_Pauli-Z_products.ipynb => dbi_strategy_Pauli-Z.ipynb} (100%) create mode 100644 examples/dbi/dbi_strategy_magnetic_field.ipynb diff --git a/examples/dbi/DBI_strategy_Pauli-Z_products.ipynb b/examples/dbi/dbi_strategy_Pauli-Z.ipynb similarity index 100% rename from examples/dbi/DBI_strategy_Pauli-Z_products.ipynb rename to examples/dbi/dbi_strategy_Pauli-Z.ipynb diff --git a/examples/dbi/dbi_strategy_magnetic_field.ipynb b/examples/dbi/dbi_strategy_magnetic_field.ipynb new file mode 100644 index 0000000000..e69de29bb2 From 588a02ae89162aadb7cbe7a22bcbaa6b28ebe325 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 15 Feb 2024 01:11:01 +0000 Subject: [PATCH 006/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- tests/test_models_dbi_utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_models_dbi_utils.py b/tests/test_models_dbi_utils.py index 1c7f825c01..89e2ce9b0d 100644 --- a/tests/test_models_dbi_utils.py +++ b/tests/test_models_dbi_utils.py @@ -1,4 +1,5 @@ """"Testing utils for DoubleBracketIteration model""" + import numpy as np import pytest From 9d663cbb21a32802174b4b9fdc745ce9705a0e41 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 15 Feb 2024 13:54:12 +0800 Subject: [PATCH 007/154] Fix test random hamiltonian seed for test coverage stability --- tests/test_models_dbi.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 6ee130f71e..3d1a72e57d 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -11,12 +11,13 @@ from qibo.quantum_info import random_hermitian NSTEPS = 1 +seed = 10 """Number of steps for evolution.""" @pytest.mark.parametrize("nqubits", [3, 4, 5]) def test_double_bracket_iteration_canonical(backend, nqubits): - h0 = random_hermitian(2**nqubits, backend=backend) + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.canonical, @@ -30,7 +31,7 @@ def test_double_bracket_iteration_canonical(backend, nqubits): @pytest.mark.parametrize("nqubits", [3, 4, 5]) def test_double_bracket_iteration_group_commutator(backend, nqubits): - h0 = random_hermitian(2**nqubits, backend=backend) + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), @@ -49,7 +50,7 @@ def test_double_bracket_iteration_group_commutator(backend, nqubits): @pytest.mark.parametrize("nqubits", [3, 4, 5]) def test_double_bracket_iteration_single_commutator(backend, nqubits): - h0 = random_hermitian(2**nqubits, backend=backend) + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), @@ -66,7 +67,7 @@ def test_double_bracket_iteration_single_commutator(backend, nqubits): @pytest.mark.parametrize("nqubits", [3, 4, 5]) def test_hyperopt_step(backend, nqubits): - h0 = random_hermitian(2**nqubits, backend=backend) + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration(Hamiltonian(nqubits, h0, backend=backend)) @@ -115,7 +116,7 @@ def test_energy_fluctuations(backend): def test_double_bracket_iteration_scheduling_grid_hyperopt( backend, nqubits, scheduling ): - h0 = random_hermitian(2**nqubits, backend=backend) + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), @@ -125,12 +126,12 @@ def test_double_bracket_iteration_scheduling_grid_hyperopt( for _ in range(NSTEPS): step1 = dbi.choose_step(d=d, scheduling=scheduling) dbi(d=d, step=step1) - step2 = dbi.choose_step(scheduling=scheduling) + step2 = dbi.choose_step() dbi(step=step2) assert initial_off_diagonal_norm > dbi.off_diagonal_norm -@pytest.mark.parametrize("nqubits", [3, 4, 5]) +@pytest.mark.parametrize("nqubits", [3, 4, 6]) @pytest.mark.parametrize("n", [2, 3]) @pytest.mark.parametrize( "backup_scheduling", [None, DoubleBracketScheduling.use_polynomial_approximation] @@ -138,7 +139,7 @@ def test_double_bracket_iteration_scheduling_grid_hyperopt( def test_double_bracket_iteration_scheduling_polynomial( backend, nqubits, n, backup_scheduling ): - h0 = random_hermitian(2**nqubits, backend=backend) + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), @@ -149,6 +150,8 @@ def test_double_bracket_iteration_scheduling_polynomial( for _ in range(NSTEPS): step1 = dbi.polynomial_step(n=n, d=d, backup_scheduling=backup_scheduling) dbi(d=d, step=step1) - step2 = dbi.polynomial_step(n=n) + step2 = dbi.choose_step( + scheduling=DoubleBracketScheduling.use_polynomial_approximation, n=n + ) dbi(step=step2) assert initial_off_diagonal_norm > dbi.off_diagonal_norm From 892fda40814fa65dcacebf34e479a01e89d2b89e Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 22 Feb 2024 15:08:28 +0800 Subject: [PATCH 008/154] Added functions for running gradient descent --- .../dbi/dbi_strategy_magnetic_field.ipynb | 242 ++++++++++++++++++ src/qibo/models/dbi/utils.py | 208 +++++++++++++-- 2 files changed, 429 insertions(+), 21 deletions(-) diff --git a/examples/dbi/dbi_strategy_magnetic_field.ipynb b/examples/dbi/dbi_strategy_magnetic_field.ipynb index e69de29bb2..6f9d7cb8f8 100644 --- a/examples/dbi/dbi_strategy_magnetic_field.ipynb +++ b/examples/dbi/dbi_strategy_magnetic_field.ipynb @@ -0,0 +1,242 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Double-Bracket Iteration Strategy: magnetic field (onsite Z)\n", + "This notebook shows the diagonalization process of DBI using the magnetic field strategy, which varies the diagonal operator $D$ by gradient descent. To find the gradient with respect to $D$, parameterization of $D$ is required. For the purpose of this notebook, we represent it by onsite Pauli-Z operators, i.e.\n", + "\n", + "$$ D = \\sum \\alpha_i Z_i $$\n", + "\n", + "Note that it is also possible to have higher-order terms, such as $ D = \\sum \\alpha_i Z_i + \\sum \\beta_{i,j}Z_iZ_j+...$\n", + "\n", + "The gradients are calculated under the premise that the diagonalization gain curve can be fitted by a polynomial, and that the iteration step duration is taken at the first dip of the curve." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.quantum_info import random_hermitian\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", + "from qibo.models.dbi.utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test on random Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", + "[Qibo 0.2.5|INFO|2024-02-22 08:22:59]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 31.576176740060667\n" + ] + } + ], + "source": [ + "# backend\n", + "set_backend(\"qibojit\", \"numba\")\n", + "# initialize dbi object\n", + "nqubits = 5\n", + "h0 = random_hermitian(2**nqubits, seed=2)\n", + "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0))\n", + "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The initial D coefficients: [(-0.2980910136757636+0j), (-0.17678355790937256+0j), (0.294550421681131+0j), (-0.2301056409534723+0j), (-0.07297191764284382+0j)]\n", + "Gradient: [ 482.57311611 272.8003344 -526.64628147 420.97083752 47.09691378]\n", + "s: 0.06953854599881942\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:228: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " grad[i] = s**3/3*da + s**2/2*db + 2*s*dc + s**2*ds*a + s*ds*b+ 2*ds*c\n" + ] + } + ], + "source": [ + "# generate the onsite Z operators\n", + "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", + "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", + "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", + "grad, s = gradient_onsite_Z(dbi,d,3, onsite_Z_ops)\n", + "print('The initial D coefficients:', d_coef)\n", + "print('Gradient:', grad)\n", + "print('s:', s)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:106: RuntimeWarning: overflow encountered in matmul\n", + " return a @ b - b @ a\n", + "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:106: RuntimeWarning: invalid value encountered in matmul\n", + " return a @ b - b @ a\n" + ] + } + ], + "source": [ + "iters = 30\n", + "off_diagonal_norm_tot = [dbi.off_diagonal_norm]\n", + "num_iters = []\n", + "s_step = [0]\n", + "for i in range(iters):\n", + " s, d_coef, d, off_diagonal_norm_history = gradient_descent_onsite_Z(dbi, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", + " off_diagonal_norm_tot.append(off_diagonal_norm_history[-1])\n", + " num_iters.append(len(off_diagonal_norm_history))\n", + " s_step.append(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(str(nqubits) + ' spins magnetic field diagonalization')\n", + "plt.plot(off_diagonal_norm_tot)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "h_input = SymbolicHamiltonian( symbols.Z(0)+symbols.X(0) + symbols.Z(0) * symbols.X(1) + symbols.Y(2) + symbols.Y(1) * symbols.Y(2), nqubits = 3 )" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|WARNING|2024-02-22 08:38:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], + "source": [ + "dbi = DoubleBracketIteration(deepcopy(h_input.dense))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "dbi(step=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "DBF_qibo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 75e1fa6de7..5ba8af5e67 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -2,8 +2,8 @@ from itertools import product from typing import Optional +import hyperopt import numpy as np -from hyperopt import hp, tpe from qibo import symbols from qibo.config import raise_error @@ -78,26 +78,16 @@ def select_best_dbr_generator( ): """Selects the best double bracket rotation generator from a list and runs the - Args: - dbi_object (`DoubleBracketIteration`): the target DoubleBracketIteration object. - d_list (list): list of diagonal operators (np.array) to run from. - step (float): fixed iteration duration. - Defaults to ``None``, uses hyperopt. - <<<<<<< HEAD - step_min (float): Minimally allowed iteration duration. - step_max (float): Maximally allowed iteration duration. - max_evals (int): Maximally allowed number of evaluation in hyperopt. - compare_canonical (bool): If `True`, the optimal diagonal operator chosen from "d_list" is compared with the canonical bracket. - ======= - step_min (float): minimally allowed iteration duration. - step_max (float): maximally allowed iteration duration. - max_evals (int): maximally allowed number of evaluation in hyperopt. - compare_canonical (bool): if `True`, the optimal diagonal operator chosen from "d_list" is compared with the canonical bracket. - mode (`DoubleBracketGeneratorType`): DBI generator type used for the selection. - >>>>>>> 056830fff9eedef0da2003a638ce4dbd30b6e3b8 - - Returns: - The updated dbi_object, index of the optimal diagonal operator, respective step duration, and evolution direction. + Args: + dbi_object (`DoubleBracketIteration`): the target DoubleBracketIteration object. + d_list (list): list of diagonal operators (np.array) to run from. + step (float): fixed iteration duration. + Defaults to ``None``, uses hyperopt. + compare_canonical (boolean): include the canonical bracket into the comparison. + scheduling: choose the method of finding optimal step. + + Returns: + The updated dbi_object, index of the optimal diagonal operator, respective step duration, and evolution direction. """ if scheduling is None: scheduling = dbi_object.scheduling @@ -160,3 +150,179 @@ def cs_angle_sgn(dbi_object, d): ) ) return np.sign(norm) + + +def dGamma_di_onsite_Z( + dbi_object: DoubleBracketIteration, n: int, i: int, d: np.array, onsite_Z_ops=None +): + """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. + + Args: + dbi_object (DoubleBracketIteration): the target dbi object + n (int): the number of nested commutators in `Gamma` + i (int): the index of onsite-Z coefficient + d (np.array): the diagonal operator + + Returns: + (list): [dGamma_0_di, dGamma_1_di, ..., dGamma_n_di] + """ + nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) + if onsite_Z_ops is None: + Z_i_str = "I" * (i) + "Z" + "I" * (nqubits - i - 1) + Z_i = SymbolicHamiltonian(str_to_symbolic(Z_i_str)).dense.matrix + else: + Z_i = onsite_Z_ops[i] + dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) + W = dbi_object.commutator(d, dbi_object.h.matrix) + dW_di = dbi_object.commutator(Z_i, dbi_object.h.matrix) + for k in range(n + 1): + if k == 0: + continue + elif k == 1: + dGamma_di[k] = dW_di + else: + dGamma_di[k] = dbi_object.commutator( + dW_di, dbi_object.Gamma(k - 1, d) + ) + dbi_object.commutator(W, dGamma_di[k - 1]) + return dGamma_di + + +def ds_di_onsite_Z( + dbi_object: DoubleBracketIteration, + d: np.array, + i: int, + taylor_coef=None, + onsite_Z_ops=None, +): + """Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients""" + # generate the list of derivatives w.r.t ith Z operator coefficient + nqubits = int(np.log2(d.shape[0])) + if onsite_Z_ops is None: + onsite_Z_ops = generate_onsite_Z_ops(nqubits) + dGamma_di = dGamma_di_onsite_Z(dbi_object, 3, i, d, onsite_Z_ops=onsite_Z_ops) + + def derivative_product(k1, k2): + r"""Calculate the derivative of a product $\sigma(\Gamma(n1,i))@\sigma(\Gamma(n2,i))""" + return dbi_object.sigma(dGamma_di[k1]) @ dbi_object.sigma( + dbi_object.Gamma(k2, d) + ) + dbi_object.sigma( + dbi_object.sigma(dbi_object.Gamma(k1, d)) + ) @ dbi_object.sigma( + dGamma_di[k2] + ) + + # calculate the derivatives of s polynomial coefficients + da = np.trace(3 * derivative_product(1, 2) + 3 * derivative_product(3, 0)) + db = np.trace(2 * derivative_product(1, 1) + 2 * derivative_product(0, 2)) + dc = np.trace(2 * derivative_product(1, 0)) + + ds = 0 + if taylor_coef != None: + a, b, c = taylor_coef[len(taylor_coef) - 3 :] + delta = b**2 - 4 * a * c + ddelta = 2 * (b * db - 2 * (a * dc + da * c)) + + ds = (-db + 0.5 * ddelta / np.sqrt(delta)) * a - (-b + np.sqrt(delta)) * da + ds /= 2 * a**2 + + return da, db, dc, ds + + +def gradient_onsite_Z( + dbi_object: DoubleBracketIteration, d: np.array, n_taylor=3, onsite_Z_ops=None +): + """Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients""" + # n is the highest order for calculating s + + # initialize gradient + nqubits = int(np.log2(d.shape[0])) + if onsite_Z_ops is None: + onsite_Z_ops = generate_onsite_Z_ops(nqubits) + grad = np.zeros(nqubits) + s, coef = dbi_object.polynomial_step( + n=n_taylor, + backup_scheduling=DoubleBracketScheduling.use_polynomial_approximation, + ) + a, b, c = coef[len(coef) - 3 :] + for i in range(nqubits): + da, db, dc, ds = ds_di_onsite_Z(dbi_object, d, i, [a, b, c], onsite_Z_ops) + grad[i] = ( + s**3 / 3 * da + + s**2 / 2 * db + + 2 * s * dc + + s**2 * ds * a + + s * ds * b + + 2 * ds * c + ) + return grad, s + + +def onsite_Z_decomposition(h_matrix: np.array, onsite_Z_ops=None): + nqubits = int(np.log2(h_matrix.shape[0])) + if onsite_Z_ops is None: + onsite_Z_ops = generate_onsite_Z_ops(nqubits) + decomposition = [] + for Z_i in onsite_Z_ops: + expect = np.trace(h_matrix @ Z_i) / 2**nqubits + decomposition.append(expect) + return decomposition + + +def generate_onsite_Z_ops(nqubits): + onsite_Z_str = ["I" * (i) + "Z" + "I" * (nqubits - i - 1) for i in range(nqubits)] + onsite_Z_ops = [ + SymbolicHamiltonian(str_to_symbolic(Z_i_str)).dense.matrix + for Z_i_str in onsite_Z_str + ] + return onsite_Z_ops + + +def gradient_descent_onsite_Z( + dbi_object: DoubleBracketIteration, + d_coef: list, + d: np.array = None, + n_taylor: int = 3, + onsite_Z_ops=None, + grad_tol: float = 1e-2, + lr_min: float = 1e-5, + lr_max: float = 1, + max_evals: int = 100, + space: callable = None, + optimizer: callable = None, + look_ahead: int = 1, + verbose: bool = False, +): + nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) + if onsite_Z_ops is None: + onsite_Z_ops = generate_onsite_Z_ops(nqubits) + off_diagonal_norm_history = [dbi_object.off_diagonal_norm] + if d is None: + d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) + grad, s = gradient_onsite_Z( + dbi_object, d, n_taylor=n_taylor, onsite_Z_ops=onsite_Z_ops + ) + off_diagonal_norm_history.append(dbi_object.loss(s, d)) + # optimize gradient descent step with hyperopt + if space is None: + space = hyperopt.hp.loguniform("lr", np.log(lr_min), np.log(lr_max)) + if optimizer is None: + optimizer = hyperopt.tpe + + def func_loss_to_lr(lr): + d_coef_eval = [d_coef[j] - grad[j] * lr for j in range(nqubits)] + d_eval = sum([d_coef_eval[i] * onsite_Z_ops[i] for i in range(nqubits)]) + return dbi_object.loss(step=s, d=d_eval) + + best = hyperopt.fmin( + fn=func_loss_to_lr, + space=space, + algo=optimizer.suggest, + max_evals=max_evals, + verbose=verbose, + ) + lr = best["lr"] + + d_coef = [d_coef[j] - grad[j] * lr for j in range(nqubits)] + d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) + dbi_object(step=s, d=d) + return s, d_coef, d, off_diagonal_norm_history From 0eb2add79684b74f0ac1a276bc0d7dcd37f6cc63 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 22 Feb 2024 16:27:32 +0800 Subject: [PATCH 009/154] Run magnetic field strategy on TFIM --- .../dbi/dbi_strategy_magnetic_field.ipynb | 224 ++++++++++++++++-- 1 file changed, 207 insertions(+), 17 deletions(-) diff --git a/examples/dbi/dbi_strategy_magnetic_field.ipynb b/examples/dbi/dbi_strategy_magnetic_field.ipynb index 6f9d7cb8f8..ea0dbb3ad2 100644 --- a/examples/dbi/dbi_strategy_magnetic_field.ipynb +++ b/examples/dbi/dbi_strategy_magnetic_field.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -26,11 +26,29 @@ "import matplotlib.pyplot as plt\n", "\n", "from qibo import hamiltonians, set_backend\n", + "from qibo.hamiltonians import SymbolicHamiltonian\n", "from qibo.quantum_info import random_hermitian\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", "from qibo.models.dbi.utils import *" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_matrix(matrix, title=\"\"):\n", + " \"\"\"Visualize hamiltonian in a heatmap form.\"\"\"\n", + " fig, ax = plt.subplots(figsize=(5,5))\n", + " ax.set_title(title)\n", + " try:\n", + " im = ax.imshow(np.absolute(matrix), cmap=\"inferno\")\n", + " except TypeError:\n", + " im = ax.imshow(np.absolute(matrix.get()), cmap=\"inferno\")\n", + " fig.colorbar(im, ax=ax)\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -40,15 +58,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", - "[Qibo 0.2.5|INFO|2024-02-22 08:22:59]: Using qibojit (numba) backend on /CPU:0\n" + "[Qibo 0.2.5|INFO|2024-02-22 16:06:03]: Using qibojit (numba) backend on /CPU:0\n" ] }, { @@ -57,6 +74,16 @@ "text": [ "Initial off diagonal norm 31.576176740060667\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -66,7 +93,8 @@ "nqubits = 5\n", "h0 = random_hermitian(2**nqubits, seed=2)\n", "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0))\n", - "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)" + "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)\n", + "visualize_matrix(dbi.h.matrix, title=f'Random hamiltonian with L={nqubits}')" ] }, { @@ -175,47 +203,209 @@ "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test on TFIM\n", + "Here we choose to customize our TFIM in the X axis using `SymbolicHamiltonian`. It is also possible to use Hadamard gate to rotate the TFIM inbuilt in `qibo`.\n", + "\n", + "$$ H = -(\\sum X_i X_{i+1} + \\sum hZ_i)" + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|WARNING|2024-02-22 16:08:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "h_input = SymbolicHamiltonian( symbols.Z(0)+symbols.X(0) + symbols.Z(0) * symbols.X(1) + symbols.Y(2) + symbols.Y(1) * symbols.Y(2), nqubits = 3 )" + "# generate the Hamiltonian\n", + "nqubits = 5\n", + "h = 1\n", + "H_TFIM = SymbolicHamiltonian( - h*symbols.Z(nqubits-1), nqubits=nqubits)\n", + "# add linear interaction terms\n", + "for i in range(nqubits-1):\n", + " H_TFIM -= SymbolicHamiltonian(symbols.X(i)*symbols.X(i+1) + h*symbols.Z(i), nqubits=nqubits)\n", + "H_TFIM = H_TFIM.dense\n", + "visualize_matrix(H_TFIM.matrix, title=f'TFIM with L={nqubits} h={h}')" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.5|WARNING|2024-02-22 08:38:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + "[Qibo 0.2.5|INFO|2024-02-22 16:13:45]: Using qibojit (numba) backend on /CPU:0\n", + "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j)]\n" ] } ], "source": [ - "dbi = DoubleBracketIteration(deepcopy(h_input.dense))" + "# backend\n", + "set_backend(\"qibojit\", \"numba\")\n", + "# initialize dbi object\n", + "dbi_TFIM = DoubleBracketIteration(deepcopy(H_TFIM))\n", + "d_coef = onsite_Z_decomposition(dbi_TFIM.h.matrix)\n", + "print(d_coef)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The initial D coefficients: [(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j)]\n", + "Gradient: [ -7.8132536 -17.94433184 -20.56560814 -17.94433184 -7.8132536 ]\n", + "s: 0.047896529653867745\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:228: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " grad[i] = s**3/3*da + s**2/2*db + 2*s*dc + s**2*ds*a + s*ds*b+ 2*ds*c\n" + ] + } + ], "source": [ - "dbi(step=0.1)" + "# generate the onsite Z operators\n", + "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", + "d_coef = onsite_Z_decomposition(dbi_TFIM.h.matrix, onsite_Z_ops)\n", + "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", + "grad, s = gradient_onsite_Z(dbi_TFIM,d,3, onsite_Z_ops)\n", + "print('The initial D coefficients:', d_coef)\n", + "print('Gradient:', grad)\n", + "print('s:', s)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:228: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " grad[i] = s**3/3*da + s**2/2*db + 2*s*dc + s**2*ds*a + s*ds*b+ 2*ds*c\n" + ] + } + ], + "source": [ + "iters = 30\n", + "off_diagonal_norm_tot = [dbi_TFIM.off_diagonal_norm]\n", + "num_iters = []\n", + "s_step = [0]\n", + "for i in range(iters):\n", + " s, d_coef, d, off_diagonal_norm_history = gradient_descent_onsite_Z(dbi_TFIM, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", + " off_diagonal_norm_tot.append(off_diagonal_norm_history[-1])\n", + " num_iters.append(len(off_diagonal_norm_history))\n", + " s_step.append(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", + "plt.plot(off_diagonal_norm_tot)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# the final matrix\n", + "visualize_matrix(dbi_TFIM.h.matrix)" + ] } ], "metadata": { From 08fb83727bafafb83b679adbcf9456f3a51f3435 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 22 Feb 2024 22:27:40 +0800 Subject: [PATCH 010/154] Complete dbi_strategies_compare notebook --- examples/dbi/dbi_strategies_compare.ipynb | 254 ++++++++++++++++ .../dbi/dbi_strategy_magnetic_field.ipynb | 272 +++--------------- src/qibo/models/dbi/double_bracket.py | 48 ++-- src/qibo/models/dbi/utils.py | 6 +- 4 files changed, 327 insertions(+), 253 deletions(-) create mode 100644 examples/dbi/dbi_strategies_compare.ipynb diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb new file mode 100644 index 0000000000..0afd3a9841 --- /dev/null +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DBI strategies comparison\n", + "\n", + "This notebook is a comparison of the so-far developed diagonalization strategies for DBI, including the canonical, Pauli-Z, and magnetic field strategies. On top of these, we also show case the use of invariant DBI generators such as 'BHMM'." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.hamiltonians import Hamiltonian, SymbolicHamiltonian\n", + "from qibo.quantum_info import random_hermitian\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", + "from qibo.models.dbi.utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_matrix(matrix, title=\"\"):\n", + " \"\"\"Visualize hamiltonian in a heatmap form.\"\"\"\n", + " fig, ax = plt.subplots(figsize=(5,5))\n", + " ax.set_title(title)\n", + " try:\n", + " im = ax.imshow(np.absolute(matrix), cmap=\"inferno\")\n", + " except TypeError:\n", + " im = ax.imshow(np.absolute(matrix.get()), cmap=\"inferno\")\n", + " fig.colorbar(im, ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test on random Hamiltonian\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# backend\n", + "set_backend(\"qibojit\", \"numba\")\n", + "# initialize dbi object\n", + "nqubits = 5\n", + "h0 = random_hermitian(2**nqubits, seed=2)\n", + "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0))\n", + "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)\n", + "visualize_matrix(dbi.h.matrix, title=f'Random hamiltonian with L={nqubits}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# iterations steps\n", + "NSTEPS = 15\n", + "# choose polynomial scheduling\n", + "scheduling = DoubleBracketScheduling.use_hyperopt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Canonical" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize DBI class for the canonical case\n", + "dbi_canonical = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.canonical, scheduling=scheduling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Canonical\n", + "off_diagonal_norm_history_canonical = [dbi_canonical.off_diagonal_norm]\n", + "steps_canonical_plot = [0]\n", + "for s in range(NSTEPS):\n", + " # same settings as iteration from list\n", + " step = dbi_canonical.choose_step(d=dbi.diagonal_h_matrix, max_evals=50)\n", + " dbi_canonical(step=step)\n", + " print(f\"New optimized step at iteration {s+1}/{NSTEPS}: {step}, loss {dbi_canonical.off_diagonal_norm}\")\n", + " off_diagonal_norm_history_canonical.append(dbi_canonical.off_diagonal_norm)\n", + " steps_canonical_plot.append(steps_canonical_plot[-1]+step)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pauli-Z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize DBI class for the Pauli-Z strategy\n", + "dbi_pauli = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generate_local_Z = generate_Z_operators(nqubits)\n", + "Z_ops = list(generate_local_Z.values())\n", + "Z_names = list(generate_local_Z.keys())\n", + "Z_optimal = []\n", + "# add in initial values for plotting\n", + "off_diagonal_norm_history_pauli = [dbi_pauli.off_diagonal_norm]\n", + "steps_pauli_plot = [0]\n", + "scheduling = DoubleBracketScheduling.use_hyperopt\n", + "for _ in range(NSTEPS):\n", + " dbi_pauli, idx, step, flip_sign = select_best_dbr_generator(dbi_pauli, Z_ops, scheduling=scheduling, compare_canonical=False, max_evals=50)\n", + " off_diagonal_norm_history_pauli.append(dbi_pauli.off_diagonal_norm)\n", + " steps_pauli_plot.append(steps_pauli_plot[-1]+step)\n", + " if flip_sign < 0:\n", + " Z_optimal.append('-' + Z_names[idx])\n", + " else:\n", + " Z_optimal.append(Z_names[idx])\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with operator {Z_optimal[-1]}, loss {dbi_pauli.off_diagonal_norm}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Magnetic field" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize DBI class for the canonical case\n", + "dbi_gradient = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", + "d_coef = onsite_Z_decomposition(dbi_gradient.h.matrix, onsite_Z_ops)\n", + "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "off_diagonal_norm_history_gradient = [dbi_gradient.off_diagonal_norm]\n", + "steps_gradient_plot= [0]\n", + "for _ in range(NSTEPS):\n", + " step, d_coef, d = gradient_descent_onsite_Z(dbi_gradient, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=50, n_taylor=5)\n", + " off_diagonal_norm_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", + " steps_gradient_plot.append(steps_gradient_plot[-1]+step)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.title(str(nqubits) + ' random Hamiltonian diagonalization')\n", + "plt.plot(off_diagonal_norm_history_canonical, label='canonical')\n", + "plt.plot(off_diagonal_norm_history_pauli, label='Pauli-Z')\n", + "plt.plot(off_diagonal_norm_history_gradient, label='gradient')\n", + "plt.legend()\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.title(str(nqubits) + ' random Hamiltonian diagonalization')\n", + "plt.plot(steps_canonical_plot, off_diagonal_norm_history_canonical, marker='o', label='canonical')\n", + "plt.plot(steps_pauli_plot, off_diagonal_norm_history_pauli, marker='o', label='Pauli-Z')\n", + "plt.plot(steps_gradient_plot,off_diagonal_norm_history_gradient, marker='o', label='gradient')\n", + "plt.legend()\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "DBF_qibo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dbi/dbi_strategy_magnetic_field.ipynb b/examples/dbi/dbi_strategy_magnetic_field.ipynb index ea0dbb3ad2..d324efdb38 100644 --- a/examples/dbi/dbi_strategy_magnetic_field.ipynb +++ b/examples/dbi/dbi_strategy_magnetic_field.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ "import matplotlib.pyplot as plt\n", "\n", "from qibo import hamiltonians, set_backend\n", - "from qibo.hamiltonians import SymbolicHamiltonian\n", + "from qibo.hamiltonians import Hamiltonian, SymbolicHamiltonian\n", "from qibo.quantum_info import random_hermitian\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", "from qibo.models.dbi.utils import *" @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -58,34 +58,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-02-22 16:06:03]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 31.576176740060667\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# backend\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -99,38 +74,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 08:22:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The initial D coefficients: [(-0.2980910136757636+0j), (-0.17678355790937256+0j), (0.294550421681131+0j), (-0.2301056409534723+0j), (-0.07297191764284382+0j)]\n", - "Gradient: [ 482.57311611 272.8003344 -526.64628147 420.97083752 47.09691378]\n", - "s: 0.06953854599881942\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:228: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " grad[i] = s**3/3*da + s**2/2*db + 2*s*dc + s**2*ds*a + s*ds*b+ 2*ds*c\n" - ] - } - ], + "outputs": [], "source": [ "# generate the onsite Z operators\n", "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", @@ -144,58 +90,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:106: RuntimeWarning: overflow encountered in matmul\n", - " return a @ b - b @ a\n", - "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:106: RuntimeWarning: invalid value encountered in matmul\n", - " return a @ b - b @ a\n" - ] - } - ], + "outputs": [], "source": [ "iters = 30\n", "off_diagonal_norm_tot = [dbi.off_diagonal_norm]\n", - "num_iters = []\n", "s_step = [0]\n", "for i in range(iters):\n", - " s, d_coef, d, off_diagonal_norm_history = gradient_descent_onsite_Z(dbi, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", - " off_diagonal_norm_tot.append(off_diagonal_norm_history[-1])\n", - " num_iters.append(len(off_diagonal_norm_history))\n", + " s, d_coef, d = gradient_descent_onsite_Z(dbi, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", + " off_diagonal_norm_tot.append(dbi.off_diagonal_norm)\n", " s_step.append(s)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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tW/SWf//999BoNHfd9/Dhw1FUVISff/5Zb/nWrVvvuq2hP+9OnTrB09MT33//vd7y1NRUHDt2TG/Zfffdh+LiYuzcuVNv+ddff617vr5kMlmtGnft2lXvLt0a//73v7Fhwwb897//xf3331/n/oC7H5d/rmPIuXjfffchLi6u1t+Tr7/+GjKZrNbcX9R6sEWHWsy0adPQvn17hIaGwtXVFQkJCVixYgWysrLuOkNxbGws5s2bh0cffRRBQUGwsrLCwYMHERsbizfeeENvXW9vbzz44INYtGgRvLy8sHnzZuzfvx9Lly6Fra1to9/H//3f/+HatWu477774OPjg4KCAqxevVpvnEBz6Ny5Mzp27Ig33ngDQgg4Ozvjl19+wf79+2ut++6772LcuHEYPXo0Xn75ZVRVVWH58uWwt7dHXl5eg/a/evVqDB48GEOGDMHzzz8Pf39/FBUVITExEb/88otuDEPHjh1hY2ODLVu2oEuXLrC3t4e3t7euC+Cf/P398eabb+K9995DWVmZ7rL+uLg45OTkYPHixQ2q9dZ9vPvuu3jrrbdw9epVjBkzBk5OTsjKysKpU6dgZ2d32/106dIFTzzxBFatWgVLS0vcf//9uHDhAj788EODJpubPn06PvroI0yfPh3vv/8+goKCsHv3bvz222933XbQoEFwcnLCnDlz8M4778DS0hJbtmypFeLNzMywePFizJ49G4888giefvppFBQUYPHixfDy8oKZ2d//n50+fTrWrl2LGTNmIDk5Gd27d8fRo0exZMkSjB07ts5gcTcPPPAANm7ciM6dO6NHjx44c+YMli9f3qDuyP/97394//338cgjjyA4OFhv3JdcLkfv3r0NPi4A0L17dwDA0qVLER4eDnNzc/To0UNv7FqNf/3rX/j6668xbtw4vPvuu/Dz88OuXbvw6aef4vnnn0dwcHC93w8ZCUmHQlObEhERIXr16iWUSqUwNzcXbm5u4qGHHhKnTp2667ZZWVli5syZonPnzsLOzk7Y29uLHj16iI8++kjvygc/Pz8xbtw4sX37dtG1a1dhZWUl/P39xcqVK/Ve705XXd24cUNv3VuvDPn1119FeHi4aNeunbCyshLu7u5i7Nix4siRI3d9HwDE3Llz66xl+fLlesvruqooLi5OjBw5UigUCuHk5CQeffRRkZqaKgCId955R2/7HTt2iO7duwsrKyvRvn178cEHH4iXXnpJODk53bUmIWpflVJT69NPPy3atWsnLC0thZubmxg0aJD4z3/+o7fet99+Kzp37iwsLS31arvdlUVff/216Nevn7C2thb29vaid+/edV61dbfjc6d97Ny5UwwfPlw4ODgIuVwu/Pz8xCOPPCJ+//33O26rVqvFq6++Ktzd3YW1tbUYOHCgOH78eJ3Hpy7Xrl0TDz/8sLC3txcKhUI8/PDD4tixYwZddXXs2DERFhYmbG1thZubm3j22WfF2bNn67yq7fPPPxeBgYHCyspKBAcHi6+++kpMmDBB9O7dW2+93NxcMWfOHOHl5SUsLCyEn5+fWLhwoSgvL9dbz9DzIj8/XzzzzDPC3d1d2NraisGDB4sjR46IoUOHiqFDh+rWM+Sqq5pjUNfjn1ePGXpc1Gq1ePbZZ4Wbm5uQyWR6+6rr55eSkiKmTZsmXFxchKWlpejUqZNYvny5qKqqqvU+bv19rTlmt/4ekvRkQtwcIEFkAvz9/dGtW7cGd9GYssrKSvTq1Qvt2rXDvn37pC6HmllBQQGCg4MxceJEfP7551KXQyQZdl0RmahnnnkGI0eOhJeXFzIzM7F+/XpcunQJq1evlro0amKZmZl4//33MXz4cLi4uCAlJQUfffQRioqK8PLLL0tdHpGkGHSITFRRUREWLFiAGzduwNLSEn369MHu3bsbNA6DjJtcLkdycjJeeOEF5OXlwdbWFgMHDsT69ev1ZhQmaovYdUVEREQmi5eXExERkcli0CEiIiKTxaBDREREJqvND0bWarVIT0+HQqG46y0IiIiIyDgIIVBUVARvb2+9iTFv1eaDTnp6erPdY4eIiIiaV1pa2h1n4m7zQUehUACoPlCGTOlORERE0lOpVPD19dV9jt9Omw86Nd1VDg4ODDpEREStzN2GnXAwMhEREZksBh0iIiIyWQw6REREZLIYdIiIiMhkMegQERGRyWLQISIiIpPFoENEREQmi0GHiIiITBaDDhEREZksBh0iIiIyWQw6REREZLIYdIiIiMhkMeg0kwqNFqeS8qQug4iIqE1j0GkG5ZVVGBhxAJM/O47U3FKpyyEiImqzGHSagbWlObp4KQAAey5kSFwNERFR28Wg00zGdPMCAOy+kClxJURERG0Xg04zGd3VAzIZcC6tANfy2X1FREQkBQadZuKusEY/f2cAwF626hAREUmCQacZje3mCYBBh4iISCoMOs2oZpzO6ZR8ZBaWS1wNERFR28Og04w8ldbo6+cEAPjtIlt1iIiIWhqDTjMLv9l9tfs8LzMnIiJqaQw6zWzMzaBzKjkPN4rUEldDRETUtjDoNDMfJ1v09FFCCHZfERERtTQGnRYQ3r16UDJnSSYiImpZDDotoGaczomrecgrqZC4GiIioraDQacF+LnYoau3A6q0Avvj2H1FRETUUhh0WsjYm91Xu88z6BAREbUUBp0WUnP11Z+JOSgsrZS4GiIioraBQaeFdHSzRycPBTRagf2XsqQuh4iIqE1g0GlB4d2rW3X2cPJAIiKiFsGg04JqxukcSchBUTm7r4iIiJqb0QaddevWoUePHnBwcICDgwPCwsKwZ88e3fNCCCxatAje3t6wsbHBsGHDcPHiRQkrvrsgd3t0dLNDRZUWB//KlrocIiIik2e0QcfHxwcffPABTp8+jdOnT2PEiBGYMGGCLswsW7YMK1euxJo1axAVFQVPT0+MHDkSRUVFEld+ezKZ7B9XX7H7ioiIqLnJhBBC6iIM5ezsjOXLl+Ppp5+Gt7c35s+fj9dffx0AoFar4eHhgaVLl2L27NkGv6ZKpYJSqURhYSEcHByaq3Sdi+mFGPfxUcgtzHD27ZGwk1s0+z6JiIhMjaGf30bbovNPVVVV+O6771BSUoKwsDAkJSUhMzMTo0aN0q0jl8sxdOhQHDt27I6vpVaroVKp9B4tKcTLAX4utlBrtDgUz+4rIiKi5mTUQef8+fOwt7eHXC7HnDlzsGPHDoSEhCAzs3rSPQ8PD731PTw8dM/dTkREBJRKpe7h6+vbbPXXRSaTIbzbzXtfcfJAIiKiZmXUQadTp06IiYnBiRMn8Pzzz2PGjBmIi4vTPS+TyfTWF0LUWnarhQsXorCwUPdIS0trltrvZOzNy8wPxWejrKKqxfdPRETUVhh10LGyskJgYCBCQ0MRERGBnj17YvXq1fD0rA4Kt7beZGdn12rluZVcLtddyVXzaGnd2ynRztEGpRVVOHz5Rovvn4iIqK0w6qBzKyEE1Go1AgIC4Onpif379+ueq6iowOHDhzFo0CAJKzRM9dVXNycPvMCrr4iIiJqL0V7y8+abbyI8PBy+vr4oKirCd999h8jISOzduxcymQzz58/HkiVLEBQUhKCgICxZsgS2traYNm2a1KUbJLy7F744koQDl7JRXlkFa0tzqUsiIiIyOUYbdLKysvDkk08iIyMDSqUSPXr0wN69ezFy5EgAwGuvvYaysjK88MILyM/Px4ABA7Bv3z4oFAqJKzdMLx9HeDpYI1NVjqMJObg/5M5dbkRERFR/rWoenebQ0vPo/NOiny9i47FkTOrTDisn92rRfRMREbVmJjWPjqmqmSX597gsVGi0EldDRERkehh0JNTXzwluCjlU5Rocu5IjdTlEREQmh0FHQuZmMozpevPqK04eSERE1OQYdCQWfvMy89/iMlFZxe4rIiKipsSgI7H+/s5wtrNCQWklTl7Nk7ocIiIik8KgIzELczOM7lp9afluTh5IRETUpBh0jEDNTT73XcxElbZNX+1PRETUpBh0jEBYRxcobSyRU1yBqGR2XxERETUVBh0jYGluhlE3Z0bec57dV0RERE2FQcdI1EweuOdCJrTsviIiImoSDDpGYlCgCxTWFsguUuNsar7U5RAREZkEBh0jIbcwx/1dbl59xckDiYiImgSDjhEJ71Y9eeDeCxlo4/daJSIiahIMOkbk3mA32FmZI72wHOeuFUpdDhERUavHoGNErC3NMaILr74iIiJqKgw6Rmbsze6r3ey+IiIiajQGHSMzrJM7bCzNkZZXhrgMldTlEBERtWoMOkbGxsocAzs4AwBv8klERNRIDDpGKNS/OuicSeF8OkRERI3BoGOEQv2cAABRyXkcp0NERNQIDDpGqKevIyzNZcguUiMtr0zqcoiIiFotBh0jZG1pju7tlADAu5kTERE1AoOOkep3c5zO6RQGHSIiooZi0DFSNQOSo5I5IJmIiKihGHSMVN+bA5ITs4uRX1IhcTVEREStE4OOkXK2s0JHNzsAvMyciIiooRh0jFjNOJ0ojtMhIiJqEAYdI1YzTuc0x+kQERE1CIOOEevnXz1OJ/ZaAcorqySuhoiIqPVh0DFi7Z1t4aaQo7JKIPZaodTlEBERtToMOkZMJpPpWnU4nw4REVH9MegYub5+HKdDRETUUAw6Rk7XopOcB62WN/gkIiKqDwYdIxfi5QBbK3OoyjVIyC6WuhwiIqJWhUHHyFmYm6F3e0cAvMEnERFRfTHotAKhunE6DDpERET1waDTCvTjDT6JiIgahEGnFejV3hFmMuB6QRkyCsukLoeIiKjVYNBpBezlFgjxdgDAy8yJiIjqg0GnleA4HSIiovpj0GklOE6HiIio/hh0WonQmxMH/pWpgqq8UuJqiIiIWgcGnVbCw8Ea7Z1toRVAdGqB1OUQERG1Cgw6rUioX3WrzhmO0yEiIjIIg04rEspxOkRERPXCoNOK1NzgMzotH5VVWomrISIiMn4MOq1IRzd7ONpaorxSi4vpKqnLISIiMnoMOq2ImZlMN06H8+kQERHdHYNOK/P3OB0GHSIiorth0GlldFdepeRDCCFxNURERMaNQaeV6e6jhJWFGXKKK5CcWyp1OUREREaNQaeVkVuYo6ePEgC7r4iIiO6GQacVqhmnwwHJREREd8ag0wrVzKdzmhMHEhER3RGDTivUt311i87VnBLkFKslroaIiMh4Mei0QkpbSwR72ANgqw4REdGdMOi0UjXjdM6kcJwOERHR7TDotFI143R4g08iIqLbY9BppUL9qlt0LlwvRFlFlcTVEBERGSejDToRERHo168fFAoF3N3dMXHiRMTHx+utM3PmTMhkMr3HwIEDJaq4Zfk42cDTwRoarUBMWoHU5RARERklow06hw8fxty5c3HixAns378fGo0Go0aNQklJid56Y8aMQUZGhu6xe/duiSpuWTKZDKH+vMEnERHRnVhIXcDt7N27V+/7DRs2wN3dHWfOnMG9996rWy6Xy+Hp6dnS5RmFUD8n/BqbgagUjtMhIiKqi9G26NyqsLAQAODs7Ky3PDIyEu7u7ggODsasWbOQnZ19x9dRq9VQqVR6j9aq5sqr6JR8VGl5g08iIqJbtYqgI4TAK6+8gsGDB6Nbt2665eHh4diyZQsOHjyIFStWICoqCiNGjIBafftJ9CIiIqBUKnUPX1/flngLzaKzpwL2cgsUqTWIzyySuhwiIiKjIxNCGH1TwNy5c7Fr1y4cPXoUPj4+t10vIyMDfn5++O677zBp0qQ611Gr1XpBSKVSwdfXF4WFhXBwcGjy2pvbk1+exJGEHLw7oSumh/lLXQ4REVGLUKlUUCqVd/38NvoWnRdffBE///wzDh06dMeQAwBeXl7w8/NDQkLCbdeRy+VwcHDQe7Rm/W52X3E+HSIiotqMdjCyEAIvvvgiduzYgcjISAQEBNx1m9zcXKSlpcHLy6sFKjQONVdeRSXlQQgBmUwmcUVERETGw2hbdObOnYvNmzdj69atUCgUyMzMRGZmJsrKygAAxcXFWLBgAY4fP47k5GRERkZi/PjxcHV1xUMPPSRx9S2nl68jzM1kyFSV43pBmdTlEBERGRWjDTrr1q1DYWEhhg0bBi8vL91j27ZtAABzc3OcP38eEyZMQHBwMGbMmIHg4GAcP34cCoVC4upbjq2VBbp5V3e/neFl5kRERHqMuuvqTmxsbPDbb7+1UDXGLdTfGeeuFSIqOQ8TerWTuhwiIiKjYbQtOmS4froZktmiQ0RE9E8MOiag780bfMZnFaGwtFLiaoiIiIwHg44JcFPIEeBqByGAs6ls1SEiIqrBoGMi+vrdvMycN/gkIiLSYdAxERynQ0REVBuDjomoucHnuWsFUGuqJK6GiIjIODDomIgOrnZwtrOCWqPFheut947sRERETYlBx0TIZDKE+tV0X3GcDhEREcCgY1J4g08iIiJ9DDompO/NAclnUvKg1d55ZmkiIqK2gEHHhHTzVkJuYYb80kpcuVEsdTlERESSY9AxIVYWZhjQwQUA8PO5dImrISIikh6DjomZEuoLAPguKg2VVVqJqyEiIpIWg46JGRniAVd7OW4UqfF7XJbU5RAREUmKQcfEWFmYYXKoDwBg66lUiashIiKSFoOOCXqsf3vIZMCRhBwk55RIXQ4REZFkGHRMkK+zLYYGuwEAvmWrDhERtWEMOiZqWv/2AID/nbnGe18REVGbxaBjokZ0doengzXySiqw90Km1OUQERFJwqIhGwUEBEAmk9V7u/nz5+Oll15qyC6pnizMzTC1vy9W/Z6ALSdTMaFXO6lLIiIianENCjobN25s0M78/f0btB01zJR+vvj4QAJOJeUhMbsIge4KqUsiIiJqUQ0KOkOHDm3qOqgZeCltcF8XD+yPy8KWk6l4Z3xXqUsiIiJqURyjY+IeH1A9KPmHM9dQXslByURE1LZwjI6JuzfIDT5ONriWX4ZfYzPwSF8fqUsiIiJqMRyjY+LMzGR4rH97LP8tHltOpjDoEBFRm8IxOm3A5FBffLT/MqJTCxCXrkKIt4PUJREREbUIjtFpA9wUcozu6gkA2HoqReJqiIiIWk6TBJ3KykqkpaUhPj4eeXl5TfGS1MRqBiXvOHsdxWqNxNUQERG1jAYHneLiYnz22WcYNmwYlEol/P39ERISAjc3N/j5+WHWrFmIiopqylqpEcI6uqCDqx1KKqrwc0y61OUQERG1iAYFnY8++gj+/v744osvMGLECPz444+IiYlBfHw8jh8/jnfeeQcajQYjR47EmDFjkJCQ0NR1Uz3JZNWDkgFgy8kUCCEkroiIiKj5yUQDPvEeffRR/N///R+6d+9+x/XUajW+/PJLWFlZ4dlnn21wkc1JpVJBqVSisLAQDg6mPUg3r6QCAyMOoEKjxU9z70FPX0epSyIiImoQQz+/GxR0TElbCjoA8K9tMdgRfR2TQ32w7JGeUpdDRETUIIZ+fter6yo8PBw7d+5EVRVn2G2tpt0clPzLuQwUllVKXA0REVHzqlfQee2117B9+3YEBQVh4cKFSExMbK66qJmE+jkh2MMeZZVV2Bl9XepyiIiImlW9gs7w4cOxefNmREdHw9fXF1OnTsV9992Hbdu2oaKiorlqpCYkk8nw+AA/AByUTEREpq/eV11pNBpotVo88MAD2LhxIx566CG8/fbb8Pb2bo76qBlM7N0O1pZmuJxVjDMp+VKXQ0RE1GzqdQsIa2truLq6onfv3rC3t4dCoYCdnR2mTp0KhULRXDVSE1PaWOLBnt74/vQ1bDmZilB/Z6lLIiIiahb1CjpbtmzBV199haKiIkyZMgWPPvoo5HJ5c9VGzejxAX74/vQ17Dqfgf97IAROdlZSl0RERNTk6tV19fDDD2PXrl3YvHkzrly5grCwMMyfPx9xcXHNVR81kx4+SnT1dkCFRosfzl6TuhwiIqJm0aCZkX18fLBw4ULs2bMHwcHBmDRpEoYMGdLUtVEz0h+UnMpByUREZJLq1XXl7+8PtVoNIQRsbW3h4OAAhUKBjh07QqlUNleN1Ewe7OWNJbsvISmnBMev5GJQoKvUJRERETWpegWdS5cuwcbGprlqoRZmL7fAhF7e2HIyFVtOpTLoEBGRyalX11VNyHniiSegUqkAALt378aOHTuavjJqETXdV79dyMSNIrXE1RARETWtBo3RiY2NhYODA+Li4rBgwQLs3bsX8+fPb+LSqCWEeDugl68jNFqB70+nSV0OERFRk2pQ0LG0tIQQAhs3bsRbb72Fzz77DEeOHGnq2qiFPH7z/lffnkqFVstByUREZDoaFHRmz56Nfv36Yfv27Zg4cSIAoKSkpCnrohb0QA9vOFhb4Fp+Gf5IuCF1OURERE2mQUHnueeew++//47Y2FjY2dkhMTERAwYMaOraqIXYWJljUh8fANWXmhMREZmKBgUdAHB0dERxcTEAIDAwEJs2bWqyoqjl1XRfHbiUhfSCMomrISIiahoNDjoAMGrUqKaqgyQW5KHAwA7O0Argk4MJUpdDRETUJBoVdDibrmlZMKoTAGBbVBoSsookroaIiKjxGhV0ZDJZU9VBRiDU3xmju3pAK4Cle/+SuhwiIqJGa1TQIdPz2pjOMDeT4fdL2ThxNVfqcoiIiBqFQYf0dHSzx7T+1QOTl+y+xHl1iIioVWtU0LGysmqqOsiIvHRfEOyszBF7rRC/ns+QuhwiIqIGa1TQOX36dFPVQUbETSHHnKEdAQDLf/sLak2VxBURERE1DLuuqE7PDAmAu0KOtLwyfHM8RepyiIiIGqTRQaewsBDPPfccAgMD0aVLF2RksKvDFNhaWeCVkcEAgE8OJqKwtFLiioiIiOqv0UHnhRdewPnz57Fs2TKkpKSgrKx6Vt358+dj9erVjS6QpPNIXx8Ee9ijsKwSnx5OlLocIiKiemt00NmzZw8+/fRTTJo0Cebm5rrlY8aMwTfffNPYlycJWZib4Y3wzgCADX8m41p+qcQVERER1U+TjNGxt7evtSwoKAiJiQ1vBYiIiEC/fv2gUCjg7u6OiRMnIj4+Xm8dIQQWLVoEb29v2NjYYNiwYbh48WKD90m1De/kjrAOLqjQaLFy32WpyyEiIqqXRgedsWPHYuvWrbWWFxcXN2rm5MOHD2Pu3Lk4ceIE9u/fD41Gg1GjRqGkpES3zrJly7By5UqsWbMGUVFR8PT0xMiRI1FUxNsXNBWZTIY3x3YBAOyIuY4L1wslroiIiMhwFo19gYiICISGhgKobmGRyWQoKyvDu+++iz59+jT4dffu3av3/YYNG+Du7o4zZ87g3nvvhRACq1atwltvvYVJkyYBADZt2gQPDw9s3boVs2fPbvibIj3dfZSY0MsbP8WkI2LPJWx+ZgBv/0FERK1Co1t0fH198eeff+Lo0aMoLS1F//794eTkhCNHjmDp0qVNUSOA6qu7AMDZ2RkAkJSUhMzMTL07qMvlcgwdOhTHjh277euo1WqoVCq9B93dglGdYGVuhj8Tc3H48g2pyyEiIjJIk4zRCQwMxP79+5GcnIyvvvoKO3fuRHx8vK6lp7GEEHjllVcwePBgdOvWDQCQmZkJAPDw8NBb18PDQ/dcXSIiIqBUKnUPX1/fJqnR1Pk622LGID8AwAd7/kIVbw1BREStQIODjhC1P+jat2+P8ePHY8yYMXBycmpUYf80b948xMbG4ttvv6313K1dKDXdZ7ezcOFCFBYW6h5paWlNVqepmzs8EA7WFvgrswg/nr0mdTlERER31eCgY29vj3vuuQcvvfQSNm3ahAsXLkCr1TZlbQCAF198ET///DMOHToEHx8f3XJPT08AqNV6k52dXauV55/kcjkcHBz0HmQYR1srzBsRCABYse8yyip4awgiIjJuDQ46y5cvR5cuXXDkyBHMmjULPXv2hEKhQFhYGObNm4cNGzbg3LlzDS5MCIF58+bhxx9/xMGDBxEQEKD3fEBAADw9PbF//37dsoqKChw+fBiDBg1q8H7pzqaH+aOdow0yVeX46s8kqcshIiK6I5moqw+qntRqNWxsbPDmm28iLy8PZ8+eRWxsLNRqNaqqGva//hdeeAFbt27FTz/9hE6dOumWK5VK2NjYAACWLl2KiIgIbNiwAUFBQViyZAkiIyMRHx8PhUJh0H5UKhWUSiUKCwvZumOgndHXMX9bDOzlFjj8/4bBxV4udUlERNTGGPr53SRBBwDMzMwQExODHj16AACqqqpw8eJF3ff1dbtxNhs2bMDMmTMBVLf6LF68GJ999hny8/MxYMAArF27Vjdg2RAMOvWn1Qo8uPYoLlxXYeYgfyx6sKvUJRERURsjedBpLRh0GuZYYg6m/fckLMxk2P/KUAS42kldEhERtSGGfn43yeXl1PYMCnTF8E5u0GgFlv/2l9TlEBER1anBQWfWrFlYv349Tp8+DbVaDeD23U1kmt4I7wIzGbD7fCbOpuZLXQ4REVEtDQ468fHxeP3119G/f3/dwN/FixdjzZo1OHbsGEpLeadrU9fJU4FH+1ZPuLhk16U651YiIiKSUqPH6CQkJODMmTM4e/Yszpw5g+joaBQUFMDc3BzBwcFGfzdxjtFpnMzCcgz78BDKK7X47Mm+GN3VU+qSiIioDTD087vRN/UMCgpCUFAQpk6dqluWlJSE06dPIzo6urEvT0bOU2mNWUM64JODiVi65y+M6OwOS3MO/SIiIuNQr0+k8PBw7Ny5865z4wQEBODRRx/FkiVLGlUctQ7P3dsBLnZWuJpTgk8PXZG6HCIiIp16BZ3XXnsN27dvR1BQEBYuXIjExMTmqotaEYW1Jd5+IAQAsPrAZUQl50lcERERUbV6BZ3hw4dj8+bNiI6Ohq+vL6ZOnYr77rsP27ZtQ0VFRXPVSK3AxN7tMKl3O2gF8PK30SgsrZS6JCIiovoPRtZoNCgqKkJRURFUKhUiIyPx8ccfIy8vDzk5Oc1VZ7PhYOSmU6zW4IGPjyA5txRjunpi3RN9OOUAERE1i2YZjGxtbQ1XV1f07t0b9vb2UCgUsLOzw9SpUw2+txSZLnu5BT55rA8mrfsTey9mYuupVDw+wE/qsoiIqA2rV9DZsmULvvrqKxQVFWHKlCl49NFHIZfzho70t+4+Srw2ujPe330J7/4Sh1A/Z3TyZAgmIiJp1GuMzsMPP4xdu3Zh8+bNuHLlCsLCwjB//nzExcU1V33UCj0zOABDg92g1mjx4rdnUVbRsDvYExERNVaDJjzx8fHBwoULsWfPHgQHB2PSpEkYMmRIU9dGrZSZmQwrJveEq70cl7OK8Z9dDMJERCSNenVd+fv7Q61WQwgBW1tbODg4QKFQoGPHjlAqlc1VI7VCrvZyfDSlJ5788hS2nEzF4EBXhHf3krosIiJqY+oVdC5dugQbG5vmqoVMzJAgN8wZ2hHrD1/B6z/EooevI9o58vwhIqKWU6+uq5qQ88QTT0ClUgEAdu/ejR07djR9ZWQSXh0VjJ6+jlCVa/Dyt9HQVGmlLomIiNqQBo3RiY2NhYODA+Li4rBgwQLs3bsX8+fPb+LSyBRYmpvhk6m9YS+3wOmUfHx8IEHqkoiIqA1pUNCxtLSEEAIbN27EW2+9hc8++wxHjhxp6trIRLR3scX7D3UDAHxyKBHHr+RKXBEREbUVDQo6s2fPRr9+/bB9+3ZMnDgRAFBSUtKUdZGJmdCrHR7t6wMhgH9ti0F+CW8ZQkREza9BQee5557D77//jtjYWNjZ2SExMREDBgxo6trIxCx6sCs6uNkhU1WO/7c9FvW8+wgREVG9NSjoAICjoyPs7e0BAIGBgdi0aVOTFUWmyU5ugY+n9oaVuRl+v5SFr4+nSF0SERGZuAYHnRqFhYV47rnnEBgYiC5duiAjI6Mp6iIT1a2dEm+EdwYAvL/7EuLSVRJXREREpqzRQeeFF17A+fPnsWzZMqSkpKCsrAwAMH/+fKxevbrRBZLpeeoef9zX2R0VN28RUVqhkbokIiIyUY0OOnv27MGnn36KSZMmwdzcXLd8zJgx+Oabbxr78mSCZDIZlj/aE+4KOa7cKMHin3mLCCIiah6NDjoAdGN1/ikoKAiJiYlN8fJkgpztrLBqai/IZMC202n45Vy61CUREZEJanTQGTt2LLZu3VpreXFxMWQyWWNfnkzYoI6umDssEADw5o/nkZzDKQqIiKhp1eteV3WJiIhAaGgoAEAIAZlMhrKyMrz77rvo06dPowsk0/by/UE4diUHZ1ML8PTGKPzw/CA42VlJXRYREZmIRrfo+Pr64s8//8TRo0dRWlqK/v37w8nJCUeOHMHSpUubokYyYZbmZlj/RF94K61xNacEs785A7WmSuqyiIjIRMhEE87alpqainPnzsHS0hIDBgyAk5NTU710s1GpVFAqlSgsLISDg4PU5bRZ8ZlFeGTdMRSpNXiwpzdWTekFMzN2fRIRUd0M/fxuUItOampqncvbt2+P8ePHY8yYMXoh5/r16w3ZDbUhnTwVWPdEX1iYyfDzuXSs3H9Z6pKIiMgENCjo9OvXD7NmzcKpU6duu05hYSG++OILdOvWDT/++GODC6S2Y3CQK5Y81B0AsOZQIr6PSpO4IiIiau0aNBj50qVLWLJkCcaMGQNLS0uEhobC29sb1tbWyM/PR1xcHC5evIjQ0FAsX74c4eHhTV03majJ/XyRll+KTw4m4s0d5+HlaI0hQW5Sl0VERK1Uo8bolJeXY/fu3Thy5AiSk5NRVlYGV1dX9O7dG6NHj0a3bt2astZmwTE6xkcIgfnbYvBTTDoUcgv87/kwdPbkz4aIiP5m6Od3owcjq1SqVh0QGHSMk1pThSf/ewqnkvPgrbTGjrn3wMPBWuqyiIjISDTrYOR/cnJywg8//NDYlyHSI7cwx+fT+6KDmx3SC8vxzKYolKh5TywiIqqfRgcdIQTWrVuHAQMGYODAgZg3bx5OnjzZFLVRG+doa4WNM/vDxc4KF66r8OK30dBUaaUui4iIWpEmudfVuXPn0L9/fwwbNgzx8fEYOnQo/vWvfzXFS1Mb197FFl/MCIXcwgwH/8rGu7/GoQmnfiIiIhPX6FtAAMDWrVsxcuRI3ffnz5/HxIkT4ePjg1dffbUpdkFtWJ/2Tlg1pRde2HoWXx9PQXtnWzw7pIPUZRERUSvQ6BYdFxcX+Pr66i3r3r07Pv74Y6xfv76xL08EAAjv7oU3w7sAAN7ffQl7L2RIXBEREbUGjQ46PXv2xJdffllreWBgINLSOOEbNZ1nhwTgyYF+EAJ4+bsYRKfmS10SEREZuUYHnf/85z9Ys2YNpk2bhqNHj0KlUiErKwtLlixBQEBAU9RIBACQyWR4Z3wIhndyg1qjxbObTiM1t1TqsoiIyIg1OugMHDgQJ06cQHp6OoYNGwYnJyd4e3tj+/btWLFiRVPUSKRjYW6GNdP6oKu3A3JLKjBz4ykUlFZIXRYRERmpJr17eXZ2Ns6cOQOtVosBAwbA1dW1qV662XDCwNYpS1WOiWv/REZhOQYEOOPrZ/pDbmEudVlERNRCWmxm5NaOQaf1+itThUfWHUexWoMhQa747Mm+sLVqkgsJiYjIyLXYzMhEUuns6YDPp/eFjaU5jiTkYPqXp1BYVil1WUREZEQa1KITEBAAmUxW753Nnz8fL730Ur23a05s0Wn9zqTk46kNp6Aq1yDEywFfP9MfrvZyqcsiIqJm1KxdV4cPH25QUf7+/vDz82vQts2FQcc0xKWrMP2rk8gprkAHNztsfmYAvB1tpC6LiIiaCcfoGIhBx3RcvVGMJ/57EumF5WjnaIPNzw5AgKud1GUREVEz4BgdanM6uNnjf88PQgdXO1wvKMOj64/jUoZK6rKIiEhCHKPDFh2Tc6NIjelfncKlDBUcrC2w8en+6NPeSeqyiIioCXGMjoEYdExTYWklntp4CmdTC2BrZY4vpofinkDjn9eJiIgMwzE6BmLQMV2lFRrM/uYMjiTkwMrcDGum9caorp5Sl0VERE2AY3SozbO1ssB/Z4RidFcPVFRp8fyWs9gRfU3qsoiIqAUx6JBJk1uYY+20PpjUpx2qtAL/2nYO3xxPlrosIiJqIQw6ZPIszM3w4SM9MXOQPwDg7Z8uYu2hRGmLIiKiFsGgQ22CmZkM74wPwYsjAgEAy3+Lxwd7/kIbH6JGRGTyGHSozZDJZHh1VCe8ObYzAGD94St4a+cFVGkZdoiITBWDDrU5z93bERGTukMmA7aeTMVzX59GUTlvBkpEZIoYdKhNeqx/e3zyWG9YWZjhwF/ZmPTpMaTklkhdFhERNTGjDjp//PEHxo8fD29vb8hkMuzcuVPv+ZkzZ0Imk+k9Bg4cKE2x1Oo80MMb388Og7tCjoTsYkxY+yeOJeZIXRYRETUhow46JSUl6NmzJ9asWXPbdcaMGYOMjAzdY/fu3S1YIbV2vXwd8cuLg9HTR4mC0ko8+dUpXn5ORGRCLKQu4E7Cw8MRHh5+x3Xkcjk8PTnbLTWch4M1ts0Ow+s/xOKnmHS8/dNF/JVZhEUPdoWluVH/X4CIiO6i1f8Vj4yMhLu7O4KDgzFr1ixkZ2ffcX21Wg2VSqX3ILK2NMeqKb3w+pjOkMmALSdT8cR/TyKvpELq0oiIqBFaddAJDw/Hli1bcPDgQaxYsQJRUVEYMWIE1Gr1bbeJiIiAUqnUPXx9fVuwYjJmMpkMzw/riC+eDIWdlTlOJuVhwtqjiM8skro0IiJqoFZzU0+ZTIYdO3Zg4sSJt10nIyMDfn5++O677zBp0qQ611Gr1XpBSKVSwdfXlzf1JD2Xs4rw7KbTSM0rhZ2VOT6a0os3BCUiMiJt8qaeXl5e8PPzQ0JCwm3XkcvlcHBw0HsQ3SrYQ4Gf5t6DsA4uKKmownPfnMGagwmcSZmIqJUxqaCTm5uLtLQ0eHl5SV0KmQAnOyt8/Ux/TA/zAwB8uO8yXvouBmUVVRJXRkREhjLqoFNcXIyYmBjExMQAAJKSkhATE4PU1FQUFxdjwYIFOH78OJKTkxEZGYnx48fD1dUVDz30kLSFk8mwNDfDuxO64f2HusHCTIZfzqVj8mfHkVFYJnVpRERkAKMeoxMZGYnhw4fXWj5jxgysW7cOEydORHR0NAoKCuDl5YXhw4fjvffeq9cAY0P7+IhOXM3F85vPIL+0Em4KOT57si/6tHeSuiwiojbJ0M9vow46LYFBh+ojLa8Uz246jfisIlhZmOHN8M6YHuYPMzOZ1KUREbUpbXIwMlFz83W2xQ8vDMLIEA9UaLRY9Escpn91CukF7MoiIjJGDDpE9WQvt8BnT/TFuxO6wtrSDEcTczB61R/48ew1XpVFRGRkGHSIGsDMTIbpYf7Y/dIQ9PJ1RFG5Bq98fw7Pbz6L3OLbT1hJREQti0GHqBE6uNlj+5wwLBgVDAszGfZezMToVUfwe1yW1KUREREYdIgazcLcDPNGBGHn3HsQ5G6PnGI1nv36NF7fHoui8kqpyyMiatMYdIiaSLd2Svzy4mDMGhIAmQzYdjoN4auP4OTVXKlLIyJqsxh0iJqQtaU53hoXgu9mDYSPkw2u5Zdh6hcn8P6uOJRXckZlIqKWxqBD1AwGdHDBnpeHYEqoL4QAvjiShAfXHMWF64VSl0ZE1KYw6BA1E4W1JZY+0gP/nR4KV3srXM4qxsS1f+KTAwnQVGmlLo+IqE1g0CFqZveHeOC3+fdiTFdPaLQCK/ZfxiPrj+NyVpHUpRERmTwGHaIW4GIvx7on+uCjKT2hsLZATFoBxq4+gojdl1Ci1khdHhGRyWLQIWohMpkMD/X2wW/z78WoEA9otAKf/XEV9688jD3nMzirMhFRM2DQIWph3o42+Hx6KL6cEQofJxtkFJbj+S1nMXNDFJJzSqQuj4jIpDDoEEnkvi4e+P2VoXhpRCCszM1w+PINjFr1Bz7af5mXohMRNREGHSIJWVua45VRnfDbv+7FkCBXVGi0WH0gAaM++gOH/sqWujwiolaPQYfICAS42uHrp/tj7bQ+8HSwRmpeKZ7aGIXZ35zG9YIyqcsjImq1GHSIjIRMJsO4Hl74/dWhmDUkAOZmMvx2MQv3rziMdZFXUKHh3DtERPUlE238Ug+VSgWlUonCwkI4ODhIXQ6Rzl+ZKry98wKikvMBAIHu9nh3QlcM6ugqcWVERNIz9PObLTpERqqzpwO+nx2GFY/2hIudFRKzizHti5N4+btopLM7i4jIIGzRYYsOtQKFpZX4cF88Np9MgRCAlYUZHh/QHi8MC4SbQi51eURELc7Qz28GHQYdakVirxXg/V2XcDIpDwBgY2mOmff4Y/a9HeBoayVxdURELYdBx0AMOtTaCCFwNDEHH+67jHNpBQAAhdwCs+7tgKcHB8BebiFtgURELYBBx0AMOtRaCSHw+6VsrNgXj78yq28Q6mRrieeHdcT0MH9YW5pLXCERUfNh0DEQgw61dlqtwK7zGfho/2VcvXkLCXeFHPNGBGJqv/awsuA1B0Rkehh0DMSgQ6ZCU6XFj9HXsfr3BN0kg+0cbfDy/UGY1LsdLMwZeIjIdDDoGIhBh0xNhUaLbVGp+ORgIrKL1ACADq52mD8yGA9094KZmUziComIGo9Bx0AMOmSqyiqqsPlECj6NTER+aSUAoLOnAvNGBCK8mxfMGXiIqBVj0DEQgw6ZumK1Bl8dTcIXf1xFkVoDAPBzscWsIR3wSF8fDlomolaJQcdADDrUVhSUVmDDn8nYdDwZBTdbeFztrfDUPQF4YqAflDaWEldIRGQ4Bh0DMehQW1NaocG2qDT890iSbtCynZU5pg1oj2cGd4Cn0lriComI7o5Bx0AMOtRWVVZp8WtsOtZHXkV8VvU8PJbmMkzs1Q6zh3ZAoLtC4gqJiG6PQcdADDrU1gkhEBl/A+sOX8Gpm7eWAICRIR6YM7Qj+vo5SVgdEVHdGHQMxKBD9LezqflYH3kF++KydMv6+ztjzrAOGN7JHTIZr9QiIuPAoGMgBh2i2hKzi/H5H1ewI/o6Kquq/0R08lBg5j3+mNDLG7ZWvJ8WEUmLQcdADDpEt5dZWI6v/kzClhMpKKmoAlB9A9GH+rTD4wP80MmT43iISBoMOgZi0CG6u8KySmyLSsXWk6lIzi3VLQ/1c8ITA/0wppsn5+MhohbFoGMgBh0iw2m1Aseu5GLLyRTsi8tClbb6z4eTrSUeDfXFtP7t4e9qJ3GVRNQWMOgYiEGHqGGyVOXYFpWGb0+lIqOwXLd8SJArHh/QHvd18YAlbyRKRM2EQcdADDpEjaOp0iIy/gY2n0zB4cs3UPMXxcNBjin92mNqP194O9pIWyQRmRwGHQMx6BA1nbS8Unx7KhXfn05DTnEFAMBMBozo7IEp/XwxrJMbW3mIqEkw6BiIQYeo6VVotPjtYia2nEzBiat/T0LoYmeFCb3a4eG+7dDVWylhhUTU2jHoGIhBh6h5JWYX47tTqdgZk46cYrVueRcvBzzcpx0m9m4HV3u5hBUSUWvEoGMgBh2ilqGp0uKPhBvYfuYafo/LRkWVFgBgbibD8E5ueLiPD0Z0cYfcgpepE9HdMegYiEGHqOUVlFbgl3Pp2H72Os6lFeiWO9pa4sGe3ni4jw96+Ch5ywkiui0GHQMx6BBJKzG7CD+cvY4fz15Dlurvrq1Ad3s80tcHD/VuBw8HawkrJCJjxKBjIAYdIuNQpRX4MzEHP5y9hr0XMqHWVHdtmcmAgR1cMLa7F8Z08+R4HiICwKBjMAYdIuOjKq/E7tgM/HD2GqKS83XLa0LPuB5eGNPVEy4MPURtFoOOgRh0iIxbWl4pdp3PwO7zGYi9VqhbbiYDwjrebOlh6CFqcxh0DMSgQ9R6pOaWYveFDOyKzcD563+HHnMzGQZ2cMa47t4Y3dWDoYeoDWDQMRCDDlHrlJpb3dKz63w6LlxX6Zabm8kQdnNMD0MPkeli0DEQgw5R65eSW6Lr3ro19PT3d8aorh4YGeIBHydbCaskoqbEoGMgBh0i05KcU6Lr3rqYrtJ7LsTLASNDPDCqqwdCvBw4Tw9RK8agYyAGHSLTlZpbin1xmdgXl4XTyXnQ/uOvXTtHm+rQE+KBfgHOvNkoUSvDoGMgBh2itiGvpAIHLmVhf1wW/ki4gfJKre45pY0lRnR2x6gQD9wb7AY7uYWElRKRIRh0DMSgQ9T2lFVU4WhiDvZdzMSBv7KRV1Khe87KwgyDA10xMsQDIzq7c1ZmIiPFoGMgBh2itq1KK3AmJR/7b3ZxpeSW6j3fxcsBQ4PdMKyTG/r6ObGLi8hIMOgYiEGHiGoIIZCQXYz9cVnYF5eF2GsF+OdfSHu5Be4JdMHQYHcM6+QGb0cb6YolauMYdAzEoENEt5NXUoEjCTcQGX8Df1y+gdx/dHEBQJC7PYZ1csPQYHf0C3CC3MJcokqJ2h4GHQMx6BCRIbRagQvphTgcfwORl28gOjVf7youG0tzDOrogmGd3DCskzt8nTlnD1FzMomg88cff2D58uU4c+YMMjIysGPHDkycOFH3vBACixcvxueff478/HwMGDAAa9euRdeuXQ3eB4MOETVEYWkljiTewOH4Gzh8+Qayi9R6z/u72OKeQFcMDnRFWEcXONpaSVQpkWky9PPbqK+hLCkpQc+ePfHUU0/h4YcfrvX8smXLsHLlSmzcuBHBwcH4z3/+g5EjRyI+Ph4KhUKCiomorVDaWuKBHt54oIc3hBC4lFGEyMvZiIy/gbMp+UjOLUVybiq2nEyFTAZ0b6fEPYGuGBLoij5+TrC2ZDcXUUsw6hadf5LJZHotOkIIeHt7Y/78+Xj99dcBAGq1Gh4eHli6dClmz55t0OuyRYeImlpReSVOXs3D0cQc/JmYg4TsYr3n5RZm6B/grGvxCfFygJkZZ2kmqg+TaNG5k6SkJGRmZmLUqFG6ZXK5HEOHDsWxY8cMDjpERE1NYW2J+0M8cH+IBwAgS1WOPxNzcDQxB0cTcpBdpMaRhBwcScgBADjZWmJQR1dd8GnvwvE9RE2l1QadzMxMAICHh4fecg8PD6SkpNx2O7VaDbX67750lUp123WJiJqCh4M1JvXxwaQ+PhBCIDG7WNfac+JqHvJLK2/eiT0DQPXtKQYEOGNAB2cMCHCBn4st78tF1ECtNujUuPWXXwhxxz8IERERWLx4cXOXRURUJ5lMhiAPBYI8FHjqngBUVmkRe60ARxNy8WdiDs6m5uN6QRl+jL6OH6OvAwA8HOQYEOCiCz4d3ewYfIgM1GqDjqenJ4Dqlh0vLy/d8uzs7FqtPP+0cOFCvPLKK7rvVSoVfH19m69QIqI7sDQ3Q18/Z/T1c8bL9wehRK3B2dR8nLyah5NJuYhJK0CWSo2fz6Xj53PpAABXezkGBDij/81Wn2B3Bcf4EN1Gqw06AQEB8PT0xP79+9G7d28AQEVFBQ4fPoylS5fedju5XA65XN5SZRIR1Yud3AJDgtwwJMgNAFBeWaUXfKJTC5BTrNbr6nKytUQ/f2cM6OCCAQHO6OypgAVvVUEEwMiDTnFxMRITE3XfJyUlISYmBs7Ozmjfvj3mz5+PJUuWICgoCEFBQViyZAlsbW0xbdo0CasmImo61pbmGNTRFYM6ugIA1JoqnEsrxMmruTiZlIczKfnIL63Evpu3rQAAWytz9G7viFA/Z4T6O6F3eyfY847s1EYZ9eXlkZGRGD58eK3lM2bMwMaNG3UTBn722Wd6EwZ269bN4H3w8nIias0qNFqcv16Ik0m5OHk1D2dT81FUrtFbx0xWfXPSfv7O6OvnhH7+zvBU8q7s1LqZxMzILYFBh4hMiVYrcDm7CFHJ+TiTnIeo5OrBzbdq52iDfv5O6OvvjH7+ThznQ60Og46BGHSIyNRlFpbjdEoeTifn43RKHuLSVXr36QIAhbUFevk6ond7J/T2dUQvX0c42fG2FWS8GHQMxKBDRG1NsVqDmNQCRCVXj/E5m5qP0oqqWuv5u9jqwk8vX0d08XKAlQUHOZNxYNAxEIMOEbV1miot/sosQnRaAaJT8xGTVoCrN0pqrWdlYYZu3g7o5euEXu0d0dvXET5ONpzThyTBoGMgBh0iotoKSysRc60AMakFiE6rDj8FpZW11nO1t0IvX0f09HFED19H9GinZJcXtQgGHQMx6BAR3Z0QAsm5pYhJy78ZfgoQl66C5tbBPgB8nW3Qw6c69PTwcUS3dg5QWFtKUDWZMgYdAzHoEBE1THllFS6mqxCdmo/Ya4U4f70QSTm1u7xkMqCDqx16+jiiu091+Onq7QBrS3MJqiZTwaBjIAYdIqKmU1haiQvphTh3rQDnrxUi9lphnZe3m5vJEOyhQE8fJbq1U6KrtwM6ezrAxorhhwzDoGMgBh0iouaVU6zG+Wt/h59z1wqRU6yutZ6ZDOjoZq8LPiHeDujqpYTSlt1eVBuDjoEYdIiIWpYQAhmF5Yi9VojYawW4mK7CxfRC5BRX1Lm+j5MNunlXh5+u7RzQ1VsJd4WcV3u1cQw6BmLQISKSnhAC2UVqXEwvxMXrKlxMV+FCeiGu5dfu9gKqr/YK8a7p8lIgxMsBAa52vJlpG8KgYyAGHSIi41VYWomLGYWIS78Zfq4X4sqN4lozOwOA3MIMwR4KdPFSoItX9ZifEC8Hdn2ZKAYdAzHoEBG1LmUVVfgrszr4XMqofvyVWVTn7M4A4K20Rhcvh+rwczME+bvYwZz39mrVGHQMxKBDRNT6abUCqXmluuATl1GESxmqOq/4AgAbS3MEe9gj2EOBTp4K3VeO/Wk9GHQMxKBDRGS6CssqEZ9ZpAtAlzJUiM8qQnmlts71HW0tq0OPhwLBntVfO3ko2P1lhBh0DMSgQ0TUtlRpBZJySnA5qwjxmUXVX7OKkJxTUufYHwDwcJAj2EOBzjdbf4I9FAh0t4ed3KJliycdBh0DMegQERFQPdPzlRvFiM+sDj6XM4twOav4tt1fANDO0QaB7vYIcq/uBgv0sEeguz0ceMuLZsegYyAGHSIiuhNVeSUSsop1LUDxmUVIyC6uc9LDGp4O1gi6GXqC3BUI8qgOQ462vOFpU2HQMRCDDhERNUR+SQUSbxQjIasYCdlFSMyu/nemqvy227jayxHoboeObvbo6GaPDm7V/27naAMzXgVWLww6BmLQISKipqQqr0RidjESbwaghJsB6E5dYHILMwS42qGju/3NEFQdgAJc7TgO6DYYdAzEoENERC2hRK1BYnYxrtwoxtUbJbhyo/rfyTmlqKiq+yowAPBSWuvCT4eb4SfA1Q7ejjZtei4gBh0DMegQEZGUNFVaXMsvw9WcYlzJ/jsAXb1RgtySuu//BQBWFmbwd7G9GXzs0cHVDgFu1SHIxc7K5OcDYtAxEIMOEREZq/ySCr0AdDWnBEk5JUjJLUFl1e0/vhXWFtXB52YICnCzQ4CLHfxcbU3mijAGHQMx6BARUWujqdIivaAcV3OKkXQz/CTllODqjRKkF5bhTp/sznZW8HOxhb+LXa2vjraWraYliEHHQAw6RERkSsorq5CSW4qknGIk5VR/vXqjBMm5pXe8JB4AHKwt4O9qBz8XO/i72MLvZgjyc7GFm71x3R6DQcdADDpERNRWFKs1SMktQUpuKZJzS5CSc/NrbukdL4sHqu8P1t7ZFr7O1cGnvbMt2t/86uNkA7mFeQu9i2oMOgZi0CEiIqq+K3xqXk3wqW4BSsktQXJO6V27w2QywMvB+pYQZFf91dkWTs3QJcagYyAGHSIiojur0GhxvaAMKbklSMsrRUpuKVLz/n6UVlTdcftdLw1GV29lk9Zk6Oc3ZyEiIiKiO7K6OaFhgKtdreeEEMgtqUBKbuktIagEqXmlyFKp4etsK0HV1Rh0iIiIqMFkMhlc7eVwtZejr59TrefLK6tgbdmy43f+yUyyPRMREZHJkzLkAAw6REREZMIYdIiIiMhkMegQERGRyWLQISIiIpPFoENEREQmi0GHiIiITBaDDhEREZksBh0iIiIyWQw6REREZLIYdIiIiMhkMegQERGRyWLQISIiIpPFoENEREQmy0LqAqQmhAAAqFQqiSshIiIiQ9V8btd8jt9Omw86RUVFAABfX1+JKyEiIqL6KioqglKpvO3zMnG3KGTitFot0tPToVAoIJPJmux1VSoVfH19kZaWBgcHhyZ7XVPEY2U4Hqv64fEyHI+V4XisDNecx0oIgaKiInh7e8PM7PYjcdp8i46ZmRl8fHya7fUdHBz4i2AgHivD8VjVD4+X4XisDMdjZbjmOlZ3asmpwcHIREREZLIYdIiIiMhkMeg0E7lcjnfeeQdyuVzqUowej5XheKzqh8fLcDxWhuOxMpwxHKs2PxiZiIiITBdbdIiIiMhkMegQERGRyWLQISIiIpPFoENEREQmi0GnmXz66acICAiAtbU1+vbtiyNHjkhdktFZtGgRZDKZ3sPT01PqsozCH3/8gfHjx8Pb2xsymQw7d+7Ue14IgUWLFsHb2xs2NjYYNmwYLl68KE2xErvbsZo5c2at82zgwIHSFCuxiIgI9OvXDwqFAu7u7pg4cSLi4+P11uG5Vc2QY8Vzq9q6devQo0cP3aSAYWFh2LNnj+55qc8pBp1msG3bNsyfPx9vvfUWoqOjMWTIEISHhyM1NVXq0oxO165dkZGRoXucP39e6pKMQklJCXr27Ik1a9bU+fyyZcuwcuVKrFmzBlFRUfD09MTIkSN1925rS+52rABgzJgxeufZ7t27W7BC43H48GHMnTsXJ06cwP79+6HRaDBq1CiUlJTo1uG5Vc2QYwXw3AIAHx8ffPDBBzh9+jROnz6NESNGYMKECbowI/k5JajJ9e/fX8yZM0dvWefOncUbb7whUUXG6Z133hE9e/aUugyjB0Ds2LFD971WqxWenp7igw8+0C0rLy8XSqVSrF+/XoIKjcetx0oIIWbMmCEmTJggST3GLjs7WwAQhw8fFkLw3LqTW4+VEDy37sTJyUn897//NYpzii06TayiogJnzpzBqFGj9JaPGjUKx44dk6gq45WQkABvb28EBARg6tSpuHr1qtQlGb2kpCRkZmbqnWNyuRxDhw7lOXYbkZGRcHd3R3BwMGbNmoXs7GypSzIKhYWFAABnZ2cAPLfu5NZjVYPnlr6qqip89913KCkpQVhYmFGcUww6TSwnJwdVVVXw8PDQW+7h4YHMzEyJqjJOAwYMwNdff43ffvsNX3zxBTIzMzFo0CDk5uZKXZpRqzmPeI4ZJjw8HFu2bMHBgwexYsUKREVFYcSIEVCr1VKXJikhBF555RUMHjwY3bp1A8Bz63bqOlYAz61/On/+POzt7SGXyzFnzhzs2LEDISEhRnFOtfm7lzcXmUym970Qotayti48PFz37+7duyMsLAwdO3bEpk2b8Morr0hYWevAc8wwU6ZM0f27W7duCA0NhZ+fH3bt2oVJkyZJWJm05s2bh9jYWBw9erTWczy39N3uWPHc+lunTp0QExODgoIC/PDDD5gxYwYOHz6se17Kc4otOk3M1dUV5ubmtZJqdnZ2rURL+uzs7NC9e3ckJCRIXYpRq7kyjedYw3h5ecHPz69Nn2cvvvgifv75Zxw6dAg+Pj665Ty3arvdsapLWz63rKysEBgYiNDQUERERKBnz55YvXq1UZxTDDpNzMrKCn379sX+/fv1lu/fvx+DBg2SqKrWQa1W49KlS/Dy8pK6FKMWEBAAT09PvXOsoqIChw8f5jlmgNzcXKSlpbXJ80wIgXnz5uHHH3/EwYMHERAQoPc8z62/3e1Y1aUtn1u3EkJArVYbxznVIkOe25jvvvtOWFpaii+//FLExcWJ+fPnCzs7O5GcnCx1aUbl1VdfFZGRkeLq1avixIkT4oEHHhAKhYLHSQhRVFQkoqOjRXR0tAAgVq5cKaKjo0VKSooQQogPPvhAKJVK8eOPP4rz58+Lxx57THh5eQmVSiVx5S3vTseqqKhIvPrqq+LYsWMiKSlJHDp0SISFhYl27dq1yWP1/PPPC6VSKSIjI0VGRobuUVpaqluH51a1ux0rnlt/W7hwofjjjz9EUlKSiI2NFW+++aYwMzMT+/btE0JIf04x6DSTtWvXCj8/P2FlZSX69Omjd0kiVZsyZYrw8vISlpaWwtvbW0yaNElcvHhR6rKMwqFDhwSAWo8ZM2YIIaovA37nnXeEp6enkMvl4t577xXnz5+XtmiJ3OlYlZaWilGjRgk3NzdhaWkp2rdvL2bMmCFSU1OlLlsSdR0nAGLDhg26dXhuVbvbseK59benn35a93nn5uYm7rvvPl3IEUL6c0omhBAt03ZERERE1LI4RoeIiIhMFoMOERERmSwGHSIiIjJZDDpERERkshh0iIiIyGQx6BAREZHJYtAhIiIik8WgQ0Rtnr+/P1atWiV1GUTUDBh0iKhFzZw5ExMnTgQADBs2DPPnz2+xfW/cuBGOjo61lkdFReG5555rsTqIqOVYSF0AEVFjVVRUwMrKqsHbu7m5NWE1RGRM2KJDRJKYOXMmDh8+jNWrV0Mmk0EmkyE5ORkAEBcXh7Fjx8Le3h4eHh548sknkZOTo9t22LBhmDdvHl555RW4urpi5MiRAICVK1eie/fusLOzg6+vL1544QUUFxcDACIjI/HUU0+hsLBQt79FixYBqN11lZqaigkTJsDe3h4ODg6YPHkysrKydM8vWrQIvXr1wjfffAN/f38olUpMnToVRUVFzXvQiKjeGHSISBKrV69GWFgYZs2ahYyMDGRkZMDX1xcZGRkYOnQoevXqhdOnT2Pv3r3IysrC5MmT9bbftGkTLCws8Oeff+Kzzz4DAJiZmeHjjz/GhQsXsGnTJhw8eBCvvfYaAGDQoEFYtWoVHBwcdPtbsGBBrbqEEJg4cSLy8vJw+PBh7N+/H1euXMGUKVP01rty5Qp27tyJX3/9Fb/++isOHz6MDz74oJmOFhE1FLuuiEgSSqUSVlZWsLW1haenp275unXr0KdPHyxZskS37KuvvoKvry8uX76M4OBgAEBgYCCWLVum95r/HO8TEBCA9957D88//zw+/fRTWFlZQalUQiaT6e3vVr///jtiY2ORlJQEX19fAMA333yDrl27IioqCv369QMAaLVabNy4EQqFAgDw5JNP4sCBA3j//fcbd2CIqEmxRYeIjMqZM2dw6NAh2Nvb6x6dO3cGUN2KUiM0NLTWtocOHcLIkSPRrl07KBQKTJ8+Hbm5uSgpKTF4/5cuXYKvr68u5ABASEgIHB0dcenSJd0yf39/XcgBAC8vL2RnZ9frvRJR82OLDhEZFa1Wi/Hjx2Pp0qW1nvPy8tL9287OTu+5lJQUjB07FnPmzMF7770HZ2dnHD16FM888wwqKysN3r8QAjKZ7K7LLS0t9Z6XyWTQarUG74eIWgaDDhFJxsrKClVVVXrL+vTpgx9++AH+/v6wsDD8T9Tp06eh0WiwYsUKmJlVN1Z///33d93frUJCQpCamoq0tDRdq05cXBwKCwvRpUsXg+shIuPArisikoy/vz9OnjyJ5ORk5OTkQKvVYu7cucjLy8Njjz2GU6dO4erVq9i3bx+efvrpO4aUjh07QqPR4JNPPsHVq1fxzTffYP369bX2V1xcjAMHDiAnJwelpaW1Xuf+++9Hjx498Pjjj+Ps2bM4deoUpk+fjqFDh9bZXUZExo1Bh4gks2DBApibmyMkJARubm5ITU2Ft7c3/vzzT1RVVWH06NHo1q0bXn75ZSiVSl1LTV169eqFlStXYunSpejWrRu2bNmCiIgIvXUGDRqEOXPmYMqUKXBzc6s1mBmo7oLauXMnnJyccO+99+L+++9Hhw4dsG3btiZ//0TU/GRCCCF1EURERETNgS06REREZLIYdIiIiMhkMegQERGRyWLQISIiIpPFoENEREQmi0GHiIiITBaDDhEREZksBh0iIiIyWQw6REREZLIYdIiIiMhkMegQERGRyWLQISIiIpP1/wFGTgmGgbBQpQAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.title(str(nqubits) + ' spins magnetic field diagonalization')\n", "plt.plot(off_diagonal_norm_tot)\n", @@ -215,27 +127,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|WARNING|2024-02-22 16:08:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# generate the Hamiltonian\n", "nqubits = 5\n", @@ -250,78 +144,28 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-02-22 16:13:45]: Using qibojit (numba) backend on /CPU:0\n", - "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 16:13:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j)]\n" - ] - } - ], + "outputs": [], "source": [ "# backend\n", "set_backend(\"qibojit\", \"numba\")\n", "# initialize dbi object\n", - "dbi_TFIM = DoubleBracketIteration(deepcopy(H_TFIM))\n", - "d_coef = onsite_Z_decomposition(dbi_TFIM.h.matrix)\n", - "print(d_coef)" + "dbi_TFIM = DoubleBracketIteration(deepcopy(H_TFIM))" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-02-22 16:26:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The initial D coefficients: [(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j)]\n", - "Gradient: [ -7.8132536 -17.94433184 -20.56560814 -17.94433184 -7.8132536 ]\n", - "s: 0.047896529653867745\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:228: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " grad[i] = s**3/3*da + s**2/2*db + 2*s*dc + s**2*ds*a + s*ds*b+ 2*ds*c\n" - ] - } - ], + "outputs": [], "source": [ "# generate the onsite Z operators\n", "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", "d_coef = onsite_Z_decomposition(dbi_TFIM.h.matrix, onsite_Z_ops)\n", "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", "grad, s = gradient_onsite_Z(dbi_TFIM,d,3, onsite_Z_ops)\n", + "print('Initial off-diagonal norm:', dbi.off_diagonal_norm)\n", "print('The initial D coefficients:', d_coef)\n", "print('Gradient:', grad)\n", "print('s:', s)" @@ -329,56 +173,25 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:228: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " grad[i] = s**3/3*da + s**2/2*db + 2*s*dc + s**2*ds*a + s*ds*b+ 2*ds*c\n" - ] - } - ], + "outputs": [], "source": [ - "iters = 30\n", + "NSTEPS = 30\n", "off_diagonal_norm_tot = [dbi_TFIM.off_diagonal_norm]\n", - "num_iters = []\n", "s_step = [0]\n", - "for i in range(iters):\n", - " s, d_coef, d, off_diagonal_norm_history = gradient_descent_onsite_Z(dbi_TFIM, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", - " off_diagonal_norm_tot.append(off_diagonal_norm_history[-1])\n", - " num_iters.append(len(off_diagonal_norm_history))\n", - " s_step.append(s)" + "for _ in range(NSTEPS):\n", + " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", + " off_diagonal_norm_tot.append(dbi_TFIM.off_diagonal_norm)\n", + " s_step.append(s)\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {s} with d_coef {d_coef}, loss {dbi_TFIM.off_diagonal_norm}\")" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", "plt.plot(off_diagonal_norm_tot)\n", @@ -388,24 +201,21 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# the final matrix\n", "visualize_matrix(dbi_TFIM.h.matrix)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Different initial `d`\n", + "Next, we show the effect of different choices of the initial direction of the gradient descent method." + ] } ], "metadata": { diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 13fbfff622..8ae4429c4a 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -225,22 +225,10 @@ def polynomial_step( if backup_scheduling is None: backup_scheduling = DoubleBracketScheduling.use_grid_search - def sigma(h: np.array): - return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) - - def Gamma(k: int): - r"""Computes the k_th Gamma function i.e $\Gamma_k=[W,...,[W,[W,H]]...]$, where we take k nested commutators with $W = [D, H]$""" - if k == 0: - return self.h.matrix - else: - W = self.commutator(d, sigma(self.h.matrix)) - result = self.h.matrix - for _ in range(k): - result = self.commutator(W, result) - return result - # list starting from s^n highest order to s^0 - sigma_gamma_list = np.array([sigma(Gamma(k)) for k in range(n + 2)]) + sigma_gamma_list = np.array( + [self.sigma(self.Gamma(k, d)) for k in range(n + 2)] + ) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients for rotation with [W,H] and H c1 = [ @@ -258,7 +246,8 @@ def Gamma(k: int): power = k + j product_matrix = c1[k] @ c2[j] trace_coefficients[power] += 2 * np.trace(product_matrix) - roots = np.roots(list(reversed(trace_coefficients[: n + 1]))) + taylor_coefficients = list(reversed(trace_coefficients[:n])) + roots = np.roots(taylor_coefficients) error = 1e-3 real_positive_roots = [ np.real(root) @@ -267,7 +256,7 @@ def Gamma(k: int): ] # solution exists, return minimum s if len(real_positive_roots) > 0: - return min(real_positive_roots) + return min(real_positive_roots), taylor_coefficients # solution does not exist, resort to backup scheduling elif ( backup_scheduling == DoubleBracketScheduling.use_polynomial_approximation @@ -277,7 +266,12 @@ def Gamma(k: int): n=n + 1, d=d, backup_scheduling=backup_scheduling ) else: - return self.choose_step(d=d, scheduling=backup_scheduling) + print( + f"Unable to find roots with current order, resorting to {backup_scheduling}" + ) + return self.choose_step(d=d, scheduling=backup_scheduling), list( + reversed(trace_coefficients[: n + 1]) + ) def choose_step( self, @@ -292,7 +286,9 @@ def choose_step( if scheduling is DoubleBracketScheduling.use_hyperopt: return self.hyperopt_step(d=d, **kwargs) if scheduling is DoubleBracketScheduling.use_polynomial_approximation: - return self.polynomial_step(d=d, **kwargs) + # omit taylor coefficients + step, _ = self.polynomial_step(d=d, **kwargs) + return step def loss(self, step: float, d: np.array = None, look_ahead: int = 1): """ @@ -330,3 +326,17 @@ def energy_fluctuation(self, state): state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ return self.h.energy_fluctuation(state) + + def sigma(self, h: np.array): + return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) + + def Gamma(self, k: int, d: np.array): + r"""Computes the k_th Gamma function i.e $\Gamma_k=[W,...,[W,[W,H]]...]$, where we take k nested commutators with $W = [D, H]$""" + if k == 0: + return self.h.matrix + else: + W = self.commutator(d, self.sigma(self.h.matrix)) + result = self.h.matrix + for _ in range(k): + result = self.commutator(W, result) + return result diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 9a311adcf8..b0a89c116a 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -250,6 +250,8 @@ def gradient_onsite_Z( + s * ds * b + 2 * ds * c ) + grad = np.array(grad) + grad = grad / np.linalg.norm(grad) return grad, s @@ -291,13 +293,11 @@ def gradient_descent_onsite_Z( nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) if onsite_Z_ops is None: onsite_Z_ops = generate_onsite_Z_ops(nqubits) - off_diagonal_norm_history = [dbi_object.off_diagonal_norm] if d is None: d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) grad, s = gradient_onsite_Z( dbi_object, d, n_taylor=n_taylor, onsite_Z_ops=onsite_Z_ops ) - off_diagonal_norm_history.append(dbi_object.loss(s, d)) # optimize gradient descent step with hyperopt if space is None: space = hyperopt.hp.loguniform("lr", np.log(lr_min), np.log(lr_max)) @@ -321,4 +321,4 @@ def func_loss_to_lr(lr): d_coef = [d_coef[j] - grad[j] * lr for j in range(nqubits)] d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) dbi_object(step=s, d=d) - return s, d_coef, d, off_diagonal_norm_history + return s, d_coef, d From 3cd9338f7acc876786150305633c7d3f45f1d8b1 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Fri, 23 Feb 2024 17:49:47 +0800 Subject: [PATCH 011/154] Fix error: missing argument d in gradient_onsite_Z --- examples/dbi/dbi_strategies_compare.ipynb | 8 + examples/dbi/dbi_strategy_Pauli-Z 2.ipynb | 514 ++++++++++++++++++ .../dbi/dbi_strategy_magnetic_field.ipynb | 112 +++- src/qibo/models/dbi/utils.py | 21 +- tests/test_models_dbi_utils 2.py | 50 ++ 5 files changed, 687 insertions(+), 18 deletions(-) create mode 100644 examples/dbi/dbi_strategy_Pauli-Z 2.ipynb create mode 100644 tests/test_models_dbi_utils 2.py diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 0afd3a9841..0bb5642abf 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -194,6 +194,7 @@ "steps_gradient_plot= [0]\n", "for _ in range(NSTEPS):\n", " step, d_coef, d = gradient_descent_onsite_Z(dbi_gradient, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=50, n_taylor=5)\n", + " dbi_gradient(d=d,step=step)\n", " off_diagonal_norm_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", " steps_gradient_plot.append(steps_gradient_plot[-1]+step)" @@ -228,6 +229,13 @@ "plt.xlabel('Iteration')\n", "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/dbi/dbi_strategy_Pauli-Z 2.ipynb b/examples/dbi/dbi_strategy_Pauli-Z 2.ipynb new file mode 100644 index 0000000000..5f563a172a --- /dev/null +++ b/examples/dbi/dbi_strategy_Pauli-Z 2.ipynb @@ -0,0 +1,514 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Double-Bracket Iteration Strategy: Pauli-Z products\n", + "\n", + "In this example, we demonstrate the usage of a DBI strategy, where the diagonal operators for double bracket iterations are variationally chosen from all possible local Pauli-Z operators." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initial setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python -m pip install hyperopt # required to optimize the DBF step" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import copy, deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketIteration\n", + "from qibo.models.dbi.utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below are some useful functions to visualize the diagonalization process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_matrix(matrix, title=\"\"):\n", + " \"\"\"Visualize absolute values of a matrix in a heatmap form.\"\"\"\n", + " fig, ax = plt.subplots(figsize=(5, 5))\n", + " ax.set_title(title)\n", + " try:\n", + " im = ax.imshow(np.absolute(matrix), cmap=\"inferno\")\n", + " except TypeError:\n", + " im = ax.imshow(np.absolute(matrix.get()), cmap=\"inferno\")\n", + " fig.colorbar(im, ax=ax)\n", + "\n", + "\n", + "def visualize_drift(h0, h):\n", + " \"\"\"Visualize drift of the evolved hamiltonian w.r.t. h0.\"\"\"\n", + " fig, ax = plt.subplots(figsize=(5, 5))\n", + " ax.set_title(r\"Drift: $|\\hat{H}_0 - \\hat{H}_{1}|$\")\n", + " try:\n", + " im = ax.imshow(np.absolute(h0 - h), cmap=\"inferno\")\n", + " except TypeError:\n", + " im = ax.imshow(np.absolute((h0 - h).get()), cmap=\"inferno\")\n", + "\n", + " fig.colorbar(im, ax=ax)\n", + "\n", + "\n", + "def plot_histories(loss_histories: list, steps: list, labels: list = None):\n", + " \"\"\"Plot off-diagonal norm histories over a sequential evolution.\"\"\"\n", + " plt.figure(figsize=(5, 5 * 6 / 8))\n", + " if len(steps) == 1:\n", + " # fixed_step\n", + " x_axis = [i * steps[0] for i in range(len(loss_histories))]\n", + " else:\n", + " x_axis = [sum(steps[:k]) for k in range(1, len(steps) + 1)]\n", + " plt.plot(x_axis, loss_histories, \"-o\")\n", + "\n", + " x_labels_rounded = [round(x, 2) for x in x_axis]\n", + " x_labels_rounded = [0] + x_labels_rounded[0:5] + [max(x_labels_rounded)]\n", + " x_labels_rounded.pop(3)\n", + " plt.xticks(x_labels_rounded)\n", + "\n", + " y_labels_rounded = [round(y, 1) for y in loss_histories]\n", + " y_labels_rounded = y_labels_rounded[0:5] + [min(y_labels_rounded)]\n", + " plt.yticks(y_labels_rounded)\n", + "\n", + " if labels is not None:\n", + " labels_copy = copy(labels)\n", + " labels_copy.insert(0, \"Initial\")\n", + " for i, label in enumerate(labels_copy):\n", + " plt.text(x_axis[i], loss_histories[i], label)\n", + "\n", + " plt.grid()\n", + " plt.xlabel(r\"Flow duration $s$\")\n", + " plt.title(\"Loss function histories\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example: TFIM\n", + "\n", + "As an example, we consider the Transverse Field Ising Model (TFIM):\n", + "$$ H_{\\rm TFIM} = - \\sum_{i=1}^{N}\\bigl( Z_i Z_{i+1} + h X_i \\bigr),$$\n", + "which is already implemented in `Qibo`. For this tutorial we set $N=5$ and $h=3$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# set the qibo backend (we suggest qibojit if N >= 20)\n", + "# alternatives: tensorflow (not optimized), numpy (when CPU not supported by jit)\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# initialize class\n", + "# Note: use deepcopy to prevent h being edited\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator)\n", + "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(H_TFIM.matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate local Pauli-Z operators" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generate_local_Z = generate_Z_operators(nqubits)\n", + "Z_ops = list(generate_local_Z.values())\n", + "Z_names = list(generate_local_Z.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Iteration from a list of operators\n", + "The idea of this strategy is to chose the Z operator that reduces the off-diagonal norm of the hamiltonian most efficiently. Given a list of operators (np.array), the function `select_best_dbr_generator_and_run` searches for the maximum decrease in off-diagonal norm for each operator and runs one double bracket rotation using the optimal operator from the list.\n", + "\n", + "Note that the hyperopt settings can be set as positional arguments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NSTEPS = 15\n", + "max_evals = 100\n", + "step_max = 1\n", + "Z_optimal = []\n", + "# add in initial values for plotting\n", + "off_diagonal_norm_history = [dbi.off_diagonal_norm]\n", + "steps = [0]\n", + "scheduling = DoubleBracketScheduling.use_hyperopt\n", + "for _ in range(NSTEPS):\n", + " dbi, idx, step, flip_sign = select_best_dbr_generator(dbi, Z_ops, scheduling=scheduling, compare_canonical=False, max_evals=max_evals, step_max=step_max)\n", + " off_diagonal_norm_history.append(dbi.off_diagonal_norm)\n", + " steps.append(steps[-1]+step)\n", + " if flip_sign < 0:\n", + " Z_optimal.append('-' + Z_names[idx])\n", + " else:\n", + " Z_optimal.append(Z_names[idx])\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with operator {Z_optimal[-1]}, loss {dbi.off_diagonal_norm}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_histories(off_diagonal_norm_history, steps, Z_optimal)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is worth noting that due to the nature of `hyperopt`, the iterations may be unstable and multiple runs may be required for the optimal result (alternatively, we can perform a grid search on the optimal step). Hence, it is sometimes needed to adjust its parameters including the following:\n", + "\n", + "- step_min\n", + "- step_max\n", + "- max_evals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare with canonical\n", + "\n", + "We compare the effectiveness at diagonalzation between the Pauli-Z operators and the canonical generator:\n", + "\n", + "$$ d = [H,\\sigma(H)]$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# set the qibo backend (we suggest qibojit if N >= 20)\n", + "# alternatives: tensorflow (not optimized), numpy (when CPU not supported by jit)\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "\n", + "# initialize class|\n", + "# Note: use deepcopy to prevent h being edited\n", + "dbi_canonical = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.canonical)\n", + "print(\"Initial off diagonal norm\", dbi_canonical.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "off_diagonal_norm_history_canonical = [dbi_canonical.off_diagonal_norm]\n", + "steps_canonical = [0]\n", + "steps_canonical_plot = [0]\n", + "for s in range(NSTEPS):\n", + " # same settings as iteration from list\n", + " step = dbi_canonical.hyperopt_step(\n", + " step_min = 1e-5,\n", + " step_max = 1,\n", + " space = hp.uniform,\n", + " optimizer = tpe,\n", + " )\n", + " dbi_canonical(step=step)\n", + " print(f\"New optimized step at iteration {s+1}/{NSTEPS}: {step}, loss {dbi_canonical.off_diagonal_norm}\")\n", + " off_diagonal_norm_history_canonical.append(dbi_canonical.off_diagonal_norm)\n", + " steps_canonical.append(step)\n", + " steps_canonical_plot.append(steps_canonical_plot[-1]+step)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "# plt.plot(steps, off_diagonal_norm_history, label=\"Pauli-Z\")\n", + "# plt.plot(steps_canonical, off_diagonal_norm_history_canonical, label=\"Canonical\")\n", + "plt.plot(off_diagonal_norm_history, label=\"Pauli-Z\")\n", + "plt.plot(off_diagonal_norm_history_canonical, label=\"Canonical\")\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Norm off-diagonal restriction\")\n", + "plt.title(\"Compare Variational Pauli-Z with Canonical\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(off_diagonal_norm_history)\n", + "print(off_diagonal_norm_history_canonical)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we make 2 observations:\n", + "\n", + "1. The canonical strategy has a steeper decrease at the beginning than Pauli-Z operators.\n", + "2. However, the canonical strategy is also prone to getting stuck at a local minimum and hence resultting in a lesser degree of diagonalization.\n", + "\n", + "Therefore, we explore the possibility of mixing the two strategies by including the canonical generator in the list." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mixed strategy: optimal at each step" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbi_mixed = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator)\n", + "print(\"Initial off diagonal norm\", dbi_mixed.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbi_eval = deepcopy(dbi_mixed)\n", + "dbi_eval.mode = DoubleBracketGeneratorType.canonical\n", + "if step is None:\n", + " step = dbi_eval.hyperopt_step(\n", + " step_max=step_max,\n", + " space=hp.uniform,\n", + " optimizer=tpe,\n", + " max_evals=max_evals,\n", + " )\n", + "dbi_eval(step=step)\n", + "print('canonical norm', dbi_eval.off_diagonal_norm, 'step', step)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z_optimal_mixed = []\n", + "# add in initial values for plotting\n", + "off_diagonal_norm_history_mixed = [dbi_mixed.off_diagonal_norm]\n", + "steps = [0]\n", + "for _ in range(NSTEPS):\n", + " dbi_mixed, idx, step, flip_sign = select_best_dbr_generator(dbi_mixed, Z_ops, scheduling=scheduling, compare_canonical=True, max_evals=max_evals, step_max=step_max)\n", + " off_diagonal_norm_history_mixed.append(dbi_mixed.off_diagonal_norm)\n", + " steps.append(steps[-1]+step)\n", + " if idx == len(Z_ops):\n", + " Z_optimal_mixed.append('Canonical')\n", + " elif flip_sign < 0:\n", + " Z_optimal_mixed.append('-' + Z_names[idx])\n", + " else:\n", + " Z_optimal_mixed.append(Z_names[idx])\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with operator {Z_optimal_mixed[-1]}, loss {dbi_mixed.off_diagonal_norm}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "# plt.plot(steps, off_diagonal_norm_history, label=\"Pauli-Z\")\n", + "# plt.plot(steps_canonical, off_diagonal_norm_history_canonical, label=\"Canonical\")\n", + "plt.plot(off_diagonal_norm_history, label=\"Pauli-Z\")\n", + "plt.plot(off_diagonal_norm_history_canonical, label=\"Canonical\")\n", + "plt.plot(off_diagonal_norm_history_mixed, label=\"Mixed\")\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Norm off-diagonal restriction\")\n", + "plt.title(\"Compare Variational Pauli-Z with Canonical\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After a few tests, we realize that the mixed strategy does not always outperform just using Pauli-Z operators. This could be caused by 2 reasons: \n", + "\n", + "1. Unstability of hyperopt\n", + "2. Tendency of canonical operator to get stuck at a near local minimum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mixed strategy: initial canonical\n", + "\n", + "Since the canonical double bracket iteration performs better at the initial steps, we attempt to combine the two strategies: iterate a few steps using the canonical bracket before switching to the variational Z-operators." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbi_mixed_can= DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.canonical)\n", + "print(\"Initial off diagonal norm\", dbi_mixed_can.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the initial iterations using canonical iterations\n", + "off_diagonal_norm_history_mixed_can = [dbi_mixed_can.off_diagonal_norm]\n", + "steps_mixed_can = [0]\n", + "cannonical_NSTEPS = 2\n", + "for i in range(cannonical_NSTEPS):\n", + " step = steps_canonical[i+1]\n", + " dbi_mixed_can(step=step)\n", + " off_diagonal_norm_history_mixed_can.append(dbi_mixed_can.off_diagonal_norm)\n", + " steps_mixed_can.append(step)\n", + " \n", + "print(\"After 2 steps, off diagonal norm:\", dbi_mixed_can.off_diagonal_norm)\n", + "print(\"By comparison, the Pauli-Z:\", off_diagonal_norm_history[2])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Continue the remaining steps with Pauli-Z operators\n", + "Z_optimal_mixed_can = [\"Cannonical\" for _ in range(cannonical_NSTEPS)]\n", + "remaining_NSTEPS = NSTEPS - cannonical_NSTEPS\n", + "dbi_mixed_can.mode = DoubleBracketGeneratorType.single_commutator\n", + "for _ in range(remaining_NSTEPS):\n", + " dbi_mixed_can, idx, step, flip_sign = select_best_dbr_generator(dbi_mixed_can, Z_ops, scheduling=scheduling, compare_canonical=False, max_evals=max_evals, step_max=step_max)\n", + " off_diagonal_norm_history_mixed_can.append(dbi_mixed_can.off_diagonal_norm)\n", + " steps_mixed_can.append(step)\n", + " if idx == len(Z_ops):\n", + " Z_optimal_mixed.append('Canonical')\n", + " elif flip_sign < 0:\n", + " Z_optimal_mixed.append('-' + Z_names[idx])\n", + " else:\n", + " Z_optimal_mixed.append(Z_names[idx])\n", + " print(f\"New optimized step at iteration {_+1}/{remaining_NSTEPS}: {step} with operator {Z_optimal_mixed_can[-1]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "# plt.plot(steps, off_diagonal_norm_history, label=\"Pauli-Z\")\n", + "# plt.plot(steps_canonical, off_diagonal_norm_history_canonical, label=\"Canonical\")\n", + "plt.plot(off_diagonal_norm_history, label=\"Pauli-Z\")\n", + "plt.plot(off_diagonal_norm_history_canonical, label=\"Canonical\")\n", + "plt.plot(off_diagonal_norm_history_mixed, label=\"Mixed: optimal steps\")\n", + "plt.plot(off_diagonal_norm_history_mixed_can, label=\"Mixed: initial canonical\")\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Norm off-diagonal restriction\")\n", + "plt.title(\"Compare Variational Pauli-Z with Canonical\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example also shows that the canonical generator is more likely to drive the model into a local minimum than variationally assigned diagonal operator, and that it is hard to get it unstuck even with the Pauli-Z operators." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dbi/dbi_strategy_magnetic_field.ipynb b/examples/dbi/dbi_strategy_magnetic_field.ipynb index d324efdb38..cb65b76541 100644 --- a/examples/dbi/dbi_strategy_magnetic_field.ipynb +++ b/examples/dbi/dbi_strategy_magnetic_field.ipynb @@ -67,7 +67,10 @@ "# initialize dbi object\n", "nqubits = 5\n", "h0 = random_hermitian(2**nqubits, seed=2)\n", - "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0))\n", + "scheduling = DoubleBracketScheduling.use_hyperopt\n", + "mode = DoubleBracketGeneratorType.single_commutator\n", + "n_taylor = 5\n", + "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)\n", "visualize_matrix(dbi.h.matrix, title=f'Random hamiltonian with L={nqubits}')" ] @@ -82,7 +85,7 @@ "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi,d,3, onsite_Z_ops)\n", + "grad, s = gradient_onsite_Z(dbi,d,5, onsite_Z_ops)\n", "print('The initial D coefficients:', d_coef)\n", "print('Gradient:', grad)\n", "print('s:', s)" @@ -95,11 +98,12 @@ "outputs": [], "source": [ "iters = 30\n", - "off_diagonal_norm_tot = [dbi.off_diagonal_norm]\n", + "off_diagonal_norm = [dbi.off_diagonal_norm]\n", "s_step = [0]\n", "for i in range(iters):\n", " s, d_coef, d = gradient_descent_onsite_Z(dbi, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", - " off_diagonal_norm_tot.append(dbi.off_diagonal_norm)\n", + " dbi(step=s, d=d)\n", + " off_diagonal_norm.append(dbi.off_diagonal_norm)\n", " s_step.append(s)" ] }, @@ -110,7 +114,7 @@ "outputs": [], "source": [ "plt.title(str(nqubits) + ' spins magnetic field diagonalization')\n", - "plt.plot(off_diagonal_norm_tot)\n", + "plt.plot(off_diagonal_norm)\n", "plt.xlabel('Iteration')\n", "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" ] @@ -151,7 +155,7 @@ "# backend\n", "set_backend(\"qibojit\", \"numba\")\n", "# initialize dbi object\n", - "dbi_TFIM = DoubleBracketIteration(deepcopy(H_TFIM))" + "dbi_TFIM = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" ] }, { @@ -164,7 +168,7 @@ "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", "d_coef = onsite_Z_decomposition(dbi_TFIM.h.matrix, onsite_Z_ops)\n", "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi_TFIM,d,3, onsite_Z_ops)\n", + "grad, s = gradient_onsite_Z(dbi_TFIM,d,5, onsite_Z_ops)\n", "print('Initial off-diagonal norm:', dbi.off_diagonal_norm)\n", "print('The initial D coefficients:', d_coef)\n", "print('Gradient:', grad)\n", @@ -177,13 +181,14 @@ "metadata": {}, "outputs": [], "source": [ - "NSTEPS = 30\n", - "off_diagonal_norm_tot = [dbi_TFIM.off_diagonal_norm]\n", - "s_step = [0]\n", + "NSTEPS = 15\n", + "off_diagonal_norm_delta = [dbi_TFIM.off_diagonal_norm]\n", + "s_step_delta = [0]\n", "for _ in range(NSTEPS):\n", - " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", - " off_diagonal_norm_tot.append(dbi_TFIM.off_diagonal_norm)\n", - " s_step.append(s)\n", + " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100, n_taylor=5, use_ds=True)\n", + " dbi_TFIM(step=s, d=d)\n", + " off_diagonal_norm_delta.append(dbi_TFIM.off_diagonal_norm)\n", + " s_step_delta.append(s)\n", " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {s} with d_coef {d_coef}, loss {dbi_TFIM.off_diagonal_norm}\")" ] }, @@ -194,7 +199,7 @@ "outputs": [], "source": [ "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", - "plt.plot(off_diagonal_norm_tot)\n", + "plt.plot(off_diagonal_norm_delta)\n", "plt.xlabel('Iteration')\n", "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" ] @@ -216,6 +221,85 @@ "## Different initial `d`\n", "Next, we show the effect of different choices of the initial direction of the gradient descent method." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H = H_TFIM.matrix\n", + "L = int(np.log2(H.shape[0]))\n", + "N = np.diag(np.linspace(np.min(np.diag(H)),np.max(np.diag(H)),2**L))\n", + "d_coef = onsite_Z_decomposition(N, onsite_Z_ops)\n", + "print(d_coef)\n", + "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "visualize_matrix(H, 'Initial hamiltonian')\n", + "visualize_matrix(N, 'Min-max diagonal matrix')\n", + "visualize_matrix(d, 'Min-max projection onsite-Z')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we see that the min-max diagonal operator can be correctly decomposed into onsite-Z operators. Then we generate the diagonalization curve and compare with other initializations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# backend\n", + "set_backend(\"qibojit\", \"numba\")\n", + "# initialize dbi object\n", + "dbi_TFIM_MMH = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NSTEPS = 15\n", + "off_diagonal_norm_MMH = [dbi_TFIM_MMH.off_diagonal_norm]\n", + "s_step_MMH = [0]\n", + "# d = np.diag(np.linspace(np.min(np.diag(dbi_TFIM_MMH.h.matrix)),np.max(np.diag(dbi_TFIM_MMH.h.matrix)),2**nqubits))\n", + "# d_coef = onsite_Z_decomposition(d, onsite_Z_ops)\n", + "for _ in range(NSTEPS):\n", + " d = np.diag(np.linspace(np.min(np.diag(dbi_TFIM_MMH.h.matrix)),np.max(np.diag(dbi_TFIM_MMH.h.matrix)),2**nqubits))\n", + " d_coef = onsite_Z_decomposition(d, onsite_Z_ops)\n", + " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM_MMH, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", + " dbi_TFIM_MMH(d=d, step=s)\n", + " off_diagonal_norm_MMH.append(dbi_TFIM_MMH.off_diagonal_norm)\n", + " s_step_MMH.append(s)\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {s} with d_coef {d_coef}, loss {dbi_TFIM_MMH.off_diagonal_norm}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", + "plt.plot(off_diagonal_norm_MMH, label='MMH')\n", + "plt.plot(off_diagonal_norm_delta, label='delta')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')\n", + "plt.legend()" + ] } ], "metadata": { diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index b0a89c116a..f42ab174d3 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -225,7 +225,11 @@ def derivative_product(k1, k2): def gradient_onsite_Z( - dbi_object: DoubleBracketIteration, d: np.array, n_taylor=3, onsite_Z_ops=None + dbi_object: DoubleBracketIteration, + d: np.array, + n_taylor=3, + onsite_Z_ops=None, + use_ds=False, ): """Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients""" # n is the highest order for calculating s @@ -236,12 +240,15 @@ def gradient_onsite_Z( onsite_Z_ops = generate_onsite_Z_ops(nqubits) grad = np.zeros(nqubits) s, coef = dbi_object.polynomial_step( + d=d, n=n_taylor, backup_scheduling=DoubleBracketScheduling.use_polynomial_approximation, ) a, b, c = coef[len(coef) - 3 :] for i in range(nqubits): da, db, dc, ds = ds_di_onsite_Z(dbi_object, d, i, [a, b, c], onsite_Z_ops) + if use_ds is True: + ds = 0 grad[i] = ( s**3 / 3 * da + s**2 / 2 * db @@ -281,7 +288,6 @@ def gradient_descent_onsite_Z( d: np.array = None, n_taylor: int = 3, onsite_Z_ops=None, - grad_tol: float = 1e-2, lr_min: float = 1e-5, lr_max: float = 1, max_evals: int = 100, @@ -289,6 +295,7 @@ def gradient_descent_onsite_Z( optimizer: callable = None, look_ahead: int = 1, verbose: bool = False, + use_ds: bool = True, ): nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) if onsite_Z_ops is None: @@ -296,7 +303,7 @@ def gradient_descent_onsite_Z( if d is None: d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) grad, s = gradient_onsite_Z( - dbi_object, d, n_taylor=n_taylor, onsite_Z_ops=onsite_Z_ops + dbi_object, d, n_taylor=n_taylor, onsite_Z_ops=onsite_Z_ops, use_ds=use_ds ) # optimize gradient descent step with hyperopt if space is None: @@ -320,5 +327,11 @@ def func_loss_to_lr(lr): d_coef = [d_coef[j] - grad[j] * lr for j in range(nqubits)] d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) - dbi_object(step=s, d=d) return s, d_coef, d + + +def diagonal_min_max(matrix: np.array): + L = int(np.log2(matrix.shape[0])) + D = np.linspace(np.min(np.diag(matrix)), np.max(np.diag(matrix)), 2**L) + D = np.diag(D) + return D diff --git a/tests/test_models_dbi_utils 2.py b/tests/test_models_dbi_utils 2.py new file mode 100644 index 0000000000..cd9f74e9de --- /dev/null +++ b/tests/test_models_dbi_utils 2.py @@ -0,0 +1,50 @@ +""""Testing utils for DoubleBracketIteration model""" + +import numpy as np +import pytest + +from qibo import set_backend +from qibo.hamiltonians import Hamiltonian +from qibo.models.dbi.double_bracket import ( + DoubleBracketGeneratorType, + DoubleBracketIteration, +) +from qibo.models.dbi.utils import * +from qibo.quantum_info import random_hermitian + +NSTEPS = 5 +"""Number of steps for evolution.""" + + +@pytest.mark.parametrize("nqubits", [3, 4, 5]) +def test_generate_Z_operators(backend, nqubits): + h0 = random_hermitian(2**nqubits) + dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0)) + generate_Z = generate_Z_operators(nqubits) + Z_ops = list(generate_Z.values()) + + delta_h0 = dbi.diagonal_h_matrix + dephasing_channel = (sum([Z_op @ h0 @ Z_op for Z_op in Z_ops]) + h0) / 2**nqubits + norm_diff = np.linalg.norm(delta_h0 - dephasing_channel) + + assert norm_diff < 1e-3 + + +@pytest.mark.parametrize("nqubits", [3, 4, 5]) +@pytest.mark.parametrize("step", [0.1, None]) +def test_select_best_dbr_generator(backend, nqubits, step): + h0 = random_hermitian(2**nqubits, seed=1, backend=backend) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + ) + generate_Z = generate_Z_operators(nqubits) + Z_ops = list(generate_Z.values()) + initial_off_diagonal_norm = dbi.off_diagonal_norm + + for _ in range(NSTEPS): + dbi, idx, step_optimize, flip = select_best_dbr_generator( + dbi, Z_ops, step=step, compare_canonical=True + ) + + assert initial_off_diagonal_norm > dbi.off_diagonal_norm From bfec99629bfa29d434aedbd284597bdb1f999db8 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 28 Feb 2024 15:09:23 +0800 Subject: [PATCH 012/154] Complete the merge in docstring --- src/qibo/models/dbi/utils.py | 30 ++++++++++-------------------- 1 file changed, 10 insertions(+), 20 deletions(-) diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 75e1fa6de7..52f8a33294 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -78,26 +78,16 @@ def select_best_dbr_generator( ): """Selects the best double bracket rotation generator from a list and runs the - Args: - dbi_object (`DoubleBracketIteration`): the target DoubleBracketIteration object. - d_list (list): list of diagonal operators (np.array) to run from. - step (float): fixed iteration duration. - Defaults to ``None``, uses hyperopt. - <<<<<<< HEAD - step_min (float): Minimally allowed iteration duration. - step_max (float): Maximally allowed iteration duration. - max_evals (int): Maximally allowed number of evaluation in hyperopt. - compare_canonical (bool): If `True`, the optimal diagonal operator chosen from "d_list" is compared with the canonical bracket. - ======= - step_min (float): minimally allowed iteration duration. - step_max (float): maximally allowed iteration duration. - max_evals (int): maximally allowed number of evaluation in hyperopt. - compare_canonical (bool): if `True`, the optimal diagonal operator chosen from "d_list" is compared with the canonical bracket. - mode (`DoubleBracketGeneratorType`): DBI generator type used for the selection. - >>>>>>> 056830fff9eedef0da2003a638ce4dbd30b6e3b8 - - Returns: - The updated dbi_object, index of the optimal diagonal operator, respective step duration, and evolution direction. + Args: + dbi_object (`DoubleBracketIteration`): the target DoubleBracketIteration object. + d_list (list): list of diagonal operators (np.array) to run from. + step (float): fixed iteration duration. + Defaults to ``None``, optimize with `scheduling` method and `choose_step` function. + compare_canonical (boolean): if `True`, the diagonalization effect with operators from `d_list` is compared with the canonical bracket. + scheduling (`DoubleBracketScheduling`): scheduling method for finding the optimal step. + + Returns: + The updated dbi_object, index of the optimal diagonal operator, respective step duration, and evolution direction. """ if scheduling is None: scheduling = dbi_object.scheduling From 54ced5bd2525c5322c305a7807ea50c414be9f7c Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 28 Feb 2024 15:20:41 +0800 Subject: [PATCH 013/154] Remove `use` in scheduling names --- examples/dbi/dbi_scheduling.ipynb | 35 ++++++++++++++++++--------- src/qibo/models/dbi/double_bracket.py | 18 +++++++------- tests/test_models_dbi.py | 8 +++--- 3 files changed, 36 insertions(+), 25 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 275d2dea73..1f53822023 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -87,7 +87,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The default scheduling strategy is grid search: `DoubleBracketScheduling.use_grid_serach`. This strategy specifies a list of step durations to test one by one and finds the one that maximizes the cost function (off-digonal norm of Hamiltonian)" + "The default scheduling strategy is grid search: `DoubleBracketScheduling.\n", + "grid_serach`. This strategy specifies a list of step durations to test one by one and finds the one that maximizes the cost function (off-digonal norm of Hamiltonian)" ] }, { @@ -97,16 +98,26 @@ "outputs": [], "source": [ "# grid_search\n", - "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search)\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n", "print('grid_search step:', step_grid)\n", "# hyperopt\n", - "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, max_evals=100, step_max=0.6)\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt, max_evals=100, step_max=0.6)\n", "print('hyperopt_search step:', step_hyperopt)\n", "# polynomial expansion\n", - "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.use_polynomial_approximation, n=5)\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, n=5)\n", "print('polynomial_approximation step:', step_poly)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "step_poly = dbi.polynomial_step(n=5)\n", + "print(step_poly)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -169,15 +180,15 @@ "outputs": [], "source": [ "# grid_search\n", - "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search, step_max=0.6, d=d)\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_max=0.6, d=d)\n", "grid_min = dbi.loss(step=step_grid, d=d)-dbi.off_diagonal_norm\n", "print('grid_search step:', step_grid, 'loss', grid_min)\n", "# hyperopt\n", - "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, d=d, max_evals=100, step_max=0.6)\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt, d=d, max_evals=100, step_max=0.6)\n", "hyperopt_min = dbi.loss(step=step_hyperopt, d=d)-dbi.off_diagonal_norm\n", "print('hyperopt_search step:', step_hyperopt, 'loss', hyperopt_min)\n", "# polynomial expansion\n", - "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.use_polynomial_approximation, d=d, n=5)\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=5)\n", "poly_min = dbi.loss(step=step_poly, d=d)-dbi.off_diagonal_norm\n", "print('polynomial_approximation step:', step_poly, 'loss', poly_min)" ] @@ -230,10 +241,10 @@ " step_min = step_poly - search_range/2\n", " step_max = step_poly + search_range/2\n", "# grid_search\n", - "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search, step_min=step_min, step_max=step_max, d=d)\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_min=step_min, step_max=step_max, d=d)\n", "print('grid_search step:', step_grid)\n", "# hyperopt\n", - "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, step_min=step_min, step_max=step_max, max_evals=100, d=d,)\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt, step_min=step_min, step_max=step_max, max_evals=100, d=d,)\n", "print('hyperopt_search step:', step_hyperopt)" ] }, @@ -313,9 +324,9 @@ "outputs": [], "source": [ "NSTEPS = 8\n", - "scheduling_list = [DoubleBracketScheduling.use_grid_search,\n", - " DoubleBracketScheduling.use_hyperopt,\n", - " DoubleBracketScheduling.use_polynomial_approximation,]\n", + "scheduling_list = [DoubleBracketScheduling.grid_search,\n", + " DoubleBracketScheduling.hyperopt,\n", + " DoubleBracketScheduling.polynomial_approximation,]\n", "scheduling_labels = ['grid search',\n", " 'hyperopt',\n", " 'polynomial',]\n", diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 1d1d48d6c9..1dbfc50b56 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -25,11 +25,11 @@ class DoubleBracketGeneratorType(Enum): class DoubleBracketScheduling(Enum): """Define the DBI scheduling strategies.""" - use_hyperopt = auto() + hyperopt = auto() """Use hyperopt package.""" - use_grid_search = auto() + grid_search = auto() """Use greedy grid search.""" - use_polynomial_approximation = auto() + polynomial_approximation = auto() """Use polynomial expansion (analytical) of the loss function.""" @@ -61,7 +61,7 @@ def __init__( self, hamiltonian: Hamiltonian, mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.canonical, - scheduling: DoubleBracketScheduling = DoubleBracketScheduling.use_grid_search, + scheduling: DoubleBracketScheduling = DoubleBracketScheduling.grid_search, ): self.h = hamiltonian self.h0 = deepcopy(self.h) @@ -223,7 +223,7 @@ def polynomial_step( d = self.diagonal_h_matrix if backup_scheduling is None: - backup_scheduling = DoubleBracketScheduling.use_grid_search + backup_scheduling = DoubleBracketScheduling.grid_search def sigma(h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) @@ -270,7 +270,7 @@ def Gamma(k: int): return min(real_positive_roots) # solution does not exist, resort to backup scheduling elif ( - backup_scheduling == DoubleBracketScheduling.use_polynomial_approximation + backup_scheduling == DoubleBracketScheduling.polynomial_approximation and n < n_max + 1 ): return self.polynomial_step( @@ -287,11 +287,11 @@ def choose_step( ): if scheduling is None: scheduling = self.scheduling - if scheduling is DoubleBracketScheduling.use_grid_search: + if scheduling is DoubleBracketScheduling.grid_search: return self.grid_search_step(d=d, **kwargs) - if scheduling is DoubleBracketScheduling.use_hyperopt: + if scheduling is DoubleBracketScheduling.hyperopt: return self.hyperopt_step(d=d, **kwargs) - if scheduling is DoubleBracketScheduling.use_polynomial_approximation: + if scheduling is DoubleBracketScheduling.polynomial_approximation: return self.polynomial_step(d=d, **kwargs) def loss(self, step: float, d: np.array = None, look_ahead: int = 1): diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 07904c551e..a573c32088 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -112,7 +112,7 @@ def test_energy_fluctuations(backend): @pytest.mark.parametrize( "scheduling", - [DoubleBracketScheduling.use_grid_search, DoubleBracketScheduling.use_hyperopt], + [DoubleBracketScheduling.grid_search, DoubleBracketScheduling.hyperopt], ) @pytest.mark.parametrize("nqubits", [3, 4, 5]) def test_double_bracket_iteration_scheduling_grid_hyperopt( @@ -136,7 +136,7 @@ def test_double_bracket_iteration_scheduling_grid_hyperopt( @pytest.mark.parametrize("nqubits", [3, 4, 6]) @pytest.mark.parametrize("n", [2, 3]) @pytest.mark.parametrize( - "backup_scheduling", [None, DoubleBracketScheduling.use_polynomial_approximation] + "backup_scheduling", [None, DoubleBracketScheduling.polynomial_approximation] ) def test_double_bracket_iteration_scheduling_polynomial( backend, nqubits, n, backup_scheduling @@ -146,14 +146,14 @@ def test_double_bracket_iteration_scheduling_polynomial( dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.single_commutator, - scheduling=DoubleBracketScheduling.use_polynomial_approximation, + scheduling=DoubleBracketScheduling.polynomial_approximation, ) initial_off_diagonal_norm = dbi.off_diagonal_norm for _ in range(NSTEPS): step1 = dbi.polynomial_step(n=n, d=d, backup_scheduling=backup_scheduling) dbi(d=d, step=step1) step2 = dbi.choose_step( - scheduling=DoubleBracketScheduling.use_polynomial_approximation, n=n + scheduling=DoubleBracketScheduling.polynomial_approximation, n=n ) dbi(step=step2) assert initial_off_diagonal_norm > dbi.off_diagonal_norm From 4b9dc91c3acc7ca1aa9524e81341579cfc0ae105 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 28 Feb 2024 15:31:04 +0800 Subject: [PATCH 014/154] Complete docstring; set default polynomial order to even number 4. --- src/qibo/models/dbi/double_bracket.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 1dbfc50b56..d017b32d7f 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -205,7 +205,7 @@ def hyperopt_step( def polynomial_step( self, - n: int = 3, + n: int = 4, n_max: int = 5, d: np.array = None, backup_scheduling: DoubleBracketScheduling = None, @@ -214,9 +214,10 @@ def polynomial_step( Optimizes iteration step by solving the n_th order polynomial expansion of the loss function. e.g. $n=2$: $2\Trace(\sigma(\Gamma_1 + s\Gamma_2 + s^2/2\Gamma_3)\sigma(\Gamma_0 + s\Gamma_1 + s^2/2\Gamma_2)) Args: - n (int, optional): The order to which the loss function is expanded. Defaults to 3. - n_max (int, optional): The maximum order allowed for recurring calls of `polynomial_step`. Defaults to 5. - d (np.array, optional): The diagonal operator, default as $\delta(H)$. + n (int, optional): the order to which the loss function is expanded. Defaults to 4. + n_max (int, optional): maximum order allowed for recurring calls of `polynomial_step`. Defaults to 5. + d (np.array, optional): diagonal operator, default as $\delta(H)$. + backup_scheduling (`DoubleBracketScheduling`): the scheduling method to use in case no real positive roots are found. """ if d is None: @@ -259,6 +260,7 @@ def Gamma(k: int): product_matrix = c1[k] @ c2[j] trace_coefficients[power] += 2 * np.trace(product_matrix) roots = np.roots(list(reversed(trace_coefficients[: n + 1]))) + print(list(reversed(trace_coefficients[: n + 1]))) error = 1e-3 real_positive_roots = [ np.real(root) From 412451987f2f1e80fa7cbed08406a1b1ab9bad8f Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 28 Feb 2024 17:15:09 +0800 Subject: [PATCH 015/154] Added documentation + test of effect of n_taylor --- .../dbi/dbi_strategy_magnetic_field.ipynb | 83 +++++++++++++++++++ src/qibo/models/dbi/double_bracket.py | 3 + src/qibo/models/dbi/utils.py | 56 +++++++++++-- 3 files changed, 133 insertions(+), 9 deletions(-) diff --git a/examples/dbi/dbi_strategy_magnetic_field.ipynb b/examples/dbi/dbi_strategy_magnetic_field.ipynb index 40e46f422a..8ab8207500 100644 --- a/examples/dbi/dbi_strategy_magnetic_field.ipynb +++ b/examples/dbi/dbi_strategy_magnetic_field.ipynb @@ -300,6 +300,89 @@ "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')\n", "plt.legend()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect of `n_taylor`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# backend\n", + "set_backend(\"qibojit\", \"numba\")\n", + "# initialize dbi object\n", + "nqubits = 5\n", + "h0 = random_hermitian(2**nqubits, seed=2)\n", + "scheduling = DoubleBracketScheduling.hyperopt\n", + "mode = DoubleBracketGeneratorType.single_commutator\n", + "n_1 = 5\n", + "n_2 = 2\n", + "dbi_1 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", + "dbi_2 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", + "print(\"Initial off diagonal norm\", dbi_1.off_diagonal_norm)\n", + "visualize_matrix(dbi_1.h.matrix, title=f'Random hamiltonian with L={nqubits}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the onsite Z operators\n", + "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", + "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", + "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", + "grad, s = gradient_onsite_Z(dbi,d,5, onsite_Z_ops)\n", + "print('The initial D coefficients:', d_coef)\n", + "print('Gradient:', grad)\n", + "print('s:', s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iters = 30\n", + "d_coef_1, d_1 = d_coef, d\n", + "d_coef_2, d_2 = d_coef, d\n", + "\n", + "off_diagonal_norm_1 = [dbi_1.off_diagonal_norm]\n", + "off_diagonal_norm_2 = [dbi_2.off_diagonal_norm]\n", + "s_step_1 = [0]\n", + "s_step_2 = [0]\n", + "for i in range(iters):\n", + " s_1, d_coef_1, d_1 = gradient_descent_onsite_Z(dbi_1, d_coef_1, d_1, onsite_Z_ops=onsite_Z_ops, n_taylor=n_1, max_evals=100)\n", + " s_2, d_coef_2, d_2 = gradient_descent_onsite_Z(dbi_2, d_coef_2, d_2, onsite_Z_ops=onsite_Z_ops, n_taylor=n_2, max_evals=100)\n", + " dbi_1(step=s_1, d=d_1)\n", + " dbi_2(step=s_2, d=d_2)\n", + " off_diagonal_norm_1.append(dbi_1.off_diagonal_norm)\n", + " off_diagonal_norm_2.append(dbi_2.off_diagonal_norm)\n", + " s_step_1.append(s_1)\n", + " s_step_2.append(s_2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.title(str(nqubits) + ' spins magnetic field diagonalization')\n", + "plt.plot(off_diagonal_norm_1, label=f'n_taylor={n_1}')\n", + "plt.plot(off_diagonal_norm_2, label=f'n_taylor={n_2}')\n", + "plt.legend()\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] } ], "metadata": { diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index e75fa16a21..46b8112712 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -280,6 +280,9 @@ def choose_step( scheduling: Optional[DoubleBracketScheduling] = None, **kwargs, ): + """ + Calculate the optimal step using respective `scheduling` methods. + """ if scheduling is None: scheduling = self.scheduling if scheduling is DoubleBracketScheduling.grid_search: diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 3ac26d0225..d179d45a4d 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -187,10 +187,19 @@ def ds_di_onsite_Z( dbi_object: DoubleBracketIteration, d: np.array, i: int, - taylor_coef=None, - onsite_Z_ops=None, + taylor_coef: Optional[list] = None, + onsite_Z_ops: Optional[list] = None, ): - """Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients""" + r"""Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients\ + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d (np.array): the diagonal operator + i (int): the index of onsite-Z coefficient + taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}`, from the highest order to the lowest. + onsite_Z_ops (list): onsite Z operators of `dbi_object.h` + Returns: + floats da, db, dc, ds + """ # generate the list of derivatives w.r.t ith Z operator coefficient nqubits = int(np.log2(d.shape[0])) if onsite_Z_ops is None: @@ -227,11 +236,18 @@ def derivative_product(k1, k2): def gradient_onsite_Z( dbi_object: DoubleBracketIteration, d: np.array, - n_taylor=3, + n_taylor=2, onsite_Z_ops=None, use_ds=False, ): - """Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients""" + r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d (np.array): the diagonal operator + n_taylor (int): the highest order of the taylore expansion of w.r.t `s` + taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` + use_ds (boolean): if False, ds is set to 0 + """ # n is the highest order for calculating s # initialize gradient @@ -244,6 +260,7 @@ def gradient_onsite_Z( n=n_taylor, backup_scheduling=DoubleBracketScheduling.polynomial_approximation, ) + a, b, c = coef[len(coef) - 3 :] for i in range(nqubits): da, db, dc, ds = ds_di_onsite_Z(dbi_object, d, i, [a, b, c], onsite_Z_ops) @@ -263,6 +280,7 @@ def gradient_onsite_Z( def onsite_Z_decomposition(h_matrix: np.array, onsite_Z_ops=None): + """finds the decomposition of hamiltonian `h_matrix` into Pauli-Z operators""" nqubits = int(np.log2(h_matrix.shape[0])) if onsite_Z_ops is None: onsite_Z_ops = generate_onsite_Z_ops(nqubits) @@ -274,6 +292,7 @@ def onsite_Z_decomposition(h_matrix: np.array, onsite_Z_ops=None): def generate_onsite_Z_ops(nqubits): + """generate the list of Pauli-Z operators of an `nqubit` system in the form of np.array""" onsite_Z_str = ["I" * (i) + "Z" + "I" * (nqubits - i - 1) for i in range(nqubits)] onsite_Z_ops = [ SymbolicHamiltonian(str_to_symbolic(Z_i_str)).dense.matrix @@ -285,18 +304,37 @@ def generate_onsite_Z_ops(nqubits): def gradient_descent_onsite_Z( dbi_object: DoubleBracketIteration, d_coef: list, - d: np.array = None, - n_taylor: int = 3, - onsite_Z_ops=None, + d: Optional[np.array] = None, + n_taylor: int = 2, + onsite_Z_ops: Optional[list] = None, lr_min: float = 1e-5, lr_max: float = 1, max_evals: int = 100, space: callable = None, optimizer: callable = None, - look_ahead: int = 1, verbose: bool = False, use_ds: bool = True, ): + """calculate the elements of one gradient descent step on `dbi_object`. + + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d_coef (list): the initial decomposition of `d` into Pauli-Z operators + d (np.array, optional): the initial diagonal operator. Defaults to None. + n_taylor (int, optional): the highest order to expand the loss function derivative. Defaults to 2. + onsite_Z_ops (list, optional): list of onsite-Z operators. Defaults to None. + lr_min (float, optional): the minimal gradient step. Defaults to 1e-5. + lr_max (float, optional): the maximal gradient step. Defaults to 1. + max_evals (int, optional): the max number of evaluations for `hyperopt` to find the optimal gradient step `lr`. Defaults to 100. + space (callable, optional): the search space for `hyperopt`. Defaults to None. + optimizer (callable, optional): optimizer for `hyperopt`. Defaults to None. + verbose (bool, optional): option to print out the 'hyperopt' progress. Defaults to False. + use_ds (bool, optional): if False, ds is set to 0. Defaults to True. + + Returns: + the optimal step found, coeffcients of `d` in Pauli-Z basis, matrix of `d` + + """ nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) if onsite_Z_ops is None: onsite_Z_ops = generate_onsite_Z_ops(nqubits) From c5ab13e2f5e8c5f011686d925f091213dc4f53e4 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 28 Feb 2024 18:16:14 +0800 Subject: [PATCH 016/154] Remove print line --- src/qibo/models/dbi/double_bracket.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index d017b32d7f..2c160ea525 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -260,7 +260,6 @@ def Gamma(k: int): product_matrix = c1[k] @ c2[j] trace_coefficients[power] += 2 * np.trace(product_matrix) roots = np.roots(list(reversed(trace_coefficients[: n + 1]))) - print(list(reversed(trace_coefficients[: n + 1]))) error = 1e-3 real_positive_roots = [ np.real(root) From 3bdee0f67597f82c3023804d0ae8b5077511fd14 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 28 Feb 2024 19:25:47 +0800 Subject: [PATCH 017/154] Test coverage for the branch --- src/qibo/models/dbi/utils.py | 3 +- tests/test_models_dbi_utils 2.py | 50 -------------------------------- tests/test_models_dbi_utils.py | 49 ++++++++++++++++++++++++++++++- 3 files changed, 50 insertions(+), 52 deletions(-) delete mode 100644 tests/test_models_dbi_utils 2.py diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index d179d45a4d..8a185dfe06 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -236,8 +236,8 @@ def derivative_product(k1, k2): def gradient_onsite_Z( dbi_object: DoubleBracketIteration, d: np.array, + onsite_Z_ops, n_taylor=2, - onsite_Z_ops=None, use_ds=False, ): r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients @@ -245,6 +245,7 @@ def gradient_onsite_Z( dbi_object (DoubleBracketIteration): the target dbi object d (np.array): the diagonal operator n_taylor (int): the highest order of the taylore expansion of w.r.t `s` + onsite_Z_ops (list): list of Pauli-Z operators taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` use_ds (boolean): if False, ds is set to 0 """ diff --git a/tests/test_models_dbi_utils 2.py b/tests/test_models_dbi_utils 2.py deleted file mode 100644 index cd9f74e9de..0000000000 --- a/tests/test_models_dbi_utils 2.py +++ /dev/null @@ -1,50 +0,0 @@ -""""Testing utils for DoubleBracketIteration model""" - -import numpy as np -import pytest - -from qibo import set_backend -from qibo.hamiltonians import Hamiltonian -from qibo.models.dbi.double_bracket import ( - DoubleBracketGeneratorType, - DoubleBracketIteration, -) -from qibo.models.dbi.utils import * -from qibo.quantum_info import random_hermitian - -NSTEPS = 5 -"""Number of steps for evolution.""" - - -@pytest.mark.parametrize("nqubits", [3, 4, 5]) -def test_generate_Z_operators(backend, nqubits): - h0 = random_hermitian(2**nqubits) - dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0)) - generate_Z = generate_Z_operators(nqubits) - Z_ops = list(generate_Z.values()) - - delta_h0 = dbi.diagonal_h_matrix - dephasing_channel = (sum([Z_op @ h0 @ Z_op for Z_op in Z_ops]) + h0) / 2**nqubits - norm_diff = np.linalg.norm(delta_h0 - dephasing_channel) - - assert norm_diff < 1e-3 - - -@pytest.mark.parametrize("nqubits", [3, 4, 5]) -@pytest.mark.parametrize("step", [0.1, None]) -def test_select_best_dbr_generator(backend, nqubits, step): - h0 = random_hermitian(2**nqubits, seed=1, backend=backend) - dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.single_commutator, - ) - generate_Z = generate_Z_operators(nqubits) - Z_ops = list(generate_Z.values()) - initial_off_diagonal_norm = dbi.off_diagonal_norm - - for _ in range(NSTEPS): - dbi, idx, step_optimize, flip = select_best_dbr_generator( - dbi, Z_ops, step=step, compare_canonical=True - ) - - assert initial_off_diagonal_norm > dbi.off_diagonal_norm diff --git a/tests/test_models_dbi_utils.py b/tests/test_models_dbi_utils.py index a05266e1de..fe202ee877 100644 --- a/tests/test_models_dbi_utils.py +++ b/tests/test_models_dbi_utils.py @@ -16,7 +16,7 @@ @pytest.mark.parametrize("nqubits", [2, 3]) -def test_generate_Z_operators(backend, nqubits): +def test_generate_Z_operators(nqubits): h0 = random_hermitian(2**nqubits) dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0)) generate_Z = generate_Z_operators(nqubits) @@ -47,3 +47,50 @@ def test_select_best_dbr_generator(backend, nqubits, step): ) assert initial_off_diagonal_norm > dbi.off_diagonal_norm + + +@pytest.mark.parametrize("nqubits", [3, 4, 5]) +@pytest.mark.parametrize("d_option", ["delta_H", "min_max"]) +def test_gradient_descent_onsite_Z(backend, nqubits, d_option): + h0 = random_hermitian(2**nqubits, seed=1, backend=backend) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + onsite_Z_ops = generate_onsite_Z_ops(nqubits) + if d_option == "delta_H": + d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops) + if d_option == "min_max": + d_min_max = diagonal_min_max(dbi.h.matrix) + d_coef = onsite_Z_decomposition(d_min_max, onsite_Z_ops) + d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) + iters = 15 + for _ in range(iters): + # calculate elements of gradient descent + s, d_coef, d = gradient_descent_onsite_Z( + dbi, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100 + ) + # double bracket rotation with the results + dbi(step=s, d=d) + # when onsite_Z_ops not given + s, d_coef, d = gradient_descent_onsite_Z(dbi, d_coef, max_evals=100) + dbi(step=s, d=d) + assert initial_off_diagonal_norm > dbi.off_diagonal_norm + + +@pytest.mark.parametrize("nqubits", [3, 4, 5]) +def test_dGamma_di_onsite_Z(nqubits): + h0 = random_hermitian(2**nqubits, seed=1) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0), + mode=DoubleBracketGeneratorType.single_commutator, + ) + onsite_Z_ops = generate_onsite_Z_ops(nqubits) + d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops) + d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) + # provide onsite_Z_ops or not gives the same result + dGamma_di_onsite_Z_with_Z_ops = dGamma_di_onsite_Z(dbi, 3, 1, d, onsite_Z_ops) + assert ( + dGamma_di_onsite_Z_with_Z_ops[-1] == dGamma_di_onsite_Z(dbi, 3, 1, d)[-1] + ).all() From f35a58d1cc530e25dde60a644561655a16026bb5 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Fri, 1 Mar 2024 12:45:43 +0800 Subject: [PATCH 018/154] Fix error in notebook call (key arguments exchanged) --- examples/dbi/dbi_strategy_magnetic_field.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/dbi/dbi_strategy_magnetic_field.ipynb b/examples/dbi/dbi_strategy_magnetic_field.ipynb index 8ab8207500..1455a1daca 100644 --- a/examples/dbi/dbi_strategy_magnetic_field.ipynb +++ b/examples/dbi/dbi_strategy_magnetic_field.ipynb @@ -85,7 +85,7 @@ "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi,d,5, onsite_Z_ops)\n", + "grad, s = gradient_onsite_Z(dbi, d, n_taylor=5, onsite_Z_ops=onsite_Z_ops)\n", "print('The initial D coefficients:', d_coef)\n", "print('Gradient:', grad)\n", "print('s:', s)" @@ -113,7 +113,7 @@ "metadata": {}, "outputs": [], "source": [ - "plt.title(str(nqubits) + ' spins magnetic field diagonalization')\n", + "plt.title(str(nqubits) + ' spins random hamiltonian')\n", "plt.plot(off_diagonal_norm)\n", "plt.xlabel('Iteration')\n", "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" @@ -168,7 +168,7 @@ "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", "d_coef = onsite_Z_decomposition(dbi_TFIM.h.matrix, onsite_Z_ops)\n", "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi_TFIM,d,5, onsite_Z_ops)\n", + "grad, s = gradient_onsite_Z(dbi_TFIM, d, n_taylor=5, onsite_Z_ops=onsite_Z_ops)\n", "print('Initial off-diagonal norm:', dbi.off_diagonal_norm)\n", "print('The initial D coefficients:', d_coef)\n", "print('Gradient:', grad)\n", @@ -322,7 +322,7 @@ "scheduling = DoubleBracketScheduling.hyperopt\n", "mode = DoubleBracketGeneratorType.single_commutator\n", "n_1 = 5\n", - "n_2 = 2\n", + "n_2 = 3\n", "dbi_1 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", "dbi_2 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", "print(\"Initial off diagonal norm\", dbi_1.off_diagonal_norm)\n", @@ -339,7 +339,7 @@ "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi,d,5, onsite_Z_ops)\n", + "grad, s = gradient_onsite_Z(dbi,d,n_taylor=5, onsite_Z_ops=onsite_Z_ops)\n", "print('The initial D coefficients:', d_coef)\n", "print('Gradient:', grad)\n", "print('s:', s)" From 80d2604d6362151c7a2a50b5a7b623a462622618 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Fri, 1 Mar 2024 14:28:50 +0800 Subject: [PATCH 019/154] Backup scheduling moved to `choose_step` --- examples/dbi/dbi_scheduling.ipynb | 382 +++++++++++++++++++++++--- src/qibo/models/dbi/double_bracket.py | 60 +++- 2 files changed, 383 insertions(+), 59 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 1f53822023..686156fa4c 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -42,9 +42,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-01 14:04:46]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 37.94733192202055\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -70,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -93,9 +108,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.030312727272727272\n", + "hyperopt_search step: 0.029554880094525483\n", + "polynomial_approximation step: 0.032960905003724034\n" + ] + } + ], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n", @@ -103,26 +128,33 @@ "# hyperopt\n", "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt, max_evals=100, step_max=0.6)\n", "print('hyperopt_search step:', step_hyperopt)\n", - "# polynomial expansion\n", "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, n=5)\n", "print('polynomial_approximation step:', step_poly)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "step_poly = dbi.polynomial_step(n=5)\n", - "print(step_poly)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.030312727272727272\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -131,7 +163,7 @@ "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", "plt.xlabel('s')\n", - "plt.title('hyperopt first step')\n", + "plt.title('First DBI step')\n", "plt.legend()\n", "print('The minimum for cost function in the tested range is:', step_grid)" ] @@ -147,23 +179,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|WARNING|2024-03-01 13:36:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:36:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], "source": [ "# Generate the digaonal operators\n", - "Z_op = SymbolicHamiltonian(str_to_symbolic(\"Z\"*nqubits)).dense.matrix\n", - "ZI_op = SymbolicHamiltonian(str_to_symbolic(\"Z\"*(nqubits-1)+\"I\")).dense.matrix" + "Z_str = \"Z\"*nqubits\n", + "ZI_str = \"Z\"*(nqubits-1)+\"I\"\n", + "Z_op = SymbolicHamiltonian(str_to_symbolic(Z_str)).dense.matrix\n", + "ZI_op = SymbolicHamiltonian(str_to_symbolic(ZI_str)).dense.matrix\n", + "op_dict = {Z_str:Z_op, ZI_str: ZI_op}" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator)\n", - "d = ZI_op\n", + "d_str = ZI_str\n", + "d = op_dict[d_str]\n", "# generate data for plotting sigma decrease of the first step\n", "s_space = np.linspace(1e-5, 0.6, 100)\n", "off_diagonal_norm_diff = []\n", @@ -175,9 +220,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.30303525252525254 loss -6.165348149025746\n", + "hyperopt_search step: 0.565048795659714 loss -6.166892748979453\n", + "polynomial_approximation step: 0.040336885340305856 loss -6.149780650249902\n" + ] + } + ], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_max=0.6, d=d)\n", @@ -195,9 +250,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.30303525252525254\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -208,7 +281,7 @@ "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", "plt.xlabel('s')\n", - "plt.title('hyperopt first step')\n", + "plt.title(f'First DBI step with D={d_str}')\n", "plt.legend()\n", "print('The minimum for cost function in the tested range is:', step_grid)" ] @@ -229,9 +302,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.04141414141414142\n", + "hyperopt_search step: 0.04175237619889543\n" + ] + } + ], "source": [ "search_range = 0.1\n", "if step_poly < search_range/2:\n", @@ -250,9 +332,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.30303525252525254\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -261,7 +361,7 @@ "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", "plt.xlabel('s')\n", - "plt.title('hyperopt first step')\n", + "plt.title(r'Restrict $s$ with polynomial')\n", "plt.legend()\n", "print('The minimum for cost function in the tested range is:', step_grid)" ] @@ -282,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -292,9 +392,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-01 13:32:30]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 15.16260860504813\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -308,9 +423,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], "source": [ "generate_local_Z = generate_Z_operators(nqubits)\n", "Z_ops = list(generate_local_Z.values())\n", @@ -319,9 +456,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------Scheduling grid search----------\n", + "New optimized step at iteration 1/8: 0.11112 with operator IIZZ, loss 11.680408968308086\n", + "New optimized step at iteration 2/8: 0.08081727272727272 with operator IIZZ, loss 9.142367366920572\n", + "New optimized step at iteration 3/8: 0.1010190909090909 with operator ZZIZ, loss 7.958198114832907\n", + "New optimized step at iteration 4/8: 0.07071636363636363 with operator IIZZ, loss 6.482023887224007\n", + "New optimized step at iteration 5/8: 0.1010190909090909 with operator ZZIZ, loss 5.771042676877126\n", + "New optimized step at iteration 6/8: 0.08081727272727272 with operator IIZZ, loss 5.140994036668525\n", + "New optimized step at iteration 7/8: 0.11112 with operator -ZZII, loss 4.728283208000788\n", + "New optimized step at iteration 8/8: 0.06061545454545455 with operator IIZZ, loss 4.40400614947187\n", + "----------Scheduling hyperopt----------\n", + "New optimized step at iteration 1/8: 0.1088441936662135 with operator IIZZ, loss 11.676654434031814\n", + "New optimized step at iteration 2/8: 0.07922158082178958 with operator IIZZ, loss 9.135794848474623\n", + "New optimized step at iteration 3/8: 0.10296369768833129 with operator ZZIZ, loss 7.935942900247105\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[32], line 20\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m----------Scheduling \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscheduling_labels[i]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m----------\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(NSTEPS):\n\u001b[0;32m---> 20\u001b[0m dbi, idx, step, flip_sign \u001b[38;5;241m=\u001b[39m \u001b[43mselect_best_dbr_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdbi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mZ_ops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscheduling\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompare_canonical\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m off_diagonal_norm_history\u001b[38;5;241m.\u001b[39mappend(dbi\u001b[38;5;241m.\u001b[39moff_diagonal_norm)\n\u001b[1;32m 22\u001b[0m steps\u001b[38;5;241m.\u001b[39mappend(steps[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m+\u001b[39mstep)\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:105\u001b[0m, in \u001b[0;36mselect_best_dbr_generator\u001b[0;34m(dbi_object, d_list, step, compare_canonical, scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flip_list[i] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 105\u001b[0m step_best \u001b[38;5;241m=\u001b[39m \u001b[43mdbi_eval\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoose_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 106\u001b[0m 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299\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrid_search_step(d\u001b[38;5;241m=\u001b[39md, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m scheduling \u001b[38;5;129;01mis\u001b[39;00m DoubleBracketScheduling\u001b[38;5;241m.\u001b[39mhyperopt:\n\u001b[0;32m--> 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhyperopt_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m scheduling \u001b[38;5;129;01mis\u001b[39;00m DoubleBracketScheduling\u001b[38;5;241m.\u001b[39mpolynomial_approximation:\n\u001b[1;32m 303\u001b[0m step, coef \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpolynomial_step(d\u001b[38;5;241m=\u001b[39md, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:197\u001b[0m, in \u001b[0;36mDoubleBracketIteration.hyperopt_step\u001b[0;34m(self, step_min, step_max, max_evals, space, optimizer, look_ahead, verbose, d)\u001b[0m\n\u001b[1;32m 194\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m 196\u001b[0m space \u001b[38;5;241m=\u001b[39m space(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep\u001b[39m\u001b[38;5;124m\"\u001b[39m, step_min, step_max)\n\u001b[0;32m--> 197\u001b[0m best \u001b[38;5;241m=\u001b[39m 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verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[1;32m 583\u001b[0m rval\u001b[38;5;241m.\u001b[39mcatch_eval_exceptions \u001b[38;5;241m=\u001b[39m catch_eval_exceptions\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# next line is where the fmin is actually executed\u001b[39;00m\n\u001b[0;32m--> 586\u001b[0m \u001b[43mrval\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexhaust\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_argmin:\n\u001b[1;32m 589\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(trials\u001b[38;5;241m.\u001b[39mtrials) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/fmin.py:364\u001b[0m, in \u001b[0;36mFMinIter.exhaust\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 362\u001b[0m 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\u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/fmin.py:278\u001b[0m, in \u001b[0;36mFMinIter.run\u001b[0;34m(self, N, block_until_done)\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrials\u001b[38;5;241m.\u001b[39mrefresh()\n\u001b[1;32m 274\u001b[0m \u001b[38;5;66;03m# Based on existing trials and the domain, use `algo` to probe in\u001b[39;00m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;66;03m# new hp points. Save the results of those inspections into\u001b[39;00m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;66;03m# `new_trials`. This is the core of `run`, all the rest is just\u001b[39;00m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;66;03m# processes orchestration\u001b[39;00m\n\u001b[0;32m--> 278\u001b[0m new_trials \u001b[38;5;241m=\u001b[39m \u001b[43malgo\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 279\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_ids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegers\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m31\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 280\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(new_ids) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(new_trials)\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(new_trials):\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/tpe.py:935\u001b[0m, in \u001b[0;36msuggest\u001b[0;34m(new_ids, domain, trials, seed, prior_weight, n_startup_jobs, n_EI_candidates, gamma, verbose)\u001b[0m\n\u001b[1;32m 931\u001b[0m memo[observed[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvals\u001b[39m\u001b[38;5;124m\"\u001b[39m]] \u001b[38;5;241m=\u001b[39m observed_vals_dict\n\u001b[1;32m 933\u001b[0m \u001b[38;5;66;03m# evaluate `n_EI_candidates` pyll nodes in `posterior` using `memo`\u001b[39;00m\n\u001b[1;32m 934\u001b[0m \u001b[38;5;66;03m# TODO: it seems to return idxs, vals, all the same. Is this correct?\u001b[39;00m\n\u001b[0;32m--> 935\u001b[0m idxs, vals \u001b[38;5;241m=\u001b[39m \u001b[43mpyll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrec_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mposterior\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_node_on_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 937\u001b[0m \u001b[38;5;66;03m# hack to add offset again for randint params\u001b[39;00m\n\u001b[1;32m 938\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m label, param \u001b[38;5;129;01min\u001b[39;00m domain\u001b[38;5;241m.\u001b[39mparams\u001b[38;5;241m.\u001b[39mitems():\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/pyll/base.py:902\u001b[0m, in \u001b[0;36mrec_eval\u001b[0;34m(expr, deepcopy_inputs, memo, max_program_len, memo_gc, print_trace, print_node_on_error)\u001b[0m\n\u001b[1;32m 899\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(_kwargs)\n\u001b[1;32m 901\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 902\u001b[0m rval \u001b[38;5;241m=\u001b[39m \u001b[43mscope\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_impls\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 904\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 905\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m print_node_on_error:\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/tpe.py:398\u001b[0m, in \u001b[0;36madaptive_parzen_normal\u001b[0;34m(mus, prior_weight, prior_mu, prior_sigma, LF)\u001b[0m\n\u001b[1;32m 394\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124;03mmus - matrix (N, M) of M, N-dimensional component centers\u001b[39;00m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 397\u001b[0m mus \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(mus)\n\u001b[0;32m--> 398\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(mus\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mus\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmus must be vector\u001b[39m\u001b[38;5;124m\"\u001b[39m, mus)\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/numpy/core/_dtype.py:42\u001b[0m, in \u001b[0;36m__str__\u001b[0;34m(dtype)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mstr\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdtype\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/numpy/core/_dtype.py:363\u001b[0m, in \u001b[0;36m_name_get\u001b[0;34m(dtype)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;66;03m# append bit counts\u001b[39;00m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _name_includes_bit_suffix(dtype):\n\u001b[0;32m--> 363\u001b[0m name \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitemsize\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;66;03m# append metadata to datetimes\u001b[39;00m\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;129;01min\u001b[39;00m (np\u001b[38;5;241m.\u001b[39mdatetime64, np\u001b[38;5;241m.\u001b[39mtimedelta64):\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "NSTEPS = 8\n", "scheduling_list = [DoubleBracketScheduling.grid_search,\n", @@ -359,7 +538,28 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "for i, scheduling in enumerate(scheduling_labels):\n", @@ -369,6 +569,100 @@ "plt.title(\"Compare Variational Pauli-Z using different scheduling strategies\")\n", "plt.legend()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## When polynomial approximation has no solution\n", + "\n", + "In some cases, the prescribed taylor expansion order `n` may not be sufficient to produce a meaningful step duration (real positive). In these cases, we rely on a backup scheduling method in `choose_step`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-01 14:05:27]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 37.94733192202055\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.canonical)\n", + "dbi.scheduling = DoubleBracketScheduling.polynomial_approximation\n", + "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For demonstration purposes, we let `n=1` which is a linear fit to the loss function. This results in no valid solutions and function `polynomial_step` returns `None`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None [(-1290240+0j), (-23040+0j)]\n" + ] + } + ], + "source": [ + "step, coef = dbi.polynomial_step(n=1)\n", + "print(step, coef)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "now n=2, step=0.03321888741718203\n", + "No solution found, going to backup DoubleBracketScheduling.grid_search\n", + "No solution found, going to backup DoubleBracketScheduling.hyperopt\n", + "0.03321888741718203 0.030312727272727272 0.029274407933556172\n" + ] + } + ], + "source": [ + "step_backup_poly = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.polynomial_approximation, n=1, n_max=5)\n", + "step_backup_grid = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.grid_search, n=1)\n", + "step_backup_hyper = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.hyperopt, n=1)\n", + "print(step_backup_poly, step_backup_grid, step_backup_hyper)" + ] } ], "metadata": { diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 2c160ea525..5c2e26f81c 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -156,14 +156,14 @@ def grid_search_step( d = self.diagonal_h_matrix loss_list = [self.loss(step, d=d) for step in space] - idx_max_loss = loss_list.index(min(loss_list)) + idx_max_loss = np.argmin(loss_list) return space[idx_max_loss] def hyperopt_step( self, step_min: float = 1e-5, step_max: float = 1, - max_evals: int = 1000, + max_evals: int = 500, space: callable = None, optimizer: callable = None, look_ahead: int = 1, @@ -205,7 +205,7 @@ def hyperopt_step( def polynomial_step( self, - n: int = 4, + n: int = 2, n_max: int = 5, d: np.array = None, backup_scheduling: DoubleBracketScheduling = None, @@ -226,6 +226,11 @@ def polynomial_step( if backup_scheduling is None: backup_scheduling = DoubleBracketScheduling.grid_search + if n > n_max: + raise ValueError( + "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." + ) + def sigma(h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) @@ -259,7 +264,9 @@ def Gamma(k: int): power = k + j product_matrix = c1[k] @ c2[j] trace_coefficients[power] += 2 * np.trace(product_matrix) - roots = np.roots(list(reversed(trace_coefficients[: n + 1]))) + # coefficients from high to low (n:0) + coef = list(reversed(trace_coefficients[: n + 1])) + roots = np.roots(coef) error = 1e-3 real_positive_roots = [ np.real(root) @@ -268,22 +275,29 @@ def Gamma(k: int): ] # solution exists, return minimum s if len(real_positive_roots) > 0: - return min(real_positive_roots) - # solution does not exist, resort to backup scheduling - elif ( - backup_scheduling == DoubleBracketScheduling.polynomial_approximation - and n < n_max + 1 - ): - return self.polynomial_step( - n=n + 1, d=d, backup_scheduling=backup_scheduling - ) + return min(real_positive_roots), coef + # solution does not exist, return None else: - return self.choose_step(d=d, scheduling=backup_scheduling) + return None, coef + + # # solution does not exist, resort to backup scheduling + # elif ( + # backup_scheduling == DoubleBracketScheduling.polynomial_approximation + # and n < n_max + 1 + # ): + # return self.polynomial_step( + # n=n + 1, d=d, backup_scheduling=backup_scheduling + # ) + # else: + # return self.choose_step(d=d, scheduling=backup_scheduling) def choose_step( self, d: Optional[np.array] = None, scheduling: Optional[DoubleBracketScheduling] = None, + backup_scheduling: Optional[ + DoubleBracketScheduling + ] = DoubleBracketScheduling.hyperopt, **kwargs, ): if scheduling is None: @@ -293,7 +307,23 @@ def choose_step( if scheduling is DoubleBracketScheduling.hyperopt: return self.hyperopt_step(d=d, **kwargs) if scheduling is DoubleBracketScheduling.polynomial_approximation: - return self.polynomial_step(d=d, **kwargs) + step, coef = self.polynomial_step(d=d, **kwargs) + # if no solution + if step is None: + if ( + backup_scheduling + == DoubleBracketScheduling.polynomial_approximation + and coef is not None + ): + # if `n` is not provided, try default value + kwargs["n"] = kwargs.get("n", 2) + kwargs["n"] += 1 + step, coef = self.polynomial_step(d=d, **kwargs) + # if n==n_max, return None + else: + # Issue: cannot pass kwargs + step = self.choose_step(d=d, scheduling=backup_scheduling) + return step def loss(self, step: float, d: np.array = None, look_ahead: int = 1): """ From 54a86a8add3bafedeb0383b92b346296db16aa1b Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi <146689118+Sam-XiaoyueLi@users.noreply.github.com> Date: Fri, 1 Mar 2024 14:30:54 +0800 Subject: [PATCH 020/154] Update src/qibo/models/dbi/double_bracket.py Co-authored-by: Edoardo Pedicillo --- src/qibo/models/dbi/double_bracket.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 5c2e26f81c..2fe515306d 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -223,8 +223,6 @@ def polynomial_step( if d is None: d = self.diagonal_h_matrix - if backup_scheduling is None: - backup_scheduling = DoubleBracketScheduling.grid_search if n > n_max: raise ValueError( From 50554e32a7ce8a6f8264af48aad8e9c2c267b9a2 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Fri, 1 Mar 2024 22:43:50 +0800 Subject: [PATCH 021/154] Simplify code structure in `polynomial_step` --- examples/dbi/dbi_scheduling.ipynb | 309 +++----------------------- src/qibo/models/dbi/double_bracket.py | 46 +--- 2 files changed, 46 insertions(+), 309 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 686156fa4c..3958aa2dff 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -42,24 +42,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-01 14:04:46]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 37.94733192202055\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -85,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -108,19 +93,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.030312727272727272\n", - "hyperopt_search step: 0.029554880094525483\n", - "polynomial_approximation step: 0.032960905003724034\n" - ] - } - ], + "outputs": [], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n", @@ -134,27 +109,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.030312727272727272\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -179,18 +136,9 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|WARNING|2024-03-01 13:36:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:36:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - } - ], + "outputs": [], "source": [ "# Generate the digaonal operators\n", "Z_str = \"Z\"*nqubits\n", @@ -202,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -220,19 +168,9 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.30303525252525254 loss -6.165348149025746\n", - "hyperopt_search step: 0.565048795659714 loss -6.166892748979453\n", - "polynomial_approximation step: 0.040336885340305856 loss -6.149780650249902\n" - ] - } - ], + "outputs": [], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_max=0.6, d=d)\n", @@ -250,27 +188,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.30303525252525254\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -302,18 +222,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.04141414141414142\n", - "hyperopt_search step: 0.04175237619889543\n" - ] - } - ], + "outputs": [], "source": [ "search_range = 0.1\n", "if step_poly < search_range/2:\n", @@ -332,27 +243,9 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.30303525252525254\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -382,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -392,24 +285,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-01 13:32:30]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 15.16260860504813\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -423,31 +301,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-01 13:32:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - } - ], + "outputs": [], "source": [ "generate_local_Z = generate_Z_operators(nqubits)\n", "Z_ops = list(generate_local_Z.values())\n", @@ -456,51 +312,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----------Scheduling grid search----------\n", - "New optimized step at iteration 1/8: 0.11112 with operator IIZZ, loss 11.680408968308086\n", - "New optimized step at iteration 2/8: 0.08081727272727272 with operator IIZZ, loss 9.142367366920572\n", - "New optimized step at iteration 3/8: 0.1010190909090909 with operator ZZIZ, loss 7.958198114832907\n", - "New optimized step at iteration 4/8: 0.07071636363636363 with operator IIZZ, loss 6.482023887224007\n", - "New optimized step at iteration 5/8: 0.1010190909090909 with operator ZZIZ, loss 5.771042676877126\n", - "New optimized step at iteration 6/8: 0.08081727272727272 with operator IIZZ, loss 5.140994036668525\n", - "New optimized step at iteration 7/8: 0.11112 with operator -ZZII, loss 4.728283208000788\n", - "New optimized step at iteration 8/8: 0.06061545454545455 with operator IIZZ, loss 4.40400614947187\n", - "----------Scheduling hyperopt----------\n", - "New optimized step at iteration 1/8: 0.1088441936662135 with operator IIZZ, loss 11.676654434031814\n", - "New optimized step at iteration 2/8: 0.07922158082178958 with operator IIZZ, loss 9.135794848474623\n", - "New optimized step at iteration 3/8: 0.10296369768833129 with operator ZZIZ, loss 7.935942900247105\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[32], line 20\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m----------Scheduling \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscheduling_labels[i]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m----------\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(NSTEPS):\n\u001b[0;32m---> 20\u001b[0m dbi, idx, step, flip_sign \u001b[38;5;241m=\u001b[39m \u001b[43mselect_best_dbr_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdbi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mZ_ops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscheduling\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompare_canonical\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m off_diagonal_norm_history\u001b[38;5;241m.\u001b[39mappend(dbi\u001b[38;5;241m.\u001b[39moff_diagonal_norm)\n\u001b[1;32m 22\u001b[0m steps\u001b[38;5;241m.\u001b[39mappend(steps[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m+\u001b[39mstep)\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:105\u001b[0m, in \u001b[0;36mselect_best_dbr_generator\u001b[0;34m(dbi_object, d_list, step, compare_canonical, scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flip_list[i] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 105\u001b[0m step_best \u001b[38;5;241m=\u001b[39m \u001b[43mdbi_eval\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoose_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mflip_list\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscheduling\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 109\u001b[0m step_best \u001b[38;5;241m=\u001b[39m step\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:301\u001b[0m, in \u001b[0;36mDoubleBracketIteration.choose_step\u001b[0;34m(self, d, scheduling, backup_scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrid_search_step(d\u001b[38;5;241m=\u001b[39md, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m scheduling \u001b[38;5;129;01mis\u001b[39;00m DoubleBracketScheduling\u001b[38;5;241m.\u001b[39mhyperopt:\n\u001b[0;32m--> 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhyperopt_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m scheduling \u001b[38;5;129;01mis\u001b[39;00m DoubleBracketScheduling\u001b[38;5;241m.\u001b[39mpolynomial_approximation:\n\u001b[1;32m 303\u001b[0m step, coef \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpolynomial_step(d\u001b[38;5;241m=\u001b[39md, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:197\u001b[0m, in \u001b[0;36mDoubleBracketIteration.hyperopt_step\u001b[0;34m(self, step_min, step_max, max_evals, space, optimizer, look_ahead, verbose, d)\u001b[0m\n\u001b[1;32m 194\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m 196\u001b[0m space \u001b[38;5;241m=\u001b[39m space(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep\u001b[39m\u001b[38;5;124m\"\u001b[39m, step_min, step_max)\n\u001b[0;32m--> 197\u001b[0m best \u001b[38;5;241m=\u001b[39m \u001b[43mhyperopt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfmin\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43mfn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlook_ahead\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlook_ahead\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mspace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mspace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 200\u001b[0m \u001b[43m \u001b[49m\u001b[43malgo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuggest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 201\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_evals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_evals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 202\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 203\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m best[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/fmin.py:586\u001b[0m, in \u001b[0;36mfmin\u001b[0;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[1;32m 583\u001b[0m rval\u001b[38;5;241m.\u001b[39mcatch_eval_exceptions \u001b[38;5;241m=\u001b[39m catch_eval_exceptions\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# next line is where the fmin is actually executed\u001b[39;00m\n\u001b[0;32m--> 586\u001b[0m \u001b[43mrval\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexhaust\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_argmin:\n\u001b[1;32m 589\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(trials\u001b[38;5;241m.\u001b[39mtrials) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/fmin.py:364\u001b[0m, in \u001b[0;36mFMinIter.exhaust\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexhaust\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 363\u001b[0m n_done \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrials)\n\u001b[0;32m--> 364\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_evals\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mn_done\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_until_done\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrials\u001b[38;5;241m.\u001b[39mrefresh()\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/fmin.py:278\u001b[0m, in \u001b[0;36mFMinIter.run\u001b[0;34m(self, N, block_until_done)\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrials\u001b[38;5;241m.\u001b[39mrefresh()\n\u001b[1;32m 274\u001b[0m \u001b[38;5;66;03m# Based on existing trials and the domain, use `algo` to probe in\u001b[39;00m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;66;03m# new hp points. Save the results of those inspections into\u001b[39;00m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;66;03m# `new_trials`. This is the core of `run`, all the rest is just\u001b[39;00m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;66;03m# processes orchestration\u001b[39;00m\n\u001b[0;32m--> 278\u001b[0m new_trials \u001b[38;5;241m=\u001b[39m \u001b[43malgo\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 279\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_ids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegers\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m31\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 280\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(new_ids) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(new_trials)\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(new_trials):\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/tpe.py:935\u001b[0m, in \u001b[0;36msuggest\u001b[0;34m(new_ids, domain, trials, seed, prior_weight, n_startup_jobs, n_EI_candidates, gamma, verbose)\u001b[0m\n\u001b[1;32m 931\u001b[0m memo[observed[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvals\u001b[39m\u001b[38;5;124m\"\u001b[39m]] \u001b[38;5;241m=\u001b[39m observed_vals_dict\n\u001b[1;32m 933\u001b[0m \u001b[38;5;66;03m# evaluate `n_EI_candidates` pyll nodes in `posterior` using `memo`\u001b[39;00m\n\u001b[1;32m 934\u001b[0m \u001b[38;5;66;03m# TODO: it seems to return idxs, vals, all the same. Is this correct?\u001b[39;00m\n\u001b[0;32m--> 935\u001b[0m idxs, vals \u001b[38;5;241m=\u001b[39m \u001b[43mpyll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrec_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mposterior\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_node_on_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 937\u001b[0m \u001b[38;5;66;03m# hack to add offset again for randint params\u001b[39;00m\n\u001b[1;32m 938\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m label, param \u001b[38;5;129;01min\u001b[39;00m domain\u001b[38;5;241m.\u001b[39mparams\u001b[38;5;241m.\u001b[39mitems():\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/pyll/base.py:902\u001b[0m, in \u001b[0;36mrec_eval\u001b[0;34m(expr, deepcopy_inputs, memo, max_program_len, memo_gc, print_trace, print_node_on_error)\u001b[0m\n\u001b[1;32m 899\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(_kwargs)\n\u001b[1;32m 901\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 902\u001b[0m rval \u001b[38;5;241m=\u001b[39m \u001b[43mscope\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_impls\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 904\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 905\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m print_node_on_error:\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/hyperopt/tpe.py:398\u001b[0m, in \u001b[0;36madaptive_parzen_normal\u001b[0;34m(mus, prior_weight, prior_mu, prior_sigma, LF)\u001b[0m\n\u001b[1;32m 394\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124;03mmus - matrix (N, M) of M, N-dimensional component centers\u001b[39;00m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 397\u001b[0m mus \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(mus)\n\u001b[0;32m--> 398\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(mus\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mus\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmus must be vector\u001b[39m\u001b[38;5;124m\"\u001b[39m, mus)\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/numpy/core/_dtype.py:42\u001b[0m, in \u001b[0;36m__str__\u001b[0;34m(dtype)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mstr\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdtype\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/numpy/core/_dtype.py:363\u001b[0m, in \u001b[0;36m_name_get\u001b[0;34m(dtype)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;66;03m# append bit counts\u001b[39;00m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _name_includes_bit_suffix(dtype):\n\u001b[0;32m--> 363\u001b[0m name \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitemsize\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;66;03m# append metadata to datetimes\u001b[39;00m\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;129;01min\u001b[39;00m (np\u001b[38;5;241m.\u001b[39mdatetime64, np\u001b[38;5;241m.\u001b[39mtimedelta64):\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "NSTEPS = 8\n", "scheduling_list = [DoubleBracketScheduling.grid_search,\n", @@ -538,33 +352,12 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "for i, scheduling in enumerate(scheduling_labels):\n", " plt.plot(s_scheduling[i], off_norm_scheduling[i], '-o', label=scheduling)\n", - "plt.xlabel(\"Iterations\")\n", + "plt.xlabel(\"Step durations\")\n", "plt.ylabel(\"Norm off-diagonal restriction\")\n", "plt.title(\"Compare Variational Pauli-Z using different scheduling strategies\")\n", "plt.legend()" @@ -581,24 +374,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-01 14:05:27]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 37.94733192202055\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -625,17 +403,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "None [(-1290240+0j), (-23040+0j)]\n" - ] - } - ], + "outputs": [], "source": [ "step, coef = dbi.polynomial_step(n=1)\n", "print(step, coef)" @@ -643,20 +413,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "now n=2, step=0.03321888741718203\n", - "No solution found, going to backup DoubleBracketScheduling.grid_search\n", - "No solution found, going to backup DoubleBracketScheduling.hyperopt\n", - "0.03321888741718203 0.030312727272727272 0.029274407933556172\n" - ] - } - ], + "outputs": [], "source": [ "step_backup_poly = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.polynomial_approximation, n=1, n_max=5)\n", "step_backup_grid = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.grid_search, n=1)\n", diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 2fe515306d..ddd7e179b9 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -9,6 +9,8 @@ from qibo.hamiltonians import Hamiltonian +error = 1e-3 + class DoubleBracketGeneratorType(Enum): """Define DBF evolution.""" @@ -223,7 +225,6 @@ def polynomial_step( if d is None: d = self.diagonal_h_matrix - if n > n_max: raise ValueError( "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." @@ -232,29 +233,18 @@ def polynomial_step( def sigma(h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) - def Gamma(k: int): - r"""Computes the k_th Gamma function i.e $\Gamma_k=[W,...,[W,[W,H]]...]$, where we take k nested commutators with $W = [D, H]$""" - if k == 0: - return self.h.matrix - else: - W = self.commutator(d, sigma(self.h.matrix)) - result = self.h.matrix - for _ in range(k): - result = self.commutator(W, result) - return result - - # list starting from s^n highest order to s^0 - sigma_gamma_list = np.array([sigma(Gamma(k)) for k in range(n + 2)]) + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + W = self.commutator(d, sigma(self.h.matrix)) + Gamma_list = [self.h.matrix] + sigma_Gamma_list = [sigma(Gamma_list[0])] + for _ in range(n + 1): + Gamma_list.append(self.commutator(W, Gamma_list[-1])) + sigma_Gamma_list.append(sigma(Gamma_list[-1])) + sigma_Gamma_list = np.array(sigma_Gamma_list) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients for rotation with [W,H] and H - c1 = [ - exp_coef * delta_gamma - for exp_coef, delta_gamma in zip(exp_list, sigma_gamma_list[1:]) - ] - c2 = [ - exp_coef * delta_gamma - for exp_coef, delta_gamma in zip(exp_list, sigma_gamma_list[:-1]) - ] + c1 = exp_list.reshape(-1, 1, 1) * sigma_Gamma_list[1:] + c2 = exp_list.reshape(-1, 1, 1) * sigma_Gamma_list[:-1] # product coefficient trace_coefficients = [0] * (2 * n + 1) for k in range(n + 1): @@ -265,7 +255,6 @@ def Gamma(k: int): # coefficients from high to low (n:0) coef = list(reversed(trace_coefficients[: n + 1])) roots = np.roots(coef) - error = 1e-3 real_positive_roots = [ np.real(root) for root in roots @@ -278,17 +267,6 @@ def Gamma(k: int): else: return None, coef - # # solution does not exist, resort to backup scheduling - # elif ( - # backup_scheduling == DoubleBracketScheduling.polynomial_approximation - # and n < n_max + 1 - # ): - # return self.polynomial_step( - # n=n + 1, d=d, backup_scheduling=backup_scheduling - # ) - # else: - # return self.choose_step(d=d, scheduling=backup_scheduling) - def choose_step( self, d: Optional[np.array] = None, From 4fcdf643171760f2f3d4df7794b20992e906bdb1 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Fri, 1 Mar 2024 22:49:51 +0800 Subject: [PATCH 022/154] Update `test_models_dbi.py` --- tests/test_models_dbi.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index a573c32088..111079f8a7 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -150,10 +150,10 @@ def test_double_bracket_iteration_scheduling_polynomial( ) initial_off_diagonal_norm = dbi.off_diagonal_norm for _ in range(NSTEPS): - step1 = dbi.polynomial_step(n=n, d=d, backup_scheduling=backup_scheduling) + step1 = dbi.choose_step(n=n, backup_scheduling=backup_scheduling) dbi(d=d, step=step1) - step2 = dbi.choose_step( - scheduling=DoubleBracketScheduling.polynomial_approximation, n=n - ) - dbi(step=step2) + # step2 = dbi.choose_step( + # scheduling=DoubleBracketScheduling.polynomial_approximation, n=n + # ) + # dbi(step=step2) assert initial_off_diagonal_norm > dbi.off_diagonal_norm From 67701e173c03d09df60080948fb6ae4513486c1f Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Fri, 1 Mar 2024 23:29:59 +0800 Subject: [PATCH 023/154] Define sigma and Gamma as class function --- src/qibo/models/dbi/double_bracket.py | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index ddd7e179b9..ffd371110c 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -230,17 +230,10 @@ def polynomial_step( "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." ) - def sigma(h: np.array): - return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = self.commutator(d, sigma(self.h.matrix)) - Gamma_list = [self.h.matrix] - sigma_Gamma_list = [sigma(Gamma_list[0])] - for _ in range(n + 1): - Gamma_list.append(self.commutator(W, Gamma_list[-1])) - sigma_Gamma_list.append(sigma(Gamma_list[-1])) - sigma_Gamma_list = np.array(sigma_Gamma_list) + W = self.commutator(d, self.sigma(self.h.matrix)) + Gamma_list = self.generate_Gamma_list(n + 2, d) + sigma_Gamma_list = list(map(self.sigma, Gamma_list)) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients for rotation with [W,H] and H c1 = exp_list.reshape(-1, 1, 1) * sigma_Gamma_list[1:] @@ -337,3 +330,14 @@ def energy_fluctuation(self, state): state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ return self.h.energy_fluctuation(state) + + def sigma(self, h: np.array): + return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) + + def generate_Gamma_list(self, n: int, d: np.array): + r"""Computes the n-nested Gamma functions, where $\Gamma_k=[W,...,[W,[W,H]]...]$, where we take k nested commutators with $W = [D, H]$""" + W = self.commutator(d, self.sigma(self.h.matrix)) + Gamma_list = [self.h.matrix] + for _ in range(n - 1): + Gamma_list.append(self.commutator(W, Gamma_list[-1])) + return Gamma_list From ca71ffaa76980a2142c37d3d60c4816a3ae60982 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 5 Mar 2024 15:12:12 +0800 Subject: [PATCH 024/154] Modified structure: moving scheduling strategies in `utils_scheduling.py` --- examples/dbi/dbi_scheduling.ipynb | 303 +++++++++++++++++++++--- src/qibo/models/dbi/double_bracket.py | 185 +++------------ src/qibo/models/dbi/utils.py | 26 +- src/qibo/models/dbi/utils_scheduling.py | 145 ++++++++++++ 4 files changed, 464 insertions(+), 195 deletions(-) create mode 100644 src/qibo/models/dbi/utils_scheduling.py diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 3958aa2dff..34a49760b5 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,8 @@ "\n", "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", - "from qibo.models.dbi.utils import *" + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_scheduling import *" ] }, { @@ -42,9 +43,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", + "[Qibo 0.2.5|INFO|2024-03-05 15:06:24]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 37.94733192202055\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -70,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -93,9 +110,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.030312727272727272\n", + "hyperopt_search step: 0.028991467713834373\n", + "polynomial_approximation step: 0.032960905003724034\n" + ] + } + ], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n", @@ -109,9 +136,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is:" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.030312727272727272\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -136,9 +188,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:24]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:24]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], "source": [ "# Generate the digaonal operators\n", "Z_str = \"Z\"*nqubits\n", @@ -150,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -168,9 +229,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.30303525252525254 loss -6.165348149025746\n", + "hyperopt_search step: 0.30457003862873383 loss -6.158069649792722\n", + "polynomial_approximation step: 0.040336885340305856 loss -6.149780650249902\n" + ] + } + ], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_max=0.6, d=d)\n", @@ -188,9 +259,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.30303525252525254\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -222,9 +311,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.04141414141414142\n", + "hyperopt_search step: 0.041686777442654525\n" + ] + } + ], "source": [ "search_range = 0.1\n", "if step_poly < search_range/2:\n", @@ -243,9 +341,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.04141414141414142\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -275,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -285,9 +401,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-05 15:06:25]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 15.582565474255802\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -301,9 +432,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], "source": [ "generate_local_Z = generate_Z_operators(nqubits)\n", "Z_ops = list(generate_local_Z.values())\n", @@ -312,9 +465,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------Scheduling grid search----------\n", + "New optimized step at iteration 1/8: 0.09091818181818181 with operator -IIIZ, loss 12.06390502880173\n", + "New optimized step at iteration 2/8: 0.08081727272727272 with operator -IIIZ, loss 9.635355222386766\n", + "New optimized step at iteration 3/8: 0.09091818181818181 with operator ZZII, loss 8.551120821934958\n", + "New optimized step at iteration 4/8: 0.06061545454545455 with operator -IIIZ, loss 7.454888988134408\n", + "New optimized step at iteration 5/8: 0.09091818181818181 with operator ZZII, loss 6.526038310796992\n", + "New optimized step at iteration 6/8: 0.07071636363636363 with operator -IIIZ, loss 5.906198775254362\n", + "New optimized step at iteration 7/8: 0.07071636363636363 with operator -IIIZ, loss 5.558604782647778\n", + "New optimized step at iteration 8/8: 0.09091818181818181 with operator IIZI, loss 5.248002043412237\n", + "----------Scheduling hyperopt----------\n", + "New optimized step at iteration 1/8: 0.0952680083828445 with operator -IIIZ, loss 12.048611508414254\n", + "New optimized step at iteration 2/8: 0.0759399266935567 with operator -IIIZ, loss 9.647010146511933\n", + "New optimized step at iteration 3/8: 0.0953305046594857 with operator ZZII, loss 8.545812587336131\n", + "New optimized step at iteration 4/8: 0.0638318397684811 with operator -IIIZ, loss 7.391689830573071\n", + "New optimized step at iteration 5/8: 0.0857170843370093 with operator ZZII, loss 6.534188669695426\n", + "New optimized step at iteration 6/8: 0.06796907471647104 with operator -IIIZ, loss 5.930126378868041\n", + "New optimized step at iteration 7/8: 0.06714742808838461 with operator -IIIZ, loss 5.582951426889378\n", + "New optimized step at iteration 8/8: 0.10283381512622272 with operator IIZI, loss 5.253925034956114\n", + "----------Scheduling polynomial----------\n" + ] + }, + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for *: 'complex' and 'NoneType'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 20\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m----------Scheduling \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscheduling_labels[i]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m----------\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(NSTEPS):\n\u001b[0;32m---> 20\u001b[0m dbi, idx, step, flip_sign \u001b[38;5;241m=\u001b[39m \u001b[43mselect_best_dbr_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdbi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mZ_ops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscheduling\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompare_canonical\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m off_diagonal_norm_history\u001b[38;5;241m.\u001b[39mappend(dbi\u001b[38;5;241m.\u001b[39moff_diagonal_norm)\n\u001b[1;32m 22\u001b[0m steps\u001b[38;5;241m.\u001b[39mappend(steps[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m+\u001b[39mstep)\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:111\u001b[0m, in \u001b[0;36mselect_best_dbr_generator\u001b[0;34m(dbi_object, d_list, step, compare_canonical, scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 110\u001b[0m step_best \u001b[38;5;241m=\u001b[39m step\n\u001b[0;32m--> 111\u001b[0m \u001b[43mdbi_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep_best\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mflip_list\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 112\u001b[0m optimal_steps[i] \u001b[38;5;241m=\u001b[39m step_best\n\u001b[1;32m 113\u001b[0m norms_off_diagonal_restriction[i] \u001b[38;5;241m=\u001b[39m dbi_eval\u001b[38;5;241m.\u001b[39moff_diagonal_norm\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:87\u001b[0m, in \u001b[0;36mDoubleBracketIteration.__call__\u001b[0;34m(self, step, mode, d)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 85\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m 86\u001b[0m operator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackend\u001b[38;5;241m.\u001b[39mcalculate_matrix_exp(\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m,\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommutator(d, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mh\u001b[38;5;241m.\u001b[39mmatrix),\n\u001b[1;32m 89\u001b[0m )\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;129;01mis\u001b[39;00m DoubleBracketGeneratorType\u001b[38;5;241m.\u001b[39mgroup_commutator:\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'complex' and 'NoneType'" + ] + } + ], "source": [ "NSTEPS = 8\n", "scheduling_list = [DoubleBracketScheduling.grid_search,\n", @@ -374,9 +566,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", + "[Qibo 0.2.5|INFO|2024-03-05 15:10:49]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 37.94733192202055\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -403,25 +611,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], "source": [ - "step, coef = dbi.polynomial_step(n=1)\n", - "print(step, coef)" + "step = dbi.choose_step(n=1)\n", + "print(step)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.026973122528658938 None None\n" + ] + } + ], "source": [ - "step_backup_poly = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.polynomial_approximation, n=1, n_max=5)\n", + "step_backup_poly = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.polynomial_approximation, n=4, n_max=5)\n", "step_backup_grid = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.grid_search, n=1)\n", "step_backup_hyper = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.hyperopt, n=1)\n", "print(step_backup_poly, step_backup_grid, step_backup_hyper)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index ffd371110c..9433befa0d 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -1,15 +1,16 @@ -import math from copy import deepcopy from enum import Enum, auto -from functools import partial from typing import Optional import hyperopt import numpy as np from qibo.hamiltonians import Hamiltonian - -error = 1e-3 +from qibo.models.dbi.utils_scheduling import ( + grid_search_step, + hyperopt_step, + polynomial_step, +) class DoubleBracketGeneratorType(Enum): @@ -27,11 +28,11 @@ class DoubleBracketGeneratorType(Enum): class DoubleBracketScheduling(Enum): """Define the DBI scheduling strategies.""" - hyperopt = auto() + hyperopt = hyperopt_step """Use hyperopt package.""" - grid_search = auto() + grid_search = grid_search_step """Use greedy grid search.""" - polynomial_approximation = auto() + polynomial_approximation = polynomial_step """Use polynomial expansion (analytical) of the loss function.""" @@ -131,135 +132,6 @@ def backend(self): """Get Hamiltonian's backend.""" return self.h0.backend - def grid_search_step( - self, - step_min: float = 1e-5, - step_max: float = 1, - num_evals: int = 100, - space: Optional[np.array] = None, - d: Optional[np.array] = None, - ): - """ - Greedy optimization of the iteration step. - - Args: - step_min: lower bound of the search grid; - step_max: upper bound of the search grid; - mnum_evals: number of iterations between step_min and step_max; - d: diagonal operator for generating double-bracket iterations. - - Returns: - (float): optimized best iteration step (minimizing off-diagonal norm). - """ - if space is None: - space = np.linspace(step_min, step_max, num_evals) - - if d is None: - d = self.diagonal_h_matrix - - loss_list = [self.loss(step, d=d) for step in space] - idx_max_loss = np.argmin(loss_list) - return space[idx_max_loss] - - def hyperopt_step( - self, - step_min: float = 1e-5, - step_max: float = 1, - max_evals: int = 500, - space: callable = None, - optimizer: callable = None, - look_ahead: int = 1, - verbose: bool = False, - d: Optional[np.array] = None, - ): - """ - Optimize iteration step using hyperopt. - - Args: - step_min: lower bound of the search grid; - step_max: upper bound of the search grid; - max_evals: maximum number of iterations done by the hyperoptimizer; - space: see hyperopt.hp possibilities; - optimizer: see hyperopt algorithms; - look_ahead: number of iteration steps to compute the loss function; - verbose: level of verbosity; - d: diagonal operator for generating double-bracket iterations. - - Returns: - (float): optimized best iteration step (minimizing off-diagonal norm). - """ - if space is None: - space = hyperopt.hp.uniform - if optimizer is None: - optimizer = hyperopt.tpe - if d is None: - d = self.diagonal_h_matrix - - space = space("step", step_min, step_max) - best = hyperopt.fmin( - fn=partial(self.loss, d=d, look_ahead=look_ahead), - space=space, - algo=optimizer.suggest, - max_evals=max_evals, - verbose=verbose, - ) - return best["step"] - - def polynomial_step( - self, - n: int = 2, - n_max: int = 5, - d: np.array = None, - backup_scheduling: DoubleBracketScheduling = None, - ): - r""" - Optimizes iteration step by solving the n_th order polynomial expansion of the loss function. - e.g. $n=2$: $2\Trace(\sigma(\Gamma_1 + s\Gamma_2 + s^2/2\Gamma_3)\sigma(\Gamma_0 + s\Gamma_1 + s^2/2\Gamma_2)) - Args: - n (int, optional): the order to which the loss function is expanded. Defaults to 4. - n_max (int, optional): maximum order allowed for recurring calls of `polynomial_step`. Defaults to 5. - d (np.array, optional): diagonal operator, default as $\delta(H)$. - backup_scheduling (`DoubleBracketScheduling`): the scheduling method to use in case no real positive roots are found. - """ - - if d is None: - d = self.diagonal_h_matrix - - if n > n_max: - raise ValueError( - "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." - ) - - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = self.commutator(d, self.sigma(self.h.matrix)) - Gamma_list = self.generate_Gamma_list(n + 2, d) - sigma_Gamma_list = list(map(self.sigma, Gamma_list)) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients for rotation with [W,H] and H - c1 = exp_list.reshape(-1, 1, 1) * sigma_Gamma_list[1:] - c2 = exp_list.reshape(-1, 1, 1) * sigma_Gamma_list[:-1] - # product coefficient - trace_coefficients = [0] * (2 * n + 1) - for k in range(n + 1): - for j in range(n + 1): - power = k + j - product_matrix = c1[k] @ c2[j] - trace_coefficients[power] += 2 * np.trace(product_matrix) - # coefficients from high to low (n:0) - coef = list(reversed(trace_coefficients[: n + 1])) - roots = np.roots(coef) - real_positive_roots = [ - np.real(root) - for root in roots - if np.imag(root) < error and np.real(root) > 0 - ] - # solution exists, return minimum s - if len(real_positive_roots) > 0: - return min(real_positive_roots), coef - # solution does not exist, return None - else: - return None, coef - def choose_step( self, d: Optional[np.array] = None, @@ -271,28 +143,25 @@ def choose_step( ): if scheduling is None: scheduling = self.scheduling - if scheduling is DoubleBracketScheduling.grid_search: - return self.grid_search_step(d=d, **kwargs) - if scheduling is DoubleBracketScheduling.hyperopt: - return self.hyperopt_step(d=d, **kwargs) - if scheduling is DoubleBracketScheduling.polynomial_approximation: - step, coef = self.polynomial_step(d=d, **kwargs) - # if no solution - if step is None: - if ( - backup_scheduling - == DoubleBracketScheduling.polynomial_approximation - and coef is not None - ): - # if `n` is not provided, try default value - kwargs["n"] = kwargs.get("n", 2) - kwargs["n"] += 1 - step, coef = self.polynomial_step(d=d, **kwargs) - # if n==n_max, return None - else: - # Issue: cannot pass kwargs - step = self.choose_step(d=d, scheduling=backup_scheduling) - return step + return scheduling(self, d=d, **kwargs) + # if scheduling is DoubleBracketScheduling.polynomial_approximation: + # step, coef = self.polynomial_step(d=d, **kwargs) + # # if no solution + # if step is None: + # if ( + # backup_scheduling + # == DoubleBracketScheduling.polynomial_approximation + # and coef is not None + # ): + # # if `n` is not provided, try default value + # kwargs["n"] = kwargs.get("n", 2) + # kwargs["n"] += 1 + # step, coef = self.polynomial_step(d=d, **kwargs) + # # if n==n_max, return None + # else: + # # Issue: cannot pass kwargs + # step = self.choose_step(d=d, scheduling=backup_scheduling) + # return step def loss(self, step: float, d: np.array = None, look_ahead: int = 1): """ diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 52f8a33294..b8f4a2ccca 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -1,9 +1,10 @@ +import math from copy import deepcopy from itertools import product from typing import Optional +import hyperopt import numpy as np -from hyperopt import hp, tpe from qibo import symbols from qibo.config import raise_error @@ -150,3 +151,26 @@ def cs_angle_sgn(dbi_object, d): ) ) return np.sign(norm) + + +def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) + Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) + sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients for rotation with [W,H] and H + c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] + c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] + # product coefficient + trace_coefficients = [0] * (2 * n + 1) + for k in range(n + 1): + for j in range(n + 1): + power = k + j + product_matrix = c1[k] @ c2[j] + trace_coefficients[power] += 2 * np.trace(product_matrix) + # coefficients from high to low (n:0) + coef = list(reversed(trace_coefficients[: n + 1])) + return coef diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py new file mode 100644 index 0000000000..d847f33ea5 --- /dev/null +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -0,0 +1,145 @@ +import math +from functools import partial +from typing import Optional + +import hyperopt +import numpy as np + +error = 1e-3 + + +def grid_search_step( + dbi_object, + step_min: float = 1e-5, + step_max: float = 1, + num_evals: int = 100, + space: Optional[np.array] = None, + d: Optional[np.array] = None, +): + """ + Greedy optimization of the iteration step. + + Args: + step_min: lower bound of the search grid; + step_max: upper bound of the search grid; + mnum_evals: number of iterations between step_min and step_max; + d: diagonal operator for generating double-bracket iterations. + + Returns: + (float): optimized best iteration step (minimizing off-diagonal norm). + """ + if space is None: + space = np.linspace(step_min, step_max, num_evals) + + if d is None: + d = dbi_object.diagonal_h_matrix + + loss_list = [dbi_object.loss(step, d=d) for step in space] + idx_max_loss = np.argmin(loss_list) + return space[idx_max_loss] + + +def hyperopt_step( + dbi_object, + step_min: float = 1e-5, + step_max: float = 1, + max_evals: int = 500, + space: callable = None, + optimizer: callable = None, + look_ahead: int = 1, + verbose: bool = False, + d: Optional[np.array] = None, +): + """ + Optimize iteration step using hyperopt. + + Args: + step_min: lower bound of the search grid; + step_max: upper bound of the search grid; + max_evals: maximum number of iterations done by the hyperoptimizer; + space: see hyperopt.hp possibilities; + optimizer: see hyperopt algorithms; + look_ahead: number of iteration steps to compute the loss function; + verbose: level of verbosity; + d: diagonal operator for generating double-bracket iterations. + + Returns: + (float): optimized best iteration step (minimizing off-diagonal norm). + """ + if space is None: + space = hyperopt.hp.uniform + if optimizer is None: + optimizer = hyperopt.tpe + if d is None: + d = dbi_object.diagonal_h_matrix + + space = space("step", step_min, step_max) + best = hyperopt.fmin( + fn=partial(dbi_object.loss, d=d, look_ahead=look_ahead), + space=space, + algo=optimizer.suggest, + max_evals=max_evals, + verbose=verbose, + ) + return best["step"] + + +def polynomial_step( + dbi_object, + n: int = 2, + n_max: int = 5, + d: np.array = None, + coef: Optional[list] = None, +): + r""" + Optimizes iteration step by solving the n_th order polynomial expansion of the loss function. + e.g. $n=2$: $2\Trace(\sigma(\Gamma_1 + s\Gamma_2 + s^2/2\Gamma_3)\sigma(\Gamma_0 + s\Gamma_1 + s^2/2\Gamma_2)) + Args: + n (int, optional): the order to which the loss function is expanded. Defaults to 4. + n_max (int, optional): maximum order allowed for recurring calls of `polynomial_step`. Defaults to 5. + d (np.array, optional): diagonal operator, default as $\delta(H)$. + backup_scheduling (`DoubleBracketScheduling`): the scheduling method to use in case no real positive roots are found. + """ + + if d is None: + d = dbi_object.diagonal_h_matrix + + if n > n_max: + raise ValueError( + "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." + ) + if coef is None: + coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n) + roots = np.roots(coef) + real_positive_roots = [ + np.real(root) for root in roots if np.imag(root) < error and np.real(root) > 0 + ] + # solution exists, return minimum s + if len(real_positive_roots) > 0: + return min(real_positive_roots) + # solution does not exist, return None + else: + return None + + +def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) + Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) + sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients for rotation with [W,H] and H + c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] + c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] + # product coefficient + trace_coefficients = [0] * (2 * n + 1) + for k in range(n + 1): + for j in range(n + 1): + power = k + j + product_matrix = c1[k] @ c2[j] + trace_coefficients[power] += 2 * np.trace(product_matrix) + # coefficients from high to low (n:0) + coef = list(reversed(trace_coefficients[: n + 1])) + return coef From 027dc0006ed12b2370f254f31ec1d67223622792 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 5 Mar 2024 16:00:38 +0800 Subject: [PATCH 025/154] Simplify backup option in `choose_step` --- examples/dbi/dbi_scheduling.ipynb | 308 +++----------------------- src/qibo/models/dbi/double_bracket.py | 32 +-- 2 files changed, 44 insertions(+), 296 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 34a49760b5..f871394c4f 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -43,25 +43,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", - "[Qibo 0.2.5|INFO|2024-03-05 15:06:24]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 37.94733192202055\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -87,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -110,19 +94,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.030312727272727272\n", - "hyperopt_search step: 0.028991467713834373\n", - "polynomial_approximation step: 0.032960905003724034\n" - ] - } - ], + "outputs": [], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n", @@ -136,34 +110,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is:" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.030312727272727272\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -188,18 +137,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:24]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:24]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - } - ], + "outputs": [], "source": [ "# Generate the digaonal operators\n", "Z_str = \"Z\"*nqubits\n", @@ -211,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -229,19 +169,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.30303525252525254 loss -6.165348149025746\n", - "hyperopt_search step: 0.30457003862873383 loss -6.158069649792722\n", - "polynomial_approximation step: 0.040336885340305856 loss -6.149780650249902\n" - ] - } - ], + "outputs": [], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_max=0.6, d=d)\n", @@ -259,27 +189,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.30303525252525254\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -311,18 +223,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.04141414141414142\n", - "hyperopt_search step: 0.041686777442654525\n" - ] - } - ], + "outputs": [], "source": [ "search_range = 0.1\n", "if step_poly < search_range/2:\n", @@ -341,27 +244,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.04141414141414142\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -391,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -401,24 +286,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-05 15:06:25]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 15.582565474255802\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -432,31 +302,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.5|WARNING|2024-03-05 15:06:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - } - ], + "outputs": [], "source": [ "generate_local_Z = generate_Z_operators(nqubits)\n", "Z_ops = list(generate_local_Z.values())\n", @@ -465,48 +313,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----------Scheduling grid search----------\n", - "New optimized step at iteration 1/8: 0.09091818181818181 with operator -IIIZ, loss 12.06390502880173\n", - "New optimized step at iteration 2/8: 0.08081727272727272 with operator -IIIZ, loss 9.635355222386766\n", - "New optimized step at iteration 3/8: 0.09091818181818181 with operator ZZII, loss 8.551120821934958\n", - "New optimized step at iteration 4/8: 0.06061545454545455 with operator -IIIZ, loss 7.454888988134408\n", - "New optimized step at iteration 5/8: 0.09091818181818181 with operator ZZII, loss 6.526038310796992\n", - "New optimized step at iteration 6/8: 0.07071636363636363 with operator -IIIZ, loss 5.906198775254362\n", - "New optimized step at iteration 7/8: 0.07071636363636363 with operator -IIIZ, loss 5.558604782647778\n", - "New optimized step at iteration 8/8: 0.09091818181818181 with operator IIZI, loss 5.248002043412237\n", - "----------Scheduling hyperopt----------\n", - "New optimized step at iteration 1/8: 0.0952680083828445 with operator -IIIZ, loss 12.048611508414254\n", - "New optimized step at iteration 2/8: 0.0759399266935567 with operator -IIIZ, loss 9.647010146511933\n", - "New optimized step at iteration 3/8: 0.0953305046594857 with operator ZZII, loss 8.545812587336131\n", - "New optimized step at iteration 4/8: 0.0638318397684811 with operator -IIIZ, loss 7.391689830573071\n", - "New optimized step at iteration 5/8: 0.0857170843370093 with operator ZZII, loss 6.534188669695426\n", - "New optimized step at iteration 6/8: 0.06796907471647104 with operator -IIIZ, loss 5.930126378868041\n", - "New optimized step at iteration 7/8: 0.06714742808838461 with operator -IIIZ, loss 5.582951426889378\n", - "New optimized step at iteration 8/8: 0.10283381512622272 with operator IIZI, loss 5.253925034956114\n", - "----------Scheduling polynomial----------\n" - ] - }, - { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for *: 'complex' and 'NoneType'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[15], line 20\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m----------Scheduling \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscheduling_labels[i]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m----------\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(NSTEPS):\n\u001b[0;32m---> 20\u001b[0m dbi, idx, step, flip_sign \u001b[38;5;241m=\u001b[39m \u001b[43mselect_best_dbr_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdbi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mZ_ops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscheduling\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompare_canonical\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m off_diagonal_norm_history\u001b[38;5;241m.\u001b[39mappend(dbi\u001b[38;5;241m.\u001b[39moff_diagonal_norm)\n\u001b[1;32m 22\u001b[0m steps\u001b[38;5;241m.\u001b[39mappend(steps[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m+\u001b[39mstep)\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:111\u001b[0m, in \u001b[0;36mselect_best_dbr_generator\u001b[0;34m(dbi_object, d_list, step, compare_canonical, scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 110\u001b[0m step_best \u001b[38;5;241m=\u001b[39m step\n\u001b[0;32m--> 111\u001b[0m \u001b[43mdbi_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep_best\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mflip_list\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 112\u001b[0m optimal_steps[i] \u001b[38;5;241m=\u001b[39m step_best\n\u001b[1;32m 113\u001b[0m norms_off_diagonal_restriction[i] \u001b[38;5;241m=\u001b[39m dbi_eval\u001b[38;5;241m.\u001b[39moff_diagonal_norm\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:87\u001b[0m, in \u001b[0;36mDoubleBracketIteration.__call__\u001b[0;34m(self, step, mode, d)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 85\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m 86\u001b[0m operator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackend\u001b[38;5;241m.\u001b[39mcalculate_matrix_exp(\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m,\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommutator(d, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mh\u001b[38;5;241m.\u001b[39mmatrix),\n\u001b[1;32m 89\u001b[0m )\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;129;01mis\u001b[39;00m DoubleBracketGeneratorType\u001b[38;5;241m.\u001b[39mgroup_commutator:\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'complex' and 'NoneType'" - ] - } - ], + "outputs": [], "source": [ "NSTEPS = 8\n", "scheduling_list = [DoubleBracketScheduling.grid_search,\n", @@ -566,25 +375,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", - "[Qibo 0.2.5|INFO|2024-03-05 15:10:49]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 37.94733192202055\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -609,50 +402,17 @@ "For demonstration purposes, we let `n=1` which is a linear fit to the loss function. This results in no valid solutions and function `polynomial_step` returns `None`." ] }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n" - ] - } - ], - "source": [ - "step = dbi.choose_step(n=1)\n", - "print(step)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.026973122528658938 None None\n" - ] - } - ], - "source": [ - "step_backup_poly = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.polynomial_approximation, n=4, n_max=5)\n", - "step_backup_grid = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.grid_search, n=1)\n", - "step_backup_hyper = dbi.choose_step(backup_scheduling=DoubleBracketScheduling.hyperopt, n=1)\n", - "print(step_backup_poly, step_backup_grid, step_backup_hyper)" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "for n in range (5):\n", + " step = polynomial_step(dbi, n=n)\n", + " print(n, step)\n", + "print(dbi.choose_step(n=1))" + ] } ], "metadata": { diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 9433befa0d..fe5ef050f8 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -136,32 +136,20 @@ def choose_step( self, d: Optional[np.array] = None, scheduling: Optional[DoubleBracketScheduling] = None, - backup_scheduling: Optional[ - DoubleBracketScheduling - ] = DoubleBracketScheduling.hyperopt, **kwargs, ): if scheduling is None: scheduling = self.scheduling - return scheduling(self, d=d, **kwargs) - # if scheduling is DoubleBracketScheduling.polynomial_approximation: - # step, coef = self.polynomial_step(d=d, **kwargs) - # # if no solution - # if step is None: - # if ( - # backup_scheduling - # == DoubleBracketScheduling.polynomial_approximation - # and coef is not None - # ): - # # if `n` is not provided, try default value - # kwargs["n"] = kwargs.get("n", 2) - # kwargs["n"] += 1 - # step, coef = self.polynomial_step(d=d, **kwargs) - # # if n==n_max, return None - # else: - # # Issue: cannot pass kwargs - # step = self.choose_step(d=d, scheduling=backup_scheduling) - # return step + step = scheduling(self, d=d, **kwargs) + if ( + step is None + and scheduling == DoubleBracketScheduling.polynomial_approximation + ): + kwargs["n"] = kwargs.get("n", 3) + kwargs["n"] += 1 + # if n==n_max, return None + step = scheduling(self, d=d, **kwargs) + return step def loss(self, step: float, d: np.array = None, look_ahead: int = 1): """ From 743f947ab79e7e5822fbc928456b6fe7231ca233 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 6 Mar 2024 09:08:29 +0800 Subject: [PATCH 026/154] Fix test for new structure --- examples/dbi/dbi_scheduling.ipynb | 4 ++-- tests/test_models_dbi.py | 23 +++++++---------------- 2 files changed, 9 insertions(+), 18 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index f871394c4f..3440716a85 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -276,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 111079f8a7..1dcbb960d0 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -72,25 +72,25 @@ def test_hyperopt_step(backend, nqubits): h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration(Hamiltonian(nqubits, h0, backend=backend)) - + dbi.scheduling = DoubleBracketScheduling.hyperopt # find initial best step with look_ahead = 1 initial_step = 0.01 delta = 0.02 - step = dbi.hyperopt_step( + step = dbi.choose_step( step_min=initial_step - delta, step_max=initial_step + delta, max_evals=100 ) assert step != initial_step - # evolve following the optimized first step + # evolve following with optimized first step for generator in DoubleBracketGeneratorType: dbi(mode=generator, step=step, d=d) # find the following step size with look_ahead look_ahead = 3 - step = dbi.hyperopt_step( + step = dbi.choose_step( step_min=initial_step - delta, step_max=initial_step + delta, max_evals=100, @@ -134,13 +134,8 @@ def test_double_bracket_iteration_scheduling_grid_hyperopt( @pytest.mark.parametrize("nqubits", [3, 4, 6]) -@pytest.mark.parametrize("n", [2, 3]) -@pytest.mark.parametrize( - "backup_scheduling", [None, DoubleBracketScheduling.polynomial_approximation] -) -def test_double_bracket_iteration_scheduling_polynomial( - backend, nqubits, n, backup_scheduling -): +@pytest.mark.parametrize("n", [2, 4]) +def test_double_bracket_iteration_scheduling_polynomial(backend, nqubits, n): h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( @@ -150,10 +145,6 @@ def test_double_bracket_iteration_scheduling_polynomial( ) initial_off_diagonal_norm = dbi.off_diagonal_norm for _ in range(NSTEPS): - step1 = dbi.choose_step(n=n, backup_scheduling=backup_scheduling) + step1 = dbi.choose_step(d=d, n=n) dbi(d=d, step=step1) - # step2 = dbi.choose_step( - # scheduling=DoubleBracketScheduling.polynomial_approximation, n=n - # ) - # dbi(step=step2) assert initial_off_diagonal_norm > dbi.off_diagonal_norm From 00f6d8c89b03fccb3494779dbb25b0f1aa360a82 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 6 Mar 2024 09:34:04 +0800 Subject: [PATCH 027/154] Test coverage for fail cases in polynomial step --- tests/test_models_dbi.py | 6 ++--- tests/test_models_dbi_utils.py | 1 + tests/test_models_dbi_utils_scheduling.py | 30 +++++++++++++++++++++++ 3 files changed, 34 insertions(+), 3 deletions(-) create mode 100644 tests/test_models_dbi_utils_scheduling.py diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 1dcbb960d0..5fa277f7ac 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -137,7 +137,6 @@ def test_double_bracket_iteration_scheduling_grid_hyperopt( @pytest.mark.parametrize("n", [2, 4]) def test_double_bracket_iteration_scheduling_polynomial(backend, nqubits, n): h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.single_commutator, @@ -145,6 +144,7 @@ def test_double_bracket_iteration_scheduling_polynomial(backend, nqubits, n): ) initial_off_diagonal_norm = dbi.off_diagonal_norm for _ in range(NSTEPS): - step1 = dbi.choose_step(d=d, n=n) - dbi(d=d, step=step1) + # by default, d is the diagonal resctriction of H + step1 = dbi.choose_step(n=n) + dbi(step=step1) assert initial_off_diagonal_norm > dbi.off_diagonal_norm diff --git a/tests/test_models_dbi_utils.py b/tests/test_models_dbi_utils.py index cd9f74e9de..a19ee502c5 100644 --- a/tests/test_models_dbi_utils.py +++ b/tests/test_models_dbi_utils.py @@ -37,6 +37,7 @@ def test_select_best_dbr_generator(backend, nqubits, step): dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.single_commutator, + scheduling=DoubleBracketScheduling.grid_search, ) generate_Z = generate_Z_operators(nqubits) Z_ops = list(generate_Z.values()) diff --git a/tests/test_models_dbi_utils_scheduling.py b/tests/test_models_dbi_utils_scheduling.py new file mode 100644 index 0000000000..392727f144 --- /dev/null +++ b/tests/test_models_dbi_utils_scheduling.py @@ -0,0 +1,30 @@ +"""Unit testing for utils_scheduling.py for Double Bracket Iteration""" + +import numpy as np +import pytest + +from qibo.hamiltonians import Hamiltonian +from qibo.models.dbi.double_bracket import ( + DoubleBracketGeneratorType, + DoubleBracketIteration, + DoubleBracketScheduling, +) +from qibo.models.dbi.utils_scheduling import polynomial_step +from qibo.quantum_info import random_hermitian + +NSTEPS = 1 +seed = 10 +"""Number of steps for evolution.""" + + +@pytest.mark.parametrize("nqubits", [5, 6]) +def test_polynomial_fail_cases(backend, nqubits): + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + scheduling=DoubleBracketScheduling.polynomial_approximation, + ) + with pytest.raises(ValueError): + polynomial_step(dbi, n=2, n_max=1) + assert polynomial_step(dbi, n=1) == None From 08f8977e924151f9d5957da2c683f6f9a0dbf426 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Tue, 12 Mar 2024 17:25:43 +0100 Subject: [PATCH 028/154] added new cost functions --- src/qibo/models/dbi/double_bracket.py | 30 +++++++++++++- src/qibo/models/dbi/utils.py | 1 + src/qibo/models/dbi/utils_scheduling.py | 52 +++++++++++++++++++++++-- 3 files changed, 78 insertions(+), 5 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index fe5ef050f8..e5db4629ee 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -35,6 +35,15 @@ class DoubleBracketScheduling(Enum): polynomial_approximation = polynomial_step """Use polynomial expansion (analytical) of the loss function.""" +class DoubleBracketCostFunction(Enum): + """Define the DBI cost function.""" + + off_diagonal_norm = auto() + """Use off-diagonal norm as cost function.""" + least_squares = auto() + """Use least squares as cost function.""" + energy_fluctuation = auto() + """Use energy fluctuation as cost function.""" class DoubleBracketIteration: """ @@ -65,11 +74,15 @@ def __init__( hamiltonian: Hamiltonian, mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.canonical, scheduling: DoubleBracketScheduling = DoubleBracketScheduling.grid_search, + cost: DoubleBracketCostFunction = DoubleBracketCostFunction.off_diagonal_norm, + state: int = 0, ): self.h = hamiltonian self.h0 = deepcopy(self.h) self.mode = mode self.scheduling = scheduling + self.cost = cost + self.state = state def __call__( self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None @@ -126,6 +139,14 @@ def off_diagonal_norm(self): return np.sqrt( np.real(np.trace(self.backend.to_numpy(off_diag_h_dag @ self.off_diag_h))) ) + @property + def least_squares(self,d: np.array): + """Least squares cost function.""" + H = self.backend.cast( + np.matrix(self.backend.to_numpy(self.h)).getH() + ) + D = d + return -(np.linalg.trace(H@D)-0.5(np.linalg.norm(H)**2+np.linalg.norm(D)**2)) @property def backend(self): @@ -166,8 +187,13 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): for _ in range(look_ahead): self.__call__(mode=self.mode, step=step, d=d) - # off_diagonal_norm's value after the steps - loss = self.off_diagonal_norm + # loss values depending on the cost function + if self.cost == DoubleBracketCostFunction.off_diagonal_norm: + loss = self.off_diagonal_norm + elif self.cost == DoubleBracketCostFunction.least_squares: + loss = self.least_squares(d=d) + else: + loss = self.energy_fluctuation(self.state) # set back the initial configuration self.h = h_copy diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index b8f4a2ccca..b1dd5daf61 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -174,3 +174,4 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): # coefficients from high to low (n:0) coef = list(reversed(trace_coefficients[: n + 1])) return coef + diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index d847f33ea5..5ceb3eba64 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -7,6 +7,15 @@ error = 1e-3 +def variance(A, state): + """Calculates the variance of a matrix A with respect to a state: Var($A$) = $\langle\mu|A^2|\mu\rangle-\langle\mu|A|\mu\rangle^2$""" + B = A@A + return B[state,state]-A[state,state]**2 + +def covariance(A, B, state): + """Calculates the covariance of two matrices A and B with respect to a state: Cov($A,B$) = $\langle\mu|AB|\mu\rangle-\langle\mu|A|\mu\rangle\langle\mu|B|\mu\rangle$""" + C = A@B + return C[state,state]-A[state,state]*B[state,state] def grid_search_step( dbi_object, @@ -64,7 +73,7 @@ def hyperopt_step( d: diagonal operator for generating double-bracket iterations. Returns: - (float): optimized best iteration step (minimizing off-diagonal norm). + (float): optimized best iteration step (minimizing loss function). """ if space is None: space = hyperopt.hp.uniform @@ -90,6 +99,7 @@ def polynomial_step( n_max: int = 5, d: np.array = None, coef: Optional[list] = None, + cost: str = None, ): r""" Optimizes iteration step by solving the n_th order polynomial expansion of the loss function. @@ -100,7 +110,8 @@ def polynomial_step( d (np.array, optional): diagonal operator, default as $\delta(H)$. backup_scheduling (`DoubleBracketScheduling`): the scheduling method to use in case no real positive roots are found. """ - + if cost is None: + cost = dbi_object.cost if d is None: d = dbi_object.diagonal_h_matrix @@ -109,7 +120,15 @@ def polynomial_step( "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." ) if coef is None: - coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n) + if cost == "off_diagonal_norm": + coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n) + elif cost == "least_squares": + coef = least_squares_polynomial_expansion_coef(dbi_object, d, n) + elif cost == "energy_fluctuation": + coef = energy_fluctuation_polynomial_expansion_coef(dbi_object, d, n, dbi_object.state) + else: + raise ValueError(f"Cost function {cost} not recognized.") + roots = np.roots(coef) real_positive_roots = [ np.real(root) for root in roots if np.imag(root) < error and np.real(root) > 0 @@ -143,3 +162,30 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): # coefficients from high to low (n:0) coef = list(reversed(trace_coefficients[: n + 1])) return coef + +def least_squares_polynomial_expansion_coef(dbi_object, d, n): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) + Gamma_list = dbi_object.generate_Gamma_list(n, d) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients + coef = np.empty(n) + for i in range(n): + coef[i] = exp_list[i]*np.trace(d@Gamma_list[i+1]) + + return coef + +#TODO: add a general expansion formula not stopping at 3rd order +def energy_fluctuation_polynomial_expansion_coef(dbi_object, d, n, state): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + Gamma_list = dbi_object.generate_Gamma_list(n, d) + # coefficients + coef = np.empty(3) + coef[0] = 2*covariance(Gamma_list[0], Gamma_list[1],state) + coef[1] = 2*variance(Gamma_list[1],state) + coef[2] = covariance(Gamma_list[0], Gamma_list[3],state)+3*covariance(Gamma_list[1], Gamma_list[2],state) + return coef From f2061f80a549d9f4289674ca8720fce96ef567ff Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Tue, 12 Mar 2024 17:40:23 +0100 Subject: [PATCH 029/154] bug fix: use string of cost function --- examples/dbi/dbi_scheduling.ipynb | 51 +++++++++++++++++++++---- src/qibo/models/dbi/double_bracket.py | 1 + src/qibo/models/dbi/utils_scheduling.py | 2 +- 3 files changed, 45 insertions(+), 9 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 3440716a85..1953c1272d 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -43,9 +43,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-12 17:24:06]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 37.94733192202055\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"qibojit\", \"numba\")\n", @@ -71,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -94,9 +109,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "loss() got an unexpected keyword argument 'state'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[16], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m# grid_search\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m step_grid \u001b[39m=\u001b[39m dbi\u001b[39m.\u001b[39;49mchoose_step(scheduling\u001b[39m=\u001b[39;49mDoubleBracketScheduling\u001b[39m.\u001b[39;49mgrid_search)\n\u001b[0;32m 3\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mgrid_search step:\u001b[39m\u001b[39m'\u001b[39m, step_grid)\n\u001b[0;32m 4\u001b[0m \u001b[39m# hyperopt\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\andre\\Documents\\GitHub\\qibo\\.conda\\lib\\site-packages\\qibo\\models\\dbi\\double_bracket.py:164\u001b[0m, in \u001b[0;36mDoubleBracketIteration.choose_step\u001b[1;34m(self, d, scheduling, **kwargs)\u001b[0m\n\u001b[0;32m 162\u001b[0m \u001b[39mif\u001b[39;00m scheduling \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 163\u001b[0m scheduling \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscheduling\n\u001b[1;32m--> 164\u001b[0m step \u001b[39m=\u001b[39m scheduling(\u001b[39mself\u001b[39m, d\u001b[39m=\u001b[39md, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 165\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[0;32m 166\u001b[0m step \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m 167\u001b[0m \u001b[39mand\u001b[39;00m scheduling \u001b[39m==\u001b[39m DoubleBracketScheduling\u001b[39m.\u001b[39mpolynomial_approximation\n\u001b[0;32m 168\u001b[0m ):\n\u001b[0;32m 169\u001b[0m kwargs[\u001b[39m\"\u001b[39m\u001b[39mn\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mn\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m3\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\andre\\Documents\\GitHub\\qibo\\.conda\\lib\\site-packages\\qibo\\models\\dbi\\utils_scheduling.py:47\u001b[0m, in \u001b[0;36mgrid_search_step\u001b[1;34m(dbi_object, step_min, step_max, num_evals, space, d, state)\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[39mif\u001b[39;00m d \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 45\u001b[0m d \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m---> 47\u001b[0m loss_list \u001b[39m=\u001b[39m [dbi_object\u001b[39m.\u001b[39mloss(step, d\u001b[39m=\u001b[39md, state\u001b[39m=\u001b[39mstate) \u001b[39mfor\u001b[39;00m step \u001b[39min\u001b[39;00m space]\n\u001b[0;32m 48\u001b[0m idx_max_loss \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margmin(loss_list)\n\u001b[0;32m 49\u001b[0m \u001b[39mreturn\u001b[39;00m space[idx_max_loss]\n", + "File \u001b[1;32mc:\\Users\\andre\\Documents\\GitHub\\qibo\\.conda\\lib\\site-packages\\qibo\\models\\dbi\\utils_scheduling.py:47\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[39mif\u001b[39;00m d \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 45\u001b[0m d \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m---> 47\u001b[0m loss_list \u001b[39m=\u001b[39m [dbi_object\u001b[39m.\u001b[39;49mloss(step, d\u001b[39m=\u001b[39;49md, state\u001b[39m=\u001b[39;49mstate) \u001b[39mfor\u001b[39;00m step \u001b[39min\u001b[39;00m space]\n\u001b[0;32m 48\u001b[0m idx_max_loss \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margmin(loss_list)\n\u001b[0;32m 49\u001b[0m \u001b[39mreturn\u001b[39;00m space[idx_max_loss]\n", + "\u001b[1;31mTypeError\u001b[0m: loss() got an unexpected keyword argument 'state'" + ] + } + ], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n", @@ -417,7 +447,7 @@ ], "metadata": { "kernelspec": { - "display_name": "DBF_qibo", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -431,7 +461,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.9.18" + }, + "vscode": { + "interpreter": { + "hash": "48caf7dabad7b721a854729228548373f17e53f40870080394d552284aea7c35" + } } }, "nbformat": 4, diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index e5db4629ee..0446ffbb9a 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -82,6 +82,7 @@ def __init__( self.mode = mode self.scheduling = scheduling self.cost = cost + self.cost_str = cost.name self.state = state def __call__( diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 5ceb3eba64..2017b57cd1 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -111,7 +111,7 @@ def polynomial_step( backup_scheduling (`DoubleBracketScheduling`): the scheduling method to use in case no real positive roots are found. """ if cost is None: - cost = dbi_object.cost + cost = dbi_object.cost.name if d is None: d = dbi_object.diagonal_h_matrix From 93eb20a5743aa57f17f31b3b72dbd6a02de64262 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Fri, 15 Mar 2024 20:18:35 +0100 Subject: [PATCH 030/154] added full diagonal gradients and a tutorial file --- examples/dbi/dbi_costs.ipynb | 548 ++++++++++++++++++++++++ src/qibo/models/dbi/double_bracket.py | 23 +- src/qibo/models/dbi/utils_scheduling.py | 103 ++++- 3 files changed, 645 insertions(+), 29 deletions(-) create mode 100644 examples/dbi/dbi_costs.ipynb diff --git a/examples/dbi/dbi_costs.ipynb b/examples/dbi/dbi_costs.ipynb new file mode 100644 index 0000000000..558f74cff3 --- /dev/null +++ b/examples/dbi/dbi_costs.ipynb @@ -0,0 +1,548 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Double-bracket Iteration other cost functions and respective scheduling\n", + "\n", + "This notebook presents two additional cost functions for the double-bracket flow: least-squares and energy fluctuation with their respectice scheduling methods." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_scheduling import *" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Least-squares\n", + "\n", + "The cost function is defined as: $\\frac{1}{2}||D-H_k||^2 =\\frac{1}{2}(||D||^2+||H||^2) -Tr(D H_k)$ as in https://epubs.siam.org/doi/abs/10.1137/S0036141092229732?journalCode=sjmael. We seek to maximize this function at each iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-15 18:17:05]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCostFunction.least_squares\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.02021181818181818\n", + "hyperopt_search step: 0.2796044748864459\n", + "polynomial_approximation step: 0.016462159944159827\n" + ] + } + ], + "source": [ + "# generate data for plotting sigma decrease of the first step\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "s_space = np.linspace(1e-5, 0.6, 1000)\n", + "off_diagonal_norm_diff = []\n", + "potential = []\n", + "for s in s_space:\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s,d=d)\n", + " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", + " potential.append(dbi_eval.least_squares(D=d))\n", + "\n", + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", + "print('hyperopt_search step:', step_hyperopt)\n", + "# polynomial\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + "print('polynomial_approximation step:', step_poly)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.02021181818181818\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(s_space, potential)\n", + "plt.xlabel('s')\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.title('First DBI step')\n", + "plt.ylabel('Least squares cost function')\n", + "plt.legend()\n", + "plt.figure()\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('First DBI step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparison of the least-squares cost function with the original cost function using the polynomial scheduling method" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", + "off_diagonal_norm_diff_least_squares = [dbi.off_diagonal_norm]\n", + "iters = 100\n", + "dbi_ls = deepcopy(dbi)\n", + "cost = DoubleBracketCostFunction.off_diagonal_norm\n", + "dbi_od = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "for _ in range(iters):\n", + " step_poly = dbi_od.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_od(step_poly,d=d)\n", + " step_poly = dbi_ls.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_ls(step_poly,d=d)\n", + " off_diagonal_norm_diff.append(dbi_od.off_diagonal_norm)\n", + " off_diagonal_norm_diff_least_squares.append(dbi_ls.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff_least_squares, label=r'Least squares')\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Energy fluctuation\n", + "\n", + "This cost function is defined as: $\\Xi_k^2 (\\mu) = \\langle \\mu | H_k^2| \\mu \\rangle - \\langle \\mu | H_k| \\mu \\rangle^2$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-15 18:17:12]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 3\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the energy fluctuation cost function\n", + "cost = DoubleBracketCostFunction.energy_fluctuation\n", + "# define the state\n", + "state = 0\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, state=state)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.8585872727272726\n", + "hyperopt_search step: 0.3413442272248831\n", + "polynomial_approximation step: 0.028303853122485182\n" + ] + } + ], + "source": [ + "# generate data for plotting sigma decrease of the first step\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "s_space = np.linspace(1e-5, 0.9, 1000)\n", + "off_diagonal_norm_diff = []\n", + "fluctuation = []\n", + "for s in s_space:\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s,d=d)\n", + " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", + " fluctuation.append(dbi_eval.energy_fluctuation(state=state))\n", + "\n", + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", + "print('hyperopt_search step:', step_hyperopt)\n", + "# polynomial\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + "print('polynomial_approximation step:', step_poly)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.8585872727272726\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(s_space, fluctuation)\n", + "plt.xlabel('s')\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label ='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.title('First DBI step')\n", + "plt.ylabel('Energy fluctuation')\n", + "plt.legend()\n", + "plt.figure()\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('First DBI step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", + "energy_fluc = [dbi.energy_fluctuation(state=state)]\n", + "iters = 50\n", + "dbi_ = deepcopy(dbi)\n", + "for _ in range(iters):\n", + " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_(step_poly,d=d)\n", + " off_diagonal_norm_diff.append(dbi_.off_diagonal_norm)\n", + " energy_fluc.append(dbi_.energy_fluctuation(state=state))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Energy fluctuation')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n", + "plt.figure()\n", + "plt.plot(range(iters+1), energy_fluc)\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'Energy fluctuation')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "iters = 10\n", + "columnNorm = np.empty((2**nqubits,iters))\n", + "d = (np.diag(np.linspace(1,2**nqubits,2**nqubits)))\n", + "for i in range(2**nqubits):\n", + " dbi_ = deepcopy(dbi)\n", + " dbi_.state = i\n", + " for j in range(iters):\n", + " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_(step_poly,d=d)\n", + " columnNorm[i,j] = np.linalg.norm(dbi_.h.matrix[:,i])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Iterations')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", + "plt.figure()\n", + "for i in range(2**nqubits):\n", + " plt.plot(range(iters), columnNorm[i], label='State ' + str(i))\n", + " plt.axhline(y=eigvals[i], color='r', linestyle='--')\n", + "plt.xlabel('Iterations')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[20], line 8\u001b[0m\n\u001b[0;32m 5\u001b[0m step \u001b[39m=\u001b[39m \u001b[39m1e-2\u001b[39m\n\u001b[0;32m 6\u001b[0m iterations \u001b[39m=\u001b[39m \u001b[39m100\u001b[39m\n\u001b[1;32m----> 8\u001b[0m d, loss, grad, diags \u001b[39m=\u001b[39m gradient_ascent(dbi, d,step, iterations)\n\u001b[0;32m 10\u001b[0m n \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m\n", + "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:253\u001b[0m, in \u001b[0;36mgradient_ascent\u001b[1;34m(dbi_object, d, step, iterations)\u001b[0m\n\u001b[0;32m 250\u001b[0m diagonals[:,\u001b[39m0\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mdiag(d)\n\u001b[0;32m 252\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(iterations):\n\u001b[1;32m--> 253\u001b[0m grad[i,:] \u001b[39m=\u001b[39m gradientDiagonal(dbi_object, d, H)\n\u001b[0;32m 254\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(d)):\n\u001b[0;32m 255\u001b[0m d[j,j] \u001b[39m=\u001b[39m d[j,j] \u001b[39m+\u001b[39m step\u001b[39m*\u001b[39mgrad[i,j] \u001b[39m# note the plus sign as we maximize the potential\u001b[39;00m\n", + "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:237\u001b[0m, in \u001b[0;36mgradientDiagonal\u001b[1;34m(dbi_object, d, H)\u001b[0m\n\u001b[0;32m 235\u001b[0m grad \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros(\u001b[39mlen\u001b[39m(d))\n\u001b[0;32m 236\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(d)):\n\u001b[1;32m--> 237\u001b[0m derivative \u001b[39m=\u001b[39m dpolynomial_diDiagonal(dbi_object,d,H,i)\n\u001b[0;32m 238\u001b[0m grad[i] \u001b[39m=\u001b[39m derivative\u001b[39m-\u001b[39md[i,i]\n\u001b[0;32m 239\u001b[0m \u001b[39mreturn\u001b[39;00m grad\n", + "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:224\u001b[0m, in \u001b[0;36mdpolynomial_diDiagonal\u001b[1;34m(dbi_object, d, H, i)\u001b[0m\n\u001b[0;32m 220\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdpolynomial_diDiagonal\u001b[39m(dbi_object, d,H,i):\n\u001b[0;32m 221\u001b[0m \u001b[39m# Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz)\u001b[39;00m\n\u001b[0;32m 222\u001b[0m \u001b[39m# Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation\u001b[39;00m\n\u001b[0;32m 223\u001b[0m derivative \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m--> 224\u001b[0m s \u001b[39m=\u001b[39m polynomial_step(dbi_object, d, H, i)\n\u001b[0;32m 225\u001b[0m A \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros(d\u001b[39m.\u001b[39mshape)\n\u001b[0;32m 226\u001b[0m Gamma_list \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mgenerate_Gamma_list(\u001b[39m4\u001b[39m, d)\n", + "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:127\u001b[0m, in \u001b[0;36mpolynomial_step\u001b[1;34m(dbi_object, n, n_max, d, coef, cost)\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[39mif\u001b[39;00m d \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 125\u001b[0m d \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m--> 127\u001b[0m \u001b[39mif\u001b[39;00m n \u001b[39m>\u001b[39;49m n_max:\n\u001b[0;32m 128\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 129\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mNo solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 130\u001b[0m )\n\u001b[0;32m 131\u001b[0m \u001b[39mif\u001b[39;00m coef \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", + "\u001b[1;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], + "source": [ + "cost = DoubleBracketCostFunction.least_squares\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "\n", + "step = 1e-2\n", + "iterations = 100\n", + "\n", + "d, loss, grad, diags = gradient_ascent(dbi, d,step, iterations)\n", + "\n", + "n = 3" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "c4f92193806e2908606a5f23edd55a5282f2f433b73b1c504507f9256ed9f0b4" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 0446ffbb9a..2069296058 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -82,7 +82,6 @@ def __init__( self.mode = mode self.scheduling = scheduling self.cost = cost - self.cost_str = cost.name self.state = state def __call__( @@ -100,7 +99,7 @@ def __call__( if d is None: d = self.diagonal_h_matrix operator = self.backend.calculate_matrix_exp( - 1.0j * step, + 1.0j*step, self.commutator(d, self.h.matrix), ) elif mode is DoubleBracketGeneratorType.group_commutator: @@ -115,6 +114,7 @@ def __call__( operator_dagger = self.backend.cast( np.matrix(self.backend.to_numpy(operator)).getH() ) + self.h.matrix = operator @ self.h.matrix @ operator_dagger @staticmethod @@ -140,20 +140,17 @@ def off_diagonal_norm(self): return np.sqrt( np.real(np.trace(self.backend.to_numpy(off_diag_h_dag @ self.off_diag_h))) ) - @property - def least_squares(self,d: np.array): - """Least squares cost function.""" - H = self.backend.cast( - np.matrix(self.backend.to_numpy(self.h)).getH() - ) - D = d - return -(np.linalg.trace(H@D)-0.5(np.linalg.norm(H)**2+np.linalg.norm(D)**2)) @property def backend(self): """Get Hamiltonian's backend.""" return self.h0.backend + def least_squares(self, D: np.array): + """Least squares cost function.""" + H = self.h.matrix + return -np.real(np.trace(H@D)-0.5*(np.linalg.norm(H)**2+np.linalg.norm(D)**2)) + def choose_step( self, d: Optional[np.array] = None, @@ -192,7 +189,7 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): if self.cost == DoubleBracketCostFunction.off_diagonal_norm: loss = self.off_diagonal_norm elif self.cost == DoubleBracketCostFunction.least_squares: - loss = self.least_squares(d=d) + loss = self.least_squares(d) else: loss = self.energy_fluctuation(self.state) @@ -213,7 +210,9 @@ def energy_fluctuation(self, state): Args: state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ - return self.h.energy_fluctuation(state) + state_vector = np.zeros(len(self.h.matrix)) + state_vector[state] = 1.0 + return np.real(self.h.energy_fluctuation(state_vector)) def sigma(self, h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 2017b57cd1..5f217cc948 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -1,12 +1,17 @@ import math from functools import partial from typing import Optional - +from copy import deepcopy import hyperopt import numpy as np + error = 1e-3 +def commutator(A, B): + """Compute commutator between two arrays.""" + return A@B-B@A + def variance(A, state): """Calculates the variance of a matrix A with respect to a state: Var($A$) = $\langle\mu|A^2|\mu\rangle-\langle\mu|A|\mu\rangle^2$""" B = A@A @@ -14,8 +19,8 @@ def variance(A, state): def covariance(A, B, state): """Calculates the covariance of two matrices A and B with respect to a state: Cov($A,B$) = $\langle\mu|AB|\mu\rangle-\langle\mu|A|\mu\rangle\langle\mu|B|\mu\rangle$""" - C = A@B - return C[state,state]-A[state,state]*B[state,state] + C = A@B+B@A + return C[state,state]-2*A[state,state]*B[state,state] def grid_search_step( dbi_object, @@ -44,6 +49,7 @@ def grid_search_step( d = dbi_object.diagonal_h_matrix loss_list = [dbi_object.loss(step, d=d) for step in space] + idx_max_loss = np.argmin(loss_list) return space[idx_max_loss] @@ -83,12 +89,14 @@ def hyperopt_step( d = dbi_object.diagonal_h_matrix space = space("step", step_min, step_max) + + best = hyperopt.fmin( - fn=partial(dbi_object.loss, d=d, look_ahead=look_ahead), - space=space, - algo=optimizer.suggest, - max_evals=max_evals, - verbose=verbose, + fn=partial(dbi_object.loss, d=d, look_ahead=look_ahead), + space=space, + algo=optimizer.suggest, + max_evals=max_evals, + verbose=verbose, ) return best["step"] @@ -112,6 +120,7 @@ def polynomial_step( """ if cost is None: cost = dbi_object.cost.name + if d is None: d = dbi_object.diagonal_h_matrix @@ -135,7 +144,11 @@ def polynomial_step( ] # solution exists, return minimum s if len(real_positive_roots) > 0: - return min(real_positive_roots) + sol = min(real_positive_roots) + for s in real_positive_roots: + if dbi_object.loss(s, d) < dbi_object.loss(sol, d): + sol = s + return sol # solution does not exist, return None else: return None @@ -167,14 +180,13 @@ def least_squares_polynomial_expansion_coef(dbi_object, d, n): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) - Gamma_list = dbi_object.generate_Gamma_list(n, d) + Gamma_list = dbi_object.generate_Gamma_list(n+1, d) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients coef = np.empty(n) for i in range(n): - coef[i] = exp_list[i]*np.trace(d@Gamma_list[i+1]) - + coef[i] = np.real(exp_list[i]*np.trace(d@Gamma_list[i+1])) + coef = list(reversed(coef)) return coef #TODO: add a general expansion formula not stopping at 3rd order @@ -182,10 +194,67 @@ def energy_fluctuation_polynomial_expansion_coef(dbi_object, d, n, state): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n, d) + Gamma_list = dbi_object.generate_Gamma_list(n+1, d) # coefficients coef = np.empty(3) - coef[0] = 2*covariance(Gamma_list[0], Gamma_list[1],state) - coef[1] = 2*variance(Gamma_list[1],state) - coef[2] = covariance(Gamma_list[0], Gamma_list[3],state)+3*covariance(Gamma_list[1], Gamma_list[2],state) + coef[0] = np.real(2*covariance(Gamma_list[0], Gamma_list[1],state)) + coef[1] = np.real(2*variance(Gamma_list[1],state)) + coef[2] = np.real(covariance(Gamma_list[0], Gamma_list[3],state)+3*covariance(Gamma_list[1], Gamma_list[2],state)) + coef = list(reversed(coef)) return coef + +def dGamma_diDiagonal(dbi_object, d, H, n, i,dGamma, Gamma_list): + # Derivative of gamma with respect to diagonal elements of D (full-diagonal ansatz) + A = np.zeros(d.shape) + A[i,i] = 1 + B = commutator(commutator(A,H),Gamma_list[n-1]) + W = commutator(d,H) + return B + commutator(W,dGamma[-1]) + +def dpolynomial_diDiagonal(dbi_object, d,H,i): + # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) + # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation + derivative = 0 + s = polynomial_step(dbi_object, n=3, d=d) + A = np.zeros(d.shape) + Gamma_list = dbi_object.generate_Gamma_list(4, d) + A[i,i] = 1 + dGamma = [commutator(A,H)] + derivative += np.real(np.trace(Gamma_list[0]@A)+np.trace(dGamma[0]@d+Gamma_list[1]@A)*s) + for n in range(2,4): + dGamma.append(dGamma_diDiagonal(dbi_object,d,H,n,i,dGamma,Gamma_list)) + derivative += np.real(np.trace(dGamma[-1]@d + Gamma_list[n]@A)*s**n/math.factorial(n)) + + return derivative + +def gradientDiagonal(dbi_object,d,H): + # Gradient of potential function with respect to diagonal elements of D (full-diagonal ansatz) + grad = np.zeros(len(d)) + for i in range(len(d)): + derivative = dpolynomial_diDiagonal(dbi_object,d,H,i) + grad[i] = d[i,i]-derivative + return grad + +def gradient_ascent(dbi_object, d, step, iterations): + H = dbi_object.h.matrix + loss = np.zeros(iterations+1) + grad = np.zeros((iterations,len(d))) + dbi_new = deepcopy(dbi_object) + s = polynomial_step(dbi_object, n = 3, d=d) + dbi_new(s,d=d) + loss[0] = dbi_new(d) + diagonals = np.empty((len(d),iterations+1)) + diagonals[:,0] = np.diag(d) + + for i in range(iterations): + dbi_new = deepcopy(dbi_object) + grad[i,:] = gradientDiagonal(dbi_object, d, H) + for j in range(len(d)): + d[j,j] = d[j,j] - step*grad[i,j] + s = polynomial_step(dbi_object, n = 3, d=d) + dbi_new(s,d=d) + loss[i+1] = dbi_new.least_squares(d) + diagonals[:,i+1] = np.diag(d) + + + return d,loss,grad,diagonals \ No newline at end of file From f17eb1d611aff90020751d669af4362cfe41f3e8 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Sat, 16 Mar 2024 09:03:10 +0100 Subject: [PATCH 031/154] Revert "Test coverage for fail cases in polynomial step" This reverts commit 00f6d8c89b03fccb3494779dbb25b0f1aa360a82. --- tests/test_models_dbi.py | 6 ++--- tests/test_models_dbi_utils.py | 1 - tests/test_models_dbi_utils_scheduling.py | 30 ----------------------- 3 files changed, 3 insertions(+), 34 deletions(-) delete mode 100644 tests/test_models_dbi_utils_scheduling.py diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 5fa277f7ac..1dcbb960d0 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -137,6 +137,7 @@ def test_double_bracket_iteration_scheduling_grid_hyperopt( @pytest.mark.parametrize("n", [2, 4]) def test_double_bracket_iteration_scheduling_polynomial(backend, nqubits, n): h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.single_commutator, @@ -144,7 +145,6 @@ def test_double_bracket_iteration_scheduling_polynomial(backend, nqubits, n): ) initial_off_diagonal_norm = dbi.off_diagonal_norm for _ in range(NSTEPS): - # by default, d is the diagonal resctriction of H - step1 = dbi.choose_step(n=n) - dbi(step=step1) + step1 = dbi.choose_step(d=d, n=n) + dbi(d=d, step=step1) assert initial_off_diagonal_norm > dbi.off_diagonal_norm diff --git a/tests/test_models_dbi_utils.py b/tests/test_models_dbi_utils.py index a19ee502c5..cd9f74e9de 100644 --- a/tests/test_models_dbi_utils.py +++ b/tests/test_models_dbi_utils.py @@ -37,7 +37,6 @@ def test_select_best_dbr_generator(backend, nqubits, step): dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.single_commutator, - scheduling=DoubleBracketScheduling.grid_search, ) generate_Z = generate_Z_operators(nqubits) Z_ops = list(generate_Z.values()) diff --git a/tests/test_models_dbi_utils_scheduling.py b/tests/test_models_dbi_utils_scheduling.py deleted file mode 100644 index 392727f144..0000000000 --- a/tests/test_models_dbi_utils_scheduling.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Unit testing for utils_scheduling.py for Double Bracket Iteration""" - -import numpy as np -import pytest - -from qibo.hamiltonians import Hamiltonian -from qibo.models.dbi.double_bracket import ( - DoubleBracketGeneratorType, - DoubleBracketIteration, - DoubleBracketScheduling, -) -from qibo.models.dbi.utils_scheduling import polynomial_step -from qibo.quantum_info import random_hermitian - -NSTEPS = 1 -seed = 10 -"""Number of steps for evolution.""" - - -@pytest.mark.parametrize("nqubits", [5, 6]) -def test_polynomial_fail_cases(backend, nqubits): - h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.single_commutator, - scheduling=DoubleBracketScheduling.polynomial_approximation, - ) - with pytest.raises(ValueError): - polynomial_step(dbi, n=2, n_max=1) - assert polynomial_step(dbi, n=1) == None From b0c6958958caa31827a4764902ad6b7855a68188 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 19 Mar 2024 14:54:14 +0800 Subject: [PATCH 032/154] Scheduling test --- tests/test_models_dbi_utils_scheduling.py | 30 +++++++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 tests/test_models_dbi_utils_scheduling.py diff --git a/tests/test_models_dbi_utils_scheduling.py b/tests/test_models_dbi_utils_scheduling.py new file mode 100644 index 0000000000..392727f144 --- /dev/null +++ b/tests/test_models_dbi_utils_scheduling.py @@ -0,0 +1,30 @@ +"""Unit testing for utils_scheduling.py for Double Bracket Iteration""" + +import numpy as np +import pytest + +from qibo.hamiltonians import Hamiltonian +from qibo.models.dbi.double_bracket import ( + DoubleBracketGeneratorType, + DoubleBracketIteration, + DoubleBracketScheduling, +) +from qibo.models.dbi.utils_scheduling import polynomial_step +from qibo.quantum_info import random_hermitian + +NSTEPS = 1 +seed = 10 +"""Number of steps for evolution.""" + + +@pytest.mark.parametrize("nqubits", [5, 6]) +def test_polynomial_fail_cases(backend, nqubits): + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + scheduling=DoubleBracketScheduling.polynomial_approximation, + ) + with pytest.raises(ValueError): + polynomial_step(dbi, n=2, n_max=1) + assert polynomial_step(dbi, n=1) == None From 032603972a3eadde65cba30334498ba2d9209f42 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 19 Mar 2024 15:30:57 +0800 Subject: [PATCH 033/154] Initial commits for SA --- src/qibo/models/dbi/double_bracket.py | 11 +- src/qibo/models/dbi/utils_scheduling.py | 189 +++++++++++++++++------- tests/test_models_dbi.py | 8 +- 3 files changed, 147 insertions(+), 61 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 2069296058..941e5dccd4 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -10,6 +10,7 @@ grid_search_step, hyperopt_step, polynomial_step, + simulated_annealing_step, ) @@ -34,6 +35,9 @@ class DoubleBracketScheduling(Enum): """Use greedy grid search.""" polynomial_approximation = polynomial_step """Use polynomial expansion (analytical) of the loss function.""" + simulated_annealing = simulated_annealing_step + """Use simulated annealing algorithm""" + class DoubleBracketCostFunction(Enum): """Define the DBI cost function.""" @@ -45,6 +49,7 @@ class DoubleBracketCostFunction(Enum): energy_fluctuation = auto() """Use energy fluctuation as cost function.""" + class DoubleBracketIteration: """ Class implementing the Double Bracket iteration algorithm. @@ -99,7 +104,7 @@ def __call__( if d is None: d = self.diagonal_h_matrix operator = self.backend.calculate_matrix_exp( - 1.0j*step, + 1.0j * step, self.commutator(d, self.h.matrix), ) elif mode is DoubleBracketGeneratorType.group_commutator: @@ -149,7 +154,9 @@ def backend(self): def least_squares(self, D: np.array): """Least squares cost function.""" H = self.h.matrix - return -np.real(np.trace(H@D)-0.5*(np.linalg.norm(H)**2+np.linalg.norm(D)**2)) + return -np.real( + np.trace(H @ D) - 0.5 * (np.linalg.norm(H) ** 2 + np.linalg.norm(D) ** 2) + ) def choose_step( self, diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 5f217cc948..548a7a8eec 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -1,26 +1,30 @@ import math +from copy import deepcopy from functools import partial from typing import Optional -from copy import deepcopy + import hyperopt import numpy as np - error = 1e-3 + def commutator(A, B): """Compute commutator between two arrays.""" - return A@B-B@A + return A @ B - B @ A + def variance(A, state): - """Calculates the variance of a matrix A with respect to a state: Var($A$) = $\langle\mu|A^2|\mu\rangle-\langle\mu|A|\mu\rangle^2$""" - B = A@A - return B[state,state]-A[state,state]**2 + """Calculates the variance of a matrix A with respect to a state: Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" + B = A @ A + return B[state, state] - A[state, state] ** 2 + def covariance(A, B, state): - """Calculates the covariance of two matrices A and B with respect to a state: Cov($A,B$) = $\langle\mu|AB|\mu\rangle-\langle\mu|A|\mu\rangle\langle\mu|B|\mu\rangle$""" - C = A@B+B@A - return C[state,state]-2*A[state,state]*B[state,state] + """Calculates the covariance of two matrices A and B with respect to a state: Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$""" + C = A @ B + B @ A + return C[state, state] - 2 * A[state, state] * B[state, state] + def grid_search_step( dbi_object, @@ -49,7 +53,7 @@ def grid_search_step( d = dbi_object.diagonal_h_matrix loss_list = [dbi_object.loss(step, d=d) for step in space] - + idx_max_loss = np.argmin(loss_list) return space[idx_max_loss] @@ -89,14 +93,13 @@ def hyperopt_step( d = dbi_object.diagonal_h_matrix space = space("step", step_min, step_max) - - + best = hyperopt.fmin( - fn=partial(dbi_object.loss, d=d, look_ahead=look_ahead), - space=space, - algo=optimizer.suggest, - max_evals=max_evals, - verbose=verbose, + fn=partial(dbi_object.loss, d=d, look_ahead=look_ahead), + space=space, + algo=optimizer.suggest, + max_evals=max_evals, + verbose=verbose, ) return best["step"] @@ -120,7 +123,7 @@ def polynomial_step( """ if cost is None: cost = dbi_object.cost.name - + if d is None: d = dbi_object.diagonal_h_matrix @@ -134,10 +137,12 @@ def polynomial_step( elif cost == "least_squares": coef = least_squares_polynomial_expansion_coef(dbi_object, d, n) elif cost == "energy_fluctuation": - coef = energy_fluctuation_polynomial_expansion_coef(dbi_object, d, n, dbi_object.state) + coef = energy_fluctuation_polynomial_expansion_coef( + dbi_object, d, n, dbi_object.state + ) else: raise ValueError(f"Cost function {cost} not recognized.") - + roots = np.roots(coef) real_positive_roots = [ np.real(root) for root in roots if np.imag(root) < error and np.real(root) > 0 @@ -176,85 +181,155 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): coef = list(reversed(trace_coefficients[: n + 1])) return coef + def least_squares_polynomial_expansion_coef(dbi_object, d, n): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n+1, d) + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients coef = np.empty(n) for i in range(n): - coef[i] = np.real(exp_list[i]*np.trace(d@Gamma_list[i+1])) + coef[i] = np.real(exp_list[i] * np.trace(d @ Gamma_list[i + 1])) coef = list(reversed(coef)) return coef -#TODO: add a general expansion formula not stopping at 3rd order + +# TODO: add a general expansion formula not stopping at 3rd order def energy_fluctuation_polynomial_expansion_coef(dbi_object, d, n, state): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n+1, d) + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) # coefficients coef = np.empty(3) - coef[0] = np.real(2*covariance(Gamma_list[0], Gamma_list[1],state)) - coef[1] = np.real(2*variance(Gamma_list[1],state)) - coef[2] = np.real(covariance(Gamma_list[0], Gamma_list[3],state)+3*covariance(Gamma_list[1], Gamma_list[2],state)) + coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1], state)) + coef[1] = np.real(2 * variance(Gamma_list[1], state)) + coef[2] = np.real( + covariance(Gamma_list[0], Gamma_list[3], state) + + 3 * covariance(Gamma_list[1], Gamma_list[2], state) + ) coef = list(reversed(coef)) return coef -def dGamma_diDiagonal(dbi_object, d, H, n, i,dGamma, Gamma_list): + +def dGamma_diDiagonal(dbi_object, d, H, n, i, dGamma, Gamma_list): # Derivative of gamma with respect to diagonal elements of D (full-diagonal ansatz) A = np.zeros(d.shape) - A[i,i] = 1 - B = commutator(commutator(A,H),Gamma_list[n-1]) - W = commutator(d,H) - return B + commutator(W,dGamma[-1]) + A[i, i] = 1 + B = commutator(commutator(A, H), Gamma_list[n - 1]) + W = commutator(d, H) + return B + commutator(W, dGamma[-1]) -def dpolynomial_diDiagonal(dbi_object, d,H,i): + +def dpolynomial_diDiagonal(dbi_object, d, H, i): # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation derivative = 0 s = polynomial_step(dbi_object, n=3, d=d) A = np.zeros(d.shape) Gamma_list = dbi_object.generate_Gamma_list(4, d) - A[i,i] = 1 - dGamma = [commutator(A,H)] - derivative += np.real(np.trace(Gamma_list[0]@A)+np.trace(dGamma[0]@d+Gamma_list[1]@A)*s) - for n in range(2,4): - dGamma.append(dGamma_diDiagonal(dbi_object,d,H,n,i,dGamma,Gamma_list)) - derivative += np.real(np.trace(dGamma[-1]@d + Gamma_list[n]@A)*s**n/math.factorial(n)) + A[i, i] = 1 + dGamma = [commutator(A, H)] + derivative += np.real( + np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s + ) + for n in range(2, 4): + dGamma.append(dGamma_diDiagonal(dbi_object, d, H, n, i, dGamma, Gamma_list)) + derivative += np.real( + np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) + ) return derivative -def gradientDiagonal(dbi_object,d,H): + +def gradientDiagonal(dbi_object, d, H): # Gradient of potential function with respect to diagonal elements of D (full-diagonal ansatz) grad = np.zeros(len(d)) for i in range(len(d)): - derivative = dpolynomial_diDiagonal(dbi_object,d,H,i) - grad[i] = d[i,i]-derivative + derivative = dpolynomial_diDiagonal(dbi_object, d, H, i) + grad[i] = d[i, i] - derivative return grad + def gradient_ascent(dbi_object, d, step, iterations): H = dbi_object.h.matrix - loss = np.zeros(iterations+1) - grad = np.zeros((iterations,len(d))) + loss = np.zeros(iterations + 1) + grad = np.zeros((iterations, len(d))) dbi_new = deepcopy(dbi_object) - s = polynomial_step(dbi_object, n = 3, d=d) - dbi_new(s,d=d) + s = polynomial_step(dbi_object, n=3, d=d) + dbi_new(s, d=d) loss[0] = dbi_new(d) - diagonals = np.empty((len(d),iterations+1)) - diagonals[:,0] = np.diag(d) + diagonals = np.empty((len(d), iterations + 1)) + diagonals[:, 0] = np.diag(d) for i in range(iterations): dbi_new = deepcopy(dbi_object) - grad[i,:] = gradientDiagonal(dbi_object, d, H) + grad[i, :] = gradientDiagonal(dbi_object, d, H) for j in range(len(d)): - d[j,j] = d[j,j] - step*grad[i,j] - s = polynomial_step(dbi_object, n = 3, d=d) - dbi_new(s,d=d) - loss[i+1] = dbi_new.least_squares(d) - diagonals[:,i+1] = np.diag(d) - - - return d,loss,grad,diagonals \ No newline at end of file + d[j, j] = d[j, j] - step * grad[i, j] + s = polynomial_step(dbi_object, n=3, d=d) + dbi_new(s, d=d) + loss[i + 1] = dbi_new.least_squares(d) + diagonals[:, i + 1] = np.diag(d) + + return d, loss, grad, diagonals + + +def simulated_annealing_step( + dbi_object, + d: Optional[np.array] = None, + initial_s=None, + step_min=1e-5, + step_max=1, + s_jump_range=None, + s_jump_range_divident=5, + initial_temp=1, + cooling_rate=0.85, + min_temp=1e-5, + max_iter=200, + verbose=False, +): + + if d is None: + d = dbi_object.diagonal_h_matrix + if initial_s is None: + initial_s = polynomial_step(dbi_object=dbi_object, d=d, n=4) + if s_jump_range is None: + s_jump_range = (step_max - step_min) / s_jump_range_divident + current_s = initial_s + current_loss = dbi_object.loss(d=d, step=current_s) + if verbose: + print("initial_s", current_s) + print("initial loss", current_loss) + temp = initial_temp + + for _ in range(max_iter): + candidate_s = max( + step_min, + min(current_s + np.random.uniform(-s_jump_range, s_jump_range, step_max)), + ) + candidate_loss = dbi_object.loss(d=d, step=candidate_s) + + # Calculate change in loss + delta_loss = candidate_loss - current_loss + + # Determine if the candidate solution is an improvement + if delta_loss < 0 or np.random.rand() < math.exp(-delta_loss / temp): + current_s = candidate_s + current_loss = candidate_loss + if verbose: + print( + f"Iter {_} s {candidate_s} accepted with loss {candidate_loss} and prob {math.exp(-delta_loss / temp)} at temp {temp}" + ) + elif verbose: + print( + f"Iter {_} s {candidate_s} loss {candidate_loss} not accepted with prob {math.exp(-delta_loss / temp)}" + ) + # Cool down + temp *= cooling_rate + if temp < min_temp: + break + + return current_s diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 1dcbb960d0..8cda5a2296 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -112,10 +112,14 @@ def test_energy_fluctuations(backend): @pytest.mark.parametrize( "scheduling", - [DoubleBracketScheduling.grid_search, DoubleBracketScheduling.hyperopt], + [ + DoubleBracketScheduling.grid_search, + DoubleBracketScheduling.hyperopt, + DoubleBracketScheduling.simulated_annealing, + ], ) @pytest.mark.parametrize("nqubits", [3, 4, 5]) -def test_double_bracket_iteration_scheduling_grid_hyperopt( +def test_double_bracket_iteration_scheduling_grid_hyperopt_annealing( backend, nqubits, scheduling ): h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) From 0b241b16fdee28c56b1470f3af44df97c4b00581 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Wed, 20 Mar 2024 11:47:42 +0100 Subject: [PATCH 034/154] Update utils_scheduling.py fixed coefficient values for energy fluctuation --- src/qibo/models/dbi/utils_scheduling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 5f217cc948..3aebfddff2 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -198,7 +198,7 @@ def energy_fluctuation_polynomial_expansion_coef(dbi_object, d, n, state): # coefficients coef = np.empty(3) coef[0] = np.real(2*covariance(Gamma_list[0], Gamma_list[1],state)) - coef[1] = np.real(2*variance(Gamma_list[1],state)) + coef[1] = np.real(2*variance(Gamma_list[1],state)+2*covariance(Gamma_list[0],Gamma_list[2],state)) coef[2] = np.real(covariance(Gamma_list[0], Gamma_list[3],state)+3*covariance(Gamma_list[1], Gamma_list[2],state)) coef = list(reversed(coef)) return coef From 10579f28789959bc73ee471170c4f1b3e353563a Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Thu, 21 Mar 2024 09:23:04 +0400 Subject: [PATCH 035/154] gate implementation --- src/qibo/backends/npmatrices.py | 16 +++++++++ src/qibo/gates/gates.py | 50 +++++++++++++++++++++++++++ src/qibo/transpiler/decompositions.py | 1 + 3 files changed, 67 insertions(+) diff --git a/src/qibo/backends/npmatrices.py b/src/qibo/backends/npmatrices.py index c6e447e9ea..0f0d5fce27 100644 --- a/src/qibo/backends/npmatrices.py +++ b/src/qibo/backends/npmatrices.py @@ -446,6 +446,22 @@ def TOFFOLI(self): dtype=self.dtype, ) + @cached_property + def CCZ(self): + return self._cast( + [ + [1, 0, 0, 0, 0, 0, 0, 0], + [0, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 0, 0, -1], + ], + dtype=self.dtype, + ) + def DEUTSCH(self, theta): sin = self.np.sin(theta) + 0j # 0j necessary for right tensorflow dtype cos = self.np.cos(theta) + 0j diff --git a/src/qibo/gates/gates.py b/src/qibo/gates/gates.py index dfbe628a00..b19ad5acb4 100644 --- a/src/qibo/gates/gates.py +++ b/src/qibo/gates/gates.py @@ -2307,6 +2307,56 @@ def congruent(self, use_toffolis: bool = True) -> List[Gate]: ] +class CCZ(Gate): + """The controlled-CZ gate. + + Corresponds to the following unitary matrix + + .. math:: + \\begin{pmatrix} + 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ + 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ + 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\\\ + 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\\\ + 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\\\ + 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\\\ + 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\\\ + 0 & 0 & 0 & 0 & 0 & 0 & 0 & -1 \\\\ + \\end{pmatrix} + + Args: + q0 (int): the first control qubit id number. + q1 (int): the second control qubit id number. + q2 (int): the target qubit id number. + """ + + def __init__(self, q0, q1, q2): + super().__init__() + self.name = "ccz" + self.draw_label = "Z" + self.control_qubits = (q0, q1) + self.target_qubits = (q2,) + self.init_args = [q0, q1, q2] + self.unitary = True + + @property + def qasm_label(self): + return "ccz" + + def decompose(self) -> List[Gate]: + """Decomposition of :math:`\\text{CCZ}` gate. + + Decompose :math:`\\text{CCZ}` gate into :class:`qibo.gates.H` in + the target qubit, followed by :class:`qibo.gates.TOFFOLI`, followed + by a :class:`qibo.gates.H` in the target qubit. + """ + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) + + class DEUTSCH(ParametrizedGate): """The Deutsch gate. diff --git a/src/qibo/transpiler/decompositions.py b/src/qibo/transpiler/decompositions.py index f559f0088a..33ab35b56b 100644 --- a/src/qibo/transpiler/decompositions.py +++ b/src/qibo/transpiler/decompositions.py @@ -465,3 +465,4 @@ def _u3_to_gpi2(t, p, l): standard_decompositions.add( gates.ECR, [gates.S(0), gates.SX(1), gates.CNOT(0, 1), gates.X(0)] ) +standard_decompositions.add(gates.CCZ, [gates.H(2), gates.TOFFOLI(0, 1, 2), gates.H(2)]) From bb513e079608f678a02bd2bac9d1ddf5615177cc Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Thu, 21 Mar 2024 09:23:59 +0400 Subject: [PATCH 036/154] api ref --- doc/source/api-reference/qibo.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/doc/source/api-reference/qibo.rst b/doc/source/api-reference/qibo.rst index c574c90145..c39b9d0f41 100644 --- a/doc/source/api-reference/qibo.rst +++ b/doc/source/api-reference/qibo.rst @@ -923,6 +923,13 @@ Toffoli :members: :member-order: bysource +CCZ +""" + +.. autoclass:: qibo.gates.CCZ + :members: + :member-order: bysource + Deutsch """"""" From aa290ed2ea7adcbd1b3f17b9453986873444e513 Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Thu, 21 Mar 2024 09:29:10 +0400 Subject: [PATCH 037/154] test --- tests/test_gates_gates.py | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/tests/test_gates_gates.py b/tests/test_gates_gates.py index d407e72e9e..759cd3961b 100644 --- a/tests/test_gates_gates.py +++ b/tests/test_gates_gates.py @@ -1206,6 +1206,39 @@ def test_toffoli(backend, applyx): assert gates.TOFFOLI(0, 1, 2).unitary +def test_ccz(backend): + nqubits = 3 + initial_state = random_statevector(2**nqubits, backend=backend) + final_state = apply_gates( + backend, + [gates.CCZ(0, 1, 2)], + nqubits=nqubits, + initial_state=initial_state, + ) + + matrix = np.array( + [ + [1, 0, 0, 0, 0, 0, 0, 0], + [0, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 0, 0, -1], + ], + dtype=np.complex128, + ) + matrix = backend.cast(matrix, dtype=matrix.dtype) + + target_state = matrix @ initial_state + backend.assert_allclose(final_state, target_state) + + assert gates.CCZ(0, 1, 2).qasm_label == "ccz" + assert not gates.CCZ(0, 1, 2).clifford + assert gates.CCZ(0, 1, 2).unitary + + def test_deutsch(backend): theta = 0.1234 nqubits = 3 From 78e9fe459349c4fad6458876017cb2f2701327cf Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Thu, 21 Mar 2024 10:37:41 +0400 Subject: [PATCH 038/154] `qibojit` branch --- pyproject.toml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 2febb035ff..b8a63978a4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,7 +69,7 @@ pylint = "^3.0.3" matplotlib = "^3.7.0" tensorflow = { version = "^2.14.1,<2.16", markers = "sys_platform == 'linux'" } torch = "^2.1.1" -qibojit = { git = "https://github.com/qiboteam/qibojit.git" } +qibojit = { git = "https://github.com/qiboteam/qibojit.git" , branch = "ccz" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } stim = "^1.12.0" @@ -87,7 +87,7 @@ optional = true [tool.poetry.group.cuda11.dependencies] cupy-cuda11x = "^12.0.0" cuquantum-python-cu11 = "^23.3.0" -qibojit = { git = "https://github.com/qiboteam/qibojit.git" } +qibojit = { git = "https://github.com/qiboteam/qibojit.git" , branch = "ccz" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } [tool.poetry.group.cuda12] @@ -96,7 +96,7 @@ optional = true [tool.poetry.group.cuda12.dependencies] cupy-cuda12x = "^12.0.0" cuquantum-python-cu12 = "^23.3.0" -qibojit = { git = "https://github.com/qiboteam/qibojit.git" } +qibojit = { git = "https://github.com/qiboteam/qibojit.git" , branch = "ccz" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } [tool.poetry.extras] From a4e53d483e2190b4e391d658559142971991630d Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Thu, 21 Mar 2024 10:42:21 +0400 Subject: [PATCH 039/154] update lock --- poetry.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index 34730c958e..e28e5bf29e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4133,8 +4133,8 @@ scipy = "^1.10.1" [package.source] type = "git" url = "https://github.com/qiboteam/qibojit.git" -reference = "HEAD" -resolved_reference = "5af642977fd66a4268608fbe2986e55160a88fe4" +reference = "ccz" +resolved_reference = "82a0bf0976a44ec54d1d46b82b716aabdf9c5d25" [[package]] name = "qibotn" @@ -5557,4 +5557,4 @@ torch = ["torch"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "b167edb192763e8c70eeef729e281f4ebbef8bdee41eb4d8692e3784babfe75e" +content-hash = "55c0840ff1101c940cc076d63aec414b437415dc62a067ac169181ad1438ca70" From 24b0f08d3ac9e4171fccb0a8ef1c707822223f73 Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Fri, 22 Mar 2024 08:35:07 +0400 Subject: [PATCH 040/154] fix coverage --- src/qibo/backends/__init__.py | 1 + tests/test_gates_gates.py | 9 ++++++++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/src/qibo/backends/__init__.py b/src/qibo/backends/__init__.py index a80c9932e1..cca6f9f010 100644 --- a/src/qibo/backends/__init__.py +++ b/src/qibo/backends/__init__.py @@ -145,6 +145,7 @@ def create(self, dtype): self.ECR = self.matrices.ECR self.SYC = self.matrices.SYC self.TOFFOLI = self.matrices.TOFFOLI + self.CCZ = self.matrices.CCZ matrices = QiboMatrices() diff --git a/tests/test_gates_gates.py b/tests/test_gates_gates.py index 759cd3961b..393a3accf5 100644 --- a/tests/test_gates_gates.py +++ b/tests/test_gates_gates.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from qibo import gates +from qibo import Circuit, gates, matrices from qibo.parameter import Parameter from qibo.quantum_info import random_hermitian, random_statevector, random_unitary @@ -1238,6 +1238,13 @@ def test_ccz(backend): assert not gates.CCZ(0, 1, 2).clifford assert gates.CCZ(0, 1, 2).unitary + # test decomposition + decomposition = Circuit(3) + decomposition.add(gates.CCZ(0, 1, 2).decompose()) + decomposition = decomposition.unitary(backend) + + backend.assert_allclose(decomposition, backend.cast(matrices.CCZ), atol=1e-10) + def test_deutsch(backend): theta = 0.1234 From 9ac51ae94975f191e5fe5a3a99fa566709c015f5 Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Sat, 23 Mar 2024 08:45:13 +0400 Subject: [PATCH 041/154] remove qibojit branch --- pyproject.toml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index b8a63978a4..2febb035ff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,7 +69,7 @@ pylint = "^3.0.3" matplotlib = "^3.7.0" tensorflow = { version = "^2.14.1,<2.16", markers = "sys_platform == 'linux'" } torch = "^2.1.1" -qibojit = { git = "https://github.com/qiboteam/qibojit.git" , branch = "ccz" } +qibojit = { git = "https://github.com/qiboteam/qibojit.git" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } stim = "^1.12.0" @@ -87,7 +87,7 @@ optional = true [tool.poetry.group.cuda11.dependencies] cupy-cuda11x = "^12.0.0" cuquantum-python-cu11 = "^23.3.0" -qibojit = { git = "https://github.com/qiboteam/qibojit.git" , branch = "ccz" } +qibojit = { git = "https://github.com/qiboteam/qibojit.git" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } [tool.poetry.group.cuda12] @@ -96,7 +96,7 @@ optional = true [tool.poetry.group.cuda12.dependencies] cupy-cuda12x = "^12.0.0" cuquantum-python-cu12 = "^23.3.0" -qibojit = { git = "https://github.com/qiboteam/qibojit.git" , branch = "ccz" } +qibojit = { git = "https://github.com/qiboteam/qibojit.git" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } [tool.poetry.extras] From e563d7f78a235b26a93d9a3f210284bb234b2bd8 Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Sat, 23 Mar 2024 08:48:30 +0400 Subject: [PATCH 042/154] update lock --- poetry.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index e28e5bf29e..34730c958e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4133,8 +4133,8 @@ scipy = "^1.10.1" [package.source] type = "git" url = "https://github.com/qiboteam/qibojit.git" -reference = "ccz" -resolved_reference = "82a0bf0976a44ec54d1d46b82b716aabdf9c5d25" +reference = "HEAD" +resolved_reference = "5af642977fd66a4268608fbe2986e55160a88fe4" [[package]] name = "qibotn" @@ -5557,4 +5557,4 @@ torch = ["torch"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "55c0840ff1101c940cc076d63aec414b437415dc62a067ac169181ad1438ca70" +content-hash = "b167edb192763e8c70eeef729e281f4ebbef8bdee41eb4d8692e3784babfe75e" From b3a5b0e77facd3063ca1ee5dc315e047b8497c53 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 25 Mar 2024 13:35:18 +0800 Subject: [PATCH 043/154] Fix lint error --- src/qibo/models/dbi/utils_scheduling.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 548a7a8eec..3c3968e773 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -308,7 +308,9 @@ def simulated_annealing_step( for _ in range(max_iter): candidate_s = max( step_min, - min(current_s + np.random.uniform(-s_jump_range, s_jump_range, step_max)), + min( + current_s + np.random.uniform(-1 * s_jump_range, s_jump_range, step_max) + ), ) candidate_loss = dbi_object.loss(d=d, step=candidate_s) From f91956b5d89e5745863b81c5930b149141fe1021 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 25 Mar 2024 14:27:23 +0800 Subject: [PATCH 044/154] Style changes for readability --- src/qibo/models/dbi/double_bracket.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 941e5dccd4..9d909d06ce 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -197,7 +197,7 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): loss = self.off_diagonal_norm elif self.cost == DoubleBracketCostFunction.least_squares: loss = self.least_squares(d) - else: + elif self.cost == DoubleBracketCostFunction.energy_fluctuation: loss = self.energy_fluctuation(self.state) # set back the initial configuration @@ -205,18 +205,20 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): return loss - def energy_fluctuation(self, state): + def energy_fluctuation(self, state=None): """ Evaluate energy fluctuation .. math:: - \\Xi_{k}(\\mu) = \\sqrt{\\langle\\mu|\\hat{H}^2|\\mu\\rangle - \\langle\\mu|\\hat{H}|\\mu\\rangle^2} \\, + \\Xi(\\mu) = \\sqrt{\\langle\\mu|\\hat{H}^2|\\mu\\rangle - \\langle\\mu|\\hat{H}|\\mu\\rangle^2} \\, for a given state :math:`|\\mu\\rangle`. Args: state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ + if state is None: + state = self.state state_vector = np.zeros(len(self.h.matrix)) state_vector[state] = 1.0 return np.real(self.h.energy_fluctuation(state_vector)) From 9e914ded35154ace6376d43dcd38fb944e895868 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 25 Mar 2024 14:38:55 +0800 Subject: [PATCH 045/154] Remove verbose option for SA --- src/qibo/models/dbi/utils_scheduling.py | 37 ++++++++----------------- 1 file changed, 12 insertions(+), 25 deletions(-) diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 3c3968e773..7972b6e8e7 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -66,7 +66,6 @@ def hyperopt_step( space: callable = None, optimizer: callable = None, look_ahead: int = 1, - verbose: bool = False, d: Optional[np.array] = None, ): """ @@ -79,7 +78,6 @@ def hyperopt_step( space: see hyperopt.hp possibilities; optimizer: see hyperopt algorithms; look_ahead: number of iteration steps to compute the loss function; - verbose: level of verbosity; d: diagonal operator for generating double-bracket iterations. Returns: @@ -99,7 +97,6 @@ def hyperopt_step( space=space, algo=optimizer.suggest, max_evals=max_evals, - verbose=verbose, ) return best["step"] @@ -182,7 +179,7 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): return coef -def least_squares_polynomial_expansion_coef(dbi_object, d, n): +def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: int = 3): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H @@ -197,7 +194,9 @@ def least_squares_polynomial_expansion_coef(dbi_object, d, n): # TODO: add a general expansion formula not stopping at 3rd order -def energy_fluctuation_polynomial_expansion_coef(dbi_object, d, n, state): +def energy_fluctuation_polynomial_expansion_coef( + dbi_object, d: np.array = None, n: int = 3, state=0 +): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H @@ -214,7 +213,7 @@ def energy_fluctuation_polynomial_expansion_coef(dbi_object, d, n, state): return coef -def dGamma_diDiagonal(dbi_object, d, H, n, i, dGamma, Gamma_list): +def dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list): # Derivative of gamma with respect to diagonal elements of D (full-diagonal ansatz) A = np.zeros(d.shape) A[i, i] = 1 @@ -236,7 +235,7 @@ def dpolynomial_diDiagonal(dbi_object, d, H, i): np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s ) for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(dbi_object, d, H, n, i, dGamma, Gamma_list)) + dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) derivative += np.real( np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) ) @@ -257,21 +256,21 @@ def gradient_ascent(dbi_object, d, step, iterations): H = dbi_object.h.matrix loss = np.zeros(iterations + 1) grad = np.zeros((iterations, len(d))) - dbi_new = deepcopy(dbi_object) + dbi_eval = deepcopy(dbi_object) s = polynomial_step(dbi_object, n=3, d=d) - dbi_new(s, d=d) - loss[0] = dbi_new(d) + dbi_eval(s, d=d) + loss[0] = dbi_eval(d) diagonals = np.empty((len(d), iterations + 1)) diagonals[:, 0] = np.diag(d) for i in range(iterations): - dbi_new = deepcopy(dbi_object) + dbi_eval = deepcopy(dbi_object) grad[i, :] = gradientDiagonal(dbi_object, d, H) for j in range(len(d)): d[j, j] = d[j, j] - step * grad[i, j] s = polynomial_step(dbi_object, n=3, d=d) - dbi_new(s, d=d) - loss[i + 1] = dbi_new.least_squares(d) + dbi_eval(s, d=d) + loss[i + 1] = dbi_eval.least_squares(d) diagonals[:, i + 1] = np.diag(d) return d, loss, grad, diagonals @@ -289,7 +288,6 @@ def simulated_annealing_step( cooling_rate=0.85, min_temp=1e-5, max_iter=200, - verbose=False, ): if d is None: @@ -300,9 +298,6 @@ def simulated_annealing_step( s_jump_range = (step_max - step_min) / s_jump_range_divident current_s = initial_s current_loss = dbi_object.loss(d=d, step=current_s) - if verbose: - print("initial_s", current_s) - print("initial loss", current_loss) temp = initial_temp for _ in range(max_iter): @@ -321,14 +316,6 @@ def simulated_annealing_step( if delta_loss < 0 or np.random.rand() < math.exp(-delta_loss / temp): current_s = candidate_s current_loss = candidate_loss - if verbose: - print( - f"Iter {_} s {candidate_s} accepted with loss {candidate_loss} and prob {math.exp(-delta_loss / temp)} at temp {temp}" - ) - elif verbose: - print( - f"Iter {_} s {candidate_s} loss {candidate_loss} not accepted with prob {math.exp(-delta_loss / temp)}" - ) # Cool down temp *= cooling_rate if temp < min_temp: From 8d2b8fc842f41c50ce17da377c57d56e26cce2d8 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 25 Mar 2024 06:51:34 +0000 Subject: [PATCH 046/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 1f0c2bce4d..d80d453f7d 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -175,4 +175,3 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): # coefficients from high to low (n:0) coef = list(reversed(trace_coefficients[: n + 1])) return coef - From 59a56ec82ad2d3565d93076fa0b7e01e932ab18f Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 26 Mar 2024 16:15:55 +0800 Subject: [PATCH 047/154] Fix errors emerged from merch --- examples/dbi/dbi_scheduling.ipynb | 48 +- examples/dbi/dbi_strategy_Pauli-Z 2.ipynb | 514 ---------------- .../dbi/dbi_strategy_magnetic_field.ipynb | 567 ++++++++++++++++-- src/qibo/models/dbi/utils.py | 48 +- src/qibo/models/dbi/utils_scheduling.py | 4 +- 5 files changed, 552 insertions(+), 629 deletions(-) delete mode 100644 examples/dbi/dbi_strategy_Pauli-Z 2.ipynb diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 1953c1272d..a7a813fcba 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -43,27 +43,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-12 17:24:06]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 37.94733192202055\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -86,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -109,24 +94,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "loss() got an unexpected keyword argument 'state'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[16], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m# grid_search\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m step_grid \u001b[39m=\u001b[39m dbi\u001b[39m.\u001b[39;49mchoose_step(scheduling\u001b[39m=\u001b[39;49mDoubleBracketScheduling\u001b[39m.\u001b[39;49mgrid_search)\n\u001b[0;32m 3\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mgrid_search step:\u001b[39m\u001b[39m'\u001b[39m, step_grid)\n\u001b[0;32m 4\u001b[0m \u001b[39m# hyperopt\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\andre\\Documents\\GitHub\\qibo\\.conda\\lib\\site-packages\\qibo\\models\\dbi\\double_bracket.py:164\u001b[0m, in \u001b[0;36mDoubleBracketIteration.choose_step\u001b[1;34m(self, d, scheduling, **kwargs)\u001b[0m\n\u001b[0;32m 162\u001b[0m \u001b[39mif\u001b[39;00m scheduling \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 163\u001b[0m scheduling \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscheduling\n\u001b[1;32m--> 164\u001b[0m step \u001b[39m=\u001b[39m scheduling(\u001b[39mself\u001b[39m, d\u001b[39m=\u001b[39md, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 165\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[0;32m 166\u001b[0m step \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m 167\u001b[0m \u001b[39mand\u001b[39;00m scheduling \u001b[39m==\u001b[39m DoubleBracketScheduling\u001b[39m.\u001b[39mpolynomial_approximation\n\u001b[0;32m 168\u001b[0m ):\n\u001b[0;32m 169\u001b[0m kwargs[\u001b[39m\"\u001b[39m\u001b[39mn\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mn\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m3\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\andre\\Documents\\GitHub\\qibo\\.conda\\lib\\site-packages\\qibo\\models\\dbi\\utils_scheduling.py:47\u001b[0m, in \u001b[0;36mgrid_search_step\u001b[1;34m(dbi_object, step_min, step_max, num_evals, space, d, state)\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[39mif\u001b[39;00m d \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 45\u001b[0m d \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m---> 47\u001b[0m loss_list \u001b[39m=\u001b[39m [dbi_object\u001b[39m.\u001b[39mloss(step, d\u001b[39m=\u001b[39md, state\u001b[39m=\u001b[39mstate) \u001b[39mfor\u001b[39;00m step \u001b[39min\u001b[39;00m space]\n\u001b[0;32m 48\u001b[0m idx_max_loss \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margmin(loss_list)\n\u001b[0;32m 49\u001b[0m \u001b[39mreturn\u001b[39;00m space[idx_max_loss]\n", - "File \u001b[1;32mc:\\Users\\andre\\Documents\\GitHub\\qibo\\.conda\\lib\\site-packages\\qibo\\models\\dbi\\utils_scheduling.py:47\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[39mif\u001b[39;00m d \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 45\u001b[0m d \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m---> 47\u001b[0m loss_list \u001b[39m=\u001b[39m [dbi_object\u001b[39m.\u001b[39;49mloss(step, d\u001b[39m=\u001b[39;49md, state\u001b[39m=\u001b[39;49mstate) \u001b[39mfor\u001b[39;00m step \u001b[39min\u001b[39;00m space]\n\u001b[0;32m 48\u001b[0m idx_max_loss \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margmin(loss_list)\n\u001b[0;32m 49\u001b[0m \u001b[39mreturn\u001b[39;00m space[idx_max_loss]\n", - "\u001b[1;31mTypeError\u001b[0m: loss() got an unexpected keyword argument 'state'" - ] - } - ], + "outputs": [], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n", @@ -306,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -321,7 +291,7 @@ "outputs": [], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "nqubits = 4\n", "h0 = random_hermitian(2**nqubits)\n", "\n", diff --git a/examples/dbi/dbi_strategy_Pauli-Z 2.ipynb b/examples/dbi/dbi_strategy_Pauli-Z 2.ipynb deleted file mode 100644 index 5f563a172a..0000000000 --- a/examples/dbi/dbi_strategy_Pauli-Z 2.ipynb +++ /dev/null @@ -1,514 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Double-Bracket Iteration Strategy: Pauli-Z products\n", - "\n", - "In this example, we demonstrate the usage of a DBI strategy, where the diagonal operators for double bracket iterations are variationally chosen from all possible local Pauli-Z operators." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!python -m pip install hyperopt # required to optimize the DBF step" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from copy import copy, deepcopy\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketIteration\n", - "from qibo.models.dbi.utils import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below are some useful functions to visualize the diagonalization process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def visualize_matrix(matrix, title=\"\"):\n", - " \"\"\"Visualize absolute values of a matrix in a heatmap form.\"\"\"\n", - " fig, ax = plt.subplots(figsize=(5, 5))\n", - " ax.set_title(title)\n", - " try:\n", - " im = ax.imshow(np.absolute(matrix), cmap=\"inferno\")\n", - " except TypeError:\n", - " im = ax.imshow(np.absolute(matrix.get()), cmap=\"inferno\")\n", - " fig.colorbar(im, ax=ax)\n", - "\n", - "\n", - "def visualize_drift(h0, h):\n", - " \"\"\"Visualize drift of the evolved hamiltonian w.r.t. h0.\"\"\"\n", - " fig, ax = plt.subplots(figsize=(5, 5))\n", - " ax.set_title(r\"Drift: $|\\hat{H}_0 - \\hat{H}_{1}|$\")\n", - " try:\n", - " im = ax.imshow(np.absolute(h0 - h), cmap=\"inferno\")\n", - " except TypeError:\n", - " im = ax.imshow(np.absolute((h0 - h).get()), cmap=\"inferno\")\n", - "\n", - " fig.colorbar(im, ax=ax)\n", - "\n", - "\n", - "def plot_histories(loss_histories: list, steps: list, labels: list = None):\n", - " \"\"\"Plot off-diagonal norm histories over a sequential evolution.\"\"\"\n", - " plt.figure(figsize=(5, 5 * 6 / 8))\n", - " if len(steps) == 1:\n", - " # fixed_step\n", - " x_axis = [i * steps[0] for i in range(len(loss_histories))]\n", - " else:\n", - " x_axis = [sum(steps[:k]) for k in range(1, len(steps) + 1)]\n", - " plt.plot(x_axis, loss_histories, \"-o\")\n", - "\n", - " x_labels_rounded = [round(x, 2) for x in x_axis]\n", - " x_labels_rounded = [0] + x_labels_rounded[0:5] + [max(x_labels_rounded)]\n", - " x_labels_rounded.pop(3)\n", - " plt.xticks(x_labels_rounded)\n", - "\n", - " y_labels_rounded = [round(y, 1) for y in loss_histories]\n", - " y_labels_rounded = y_labels_rounded[0:5] + [min(y_labels_rounded)]\n", - " plt.yticks(y_labels_rounded)\n", - "\n", - " if labels is not None:\n", - " labels_copy = copy(labels)\n", - " labels_copy.insert(0, \"Initial\")\n", - " for i, label in enumerate(labels_copy):\n", - " plt.text(x_axis[i], loss_histories[i], label)\n", - "\n", - " plt.grid()\n", - " plt.xlabel(r\"Flow duration $s$\")\n", - " plt.title(\"Loss function histories\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example: TFIM\n", - "\n", - "As an example, we consider the Transverse Field Ising Model (TFIM):\n", - "$$ H_{\\rm TFIM} = - \\sum_{i=1}^{N}\\bigl( Z_i Z_{i+1} + h X_i \\bigr),$$\n", - "which is already implemented in `Qibo`. For this tutorial we set $N=5$ and $h=3$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# set the qibo backend (we suggest qibojit if N >= 20)\n", - "# alternatives: tensorflow (not optimized), numpy (when CPU not supported by jit)\n", - "set_backend(\"qibojit\", \"numba\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 5\n", - "h = 3\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# initialize class\n", - "# Note: use deepcopy to prevent h being edited\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator)\n", - "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(H_TFIM.matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate local Pauli-Z operators" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "generate_local_Z = generate_Z_operators(nqubits)\n", - "Z_ops = list(generate_local_Z.values())\n", - "Z_names = list(generate_local_Z.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Iteration from a list of operators\n", - "The idea of this strategy is to chose the Z operator that reduces the off-diagonal norm of the hamiltonian most efficiently. Given a list of operators (np.array), the function `select_best_dbr_generator_and_run` searches for the maximum decrease in off-diagonal norm for each operator and runs one double bracket rotation using the optimal operator from the list.\n", - "\n", - "Note that the hyperopt settings can be set as positional arguments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NSTEPS = 15\n", - "max_evals = 100\n", - "step_max = 1\n", - "Z_optimal = []\n", - "# add in initial values for plotting\n", - "off_diagonal_norm_history = [dbi.off_diagonal_norm]\n", - "steps = [0]\n", - "scheduling = DoubleBracketScheduling.use_hyperopt\n", - "for _ in range(NSTEPS):\n", - " dbi, idx, step, flip_sign = select_best_dbr_generator(dbi, Z_ops, scheduling=scheduling, compare_canonical=False, max_evals=max_evals, step_max=step_max)\n", - " off_diagonal_norm_history.append(dbi.off_diagonal_norm)\n", - " steps.append(steps[-1]+step)\n", - " if flip_sign < 0:\n", - " Z_optimal.append('-' + Z_names[idx])\n", - " else:\n", - " Z_optimal.append(Z_names[idx])\n", - " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with operator {Z_optimal[-1]}, loss {dbi.off_diagonal_norm}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_histories(off_diagonal_norm_history, steps, Z_optimal)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is worth noting that due to the nature of `hyperopt`, the iterations may be unstable and multiple runs may be required for the optimal result (alternatively, we can perform a grid search on the optimal step). Hence, it is sometimes needed to adjust its parameters including the following:\n", - "\n", - "- step_min\n", - "- step_max\n", - "- max_evals" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compare with canonical\n", - "\n", - "We compare the effectiveness at diagonalzation between the Pauli-Z operators and the canonical generator:\n", - "\n", - "$$ d = [H,\\sigma(H)]$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# set the qibo backend (we suggest qibojit if N >= 20)\n", - "# alternatives: tensorflow (not optimized), numpy (when CPU not supported by jit)\n", - "set_backend(\"qibojit\", \"numba\")\n", - "\n", - "\n", - "# initialize class|\n", - "# Note: use deepcopy to prevent h being edited\n", - "dbi_canonical = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.canonical)\n", - "print(\"Initial off diagonal norm\", dbi_canonical.off_diagonal_norm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "off_diagonal_norm_history_canonical = [dbi_canonical.off_diagonal_norm]\n", - "steps_canonical = [0]\n", - "steps_canonical_plot = [0]\n", - "for s in range(NSTEPS):\n", - " # same settings as iteration from list\n", - " step = dbi_canonical.hyperopt_step(\n", - " step_min = 1e-5,\n", - " step_max = 1,\n", - " space = hp.uniform,\n", - " optimizer = tpe,\n", - " )\n", - " dbi_canonical(step=step)\n", - " print(f\"New optimized step at iteration {s+1}/{NSTEPS}: {step}, loss {dbi_canonical.off_diagonal_norm}\")\n", - " off_diagonal_norm_history_canonical.append(dbi_canonical.off_diagonal_norm)\n", - " steps_canonical.append(step)\n", - " steps_canonical_plot.append(steps_canonical_plot[-1]+step)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "# plt.plot(steps, off_diagonal_norm_history, label=\"Pauli-Z\")\n", - "# plt.plot(steps_canonical, off_diagonal_norm_history_canonical, label=\"Canonical\")\n", - "plt.plot(off_diagonal_norm_history, label=\"Pauli-Z\")\n", - "plt.plot(off_diagonal_norm_history_canonical, label=\"Canonical\")\n", - "plt.xlabel(\"Iterations\")\n", - "plt.ylabel(\"Norm off-diagonal restriction\")\n", - "plt.title(\"Compare Variational Pauli-Z with Canonical\")\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(off_diagonal_norm_history)\n", - "print(off_diagonal_norm_history_canonical)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we make 2 observations:\n", - "\n", - "1. The canonical strategy has a steeper decrease at the beginning than Pauli-Z operators.\n", - "2. However, the canonical strategy is also prone to getting stuck at a local minimum and hence resultting in a lesser degree of diagonalization.\n", - "\n", - "Therefore, we explore the possibility of mixing the two strategies by including the canonical generator in the list." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mixed strategy: optimal at each step" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dbi_mixed = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator)\n", - "print(\"Initial off diagonal norm\", dbi_mixed.off_diagonal_norm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dbi_eval = deepcopy(dbi_mixed)\n", - "dbi_eval.mode = DoubleBracketGeneratorType.canonical\n", - "if step is None:\n", - " step = dbi_eval.hyperopt_step(\n", - " step_max=step_max,\n", - " space=hp.uniform,\n", - " optimizer=tpe,\n", - " max_evals=max_evals,\n", - " )\n", - "dbi_eval(step=step)\n", - "print('canonical norm', dbi_eval.off_diagonal_norm, 'step', step)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Z_optimal_mixed = []\n", - "# add in initial values for plotting\n", - "off_diagonal_norm_history_mixed = [dbi_mixed.off_diagonal_norm]\n", - "steps = [0]\n", - "for _ in range(NSTEPS):\n", - " dbi_mixed, idx, step, flip_sign = select_best_dbr_generator(dbi_mixed, Z_ops, scheduling=scheduling, compare_canonical=True, max_evals=max_evals, step_max=step_max)\n", - " off_diagonal_norm_history_mixed.append(dbi_mixed.off_diagonal_norm)\n", - " steps.append(steps[-1]+step)\n", - " if idx == len(Z_ops):\n", - " Z_optimal_mixed.append('Canonical')\n", - " elif flip_sign < 0:\n", - " Z_optimal_mixed.append('-' + Z_names[idx])\n", - " else:\n", - " Z_optimal_mixed.append(Z_names[idx])\n", - " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with operator {Z_optimal_mixed[-1]}, loss {dbi_mixed.off_diagonal_norm}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "# plt.plot(steps, off_diagonal_norm_history, label=\"Pauli-Z\")\n", - "# plt.plot(steps_canonical, off_diagonal_norm_history_canonical, label=\"Canonical\")\n", - "plt.plot(off_diagonal_norm_history, label=\"Pauli-Z\")\n", - "plt.plot(off_diagonal_norm_history_canonical, label=\"Canonical\")\n", - "plt.plot(off_diagonal_norm_history_mixed, label=\"Mixed\")\n", - "plt.xlabel(\"Iterations\")\n", - "plt.ylabel(\"Norm off-diagonal restriction\")\n", - "plt.title(\"Compare Variational Pauli-Z with Canonical\")\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After a few tests, we realize that the mixed strategy does not always outperform just using Pauli-Z operators. This could be caused by 2 reasons: \n", - "\n", - "1. Unstability of hyperopt\n", - "2. Tendency of canonical operator to get stuck at a near local minimum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mixed strategy: initial canonical\n", - "\n", - "Since the canonical double bracket iteration performs better at the initial steps, we attempt to combine the two strategies: iterate a few steps using the canonical bracket before switching to the variational Z-operators." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dbi_mixed_can= DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.canonical)\n", - "print(\"Initial off diagonal norm\", dbi_mixed_can.off_diagonal_norm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run the initial iterations using canonical iterations\n", - "off_diagonal_norm_history_mixed_can = [dbi_mixed_can.off_diagonal_norm]\n", - "steps_mixed_can = [0]\n", - "cannonical_NSTEPS = 2\n", - "for i in range(cannonical_NSTEPS):\n", - " step = steps_canonical[i+1]\n", - " dbi_mixed_can(step=step)\n", - " off_diagonal_norm_history_mixed_can.append(dbi_mixed_can.off_diagonal_norm)\n", - " steps_mixed_can.append(step)\n", - " \n", - "print(\"After 2 steps, off diagonal norm:\", dbi_mixed_can.off_diagonal_norm)\n", - "print(\"By comparison, the Pauli-Z:\", off_diagonal_norm_history[2])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Continue the remaining steps with Pauli-Z operators\n", - "Z_optimal_mixed_can = [\"Cannonical\" for _ in range(cannonical_NSTEPS)]\n", - "remaining_NSTEPS = NSTEPS - cannonical_NSTEPS\n", - "dbi_mixed_can.mode = DoubleBracketGeneratorType.single_commutator\n", - "for _ in range(remaining_NSTEPS):\n", - " dbi_mixed_can, idx, step, flip_sign = select_best_dbr_generator(dbi_mixed_can, Z_ops, scheduling=scheduling, compare_canonical=False, max_evals=max_evals, step_max=step_max)\n", - " off_diagonal_norm_history_mixed_can.append(dbi_mixed_can.off_diagonal_norm)\n", - " steps_mixed_can.append(step)\n", - " if idx == len(Z_ops):\n", - " Z_optimal_mixed.append('Canonical')\n", - " elif flip_sign < 0:\n", - " Z_optimal_mixed.append('-' + Z_names[idx])\n", - " else:\n", - " Z_optimal_mixed.append(Z_names[idx])\n", - " print(f\"New optimized step at iteration {_+1}/{remaining_NSTEPS}: {step} with operator {Z_optimal_mixed_can[-1]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "# plt.plot(steps, off_diagonal_norm_history, label=\"Pauli-Z\")\n", - "# plt.plot(steps_canonical, off_diagonal_norm_history_canonical, label=\"Canonical\")\n", - "plt.plot(off_diagonal_norm_history, label=\"Pauli-Z\")\n", - "plt.plot(off_diagonal_norm_history_canonical, label=\"Canonical\")\n", - "plt.plot(off_diagonal_norm_history_mixed, label=\"Mixed: optimal steps\")\n", - "plt.plot(off_diagonal_norm_history_mixed_can, label=\"Mixed: initial canonical\")\n", - "plt.xlabel(\"Iterations\")\n", - "plt.ylabel(\"Norm off-diagonal restriction\")\n", - "plt.title(\"Compare Variational Pauli-Z with Canonical\")\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example also shows that the canonical generator is more likely to drive the model into a local minimum than variationally assigned diagonal operator, and that it is hard to get it unstuck even with the Pauli-Z operators." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/dbi/dbi_strategy_magnetic_field.ipynb b/examples/dbi/dbi_strategy_magnetic_field.ipynb index 1455a1daca..fc1c51bd40 100644 --- a/examples/dbi/dbi_strategy_magnetic_field.ipynb +++ b/examples/dbi/dbi_strategy_magnetic_field.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -58,12 +58,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-03-26 16:07:47]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 31.576176740060667\n" + ] + }, + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# backend\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "# initialize dbi object\n", "nqubits = 5\n", "h0 = random_hermitian(2**nqubits, seed=2)\n", @@ -77,15 +102,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 691.96trial/s, best loss: 27.607175404720753]\n", + "The initial D coefficients: [(-0.2980910136757636+0j), (-0.17678355790937256+0j), (0.294550421681131+0j), (-0.2301056409534723+0j), (-0.07297191764284382+0j)]\n", + "Gradient: [-0.20478337 0.418433 -0.03167988 0.18669773 -0.86435984]\n", + "s: 0.11660954506915275\n" + ] + } + ], "source": [ "# generate the onsite Z operators\n", "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi, d, n_taylor=5, onsite_Z_ops=onsite_Z_ops)\n", + "grad, s = gradient_onsite_Z(dbi, d=d, onsite_Z_ops=onsite_Z_ops)\n", "print('The initial D coefficients:', d_coef)\n", "print('Gradient:', grad)\n", "print('s:', s)" @@ -93,15 +140,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 703.22trial/s, best loss: 27.607182422340095] \n", + "100%|██████████| 500/500 [00:00<00:00, 729.43trial/s, best loss: 24.35179754917795] \n", + "100%|██████████| 500/500 [00:00<00:00, 729.08trial/s, best loss: 22.089444283591433] \n", + "100%|██████████| 500/500 [00:00<00:00, 698.65trial/s, best loss: 20.347120765676763]\n", + "100%|██████████| 500/500 [00:00<00:00, 623.16trial/s, best loss: 18.94635121785982]\n", + "100%|██████████| 500/500 [00:00<00:00, 605.13trial/s, best loss: 17.773702241529776] \n", + "100%|██████████| 500/500 [00:00<00:00, 687.10trial/s, best loss: 16.784805711373227] \n", + "100%|██████████| 500/500 [00:00<00:00, 695.51trial/s, best loss: 15.934402363491223] \n", + "100%|██████████| 500/500 [00:00<00:00, 652.85trial/s, best loss: 15.197822552085507] \n", + "100%|██████████| 500/500 [00:00<00:00, 685.77trial/s, best loss: 14.481250187299748] \n", + "100%|██████████| 500/500 [00:00<00:00, 692.66trial/s, best loss: 14.044172334074341] \n", + "100%|██████████| 500/500 [00:00<00:00, 692.91trial/s, best loss: 13.670766358199891] \n", + "100%|██████████| 500/500 [00:00<00:00, 684.26trial/s, best loss: 13.325331286760488] \n", + "100%|██████████| 500/500 [00:00<00:00, 636.50trial/s, best loss: 13.01668686825596] \n", + "100%|██████████| 500/500 [00:00<00:00, 612.11trial/s, best loss: 12.711623339299685] \n", + "100%|██████████| 500/500 [00:00<00:00, 697.36trial/s, best loss: 12.409020875491057] \n", + "100%|██████████| 500/500 [00:00<00:00, 683.72trial/s, best loss: 12.08748982503799] \n", + "100%|██████████| 500/500 [00:00<00:00, 738.27trial/s, best loss: 11.75348065601818]\n", + "100%|██████████| 500/500 [00:00<00:00, 724.55trial/s, best loss: 11.410208539441799] \n", + "100%|██████████| 500/500 [00:00<00:00, 729.97trial/s, best loss: 11.06582875641592] \n", + "100%|██████████| 500/500 [00:00<00:00, 723.17trial/s, best loss: 10.734278849532725] \n", + "100%|██████████| 500/500 [00:00<00:00, 736.09trial/s, best loss: 10.391106227243483] \n", + "100%|██████████| 500/500 [00:00<00:00, 695.73trial/s, best loss: 9.835687097799866] \n", + "100%|██████████| 500/500 [00:00<00:00, 645.00trial/s, best loss: 9.836151362023536]\n", + "100%|██████████| 500/500 [00:00<00:00, 662.94trial/s, best loss: 9.83679254247866] \n", + "100%|██████████| 500/500 [00:00<00:00, 692.56trial/s, best loss: 9.83815541734947] \n", + "100%|██████████| 500/500 [00:00<00:00, 683.98trial/s, best loss: 9.838623865790995] \n", + "100%|██████████| 500/500 [00:00<00:00, 691.24trial/s, best loss: 9.83915601848446] \n", + "100%|██████████| 500/500 [00:00<00:00, 697.64trial/s, best loss: 9.843010172903082] \n", + "100%|██████████| 500/500 [00:00<00:00, 669.37trial/s, best loss: 9.844996826247685] \n" + ] + } + ], "source": [ "iters = 30\n", "off_diagonal_norm = [dbi.off_diagonal_norm]\n", "s_step = [0]\n", "for i in range(iters):\n", - " s, d_coef, d = gradient_descent_onsite_Z(dbi, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", + " s, d_coef, d = gradient_descent_onsite_Z(dbi, d_coef=d_coef, d=d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", " dbi(step=s, d=d)\n", " off_diagonal_norm.append(dbi.off_diagonal_norm)\n", " s_step.append(s)" @@ -109,9 +193,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.title(str(nqubits) + ' spins random hamiltonian')\n", "plt.plot(off_diagonal_norm)\n", @@ -131,9 +236,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# generate the Hamiltonian\n", "nqubits = 5\n", @@ -148,27 +271,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-03-26 16:08:14]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], "source": [ "# backend\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "# initialize dbi object\n", "dbi_TFIM = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:01<00:00, 383.66trial/s, best loss: 8.1443761719701] \n", + "Initial off-diagonal norm: 9.844996868109437\n", + "The initial D coefficients: [(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j)]\n", + "Gradient: [-0.22567346 -0.52080864 -0.59637211 -0.52080864 -0.22567346]\n", + "s: 0.05271207518843116\n" + ] + } + ], "source": [ "# generate the onsite Z operators\n", "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", "d_coef = onsite_Z_decomposition(dbi_TFIM.h.matrix, onsite_Z_ops)\n", "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi_TFIM, d, n_taylor=5, onsite_Z_ops=onsite_Z_ops)\n", + "grad, s = gradient_onsite_Z(dbi_TFIM, d, n=5, onsite_Z_ops=onsite_Z_ops)\n", "print('Initial off-diagonal norm:', dbi.off_diagonal_norm)\n", "print('The initial D coefficients:', d_coef)\n", "print('Gradient:', grad)\n", @@ -177,15 +331,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 591.53trial/s, best loss: 8.145383187499851]\n", + "New optimized step at iteration 1/15: 0.05372645731587117 with d_coef [(-0.9894224152921011+0j), (-0.9787695132043422+0j), (-0.9750406784474285+0j), (-0.9787695132043422+0j), (-0.9894224152921011+0j)], loss 8.143621474679835\n", + "100%|██████████| 500/500 [00:00<00:00, 645.70trial/s, best loss: 7.605789256028495]\n", + "New optimized step at iteration 2/15: 0.05650469375482817 with d_coef [(-0.967659680502992+0j), (-1.1697984701193866+0j), (-1.1812229848159992+0j), (-1.169798470119386+0j), (-0.9676596805029926+0j)], loss 7.597613753384701\n", + "100%|██████████| 500/500 [00:00<00:00, 655.89trial/s, best loss: 7.428351470243482] \n", + "New optimized step at iteration 3/15: 0.0398775478554277 with d_coef [(-0.9211510601202757+0j), (-1.8583923563596692+0j), (-1.3920649030243233+0j), (-1.8583923563596683+0j), (-0.9211510601202774+0j)], loss 7.340829616091421\n", + "100%|██████████| 500/500 [00:00<00:00, 662.71trial/s, best loss: 7.044497387511533] \n", + "New optimized step at iteration 4/15: 0.04899248221924902 with d_coef [(-0.6972643143931548+0j), (-2.4626900070115862+0j), (-0.9844514837970455+0j), (-2.462690007011585+0j), (-0.6972643143931571+0j)], loss 6.642213913279594\n", + "100%|██████████| 500/500 [00:00<00:00, 657.76trial/s, best loss: 5.999678025090855] \n", + "New optimized step at iteration 5/15: 0.022874588399740523 with d_coef [(-0.5466790556822081+0j), (-2.999350700331548+0j), (-0.3794556667684317+0j), (-2.9993507003315476+0j), (-0.5466790556822098+0j)], loss 5.886447625252318\n", + "100%|██████████| 500/500 [00:00<00:00, 574.02trial/s, best loss: 5.3112300777947405]\n", + "New optimized step at iteration 6/15: 0.019141369630992236 with d_coef [(-0.7333858133569751+0j), (-3.0535952858417934+0j), (0.5756460221651358+0j), (-3.053595285841793+0j), (-0.7333858133569763+0j)], loss 5.250048883689106\n", + "100%|██████████| 500/500 [00:00<00:00, 675.11trial/s, best loss: 4.816608426854996] \n", + "New optimized step at iteration 7/15: 0.023987091082236008 with d_coef [(-1.0481221272508368+0j), (-3.1124649418400363+0j), (1.4655494332027308+0j), (-3.1124649418400367+0j), (-1.048122127250837+0j)], loss 4.718211391122002\n", + "100%|██████████| 500/500 [00:00<00:00, 679.58trial/s, best loss: 4.284286113986318] \n", + "New optimized step at iteration 8/15: 0.02139251957239659 with d_coef [(-1.4568864129920867+0j), (-3.060562777832911+0j), (2.2749298849030803+0j), (-3.0605627778329114+0j), (-1.4568864129920873+0j)], loss 4.141138157155743\n", + "100%|██████████| 500/500 [00:00<00:00, 707.05trial/s, best loss: 3.539292516453598] \n", + "New optimized step at iteration 9/15: 0.02678154139520766 with d_coef [(-1.8702833982016478+0j), (-2.970343142930349+0j), (3.048114960756174+0j), (-2.9703431429303495+0j), (-1.8702833982016478+0j)], loss 3.3574911798518396\n", + "100%|██████████| 500/500 [00:00<00:00, 712.38trial/s, best loss: 2.874231151326864] \n", + "New optimized step at iteration 10/15: 0.01690916984115942 with d_coef [(-2.3030995103215814+0j), (-2.7605226880132614+0j), (3.777805532801257+0j), (-2.7605226880132614+0j), (-2.3030995103215792+0j)], loss 2.8230284010126816\n", + "100%|██████████| 500/500 [00:00<00:00, 666.99trial/s, best loss: 2.6088111056187437]\n", + "New optimized step at iteration 11/15: 0.026384092579150507 with d_coef [(-2.303103545745607+0j), (-2.76052331639202+0j), (3.7778137111811922+0j), (-2.76052331639202+0j), (-2.303103545745605+0j)], loss 2.6088111361606745\n", + "100%|██████████| 500/500 [00:00<00:00, 698.11trial/s, best loss: 2.546900361123962] \n", + "New optimized step at iteration 12/15: 0.015856203984303208 with d_coef [(-2.4537053596179947+0j), (-2.9677391380139637+0j), (4.069082377286002+0j), (-2.9677391380139513+0j), (-2.453705359617996+0j)], loss 2.546553818422246\n", + "100%|██████████| 500/500 [00:00<00:00, 742.58trial/s, best loss: 2.5253329418331236] \n", + "New optimized step at iteration 13/15: 0.022393088088665674 with d_coef [(-2.3237315151526348+0j), (-3.577847645999822+0j), (4.518564738562181+0j), (-3.577847645999736+0j), (-2.323731515152562+0j)], loss 2.500500419765173\n", + "100%|██████████| 500/500 [00:00<00:00, 746.30trial/s, best loss: 2.477865397687387] \n", + "New optimized step at iteration 14/15: 0.015967541185165194 with d_coef [(-1.9364597181792154+0j), (-4.149207093868703+0j), (4.441437397182407+0j), (-4.149207093868369+0j), (-1.936459718178632+0j)], loss 2.4579020061866172\n", + "100%|██████████| 500/500 [00:00<00:00, 742.62trial/s, best loss: 2.435713071167654] \n", + "New optimized step at iteration 15/15: 0.01431296703708781 with d_coef [(-1.5093021425133906+0j), (-4.700001486852238+0j), (4.295827265099874+0j), (-4.700001486851165+0j), (-1.5093021425092228+0j)], loss 2.422561227610107\n" + ] + } + ], "source": [ "NSTEPS = 15\n", "off_diagonal_norm_delta = [dbi_TFIM.off_diagonal_norm]\n", "s_step_delta = [0]\n", "for _ in range(NSTEPS):\n", - " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100, n_taylor=5, use_ds=True)\n", + " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100, n=5, use_ds=True)\n", " dbi_TFIM(step=s, d=d)\n", " off_diagonal_norm_delta.append(dbi_TFIM.off_diagonal_norm)\n", " s_step_delta.append(s)\n", @@ -194,9 +385,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", "plt.plot(off_diagonal_norm_delta)\n", @@ -206,9 +418,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# the final matrix\n", "visualize_matrix(dbi_TFIM.h.matrix)" @@ -224,9 +447,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(-2.580645161290323+0j), (-1.2903225806451613+0j), (-0.6451612903225807+0j), (-0.32258064516129037+0j), (-0.16129032258064518+0j)]\n" + ] + } + ], "source": [ "H = H_TFIM.matrix\n", "L = int(np.log2(H.shape[0]))\n", @@ -238,9 +469,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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CDwCgU8rLy7Vy5UoNH97xatDfReABALfyG3tSJ5w5c0Y//vGP9eqrr+qyyy4L6diofcez+/waSWG2Xcr+0c5Hp95sab+rN9k3etrqNVgRS6O6Y+laY4GdM1aEkl9UM37JhPl70jQHntra2qDNXq9XXq/3kofNmTNHd955p37wgx/o+eefD+mU1HgAAMrIyJDP5wuk4uLiS+67bt06/eEPf2h3n/ZEbY0HANABY8M4nm9rPBUVFUpKSgpsvlRtp6KiQj/72c+0fft29enTp1OnJPAAgFvZOIA0KSkpKPBcyv79+1VVVaWRI0cGtjU1NWnXrl1avny5Ghoa1LNnz3bzIPAAACy77bbbdOjQoaBtDz74oK699lo9+eSTHQYdicADAO4VgSlzEhMTlZ2dHbStX79+uvzyy1ttvxQCDwC4lPGHv3J1BFa+JvAAAMJTWloa0v4EHgBwK5fOTt3tA4/dg86sDgyN1kGfMTW4DjEppgaa+mVD4LGjIKFhACkAwFG2B56ioiJ5PJ6glJqaavdpAAB+m5LDuqSp7YYbbtCHH34Y+GylXzcAIETm2xRuHg7rksDTq1cvy7WchoYGNTQ0BD5fPFEdAKB76ZJ3PEeOHFF6erqysrL0ox/9SF988cUl9y0uLg6amC4jI6MrigQA3Y7xe2xJTrM98IwZM0avv/66PvjgA7366quqrKzUuHHjVF1d3eb+CxYsUE1NTSBVVFTYXSQA6J54x9MsPz8/8Pdhw4YpJydHgwcP1po1a1RYWNhq/47WfAAAdC9dPo6nX79+GjZsmI4cOdLVpwKA2GI8UrhNZRHoXNDl43gaGhr02WefKS0tratPBQAxxa3veGyv8Tz++OOaMmWKBg0apKqqKj3//POqra1VQUFBSPlM6FOgXp64dvexc0RxVI9OtlFMjeoG2mHnvwX+HYTG9sDz1Vdf6f7779c333yj/v37a+zYsdq7d68yMzPtPhUAxDa/DU1t3aFzwbp16+zOEgDQFuNpTmHlYU9RQsFcbQAAR3X72akBoLuyo3MAC8EBAKzz97DhHY/zbW00tQEAHEWNBwDcil5tAAAnGeORCbNXm6FXGwCgu4vaGs/u82sktR/JIzGiOBIj+iNxTmY4AJpZ+dnt6N/BBdOo3edX21WkP3Np54KoDTwAgPYZv2zoTk2vNgBAN0eNBwDcypZlEbrB7NQAAGfY06utGyx9DQBAe6jxAIBb+Xs0p7DysKcooSDwAIBL2TNJKE1tAIBuztU1nlgZlGj1Oo9OvbnDfa7e9G/hFifi7By0ygBY2KHjn4+uGSvj1s4Frg48ABDTXPqOh6Y2AICjqPEAgEu5tXMBgQcAXMqt73hoagMAOIoaDwC4lUs7FxB4AMCl3PqOh6Y2AICjqPEAgEu5tXMBgSdEdi4JbfdoeCuzElgdqW9VJEb0R+IZMMMBopKx4R2P8wuQ0tQGAHAWNR4AcCm3di4g8ACASxkT/jsaQ1MbAKC7o8YDAG5lQ1ObaGoDAFhlTA8ZE17DlYlAWxtNbQAAR1HjAQC38nvCbyqjqQ0AYBUzFyCIlRHssTQaPhIzOdiZHzMcAPYJ+R3Prl27NGXKFKWnp8vj8WjTpk1B3xtjVFRUpPT0dMXHxys3N1eHDx+2q7wAgG+1DCANNzkt5MBTX1+vESNGaPny5W1+v2TJEi1dulTLly9XeXm5UlNTNXnyZNXV1YVdWADAn7X0ags3OS3kprb8/Hzl5+e3+Z0xRsuWLdMzzzyjadOmSZLWrFmjlJQUrV27Vo8++mh4pQUAuJ6toe7YsWOqrKxUXl5eYJvX69Wtt96qPXv2tHlMQ0ODamtrgxIAoGMx09TWnsrKSklSSkpK0PaUlJTAdxcrLi6Wz+cLpIyMDDuLBADdVkuvtnCT07qkcc/jCb4QY0yrbS0WLFigmpqaQKqoqOiKIgEAooSt3alTU1MlNdd80tLSAturqqpa1YJaeL1eeb1eO4sBADHBreN4bK3xZGVlKTU1VSUlJYFtjY2NKisr07hx4+w8FQDEPGNseMfjhgGkZ86c0dGjRwOfjx07poMHDyo5OVmDBg3S/PnztWjRIg0ZMkRDhgzRokWL1LdvX82YMcPWggMA3CnkwLNv3z5NnDgx8LmwsFCSVFBQoNdee01PPPGEzp07p9mzZ+vUqVMaM2aMtm/frsTERPtK3U3YPRreznNaZedI/e4w6t/OZxrN14no4NbZqUMOPLm5ue0W1OPxqKioSEVFReGUCwDQAbcufc2yCAAARzFJKAC4lFt7tRF4AMCl3Bp4aGoDADiKwAMALmX8dszXFto5V6xYoeHDhyspKUlJSUnKycnR1q1bQ8qDpjYAcKlINLUNHDhQixcv1tVXXy2peQWCe+65RwcOHNANN9xgKQ8CDwDAsilTpgR9/sUvfqEVK1Zo79697g88E/oUqJcnrt19YmWAXSSu0+7BnJEYMBnNgzRjZUAtupY9A0ibj794SRor82g2NTXp7bffVn19vXJyciyfk3c8AOBSfuOxJUlSRkZG0BI1xcXFlzzvoUOHlJCQIK/Xq1mzZmnjxo26/vrrLZc7ams8AADnVFRUKCkpKfC5vdrONddco4MHD+r06dPasGGDCgoKVFZWZjn4EHgAwK3sWEH02+NbeqlZERcXF+hcMGrUKJWXl+ull17Sr3/9a0vHE3gAwKWiZQCpMUYNDQ2W9yfwAAAse/rpp5Wfn6+MjAzV1dVp3bp1Ki0t1bZt2yznQeABAJeKRI3n66+/1gMPPKCTJ0/K5/Np+PDh2rZtmyZPnmw5DwIPALhUJALPqlWrwjqfRHdqAIDDqPEAgEv5TQ/5wxxAGu7xnRG1gWf3+TWS2q8CRvPIdLeze1luO0fqW+X2Zx+JZwB3McaGFUhZFgEA0N1FbY0HANC+aBnHEyoCDwC4lFsDD01tAABHUeMBAJf67uzS4eThNAIPALgUTW0AAFhAjQcAXMqtNR4CDwC4FO94IoB164NFYiaH7nDf3I4ZDuA2rg48ABDLjAm/qcwYmwoTAgIPALiUW9/x0KsNAOAoajwA4FLGhs4F9GoDAFhGUxsAABZQ4wEAl3JrjYfAAwAuxQDSKBVLg+uieUBtJAa3Hp16c4f7XL3p32w9ZzSz899CNP87QPTr9oEHALortza1hdy5YNeuXZoyZYrS09Pl8Xi0adOmoO9nzpwpj8cTlMaOHWtXeQEA32ppags3OS3kwFNfX68RI0Zo+fLll9zn9ttv18mTJwNpy5YtYRUSANB9hNzUlp+fr/z8/Hb38Xq9Sk1NtZRfQ0ODGhoaAp9ra2tDLRIAxCQjj4zCbGoL8/jO6JJxPKWlpRowYICGDh2qRx55RFVVVZfct7i4WD6fL5AyMjK6okgA0O20vOMJNznN9sCTn5+vN998Uzt27NCLL76o8vJyTZo0KahW810LFixQTU1NIFVUVNhdJABAFLG9V9v06dMDf8/OztaoUaOUmZmp999/X9OmTWu1v9frldfrtbsYANDtMY7nEtLS0pSZmakjR4509akAIKbETHfqUFVXV6uiokJpaWldfSoAgAuEXOM5c+aMjh49Gvh87NgxHTx4UMnJyUpOTlZRUZHuu+8+paWl6csvv9TTTz+tK664Qvfee6+tBbdbdxiJHc0jziNxXiuzEliZ3cBqXqGIxLPqDrNzWBEr1ylJftnQ1BaBXm0hB559+/Zp4sSJgc+FhYWSpIKCAq1YsUKHDh3S66+/rtOnTystLU0TJ07U+vXrlZiYaF+pAQCuFXLgyc3NlWlnke4PPvggrAIBAKxx6zse5moDAJfyyxN2U1kkmtpYCA4A4ChqPADgVnbMPEBTGwDAKrcOIKWpDQDgKGo8AOBS9GoDADjK/20KNw+nEXi6ESsjsWNpVLcVVmcksHrfrIrE/bV6zmieAcMKO68zlPxgHYEHAFyKpjYAgKP8Jvxeaf5LT0TTZejVBgBwFDUeAHApI49MmFPehHt8ZxB4AMClGEAKAIAF1HgAwKWaOxeEn4fTCDwA4FK844ErMLjOPSL1DGLlmcbKgNpoROABAJdya+cCAg8AuJQxzSncPJxGrzYAgKOo8QCASxl55KdzAQDAKW6dJJSmNgCAo6jxAIBL0asNAOAo820KNw+n0dQGAHAUNR60KVZmOIhU+e0cDe/2ZxDt7FhS/oJp1O7zq+0qUgBNbQAAR/m/TeHm4TSa2gAAjqLGAwAu5dZxPAQeAHApt77joakNAOAoAg8AuJSxKYWiuLhYo0ePVmJiogYMGKCpU6fq888/DykPAg8AuFRLU1u4KRRlZWWaM2eO9u7dq5KSEl24cEF5eXmqr6+3nAfveAAAlm3bti3o8+rVqzVgwADt379ft9xyi6U8CDwA4FJ2juOpra0N2u71euX1ejs8vqamRpKUnJxs+ZwEHoTF7evWR2JGAqvnjeZZFayKldkSOr7OrpkRzc7u1BkZGUHbFy5cqKKiog6ONSosLNT48eOVnZ1t+ZwhveOx8lLJGKOioiKlp6crPj5eubm5Onz4cCinAQA4rKKiQjU1NYG0YMGCDo+ZO3euPvnkE7311lshnSukwGPlpdKSJUu0dOlSLV++XOXl5UpNTdXkyZNVV1cXUsEAAO0z+nNzW2dTS10sKSkpKHXUzDZv3jxt3rxZO3fu1MCBA0Mqd0hNbR29VDLGaNmyZXrmmWc0bdo0SdKaNWuUkpKitWvX6tFHHw2pcACASzOyoaktxKWvjTGaN2+eNm7cqNLSUmVlZYV8zrC6U1/8UunYsWOqrKxUXl5eYB+v16tbb71Ve/bsaTOPhoYG1dbWBiUAQHSaM2eO3njjDa1du1aJiYmqrKxUZWWlzp07ZzmPTgeetl4qVVZWSpJSUlKC9k1JSQl8d7Hi4mL5fL5AuvgFFwCgbX5jTwrFihUrVFNTo9zcXKWlpQXS+vXrLefR6V5tLS+VPvroo1bfeTzBVTdjTKttLRYsWKDCwsLA59raWoIPAFgQiRVIjQm/h16nAk/LS6Vdu3YFvVRKTU2V1FzzSUtLC2yvqqpqVQtqYbWvOACgewipqc0Yo7lz5+qdd97Rjh07Wr1UysrKUmpqqkpKSgLbGhsbVVZWpnHjxtlTYgCApMhMmWOHkGo8c+bM0dq1a/Xuu+8GXipJks/nU3x8vDwej+bPn69FixZpyJAhGjJkiBYtWqS+fftqxowZXXIBcIdoHjBpp0gMqLVzwGco57WiOzzTaObWFUhDCjwrVqyQJOXm5gZtX716tWbOnClJeuKJJ3Tu3DnNnj1bp06d0pgxY7R9+3YlJibaUmAAgLuFFHisvFTyeDwqKirqcKoFAEB4WIEUAOAotza1sR4PAMBR1HgAwKWMaU7h5uE0Ag8AuJRfHvlDnGutrTycRlMbAMBR1HgAwKU6M9daW3k4jcADAG5lwzueLloctV0EHkQNu0fqR/NsCbEyUt/tS6OjaxB4AMCl3Nq5gMADAC7l1u7U9GoDADiKGg8AuJRbp8wh8ACAS7m1OzVNbQAAR1HjAQCXMgp/GE4EKjwEHgBwq+amtjC7U9PUBgDo7qjxwHUiMRo+EjMcRGpWhaNTb+5wn6s3/Zut54zmWSaimVvH8RB4AMCl3NqdmqY2AICjqPEAgEvR1AYAcBRNbQAAWECNBwBcytgwZQ5NbQAAy9w6cwFNbQAAR0VtjWdCnwL18sS1u08sDRRD6Oz8+YilnzUrg0NjaTBnNC/L7dbZqaM28AAA2ufW7tQ0tQEAHEWNBwBcyq3jeAg8AOBSbn3HQ1MbAMBR1HgAwKXcOo6HwAMALkVTGwAAFlDjAQCXcus4nqgNPLvPr5HkaXefaB5RDIQrmpfltrtsdp7TbnYsy33BNGr3+dV2FSnArd2paWoDADgqpMBTXFys0aNHKzExUQMGDNDUqVP1+eefB+0zc+ZMeTyeoDR27FhbCw0A+LbGY8JMESh3SIGnrKxMc+bM0d69e1VSUqILFy4oLy9P9fX1QfvdfvvtOnnyZCBt2bLF1kIDAP7cnTrc5LSQ3vFs27Yt6PPq1as1YMAA7d+/X7fccktgu9frVWpqqqU8Gxoa1NDQEPhcW1sbSpEAAC4T1juempoaSVJycnLQ9tLSUg0YMEBDhw7VI488oqqqqkvmUVxcLJ/PF0gZGRnhFAkAYobpbPPad5KrZqc2xqiwsFDjx49XdnZ2YHt+fr7efPNN7dixQy+++KLKy8s1adKkoFrNdy1YsEA1NTWBVFFR0dkiAUBMaelOHW5yWqe7U8+dO1effPKJPvroo6Dt06dPD/w9Oztbo0aNUmZmpt5//31NmzatVT5er1der7ezxQAAuEynAs+8efO0efNm7dq1SwMHDmx337S0NGVmZurIkSOdKiAAoG1uHccTUuAxxmjevHnauHGjSktLlZWV1eEx1dXVqqioUFpaWqcLCQBorbk7dHhtZVG/9PWcOXO0du1avfvuu0pMTFRlZaUkyefzKT4+XmfOnFFRUZHuu+8+paWl6csvv9TTTz+tK664Qvfee6/thbdjRHEoeQHRKJpnOIiE6JzJIRKdlqNXSIFnxYoVkqTc3Nyg7atXr9bMmTPVs2dPHTp0SK+//rpOnz6ttLQ0TZw4UevXr1diYqJthQYAxMiyCKaD7g/x8fH64IMPwioQAMAaO2YeYFkEAEC3F7WzUwMA2me+/RNuHk4j8ACAS9HUBgCABdR4AMClYmIAKQAgehhjwzueCEzWRlMbAMBR3b7Gw6huoJnd/xbsPKcVR6febGm/qzfFzr95mtoAAI6iqQ0AAAuo8QCASxmF31QW9XO1AQCih98YG5ZFoKkNABDFdu3apSlTpig9PV0ej0ebNm0KOQ8CDwC4lLHpTyjq6+s1YsQILV++vNPlpqkNAFwqEt2p8/PzlZ+fH9Y5CTwAANXW1gZ99nq98nq9XXIuAs+3GGgam3ierUXiWq08h2geGNrROS+YRu0+v9q287Xwy4bOBd8en5GREbR94cKFKioqCivvSyHwAIBL2dmrraKiQklJSYHtXVXbkQg8AABJSUlJQYGnKxF4AMClWIEUAOAoO9/xWHXmzBkdPXo08PnYsWM6ePCgkpOTNWjQIEt5EHgAAJbt27dPEydODHwuLCyUJBUUFOi1116zlAeBBwBcKhI1ntzc3LBntCbwAIBLufUdD1PmAAAcRY0HAFzK2NDURq82F7BzhoNYGg0frZixIjpYuW92PwOrS2k/8sFwG87ZNb/c/R6/PJ7wZmvzR2Dxa5raAACOosYDAC7ll5HH4V5tdiDwAIBLmW87VIebh9NoagMAOIoaDwC4lF+yoanNeQQeAHAperUBAGABNR4AcCm//PKEWWOJRI2HwAMALkXgQZBIjMRG12GGg2CRuE6775mVGQkk6dUfftLhPldvCrMwMSakdzwrVqzQ8OHDA0uk5uTkaOvWrYHvjTEqKipSenq64uPjlZubq8OHD9teaADAn8fxhJucFlLgGThwoBYvXqx9+/Zp3759mjRpku65555AcFmyZImWLl2q5cuXq7y8XKmpqZo8ebLq6uq6pPAAEMv8Hr8tyWkhBZ4pU6bojjvu0NChQzV06FD94he/UEJCgvbu3StjjJYtW6ZnnnlG06ZNU3Z2ttasWaOzZ89q7dq1l8yzoaFBtbW1QQkA0H11ujt1U1OT1q1bp/r6euXk5OjYsWOqrKxUXl5eYB+v16tbb71Ve/bsuWQ+xcXF8vl8gZSRkdHZIgFATDHyh/0n6pvaJOnQoUNKSEiQ1+vVrFmztHHjRl1//fWqrKyUJKWkpATtn5KSEviuLQsWLFBNTU0gVVRUhFokAIhJRk22JKeF3Kvtmmuu0cGDB3X69Glt2LBBBQUFKisrC3zv8XiC9jfGtNr2XV6vV16vN9RiAABcKuTAExcXp6uvvlqSNGrUKJWXl+ull17Sk08+KUmqrKxUWlpaYP+qqqpWtSAAQPiax+C4bxxP2FPmGGPU0NCgrKwspaamqqSkJPBdY2OjysrKNG7cuHBPAwC4iN+mtzxOC6nG8/TTTys/P18ZGRmqq6vTunXrVFpaqm3btsnj8Wj+/PlatGiRhgwZoiFDhmjRokXq27evZsyY0VXldzUGJXY/sbI0ejT/7Np9TiuDQy/417T7fW3tWSX/xf+zdL5YEFLg+frrr/XAAw/o5MmT8vl8Gj58uLZt26bJkydLkp544gmdO3dOs2fP1qlTpzRmzBht375diYmJXVJ4AIhlzZ0DLv0O3WoeTgsp8Kxatard7z0ej4qKilRUVBROmQAAFsTsOx4AAELBJKEA4FJ2zLUWiQGkBB4AcCm/mqQw3/H4I/COh6Y2AICjqPEAgEvR1AYAcJTf2NDUZqK8O7UTjGkZRev8aNpodcE0WtyTe+YW1p6p+59nJH52I3HO2tqzHXx/rvmMxv3P1A4eE2V34quvvmJpBADdUkVFhQYOHBh2PrW1tfL5fLq870j18IRXf/CbC6o+u181NTVKSkoKu2xWRF2NJz09XRUVFUpMTAzMal1bW6uMjAxVVFQ4dmPs5vZrcHv5Jfdfg9vLL7n/GjpbfmOM6urqlJ6ebmt5mt/xhNdUxjseST169Ljk/wiSkpJc+cP6XW6/BreXX3L/Nbi9/JL7r6Ez5ff5fF1UGveJusADALDGGL/84c7VZqjxAAAsam4mC3eSUOZqa5PX69XChQtdvVKp26/B7eWX3H8Nbi+/5P5rcHv5o0XU9WoDALSvpVebr8/18nh6hpWXMU2qOf9pbPdqAwBY0/yGh6Y2AADaRY0HAFyquUcavdoAAA6xY9nqSCx9TVMbAMBRrgg8r7zyirKystSnTx+NHDlSu3fvjnSRLCkqKpLH4wlKqampkS5Wu3bt2qUpU6YoPT1dHo9HmzZtCvreGKOioiKlp6crPj5eubm5Onz4cGQK24aOyj9z5sxWz2Ts2LGRKWwbiouLNXr0aCUmJmrAgAGaOnWqPv/886B9ov0ZWLmGaH4OK1as0PDhwwOzE+Tk5Gjr1q2B76Pp/htjZIw/zOR8x+aoDzzr16/X/Pnz9cwzz+jAgQOaMGGC8vPzdfz48UgXzZIbbrhBJ0+eDKRDhw5Fukjtqq+v14gRI7R8+fI2v1+yZImWLl2q5cuXq7y8XKmpqZo8ebLq6uocLmnbOiq/JN1+++1Bz2TLli0OlrB9ZWVlmjNnjvbu3auSkhJduHBBeXl5qq+vD+wT7c/AyjVI0fscBg4cqMWLF2vfvn3at2+fJk2apHvuuScQXKLp/resxxNucr7gUe7mm282s2bNCtp27bXXmqeeeipCJbJu4cKFZsSIEZEuRqdJMhs3bgx89vv9JjU11SxevDiw7fz588bn85l//ud/jkAJ23dx+Y0xpqCgwNxzzz0RKU9nVFVVGUmmrKzMGOO+Z2BM62swxn3P4bLLLjO/+c1voub+19TUGEkmPu4q09f7vbBSfNxVRpKpqalxrPxRXeNpbGzU/v37lZeXF7Q9Ly9Pe/bsiVCpQnPkyBGlp6crKytLP/rRj/TFF19EukidduzYMVVWVgY9D6/Xq1tvvdU1z0OSSktLNWDAAA0dOlSPPPKIqqqqIl2kS6qpqZEkJScnS3LnM7j4Glq44Tk0NTVp3bp1qq+vV05OTtTdf2OabElOi+rA880336ipqUkpKSlB21NSUlRZWRmhUlk3ZswYvf766/rggw/06quvqrKyUuPGjVN1dXWki9YpLffcrc9DkvLz8/Xmm29qx44devHFF1VeXq5JkyapoaEh0kVrxRijwsJCjR8/XtnZ2ZLc9wzaugYp+p/DoUOHlJCQIK/Xq1mzZmnjxo26/vrro+7+h/9+x0936ktpWZenhTGm1bZolJ+fH/j7sGHDlJOTo8GDB2vNmjUqLCyMYMnC49bnIUnTp08P/D07O1ujRo1SZmam3n//fU2bNi2CJWtt7ty5+uSTT/TRRx+1+s4tz+BS1xDtz+Gaa67RwYMHdfr0aW3YsEEFBQUqKysLfO+W+x+torrGc8UVV6hnz56t/idRVVXV6n8cbtCvXz8NGzZMR44ciXRROqWlR153eR6SlJaWpszMzKh7JvPmzdPmzZu1c+fOoPWp3PQMLnUNbYm25xAXF6err75ao0aNUnFxsUaMGKGXXnop6u6/WzsXRHXgiYuL08iRI1VSUhK0vaSkROPGjYtQqTqvoaFBn332mdLS0iJdlE7JyspSampq0PNobGxUWVmZK5+HJFVXV6uioiJqnokxRnPnztU777yjHTt2KCsrK+h7NzyDjq6hLdH2HC5mjFFDQ0PU3X+3NrVFfa+2devWmd69e5tVq1aZTz/91MyfP9/069fPfPnll5EuWocee+wxU1paar744guzd+9ec9ddd5nExMSoLntdXZ05cOCAOXDggJFkli5dag4cOGD++Mc/GmOMWbx4sfH5fOadd94xhw4dMvfff79JS0sztbW1ES55s/bKX1dXZx577DGzZ88ec+zYMbNz506Tk5Njrrzyyqgp/9/+7d8an89nSktLzcmTJwPp7NmzgX2i/Rl0dA3R/hwWLFhgdu3aZY4dO2Y++eQT8/TTT5sePXqY7du3G2Oi4/639Grr3TPFxPVKCyv17pnieK+2qA88xhjz8ssvm8zMTBMXF2duuummoG6Z0Wz69OkmLS3N9O7d26Snp5tp06aZw4cPR7pY7dq5c6eR1CoVFBQYY5q78y5cuNCkpqYar9drbrnlFnPo0KHIFvo72iv/2bNnTV5enunfv7/p3bu3GTRokCkoKDDHjx+PdLED2iq7JLN69erAPtH+DDq6hmh/Dg899FDg903//v3NbbfdFgg6xkTH/W8JPL169je9e6WElXr17O944GE9HgBwmZb1eHr2SJbHE94bE2P8avL/n6Pr8UT1Ox4AQPfjiu7UAIC2GCnsXmnON3oReADApexZj4dJQgEA3Rw1HgBwqebBn2HWeGhqAwBYF37gicQ7HpraAACOosYDAG5lQ+cCRaBzAYEHAFzKre94aGoDADiKGg8AuBadCwAAjjLN72jCSZ0MPK+88oqysrLUp08fjRw5Urt377Z8LIEHABCS9evXa/78+XrmmWd04MABTZgwQfn5+Tp+/Lil45mdGgBcpmV2aqmn7GlqawppduoxY8bopptu0ooVKwLbrrvuOk2dOlXFxcUdHk+NBwBc7ZJLIFlMzWpra4NSQ0NDm2drbGzU/v37lZeXF7Q9Ly9Pe/bssVRiAg8AuExcXJxSU1MlNdmSEhISlJGRIZ/PF0iXqrl88803ampqUkpKStD2lJQUVVZWWio/vdoAwGX69OmjY8eOqbGx0Zb8jDHyeIKb7Lxeb7vHXLx/W3lcCoEHAFyoT58+6tOnj+PnveKKK9SzZ89WtZuqqqpWtaBLoakNAGBZXFycRo4cqZKSkqDtJSUlGjdunKU8qPEAAEJSWFioBx54QKNGjVJOTo5Wrlyp48ePa9asWZaOJ/AAAEIyffp0VVdX67nnntPJkyeVnZ2tLVu2KDMz09LxjOMBADiKdzwAAEcReAAAjiLwAAAcReABADiKwAMAcBSBBwDgKAIPAMBRBB4AgKMIPAAARxF4AACOIvAAABz1/wGHOV5CNV4bwgAAAABJRU5ErkJggg==", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "visualize_matrix(H, 'Initial hamiltonian')\n", "visualize_matrix(N, 'Min-max diagonal matrix')\n", @@ -256,21 +518,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-03-26 16:08:28]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], "source": [ "# backend\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "# initialize dbi object\n", "dbi_TFIM_MMH = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 688.27trial/s, best loss: 9.336239342915379]\n", + "New optimized step at iteration 1/15: 0.039240166337035656 with d_coef [(-2.3180340693309422+0j), (-0.9042157574954297+0j), (-0.6267094129284807+0j), (-0.37510402952816974+0j), (-0.16137910360026844+0j)], loss 9.263805656974093\n", + "100%|██████████| 500/500 [00:00<00:00, 654.86trial/s, best loss: 8.253271106315344] \n", + "New optimized step at iteration 2/15: 0.0636971166898561 with d_coef [(-2.8893154826347565+0j), (-1.3328071932958503+0j), (-0.5996311871447069+0j), (-0.38812640871658144+0j), (-0.16592899239661785+0j)], loss 8.248988639626276\n", + "100%|██████████| 500/500 [00:00<00:00, 705.90trial/s, best loss: 7.820911729728226] \n", + "New optimized step at iteration 3/15: 0.026774099108320803 with d_coef [(-3.9047191557345737+0j), (-1.3620955366051533+0j), (-1.094932722170599+0j), (-0.5744178736473565+0j), (-0.04727696085745736+0j)], loss 7.79237041903216\n", + "100%|██████████| 500/500 [00:00<00:00, 522.76trial/s, best loss: 7.506187222292692]\n", + "New optimized step at iteration 4/15: 0.029295596624437686 with d_coef [(-3.894655859483571+0j), (-1.866243073713661+0j), (-0.7092145648013096+0j), (-0.7039608847825699+0j), (-0.023283739763302808+0j)], loss 7.4962199726801755\n", + "100%|██████████| 500/500 [00:00<00:00, 576.24trial/s, best loss: 7.262455656549131]\n", + "New optimized step at iteration 5/15: 0.02836170693029348 with d_coef [(-3.9671435812064013+0j), (-1.748374386604198+0j), (-0.9350901630745362+0j), (-0.6281543245247632+0j), (-0.021512156595171472+0j)], loss 7.242940534826334\n", + "100%|██████████| 500/500 [00:00<00:00, 671.15trial/s, best loss: 7.044926289914773]\n", + "New optimized step at iteration 6/15: 0.027897015043668715 with d_coef [(-3.948390041694754+0j), (-1.8847156433519916+0j), (-0.8262997874928508+0j), (-0.6276868981090158+0j), (-0.03885078124692265+0j)], loss 7.026681601151195\n", + "100%|██████████| 500/500 [00:00<00:00, 690.94trial/s, best loss: 6.855318614477858] \n", + "New optimized step at iteration 7/15: 0.027095778110113995 with d_coef [(-3.9519653973013913+0j), (-1.911636257457286+0j), (-0.8907292589911223+0j), (-0.6344354980656255+0j), (-0.0239873577390306+0j)], loss 6.826359605831807\n", + "100%|██████████| 500/500 [00:00<00:00, 520.20trial/s, best loss: 6.6782408641935875]\n", + "New optimized step at iteration 8/15: 0.027670995126608866 with d_coef [(-3.9302491674477538+0j), (-1.9666365073691627+0j), (-0.8561543524586357+0j), (-0.6383800207112388+0j), (-0.004655769048021813+0j)], loss 6.636290444352086\n", + "100%|██████████| 500/500 [00:00<00:00, 576.02trial/s, best loss: 6.500633770102917]\n", + "New optimized step at iteration 9/15: 0.027675484066740867 with d_coef [(-3.910374644169554+0j), (-1.9831418560231258+0j), (-0.9056736621483122+0j), (-0.6540987589359828+0j), (0.02406147464053876+0j)], loss 6.447464047229631\n", + "100%|██████████| 500/500 [00:00<00:00, 686.99trial/s, best loss: 6.319748615035787]\n", + "New optimized step at iteration 10/15: 0.026930095210157 with d_coef [(-3.886824281463916+0j), (-1.99625546879924+0j), (-0.9414450075378732+0j), (-0.6563760409606512+0j), (0.03970055245590362+0j)], loss 6.2592485638502575\n", + "100%|██████████| 500/500 [00:00<00:00, 678.32trial/s, best loss: 6.1400705423264075]\n", + "New optimized step at iteration 11/15: 0.027416250931757133 with d_coef [(-3.8475665420145373+0j), (-2.037392997099672+0j), (-0.9264643353804642+0j), (-0.6830139042784837+0j), (0.08321313069136971+0j)], loss 6.056764516965165\n", + "100%|██████████| 500/500 [00:00<00:00, 729.97trial/s, best loss: 5.940597947808348] \n", + "New optimized step at iteration 12/15: 0.028200202317592835 with d_coef [(-3.82460449840812+0j), (-2.035906559623582+0j), (-0.9702033338205296+0j), (-0.6848609304443387+0j), (0.11118456157172787+0j)], loss 5.848596463276441\n", + "100%|██████████| 500/500 [00:00<00:00, 743.16trial/s, best loss: 5.72938416138] \n", + "New optimized step at iteration 13/15: 0.025525484486623708 with d_coef [(-3.8277137978993436+0j), (-2.0402358325723036+0j), (-0.9967614632890175+0j), (-0.6822006377994072+0j), (0.09661303923602668+0j)], loss 5.643243093952352\n", + "100%|██████████| 500/500 [00:00<00:00, 722.17trial/s, best loss: 5.532276668994669] \n", + "New optimized step at iteration 14/15: 0.028418788139760974 with d_coef [(-3.7489089984244486+0j), (-2.114017010442895+0j), (-0.9183197191620466+0j), (-0.7036125621442609+0j), (0.16285610695072883+0j)], loss 5.4057916657046725\n", + "100%|██████████| 500/500 [00:00<00:00, 739.76trial/s, best loss: 5.288910417094644] \n", + "New optimized step at iteration 15/15: 0.02625000676004677 with d_coef [(-3.789232226109539+0j), (-2.092494639505251+0j), (-1.0022140546781002+0j), (-0.6714823814533052+0j), (0.13551681944910254+0j)], loss 5.191808803025761\n" + ] + } + ], "source": [ "NSTEPS = 15\n", "off_diagonal_norm_MMH = [dbi_TFIM_MMH.off_diagonal_norm]\n", @@ -289,9 +596,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", "plt.plot(off_diagonal_norm_MMH, label='MMH')\n", @@ -305,17 +633,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Effect of `n_taylor`" + "## Effect of `n`" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-03-26 16:08:41]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 31.576176740060667\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# backend\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "# initialize dbi object\n", "nqubits = 5\n", "h0 = random_hermitian(2**nqubits, seed=2)\n", @@ -331,15 +684,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 697.70trial/s, best loss: 9.558186537868679] \n", + "The initial D coefficients: [(-3.321354431855655-1.7961649980378765e-16j), (-0.7143725995296772+3.608986798092513e-17j), (0.472710854506152+9.347215093087467e-17j), (-0.5707798509274735-1.3813111045761499e-17j), (0.34536980200226214-1.1499770144849896e-16j)]\n", + "Gradient: [ 0.65534217 0.16603388 -0.31270245 0.27247095 0.60904527]\n", + "s: 0.024282460160549718\n" + ] + } + ], "source": [ "# generate the onsite Z operators\n", "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi,d,n_taylor=5, onsite_Z_ops=onsite_Z_ops)\n", + "grad, s = gradient_onsite_Z(dbi,d,n=5, onsite_Z_ops=onsite_Z_ops)\n", "print('The initial D coefficients:', d_coef)\n", "print('Gradient:', grad)\n", "print('s:', s)" @@ -347,9 +722,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 596.09trial/s, best loss: 27.467491165569765]\n", + "100%|██████████| 500/500 [00:00<00:00, 689.73trial/s, best loss: 27.469650148347917] \n", + "100%|██████████| 500/500 [00:00<00:00, 684.87trial/s, best loss: 23.138955844687388] \n", + "100%|██████████| 500/500 [00:00<00:00, 680.99trial/s, best loss: 23.147351603039073]\n", + "100%|██████████| 500/500 [00:00<00:00, 709.00trial/s, best loss: 20.03558303567074] \n", + "100%|██████████| 500/500 [00:00<00:00, 638.55trial/s, best loss: 20.01446017712839] \n", + "100%|██████████| 500/500 [00:00<00:00, 659.72trial/s, best loss: 18.534762036988734]\n", + "100%|██████████| 500/500 [00:00<00:00, 702.62trial/s, best loss: 18.511235299525854]\n", + "100%|██████████| 500/500 [00:00<00:00, 716.91trial/s, best loss: 17.667631299146947] \n", + "100%|██████████| 500/500 [00:00<00:00, 717.85trial/s, best loss: 17.623675374778802] \n", + "100%|██████████| 500/500 [00:00<00:00, 626.07trial/s, best loss: 17.043867777621116]\n", + "100%|██████████| 500/500 [00:00<00:00, 662.03trial/s, best loss: 16.99411319096819] \n", + "100%|██████████| 500/500 [00:00<00:00, 694.96trial/s, best loss: 16.644107561332255]\n", + "100%|██████████| 500/500 [00:00<00:00, 712.76trial/s, best loss: 16.467189206952877] \n", + "100%|██████████| 500/500 [00:00<00:00, 713.76trial/s, best loss: 16.046314213095602]\n", + "100%|██████████| 500/500 [00:00<00:00, 699.95trial/s, best loss: 15.878928292681035] \n", + "100%|██████████| 500/500 [00:00<00:00, 691.24trial/s, best loss: 15.62141427589626] \n", + "100%|██████████| 500/500 [00:00<00:00, 667.73trial/s, best loss: 15.306325490043811] \n", + "100%|██████████| 500/500 [00:00<00:00, 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9.063835314892646]\n", + "100%|██████████| 500/500 [00:00<00:00, 717.35trial/s, best loss: 8.149990124972552] \n", + "100%|██████████| 500/500 [00:00<00:00, 715.79trial/s, best loss: 8.87960896954228] \n", + "100%|██████████| 500/500 [00:00<00:00, 726.35trial/s, best loss: 7.913881055204248]\n", + "100%|██████████| 500/500 [00:00<00:00, 724.78trial/s, best loss: 8.697803369655396] \n", + "100%|██████████| 500/500 [00:00<00:00, 687.97trial/s, best loss: 7.678966990725647] \n", + "100%|██████████| 500/500 [00:00<00:00, 720.81trial/s, best loss: 8.529279658079181] \n", + "100%|██████████| 500/500 [00:00<00:00, 728.49trial/s, best loss: 7.4907779318523815]\n", + "100%|██████████| 500/500 [00:00<00:00, 721.37trial/s, best loss: 8.367946297589626] \n", + "100%|██████████| 500/500 [00:00<00:00, 723.56trial/s, best loss: 7.305839659415738] \n" + ] + } + ], "source": [ "iters = 30\n", "d_coef_1, d_1 = d_coef, d\n", @@ -360,8 +802,8 @@ "s_step_1 = [0]\n", "s_step_2 = [0]\n", "for i in range(iters):\n", - " s_1, d_coef_1, d_1 = gradient_descent_onsite_Z(dbi_1, d_coef_1, d_1, onsite_Z_ops=onsite_Z_ops, n_taylor=n_1, max_evals=100)\n", - " s_2, d_coef_2, d_2 = gradient_descent_onsite_Z(dbi_2, d_coef_2, d_2, onsite_Z_ops=onsite_Z_ops, n_taylor=n_2, max_evals=100)\n", + " s_1, d_coef_1, d_1 = gradient_descent_onsite_Z(dbi_1, d_coef_1, d_1, onsite_Z_ops=onsite_Z_ops, n=n_1, max_evals=100)\n", + " s_2, d_coef_2, d_2 = gradient_descent_onsite_Z(dbi_2, d_coef_2, d_2, onsite_Z_ops=onsite_Z_ops, n=n_2, max_evals=100)\n", " dbi_1(step=s_1, d=d_1)\n", " dbi_2(step=s_2, d=d_2)\n", " off_diagonal_norm_1.append(dbi_1.off_diagonal_norm)\n", @@ -372,9 +814,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.title(str(nqubits) + ' spins magnetic field diagonalization')\n", "plt.plot(off_diagonal_norm_1, label=f'n_taylor={n_1}')\n", diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 6626c3db03..f478777e93 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -96,14 +96,13 @@ def select_best_dbr_generator( """ if scheduling is None: scheduling = dbi_object.scheduling - norms_off_diagonal_restriction = [ - dbi_object.off_diagonal_norm for _ in range(len(d_list)) - ] - optimal_steps, flip_list = [], [] + norms_off_diagonal_restriction = [dbi_object.off_diagonal_norm] * (len(d_list) + 1) + optimal_steps = np.zeros(len(d_list) + 1) + flip_list = np.ones(len(d_list) + 1) for i, d in enumerate(d_list): # prescribed step durations dbi_eval = deepcopy(dbi_object) - flip_list.append(cs_angle_sgn(dbi_eval, d)) + flip_list[i] = cs_angle_sgn(dbi_eval, d) if flip_list[i] != 0: if step is None: step_best = dbi_eval.choose_step( @@ -112,11 +111,10 @@ def select_best_dbr_generator( else: step_best = step dbi_eval(step=step_best, d=flip_list[i] * d) - optimal_steps.append(step_best) + optimal_steps[i] = step_best norms_off_diagonal_restriction[i] = dbi_eval.off_diagonal_norm # canonical if compare_canonical is True: - flip_list.append(1) dbi_eval = deepcopy(dbi_object) dbi_eval.mode = DoubleBracketGeneratorType.canonical if step is None: @@ -124,8 +122,8 @@ def select_best_dbr_generator( else: step_best = step dbi_eval(step=step_best) - optimal_steps.append(step_best) - norms_off_diagonal_restriction.append(dbi_eval.off_diagonal_norm) + optimal_steps[-1] = step_best + norms_off_diagonal_restriction[-1] = dbi_eval.off_diagonal_norm # find best d idx_max_loss = np.argmin(norms_off_diagonal_restriction) flip = flip_list[idx_max_loss] @@ -175,6 +173,7 @@ def dGamma_di_onsite_Z( else: Z_i = onsite_Z_ops[i] dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) + Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) W = dbi_object.commutator(d, dbi_object.h.matrix) dW_di = dbi_object.commutator(Z_i, dbi_object.h.matrix) for k in range(n + 1): @@ -184,7 +183,7 @@ def dGamma_di_onsite_Z( dGamma_di[k] = dW_di else: dGamma_di[k] = dbi_object.commutator( - dW_di, dbi_object.Gamma(k - 1, d) + dW_di, Gamma_list[k - 1] ) + dbi_object.commutator(W, dGamma_di[k - 1]) return dGamma_di @@ -193,6 +192,7 @@ def ds_di_onsite_Z( dbi_object: DoubleBracketIteration, d: np.array, i: int, + n: int = 3, taylor_coef: Optional[list] = None, onsite_Z_ops: Optional[list] = None, ): @@ -210,15 +210,14 @@ def ds_di_onsite_Z( nqubits = int(np.log2(d.shape[0])) if onsite_Z_ops is None: onsite_Z_ops = generate_onsite_Z_ops(nqubits) - dGamma_di = dGamma_di_onsite_Z(dbi_object, 3, i, d, onsite_Z_ops=onsite_Z_ops) + dGamma_di = dGamma_di_onsite_Z(dbi_object, n=4, i=i, d=d, onsite_Z_ops=onsite_Z_ops) + Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) def derivative_product(k1, k2): r"""Calculate the derivative of a product $\sigma(\Gamma(n1,i))@\sigma(\Gamma(n2,i))""" return dbi_object.sigma(dGamma_di[k1]) @ dbi_object.sigma( - dbi_object.Gamma(k2, d) - ) + dbi_object.sigma( - dbi_object.sigma(dbi_object.Gamma(k1, d)) - ) @ dbi_object.sigma( + Gamma_list[k2] + ) + dbi_object.sigma(dbi_object.sigma(Gamma_list[k1])) @ dbi_object.sigma( dGamma_di[k2] ) @@ -242,9 +241,10 @@ def derivative_product(k1, k2): def gradient_onsite_Z( dbi_object: DoubleBracketIteration, d: np.array, - onsite_Z_ops, - n_taylor=2, + onsite_Z_ops: list, use_ds=False, + n=3, + **kwargs, ): r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients Args: @@ -262,15 +262,17 @@ def gradient_onsite_Z( if onsite_Z_ops is None: onsite_Z_ops = generate_onsite_Z_ops(nqubits) grad = np.zeros(nqubits) - s, coef = dbi_object.polynomial_step( + coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) + s = dbi_object.choose_step( d=d, - n=n_taylor, - backup_scheduling=DoubleBracketScheduling.polynomial_approximation, + **kwargs, ) a, b, c = coef[len(coef) - 3 :] for i in range(nqubits): - da, db, dc, ds = ds_di_onsite_Z(dbi_object, d, i, [a, b, c], onsite_Z_ops) + da, db, dc, ds = ds_di_onsite_Z( + dbi_object, d=d, i=i, n=n, taylor_coef=[a, b, c], onsite_Z_ops=onsite_Z_ops + ) if use_ds is True: ds = 0 grad[i] = ( @@ -312,7 +314,7 @@ def gradient_descent_onsite_Z( dbi_object: DoubleBracketIteration, d_coef: list, d: Optional[np.array] = None, - n_taylor: int = 2, + n: int = 2, onsite_Z_ops: Optional[list] = None, lr_min: float = 1e-5, lr_max: float = 1, @@ -348,7 +350,7 @@ def gradient_descent_onsite_Z( if d is None: d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) grad, s = gradient_onsite_Z( - dbi_object, d, n_taylor=n_taylor, onsite_Z_ops=onsite_Z_ops, use_ds=use_ds + dbi_object, d, n=n, onsite_Z_ops=onsite_Z_ops, use_ds=use_ds ) # optimize gradient descent step with hyperopt if space is None: diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 7972b6e8e7..488d52ca40 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -294,6 +294,8 @@ def simulated_annealing_step( d = dbi_object.diagonal_h_matrix if initial_s is None: initial_s = polynomial_step(dbi_object=dbi_object, d=d, n=4) + if initial_s is None: + initial_s = step_min if s_jump_range is None: s_jump_range = (step_max - step_min) / s_jump_range_divident current_s = initial_s @@ -304,7 +306,7 @@ def simulated_annealing_step( candidate_s = max( step_min, min( - current_s + np.random.uniform(-1 * s_jump_range, s_jump_range, step_max) + current_s + np.random.uniform(-1 * s_jump_range, s_jump_range), step_max ), ) candidate_loss = dbi_object.loss(d=d, step=candidate_s) From e8ab44736f8a75793863598cc5dc7814d074670a Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Tue, 26 Mar 2024 10:44:31 +0100 Subject: [PATCH 048/154] Added numerical gradient as well as Z-ansatz --- examples/dbi/dbi_costs.ipynb | 287 +++++-- examples/dbi/dbi_misc.ipynb | 944 ++++++++++++++++++++++++ src/qibo/models/dbi/utils_scheduling.py | 67 +- 3 files changed, 1208 insertions(+), 90 deletions(-) create mode 100644 examples/dbi/dbi_misc.ipynb diff --git a/examples/dbi/dbi_costs.ipynb b/examples/dbi/dbi_costs.ipynb index 558f74cff3..2170ce8c9b 100644 --- a/examples/dbi/dbi_costs.ipynb +++ b/examples/dbi/dbi_costs.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 192, "metadata": {}, "outputs": [], "source": [ @@ -39,14 +39,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 184, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.5|INFO|2024-03-15 18:17:05]: Using qibojit (numba) backend on /CPU:0\n" + "[Qibo 0.2.5|INFO|2024-03-20 10:46:15]: Using qibojit (numba) backend on /CPU:0\n" ] } ], @@ -55,7 +55,7 @@ "set_backend(\"qibojit\", \"numba\")\n", "\n", "# hamiltonian parameters\n", - "nqubits = 5\n", + "nqubits = 9\n", "h = 3.0\n", "\n", "# define the hamiltonian\n", @@ -70,23 +70,23 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 189, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "grid_search step: 0.02021181818181818\n", - "hyperopt_search step: 0.2796044748864459\n", - "polynomial_approximation step: 0.016462159944159827\n" + "grid_search step: 0.39394545454545454\n", + "hyperopt_search step: 0.017463998220887386\n", + "polynomial_approximation step: 0.0010293852957746303\n" ] } ], "source": [ "# generate data for plotting sigma decrease of the first step\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "s_space = np.linspace(1e-5, 0.6, 1000)\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", + "s_space = np.linspace(1e-5, 0.3, 100)\n", "off_diagonal_norm_diff = []\n", "potential = []\n", "for s in s_space:\n", @@ -96,31 +96,39 @@ " potential.append(dbi_eval.least_squares(D=d))\n", "\n", "# grid_search\n", - "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", + "#step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", "print('grid_search step:', step_grid)\n", "# hyperopt\n", - "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", + "#step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", "print('hyperopt_search step:', step_hyperopt)\n", "# polynomial\n", - "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + "#step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", "print('polynomial_approximation step:', step_poly)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 191, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "The minimum for cost function in the tested range is: 0.02021181818181818\n" + "The minimum for cost function in the tested range is: 0.39394545454545454\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -144,17 +152,17 @@ "plt.figure()\n", "plt.plot(s_space, potential)\n", "plt.xlabel('s')\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "#plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "#plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "#plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", "plt.title('First DBI step')\n", "plt.ylabel('Least squares cost function')\n", "plt.legend()\n", "plt.figure()\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "#plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "#plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "#plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", "plt.xlabel('s')\n", "plt.title('First DBI step')\n", @@ -200,7 +208,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -239,14 +247,28 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 183, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.5|INFO|2024-03-15 18:17:12]: Using qibojit (numba) backend on /CPU:0\n" + "[Qibo 0.2.5|INFO|2024-03-20 10:23:17]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3. -1. -1. -0. -1. -0. -0. -0.]\n", + " [-1. 1. -0. -1. -0. -1. -0. -0.]\n", + " [-1. -0. 1. -1. -0. -0. -1. -0.]\n", + " [-0. -1. -1. 1. -0. -0. -0. -1.]\n", + " [-1. -0. -0. -0. 1. -1. -1. -0.]\n", + " [-0. -1. -0. -0. -1. 1. -0. -1.]\n", + " [-0. -0. -1. -0. -1. -0. 1. -1.]\n", + " [-0. -0. -0. -1. -0. -1. -1. -3.]]\n" ] } ], @@ -256,7 +278,7 @@ "\n", "# hamiltonian parameters\n", "nqubits = 3\n", - "h = 3.0\n", + "h = 1.0\n", "\n", "# define the hamiltonian\n", "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", @@ -266,27 +288,28 @@ "# define the state\n", "state = 0\n", "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, state=state)" + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, state=state)\n", + "print(np.real(dbi.h.matrix))" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 175, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "grid_search step: 0.8585872727272726\n", - "hyperopt_search step: 0.3413442272248831\n", - "polynomial_approximation step: 0.028303853122485182\n" + "grid_search step: 0.33334\n", + "hyperopt_search step: 0.33819673200950817\n", + "polynomial_approximation step: 0.10712604100508318\n" ] } ], "source": [ "# generate data for plotting sigma decrease of the first step\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))\n", "s_space = np.linspace(1e-5, 0.9, 1000)\n", "off_diagonal_norm_diff = []\n", "fluctuation = []\n", @@ -309,19 +332,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The minimum for cost function in the tested range is: 0.8585872727272726\n" + "The minimum for cost function in the tested range is: 0.33334\n" ] }, { "data": { - "image/png": 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ES7lU/6LXA52d0jyuh7iA9fTmc6cHsgbDwH9nww18b731VsydOxdff/01vvvuO6xYsQLPP/887rrrLjidTlx66aV4+umne9/I4UCSO8i49IorkJqairfeegvJyclwOp0YM2YMrFZrvz9HSIhnr3Onj1FgGOaM7S6hUeZGIAzDIMagBQA0dVokXg0hxJe2lzbjaF0nQtRKLJie0et7qdF6XDkhBQCw+pcy3y/OnzEMYDD4/sOLLrkjR46EWq3G9u3b+cva29tx7Ngxr37U/Px8bN26tddlp389mNTUVNx+++349NNP8cADD+Ctt94CAEycOBEHDx5ERkYGRo0a1evDEBKCptZWHDp0CI8//jjmzJmDvLw8tLS0DPp4YWFhyMjIwPr1671apy9QcCOgmFDXTIymTusg1+ym0CswvX46ptdPh0JP/zsI8Uf/2lEJALhiQjLCdWcO+7vJHfB8c6AWjfTmJ6CEhYXhpptuwoMPPogff/wRBw8exC233AKFQuHVKIHbb78dJ06cwP33348jR47gn//8J9asWePx7e+99158++23KC0txe7du/G///0PeXl5AFzFxs3Nzfj1r3+N7du34+TJk/juu+9wy623wuFwICo8HDExMXjzzTdx/Phx/O9//8P999/v0eMuW7YMzz//PF5++WUcO3YMu3fvxiuvvOLxusVCr6YCigl1ZW68efJiGAaaOA00cRqaqUKIH7Lanfj+UB0A4KqJI/q8Tn5yOApHRMDhZPH1vhpfLo/4wAsvvICioiLMnz8f559/PmbMmIG8vDzodJ6fRkxLS8PatWvx5ZdfYty4cXj99dfx17/+1ePbOxwOLF68GHl5ebjooouQm5uLVatWAQCSk5OxefNmOBwOzJ07F2PGjME999yDiPBwKBQKKBQKfPSPf2DXrl0YM2YM7rvvPjz77LMePe5NN92ElStXYtWqVSgoKMD8+fO9zlqJgWGDrMKtvb0dERERaGtrQ3h4uKD3ff+/9uDT4io8cvFo3D5zpKD3TYhQqOZGWBuPNuCmd7cjLkyLbUvnQKHo+03KOz+X4s9flWBiWiQ+vWOGj1cpf2azGaWlpcjMzPQqKJAjo9GIlJQUPP/88/jd734n9XL653AAxcWuzydMAJTS13wO9Hvgzes3ZW4ExG1LNRs935ZyWpw4uvgoji4+CqdF3AIrQojwvjlQCwC4MD+h38AGAC4tTAIA7K5oRX2H2SdrI75RXFyMDz/8ECdOnMDu3btxww03AAAuv/xyiVcWvCi4EdBQtqVYO4vqVdWoXlUN1h5USTRC/J7TyeL7Eldwc9GYxAGvGx+uQ+GICADAhiMNoq+N+NZzzz2HcePG4fzzz4fRaMSmTZsEnRt18cUX9zrK3fPDm+2rYEFHwQUUY/C+oJgQX1MqlJiXPY//nAzd0foONHZaEaJW4qzMmEGvPzs3HvtOteHHw/W4bnKqD1ZIfGHChAnYtWuXqI/x9ttvo6urq8/vRUdTe4HTUXAjoFh35qbJSKchiHzpVDp8/ZuvpV5GQNhyogkAMDkjChrV4Inw80bH46X1x7DpWCOsdqdHtyEEAFJSUqRegl+hvywBDeUoOCHEf2096QpuikYOnrUBgLEpEYgN1aLTYsfOcpFn6xASxCi4ERBXc9PUaaU264QEOKeTxbZSV4AyLcuz4EahYDBjlOu6205ScEOIWCi4ERBXc2N1ONFhsUu8GkL6ZrQaYfirAYa/GmC0GqVejt86XNuBVpMNBo0SY1MiPL4dV5uzrbRJrKUREvQouBGQTq2EQeMq0KStKSJnJpsJJptJ6mX4tV3ubaWJ6VFQKz1/Kp2a6Sr+LK5ohcVOQ3YJEQMFNwLr3pqiomJCAtmeyjYAwIS0KK9uNzLOgNhQDSx2J/adahNjaYQEPQpuBMYVFTdS5oaQgLb3VCsAYNwIz7ekANfIFS57s72U6m4CwaxZs3DvvfdKvQzSAwU3AuMng9NxcEICVofZhhMNnQCAwhGRXt9+SoYruNlRRsENkb9ly5Zh/PjxUi/DKxTcCCyWG8FAmRtCAtb+qjawLJASGYK4MK3Xt+e2svZWttLJSiIKh8MBpzN4R/pIGtwsW7YMDMP0+khMHLiF+caNGzFp0iTodDpkZWXh9ddf99FqPcP3uvFivhQhxL/sddfbjEv1bkuKk5cUBo1SgRaTDZXNfXedJf7F6XTioYceQnR0NBITE7Fs2TIAwC233IL58+f3uq7dbkdiYiLeffddAK5trTvvvBN33nknIiMjERMTg8cff7xX4Gu1WvHQQw8hJSUFBoMBZ511FjZs2MB/f82aNYiMjMRXX32F/Px8aLValJeXo6WlBQsWLEBUVBT0ej0uvvjiXlO713z5JSJnz8bn//kPcnJyoNPpcMEFF6CyspK/3+XLl2Pv3r386/SaNWvE+UcUkOQdigsKCvDDDz/wXysHmEpaWlqKefPmYdGiRfjggw+wefNm3HHHHYiLi8PVV1/ti+UOituW8ni+lAKImBnBf06I2BSMAjPTZ/KfE+/tr2oFMLQtKQDQqpTISw7H3spW7DnVirQYvXCLC0ADtSxQKpTQqXQeXVfBKBCiDhnwugaNYUhrfO+993D//fdj27Zt2LJlCxYuXIgZM2bg1ltvxbnnnouamhokJbmGp65btw6dnZ247rrret3+d7/7HbZt24adO3fitttuQ3p6OhYtWgQAuPnmm1FWVoaPPvoIycnJ+Oyzz3DRRRdh//79yM7OBgCYTCasWLECb7/9NmJiYhAfH4/f/OY3OHbsGL744guEh4fj4Ycfxrx581BSUgK1wvX3bzKb8eSKFXjvvfeg0Whwxx134Prrr8fmzZvxq1/9CgcOHMA333zDv1ZHRAwtqPclyYMblUo1aLaG8/rrryMtLQ0rV64EAOTl5WHnzp147rnn5BPceNmlWBmixIQNE8RcEiG9hKhDsGHhBqmX4dcO1XQAAAqSw4d8H+NHRLiCm4pWXDYuWailBaTQFaH9fm9e9rxe40Tin4vvt83BzPSZvX73M17KQKOpsdd12CeGtk1YWFiIJ554AgCQnZ2NV199FevXr8dTTz2F3Nxc/P3vf8dDDz0EAFi9ejWuvfZahIZ2/1ypqal48cUXwTAMcnNzsX//frz44otYtGgRTpw4gQ8//BCnTp1CcrLrd2XJkiX45ptvsHr1an5wps1mw6pVqzBu3DgA4IOazZs3Y/r06QCAf/zjH0hNTcXnn3+Oa6+6ynU7ux2vvvQSzioqAuAKtPLy8rB9+3ZMnToVoaGhXr1Wy4Hkb9uOHTuG5ORkZGZm4vrrr8fJkyf7ve6WLVtw4YUX9rps7ty52LlzJ2w2m9hL9QjNlyIksJltDpQ1ud7x5yaGDfl+xqdFAug+dUX8W2FhYa+vk5KSUF9fDwC49dZbsXr1agBAfX09vv76a9xyyy29rj9t2jQwDMN/XVRUhGPHjsHhcGD37t1gWRY5OTm9poFv3LgRJ06c4G+j0Wh6rePQoUNQqVQ466yz+MtiYmKQm5uLQ4cO8ZeplEpMnjyZ/3r06NGIjIzsdR1/I2nm5qyzzsL777+PnJwc1NXV4S9/+QumT5+OgwcPIibmzHbmtbW1SEhI6HVZQkIC7HY7Ghsb+ZRfTxaLBRZLd6DR3t4u/A/SA82XIiSwHavrBMsC0QYN4kK9LybmjHNvaR2oaoPd4YTKi0aAwaZzaWe/3zt9sn39kvp+r3v6NmzZPWXDWldParW619cMw/AFvQsWLMAjjzyCLVu2YMuWLcjIyMA555zj8X07nU4olUrs2rXrjNKNntmfkJCQXgFSf8XqLMv2uh633tP1dZm/kDS4ufjii/nPx44di6KiIowcOZLfu+zL6f/Y3P+8/v4nrFixAsuXLxdoxYPjam6aTVY4nCyUioF/ORxGB7ZmbAUATCubBqWh/5ojQoRgtBqR8VIGANeT+1BrDILV4VrXG6TchLBhPflnxBhg0ChhtDpwstGInIShZ4ECnTe/o2JddzhiYmJwxRVXYPXq1diyZQtuvvnmM66zdevWM77Ozs6GUqnEhAkT4HA4UF9f71VQlJ+fD7vdjm3btvHbUk1NTTh69Cjy8vL469kdDuzcuRNT3dtSR44cQWtrK0aPHg3AlRFyOPyrm7as3ioYDAaMHTu2VyV3T4mJiaitre11WX19PVQqVZ+ZHgBYunQp2tra+A+uAlwsUXo1GAZgWaDF5Fn2xtZog61RHttqJDg0mhrPqDUgnjlS66q3Gc6WFOAaopmX5KrZKakWN6NMpHfrrbfivffew6FDh3DTTTed8f3Kykrcf//9OHLkCD788EO88soruOeeewAAOTk5uOGGG7BgwQJ8+umnKC0txY4dO/D0009j3bp1/T5mdnY2Lr/8cixatAg///wz9u7di9/+9rdISUnB5Zdfzl9PrVLhrnvuwbZt27B7927cfPPNmDZtGqZOnQoAyMjIQGlpKfbs2YPGxsZeuyFyJavgxmKx4NChQ31uLwGuPcjvv/++12XfffcdJk+efEZKkKPVahEeHt7rQ0wqpQJRes+3phQhCkw5MAVTDkyBIkRW/zsIIX04UucKbkYPM7gBgHx3QXJJDQU3ge78889HUlIS5s6dyxcF97RgwQJ0dXVh6tSpWLx4Me666y7cdttt/PdXr16NBQsW4IEHHkBubi4uu+wybNu2DampqQM+7urVqzFp0iTMnz8fRUVFYFkW69at6/Waqdfp8PBDD+E3v/kNioqKEBISgo8++oj//tVXX42LLroIs2fPRlxcHD788EMB/kXEJem21JIlS3DppZciLS0N9fX1+Mtf/oL29nY+ql26dCmqqqrw/vvvAwBuv/12vPrqq7j//vuxaNEibNmyBe+8847s/qGjDRo0G63u+VIDPwEyCgaGAtoWIMRfHBYocwMA+ZS5CQg9+81wPv/8815fd3V1obW1Fb/73e/6vA+1Wo2VK1fitdde6/f7y5cv77fMYuHChVi4cOEZl0dFRfGvoQO56sorcdU11/T5Pa1Wi08++WTQ+5ATSYObU6dO4de//jUaGxsRFxeHadOmYevWrUhPTwcA1NTUoKKigr9+ZmYm1q1bh/vuuw9/+9vfkJycjJdfflk2x8A50QZ3l2IPt6UIIf6h2WhFQ4crJS9EjQyXuTlU095nkSfxf06nE7W1tXj++ecRERGByy67TOolBQVJg5ueaa++9NUFcebMmdi9e7dIKxJGtHtbqsWDLsVOqxPlfy0HAKQ/mg6FhramCJErrpg4LVoPg3b4T585CWFQKhg0Ga2o77AgIVw3+I2IX6moqEBmZiZGjBiBNWvWQKWSvL1cUKB/ZRFEcZkb4+BFwqyNRflyV3CT9mAaoBF1aYSQYRCqmJijUysxMs6Ao3WdKKlup+AmAGVkZAw6P6yvbS1fWXjppVh46aWSPb5YKE0gghh3cOPpaSlCfEnBKDA5eTImJ0+m8Qte4iaBZ8f33zHXW/yJKSoqJkQwlLkRQXfmhoIbIj8h6hDsWLRD6mX4pZMNrs7EWXHCBTf5SeH4z55qKiomRED0tk0E0QbXETsKbggJLKWNXHAj3AlHLnNzqJaCG0KEQsGNCLg+NxTcEBI4TFY7atrMAICsWOGCG65+p7zJBIvdv7rAEiJXFNyIIJpqboiMmWwmZKzMQMbKjH6nJ5MzcVtS0QYNIvXCVf7Hh2kRplPB4WT5zBAhZHgouBFBz8zNYFXyhPgay7IobytHeVs5/X56gd+SEjBrA7jm4nE9c47W9T8gkhDiOQpuRMBNBrfYnTBZKc1MSCDgMjeZAgc3QPfpq+Pu0Q4kuKxZswaRkZFSL8Mjy5Ytw/jx4726DcMwZ3RsFhsFNyIIUSuhVbn+aanuhpDAUNroyqoIeVKKk02ZG+InlixZgvXr10u9jEFRcCMChmGo7oaQAHOyUfzMzbF6ytwQeQsNDUVMTIzUyxgUBTcioRNThAQOlmVR6t6WGingMXAOV3NTRiem/NKsWbNw55134s4770RkZCRiYmLw+OOP8zVtLS0tWLBgAaKioqDX63HxxRfj2LFjfd5XWVkZFAoFdu7c2evyV155Benp6WBZFhs2bADDMFi/fj0mT54MvV6P6dOn48iRI71u89prr2HkyJHQaDTIzc3F3//+917fZ1QqvPHpp5h/333Qh4UhLy8PW7ZswfHjxzFr1iwYDAYUFRXhxIkT/G1O35basWMHLrjgAsTGxiIiIkI2I5IouBEJZW4ICRwNnRZ0WOxQMEBajF7w+08I1yJMSyem+uMwOrz+cNqd/O2ddqfr8i7HoPc7VO+99x5UKhW2bduGl19+GS+++CLefvttAK6J3Tt37sQXX3yBLVu2gGVZzJs3DzbbmSN6MjIycP7552P16tW9Ll+9ejUWLlzYa7jqY489hueffx47d+6ESqXCLbfcwn/vs88+wz333IMHHngABw4cwO9//3vcfPPN+PHHH3vd75/feQcL5s3Dnl27MHr0aPzmN7/B73//eyxdupQPsO68885+f+6Ojg7cdNNN2LRpE7Zu3Yrs7GzMmzcPHR3SZiGpQ7FIuOCmqXOQ4IYB9Pl6/nNCxMYwDPLj8vnPyeC4rM2IKD20KqXg988wDLITQrG7ohXH6joxOjFc8MfwZ5tCN3l9m/x/5yP+2ngAQONnjSi5rgQRMyMwYcME/jpbM7bC1tg7wJjFzhrSGlNTU/Hiiy+CYRjk5uZi//79ePHFFzFr1ix88cUX2Lx5M6ZPnw4A+Mc//oHU1FR8/vnnuPbaa8+4r1tvvRW33347XnjhBWi1Wuzduxd79uzBp59+2ut6Tz75JGbOnAkAeOSRR3DJJZfAbDZDp9Phueeew8KFC3HHHXcAAO6//35s3boVzz33HGbPns3fx83z5+O6Cy4AcnLw8MMPo6ioCH/84x8xd+5cAMA999yDm2++ud+f+7zzzuv19RtvvIGoqChs3LgR8+fPH8K/pDAocyMSTzM3Sr0SUw9OxdSDU6HUC/+kScjp9Go9Dt5xEAfvOAi9WvgsRCAqb3b1A0oXIWvDyY53bU0doxNTfmnatGm93iwUFRXh2LFjKCkpgUqlwllnncV/LyYmBrm5uTh06FCf93XFFVdApVLhs88+AwC8++67mD17NjIyMnpdr7CwkP88KSkJAFBfXw8AOHToEGbMmNHr+jNmzDjjMQuzs/nPExISAABjx47tdZnZbEZ7e98dtOvr63H77bcjJycHERERiIiIQGdnJyoqKvq8vq9Q5kYk3TU3g08GJ4TIW6U7uEmLFjG4SeCKiunE1OnO6TzH69sw2u5AI/bKWNd9nPZ2flrZtOEubchYlu03c6rRaHDjjTdi9erVuOqqq/DPf/4TK1euPON6arWa/5y7L6fTecZlAz2mWqU64/qD3W9PCxcuRENDA1auXIn09HRotVoUFRXBapW2JIMyNyLh5ku1UEExIX6vwifBDXccnDI3p1MalF5/KFTdL28KlcJ1eYhy0Psdqq1bt57xdXZ2NvLz82G327Ft2zb+e01NTTh69Cjy8vL6vb9bb70VP/zwA1atWgWbzYarrrrKq/Xk5eXh559/7nXZL7/8MuBjDsWmTZtw9913Y968eSgoKIBWq0VjY6OgjzEUlLkRiaeTwR0mB3ZN2QUAmLRjEm1NEdGZbCZMeWsKAGDHoh20NeUBLnOTKmJwk+PO3HAnpsSo7SHiqaysxP3334/f//732L17N1555RU8//zzyM7OxuWXX45FixbhjTfeQFhYGB555BGkpKTg8ssv7/f+8vLyMG3aNDz88MO45ZZbEBIS4tV6HnzwQVx33XWYOHEi5syZgy+//BKffvopfvjhh+H+qL2MGjUKf//73zF58mS0t7fjwQcf9HqtYqDMjUi4mpvmwU5LsYCpxARTiQmgTvjEB1iWRUlDCUoaSmj8gocqmrsAiJu5SQzXIdR9YooLpoj/WLBgAbq6ujB16lQsXrwYd911F2677TYArpNOkyZNwvz581FUVASWZbFu3bpe2z99+d3vfger1drrFJSnrrjiCrz00kt49tlnUVBQgDfeeAOrV6/GrFmzhvLj9evdd99FS0sLJkyYgBtvvBF333034uPjBX2MoWDYIHt2a29vR0REBNra2hAeLt6JhMO17bho5SbEGDTY9ccL+r0e62DRuqkVABB5TiQYJZ1eIeIyWo0IXeHKEnQu7YRBI3zflkBistqR/3/fAgD2PnEhIkIGfkEajktf+Rn7q9rwxo2TMLcgUbTHkSOz2YzS0lJkZmZCp9NJvRyvzJo1C+PHj++zLmY4nnzySXz00UfYv3+/oPfLcziA4mLX5xMmAErps4UD/R548/pN21IiidZ3n5ZyOlkoFH0HLYySQdSsKF8ujRDihUp31iZcpxI1sAGArDgD9le18XOsSHDq7OzEoUOH8Morr+DPf/6z1MvxS7QtJZJId3DjZIF2M52YIsRf8SelRDwGzuFGO5xsoBNTwezOO+/E2WefjZkzZw5pS4pQ5kY0GpUCYVoVOix2NBmtfLBzOqfNiZo3awAASbclQaGmeJMQOfHFSSkON5TzJHUp9isbNmwQ9P7WrFmDNWvWCHqfwYZeSUUUHeremhrgxBRrZXHszmM4ducxsNagKn8ixC9wwU1qlA+CG3fmhkYwEDI8FNyIiIZnEjliGAbpEelIj0in8Qse8MUxcE6Weyhns9GK1iCdSxdkZ1zIaYT6/0/bUiKi4ZlEjvRqPcruLZN6GX6jssV321J6jQpJETrUtJlxosGISel9b2cHIu5YtMlkkkWfFCINrrOxcpgntyi4ERGNYCDEv7Es270t5YPgBnAVFde0mXGyoROT0oPnJKVSqURkZCQ/G0mv11NmUWyOHlPQzWbJj4I7nU40NDRAr9dDpRpeeELBjYi4EQzNRovEKyGEDEVDpwVmmxMMA6RE+iabkBVnwC8nmoKy7iYx0dXbhwtwiMicToAblVBWBiikr1RRKBRIS0sbdmBLwY2Iog1aAJS5IfLSZevCuWvOBQD8tPAnhKhpC6A/XI+b5IgQaFS+eeLPinWfmArCXjcMwyApKQnx8fGw2eh5U3QmE3DJJa7Pd+8G9NKPYtFoNFAIEGRRcCMifngm1dwQGXGyTuys3sl/TvrHFROPiPJdAMgVFZ9sDN5eN0qlctg1F8QDDgdQXu76XKsF/Kwz9ECkz0EFMDotRYh/82WPG87IuO4Bmg4nnRwiZCgouBFRtIeTwQkh8nSqhcvc+C64SY50bYFZ7U5UtXT57HEJCSQU3IgoyjB4Ez9CiHxVt5oBACk+3JZSKhhkuEc9BPPWFCHDQcGNiGLcwU2HxQ6rnWobCPE31a3uguJI39YiBHNRMSFCoOBGROE6Nbhh4MHabZQQf8WyLKrcwc2ISN+eIqGiYkKGh4IbESkUTHdR8QDBjTpWDXWs2lfLIgSx+ljE6mOlXoasNRmtsNhdPW4SIrQ+fWx+gCZlbggZEjoKLrIogwZNRiuaO/sObpQGJWY0zPDxqkgwM2gMaHiwQeplyB63JRUXqoVW5dtjyVzmpiwIG/kRIgTK3Igs2oPMDSFEfrrrbXzf5JCbDl7dZkaX1THItQkhp6PgRmR0HJwQ/3TKfQzblyelOJF6DaL0rq3qsibK3hDiLQpuRNZ9HLzvVuKOLgeKZxWjeFYxHF30Do2Ir8vWhVlrZmHWmlnoslEflf7wx8AlyNwArgGaAIJyxhQhw0U1NyLj3n31O4LBCbRtbOM/J0RsTtaJjeUb+c9J3/htqQhpWtJnxoZid0UrBTeEDAEFNyIbbFuK0TLI/3c+/zkhRB6q27htKWmGCfLHwenEFCFeo+BGZNxR8P4yNwqVAvHXxvtySYQQD3CjD3zdwI/TvS1FvW4I8RbV3IiMy9zQZHBC/IfZ5kCTO9tKNTeE+B8KbkQ2WEGx0+5E/cf1qP+4Hk4a0UCILHD1NgaNEhEh0jTYzIhxBTctJhvNpyPESxTciIzvc9PPkxNrYVFyXQlKrisBa2F9uTRCSD+4k1LJkSFgGGlq4UI0SiS5i5lL6Tg4IV6h4EZkUQbXu74um4OacRHZ0Kv10KulKZT1B1WtJgDSNPDrid+aoqJiQrxCBcUiC9WqoFYysDlYtJisCNFI+2RJiEFjgPFRerEcSFWPzI2UMmMN+OVEE9XdEOIl2WRuVqxYAYZhcO+99/Z7nQ0bNoBhmDM+Dh8+7LuFeolhegzPpH1zQvwCV3MzQoLuxD35c1FxY6cFf/mqBHNf/AkXrfwJT39zGO3mvmsPCRGaLDI3O3bswJtvvonCwkKPrn/kyBGEh4fzX8fFxYm1NEFE6TWo77DQiSlC/ET3XClpjoFz+F43fhbcHKhqwy1rdqC+w8Jfdri2A1/sqcaHi6YhLYa2RIm4JM/cdHZ24oYbbsBbb72FqKgoj24THx+PxMRE/kOp9O3EXm9xdTeUuSFyYLabcck/L8El/7wEZrtZ6uXIEhfcJEVInbkJBeCaDu50+seBg8pmE256dzvqOyzIjg/FazdMxEvXj0dqdAiqWrtwwztb0dZFGRwiLsmDm8WLF+OSSy7B+eef7/FtJkyYgKSkJMyZMwc//vjjgNe1WCxob2/v9eFrfK8bCm6IDDicDqw7tg7rjq2Dw0lF7qdjWRa17a6gL0mi0QucEVEhUCkYdNkcqOuQfyDqdLJ44OO9aDJaMSYlHJ/eMR0Xj03C5eNTsPb26UiL1qOyuQuPf35A6qWSACdpcPPRRx9h9+7dWLFihUfXT0pKwptvvom1a9fi008/RW5uLubMmYOffvqp39usWLECERER/EdqaqpQy/dYd5dierdCiNy1ddlgtrl6TiWESxvcqJUKpEW7tnD84cTU37eWY3tpM/QaJVb9ZhLCdN09guLDdXj51xOgVDD4cm81fjnRKOFKSaCTLLiprKzEPffcgw8++AA6nWdPILm5uVi0aBEmTpyIoqIirFq1Cpdccgmee+65fm+zdOlStLW18R+VlZVC/Qgeoy7FhPiPmjZXhiTaoIFOLf2WN1dULPe6mw6zDS/+cBQA8MjFo/usqxmfGokbzkoDAKxYdxgs6x9bbcT/SBbc7Nq1C/X19Zg0aRJUKhVUKhU2btyIl19+GSqVCg6HZ+nyadOm4dixY/1+X6vVIjw8vNeHr9FpKUL8B7cllShx1objLyemVm8uQ6vJhpFxBtxwVnq/17tnTjZ0agX2V7Vhy8kmH66QBBPJgps5c+Zg//792LNnD/8xefJk3HDDDdizZ4/HRcLFxcVISkoSebXDQ5kbQvxHrTtzkyhxvQ0nM07+wU2H2Ya3Np0EANx7fg6Uiv67OseEanHNpBEAgLc3lfpkfST4SHYUPCwsDGPGjOl1mcFgQExMDH/50qVLUVVVhffffx8AsHLlSmRkZKCgoABWqxUffPAB1q5di7Vr1/p8/d7g5ks19zNfihAiHzVyC278IHPzWXEVOsx2jIwz4JKxg7/Z/N3ZWfjHtgr873A9ShuN/M9IiFAkPy01kJqaGlRUVPBfW61WLFmyBIWFhTjnnHPw888/4+uvv8ZVV10l4SoHx82XotNShMhfnTu4SZLJtlSW+zh4RbMJNof8huuyLIv3t5QDABYUZUAxQNaGkxlrwMwcV3+yz3afEnV9JDjJookfZ8OGDb2+XrNmTa+vH3roITz00EO+W5BAIvXuPjcmK1iW7TWIT2lQYhY7S6KVkWBk0BjAPkGFnP2pcdfcJMgkc5MQrkWIWokumwOVzSZkxYVKvaRetpxowvH6Thg0Slw1McXj2105IQUbjjTg0+Iq3Ht+jkdBESGeknXmJlBwNTdWuxMmGp5JiKzVtnEN/OQR3DAMI+utqU/cmZfLJ6T0Ovo9mAvzExGqVeFUSxd2lreItTwSpCi48QG9RgmNyvVPTSemCJE3ruZGLsENIN+iYrPNgW8P1AIArprgedYGAEI0Slw8JhEA8OXeasHXRoIbBTc+wDBMd93NaSemHGYHDl57EAevPQiHmbI6RHxmuxnXfnwtrv34Whq/cBqjxY4Osx0AkCjx6IWesmTa6+aHQ3UwWh0YERWCSemejc/p6eKxruBm/aE66nlDBEXBjY9EGfrpUuwAGj5pQMMnDQDFNsQHHE4HPin5BJ+UfELjF07D9bgJ1aoQqpVPSSK/LSWzLsX/2ePKuFw2LrlXLaGnpo+MRYhaieo2M0pqfD8ahwQuCm58JNo9PPP0E1OMhkH2q9nIfjUbjIYK6giRktx63HDkWHPTZrJhw5F6AMAVXm5JcXRqJc7JjgUA/FBSL9jaCKHgxkf661KsUCuQsjgFKYtToFDT/w5CpFQrw3oboDu4qW03w2ixS7walx8O1cHmYJGbEIachLAh38/5+Qn8/REiFHo19RHqUkyI/HHbUlIPzDxdpF7DP4eUNckje8MFI3MLEoZ1P7Nz4wEA+6va6MAFEQwFNz7SX+aGdbBo2dCClg0tYB1UUEeIlGpkdgy8JzltTVnsDvx0tAFAd+ZlqOLCtMh1Z362nKBZU0QYFNz4SH+ZG6fZib2z92Lv7L1wmuXXfZSQYFLbZgEgv5obQF5FxVtPNsNodSAhXIsxyRHDvr/po2IAAJtPNA77vggBKLjxGb5LMaVdCZGt2nbK3HjihxLXltScvARBOgufPcpVVLz5OAU3RBjyOesY4PjMDQ3PJBLTq/XoXNrJf066cQXFcqu5AeTT64ZlWb7e5oK84W1JcaZmRkOpYFDeZMKpFhNGRNHvJRkeCm58hK+58dOC4tJGI/6zpwqtJhsmpEXikrFJUCkp8eePGIaBQUNTmE9nsTvQ2On6+0ySUQM/Dtel+GRD5xkz6nzpeH0natrM0KkVKBoZI8h9hunUKBwRgeKKVmwvbabghgwbBTc+wmVuWvsYnil3b286iRX/PQyH01XwvOYX4PWNJ/H2TZOREim/FwFChqK+3VVvo1EpEKX3fEaSr2TEuIKbdrMdLSYb/5zia1tOuop+J6dHQ6dWCna/k9OjUFzRil3lLbhq4gjB7pcEJ3rr7SNc5sbmYNEpkz4Vnvj7ljL85etDcDhZnJMdi4XTMxCpV+NQTTuue30LGjosUi+ReMlit2Dh5wux8POFsNjp/x+HOwaeGK6T5ZsPnVrJv5kobeyUbB2/HHcFN0JlbTjc+IZdNESTCICCGx8J0SgR4n6X4y91NyXV7fjTVyUAgHvPz8b7t0zFsssK8N97zkFmrAFVrV2468PdcDrpCLs/sTvteG/ve3hv73uwO/0n0BZbjUy7E/fEFRWflOjElNPJYmupOMHNRHdwc6SuA+1m/3iOJPJFwY0PcWlkf6i7YVkW//efA7A5WFyYn4B75mTz72aTIkLw1oLJ0GuU2HqyGR/uqJB4tYQMX51MuxP3JPWJqUO17Wg12RCqVaEwZfhHwHuKD9MhLVoPlgWKK1oFvW8SfCi48aGofuZLydH6Q/XYWd4CvUaJ5ZcXnJGmHxUfiiUX5gIAnvnmCDrondaAHE4WFU0mekcqY3zmRoYnpThSBzdck70pGVGiHCiYTFtTRCBDKihev3491q9fj/r6ejidvRvPvfvuu4IsLBD116VYjt746QQA4Mai9H5Pjtw0PQP/2FaOEw1GrN5chrvnZPtyiX7j8+Iq/HXdIdR3WKBgXBOUl11WgEi9NAWhpG9cjxtZb0vFySO4mT4yVpT7n5QRhU+Lq7CrvFmU+yfBw+vQe/ny5bjwwguxfv16NDY2oqWlpdcH6R8X3Mh9vtTuihbsKGuBWsnglhmZ/V5PqWD4gObtTScpK9GHNzaewL3/2oP6DguUCgZOFvh8TzVueHsbZbtkRq5DM3vK6pG58XWtm93hxLZSV9AhdL0NhysqLq5ohd0R2B3bD9e2Y/2hOnreFInXmZvXX38da9aswY033ijGegIaX3Mj88zNB1vKAQCXj08ZtJnZ/MJkvPK/4zhe34l/76jEredk+WKJfmHDkXqs+O9hAMAds0binvOzUVLdjkXv78TB6nY89tkBvPzrCRKvknBq+YJi+bY3SIkMgVrJwGJ3oqbd7NNWDAeq29FpsSMiRI28pHBRHiMnPgyhWhU6LXacaDAiN3Ho08blyu5w4o//OYgPt7tqFSNC1HjjxkmYliVOwBisvM7cWK1WTJ8+XYy1BDx/yNyYrHZ8c7AWAPDrqWmDXl+pYHDzjAwAwD+2VdDJKTeT1Y6H1+4DANw4LR0PXTQaWpUSE9Ki8OaCyVAwwBd7q/HjkXqJV0oAV01UnbutgZxrblRKBdKiXQ3ufD1j6hf33Kez3N2ExaBQMMhPdgVO+6vaRHkMqT3//VE+sEkI16Kty4bbP9iFUy0miVcWWLwObm699Vb885//FGMtAS+aLyjuTkMq9ApMr5+O6fXTodBLX9/9fUkdTFYH0mP0mJgW6dFtrhifgjCtCqWNRhp85/bmTydR125BanQIHrskr9f3JqZF4Wb3dt9z3x4By/o2INSr9ahfUo/6JfU0fsGtsdMCh5OFUsEgLkwr9XIGlBkbCsD3vW66623EzTCMdZ/C2n+qVdTHkcLB6ja8tsFVz/jS9eOx8cHZGDciAq0mG/7y1SGJVxdYvN6WMpvNePPNN/HDDz+gsLAQanXvTp4vvPCCYIsLNFF9HAVnGAaaOPkUln66uwqAK2DxtJGZQavC1ZNGYM0vZfjntgqckx0n5hJlr6HDgjc2ngQAPHzR6D67uN45exQ+2l6Bg9XtWH+oHufnCzOjxxMMwyDOENz/j07HbUnFh2lFy0oIJSvOABzy7Ywpq92JnWWumsoikYqJOYUj3MFNAGZunvv2CABgfmESLh+fAgB49tpxmLvyJ3xzsBYHqtowRuAj9sHK61TBvn37MH78eCgUChw4cADFxcX8x549e0RYYuCI5ralZFpz02628VN5Lxuf7NVtr53sape+/nB90BfI/X1rObpsDowbEYFLxib1eZ0ogwY3TEsHALy/tdyXyyN9qJHxwMzTcWMYfHliau+pVnTZHIgxaJCTECrqY3Ev7iU17QFVVHy8vhM/HmmAggEecLfRAICchDBcWuh6vn1/S5lEqws8XmdufvzxRzHWERS4zE3PmhunxYnj9x8HAIx6YRQUWum2pjYdbYTdySIrzoCRcd49geUnhSM7PhTH6jvxzf5aXDclVaRVypvZ5sA/3MHKonOzBsx+3XBWGt7adBI/HW1ARZMJaTG+2SKy2C24/9v7AQAvzH0BWpW8t2F8obbNdQxczielOFL0uuFGLkwbGSP6aIrMGANfVHy8oROjE8UpXvY1rs7mvNEJ/P9Dzo1F6fhibzW+2leDJy4tgEFLYx+Ha1ivpKdOnUJVVZVQawl40XxwY+MLb1k7i+pV1aheVQ3WLm0x7vrDdQCAOaPjvb4twzC4YoIrzfpZcfD+Tny5txpNRiuSI3S4qCBxwOumxxgww53i/3JftS+WB8A1fmHVzlVYtXMVjV9wq3UPzZRzjxvOSHevm8pmE8w2h08ekysmFrveBjitqPhUYGxNOZwsPnc/L95w1pkHNSanRyEr1gCT1YHvSmp9vbyA5HVw43Q68ac//QkRERFIT09HWloaIiMj8ec///mMhn6kt0j3pGGHk0WH2fWiwqgZpD+RjvQn0sGopdvrdzhZbDjSAACYkze0+o/L3VtZW0ubgnag5sc7TwEAbpiW7lEH10vHubatvtpXI+q6yMD8KXMTF6ZFuE4FJ+ubGVNmm4MfhyBW877TcaMdDgRI3c2OsmY0Ga2I1KtxTvaZ/4YMw+CSQtdzwfcldb5eXkDyOrh57LHH8Oqrr+Kpp55CcXExdu/ejb/+9a945ZVX8Mc//lGMNQYMrUoJg8ZVXMoVFSs0CmQuy0TmskwoNNJtSe091YpmoxXhOhXfSMtbI6L0KBwRAZYF1h8Kvj/QymYTtpc1g2GAqyameHSbuQWJUCkYHKppx4kG6SY9Bzt/qrlhGIbv/3KsvkP0x9tV3gKrw4nEcB0yfLR1OtZdVLwvQIKbb93tNeaMTuj3Tc/57jeVG480wGL3TUYukHn9avree+/h7bffxh/+8AcUFhZi3LhxuOOOO/DWW29hzZo1IiwxsETJtJFfz7bq6mHMjLnQfernuyB898Ftx00fGdPvyIrTReo1ONv9Tu5ryt5Ipq6d604s3wZ+PWUnuIKbI7XiBzc9j4CLXW/DKUh2BTeHazr8vncWy7L47qDr+fDCgv6z4mNTIhAfpoXR6sCOUur2P1xev4o1Nzdj9OjRZ1w+evRoNDfTPJDB8HU37uCGdbIwHjTCeNAIVsI/Yu4JbLht1S9015n8fLwRnZbgqedgWRaf73EFN1dOGOHVbS8e4/o3o4Z+0mBZls/c+MO2FADkuoObo3XiBzdcvc00H9TbcDJi9NCoFOiyOVDR7N/N7Q7VdKCqtQs6tQLnDtAmQ6FgcPYo1xudX6hf2LB5HdyMGzcOr7766hmXv/rqqxg3bpwgiwpk/PBM97aUs8uJHWN2YMeYHXB2SVOzZLE7sLNcmJkx2fGhyIjRw2p34qejDUIszy+caOjEyQYjNEoF5g7w7qwvXF+gvZWtaDMF9zF6KbSabLDYXX978eH+cXIshw9uxN3K7LTYsc9d1OuLYmKOSqngj5wf9kF2SkxcoFKUFYMQzZk9r3riAsgtJ5tEX1eg8zq4eeaZZ/Duu+8iPz8fv/vd73DrrbciPz8fa9aswbPPPivGGgMKl7lpldEIhr2VbTDbnIgN1SA7fng9LBiG4bM33x0Mnqp/bhuuaGQMwnTqQa7dW3JkCEbFh8LJgjo8S4DL2sQYNNCqBn7xkQvuhb+i2QSTVbwM6Y6yZtidLFKjQzAiyrfdrLkj4Idr2336uELzJite5J4vte9UW1BlvsXgdXAzc+ZMHD16FFdeeSVaW1vR3NyMq666CkeOHME555wjxhoDCp+5McrnHTr3x3dWljB76nxh3NEGOPx8v9xTnuypD4RLV286Jn62K0QdgtJ7SlF6TylC1P5RYyImrt7GH46Bc2JCtYgNdT2XHBMxe7OVq7fJ8s0pqZ5GJ/qurkgsdocT292T1D0ZjJkarUdKZAgcThb7AnD8hC8NqVNQcnIynnzySaHXEhS650vJJ3Ozq8JVvDYtM1qQ+5uQFokwrQotJhsOVLVhXGqkIPcrV/XtZuypbAXQHdh565ycWLy7uRQ/HW0Ey7KiFm4qGAUyIjNEu39/42/1Npzs+DA0djbhaF2HaH9jvwhUizcU3Zkb/w1uDla3o8NiR5hOxRdJD2Z8aiSqWruwt7LNZ0fvA5FHwc2+ffswZswYKBQK7Nu3b8DrFhYWCrKwQNXXfCkpOZ0s9riDmwlpQzsCfjq1UoHpo2Lw7cE6bDzaEPDBzffuY+/jUiOHfJT4rMxoqBQMqlq7UNXa5fMtgGDG9bjxh2PgPeUmhmHLySbRiorbTDYcrHbV20gR3HDH3cuajOiyOgatV5Gjre7aGW8mqY9LjcDX+2uw1/2GiQyNR8HN+PHjUVtbi/j4eIwfPx4Mw/Q5yZhhGDgcdD5/IHKbL3Wy0Yh2sx06tYJPAwvh3Jw4fHuwDj8dbcDdc7IFu185Wn/IdcrpwmEMv9RrVChIicDeylbsLGsRNbixOqx4bP1jAIAn5zwJjVI+g1ulUNvun5kbsYuKt5U2wcm6BnVKEfjFhbm23ho7raJmp8S0s9z1xnGqF1nxcSMiAbh6j5Gh8yi4KS0tRVxcHP85GbpIvbwyN8XurE3hiEiPOup6iqshKa5sRbvZhnAvi2z9hdXu5N+dzcod3qTtKelRruCmvJkfZSEGm8OG57Y8BwBYNmtZ0Ac33LZUop/0uOFwRcViZW5+6dHfRiq5iWFoPN6Ew7XtfhfcsCzLb1d7kxUfkxIBBeP6vaxrN/tdRlEuPHo1S09P52sAysvLkZKSgvT09F4fKSkpKC+n6caDOb3PjdSK+T++SEHvNzVaj6xYAxxOFr8cD9wTQMUVLTBZXdOS84Y54G9yhuvd3c4yauDlS7VccONnLyJcI7+aNjPauoQ/oMAF7UUSFBNz/LnuprrNjIYOC5QKBmM8rLcBAINWxWflaGtq6Lx+qz579uw+m/W1tbVh9uzZgiwqkEW5C4pbu2yyOEnEzYyZkCpMvU1P5+a4Mhk/HQvc4GazO3CbPioWCg/31PvDjb04UtdB/W58iA9u/GxbKiJEzW+lCZ29aeq08AHFtCxhDhoMBVd3c7jG/4KbPe7n1tGJYV7XC3FbU/sCZHCoFLwObvo7ydHU1ASDwdDHLUhP3FFwloUo77a8YbTYccTdQ0LozA3Qnc7mjpMGop/dwc05o4b/7jYuTIvMWANYFthdQdkbX+i02NHh7ifib8ENAOQluTIbJdXC9oLZetL1BnZ0YhhiQqVrbJjXo9dNX3Wecran0vU3PH4I22l5Se6gzg8zVnLh8VHwq666CoCraHjhwoXQart/4R0OB/bt24fp06cLv8IAo1YqEKZTocNsR4vJigi9dPv8+061wckCKZEhouzrnpUZA4ZxFS0H4t5xu9mGve53VjP6mPQ7FBNSI1HaaMTeU62YPTpekPsk/eOyNmE6FUK1Q+qMIakxyeH43+F6wadn8111Jay3AYDshFAoGKDFZENDhwXxfvQcsrfS9f9kKMFNDtfjp86/GxhKyePMTUREBCIiIsCyLMLCwvivIyIikJiYiNtuuw0ffPCBmGsNGHKpu+Gq8celer4f7I0IvRoFya53XlsDsJ341hNNcDhZZMYakBIpTJBa6J6GvJ/S0T5R66c9bjgFKa7flwMCZ2628PU20gY3OrUSadGuk4PH68UdNSEkh5PFgeqhBzdcrVFlcxd1Kh4ij9+qrF69GgCQkZGBJUuW0BbUMETpNShvMrkmgw/vgM2wHHQ/IXraXGooirJicKCqHVtONOHy8eKdAJICtyV1tgBbUpyx3F57VZvozfwIUOPuceNvJ6U4Y9zBzbG6DphtDujUw+8FU9duxskGIxSMq2u51EbFh6GsyYRj9Z2YLuDfmpjKm4wwWR3QqRXIivN+pE20QYO4MC0aOiw4WteBiQL1IAsmXtfcPPHEExTYDBOfuTFZoQhRYMqBKZhyYAoUIcIdxfZEifudBZddEQPXcjwQMzdccDNDwCfc/KRwKBUMGjosfP8VoYWoQ3DgDwdw4A8Hgn78QvdJKf8YmHm65AgdovRq2J2sYEXFP7sPAIxJiUBEiPQtHEa55935U+ampMb1xjE3Mdzj5n2nC4TxE1Ia0ibzJ598gn//+9+oqKiA1dp7a2X37t2CLCyQ9ZwvxSgYGAp8HyyarHacbDQCEDdzMyUzGgoGKGsyoaatC0l++g75dI2dFpxscP37CZm6D9EokR0fisO1Hdh3qk2Ufy8Fo0BBfIHg9+uPatr9s8cNh2EYjEmJwKZjjThQ1Y5Cd+ZvODaLkJEcDm6Y77F6/3mRP+QObvKTht4YNTchDJuONVJwM0Repwpefvll3HzzzYiPj0dxcTGmTp2KmJgYnDx5EhdffLEYaww4UXr3fCkJG/kdqukAywLxYVrEhYn3rjVcp8ZYd+p8SwCdmtrt7jyakxCKCL2w726p7sZ3/L3mBuh+c8LVeAwHy7KibLcOR3fmxijxSjzHnV7LTxp6Vpw/Bu/nU9Gl4nVws2rVKrz55pt49dVXodFo8NBDD+H777/H3Xffjba2of9xrVixAgzD4N577x3wehs3bsSkSZOg0+mQlZWF119/fciPKRV+vpTRCqfVidJlpShdVgqn1emzNXBbUvkibklxpo0MvK2p3e4eFmLshXN1N/sFPgHDsTqsWLZhGZZtWAarQx7NJKXirz1uehqT4vobPihAUfGx+k7Ud1igUyswMV0edR4j3cFNY6cFrTLp7D4YblsqT4Dgxp+24+TE6+CmoqKCP/IdEhKCjg5XyuzGG2/Ehx9+OKRF7NixA2+++eagQzdLS0sxb948nHPOOSguLsajjz6Ku+++G2vXrh3S40ql52kp1saifHk5ypeXg7X5ro9DdzGxD4Ibvu7mzOaP/orL3IjxAsClssVKR9scNizfuBzLNy6HzRHczQL9da5UT1z320M17bA5hvcGiau3mZIRLUhxshBCtSoku///+MMLfVOnBXXtFgDA6GEEN5mxrnKFxk6r5D3R/JHXwU1iYiKamlzvwNPT07F161YArsBjKE2WOjs7ccMNN+Ctt95CVNTALxSvv/460tLSsHLlSuTl5eHWW2/FLbfcgueee87rx5VSVI/5UoyKQfIdyUi+IxmMyncnY3xxUoozOT0KCgaoaDahXqQiWV+yOZz8MXoxMjdc6/XadrPk7QICmdnmcJ1YBJAU7p81NwCQFq1HRIgaVruTr/UYKrltSXFG+lFR8SF3N+X0GP2weieF6dSId5cMlDb6z5acXHgd3Jx33nn48ssvAQC/+93vcN999+GCCy7Ar371K1x55ZVeL2Dx4sW45JJLcP755w963S1btuDCCy/sddncuXOxc+dO2Gx9R7YWiwXt7e29PqTGZW5aTTYotArk/C0HOX/LgULrm9NSNocTR9wnK3yRuQnTqfm+DdyUXH9WUt0Oi92JSL0aWbHCF4OH6dRIjXa92FKHUvHUuQPtELUS4SH+18CPo1AwmOjuML5rGH9fNkf3ENizBWpKKZTseFfAf8wvgpvh19twuOzNyQb5/9xy4/Wr6ZtvvonHHnsMAHD77bdjzZo1yMvLw/Lly/Haa695dV8fffQRdu/ejRUrVnh0/draWiQkJPS6LCEhAXa7HY2Nfc8vWrFiRa+Gg6mpqV6tUQzR7vlSzRK9Kz/R0Amr3YlQrQqpUXqfPOaUDFeGY0eZ/29NcaMRJqRGDnueVH9G92g7T8RR06Pext/7CXFzyYYT3OypbIXJ6kC0AENgheZPx8GFqLfhcD1yKHPjPa+DG4VCAZWq+13Oddddh5dffhl33303NBqNx/dTWVmJe+65Bx988AF0Os/3u09/EuK2wvp7clq6dCna2tr4j8rKSo8fSyzctlRblw02uwPWBiusDVafzU7hajlyE8NEe3E+3aQAmnjNvYBMErHgMs+PBwb6C3+dBt4XrvZr9zCCmw1H6gG4ZsL56nnBU9kJ/hPccM+vXJ+a4RgZx2VuKLjxlte52J9++mnA75977rke3c+uXbtQX1+PSZMm8Zc5HA789NNPePXVV2GxWKBU9i5oS0xMRG1tba/L6uvroVKpEBPTd68RrVbbaw6WHESEqMEwruGZLU0WlCRuBwCc03kOlAbxi/i4Zl9cbYcvcJmbg9Vt6LTY/XKOD6dYxJNSHK4QkTI34qkJgGPgnHEjIqFUMKhuM6O6tQvJQxgHsv6QK7iZkye/mWaj3BmMqtYuGC12GGT6/OFwsjjh3kLKFuD5lduWOkHbUl7z+jdk1qxZZ1zWM2vicDg8up85c+Zg//79vS67+eabMXr0aDz88MNnBDYAUFRUxNf7cL777jtMnjwZarX0nTQ9pVIqEBGiRqvJJknB6NE61x9KToL3bcGHKikiBCmRIahq7cKeilbZ7el7qrbNjKrWLigYYNwQZsZ4KpcfnNcBh5MdcpdT0r+6dv8/Bs4xaFXISwrDgap27Cpv8Tq4OdViwuHaDigYYFaO/IKbKIMGMQYNmoxWnGwwYuwI8Q9CDEVVSxcsdic0KgVSo4ZfpM5tS5U1GeF0srLLqMmZ19tSLS0tvT7q6+vxzTffYMqUKfjuu+88vp+wsDCMGTOm14fBYEBMTAzGjBkDwLWltGDBAv42t99+O8rLy3H//ffj0KFDePfdd/HOO+9gyZIl3v4YkuO2plpMvj/id8yducn1YeYG6M7e7Cz337obrt5mdGK4qO8eM2IM0KoUMNucqGg2CXrfOpUO22/dju23bodO5f8v7EPFzZUKhMwN4Dq+DQC/DKFZJpe1mZwezffhkptRftCpmFtbVqwBKuXwD4ikRoVArWRgtjn5btrEM17/6/cszo2IiEBsbCwuuOACPPPMM3jooYcEXVxNTQ0qKir4rzMzM7Fu3Tps2LAB48ePx5///Ge8/PLLuPrqqwV9XF/guhT7uilVl9WBcveLpRBpU29MDoC6m118f5tIUR9HqWC6O5QO83jvmfetxJSUKZiSMgVKhTx6mUihu4Gf/x4D7+kcdzaUG5/gjR8O1QGQ55YUxx+Ka7maIC4QGy6VUoFU91T0Mhn/3HIk2FvPuLg4HDlyZFj3sWHDhl5fr1mz5ozrzJw5MyDmV3UPz7QhyYePe6KhEyzrevzYUN++Q5vsztzsrmiB3eEU5J2Nr3GZGzGLiTm5CWHYd6oNh2o7cPFYX/6WBIeaACooBoCpmTFQKRhUNJtQ0WRCWoxnJyE7LXZsczfYnJOXMMi1pcMX18r4RZ47qs4dXRdCWrQeJxuMqBQ4gxvovA5u9u3b1+trlmVRU1ODp556CuPGjRNsYYGue1vK6tPghqvkz44P9fnx15z4MITpVOgw23GopkO2++b9MdscOOAeiSBmMTGHOyEidDGh1WHFS1tfAgDcM+0eaJTy3IYQk83hREOnq4tsINTcAK5OvhPTorC9rBk/H2/Eb2LSPLrd/w7Xw+pwIiNGzwcQctTd80W+wY3QmRsAfLuOyhYKbrzhdXAzfvx4MAxzxrHladOm4d133xVsYYGuZ+bGl47Wdx8D9zWFgsHk9Cj8eKQBO8ub/S64OVjdBpuDRWyoBmnR4vcH4p4gTwh8/NXmsOGhH1xbyHdMuSMog5v6DgtYFlArGcTItMZkKGaMisX2smZsOtaA35zlWXDzxZ5qAMD8wmRZ9/vhgpuyRnkW17Isywc32QIe1uCeayqauwS7z2DgdXBTWlra62uFQoG4uDivetWQ7uGZvq65OVYn3DHFoZicEe0KbspacPOMTEnWMFS7y1sBABPSonzyIjAqzvX/6GSjkU5MCazWXUycEK6T3YvkcMzMjcOLPxzFxqMNMNscg86HajVZsfGoq5j4svHJvljikKVG66FSMOiyOVDXYUaSzGql6tot6LTYoVQwyIgRLgOWygc3lLnxhtdFDxs3bkRiYiLS09ORnp6O1NRU6HQ6WK1WvP/++2KsMSBF99iW8iVuWypHwLSpNyand3cq9lXTQqH4onlfTylRIdCqFLDanbTfLrDaNteWVKCclOKMGxGBlMgQmKwObDjSMOj1vzlQC5uDxejEMJ/2vRoKtVLBZzHkuDXFnZRKj9FDoxKunpAbxXKKngO84vX/gZtvvhltbW1nXN7R0YGbb75ZkEUFA25bypcjGIwWO6paXe9YpXoiG5caCbWSQX2HBada/CfNyrIsdrmLiX1RbwO4TkxxJ0SoiZewuGPggXJSisMwDC4ekwgAWLe/ZtDrf7jD1bH98vEpoq5LKHzdjQyLivktKYHfOHKZmyajFZ0Wu6D3Hci8Dm5Ylu0zJX/q1ClERPhXDYWUYt3TXps6fRfccJX8cWFayXpZ6NRKjElx/Z74U7+bUy1daOiwQKVgUOjDWiGuwNMf2s77k+7RC/LqXi6EeYWuIwrrD9XBbOu/qereylbsrWyFRqnAtZNH+Gp5w5Ll/nsolWXmRvhiYgAI16kR6W4dQhlcz3lcczNhwgQwDAOGYTBnzpxe86UcDgdKS0tx0UUXibLIQMQVMTYZrQB88+7xaK00zftONyktCsUVrdhZ1oIrJ/jHkyp3BLwgOXzQOgYh+dPAQH9S0x5YPW56mpAaiRFRITjV0oUv9lbjusl9Dwt+f0s5AOCSwiTEhvpHkJcZ6/p7ONkov7+H43XCHwPnpEXr0WpqQ2WzSZCBnMHA4+DmiiuuAADs2bMHc+fORWhod3Sq0WiQkZHhl830pMI9mVjtTp895vEGcd5ZeGtyRhTe/rl0WBOMfW0337zPN1tSHD64oW0pQdUG0Fyp0zEMgxvOSsfT3xzGe7+U4dpJI87Itp9qMeHLva5TUjcWpUuxzCHhtqXk2MhPzOfX1Gg99p1qo6JiL3gc3DzxxBMAgIyMDFx//fWyG0bpb0I0Shg0SpicDiR8novECB0UOnGb2p10//FJ3ctiUrqrU/GRug60ddkQESL/uWC7fTAssy89j4P3tyXsLZ1Khx9v+pH/PBh1dycOzJ//V1NS8dL6ozhY3Y7/Ha4/oznfyh+OwepwYvrIGJ//Tg8Hty1V2WyC1T3DSQ5aTVa+fjJLhOdXrteNP9UpSs3r34z8/Hzs2bPnjMu3bduGnTt3CrGmoBETqgWrAIzjtIiaFQVGKe6RVO6EAVekKpW4MC3SY/RgWaC4Qv7ZG5PVjhL3CARfZ24yYgxQMEC72c43nRsupUKJWRmzMCtjVlCOX3A4WX5oZiBmbgDXgYWF012tFv667lCv2ptfTjTik12nAABL5uZKsr6hig/TwqBRwskCFc3yyd5wmaTEcB30GuFnzqXRcXCveR3cLF68GJWVlWdcXlVVhcWLFwuyqGAR4x5/0OiDomKbo3sAoxjvLLzFHafe7QdbU/tOtcHhZJEYrkOyj18MdWolf1qC6m6EUd9hht3JQqVgEB8WmMENAPxh5kjEhmpxosGIR9bug83hxOHadtz1z2IAwA1npflV1gZwbbllxsmvUzEX3GTEitPck4Ib73kd3JSUlGDixIlnXD5hwgSUlJQIsqhgERuqhdIBmN9rQNXfquC0iVd/U9Fsgt3JQq9RymKWDhfc7PSD4KbnsEwpOrhmCVxnYHPY8Lftf8Pftv8NNofvp9JLrbqVOwauC+jGiBF6NV781TgoGODzPdWY9OfvMe+lTWgyWjEmJRxL5+VJvcQh4YqK5VR3ww215NYmNK7XTWWzye/6g0nF6+BGq9Wirq7ujMtramp6naAig4sN1UDpACKfa8KxO4+BtYr3S8u9y8mMNciixfpkd93NnspW2B2+K6oeimIf97c5XYY7uClvEuZdm9VhxZ3/vRN3/vdOWB2+a0XQarKiwyx9MFXV6tqSSo4MvJNSpzsnOw5v3jgZkXo12s12OFlgdm4c3rt5KkK1/vl8nSXDGVMn+eBGnMxNcmQIGAaw2J3uE7ZkMF7/dl9wwQVYunQp/vOf//B9bVpbW/Hoo4/iggsuEHyBgSzG4Kq5qZ+uQUFyBCBi+QNXTCx1vQ0nOz4U4ToV2mU+RJNl2e5iYh/X23DkfELEE6WNRjz48V7sLG+BSsHg+qmpeGxePkI00tT7VLmLMlOCILgBgPPzE7B16Rzsr2pDjEEjmzc4Q8X3upHR30NZk7iZG7VSgfgwLeraLahu7fKbo/tS8jpz8/zzz6OyshLp6emYPXs2Zs+ejczMTNTW1uL5558XY40BKzZUA5sK2HiHDgUfF0CpE+/Jni8mjpW+3gZwDdGcyG9NybeZX1mTCc1GKzQqBQqSpekvwc2pKZPRk7mnqlq7cM1rv/Dbj3Yniw+2VuC2v++ETaKMHbctFSzBDeCq3ZqSEY2suFC/DmwA+XUpZlmWbyooVuYGAD9Lq9qdeSQD8zq4SUlJwb59+/DMM88gPz8fkyZNwksvvYT9+/cjNbXvZlGkbzHu6FuoUzAD4ZpeyaGYmDPZD+puuILnsSkR0KqkyTRwT+blzSY4nf6z386yLO7+sBhNRitGJ4Zh00Oz8d4tU6HXKLHpWCNW/XhCknVxwU0wbEsFIu7vobHTgnYZbHM2dFpgtDqgYLpHJYiBC8a5318ysCFtuhoMBtx2221CryXocKelmnwR3PCZG3lsSwHd2zy7yloE6+EiNG6elK+GZfYlOTIEaiUDq92J6rYujIgS7wlUSF/srcau8hboNUq8tWAyUqP1SI3W46mrC3H3h8V45X/HcPn4ZL6myFeq+OBG+sJ64r0wnRpxYVo0dFhQ2mDEuNRISddT1uiqhXMNuhXvDRDXtoCCG894HdwMNvl7wYIFQ15MsIkL1UJjBf64BNiwZAPO6TwHSoPwfxxtJhtfhJYpo8zN+NRIKBUMatvNqG4zy3KbgO9MnBYp2RqUCgap0XqcbDCirNHkF8GN08li5Q/HAAB3zBrZ6x3tpYVJWLvrFDYebcBL64/hxV+N9+naqoJwWyrQZMYaXMFNo/TBTak7K85tH4uFyzTWtNG2lCe8Dm7uueeeXl/bbDaYTCZoNBro9XoKbrwQ46OisBPuP76EcK2sTkjoNSoUJIdj36k27CxrRorMJhN3mG04UueaxyV1P5DMGANONhhR2mTE2dmxkq7FEz8eqUdpoxFhOhVunpHZ63sMw2DJhbnYeLQB/9lThSVzc30WaLSbbegwuyYr07aU/xoZZ8D20mb+oISUSt2ZG7HrGblMYxVlbjzidc1NS0tLr4/Ozk4cOXIEZ599Nj788EMx1hiwIkPU8EWbDTluSXG47R45zpnaW9kGlgVGRIUgXuLeQNzWjRBFxVqVFl/9+it89euvoFWJE2D/Y1sFAOD6Kakw9BFQjx0RgWlZ0XCywL+2V4iyhr7UuIsxI/XqPtdF/AN/glCg9gjDwWduRA9uuMwNBTeeEGQwR3Z2Np566qkzsjpkYAoFg2iD+Nmb7mPg8tmS4nD9bnaWyS+44QIuKettON29boYf3KgUKlyScwkuybkEKoXwL/CtJit+OtoAwDXjqD+/neYa2PjRjkqfnZzii4kDcBp4MOG2gEplMB2cq7kRO7jhTkvVd1h8OnDZXwk2dUypVKK6ulqouwsaMQbxh0bKZaZUXyZnuAKHw7Xt6LTYJV5Nb7slbt7XU2aM/Hp79Oe/B2phd7LISwrHqPiwfq93YX4iYkO1qO+w4H+H632ytlN0Uiog8L1uGoySdux1Olm+x43Y21IxBg00KgVYFvxsNNI/r9+2ffHFF72+ZlkWNTU1ePXVVzFjxgzBFhYsXHU34v6iyvEYOCchXIeUyBBUtXahuKIF52THSb0kAK4nrd0yOCnF4WbWVDZ3weFkhzU2wOaw4R/7/wEAuGHsDVArhQ2wv9zrepNz2bjkAa+nUSlw5YRkvLWpFF/vq8HcgkRB19EXLnMzIoqCG3+WGq2HggGMVgcaOiySbRvXtJthsTuhUjCi140pFAySInQobzKhurVL1GPngcDr4OaKK67o9TXDMIiLi8N5551HTfyGINqggZjBjcPJosy9Lz1ShjU3gCt7U7WnC7vK5RPcnGjoRIfZjhC1EqMT+88++EpyRAg0KoXrOPgwn9isDitu/s/NAIBr868VNLhpMVqx9WQTAGB+YdKg1583NglvbSrF+kN1MNsc0KnF7SVUTcfAA4JWpURKVAgqm7tQ2miULLjhauDSYvRQKQXbCOlXckSIK7ihuptBef1/w+l09vpwOByora3FP//5TyQlDf5kRnoT+8RUdWsXrHYnNCoFUmT6bnWyDIuKuaxN4YgInzxpDUahYPjJwHLemvr5eCOcLJCbEOZRADY+NRIpkSEwWh3YcKRB9PVRA7/AIYcBmvxMKZGPgXOSIrleN7QtNRjpn7WDXLTINTcnGrgeDHrZTkCe5C4qLq5ohUMmHXjlVEzM4ccwCFBULBaukPjcHM+OqzMMg4vHuLaj/nugRrR1cbi5UhTc+L8sGcxc4zI3vmpESV2KPefRttT999/v8R2+8MILQ15MMIoR+bSUnI+Bc3ITwxCqVaHTYsfh2nbXEFGJ8cMyZVBMzOHm1nCnM+SGZVn8dIwLbjzfXpw7JhFv/1yKjUcbhl1PNBC7w4ladyHmCApu/J4cBsqW8tPAfZS5iaDgxlMeBTfFxcUe3Zkc2+fLXax7BINY5FxMzFEqGExIi8SmY43YVd4ieXDTarLieL3r302qSeB9SXNnbiqa5RncHK3rRF27BVqVAlMyoj2+3YTUSITpVGg12bDvVCsmiBRQ1nVY4GQBtZKhqcoBIEMGwU2Zj4MbrlaMuhQPzqPg5qWXXkJBQQGUSmkGBwayaIMGrSLev5yPgfc0KT0Km441YmdZCxYUZUi6luLKVgCuJyxXwbc8cDU3lTINbn450QgAmJoZ7VVhsEqpwNmjYvHfA7XYeLRBtODmlPvfLSkiBAqZbtESz2XxvZ9Momb8+mN3OPk3Gr4KbhLd86Vq6Sj4oDyquZkwYQKam5sBAFlZWWhqahJ1UcGk54unGP0auODGV398Q8U185NDUfEud0PBCRLOk+oLF9xUNJsk7e3RH64R47SsGK9vOyvXtY218ah4RcXcC1F6DB2hDQTJkSHQKBWwOpySbNOcaumC3clCq1Ig0UentbjHaTXZYLY5fPKY/sqjzE1kZCROnjyJuLg4lJWVwemk7ohCiY8NwQOXu6LwQocD0UMb1N4no8XOR/gjZbwtBQDj0yKhYFxzU2rbzPw7FCnsLHcF8lzAJRcpkSFgGKDL5kBjpxVxYUPbWtGqtPj3Nf/mPxcCy7L8v9tQirC5Gp09la1oMVoRJULGjMt4UX+QwKBUMEiL0eN4fSdKG40+//9a2tT9xtFXmcCIEDXfEqK+3YI0CtT75VHm5uqrr8bMmTORmZkJhmEwefJkZGVl9flBvKPTqXBsogI7RjvQ0GUV9L65vehogwaRevlsr/QlVKtCXlI4gO7gQgo2hxN73NtSXPdkudCoFPzYgOHU3agUKlxbcC2uLbhWsPELp1q6UNdugVrJYNyISK9vnxQRgtyEMLAs+KJkoXH/ZmkU3AQMKYuKS91ZcbGngffEMAyfvanroK2pgXj0zPbmm2/iqquuwvHjx3H33Xdj0aJFCAuTvrFZoEgI06HVZEN9hxm5AjaM446Bi90WXCiT0qNwsLodO8taML9w4O62YimpbofZ5kREiBqjZFinlBrt6uZ8qsUkq2PqO8pcAemYlAiEaIZWm3duTiyO1HXgl+NNuFyECfEU3AQeKY+Dcy0ZfHUMnJMQrkVFswm1VFQ8II/ftl100UUAgF27duGee+6h4EYgTrsTU4+qEF6jRF2zsL+s3cXE/hPcvL+lXNK6m509+tvIseg0LVqPrSebUTGMach2px2fHfoMAHBl3pWCZG+4fzdvTkmdrmhkDN7aVIqtpeLU9FU0u+oyKLgJHJJmbviTUr79fUrgMjdUVDwgr5/VVq9eLcY6ghZrYTHndSvmQIdDvxe2KI7rnin3k1Kcye4XxpKadhgtdhi0wk+sHsyuYdSN+ELPouKhstgtuO6T6wAAnUs7odIM/995D98XKHLI9zE5IxoKxnX6paati+/pIQST1Y7GTgsAqrkJJFIeB+czNz7clgK6i4opuBkYdSiWmgJoH6vG4VQHGjqFrbk56WfbUimRIUiK0MHhZLHXXffiSyzL8id+Jss0uEkVILgRmtnmwNG6DgBA4RDqbTjhOjXGpLh6HG07KWzdVaU7axMRokZEiLhdwYnvcM9tp1pMsNp9d9DFanfy3a59fRKVy9zUtlt8+rj+hoIbiSlDlGh+LRlP/caMWqtwv6wsy/LvZvwlcwN0Z0yk2Jo61dKF+g53UWxqpM8f3xNy7HVzuLYDdieLGIMGScM85cYdI+eGbwqF6m0CU1yYFgaNEk7WtwF/ZYsJThbQa5RDPrU4VAkRlLnxBAU3MsBNtK0XMBKvbTfDZHW4jkv60RM6lzHZKUFww53SKkiOEH069VBx/y9r2s2w2OXR52L/qVYArmLi4XYpPyvTtTVJwQ3xBMMwkmxNcZ2J02MMPu/Mn+AOpii4GZjXwY3RKN+hff4qnvtlFfBoH1dMnBath0blPzEsN0Rzd0ULnD4eoin3LSnAdazfoFGCZbuHQEptf1UbANcE9eHi6m7KmoQ9DUI9bgJXd1Fxp88eU6piYqC7S3Fdu1mWzTzlwutXvYSEBNxyyy34+eefxVhP0HEYHTBNLsHLL+vR2mgR7JfV3+ptOHlJYdBrlOgw23Gott2nj81thcmtv01PDMPIru5m3ylXcMPVywxHRIiany22TcBTU5S5CVzdx8F99/cgVTEx0F1zY7Y50d5l9/nj+wuvg5sPP/wQbW1tmDNnDnJycvDUU0+hurpajLUFDWeTHeFdDCx24X5ZT/jZMXCOStk9dHHLCd+N+Wg323DEXRQ7SWadiU8np7obs82BY+4ho0JkbgBgWpbwW1MU3ASuDAkyN+XuVgy+7nEDADq1ki+KpxlT/fM6uLn00kuxdu1aVFdX4w9/+AM+/PBDpKenY/78+fj0009ht1MkORz1Am1N+dsx8J6mj3QVlfoyuNlR2gyWBTJi9D4vEPTWcI+Da5QarL58NVZfvhoa5fA6V5fUtMPhZBEbqhFsvs5ZmVxRsTAnppxOtse2lHDHy4k8SNHrptTH08BPR8fBBzfkYoyYmBjcd9992Lt3L1544QX88MMPuOaaa5CcnIz/+7//g8kk/btKf1TfIUxR8Qn3u+mRfhncxAIAtpU2w+7wzfFOLpAqGun90Edf4+bJDDW4USvVWDh+IRaOXwi1cnjHog+4623GClBMzJmSEQ2Gcb2ACBHsV7d1wWJ3Qq1kkBJJwU2g4QKMunYLjBbx31xb7A5+UKdUQ1jjw11vwChz078hBze1tbV45plnkJeXh0ceeQTXXHMN1q9fjxdffBGfffYZrrjiCgGXGTyEiMTNNgeq21x/fP62LQUA+cnhCNep0Gmx88WqYtvi3gIZykRrX+uuuZG+oLik2lUXxdXJCCFCr8boRNecsR2lwz81x73LTovWQ6X0n+J64plIvQbR7kGrXC2MmCqbXcfADRol4kKlyfIm8idsKbjpj9etST/99FOsXr0a3377LfLz87F48WL89re/RWRkJH+d8ePHY8KECUKuM2gIkbkpbTSCZYFwnQoxIkxXFptSwWBaVgy+K6nDLyeaMCFN3ALfNpMNJTWuF+kiPwhuetbcsCzrdcbE7rTj2+PfAgDmjpo7rPELh2tddUqjk4Qdx3JWZjQO1bRje2kTLilMGtZ9dY8h8b8sJvFMRowezUYrShuNggbafSlr7K638fUxcA53YooyN/3z+m3MzTffjOTkZGzevBl79uzBnXfe2SuwAYCsrCw89thjQq0xqAiRueGezEfGh0r2xzdcvqy72VbaBJZ1ZbniBaobEVNKZAgYBui02NFisnl9e4vdgvkfzsf8D+fDYh96MO10snxn4tECDnwFgKnufjfbSodfd8OfHPTDLCbxTGasK3DlJnWLSaqBmT3F8zU31KW4P14HNzU1NXjjjTcwZcqUfq8TEhKCJ554YtD7eu2111BYWIjw8HCEh4ejqKgI//3vf/u9/oYNG8AwzBkfhw8f9vbHkC0hMjfd08D9953q9FGuupsdZc2iN6vjtqT8IWsDuE5LcGlpKY+DV7V2wWR1QKNUIF3gI7HcibnDtR1oNQ1vLAlXXD/Sj/8eyMC4wLXUB9tS3DZnhkT1NgAVFHvC6+DGbrejvb39jI+Ojg5Yrd49CY0YMQJPPfUUdu7ciZ07d+K8887D5ZdfjoMHDw54uyNHjqCmpob/yM7O9vbHkC0h9lAD4Z1qdnwoYkO1sNid2F3eKupj+VMxMUcOvW64LamR8aFQC1zLEhemxUj37++OsuHV3Zz007YIxHNcvxlfnJiSsscNJ4ErKBaw0WWg8foZKTIyElFRUWd8REZGIiQkBOnp6XjiiSfgdA5+yuXSSy/FvHnzkJOTg5ycHDz55JMIDQ3F1q1bB7xdfHw8EhMT+Q+lUp6t8oeiRoBfVv6dqh/XGDAMgxmjXMHGz8cbRHucZqOVf5HmjiD7Azn0ujnibrIo9JYUZ6r7/8f2YTTz61lcL9WxXSI+Xx4H52pupPx94jI3jZ0Wn50o9TdeBzdr1qxBcnIyHn30UXz++ef47LPP8OijjyIlJQWvvfYabrvtNrz88st46qmnvLpfh8OBjz76CEajEUVFRQNed8KECUhKSsKcOXPw448/evsjyFpduxmOYYwdYFm2xzFw/34yn5kTBwD48bB4wc0295ZUdnyo7Pvb9MT3ummSPnOTK1Jww82Z2j6MuhuuuD4iRM2fqCGBJ8M9BqHVZEOLcXjbmAPpGSxLWXMTE6qFUsHAyQKNneL9vP7M62MS7733Hp5//nlcd911/GWXXXYZxo4dizfeeAPr169HWloannzySTz66KOD3t/+/ftRVFQEs9mM0NBQfPbZZ8jPz+/zuklJSXjzzTcxadIkWCwW/P3vf8ecOXOwYcMGnHvuuX3exmKxwGLprmNpb/dtS39vKBjA7GDR2GnhW2x7q77DAqPVAQXT3Q/FX52bEweGcTWKq20z8ycEhPTTsUYA3QXM/oJrRiflttQRkYMbrqj4QHU7Oi12hGq9P9XVc0vKX4vryeD0GhWSInSoaTOjtMmIKJECWdcJRSBMK+1JVKWCQYxBg/oOCxo6LKI8N/o7rzM3W7Zs6fOY94QJE7BlyxYAwNlnn42KigqP7i83Nxd79uzB1q1b8Yc//AE33XQTSkpK+r3uokWLMHHiRBQVFWHVqlW45JJL8Nxzz/V7/ytWrEBERAT/kZqa6tG6pMAFNFyDqKHgionTovXQqvx7uy42VIvCEZEAgI1H6wW/f5ZlsfGI635n5cYLfv9i4relWqQJbix2B7/9mZsgTnCTHBmCEVEhcDhZfu6Xt7its5x4cdZI5IOvuxHxxBS37ZUeq5c8WOYyzQ2dVHfTF6+DmxEjRuCdd9454/J33nmHDxyampoQFeVZbxKNRoNRo0Zh8uTJWLFiBcaNG4eXXnrJ4/VMmzYNx44d6/f7S5cuRVtbG/9RWVnp8X37AqNhkP1qNrJfzUZ8jCu4GU7dTaD19JidK97W1PH6TlS3maFRKfyieV9PXEFxdWsXbF7uuWuUGrx68at49eJXhzx+4WSDEQ4nizCd6x2zWM4aZt2N2FtnRD4y3dvwYjby42dKSVhMzInnghuButoHGq/zvM899xyuvfZa/Pe//8WUKVPAMAx27NiBw4cP45NPPgEA7NixA7/61a+GtCCWZXttIw2muLgYSUn9N/nSarXQauVbS6FQK5CyOAUAkPDPOuBUqyCZG3+vt+HMzo3Hyh+O4efjjbA5nIKeytlwxBUwnZUZjRCNf2W54kK10KkVMNucqG7t8uootlqpxuKpi4f1+NyW1OjEMFHfwZ6VGY21u08Nue7mcK04fXiI/HDTwU+KWFTMHTWXQ3E6l7mpp143ffI6uLnssstw9OhRvP766zhy5AhYlsXFF1+Mzz//HBkZGQCAP/zhDx7d16OPPoqLL74Yqamp6OjowEcffYQNGzbgm2++AeDKulRVVeH9998HAKxcuRIZGRkoKCiA1WrFBx98gLVr12Lt2rXe/hiyxM29qW6lzA1nbEoEYgwaNBmt2FnWIuhx7Y1HXcGNv21JAa7TZKlRehyr70Rls3fBjRB8lRHh6m72VrbBbHNAp/Y8CDVa7HxNEmVuAp8vtqXKGqU/Bs6JD3NlTBs6Kbjpi1fBjc1mw4UXXog33ngDK1asGPaD19XV4cYbb0RNTQ0iIiJQWFiIb775BhdccAEAV8PAnrU7VqsVS5YsQVVVFUJCQlBQUICvv/4a8+bNG/ZapMI6WLRuagUAJLnnlNS0DT1zc7KRa+An/R+fEBQKBjNz4vBpcRV+PFIvWHBjstr5bMAs99aXv0mNdgU33hYVO5wObKrYBAA4J+0cKBXeZ624WpZc9wwosaTH6BEfpkV9hwXFFa1e/f/nuifHhWkRI9EMIOI7PbelhjKWxBN8cCOD51fK3AzMq+BGrVbjwIEDgv3S9FW709OaNWt6ff3QQw/hoYceEuSx5cJpdmLv7L0AgKTtOQCGXlBstjlwqsV125HxgZG5AYDz8uLxaXEVvj1Yi6UXjxbk9++no42wOpwYERXit4Fg2hAb+ZntZsx+bzYAoHNpJwwa73/+Y+52Azki/54xDIOpmdH4al8Ntpc2exXc0JZUcEmN0kOpYGCyOlDfMfQTp/1xHQN3ZdWl7E7M4WtuKHPTJ68LGBYsWDBoUEK8wAD6fD30+XokR7m3pYZYUOzvAzP7Mzs3HlqVAuVNJhysFuYo/zcHagAAcwsSJT/1MFSpEp2Y6rI6UOUOwEf5IIg+y13svb3Mu6Ji/qi6SKe5iLxoVAqkup9DT4qwNcW9iQjTqWTRMymOCooH5HXNjdVqxdtvv43vv/8ekydPhsHQ+13fCy+8INjigoFSr8TUg1MBAE3uCLyx0wKr3QmNyrvYk0vD5ySIW+TpawatCrNz4/HNwVqs21+DMSnDm/prsTuw/pDrCPi8sYlCLFES3BO5r7sUc0F0pN43jfG4Zn67ylu8+rs4WN0GABidJO7WGZGPjFgDyppMKG00Cj5OhR9rI+E08J74bakOs2jbcP7M68zNgQMHMHHiRISHh+Po0aMoLi7mP/bs2SPCEoNHtEEDrUoBlh3azBA+uAnANPy8QteJuHX7a8CyQ+/gDAC/HG9Ch8WOhHAtJqR61rJAjrgmjb5u5Nd9Is83U+dHxYUiSq+G2ebE/qpWj25jczixv8oV3IxPHV4wTPwHd4pJjOPgJ9zZILls+XPBjdnmRKfFLvFq5MfrzE2gjTuQE4ZhMCIqBCcajKhoNnndYfhonW/qIKQwZ7Rra6qsyYSSmnYUJA/9BWvd/u4tKYXCf9/tpEZ1t5xvN9sQrlP75HF93W5AoWBQNDIG6/bXYuPRRkxKjx70NkdqO2C2ORGmUyGLpoEHDf44uAjbUt1jbeTx+6TXqBCqVaHTYkd9hwVhPvr79xdDbhpy/PhxfPvtt+jqcu29D/fddLBymBzYXrAd2wu2w2Fy8Ed6y5u9/+PsuS0VaAxaFX+q6Ys91UO+H6vdie8P1QEALhrjv1tSgOvfhKut8uXW1HH3k7wv6m043HF9rqP0YIorWwEA41Mj/TqAJd7J4HvddAp+3z0zlnJBjfz653Vw09TUhDlz5iAnJwfz5s1DTY3rXfCtt96KBx54QPAFBjwWMJWYYCoxAezQT8B0WR38bQJxWwoArpo4AgCwdneV1115Of87XI9Wkw1xYVpMzRg8AyB3fFFx89DbB3iLT8/78El+lnuI6t5TbR49ke+paAXgCm5I8OAC7vImE6x24aZlsyzb4/dePqcrYym46ZfXwc19990HtVqNiooK6PXd2ya/+tWv+OZ7ZOiGOu35eH0nWNZVtxMboD09zhsdj9hQDRo7Ldh4ZGjjGD7Z5Rq/cdXEFKgE7HYsle7gxvPfF7VSjWfOfwbPnP8M1ErvUtlOJ8sXVvoyuIkP12FMiqsw+Kejg/+/L650zaKakBYp5rKIzCSG6xCmVcHhZAWtu6nvsKDTYodSwchqIHF3UTEFN6fz+tn9u+++w9NPP40RI0b0ujw7Oxvl5eWCLSxYpbv/cMq9DG66t6TkkzIVmlqpwJUTXKMqPtrh/Yyw+g4zfnQHRddOku8AVW+kDWE6uEapwYMzHsSDMx70erZUVWsXLHYnNEoFRrhPa/nKbPfW1I+DbE21GK18zcU49+BVEhwYhsEo93Mg95woBK7eRm4DiWlbqn9eBzdGo7FXxobT2Ngo6xlO/oILbiqbTV7VMQVyvU1Pv5riCkrWH65DuZfvzP61vRIOJ4sJaZE+rRcRk6+ng3N1Bxmxep9nvri6m5+ONsA+wLbkLydc/XByEkKpM3EQynb/bR+rE67uRq4z+3oeB5eT4ooWQbcFh8LrZ6dzzz2Xn/UEuCJlp9OJZ599FrNnzxZ0ccFohPsETIfFjhaTzePbBUtwMyo+DDNz4sCywOrNZR7fzmJ34L0trsziTUUZ4ixOAtyJKW8yNw6nAzuqdmBH1Q44nA6vHk+KYmLO+NRIxBg0aDfbsflE/w39fj7uys6dPco/x2qQ4eGeA4/VC5i5kaDOzBP8fCkZZW5q2rpw5apfMOnP36PL6t3zi5C8Dm6effZZvPHGG7j44othtVrx0EMPYcyYMfjpp5/w9NNPi7HGoKJTK5HobhvuTWaCPwYe4MENACw6JwsA8O+dlWg2Wj26zX/2VKOx04KkCB0uKex/iry/4WpuTjV3wen0LNNntpsx9e2pmPr2VJjt3r3jk/JJXqlg+P93nxdX9XkdlmXx09FGAMA52bE+WxuRj1GiZm7kFdzIsUvxz8dcf39Z8aEI0Ui3hed1cJOfn499+/Zh6tSpuOCCC2A0GnHVVVehuLgYI0eOFGONQcfb5mxtJhvfDj8YWs3PGBWDguRwmKwO/O3H44Ne32p3YpX7egunZ0AdAIXEnKQIHZQKBlaH0ydFhVI/yV/hrrn69mAtTNYzG5cdrG5HVWsXdGoFzsry/9NwxHvcG7zSRuOQT1Weju9xEy+vbSk51txsPu5+czFK2jcXQ3qWT0xMxPLly/HVV19h3bp1+Mtf/oKkpMB5Nyy19Gjvioq5NvOp0SGI0Ad+IyeGYfDQRaMBAH/fUj7oSaEPtpajrMmE2FAtbpiW7osl+oxKqUBKpPdFxUMlxUmpniakRiI9Rg+T1YFvDtSe8f0v97l6IJ03Oh56jdc9SkkASIrQwaBRwu5k+Snew2Gy2vl5f3JrCMllbppNVsECueFgWRY/H3dtGc/wx+CmtbUV3333HT744AO8//77vT7I8HHtvbnJy4PhhkkWJAVPm/lzs2Nx9qhYWB1OPPrZ/n63ZOo7zHj5f8cAAA9cmINQbeC94A21N5K3Wk1WNHa6tgGzJCqsZBgG17j7Hb3zc2mvonubw8k3eJxfmCzJ+oj0XCemuLqb4W9NcSfvYgwaRMlgYGZP0XoNlAoGLAs0dXq2RS+mEw1GNHZaoFUpMDE9UtK1eP1M/+WXX+KGG26A0WhEWFjvAY0Mw2DBggWCLjAYcce5j3l4lPGAO3PD9QEJBgzDYNllBZj/yiZsOtaIdzeX4lZ3LQ7H7nBiycf70GqyIT8pHNdOGtHPvfm31GjfDNDktqSSI3QwSBgk/nZaOlZtOIGD1e3YcLSBPyL+9b4a1LSZERuqxXmj4yVbH5FeTnwo9la24mhdB+aNHd6ugtRbsQNRKBjEhmpQ125BQ4cFiRE6Sdezu8LVX2rciEjJj8x7nbl54IEHcMstt6CjowOtra1oaWnhP5qbm8VYY9DJjne96zjR0OlRqpHP3Axj3pI/GhUfiscuyQcA/HXdIazddYr/ntXuxCOf7sdPRxugVSmw8vrxAdG0ry9DaeQ3FCfq5TE4MMqgwW+npQEAln9xECarHUaLHS/+cBQAsHB6OnRq+fQiIb6XneBd9nsgx2Vab8OR03HwYndwM0HirA0whMxNVVUV7r777j573RBhpESGQK9RwmR1oLzJiFHx/RcJm6x2/p1FQRBlbji/PSsNJdVt+HB7JR74eC++3FeNvKRwrD9Uh6N1nWAY4KXrxwf0KTJf9bqR0zvYu+dk44u91ShrMuGmd7dDr1GhvMmEpAgdFkzPkHp5RGLZ7r/34wKcmDpU48qgy/Wwhus4eLssiop3l7cCACamRUm7EAwhuJk7dy527tyJrKyswa9MBsWoGaQ/kc5/DrhSjdnxodh7qg1H6zoHDG4O1XSAZV3RO9fzIJgwDIO/XDEWMQYt/rbhODYcacAGdxficJ0KL1w3HufnJ0i8SnF52+tGrVTjiZlP8J97Sk6NzMJ0aqy6YRIWvLMNO8pc7xY1SgVeuG68z6ajE/niGvmdbHRlv4dzQvJInSsznpsozzePcaHyODHVbrbhqLu3kBzGnngd3FxyySV48MEHUVJSgrFjx0Kt7v1Ectlllwm2uGCg0CiQuSzzjMuzE8Lcwc3Ae8ZcGrAwJbi2pHpSKhgsmZuLqyam4Mu9NWjoNCM7PgxXTEhBREjgv9BxmZu6dgvMNsegWzIapQbLZi3z+nH49LwMMjcAMCk9Cp8tnoHVm8vgcDpx47QMjB0RvH8HpFtyRAgMGiWMVgdKG41Dztx2Wuz8UNrRMh1IHB8uj/lSeytbwbLAiKgQWbzR9jq4WbRoEQDgT3/60xnfYxgGDod0HQkDSXdR8cBp1Z3ud62TMqRPA0otKy4U95yfLfUyfC5Sr0aoVoVOix2nWrpE6R5ssXdPnZfT6IqchDCsuGqs1MsgMqNQMMhLCsfO8haUVLcPObg5UuvKRMSHaWV3Uoojl0Z++065DrZMkMGWFDCEgmKn09nvBwU23mOdLIwHjTAeNILtcZyZ+2M8XNve/21ZFjvLXcHN5HRqWBasGIbxqqjYyTpxsP4gDtYfhJP1rDdGeZMJThYI06r4J1NC5Cw/2bWNxPUBGwouuBmdJM8tKaB7W0rqguIS98GWMcny+LcKzOMjfsTZ5cSOMTuwY8wOOLu6X2jGuLeZTjYa0WHue8ZURbMJjZ0WaJQKFFI6Pqh5Mx28y9aFMa+NwZjXxqDL1uXR/XMdWrPiQ3u1fyBErgrcL7IlNf2/QRzMEfebS7luSQHd21INndJmbrh/53x/C27mzZuHtrbuCPjJJ59Ea2sr/3VTUxPy8/MFXVywUMeqoY7tXRsSG6rFiKgQsGx3uu903JbUmJRwOvoa5LiiYrGOg3fX20hfTEyIJ/LdTU0PVrf3avbojUO18j4pBQBxod3DM4f6cw5Xp8WOMvcsxDyZZLk8Dm6+/fZbWCzdkeHTTz/dq6+N3W7HkSNHhF1dEFAalJjRMAMzGmZAaegdoIxPjQQA7Kls7fO2O8td//6TM2hLKth5O4/MW9xJKTnV2xAykOyEUKgUDFpNNtS0eb9lw7Isvy2VK+PMTWyYqxbIbHOi03LmvDVfOFLbDpYFEsK1iA2Vx7a1x8HN6RGhVBFiMOGCG+5EVE89px9PowGBQS9V5BEMUk4DJ2QodGolH4xzjU69UdduQVuXDUoFI+ugXq9R8WNlpCoq5upt8mWStQGo5kbWpma6gpatJ5vP6FR8rL4TVa1d0KgUKMqSdkAZkR63LXWqpUvwNx5OJyurHjeEeIqr/ygZQnBzyF1DkhlrkP22v9QnpuRWbwN4EdwwDHNGISEVFg6fo8uB4lnFKJ5VDEdX79NmY5IjEG3QoNNiR3FFa6/v/Xi4HgAwLSsGIRp5/+ER8Y2IchUUd1rsaDH1XYA+VLXtZpisDqgUDNJjKLgh/oPLJAzlxBRX6zjWD3qI8Y38JCoqLnF3cZZLvQ3gRZ8blmWxcOFCaLWuf0Sz2Yzbb78dBoPrya5nPQ7xghNo29jGf96TQsHg7FGx+GJvNTYereczOQDw5T7X9OPz82hAIHGl4BPCtahrt6Cy2YRoAXtycMXE6TH6YXV6JcTXuHl7Q9mW2neqFQD84iSqlJkblmX505RyGnPjcXBz00039fr6t7/97RnXoYngwjtvdDy+2FuNL/ZW44ELcqFQMDhS24EDVe1QKxnML0yWeolEJtKi9ahrt6Ci2YRx7nqtvqiVaiwpWsJ/Phg5zZQixBsFKeFgGKCqtQsNHRaPezSxLIu97swNBTcDq203o9Nih1LBIENGmV2Pg5vVq1eLuQ7Sj7kFiQjTqVDZ3IVfTjTh7OxYrN5cCgCYlRsv6Dt04t9So/XYUdYyaFGxRqnBsxc+6/H9cpkbORdVEtKXcJ0a2fGhOFrXid0VLZhbkOjR7WrbzWjstECpYPgj5XImZXDTM7OrUcknsyuflZA+hWiUuGJ8CgDg5fXHcLi2HR/vOgUA+P25NLyUdBOr1w1lbog/4yZU7+7j1Gl/9la6sjY5CWF+UdMoZc0N/+ZHZs8PFNz4gd/PzIJOrcD2smZctHITHE4W5+fFU38b0ku6u9dNedPAwY2TdaKstQxlrWUejV84Xu86Bk6ZG+KPuODm9EMZA+HrbfygmBiQR+ZGbs8PFNz4gRFRerxw3Xg+5Tc6MQxPXV0o8aqI3GTGuva7SxuNA16vy9aFzJcykflS5qDjF9pMNjS63w1m0TFw4ocmpkcCcAUsp7fU6A83s298WqRIqxIWBTdn8noqOJHGvLFJmJoZjaqWLhQkh0NFp1bIabjgprbdDKPFDoN2+H/ex91bUonhOoTpBi8+JkRusmJDEa5Tod1sx6GadhSOiBzw+mabg+8Kf1amf2THueCmyWiFw8lCqfBdmxa5di+nV0g/EhuqxbjUSApsSJ8i9RpE6V0BCDfnZbj4ept4ytoQ/6RQMJji3sLfcqJp0OvvqWyF1e5EbKiWf8Mgd9EGDRgGcDhZtJisPnvcVpMVjZ2ux5NbTR69ShISQLgn47JGYYqK+XdlMnviIsQbM0a5urj/fLxx0OtuO+ma2XdWVrTfNKpVKxWI1rtOzvpya4rbkkqO0AmSKRYSBTeEBJDMWFcQUtrYKcj9cc25Rsos5UyIN87JdgU320ubYbY5BrzutlJXdmean2xJcaSouzku4+cHCm4ICSBc0e/JQYqKPcUNzKTMDfFno+JDkRCuhcXuxK7y/o+Em20O/sj4WVkxvlqeIKQMbuRWbwNQcENIQOE6hA52YsoTFrsD5U10DJz4P4Zh+K2pn4419Hu9X040wmxzIilCh2w/+52XotdNmbvtRJYMa5PktUkWhBgVg+Q7kvnPCRkOT46DqxQq3DH5Dv7z/pQ3meBkgTCtyuO29YTI1azceHy6uwrfH6zDIxeN7rOe5odDroHE542O95t6G44UmZtTLa7gJjVa77PH9BQFNxJTaBXI+VuO1MsgASIj1vUk02qyocVoRVQf4zm0Ki3+dsnfBr2vnvvp/vZET8jpzhsdD41KgZONRhyq6UB+cu8J1g4nix9K6gAA5+clSLHEYfF1cMOyLD/qRY7BDW1LERJA9BoVkiJ0AIDSYR4H54MbqrchASBUq8KsnDgAwH/2Vp3x/Z+PN6K+w4JIvRrTR/lXvQ3g++Cm2WiFyeoAwwApkSE+eUxvUHAjMZZlYW2wwtpgBcuyUi+HBAB+a6qh7+CGZVk0GBvQYGwY8HfuaF0HAKq3IYHjqokjAAAf7zx1xqmpj3dWAgAuG5cMrUr+86RO5+uaGy5rkxCmg04tv38vCm4k5jQ58Uv8L/gl/hc4TZ61BidkIBmD1N2YbCbEPxeP+OfiYbL13w+HC25GJ4YJv0hCJHB+XjySI3RoNlrxeXF39qas0Yj/HqgFAFw3OVWq5Q2LrzM3XHCTJsMtKYCCG0ICTpaHM6YGYrU7cdKd+cml4IYECJVSgVvOzgQAPP/9UXSYbWBZFs9+ewQOJ4vZuXEY4yfDMk/HBTdtXTZY7AP38hHCqRbXXDo51tsAVFAsOaVBiVnsLKmXQQIIty01nF43Jxs7YXeyCNN11/AQEghuLErHB1vLUdZkwqL3d2JCWhS+3l8DpYLB/RfkSr28IYsIUUOtZGBzsGjstIpeB1PRxBUTy6/eBqDMDSEBp/s4eCeczqHVcR2pdW1J5SaE0UkpElC0KiVeun4CDBoltp5sxmsbTgAAHpqbi7Ej/DNrA7h6+fB1Nz7YmqpsoW2pfr322msoLCxEeHg4wsPDUVRUhP/+978D3mbjxo2YNGkSdDodsrKy8Prrr/totYT4h/QYAzQqBcw2J/8E5C0uuMmhLSkSgMalRuKj24pwfl48pmZE45lrCnHbuVlSL2vYfFl3I+dj4IDE21IjRozAU089hVGjRgEA3nvvPVx++eUoLi5GQUHBGdcvLS3FvHnzsGjRInzwwQfYvHkz7rjjDsTFxeHqq6/29fIF4TA7cPjGwwCA0X8fDaVOflXnxL8oFQyy40NxsLodh2s7kB7jffdQKiYmgW7siAi8fdMUqZchKF8FNzaHEzVtZgCUuenTpZdeinnz5iEnJwc5OTl48sknERoaiq1bt/Z5/ddffx1paWlYuXIl8vLycOutt+KWW27Bc8895+OVC8gBNHzSgIZPGgDxa8BIkMhNcAUlR90ZGG8d5jI3CRTcEOIvfBXc1LSa4XCy0KgU/FaY3MimoNjhcODjjz+G0WhEUVFRn9fZsmULLrzwwl6XzZ07F++88w5sNhvUavUZt7FYLLBYuv9Ht7e3C7twQmSI2046UndmcKNSqHDTuJv4z0/XabHzJyFyKbghxG/EhbmK/xs6zaI+DrfdnRoVAoVCnjV5kgc3+/fvR1FREcxmM0JDQ/HZZ58hPz+/z+vW1tYiIaF3W+yEhATY7XY0NjYiKSnpjNusWLECy5cvF2XthMgVn7npI7jRqrRYc8Wafm/L3SY+TNvn+AZCiDz5KnMj9x43gAxOS+Xm5mLPnj3YunUr/vCHP+Cmm25CSUlJv9c//eQG12G1vxMdS5cuRVtbG/9RWVkp3OIJkSmuN83JBiOsdu+aQx6u6eh1H4QQ/+Cr01KVMi8mBmSQudFoNHxB8eTJk7Fjxw689NJLeOONN864bmJiImpra3tdVl9fD5VKhZiYvmeBaLVaaLXy3BMkRCxJETqEaVXosNhR2mjsFaiwLMt3Jtar9We8Mdhf1QYAftvMjJBgxWVu6ilzI33m5nQsy/aqkempqKgI33//fa/LvvvuO0yePLnPehtCghXDMHzdzeHa3nVmJpsJoStCEboitM/xCwer3cFNMgU3hPiT+B7bUmLOKuQyNyOiKLjp06OPPopNmzahrKwM+/fvx2OPPYYNGzbghhtuAODaUlqwYAF//dtvvx3l5eW4//77cejQIbz77rt45513sGTJEql+BEJkK2eAupv+WO1OfltqLGVuCPErse5tKYvdiQ6LXbTHqXQfOJBz5kbSbam6ujrceOONqKmpQUREBAoLC/HNN9/gggsuAADU1NSgoqKCv35mZibWrVuH++67D3/729+QnJyMl19+2W973BAiptwE1zTvI14cBz9a1wGrw4lwnUq2bdUJIX0L0Sj57eiGDgvCdcLvaHRa7Gg2WgHId/QCIHFw88477wz4/TVr1pxx2cyZM7F7926RVkRI4Mh3bytxNTSe4LekUiJo7AIhfiguTMsHNyPjQgW/f25LKkqvRpgIwZNQZFdzQwgRxpiUcCgYoK7dgrp2z/peUDExIf4tVuTj4HIfu8Ch4IaQAKXXqPi6m72VrR7dZm+lK7ihehtC/JPYvW784Rg4QMENIQGt0D3leN+pwbemjBY7SmpcJ6smpUeJui5CiDj4Xjed4gY3ci4mBmTQ5yboKYG4a+L4zwkRUuGISPx75ynsPdXKX6ZUKHFN/jX855w9la1wOFkkR+iQHCnfQkFCSP9Ez9y4T0qlyvgYOEDBjeSUOiUKPj5zAjohQhg3IhKAK3PDsiwYhoFOpcPH1358xnV3lrUAACZnRPtyiYQQAYkd3PhDAz+AtqUICWi5iWHQqhRo67LhREPngNfdWd4MAJicQVtShPgrMYMblmV71NzIO7tLwQ0hAUyjUvDByubjTf1ez+FkUVzRCoDqbQjxZ2LW3DR0WGCxO6FgIPutawpuJOYwOrCB2YANzAY4jA6pl0MC0PSRsQCAzccbAQBGqxHMcgbMcgZGqxEAsPdUKzotdoTrVBidGC7ZWgkhw8ONYGjqtMDhFHYEA7cllRQRArVS3uGDvFdHCBm2GaNcwc3Wk039PtltPNIAADg7OxZKBTXvI8RfRRs0YBjAyYLvJCyUyhb/qLcBKLiRnEKvwPT66ZhePx0KPf3vIMIbmxKBMJ0K7WY7DvTTrfinY67gZmZOnC+XRggRmEqpQIxBA0D4upuKJvnPlOLQq6nEGIaBJk4DTZyG2t0TUSgVDKZlxQAANrgzND3VtZuxx93k71wKbgjxe7Ei1d1wmRu5FxMDFNwQEhTmFiQCAL7cVw2W7b019fW+GrCsq5A4KUL+T1qEkIGJdWLKX0YvABTcSM5pceLo4qM4uvgonBan1MshAerCggRolAocr+/EkbreU8L/s6cKADC/MEmKpRFCBCZWcHOKghviKdbOonpVNapXVYO1C1vZTggnXKfGeaPjAQD/2FbOX15c0Yy9p9qgUSowvzBZquURQgQkRnBjsTtQ4x7A6w81N9ShmJAgces5mfjmYC2+2FOH87LnQqdWYtWGUgDAlRNS+CdEQoh/E6PXTXWrGSwLhKiVfMGynFHmhpAgMTkjGtOyomF3qBBm/CPOi3sBvxzvgEalwO9nZkm9PEKIQLozN2bB7rPn2AV/OPxCwQ0hQeTZa8YhTKfCnspWvPK/4wCAh+bmIisuVOKVEUKEIsa2lD8VEwMU3BASVFKj9fjgd2fhrMxojIoPxf/Nz8fvzs6UelmEEAHFixDcnPKTmVIcqrkhJMiMSlDjq6bzAQD/mVrvFylmQojn4kJ1AIB2sx1mmwM6tXLY9+kv08A5lLkhJAiZbCaYbCapl0EIEUF4iAoa9+ynRoGKivkGflEU3BBCCCHExxiG4etu6tqFCW4qmtyZmxgKbgghhBAigYRwV3BT3z78E1NtJhvazXYAwIgo/6i5oeCGEEIICTCJEa66m1oBghtuSyo2VAu9xj9KdSm4IYQQQgJMQriAwY2fnZQCKLghhBBCAk6iO7ipaxt+cONvJ6UAOgouPQUQMTOC/5wQsSkYBWamz+Q/J4QEHjG2pSi4IR5ThigxYcMEqZdBgkiIOgQbFm6QehmEEBFx21JCnJaqaO4C4D/HwAHKFRBCCCEBh9uWqm0zg2XZYd1XpZ+NXgAouCGEEEICDrct1WVz8Me4h8LhZFHV4s7cUEEx8ZTD6MDmuM3YHLcZDqND6uWQIGC0GhH3bBzino2D0WqUejmEEBHo1EpEhKgBAHXDqLupazfD6nBCpWCQFEHBDfGCrdEGW6NN6mWQINJoakSjqVHqZRBCRNRza2qouC2plKgQKBX+M4eOCoolpghRYMqBKfznhBBCiBASInQ4UtcxrBNT/ngMHKDgRnKMgoGhwCD1MgghhASYRPcIhuH0uqnk6238K7ihVAEhhBASgBIF6FLMn5Tyo2PgAGVuJOe0OlH+13IAQPqj6VBoKN4khBAyfAkRXK+b4Qc3tC1FvMLaWJQvdwU3aQ+mARqJF0QIISQgJIQNP3NT7odzpQAKbggJOgpGgcnJk/nPCSGBiR/B0Da0LsUmqx0NHa7bpkf7V20oBTeEBJkQdQh2LNoh9TIIISLjgpsmowVWuxMalXdvZriTUhEhakTo1YKvT0z0to0QQggJQDEGDTQqBVh2aHU35U2u4CY9xr/qbQAKbgghhJCAxDAMUiJdtTKn3Ee6vVHR5J/FxAAFN4QEHZPNhIyVGchYmQGTzST1cgghIuKCm6pW74Ob8mbXeBZ/zNxQzQ0hQYZlWZS3lfOfE0ICFxfcVA8huKlodt3G34qJAcrcEEIIIQErJcqduRnStpQrc+Nv3YkBCm4IIYSQgJU8xG0pu8PJ1+n447YUBTeEEEJIgBrqtlRNmxl2JwuNSsGPcfAnFNwQQgghAWpEVHfmxpsaO+4YeGpUCBQKRpS1iUnS4GbFihWYMmUKwsLCEB8fjyuuuAJHjhwZ8DYbNmwAwzBnfBw+fNhHqyaEEEL8Q0K4DgwDWOxONHZaPb5d90kp/ysmBiQ+LbVx40YsXrwYU6ZMgd1ux2OPPYYLL7wQJSUlMBgG/gc9cuQIwsPD+a/j4uLEXq44GECfr+c/J0RsDMMgPy6f/5wQErg0KgUSwnSobTejurULcWFaj27nzz1uAImDm2+++abX16tXr0Z8fDx27dqFc889d8DbxsfHIzIyUsTV+YZSr8TUg1OlXgYJInq1HgfvOCj1MgghPpISFYLadjOqWrswLjXSo9uUNvpvjxtAZjU3bW1tAIDo6OhBrzthwgQkJSVhzpw5+PHHH8VeGiGEEOKXuOwLNyvKEyfdwc3IuFBR1iQ22TTxY1kW999/P84++2yMGTOm3+slJSXhzTffxKRJk2CxWPD3v/8dc+bMwYYNG/rM9lgsFlgs3RNR29vbRVk/IYQQIkdc9qXc3bdmMHaHk79uVhzV3AzLnXfeiX379uHnn38e8Hq5ubnIzc3lvy4qKkJlZSWee+65PoObFStWYPny5YKvVygOkwO7puwCAEzaMQlKvVLiFZFAZ7KZMOWtKQCAHYt2QK/2z7QzIcQzXHBT1uhZ5qaypQs2BwudWoHkiBAxlyYaWWxL3XXXXfjiiy/w448/YsSIEV7fftq0aTh27Fif31u6dCna2tr4j8rKyuEuV1gsYCoxwVRiAqgTPvEBlmVR0lCCkoYSGr9ASBDgTjx5mrk52dAJAMiMDfXLY+CAxJkblmVx11134bPPPsOGDRuQmZk5pPspLi5GUlJSn9/TarXQaj2rDpeCQqfAuB/H8Z8TQgghQspwBzfVbWaYbQ7o1APvEJxwBzf+uiUFSBzcLF68GP/85z/xn//8B2FhYaitrQUAREREICTElQpbunQpqqqq8P777wMAVq5ciYyMDBQUFMBqteKDDz7A2rVrsXbtWsl+juFglAyiZkVJvQxCCCEBKkqvRphOhQ6zHRXNJuQkhA14/ZMN/l1MDEgc3Lz22msAgFmzZvW6fPXq1Vi4cCEAoKamBhUVFfz3rFYrlixZgqqqKoSEhKCgoABff/015s2b56tlE0IIIX6DYRhkxBiwv6oNZY1GL4IbytwMiSf7/WvWrOn19UMPPYSHHnpIpBX5ntPmRM2bNQCApNuSoFDT1hQhhBBhpcfosb+qjR+rMBBuW4oyN2TIWCuLY3e6iqETFyYCaokXRAghJOBwdTdlgxQVN3Za0GS0gmGo5oYQ4kcYhkF6RDr/OSEk8HHHwbnOw/05VOPqBZcRY4Be478hgv+unBAyJHq1HmX3lkm9DEKID2W762yO1nUOeL3DNR0AgNGJA9flyB0VeBBCCCEBLjveVT/T2GlBs7H/6eBc5iYvKbzf6/gDCm4IIYSQAGfQqjAiytVi5WhdR7/XK6HghhDij7psXZjy1hRMeWsKumxdUi+HEOIjue6tqWP9BDdWu5M/KeXv21JUc0NIkHGyTuys3sl/TggJDtkJYVh/uB6Ha/sObk40dMLmYBGm687y+CvK3BBCCCFBID/ZtdV0oKqtz+9zl+clhvv9SUoKbgghhJAgMG5EBADgUE0HrPYzs7a7K1oAABPSIn25LFFQcEMIIYQEgbRoPSL1algdThyubT/j+7vKXcHNxHT/n3dIwQ0hhBASBBiGwdgUV/Zm76neW1NtXTa+B87ENApuCCGEEOInuMBl28mmXpfvKmsG4OpkHBem9fm6hEbBjQyoY9VQx9JQKeI7sfpYxOpjpV4GIcTHzsl2/d1vPt4Ip7N7ePVPxxoAAOdmx0myLqHRUXCJKQ1KzGiYIfUySBAxaAxoeLBB6mUQQiQwLjUSYVoVWkw2lNS0Y4z78k3HGgEA542Ol25xAqLMDSGEEBIk1EoFikbGAAC+K6nlL69uNUOrUmBaVoxUSxMUBTeEEEJIELlq4ggAwKe7T/W6/JLCJIRolFIsSXAU3EjM0eVA8axiFM8qhqPLIfVySBDosnVh1ppZmLVmFo1fICQIzcmLR0K4Fk2dtl6X31SUIc2CREDBjdScQNvGNrRtbAOoEz7xASfrxMbyjdhYvpHGLxAShNRKBRbPHtXrspm5cRiXGinNgkRABcUSY7QM8v+dz39OCCGEiO23Z6Wjub4ZeNH19VNXjZV2QQKj4EZiCpUC8dcGRnU6IYQQ/6BQMLj3/Fz+68j/b+/+Q6uu9ziOv3Z29pvOIE1bHjm50NSwVRszJyLUWtRAEkqpCIuCRkSrUbFu0RKCyDDK0IJh9s9W0nDQH1YbQeuosVATohMoTsOhq2bJOc6m7ex9//But+WyndP5sX2+zwcccF8/Z7wOr52d974/zinOz2Ka1OOwFAAAcAp7brJsdGRUg50X319g9trZ8vmZNwEA+DcYbrLMzpsi6yKSpFVnV9EIAAD/Ei+lgAcV5xVnOwIApA3DDeAxJfklGvrPULZjAEDacIIHAABwCsMNAABwCsMN4DHDI8Oqb69XfXu9hkeGsx0HAFKOc24Aj4mPxrX7yO7xfwOAa9hzAwAAnMJwAwAAnMJwAwAAnMJwAwAAnMJwAwAAnOK5q6XMTJIUjUaznOSi+FBcQ7r4brHRaFS58dwsJ4Lrhi4MSf+7AjwajSqezxVTgCcN/emdyqNRKT69fxeMvW6PvY5fTo5NZZVD+vv7NX/+/GzHAAAASThx4oSCweBl13huuBkdHdXJkyd1xRVXKCcnJ6XfOxqNav78+Tpx4oQCgUBKvzcSRx/TC31MP3QyvdDH5ZmZYrGYrrnmGvl8lz+rxnOHpXw+3z9OfP9WIBDgB3MaoY/phT6mHzqZXujj75WWlk5pHScUAwAApzDcAAAApzDcpFBBQYFaWlpUUFCQ7SgQfUw39DH90Mn0Qh+p47kTigEAgNvYcwMAAJzCcAMAAJzCcAMAAJzCcAMAAJzCcJOgbdu2acGCBSosLFRlZaXC4fBl1/f09KiyslKFhYUqLy/Xe++9l6Gk3pBIH7t27dIdd9yhq666SoFAQCtWrNDnn3+ewbTuS/T5MWbv3r3y+/266aab0hvQYxLt4/z583rxxRcVCoVUUFCg6667Tu+//36G0npDop20tbWpoqJCxcXFKisr0yOPPKLTp09nKO0MZpiyjz76yPLy8qy1tdUikYg1NjZaSUmJ/fjjj5Ou7+vrs+LiYmtsbLRIJGKtra2Wl5dnHR0dGU7upkT7aGxstNdff92++eYbO3z4sL3wwguWl5dnBw8ezHByNyXax5gzZ85YeXm51dXVWUVFRWbCekAyfaxZs8aWL19u3d3dduzYMevt7bW9e/dmMLXbEu0kHA6bz+ezt99+2/r6+iwcDtsNN9xg99xzT4aTzzwMNwmorq62hoaGCdsWL15szc3Nk65//vnnbfHixRO2Pf7443brrbemLaOXJNrHZJYuXWobN25MdTRPSraP9evX20svvWQtLS0MNymUaB+ffvqplZaW2unTpzMRz5MS7eSNN96w8vLyCdu2bNliwWAwbRldwWGpKbpw4YIOHDigurq6Cdvr6uq0b9++Se/z9ddfX7L+zjvv1P79+/XHH3+kLasXJNPHX42OjioWi+nKK69MR0RPSbaPHTt26OjRo2ppaUl3RE9Jpo9PPvlEVVVV2rRpk+bNm6dFixbp2Wef1e+//56JyM5LppOamhr19/dr9+7dMjP99NNP6ujoUH19fSYiz2ie++DMZA0ODioej2vu3LkTts+dO1cDAwOT3mdgYGDS9SMjIxocHFRZWVna8roumT7+avPmzRoaGtK6devSEdFTkunjyJEjam5uVjgclt/Pr6JUSqaPvr4+7dmzR4WFhers7NTg4KCeeOIJ/frrr5x3kwLJdFJTU6O2tjatX79ew8PDGhkZ0Zo1a/TOO+9kIvKMxp6bBOXk5Ez42swu2fZP6yfbjuQk2seYDz/8UK+88op27typOXPmpCue50y1j3g8rgceeEAbN27UokWLMhXPcxJ5foyOjionJ0dtbW2qrq7W3XffrTfffFMffPABe29SKJFOIpGInnrqKb388ss6cOCAPvvsMx07dkwNDQ2ZiDqj8efSFM2ePVu5ubmXTNg///zzJZP4mKuvvnrS9X6/X7NmzUpbVi9Ipo8xO3fu1KOPPqqPP/5YtbW16YzpGYn2EYvFtH//fn377bd68sknJV18cTUz+f1+dXV16bbbbstIdhcl8/woKyvTvHnzVFpaOr5tyZIlMjP19/dr4cKFac3sumQ6ee2117Ry5Uo999xzkqQbb7xRJSUlWrVqlV599VX2/l8Ge26mKD8/X5WVleru7p6wvbu7WzU1NZPeZ8WKFZes7+rqUlVVlfLy8tKW1QuS6UO6uMfm4YcfVnt7O8etUyjRPgKBgL777jsdOnRo/NbQ0KDrr79ehw4d0vLlyzMV3UnJPD9WrlypkydP6uzZs+PbDh8+LJ/Pp2AwmNa8XpBMJ+fOnZPPN/FlOjc3V9L/jwLgb2TrTOaZaOwyvu3bt1skErGnn37aSkpK7Pjx42Zm1tzcbA899ND4+rFLwZ955hmLRCK2fft2LgVPoUT7aG9vN7/fb1u3brVTp06N386cOZOth+CURPv4K66WSq1E+4jFYhYMBu3ee++177//3np6emzhwoX22GOPZeshOCfRTnbs2GF+v9+2bdtmR48etT179lhVVZVVV1dn6yHMGAw3Cdq6dauFQiHLz8+3W265xXp6esb/b8OGDbZ69eoJ67/88ku7+eabLT8/36699lp79913M5zYbYn0sXr1apN0yW3Dhg2ZD+6oRJ8ff8Zwk3qJ9vHDDz9YbW2tFRUVWTAYtKamJjt37lyGU7st0U62bNliS5cutaKiIisrK7MHH3zQ+vv7M5x65skxY98WAABwB+fcAAAApzDcAAAApzDcAAAApzDcAAAApzDcAAAApzDcAAAApzDcAAAApzDcAAAApzDcAAAApzDcAAAApzDcAHBGR0eHli1bpqKiIs2aNUu1tbUaGhrKdiwAGebPdgAASIVTp07p/vvv16ZNm7R27VrFYjGFw2Hx8XmA9/DBmQCccPDgQVVWVur48eMKhULZjgMgizgsBcAJFRUVuv3227Vs2TLdd999am1t1W+//ZbtWACygD03AJxhZtq3b5+6urrU2dmpgYEB9fb2asGCBdmOBiCDGG4AOCkejysUCqmpqUlNTU3ZjgMggzihGIATent79cUXX6iurk5z5sxRb2+vfvnlFy1ZsiTb0QBkGMMNACcEAgF99dVXeuuttxSNRhUKhbR582bddddd2Y4GIMM4LAUAAJzC1VIAAMApDDcAAMApDDcAAMApDDcAAMApDDcAAMApDDcAAMApDDcAAMApDDcAAMApDDcAAMApDDcAAMApDDcAAMApDDcAAMAp/wVk6Hx8LNffuAAAAABJRU5ErkJggg==", + "image/png": 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", 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", 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", 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" ] @@ -365,14 +388,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", "energy_fluc = [dbi.energy_fluctuation(state=state)]\n", - "iters = 50\n", + "iters = 10\n", "dbi_ = deepcopy(dbi)\n", "for _ in range(iters):\n", " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", @@ -383,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -392,13 +415,13 @@ "Text(0, 0.5, 'Energy fluctuation')" ] }, - "execution_count": 11, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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iVYqiSwvzdoe/SokWo4jDBTqpyyEiInI4FndLLVy4ELfddhu+//57DB8+HIIgYM+ePcjPz8c333xjixrpNwRBQHKkNzJzSqDNr0JKtK/UJRERETkUi1tuRo0ahRMnTuCOO+5AVVUVKioqcOedd+L48eO48cYbbVEj/Q53CCciIro0i1tuACA0NJQDhyXEHcKJiIgurVvh5uDBg0hISIBMJsPBgwe7vHbw4MFWKYwubXC4N2QCUFBVjxJ9A4LUblKXRERE5DC6FW6SkpJQXFyMwMBAJCUlQRAEiKLY4TpBEGAwcAaPramUcsQFeeFYcTWy86pwa0Kw1CURERE5jG6Fm9zcXAQEBJh+J+klR3rjWHE1tPkMN0RERL/VrQHFUVFRpk0az507h7CwMERFRZk9wsLCcO7cOZsWSxclR7RuoskdwomIiMxZPFvqpptu6nSxPp1Oh5tuuskqRdHlJbUNKj54XocWA/f0IiIiamdxuBFF0dSK81vl5eXw9PS0SlF0ebEBKngp5ahvNuBESY3U5RARETmMbk8Fv/POOwG0DhqePn06lEql6ZzBYMDBgwcxYsQI61dInZLJBCRGeGPXqTJk51civm23cCIiot6u2y03Go0GGo0GoijCy8vL9Fyj0SA4OBiPPvooPv30U4vefOfOnZg4cSJCQ0MhCAI2b97c5fUbN27E2LFjERAQALVajeHDh+O7776z6D2dCRfzIyIi6qjbLTerVq0CAERHR+PZZ5+1ShdUbW0tEhMTMWPGDNx1112XvX7nzp0YO3YsFixYAG9vb6xatQoTJ07EL7/8guTk5Kuup6fhYn5EREQdCWJnC9ZIQBAEbNq0CZMnT7bovoEDB2Lq1Kl48cUXOz3f2NiIxsZG03O9Xo+IiAjodDqo1T27K6e8phFDX/seAHDgpXHQuLtKXBEREZFt6PV6aDSabn1/X9H2C59//jn+/e9/Iy8vD01NTWbnfv311yt5yStiNBpRXV0NX99Lbx6ZkZGBefPm2a0me/JTKRHp64G8ijocyK/CyLgAqUsiIiKSnMWzpd59913MmDEDgYGByM7OxrXXXgs/Pz+cOXMGaWlptqjxkhYtWoTa2lpMmTLlktfMnTsXOp3O9MjPz7djhbbHrikiIiJzFoebpUuXYsWKFViyZAkUCgX+9re/ITMzE08++SR0Op0tauzU+vXr8fLLL2PDhg0IDAy85HVKpRJqtdrs4UySTYOKuZgfERERcAXhJi8vzzTl293dHdXV1QCAadOmYf369dat7hI2bNiAhx9+GP/+979xyy232OU9HVVSZOtKxdr8qk73+yIiIuptLA43wcHBKC8vB9C6LcP//vc/AK17Ttnjy3X9+vWYPn06PvvsM9x22202fz9HFx+ihkIuQ2VdM86V10ldDhERkeQsDjc333wzvv76awDAww8/jGeeeQZjx47F1KlTcccdd1j0WjU1NdBqtdBqtQBaA5JWq0VeXh6A1vEy6enppuvXr1+P9PR0LFq0CNdffz2Ki4tRXFxs1+4wR6OQy5DQtoBfdj67poiIiCyeCm40GmE0GiGXt060+ve//41du3YhNjYWs2bNgkKh6PZr/fjjj53uR/XQQw9h9erVmD59Os6ePYsff/wRADB69Gjs2LHjktd3hyVTyXqKV77OwcrduUgfHoVXbk+QuhwiIiKrs+T722HWubEXZww3Xx8oxJ/XZ2NwuAZfPXGD1OUQERFZnU3Xudm5c2eX50eOHGnpS9JVap8OnlOoR0OzAW6uLtIWREREJCGLw83o0aM7HPvtLuEGg+GqCiLLhXm7w1+lRFlNIw4X6JASfelFDYmIiJydxQOKKysrzR6lpaXYsmULhg0bhq1bt9qiRroMQRC4mB8REVEbi1tuNBpNh2Njx46FUqnEM888g/3791ulMLJMUoQ3MnNKuEM4ERH1eha33FxKQEAAjh8/bq2XIwu1t9xwpWIiIurtLG65OXjwoNlzURRRVFSE119/HYmJiVYrjCwzONwbMgEo1DWgRN+AILWb1CURERFJwuJwk5SUBEEQOqxGfP3112PlypVWK4wso1LKERfkhWPF1cjOq8KtCcFSl0RERCQJi8NNbm6u2XOZTIaAgAC4ubGlQGrJkd6t4Sa/kuGGiIh6LYvH3OzYsQPBwcGIiopCVFQUIiIi4ObmhqamJnzyySe2qJG6KTmibRNNDiomIqJezOJwM2PGjE73cqqursaMGTOsUhRdmaS2QcUHz+vQYjBKWwwREZFELA43oiiaLdrX7vz5851OEyf7iQ1QwUspR32zAcdLqqUuh4iISBLdHnOTnJwMQRAgCALGjBlj2jgTaF2VODc3F7feeqtNiqTukckEJEZ4Y9epMmjzqzAwlGGTiIh6n26Hm8mTJwMAtFotxo8fD5VKZTqnUCgQHR2Nu+66y+oFkmWS2sJNdl4VHrguSupyiIiI7K7b4eall14CAERHR+Pee++FUqm0WVF05bgNAxER9XYWj7mJj4+HVqvtcPyXX35BVlaWNWqiq5AU4Q0AOFVaA119s7TFEBERScDicDN79mzk5+d3OF5QUIDZs2dbpSi6cn4qJSJ9PQAAB9h6Q0REvZDF4SYnJwdDhgzpcDw5ORk5OTlWKYquDrumiIioN7M43CiVSpSUlHQ4XlRUZDaDiqST3NY1xU00iYioN7I43IwdOxZz5841W8ivqqoKzz//PMaOHWvV4ujKJEW2rVScX9VhDzAiIiJnZ3FTy6JFizBy5EhERUUhOTkZQOv08KCgIKxdu9bqBZLl4kPUUMhlqKxrxrnyOkT7e0pdEhERkd1YHG7CwsJw8OBBrFu3DgcOHIC7uztmzJiB++67D66urraokSykkMuQEKrGr3lVyM6vZLghIqJe5YoGyXh6euLRRx+1di1kRUkRPq3hJq8KdySHS10OERGR3Vgcbi6383d6evoVF0PWkxzpDezmjCkiIup9LA43Tz31lNnz5uZm1NXVQaFQwMPDg+HGQbRPB88p1KOh2QA3VxdpCyIiIrITi2dLVVZWmj1qampw/Phx3HDDDVi/fr0taqQrEObtDn+VEi1GEYcLdJe/gYiIyElYHG46069fP7z++usdWnVIOoIgcDE/IiLqlawSbgDAxcUFhYWF1no5soIk02J+VZLWQUREZE8Wj7n56quvzJ6LooiioiIsWbIEqampViuMrl57yw1XKiYiot7E4nAzefJks+eCICAgIAA333wzFi1aZK26yAoGh3tDJgCFugaU6BsQpHaTuiQiIiKbszjcGI1GW9RBNqBSyhEX5IVjxdXIzqvCrQnBUpdERERkc1Ybc0OOydQ1lc+uKSIi6h261XIzZ86cbr/g4sWLr7gYsr7kCB+s35sPLQcVExFRL9GtcJOdnd2tFxME4aqKIetLamu5OXhehxaDEXIXNtYREZFz61a4eeeddzBw4EC4uHCV254mNkAFL6Uc1Y0tOF5SjYGhGqlLIiIisqlu/W98cnIyKioqAAB9+vRBeXm5TYsi65HJBCS2rXfDxfyIiKg36Fa48fb2xpkzZwAAZ8+e5YypHoaL+RERUW/SrW6pu+66C6NGjUJISAgEQUBKSsolu6jaQxA5Di7mR0REvUm3ws2KFStw55134tSpU3jyySfxyCOPwMvLy9a1kZW0t9ycvlALXX0zNO6u0hZERERkQ91exO/WW28FAOzfvx9PPfUUw00P4qdSItLXA3kVdTiQX4WRcQFSl0RERGQzFs8LXrVqldWCzc6dOzFx4kSEhoZCEARs3rz5svfs2LEDQ4cOhZubG/r06YPly5dbpRZnd7FrqkrSOoiIiGxN0kVPamtrkZiYiCVLlnTr+tzcXEyYMAE33ngjsrOz8fzzz+PJJ5/EF198YeNKe75k04wpjrshIiLnZvHeUtaUlpaGtLS0bl+/fPlyREZG4u233wYADBgwAFlZWXjrrbdw11132ahK55AU6QOgdTq4KIpccJGIiJxWj1qu9ueff8a4cePMjo0fPx5ZWVlobm7u9J7Gxkbo9XqzR28UH6KGQi5DZV0zzpXXSV0OERGRzVgcbmpra21RR7cUFxcjKCjI7FhQUBBaWlpQVlbW6T0ZGRnQaDSmR0REhD1KdTgKuQwJoWoA3ESTiIicm8XhJigoCDNnzsSuXbtsUc9l/b47RRTFTo+3mzt3LnQ6nemRn59v8xodVVJEa9cUBxUTEZEzszjcrF+/HjqdDmPGjEFcXBxef/11FBYW2qK2DoKDg1FcXGx2rLS0FHK5HH5+fp3eo1QqoVarzR69VfuMKW7DQEREzszicDNx4kR88cUXKCwsxGOPPYb169cjKioKf/jDH7Bx40a0tLTYok4AwPDhw5GZmWl2bOvWrUhJSYGrKxemu5z2cJNTqEdDs0HaYoiIiGzkigcU+/n54ZlnnsGBAwewePFifP/997j77rsRGhqKF198EXV1lx+0WlNTA61WC61WC6B1qrdWq0VeXh6A1i6l9PR00/WzZs3CuXPnMGfOHBw9ehQrV67Exx9/jGefffZK/4xeJczbHf4qJVqMIg4X6KQuh4iIyCauONwUFxdj4cKFGDBgAJ577jncfffd2LZtG/75z39i06ZNmDx58mVfIysrC8nJyUhOTgYAzJkzB8nJyXjxxRcBAEVFRaagAwAxMTH45ptv8OOPPyIpKQmvvvoq3n33XU4D7yZBENg1RURETs/idW42btyIVatW4bvvvkN8fDxmz56NBx98EN7e3qZrkpKSTIGlK6NHjzYNCO7M6tWrOxwbNWoUfv31V0vLpjZJEd7IzCnhoGIiInJaFoebGTNm4N5778Xu3bsxbNiwTq/p06cPXnjhhasujqyPO4QTEZGzszjcFBUVwcPDo8tr3N3d8dJLL11xUWQ7g8O9IROAQl0DSvQNCFK7SV0SERGRVVk85qalpaXDir96vR7V1dVoamqyRY1kRSqlHHFBrRufsmuKiIickcXhxtvbGz4+Ph0e3t7ecHd3R1RUFF566SUYjUZb1EtWYOqa4krFRETkhCzullq9ejVeeOEFTJ8+Hddeey1EUcS+ffuwZs0a/OMf/8CFCxfw1ltvQalU4vnnn7dFzXSVkiN8sH5vPrRsuSEiIidkcbhZs2YNFi1ahClTppiOTZo0CYMGDcIHH3yAbdu2ITIyEvPnz2e4cVBJbS03B8/r0GIwQu7So/ZPJSIi6pLF32o///xzp9O8k5OT8fPPPwMAbrjhBrP1acixxAao4KWUo77ZgOMl1VKXQ0REZFUWh5vw8HB8/PHHHY5//PHHph23y8vL4ePjc/XVkU3IZAISI7wBcDE/IiJyPhZ3S7311lu455578O2332LYsGEQBAH79u3DsWPH8PnnnwMA9u3bh6lTp1q9WLKepAhv7DpVhuy8KjxwXZTU5RAREVmNxeFm0qRJOHHiBJYvX47jx49DFEWkpaVh8+bNiI6OBgA89thj1q6TrIyL+RERkbOyKNw0Nzdj3Lhx+OCDD5CRkWGrmsgOktq6pU5fqIWuvhkad+6qTkREzsGiMTeurq44fPgwBEGwVT1kJ34qJSJ9W1eaPsBxN0RE5EQsHlCcnp7e6YBi6nkudk1VSVoHERGRNVk85qapqQkfffQRMjMzkZKSAk9PT7PzixcvtlpxZFvJEd74UlsILVcqJiIiJ2JxuDl8+DCGDBkCADhx4oTZOXZX9SxJka3T9X/Nq8L5yjqE+3S9ISoREVFPYHG42b59uy3qIAnEh6jhr1KgrKYJN731I+6/NhKzb45FoBd3Ciciop7ritfdP3XqFL777jvU19cDAERRtFpRZB8KuQyfzLwOqbF+aDaIWPPzOYxcuB1vbDmGqjru8E5ERD2TIFqYSsrLyzFlyhRs374dgiDg5MmT6NOnDx5++GF4e3tj0aJFtqrVKvR6PTQaDXQ6HdRqtdTlOIw9p8rw5tbjpsHFXko5Hh3ZBzNuiIFKaXEDHxERkVVZ8v1tccvNM888A1dXV+Tl5cHD4+IYjalTp2LLli2WV0sOYUSsPzY+NgIfpaegf7AXqhtbsCjzBEYt3I6Pd+WiodkgdYlERETdYnHLTXBwML777jskJibCy8sLBw4cQJ8+fZCbm4tBgwahpqbGVrVaBVtuLs9oFPHfQ0X4Z+YJ5JbVAgBCNG54ckw/3D00HK7cRZyIiOzMpi03tbW1Zi027crKyqBUKi19OXJAMpmASYmhyHxmJN64axBCNW4o0jVg7sZDuGXxDnypLYDRyDFWRETkmCwONyNHjsQnn3xiei4IAoxGI958803cdNNNVi2OpCV3kWHqsEj88OxovPiHePh5KnCuvA5P/UuLCe/+hMycEg4kJyIih2Nxt1ROTg5Gjx6NoUOH4ocffsCkSZNw5MgRVFRUYPfu3ejbt6+tarUKdktdudrGFqzecxbLd5xGdUMLACAxwht/G38NUmP9Ja6OiIicmSXf3xaHGwAoLi7GsmXLsH//fhiNRgwZMgSzZ89GSEjIFRdtLww3V09X14wVP53Gyl1nUd820Hh4Hz88O/4aDI3ykbg6IiJyRjYPNz0Zw431XKhuxPvbT+GzX/LQZDACAG4ZEIi/jLsGA0L42RIRkfXYPNxUVVVh7969KC0thdFoNDuXnp5u6cvZFcON9RVU1ePd70/i81/Pw9A20HhiYiieuaUf+gSoJK6OiIicgU3Dzddff40HHngAtbW18PLyMttPShAEVFRUXFnVdsJwYztnLtTgn9+fxNcHCgEALjIB9wwNx5Nj+iHU213i6oiIqCezabiJi4vDhAkTsGDBgk6nhDs6hhvbO1Kow+KtJ7DtWCkAQOEiwwPXR+Lx0bEI8OJyAUREZDmbhhtPT08cOnQIffr0uaoipcJwYz/7z1Xize+O4X9nWlvz3F1dMPOGaDx6Y19oPFwlro6IiHoSmy7iN378eGRlZV1xcdR7DI3ywfpHrsenD1+HxHAN6psNeH/7ady48Ae8v/0U6ppapC6RiIickMUtNx9//DFeeeUVzJgxA4MGDYKrq/n/gU+aNMmqBVobW26kIYoiMnNKsGjrCRwvqQYA+KsUmH1TLO6/LhJKuYvEFRIRkSOzabeUTHbpxh5BEGAwOPYGiww30jIYRXx9oBD//P4EzpXXAQDCvN3x5JhY3DUkHHLuW0VERJ3gOjddYLhxDM0GI/6TdR7vbjuJYn0DAKCPvyeeGRuH2waFQCYTLvMKRETUmzDcdIHhxrE0NBvw6f/OYemPp1FR2wQAGBCixrPj4nBz/0CzpQaIiKj3ssmA4gkTJkCn05mez58/H1VVVabn5eXliI+Pt7xa6tXcXF3wxxv7YOffbsJfxsbBSynH0SI9Hl6ThbuW7cHPp8ulLpGIiHqYbrfcuLi4oKioCIGBgQAAtVoNrVZrmhJeUlKC0NBQjrmhq1JV14TlO85g9Z5cNDS3rn59Q6w/nh1/DZIivKUtjoiIJGOTlpvfZ6Be1ptFduLtocBzaf2x86834aHhUXB1EbDrVBkmv78bj3yShWPFeqlLJCIiB8epKeSQAtVumHd7An74y2jcMzQcMgHIzClB2js/4el/ZeNcea3UJRIRkYPqdrgRBKHD4E4O9iRbi/D1wJv3JGLrM6Nw26AQiCKwWVuIsf/cadrDioiI6Lfk3b1QFEVMnz4dSmXr3kANDQ2YNWsWPD09AQCNjY22qZAIQGygCu8/MASPFeiQ8e1R7D5Vjj+vz0ZeRR0eH92XQZuIiEy63XLz0EMPITAwEBqNBhqNBg8++CBCQ0NNzwMDA5Genm5xAUuXLkVMTAzc3NwwdOhQ/PTTT11ev27dOiQmJsLDwwMhISGYMWMGyss5o6a3SAjT4JOZ1+GPN8QAAN787jj+/sVBNBuMEldGRESOQtJ1bjZs2IBp06Zh6dKlSE1NxQcffICPPvoIOTk5iIyM7HD9rl27MGrUKPzzn//ExIkTUVBQgFmzZqFfv37YtGlTt96Ts6Wcx9qfz+Klr47AKAKpsX5Y+sBQaNy5IScRkTPqMYv4XXfddRgyZAiWLVtmOjZgwABMnjwZGRkZHa5/6623sGzZMpw+fdp07L333sPChQuRn5/f6Xs0NjaadZnp9XpEREQw3DiJ7cdK8cRnv6K2yYDYQBVWTR+GCF8PqcsiIiIrs+mu4NbS1NSE/fv3Y9y4cWbHx40bhz179nR6z4gRI3D+/Hl88803EEURJSUl+Pzzz3Hbbbdd8n0yMjJMXWcajQYRERFW/TtIWjf1D8R/Zo1AsNoNp0prcMfS3cjOq5S6LCIikpBk4aasrAwGgwFBQUFmx4OCglBcXNzpPSNGjMC6deswdepUKBQKBAcHw9vbG++9994l32fu3LnQ6XSmx6VaeKjnig9VY/PsVAwMVaOspgn3rvgfvj1UJHVZREQkEcnXufn9LBdRFC858yUnJwdPPvkkXnzxRezfvx9btmxBbm4uZs2adcnXVyqVUKvVZg9yPsEaN/z7T8Mxpn8gGluMeGzdr/hgx2kuNklE1AtJFm78/f3h4uLSoZWmtLS0Q2tOu4yMDKSmpuKvf/0rBg8ejPHjx2Pp0qVYuXIlior4f+q9nadSjhXpKZg+IhoAkPHtMTy/6TBnUhER9TKShRuFQoGhQ4ciMzPT7HhmZiZGjBjR6T11dXWQycxLdnFxAcDtIKiVi0zAy5MG4qWJ8ZAJwPq9eZi5eh/0Dc1Sl0ZERHYiabfUnDlz8NFHH2HlypU4evQonnnmGeTl5Zm6mebOnWu2ds7EiROxceNGLFu2DGfOnMHu3bvx5JNP4tprr0VoaKhUfwY5oBmpMfgwPQUeChf8dLIMdy/bg/OVdVKXRUREdtDtFYptYerUqSgvL8crr7yCoqIiJCQk4JtvvkFUVBQAoKioCHl5eabrp0+fjurqaixZsgR/+ctf4O3tjZtvvhlvvPGGVH8CObAxA4Lw7z8Nx8Nr9uFESQ0mv78HHz+UgkTuLk5E5NQkXedGClzEr/cp0tVj5uosHC3Sw81VhrenJuPWhGCpyyIiIgv0iHVuiOwlROOO/8wajpuuCUBDsxGPrduPD3ee4TgtIiInxXBDvYJKKceH6SmYdn0URBGY/81R/GPzYbRwJhURkdNhuKFeQ+4iwyu3D8T/+0M8BAFY90seHl6ThWrOpCIicioMN9SrCIKAh2+IwQcPDoW7qwt2nLiAe5b/jMKqeqlLIyIiK2G4oV5p3MBgbPjT9QjwUuJYcTUmv78bh87rpC6LiIisgOGGeq3B4d7YPDsV1wR5obS6EVM++BmZOSVSl0VERFeJ4YZ6tTBvd/znseG4sZ8/6psNeHRtFlbuyuVMKiKiHozhhno9tZsrVk4fhvuujYQoAq/8Nwcvf3WEM6mIiHoohhsiAK4uMiy4IwHPT+gPAFjz8zk88kkWahpbJK6MiIgsxXBD1EYQBDw6si+WPTAESrkM24+3zqQq0nEmFRFRT8JwQ/Q7aYNC8K9Hr4e/SoGjRXpMfn83DhdwJhURUU/BcEPUieRIH2x6PBX9AlUo0bfOpNp2lDOpiIh6AoYbokuI8PXA54+NQGqsH+qaDHjkkyys2XNW6rKIiOgyGG6IuqBxd8XqGddiakoEjCLw0ldHMO/rIzAYOVWciMhRMdwQXYariwyv3zUIf7v1GgDAqt1n8ae1WajlTCoiIofEcEPUDYIg4PHRsVhyfzIUchm+P1qKqSt+Rom+QerSiIjodxhuiCzwh8GhWP/I9fD1VOBwQetMqqNFeqnLIiKi32C4IbLQ0CgfbH48FX0DPFGka8Ddy/bgx+OlUpdFRERtGG6IrkCknwc2PpaK4X38UNtkwMNrsrD2f+ekLouIiMBwQ3TFNB6uWDPzWtw9NBwGo4j/t/kwXvtvDmdSERFJjOGG6Coo5DK8efdgPDsuDgDw0a5cPPbpftQ1cSYVEZFUGG6IrpIgCHji5n54594kKFxk2JpTgntX/A+l1ZxJRUQkBYYbIiu5PSkM6x65Dj4erjh4Xoc73t+D48XVUpdFRNTrMNwQWdGwaF9sejwVMf6eKKiqx93L9mDniQtSl0VE1Ksw3BBZWbS/JzY+NgLXxviiurEFM1bvw/q9eVKXRUTUazDcENmAj6cCax++Fnckh8FgFDF34yG89t8cVNQ2SV0aEZHTE0RR7FXzVvV6PTQaDXQ6HdRqtdTlkJMTRRHvbDuJt78/CQCQywSMigvApKRQjI0PgodCLnGFREQ9gyXf3ww3RHbwzaEivL/9FI4UXtyqwUPhgnHxQbg9OQw3xPrD1YUNqUREl8Jw0wWGG5LSqdJqfKktxJfaQuRV1JmO+3kqcNvgENyeFIYhkd4QBEHCKomIHA/DTRcYbsgRiKKI7PwqfJldgP8eLEL5b8biRPi64/bEMNyeFIp+QV4SVklE5DgYbrrAcEOOpsVgxK5TZfhKW4jvjhSjtslgOhcfosbtSaGYlBSKEI27hFUSEUmL4aYLDDfkyOqbDPj+aAm+1Bbgx+MX0NK2T5UgANfF+OL2pDBMSAiBxsNV4kqJiOyL4aYLDDfUU1TWNuGbw0X4MrsQe89WmI67uggYfU0gJieFYcyAQLi5ukhYJRGRfTDcdIHhhnqigqp6fKUtxJfaAhz7zZYOKqUc4wcGY3JyKIb38YOcM66IyEkx3HSB4YZ6uuPF1fhSW4AvtYUoqKo3HfdXKTExsXXGVWK4hjOuiMipMNx0geGGnIXRKGJ/XiW+1Bbg/w4WobKu2XQuxt8TkxJDcXtSKPoEqCSskojIOhhuusBwQ86oqcWIXacuYHN2ITJzSlDffHHG1eBwDSYlhmJSYigC1W4SVklEdOUYbrrAcEPOrraxBZk5JdisLcBPJ8tgaJtxJROAEX39MSkpFLcmBEPtxhlXRNRzMNx0geGGepPymkb836EifKktxP5zlabjCrkMY/oH4vakMIy+JoAzrojI4Vny/S351IqlS5ciJiYGbm5uGDp0KH766acur29sbMQLL7yAqKgoKJVK9O3bFytXrrRTtUQ9i59KifTh0fjisRH46W834a/jr0G/QBWaWoz49nAxZn26H8Pmf4+/f34Qe05dbOUhIurJJG252bBhA6ZNm4alS5ciNTUVH3zwAT766CPk5OQgMjKy03tuv/12lJSU4LXXXkNsbCxKS0vR0tKCESNGdOs92XJDvZ0oisgp0uMrbSG+OlCIIl2D6VyQWtk2EDkMA0PVnHFFRA6jx3RLXXfddRgyZAiWLVtmOjZgwABMnjwZGRkZHa7fsmUL7r33Xpw5cwa+vr5X9J4MN0QXGY0i9p6tMM240je0mM71DfBsXRF5UDBiA7nHFRFJq0eEm6amJnh4eOA///kP7rjjDtPxp556ClqtFjt27Ohwz+OPP44TJ04gJSUFa9euhaenJyZNmoRXX30V7u6d77vT2NiIxsZG03O9Xo+IiAiGG6LfaWwxYMfxC/hSW4jvj5agscVoOhcbqEJaQjBuTQhGfAhbdIjI/iwJN3I71dRBWVkZDAYDgoKCzI4HBQWhuLi403vOnDmDXbt2wc3NDZs2bUJZWRkef/xxVFRUXHLcTUZGBubNm2f1+omcjVLugnEDgzFuYDCqG5rx3ZES/N/BQuw6VYZTpTV474dTeO+HU4jy88CtA1uDTlKEN4MOETkcyVpuCgsLERYWhj179mD48OGm4/Pnz8fatWtx7NixDveMGzcOP/30E4qLi6HRaAAAGzduxN13343a2tpOW2/YckN0dXT1zfjhWAm+PVSMHScumLXohGrcMD4hGGkJIRga5QMXGYMOEdlGj2i58ff3h4uLS4dWmtLS0g6tOe1CQkIQFhZmCjZA6xgdURRx/vx59OvXr8M9SqUSSqXSusUT9SIad1fckRyOO5LDUdvYgh+PX8C3h4uw/VgpCnUNWLX7LFbtPgt/lRLjBwYhLSEE1/XxhSv3uSIiiUgWbhQKBYYOHYrMzEyzMTeZmZm4/fbbO70nNTUV//nPf1BTUwOVqnVJ+RMnTkAmkyE8PNwudRP1Zp5KOW4bHILbBoegodmAn06W4dvDRfg+pwRlNY1Y90se1v2SB28PV4wdEIS0QcFIjfWHUs51dIjIfhxiKvjy5csxfPhwrFixAh9++CGOHDmCqKgozJ07FwUFBfjkk08AADU1NRgwYACuv/56zJs3D2VlZfjjH/+IUaNG4cMPP+zWe3K2FJH1NbUY8fOZcmw5XIStR0pQXttkOuellOPmAYFISwjGqLhAuCsYdIjIcj2iWwoApk6divLycrzyyisoKipCQkICvvnmG0RFRQEAioqKkJeXZ7pepVIhMzMTf/7zn5GSkgI/Pz9MmTIFr732mlR/AhGhdcXjUXEBGBUXgFdvN2Lv2QpsOVyMLYeLUVrdiC+1hfhSWwh3VxeMviYAtyYE4+b+gfDiFhBEZAPcfoGIbMZoFJGdX4lvDxXj28PFKKiqN51TyGUY2c8ftyaEYOyAIGg8GHSI6NJ6xDo3UmG4IZKGKIo4XKDHt4eLsOVwMc6U1ZrOyWUChvf1Q1pCCMYNDIK/ipMAiMgcw00XGG6IpCeKIk6U1JiCzrHiatM5mQAMi/ZtWzQwBMEaNwkrJSJHwXDTBYYbIsdz5kINthxpHaNz8LzO7FxypDfS2tbSifD1kKhCIpIaw00XGG6IHFt+RR2+O9I6Rmf/uUqzcwND1aYWndhAlUQVEpEUGG66wHBD1HOU6Btag86hYvySWw7jb/616heoQtqgEKQlBKN/sBe3gSBycgw3XWC4IeqZymsakZlTgm8PF2PP6TI0Gy7+0xXt54FbE1qDzuBwDYMOkRNiuOkCww1Rz6erb8a2o61BZ8eJC2j63X5X1/f1w7BoXwyL9kXfAE+GHSInwHDTBYYbIudS29iC7cdL8e3hYmw/Voq6JoPZeV9PBVKifDAs2hcp0T5ICNNw3yuiHojhpgsMN0TOq6HZgF9yK5B1tgJ7cyugza8y28UcANxcZUiO8MGwGF8Mi/bBkEgfeColXaydiLqB4aYLDDdEvUdTixGHC3XYl1uBfWcrkXWuAlV1zWbXuMgExIeo27qxfJAS7YsALy4iSORoGG66wHBD1HsZjSJOX6jB3rMVyDpbib25FWZbQrSL8fds7cqKaR23E+3nwXE7RBJjuOkCww0R/VZhVT2yzlW2te5U4HhJNX7/r6K/Solh0T6mQcoDQrwg57gdIrtiuOkCww0RdUVX14xf8yrbWncqcCBfhyaD+bgdT4ULhvxmkHJyhA/cFS4SVUzUOzDcdIHhhogs0dBswKECHfadrcC+3ApknatEdUOL2TVymYCEMI2pdScl2he+ngqJKiZyTgw3XWC4IaKrYTSKOF5SjayzrYOU952tQJGuocN1sYEqs66scB93jtshugoMN11guCEiaxJFEQVV9a0tO2dbx+6cLK3pcF2QWmkKOsOifXFNsBdcZAw7RN3FcNMFhhsisrXK2iZknatsa92pwKECndl2EQDg5SbH0PZxO1GtiwtyvR2iS2O46QLDDRHZW32TAQfOV7XOyDpXiV/PVaKm0XzcjiAAMX6eiA9VIyFMg4GhagwM1XDsDlEbhpsuMNwQkdRaDEYcK744bmf/uUoU6zuO2wGAEI0bBoaqER+qQUKoGgPDNAjVuHH8DvU6DDddYLghIkdUXtOII4V6HCnU43ChDjmFeuSW1XZ6rbeHq6llp/1njL8nx/CQU2O46QLDDRH1FDWNLThapMeRAl1b6NHjZEk1Wowd/9l2d3XBgBAvs8ATF6yCUs71d8g5MNx0geGGiHqyxhYDTpbU4EhhW+Ap0OFoUTXqmw0drpXLBPQL8moLO62BJz5UDRUHLlMPxHDTBYYbInI2BqOI3LJaHGnrzmrv2vr9JqHtov08MPA3g5YHhqrhr+JmoeTYGG66wHBDRL2BKIoo1DWYurSOFOqRU6hDYScLDgKt6/AMbBu0HN8WeLjwIDkShpsuMNwQUW9WUdtk6tJqfeiQW1bbYbNQANC4uyI+pLVLq316ep8AFQcukyQYbrrAcENEZK62sQXHivU4XKA3BZ8TJdUdFh4EADdXGfoHtwaefoEqxASo0MffE6He7gw9ZFMMN11guCEiurymFiNOlla3tu60dW0dLdKjtqnjwGUAULjIEOXngT4Bnojxbw08MQGeiPH3hJ+ngt1bdNUYbrrAcENEdGWMRhFny2tbx+8U6ZF7oRZnympwtrwOTS3GS96ndpObWnhifvPoE+AJDwVnblH3MNx0geGGiMi6DEYRhVX1yC2rRW5ZLc5cqMGZtt8Lquo7Hc/TLljt1hp2AjxN4adPgArhPu5wdZHZ748gh8dw0wWGGyIi+2loNiCvou5i4LlQawpB5bVNl7xPLhMQ6evxm1Yelam1J9BLyW6uXsiS72+2BxIRkc24ubogLsgLcUFeHc5V1TWZgk5uWS3OlNXizIVa5JbVoKHZ2Pq8ky0oPBQuZqHH1N0V4Am1m6s9/ixycGy5ISIih2I0iiipbmgb03Mx8OSW1SK/sh6GTrafaOevUrSGHn+VaUBzH39PRPp5cCuKHo7dUl1guCEi6rmaWozIr6wzCzxn2kLQherGS94nE4AwH3dE+HggzNsdod7uCPNxR3jb7yHebgw/Do7dUkRE5JQUchn6BqjQN0AFIMjsXHVDM86W1eFMW+jJNbX61KKmsQX5FfXIr6i/5GsHeCkR1hZ6wrzdL4agtmNqNznH+vQQbLkhIiKnJooiLtQ0IvdC6+ytgsp6FOrqcb6yHgVV9SisqkdD86WnsrdTKeVtgcetLQB5INTbDeE+rSEo0MuNCxnaEFtuiIiI2giCgEAvNwR6uXV6XhRFVNQ2obCqAQVVdThfWW/6vfVnPSpqm1DT2ILjJdU4XlLd6evIZQJCvN0QqjFv/QnzudgC5ObKri97YLghIqJeTRAE+KmU8FMpMShc0+k1dU0tpqBT2N76U1WP822/F+sb0GIUL3Z95Xb+Xv4qhSno/PZneFsY8vZwZdeXFTDcEBERXYaHQo7YQBViA1WdnjcYRZToG1qDT1V9W+tPvVkYqm0yoKymCWU1TTh4XneJ93FB6G+CT7iPOwK9lAhUuyFApUSgWglfDwVk7P7qEsMNERHRVXKRCaZQktLJeVEUoa9vwfmqOlOrT2vwaTC1/pTVNKKuyYBTpTU4VVrT5Xv5qxQI9HJDgJcSgV5Ks58BXm6m33trNxjDDRERkY0JggCNhys0HhoMDO2866uh2YAiXYOppaegLQCVVjeiVN+AC9WNqKhramslakSJ/tJT39t5ucl/E35+H4YuPne27jDJw83SpUvx5ptvoqioCAMHDsTbb7+NG2+88bL37d69G6NGjUJCQgK0Wq3tCyUiIrIhN9eLKy9fSrPBiPKaJlyobkRpdUPbz8YOz0urG9HUYkR1QwuqG1pw+kLHlZ5/y9VFQIDqYstPZy1CgWo3+KsUPWI9IEnDzYYNG/D0009j6dKlSE1NxQcffIC0tDTk5OQgMjLykvfpdDqkp6djzJgxKCkpsWPFRERE0nF1kSFY44ZgjRuAzluAgLZusIYWs9BzwRSCzMNQZV0zmg0iCnUNKNQ1AOh8PFA7bw9X0/if1p9uv3uuRIDKDRoP6bbCkHSdm+uuuw5DhgzBsmXLTMcGDBiAyZMnIyMj45L33XvvvejXrx9cXFywefPmLltuGhsb0dh4selOr9cjIiKC69wQERGhddXnsprOW4Daf5a1/d5kuPx6QADgpZTj0LzxVq2zR6xz09TUhP379+O5554zOz5u3Djs2bPnkvetWrUKp0+fxqefforXXnvtsu+TkZGBefPmXXW9REREzkghl5kGQ3dFFEXo6ps7hiB9Iy7U/PZnA/xVSjtV3znJwk1ZWRkMBgOCgsyXzw4KCkJxcXGn95w8eRLPPfccfvrpJ8jl3St97ty5mDNnjul5e8sNERERdZ8gCPD2UMDbQ9HpLu+/1dzNFh5bkXxA8e9HZ4ui2OmIbYPBgPvvvx/z5s1DXFxct19fqVRCqZQ2QRIREfUmri4ySd9fsnDj7+8PFxeXDq00paWlHVpzAKC6uhpZWVnIzs7GE088AQAwGo0QRRFyuRxbt27FzTffbJfaiYiIyHFJFq0UCgWGDh2KzMxMs+OZmZkYMWJEh+vVajUOHToErVZresyaNQvXXHMNtFotrrvuOnuVTkRERA5M0m6pOXPmYNq0aUhJScHw4cOxYsUK5OXlYdasWQBax8sUFBTgk08+gUwmQ0JCgtn9gYGBcHNz63CciIiIei9Jw83UqVNRXl6OV155BUVFRUhISMA333yDqKgoAEBRURHy8vKkLJGIiIh6GEnXuZGCJfPkiYiIyDFY8v0t7XBmIiIiIitjuCEiIiKnwnBDREREToXhhoiIiJwKww0RERE5FYYbIiIicioMN0RERORUGG6IiIjIqUi+K7i9ta9ZqNfrJa6EiIiIuqv9e7s7aw/3unBTXV0NAIiIiJC4EiIiIrJUdXU1NBpNl9f0uu0XjEYjCgsL4eXlBUEQrPraer0eERERyM/P59YONsTP2T74OdsHP2f74WdtH7b6nEVRRHV1NUJDQyGTdT2qpte13MhkMoSHh9v0PdRqNf/DsQN+zvbBz9k++DnbDz9r+7DF53y5Fpt2HFBMREREToXhhoiIiJwKw40VKZVKvPTSS1AqlVKX4tT4OdsHP2f74OdsP/ys7cMRPudeN6CYiIiInBtbboiIiMipMNwQERGRU2G4ISIiIqfCcENEREROheHGSpYuXYqYmBi4ublh6NCh+Omnn6QuyelkZGRg2LBh8PLyQmBgICZPnozjx49LXZbTy8jIgCAIePrpp6UuxekUFBTgwQcfhJ+fHzw8PJCUlIT9+/dLXZZTaWlpwT/+8Q/ExMTA3d0dffr0wSuvvAKj0Sh1aT3ezp07MXHiRISGhkIQBGzevNnsvCiKePnllxEaGgp3d3eMHj0aR44csUttDDdWsGHDBjz99NN44YUXkJ2djRtvvBFpaWnIy8uTujSnsmPHDsyePRv/+9//kJmZiZaWFowbNw61tbVSl+a09u3bhxUrVmDw4MFSl+J0KisrkZqaCldXV3z77bfIycnBokWL4O3tLXVpTuWNN97A8uXLsWTJEhw9ehQLFy7Em2++iffee0/q0nq82tpaJCYmYsmSJZ2eX7hwIRYvXowlS5Zg3759CA4OxtixY017PNqUSFft2muvFWfNmmV2rH///uJzzz0nUUW9Q2lpqQhA3LFjh9SlOKXq6mqxX79+YmZmpjhq1Cjxqaeekrokp/L3v/9dvOGGG6Quw+nddttt4syZM82O3XnnneKDDz4oUUXOCYC4adMm03Oj0SgGBweLr7/+uulYQ0ODqNFoxOXLl9u8HrbcXKWmpibs378f48aNMzs+btw47NmzR6KqegedTgcA8PX1lbgS5zR79mzcdtttuOWWW6QuxSl99dVXSElJwT333IPAwEAkJyfjww8/lLosp3PDDTdg27ZtOHHiBADgwIED2LVrFyZMmCBxZc4tNzcXxcXFZt+NSqUSo0aNsst3Y6/bONPaysrKYDAYEBQUZHY8KCgIxcXFElXl/ERRxJw5c3DDDTcgISFB6nKczr/+9S/8+uuv2Ldvn9SlOK0zZ85g2bJlmDNnDp5//nns3bsXTz75JJRKJdLT06Uuz2n8/e9/h06nQ//+/eHi4gKDwYD58+fjvvvuk7o0p9b+/dfZd+O5c+ds/v4MN1YiCILZc1EUOxwj63niiSdw8OBB7Nq1S+pSnE5+fj6eeuopbN26FW5ublKX47SMRiNSUlKwYMECAEBycjKOHDmCZcuWMdxY0YYNG/Dpp5/is88+w8CBA6HVavH0008jNDQUDz30kNTlOT2pvhsZbq6Sv78/XFxcOrTSlJaWdkisZB1//vOf8dVXX2Hnzp0IDw+Xuhyns3//fpSWlmLo0KGmYwaDATt37sSSJUvQ2NgIFxcXCSt0DiEhIYiPjzc7NmDAAHzxxRcSVeSc/vrXv+K5557DvffeCwAYNGgQzp07h4yMDIYbGwoODgbQ2oITEhJiOm6v70aOublKCoUCQ4cORWZmptnxzMxMjBgxQqKqnJMoinjiiSewceNG/PDDD4iJiZG6JKc0ZswYHDp0CFqt1vRISUnBAw88AK1Wy2BjJampqR2WMjhx4gSioqIkqsg51dXVQSYz/6pzcXHhVHAbi4mJQXBwsNl3Y1NTE3bs2GGX70a23FjBnDlzMG3aNKSkpGD48OFYsWIF8vLyMGvWLKlLcyqzZ8/GZ599hi+//BJeXl6m1jKNRgN3d3eJq3MeXl5eHcYxeXp6ws/Pj+ObrOiZZ57BiBEjsGDBAkyZMgV79+7FihUrsGLFCqlLcyoTJ07E/PnzERkZiYEDByI7OxuLFy/GzJkzpS6tx6upqcGpU6dMz3Nzc6HVauHr64vIyEg8/fTTWLBgAfr164d+/fphwYIF8PDwwP3332/74mw+H6uXeP/998WoqChRoVCIQ4YM4fRkGwDQ6WPVqlVSl+b0OBXcNr7++msxISFBVCqVYv/+/cUVK1ZIXZLT0ev14lNPPSVGRkaKbm5uYp8+fcQXXnhBbGxslLq0Hm/79u2d/pv80EMPiaLYOh38pZdeEoODg0WlUimOHDlSPHTokF1qE0RRFG0foYiIiIjsg2NuiIiIyKkw3BAREZFTYbghIiIip8JwQ0RERE6F4YaIiIicCsMNERERORWGGyIiInIqDDdERETkVBhuiKhXiI6Oxttvvy11GURkBww3RGR106dPx+TJkwEAo0ePxtNPP2239169ejW8vb07HN+3bx8effRRu9VBRNLhxplE1CM0NTVBoVBc8f0BAQFWrIaIHBlbbojIZqZPn44dO3bgnXfegSAIEAQBZ8+eBQDk5ORgwoQJUKlUCAoKwrRp01BWVma6d/To0XjiiScwZ84c+Pv7Y+zYsQCAxYsXY9CgQfD09ERERAQef/xx1NTUAAB+/PFHzJgxAzqdzvR+L7/8MoCO3VJ5eXm4/fbboVKpoFarMWXKFJSUlJjOv/zyy0hKSsLatWsRHR0NjUaDe++9F9XV1aZrPv/8cwwaNAju7u7w8/PDLbfcgtraWht9mkTUXQw3RGQz77zzDoYPH45HHnkERUVFKCoqQkREBIqKijBq1CgkJSUhKysLW7ZsQUlJCaZMmWJ2/5o1ayCXy7F792588MEHAACZTIZ3330Xhw8fxpo1a/DDDz/gb3/7GwBgxIgRePvtt6FWq03v9+yzz3aoSxRFTJ48GRUVFdixYwcyMzNx+vRpTJ061ey606dPY/Pmzfjvf/+L//73v9ixYwdef/11AEBRURHuu+8+zJw5E0ePHsWPP/6IO++8E9yLmEh67JYiIpvRaDRQKBTw8PBAcHCw6fiyZcswZMgQLFiwwHRs5cqViIiIwIkTJxAXFwcAiI2NxcKFC81e87fjd2JiYvDqq6/isccew9KlS6FQKKDRaCAIgtn7/d7333+PgwcPIjc3FxEREQCAtWvXYuDAgdi3bx+GDRsGADAajVi9ejW8vLwAANOmTcO2bdswf/58FBUVoaWlBXfeeSeioqIAAIMGDbqKT4uIrIUtN0Rkd/v378f27duhUqlMj/79+wNobS1pl5KS0uHe7du3Y+zYsQgLC4OXlxfS09NRXl5uUXfQ0aNHERERYQo2ABAfHw9vb28cPXrUdCw6OtoUbAAgJCQEpaWlAIDExESMGTMGgwYNwj333IMPP/wQlZWV3f8QiMhmGG6IyO6MRiMmTpwIrVZr9jh58iRGjhxpus7T09PsvnPnzmHChAlISEjAF198gf379+P9998HADQ3N3f7/UVRhCAIlz3u6upqdl4QBBiNRgCAi4sLMjMz8e233yI+Ph7vvfcerrnmGuTm5na7DiKyDYYbIrIphUIBg8FgdmzIkCE4cuQIoqOjERsba/b4faD5raysLLS0tGDRokW4/vrrERcXh8LCwsu+3+/Fx8cjLy8P+fn5pmM5OTnQ6XQYMGBAt/82QRCQmpqKefPmITs7GwqFAps2ber2/URkGww3RGRT0dHR+OWXX3D27FmUlZXBaDRi9uzZqKiowH333Ye9e/fizJkz2Lp1K2bOnNllMOnbty9aWlrw3nvv4cyZM1i7di2WL1/e4f1qamqwbds2lJWVoa6ursPr3HLLLRg8eDAeeOAB/Prrr9i7dy/S09MxatSoTrvCOvPLL79gwYIFyMrKQl5eHjZu3IgLFy5YFI6IyDYYbojIpp599lm4uLggPj4eAQEByMvLQ2hoKHbv3g2DwYDx48cjISEBTz31FDQaDWSyS/+zlJSUhMWLF+ONN95AQkIC1q1bh4yMDLNrRowYgVmzZmHq1KkICAjoMCAZaG1x2bx5M3x8fDBy5Ejccsst6NOnDzZs2NDtv0utVmPnzp2YMGEC4uLi8I9//AOLFi1CWlpa9z8cIrIJQeS8RSIiInIibLkhIiIip8JwQ0RERE6F4YaIiIicCsMNERERORWGGyIiInIqDDdERETkVBhuiIiIyKkw3BAREZFTYbghIiIip8JwQ0RERE6F4YaIiIicyv8HGwefBbycCOgAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -431,41 +454,62 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 179, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\andre\\AppData\\Local\\Temp\\ipykernel_23312\\3703842558.py:14: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " energy[i,j] = dbi_.h.matrix[states[i],states[i]]\n" + ] + } + ], "source": [ - "iters = 10\n", - "columnNorm = np.empty((2**nqubits,iters))\n", + "iters = 30\n", + "states = [0,1,2,3,4,5,6,7]\n", + "energy = np.empty((len(states),iters))\n", + "\n", + "\n", "d = (np.diag(np.linspace(1,2**nqubits,2**nqubits)))\n", - "for i in range(2**nqubits):\n", + "for i in range(len(states)):\n", " dbi_ = deepcopy(dbi)\n", - " dbi_.state = i\n", + " dbi_.state = states[i]\n", " for j in range(iters):\n", " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " dbi_(step_poly,d=d)\n", - " columnNorm[i,j] = np.linalg.norm(dbi_.h.matrix[:,i])\n", + " if step_poly is not None:\n", + " dbi_(step_poly,d=d)\n", + " energy[i,j] = dbi_.h.matrix[states[i],states[i]]\n", " " ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 181, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eigenvalues: [-4.00000000e+00 -3.46410162e+00 -8.06606266e-16 3.61709753e-17\n", + " 2.32474753e-15 2.00000000e+00 2.00000000e+00 3.46410162e+00]\n" + ] + }, { "data": { "text/plain": [ "Text(0.5, 0, 'Iterations')" ] }, - "execution_count": 13, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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Fs+sPocmpB5tReem474qx+NL5JUl/tZPP58Pu3buxefNm1NfXB+ePHTsWs2bNwqhRoxhmiIiGGAacFObyqnhtyzH8dt0hNLa5AQAjc6z43uVj8eWpJTAoyX1b/vb2dmzbtg1btmxBe3s7AMBoNGLq1KmYMWMG8vLyEtxCIiJKFAacFOTxaXhjWy1+s/Yg6uwuAPpl3t+7fAyunzYCxiQPNg0NDdi8eTM+++wzqKp+OXtmZiZmzJiBadOmwWq1JriFRESUaAw4KcSranhz+3H8+oODONHaCQAotllwzxfG4KbppTAZkjfYaJqGQ4cOoaqqCocPHw7OLykpwaxZszBp0iQoipLAFhIR0WDCgJMCfKqGt3acwP9+cAC1zXqwKcg04+7LxuDWi0thNiTvB7/H48Fnn32GzZs3o6mpCYB+OffEiRMxc+ZMlJaW8vwaIiLqJuEBp7y8HEePHu02f8mSJfjNb37Tbf66detw2WWXdZtfXV2NCRMmDEgbBytVE3j305P41b8PoKZJPwclL8OEuy4dg/+YMRIWY/IGG4fDgS1btmD79u3o7NRDm9lsxrRp03DxxRdj2LBhCW4hERENZgkPOFu3bg2eRwEAu3fvxpVXXombbrop7nr79u1DVlZWcDo/P3/A2jjYaJrA+7vq8PSa/Th0Sg82Oekm3Dl/NP5zZllS33X45MmTqKqqwueffx68GV92djZmzpyJqVOnwmKxJLiFRESUDBL+Sdg1mPzsZz9DRUUF5s+fH3e9goICZGdnD2DLBh9NE/jX5/V4es0B7GtoAwBkW4349rzRuH1WOdLNCf9xnhFN07Bv3z5UVVXh2LFjwfkjR47ErFmzMH78eMhy8p4/RERE596g+kT0eDx45ZVX8MADD/R4XsWFF14Il8uFSZMm4aGHHop62CpVCCGwek8DfrnmAKrrHACATIsBd1wyGt+YU45MS3I+qdrlcmHnzp34+OOP0dLSAgCQZRlTpkzBzJkzUVJSkuAWEhFRshpUAWflypVobW3F4sWLY9YUFxfjueeew/Tp0+F2u/Hyyy/j8ssvx7p16zBv3ryo67jdbrjd7uC0w+Ho76YPCCEE1u07hadW78euE3YAQIbZgG/OHYVvzR0FW1pyBpuWlhZ8/PHH2LFjR/DnkpaWhsrKSlx00UURhx6JiIjOhCSEEIluRMDChQthMpnw7rvv9mm9a665BpIk4Z133om6/JFHHsGjjz7abb7dbh+UH6ZCCGw80ISnVu/HztpWAIDVpOAbc8pxxyWjkW1NvuclCSFQW1uLqqoq7N27F4Ffu7y8PMycORPnn38+nwNFRERxORwO2Gy2Xn1+D5oenKNHj2LNmjX429/+1ud1Z86ciVdeeSXm8mXLluGBBx4ITjscDpSWlp5ROwfaRwf1YLPtqH7IxmKUcfuscnx73mjkZpgT3Lq+U1UVe/bsQVVVFU6ePBmcP3r0aMyaNQsVFRU8v4aIiPrdoAk4K1asQEFBAa6++uo+r7tjxw4UFxfHXG42m2E2D+5wsKWmGU+t3ofNh5sBAGaDjP+cWYY751cgP3Nwtz2ajo4ObN++HVu2bEFbm35CtKIouOCCCzBjxgwUFhYmuIVERJTKBkXA0TQNK1aswO233w6DIbJJy5Ytw4kTJ/DSSy8BAJ5++mmUl5dj8uTJwZOS33zzTbz55puJaPpZ2360Bb9cvR+bDuo3sTMpMr42YyTuurQChVnJd0l0U1MTNm/ejE8//RRerxcAkJ6ejosvvhiVlZVIT09PcAuJiGgoGBQBZ82aNTh27Bi++c1vdltWV1cXcemwx+PB0qVLceLECaSlpWHy5Ml4//33sWjRonPZ5LO2s7YVv1y9H+v3nwIAGBUJN1eW4u7LxqAkOy3BresbIQRqampQVVWFAwcOBOcXFhZi1qxZmDJlSrfgSkRENJAG1UnG50pfTlLqb7tP2PH0mv1YU90IAFBkCTdNH4G7LxuD0pzkekik1+vF7t27sXnzZjQ0NATnjx8/HjNnzkR5eTkfo0BERP0mKU8yTnXVdQ48vWY//vW5HgRkCbh+2gh89wtjUJabXIdtnE4ntm7dim3btqG9Xb+TstFoxIUXXogZM2YgNzc3wS0kIqKhjgFngO1vaMOv1hzA+7vqAACSBFx3QQm+d/lYjM7PSHDr+qahoQFVVVXYtWtX8PEaWVlZmDFjBqZNm4a0tOQ6tEZERKmLAWeAHDrlxK/WHMC7n51E4CDgl84vxn1XjMWYgszENq4PNE3DwYMHUVVVhZqamuD84cOHY9asWZg4cSIUJXkf6klERKmJAaefHWlqx/9+cAArd5yA5g82X5xShHuvGIsJRYPvpoKxeDye4GMUTp8+DQCQJAkTJ07ErFmzBu19hIiIiAAGnH61dl8j/s+L26D6k80VEwtx/5VjMbnEluCW9UwIgcbGRhw6dAiHDh3C0aNH4fP5AOj3EZo+fTouvvjiIfeAUyIiSk4MOP1oxqgcDLOacN7wLNx/5TicPyI70U2Kq729PRhoDh06BKfTGbE8JycHM2bMwNSpUwf9jRKJiIjCMeD0I6vJgFX3z0NO+uB8ppLP50NtbW0w0NTV1UUsNxgMKC8vR0VFBSoqKpCfn8/LvImIKCkx4PSzwRRuhBBoamoKBpojR44E7y4cUFRUFAw0paWlMBqT8wnlRERE4RhwUkxHRwcOHz4cDDUOhyNieXp6ejDQjB49GpmZyXNFFxERUW8x4CQ5VVVx/PhxHDx4EIcOHYp4YjegP+CyrKwsGGoKCwt52ImIiFIeA06SEUKgubk52ENTU1MDj8cTUVNQUBAMNGVlZTzsREREQw4DThLo7OxETU1NMNS0trZGLLdarRGHnc7187WIiIgGGwacQUhVVZw4cSIYaE6cOIHwZ6LKsoyRI0cGQ01RURFkWU5gi4mIiAYXBpxBoqWlJRhoDh8+DLfbHbE8Ly8v4rAT70tDREQUGwNOgrhcLhw5ciQYapqbmyOWp6WlYfTo0cHDTryDMBERDSaaR4XW5oHq9EJzeqC2+V+dXmhtHsgZRgz7ytiEtY8B5xzRNA0nT54MBprjx49D07TgclmWMWLECIwZMwYVFRUoLi7mYSciIjqnNHcgtHigOb1QowSXQKARHi3utpQcyzlqdXQMOAOotbU14rCTy+WKWJ6TkxM87FReXg6LJbG/DERElFqEEBBuNWYvS3C+f1p444eWriSjDDnDCCXDpL9mhl4VW2JPpWDA6UcejyfisFNTU1PEcrPZHDzsVFFRgWHDhiWopURElKyCoaUt1MuitXlDvS5tkb0v8J1BaMk0QckwQs4wQcn0v4ZNKxkmyJlGSCZl0N5bjQGnH9XV1eHVV18NTkuShBEjRgQDTUlJCRRFSWALiYhoMFPtbvhOd3bpYQmElVBwgU/0vLEwkkn2hxN/D0u3sBIKMLI5NT6nGHD60YgRI1BYWBgMNaNGjUJaWlqim0VERIOc8GmwrzoC54YTvV5HMiuhoJJhDIWU8N4X/7RsSo3Q0hcMOP1IURTcddddiW4GERElEW9TJ5r/shfeE04AgCHX0uUQkSl0nktm6HyXoRha+oIBZ4gTQgAaAE2DUAWEKgBNQKgaZCv/gIiIBooQAh2fNKL17YMQHg2y1YBhN4xF2uS8RDctJTDg9COhalAdHkDVA0IoLAh9nqb5l4XmC00Agdrgsq5hw1+jhdX4p0PbDttOjPcM33ZonTjHcWXANCIT5tHZMI+2wVSexcBDRNQPNJcPLSsPonPnKQCAebQNw24ZD0OCrzxKJQw4/ch32oWGp7YnuhlnTwIgS4Aq4DnWBs+xNrStqwUUyR94bDBX2GAuy4JkZOAhIuoL9zEHml/bB7XZBchA1hVlyLy0FJI8OK9GSlYMOP1IUiTAIEGSZUCRIPkHKLL+i6tIoVdFBuQuNYoUnBdaFqoLrhexncj3C9XK8bcjh2pC88K2DcDX7IL7sB3uw61wH7JDtbvhOeqA56gDbWv9gWdkqIfHPDILkpE3JyQiikZoAm3rj8Ox+iigCSjDzMi5dQLMZXxA8kCQRPhTHIcIh8MBm80Gu93OJ2/3khACaiDwHGqF67AdmsMTWWSQYB6Z5e/hyYapNBOSgYGHiEh1uNH8+j64D9kBAGnn52HY9WMhW9jP0Bd9+fxmwGHAOSNCCPhOu+A+1Brs5dHavBE1klGGqSws8AzPYOAhoiGnc89ptPx1P7QOHySTjOxrK2CdXjhob5A3mPXl85vRkc6IJEkw5qXBmJeGjBnFeuA51Rk6pHXYDs3phftgK9wHWwEc1QNPeRbMFf6Tlodn6ofHiIhSkPBqaP37YbRX1QEAjCXpyPnqBBjzrQlu2dDAgEP9QpIkGAusMBZYkTHTH3gaO4KHtNyH7dA6fHAfaIX7QKu+jkmBeZS/h2d0NowlGQw8RJQSvA3t+r1t6jsAABlzh8N2VTl7sc8hBpx+tq9qE0ZMnIz07KH9nClJkmAsTIexMB0Zs0ogND3wuIKHtOwQnT649rXAta9FX8eswDzKFjykZSxO51UFRJRUhBBo31IP+3uHIbwa5Awjcm4aB8v4nEQ3bchhwOlHjqZTeO9XT0CSJJSdfyEmzb0UYy6aBSOfEg5JlmAsSoexKB2Zc4ZDaALe+na4D/kPadXYIVwqXHub4drbrK9jMeg9PP5DWsaiXgSezlag9RjQelR/bTkKuOz+RkgApLBXhE2jy7IYr1G3E23+2Wwv2roAJAWQDfqgGELjshGQFUAxhs0LGxT/ctkYf/1grUGfJqI+0Tq8aHnzADo/Pw0AMI8bhpybxkHJNCW4ZUMTA04/6mg5jVxTGk67O3Bk53Yc2bkdRrMFYy6ehUlzL8XI86ZC5sM2AeiBx1SSAVNJBjIv8Qeek85g744eeHxwVTfDVa0HHtlqgKksHeZCLyy2UzDgCCT70VCgaTkGuO0J/spShRQlIBlCIUmJEqT6EqZihjFD5PtE1EYLYv3w3jzRk/qB+7Adza/vhWr3AIoE21XlyJgznL3QCcSrqPrxKir3gQM4fM21aDcZcWJYBk4Oy0SH2RhcnmZNx/iZczHlyi+iYFQFz6CPxtMOtNZCNB+F90gj3LVuuBrT4HEWQojIO3zKsMMs7/IPn8Eg1eqfVen5QPZIILtMf7Xm+tcQgBBRXgPL4tX462It67a9XtT2anvBjQGaCggVUL2A5osc1PBpr14brS6i1r9caP3wg0tikhLZcxUMcb0IduG67UpFD8t7U3O2y89gG73dTp/qopT1+/ueI13220LIcNgvR5vjcgAyDIZTyMl7FSZT7x+a2f09FECW/QFcCf1uSnLY76khcjpY15v1Ymw/uJ3wuhjTEduP8d4GMzCs7Oy+313wMvEeDFTA8dbXo/XNN+HaUw3Xnj3w1tWh1WrGyWGZOJmdAa8h1HuTqRhRUToKE+bMQ8HM2TAWFPRbOwY1rwuw1+qHjlqPdj+c1NEUdTUhFHjEGLi18+HGNHjU8RAisttXtkr6+Ttj8mCusMGQl8YQ2RuaFj0Iab5ehqmu8/q6fqzQFpinhr2Pvz78veOuH7au5kv0d5pSjE/ko9mzFB4xGQBgVVYj2/B7yJIrwS0bJHLHAN/t37v7M+D04FzdB8fX0gLXnj1w7dmDjs8/x7EDe3HM24kGWzo0OXQm/TBnJ0Z4gdEjRyFryhRYJk2CZdJkGIeXJN8HtM+jB5iuwaX1mD4463vehtkGDAv0wPh7YbJH6v8TsJUCliwInwbP8bbQIa0jDsAX2RMhZ5r0y9FLM6HYzFCyTPqQaeKVDEORED2Eo7Dw1NtwhS5/n93+Xnta3puaBLxHb7cTY9bZbe9s6s5SHz4OOw4DLRskCI8EySgw7BIB65h++DgVQu+p1TT/qy/0eye0sPHAssFS5+9hDq/LGQ3c8cHZf0/CJFXAeeSRR/Doo49GzCssLER9fewPwvXr1+OBBx7A559/jpKSEnz/+9/HnXfe2ev3TOSN/tS2NrR9+in2rf83Duzfg4bO9tC5pppAQVs7SlqcKHB0wJiVBcvEif7Aow+m8jL9kQqJonoBx4kuwSUswDhOIkZ/dIgxXQ8rgfAyrCzykFJadp+bJXwaPMfagvfgcR9zAL7Y7ZDTjaHAk2WGHBzXp5UsE+R0I4+fE1EEzaPC/u5htG/VP6NMIzORc+sEGHJ4Mcm5kHQ3+ps8eTLWrFkTnFbinIhbU1ODRYsW4Y477sArr7yCDz/8EEuWLEF+fj5uuOGGc9Hcs6JkZiJ77lzMmDsXMwC0NTehet2/Ub1uDZoa6tBgy0CDLQMGVUNRqxPDd+1EzubNwf+/yFYrzBMmhELP5Ekwjx4NyWiM97a943MDHc36YaKO03pYCQSXQJhxnNCTeTyGtOjBJXskMKwcSBvW7//7kgyy/346NgD6DbbcxxxwH7bD19gB1e6G6vBAbdOf9q61e6G1e+Gta4+9UVmCkmnsEoDCeoL805JFSb6eNiLqM89JJ5r/she+U52ABGReWoqsK0bqz/qjQWdQ9OCsXLkSO3fu7FX9D37wA7zzzjuorq4Ozrvzzjvx6aefoqqqqlfbGKyPamg6dgTVm9ahetN6tJ0+FZxvNZowwgsU1dQiw+Hstp5kMsE8fnyot2fyJJjHjIEsOoF2f1gJDk3+EBM2r90/z9PWu4YqplBgieiF8Q/peYP2yhShCWgd3mDY0eweqA431DYPVLs+T3W4oTm9PXZEBUhGWe/xiRGAAuN88jpRchJCwPnhSdj/UQOoAnKWCTm3jIelIjvRTRtykq4H58CBAygpKYHZbMaMGTPw+OOPY/To0VFrq6qqsGDBgoh5CxcuxPPPPw+v1wtjlJ4Mt9sNt9sdnHY4HP37BfSTvJHluORrizH31ttwfO/nqN64Fvs3f4iOjnbsB7B/VCHy8iZhdHYGRrS3Qa45AdfRU9BcHrh27YJr167QxiQBs80HyzBvcDBne6EYe/jUlhTAmqNfeZRR2KUXxj+eUaifLZ+EJFmCkmGCkhH/vhRC1aA6vdAcHr33p82jhyKHPxD5x0WnD8KrwXfaBZyOf2KhlGaIGnwiDotlmHg3Z6JBRHV60PL/7Q/ekNQyMQfDbhwHJb0fes1pQCU84MyYMQMvvfQSxo0bh4aGBvz0pz/F7Nmz8fnnnyM3N7dbfX19PQoLCyPmFRYWwufzoampCcXFxd3WWb58ebfzfBLK5+nSqxI5SB2nUdpxGqXaaXxhUjMON6iobhmGw84cNDWdRlPTaWyBwMgcFyaWd6JcboVml+FqMQYH1a3A3WqEu9UIe03orU35VlhG5sIyqgSWcaNhmTgJSmGpHmjSc/UTfJM0vPQnSZFhsJkBmxkozYxZp3lUaG3dg09gWgsEIa8G0emDr9MHX0NHnDcG5AxjlwAUdk6QWYFsVvRXk/4qmRSeK0Q0AFwHWtD8xj79QcIGCdlXj0b6zGIekk4SCT9E1VV7ezsqKirw/e9/Hw888EC35ePGjcM3vvENLFu2LDjvww8/xNy5c1FXV4eioqJu60TrwSktLe3/Q1QuO3BkU5fDQs1hh4b80+4z60HqFFbsd41EdcswnLCHsqlBkVExphgTL5yE8vPOh5yRD1+bgOtIHVx79wev5PI1NETdrnH48OChrcBhLkN+/hm1kboTQkC41KgBSHV4/CFI7yXCGd6SRjLKetCJCD5yaDwwP1BjDhs3yZDMBr0+MJ+hiYYw4dNgX3UUzg3HAQCGQityvzoBxqL0BLeMku4QVbj09HScd955OHDgQNTlRUVF3a6wamxshMFgiNrjAwBmsxlmsznqsn7lOAm89rXe1UoykJajn69izQ0dFrLmAta8sPFQTZrRigskCRcAsDfWo3rTelRvXIvmk8exb98J7Nt3Amnvf4zxsy/BxLmXofiKK5F5Zehwnq+pCa7q6uB9elx79sBbWwvviRPwnjiBttWrg7WG/HykVU6H7ZprkXHJ3P45iXmIkiQJUpoBcpoBxsLYO0ih6Sc/d+0NCgagdi+EW4XwqNDcGoTHFwxEwqtBeDWg3YseTgHvfbuNcrCHqMdA1KVHKep8o8zQRIOer6kTp1/bC+9x/XzH9JnFyL56FM+hS0KDrgfH7XajoqIC3/72t/E///M/3Zb/4Ac/wLvvvos9e/YE5911113YuXNn4k8ybm8CXr0lajiJDC85gCW7Xw4FCSHQWHMI1ZvWonrTenTYW4PLsguLMfGSSzFx7qUYVjw86vqq3Q5X9V498FTrwcdz+HDE/SCUYcOQdfXVsF13HSxTJrN7dpAQQgA+Ac2jhgUf/VW41SjztRjzI+t7e3L1mZBMcqiHyN/rJBvlUA9U+GvYuBycp/dMda2X/ctgkPj7SWes/ZMGtK48BOFRIaUZkHPjWKRNzkt0syhMUt0HZ+nSpbjmmmswcuRINDY24qc//SnWr1+PXbt2oaysDMuWLcOJEyfw0ksvAdAvE58yZQq+853v4I477kBVVRXuvPNO/OUvf+n1ZeKD9Sqqs6WpKo7t/hTVG9fiwJYqeN2hk16Lx4zHxEsuxfjZ82DNssXfTns7XHv3om3Vatjfew/q6dPBZabRo2G77jrYrr0GxijnO1Fy00OTFgxE4QEoPBBFBKPAeJf68CA1kKEpgoTIEBQc9/c8dZkXfA2EqF7UMESlHs3lQ+vKg+jYqV+9ahplQ86t4/Xz8GhQSaqAc+utt2LDhg1oampCfn4+Zs6ciZ/85CeYNGkSAGDx4sU4cuQI1q1bF1xn/fr1uP/++4M3+vvBD36QNDf6O1e8LhcObtuM6o1rceSzHRCafixDkmWMmjodE+ZeijGVM2A0x785lfD50P7hh7C//Q7a/v1viMC5TJIE64wZsF17LTIXLICSwWPTFJ0QQj+EFh6IfFowFOmH19RgjRY+z9PlNVDj1SKWQTuHuzEJoR4mRQYMMiSDBEmR9TtkGyRIBtk/LenL/eP6chmS4q/xj/dUg/B6gwQoYeMyA9fZ8NS24fRf9kJtdgEykHV5GTIvK+Xh1EEqqQJOIgyFgBOuvbUF+6o2onrjWtQfCp3bZLSkYezFszDxksswcsr5kOX4x5jVtja0rVoF+8q30bF1a3C+ZLEg84orYLvuOqTPngWJT0ync0yo/lDkDz1ajGB0xjVeDVAH6a5Sgj/wSLEDU/jy8BqDDMkkw5hvhXF4BowF1iHzGBOhCbRtOA7HqqOAJqBkm5Hz1Qkwl6X+Z0IyY8DpwVALOOGaTx7Xbya4cS3sjaGrqtKH5WDC7HmYeMllKCgf3eP/CD3HT8Dx3ruwr3wbniNHgvMN+fnI+tKXYPvydbCMHz9QXwbROdc1RAmvpvdE+TQIn4BQNSBiXH8NLvdpgBoa16dD40IV/vX18W7LfcK/vjZwh/wUCcZCK4wlGTANz4CxJAPG4nT90FwKUR1uNL+xH+6DrQCAtPPzMOwrYyGnDbrrbqgLBpweDOWAEyCEwMn9e1G9cS32VW2Eyxm6i3HuiJGYOPdSTJgzH7aCwjhb0bfj+uwz2N9+B47334dqtweXmSdMgO3aa5H1pauHztPSic4BoXYJVD7NH6b8IUmNErr889GlXrh88Na1w3OyHcIV5YnrEmDIT9NDT0mG/zUdsjU5r6zsrD6Nlr/uh9bug2SUkX1tBayVhTzMlyQYcHrAgBNJ9XlRs/MTVG9ci0PbP4bq9QaXFVWMxdgZczBu5lxkF3a/x1A44fHAuXEj7CvfRtu6dUBgO7KM9NmzYbvuOmRecTnktLQB/GqI6EwIIaC2uOE96YTnhFN/PenUb3IXhTLMHAw8xuF6+FGy4t8hPJGEV4P9HzVwfnQSAGAsSUfOVyfAmG9NcMuoLxhwesCAE5u7ox0HPv4I1ZvWovbz3RAidOe5glEVGDdzLsbNnINhRSVxt6O2tsLxz3/CvvJtdIY9Z0y2WpG5cCFs110H68UXJfbJ6ETUI9XhgeekHnj00NOun5AbhZxhDB3a8vf0KDmWhPeOeBs70PzqXnjr9YfrZswdDttV5UPmfKNUwoDTg4EKOEIIbHrjAIaPH4ayKblQkvyPp721BQe3VmH/5g9R+/muiLCTXz4a4/w9Ozkl0e+xE+A5cgT2d96F/Z134D1+PDjfUFwM2zXXwHbdtTBXVAzY10FE/Uvr8MJT166HnhN66PGd6oh6bpBkMcBUkh7W05MOQ571nDxzTQiB9q31sL97GMKrQc4wIuemcbCMzxnw96aBwYDTg4EKOA1H7fjr8u0AAEuGAeMuLsKEWcXIj/Mso2TR4bAHw86x3Z8GLzsH9IeEjps5B+NmzEXuiNKY2xBCoPOTT2Bf+TYc//wntLbQeT+WKVNgu+46ZF29CIYc7nyIko3mUeGtD4Sedr3Xp7496tVnklGGsShdv3KrJF0/1FWU3q89KlqHFy1/O4DO3fp9vMxjs5Fz83gomYP3MBr1jAGnBwMVcA7VHsPPVvweY09VIt0bupmeltOJYRdIGH9xMcaVjIbNHP9Ge4NdZ5sDB7dt1sPOrp3Q1NDDAXJHjNTDzsy5yB0xMmbXtOZ2w7l2Lewr34Zz40YgsA2DARmXXALbddch47JLIZ+LR2wQ0YAQPg3exg7/4a324KEu4Yny0DU57AquEn/4KU6HbO77lU3uI3Y0/2UfVLsbUCTYFpYjY+5w3tsmBTDg9GCgAs6BlgP40Yc/wpHWo8hpKsX4UxdjVPP5UIT+B6pKKo5lf46Tw6thGe1D+bAyjLKNCg7F6cWQpeQ6rOVyOnFw22Yc+PhDHPl0BzQ1dBVGTskIjJs1F+NmzEHeyPKYYcd3+jQc7/8d9rffhuvzz4Pz5awsZF11FWxfvg5pF16Y8OP4RHT2hCbgO90ZPJ/H6z+hWeuIcQVXbpreyxN2bo+SHv0KLqEKOD44hrYPjgECMORakPPVCTCNSP5edNIx4PRgoE8yFkKgsaMRNY4aHKyvQd2n7RD7spDeEnqmSafBiYN527G34GOctp4AJMCsmFGeVR4RekbZRqEsqwxphsF/5ZGr3YnD27dg3+ZNOPrpJ1B9oR3WsOLhGDdzDsbOmBP3Pjvugwdhf/sd2N99F76wh6oaS0thu/Za2K67FqaRIwf8ayGic0cIAdXuDh3a8g+q3RO1XrGZg+fzBM7tgRBofm0fPEccAADrtAJkX1dxRj1ANHgx4PQg+A06eBBZmVGSvckEZGeHphsbY2/MYADCzxmJU9t8yo1d1U7s/7gBnjYNls5WQAg4M5qxL2cLDuR8ArexAwCgSYA9M/SHOV7NQ3lGKUZmlWFk1kiUZY1EaeZI5Fpy9DsH54U9EK65GfBF+d9QQPg9afpS29oKeKLvcLrWuk+exJFtm3H4ky04uvvTiEvPs/MLUXrp5Rg/ex4KRlVAamsDXJFXZQhVRceOHWj7xz9h37gRmn+57PMh7bzzkHnVVci6/AtQuv6C5+ToPxMAcDqBjo7Y7e1LbXa2/nvR19qODr0+lqwswGLpe63LBTgcsWszMgCrte+1Ho/+c47FatXr+1rr8+m/a/1Ra7Ho3wsA0DSgqal/avvydz9A+4hutU1NerujkeXIv/u+1A6CfUSPtXl5UDt8+qGtgw3wnrDD1+CCr9ndrVQYLYAsAQKQFB+GXVEC6+Rh0bfLfYQuSfcRfeqgEEOQ3W4XAIRdf2Z29yE/P3KFWHWAEDZbZK0kxa61WoUQQqg+VRzZ1SRUWYlZ6zUq4ra/3ybm/mWumPLCFOEyxN6uT5bEf2/8b/GHz/4g1hxdI1RrWuw2SFJke222+F9fuPz83tcOHx639plrLxc/v/lq8Yd7vik6CuJvV92+XbS+8444+s1viU6zOX4bNm4MtaGyMn7typWh2vnz49e+8EKodtGi+LW//GWo9qab4tc+/HCo9lvfil97//2h2vvvj1/7rW+Fah9+OH7tTTeFan/5y/i1ixaFal94IX7t/Pmh2pUr49dWVoZqN26MXztlSqh2z574tRUVodqGhvi1w4dH/g7Hqx3gfUSQEnsfIczmyNp4fxuKEllrtcauHST7iLZ9e8XpE7Xi5IG9wlM6Im7t8e+uFLU/2CA+/+HboqU4/nb/ddst4s8PPiD+/OAD4lRxYdzatTdcI15Zdp94Zdl9or40/nY3LbpCvPSD74mXfvA9UTu6LG7tx5fNES/+1z3ixf+6R9SMGxO3dsesSvHi0rvFi0vvFvunTIxbu3v6BcHa3dMviFu7f8rEYO2OWfH3lTXjxgRrP750Ttza2lEjg7Xbbv5K/N+HM9hHBD+/7XbRE/bdJYCsyCibkqs/QyYGSZPxLceDmDi7GEq+F8ZvFwGIfsMtAYF3Dr0TnP7Y50KsW1cJxH3bc2bM9JnYe7Aa9sYGuNrbEe8AnJyWpl9Ofs010D76ENi7N2at+9AhmObM4fk6ROeY0DRAiLj7l6q//gXuzg54OjtwibMt7t/9i/91N1z+3ohvNjcjRn8MAGD1gV/BlVOMdp8dt7Xb41QCp2uPos6l94J4XdHv5xPQWn8S9YreK+bp7Ixb6zjViMYa/aIId7zeGwDO5macOlrjr43TIwOgw96KU8eOAABc7W1xazvbHMHazrY4PTL+bQVqO+ytcWvdHc5g7YiWOL0sANydncHa9pb2uLUDjYeozuEhqp66n5sb2nFoWwMObm9ER5sPrjS9DbnD0zFpohljLsyDNewSR7fqxvG2EzjqrMUBpRk1jhocsR9B04kD8Hhi/4HJBYXB83vGi3yUpQ9HWWYZCtMLu5/kPIDdz16vBzU7t+Pwun/j2LYt8HpCO5uMYbkYPe0ijJk+A0UzZkEKdBM7HMHDWa4DB2D/xz/RtmoVVH/XpibLMFVUwHbddbB94TIYw3+OXbH7uXstD1GFpofIISpNU+F1ueDu7ISnswNelwsuaxo8nR1wd3TA19AAX4cTHlcnPB0ueDs74HZ1wNPpgtfdCYcEeDo64HF1wuR2Q47VBiAYWADA5PHErXWbLTBlpMOUZkW6bITFaIQ5zQKTxQpjWhqMaWkwm9NgSkuDUlQEozUdsiJDcjohBfY9Yf/RkfzRS8vK0r/PkgR0dEB2udD9/0ORtVJYbdcEF9xuRgYQuOozsN2IwtBURG1nJ+SOzuB2utLSraG/e39tLBG1Lhfk9tj7Kc2aBgTuKt+XWo8Hclvs/ZSWZgnuTwyShOGFxTFrB/oQ1dAOOIP0TsaaJnC8uhl7q+pweGcTVJ++E5BkCWVTcjFhVhHKz8uLeSNBIUInOR9uPYwaew1qHDWosdegsSP2ztWiWFCWVRZxVZfNbNMHky04blL6/z4SXo8bRz79BPurNuHwJ1si/reUMSxHf1zEjDkomTCx21PPhc+H9qoq/RERa9ZAuEPH6K0XX4zsG65H1qJFkIzJ+ewcGhqEEFC9Xvi8Hvg8Hqj+14ghOM+t13rc8IbXhq2jdpkOX66Gbyfe+TVnQFYUmNKsMKVZYbZGvprS0vTpNCtMXZZFzEuzwmA2syeWumHA6cFgDzjhXO1eHNzeiL1VdWioCf0v3JJuxNiLCjFhVhHyR2b2ekfQ7m3HEfsRHLbrweeI4whq7DU46jgKrxb9EFhXaYY0ZJmyuoWfLHNWaH5YIAqMpxnSetVOn8eDI5/twIHNm3Bw28fwdIb+Z5GePQxjLp6N8TPnYPjEyd3Cjup0ou1fq2B/+210bNkSnG8sLUXenXfCdu01DDpDnBACQtOgqj4IVYWqqv5XHzSfCk1T9VfVB01VwwZfWK0+HR4ewkODzx8+VK8X3i6BxOfxv3r9IcTj0eu9Hv1sgwSRFYMeNqIFD3/4MIeFFH063V8XmmcwmhhMaMAw4PQgmQJOuJb6duytqse+zXVoD7t8MqckHRNmFWPcxYVIt53ZjfF8mg8nnSf13h5/j09jRyMcbgfsHjvsbjscHgc0EbtLuScG2dAt+ESEoijBKF1OQ/O+Qzj48Uc4uG0z3O2hY7pWWzbGXjwLY2fMQemk8yArkWHHe+IE7O+8g+aXXwkewjKOHBkKOgaegtbfVJ8PXpcLXrcLHldncDx8ns/thqZqXQKEzx8aAsHCvzwYOHz6qxoY10LzfGpEWIlYP9o2w25MOWhJEgwmEwwmMwxGY5dxMxT/q8FkgsFo8i/3zzOaoJhMXZaFxpXgPHNwHVOaFQYGf0oCDDg9SNaAExBxCOvTJqjesENYk3MwYVaxfgjL2L83DdSEBqfXGRl63A7Y3fbgdGC86/ze9g5FI0FCljkL2YoNI1qsKKiVkHnUDdkd+qBSrBYMO28cRlROQ9nkCzDMmoMscxaMshFaRwda/vIaTj//PIMO/IdCfD5/8OiE1+XWX90ueFz+eW538NXjX+Z16ct9wfCi13jcLvj8y8Jv9JhsZEWBrBggK7L/Vek2T1EUSIoCRTFANiiRYSEQRMLmhYJI90CiRAkfgUFWDOwFIYqCAacHyR5wwrk7Qoew6g+HDmGZ0w0Yd1FRnw9hDQQhBDp9nXB4HN1DUFgw6rrc7raj0xf9hDpJA4pPW1BWb0VZvRUWb6j3xmVUcaywA0eLO2AvUpBpyUKmKRM5woqZm1sxbfUxWJx6D5iraBhabr0cYuElyEobhkxTZnDIMGb0252lhRAQQoPQRNh4YNo/7j90EhzvUhMKJa6wMBIYwgJKWI0eSsIDiz5voHsxZMUAk8UCg8UCk9kCoyUNRosZJksaDEYTZIOhe3gwKJBkBYrBoL8qil4ny/767uFDUQz+wKEEXyPDiX/aoECWFT2UdFvHAEmWGSiIkgADTg9SKeCEa6lvx97N9di3uR7traETbXNK0jFhZjHGzTjzQ1iJ4lE9kcGnS2+Rw+OAo9MOz9FGmA/akX3MC5M79EHlNqo4la2HGUnog6wBuW1ArgNQBCAgwWMAWjIkONMASUjBegUKFMiQJRmKkCBDhgxJ/yckyADg325gHaGF7t6g+cNKIs+tiEcxGmE0W2C0WGA0W2Dyvxot/lASmBecHwoq4esZLZaweWYoBh7uIKL+x4DTg1QNOAGaJnB8bzP2VtXj8M5TEYewRk7OwYSZxRh1fv8fwhoMNE3F8T2fY9/mjTiw5SN02uPfE2PQkSRIkuTvUZAhyRIkSYYsy5AUJSKAmCwWGMz+YBEWUAwRQSWtS2gJCyVmC5QhdniOiJIbA04PUj3ghHN3+nBwWwP2VtWj/nDow95sNWDcRYUYP6sYBWWJPYQ1UDRNxcl91Witr9MDg/8whB4gFH94kACPF+0bN6Jt1WoIZxsgAENBPsyLFkCtnIJOuNDh60SH6h98HWj3Dx2+drT7OuD0tcPpc8LpbUebzwmv8EIAEBIgJAEBAGHj+nz9Jo1C8i/z1wQokoIMUwYyjf5DZqYMmBUzLIoFZoP/VTHrg8HcbZlJMXWv9deF1xpl9rYQUXJgwOnBUAo44VobOrC3qg77Pq6HsyV0CGtYcTomzCrC+BlFSXcIqz9p7e1o+ctfcPr5P0FtaQEAGMtGIu+uu2D70pd6fTKyEAJu1Y02TxvaPG1weBzB8TZPG9q83ec53A60eUPjPnHuTtZVJEUPPIawUOQPQ11DVNdl0UKUxRC2Tpf1AssMMnuOiKjvGHB6MFQDToCmCZzY24LqqrrIQ1gSMHJyrn4V1vm5MBiVHraUmvor6JwpIQRcqisyAHkcaPe2w+Vzwa26g0P4dGDcpbrgUT1Rl4UPiZRhzAjeHiDLlIUsc2g8/BYC4a99uZcSEaUmBpweDPWAE87d6cMh/1VYdYciD2GNqSzE2OkFKB6bDVkeeh8qWns7ml99Fc3P/wmq/5EEprIy5C25C1lXX53Ul5drQoNH9QTDj0f1wKW6ugUlt88dNVB1DVE91bp9bni0OI/u6CWDZOgWhrqGoq7zAwGKh+KIkh8DTg8YcKJrbejA3s112Lc58hCW1WbCmGkFGFNZiKJRWZCGWNhJ5aBzLgVCVYevAw63I3R1XNgtA8Lnd71tgE87u8N26cb0qGEovIco/KaTgXGrwcpeI6JBggGnBww48WmawIl9LTiwtQGHd56CuyP0wZIxzIwx0/Wwk6onJ8eiOtvR8uqraP5TWNApL9eDzqJFDDoDqOu9lBweR7e7bHe9n1Kgps0b/wnMPQn0GnUNQ5mmzOB5SGbFHDwfKTAdmBdcZoixTOajDYh6iwGnBww4vaf6NNRWN+PgtkYc/vQUvK7QDeKy8iz6YazKAuQOzxgyO2kGneTi03xwepzdwlDXUBTei9Qfd+Dui/DA03W8a1CKOIG7l8vibVORh+a5dpScGHB6wIBzZnxeFcd2N+PA9gYc+awJPk/ouVTZhVaMqSzA2OmFyClJT2Arz51g0Hn+eaj+++0Eg87VV0NS+MGRzAIne0eEoi6H0ALnMXk0/TVwHlJgftfx8Gn/zQMSzqyYcWHBhZg3Yh7mjZiHsqyyRDeJKCYGnB4w4Jw9r1vFkV1NOLitEUd3n4bqC4Wd3OHpGDO9EGMqC5BdYE1gK88N1dmOlj//We/RCQ86dy/Re3QYdKgLIQR8mq/blW29CUa9XuZzw62FpsNr4p3PVJZVhkuGX4J5I+ahsrASRoUnZ9PgwYDTAwac/uXp9KHmsyYc3NaAY3uaoamhX6n8kZkYU1mAMdMLkJWblsBWDryoQWfUqNChKwYdGiR8mg8e1QOP6kFTZxM+PPkhNh7fiO0N2yPuwWQ1WDGrZBYuGX4JLhlxCQqsBQlsNREDTo8YcAaOq92LwztP4eD2Rhzf26I/l8mvcFQWxlYWomJaATKGpe4NBWMHnSXIWvRFBh0atJweJzbXbcaG4xuw8cRGNHU2RSyfmDMRl4zQe3em5E7h+Tt0zjHg9IAB59zobPPg0I5TOLitAScOtCJ4yoEElIzJxpjpBaiYVgBrlimRzRwwqtOJllf+jOYVKxh0KOloQkN1c7Uedo5vxO6m3RHnDQ0zD8Pc4XMxb8Q8zB4+G1km7ktp4DHg9IAB59xrt7tx6JNGHNzWGHFDQUkCho8fpoedCwtgyUi94/2BoHN6xQpoDDqUpE53nsamE5uw4fgGfHTyIzi9zuAyRVIwtWCqfqLy8HmoyK4YMldV0rnFgNMDBpzEamt24eD2Rhzc1oDGo6F7lMiyhBETczC2sgCjpubDnJZal1vrQecVnF7xQijojB6tB50vXsWgQ0nDq3mxs3EnNh7fiA3HN+CQ/VDE8pL0kuChrIuLLobFYElQSynVJFXAWb58Of72t79h7969SEtLw+zZs/HEE09g/PjxMddZt24dLrvssm7zq6urMWHChB7fkwFn8LCf6sTB7Q04sK0Rp4+H/kcoGySMnJSLsRcVoPy8PJgsqRN2GHQo1RxvO46NJ/Sws6VuS8RjOcyKGRcXXRy8DL0koySBLaVkl1QB56qrrsKtt96Kiy66CD6fDw8++CB27dqFPXv2ID09+v1UAgFn3759EV9gfn4+lF58ODDgDE4t9e04uL0RB7Y2oKW+IzjfYJRRdl4uxkwvRPl5uTCYUiMARA06FRX6VVdXMehQcur0dWJL3RZsOL4BG05sQH17fcTyMdlj9N6d4fMwtWAqnyxPfZJUAaerU6dOoaCgAOvXr8e8efOi1gQCTktLC7Kzs/v8Hgw4g5sQAs0n23FgWwMObmuE/VRncJnRrKD8/DyMrSzAyEm5UIxyAlvaP9S2tlDQcTgAMOhQahBC4EDrgeCJyjtP7YQmQvfMyjRlYk7JHMwbMQ9zhs9BjiUnga2lZJDUAefgwYMYO3Ysdu3ahSlTpkStCQSc8vJyuFwuTJo0CQ899FDUw1YA4Ha74XaHHh7pcDhQWlrKgJMEhBBoqnUGw05bsyu4zJRmwOgL8jCmshAjJg6DoiR32GHQoVRnd9vx0cmPsOH4Bmw6sQmt7tbgMgkSzss7L3juzsSciTxRmbpJ2oAjhMB1112HlpYWbNy4MWbdvn37sGHDBkyfPh1utxsvv/wynn32Waxbty5qr88jjzyCRx99tNt8BpzkIoRAQ40DB7c14uD2BrTbw47zpxtQcWEBxlQWYPi4YZCT+Innalsbml9+Gc0vvBgKOmMqkPed7yDz8sshW1P/7tCU+lRNxa6mXcF77uxt3huxPD8tP3goa2bJTKQbh8YjYCi+pA04d999N95//31s2rQJI0aM6NO611xzDSRJwjvvvNNtGXtwUo/QBOoOteLAtkYc+qQRnW2hhyKmZRpRMa0AYysLUFyRDSlJw060oCNZLMi45BJkLlyIjEvnQ8nISHArifpHQ3tD8ETlzXWb0ekLHZo2yAZUFlbyeVmUnAHnu9/9LlauXIkNGzZg1KhRfV7/sccewyuvvILq6uoea3kOTmrRVA0nDrTi4LZGHNrRCHd76Fbz6dlmDB+fDaPZAINJhsEow2BU9HGTAoNRhmKUYTQpUExhy4yh5YHXRAWlQNCx/+0teI8fD86XjEakz5mDzIULkfmFy6DYbAlpH1F/86gebKvfFgw8x9qORSwPPC/rkhGXoLKwEiYlNW8WSt0lVcARQuC73/0u3nrrLaxbtw5jx449o+3ceOONaG5uxgcffNBjLQNO6lJVDcerW3BwWwMO7zwFj0vtt23LBkkPQsboAcgQWBYWnoLL/eEpMkyFh63wcKVANkjdzj8QQsBdXQ3Hv1ahbdUqeGpqQgsNBqTPmIHMhQuQecUVMOTwZE1KHUfsR4JXZW1v2B7xsFCrwYqZxTMxd8Rc5FpyoUgKJEmCIimQJTli6DovvLbrOt3WhwxZ9q8DCYqshOZLMs8XOkeSKuAsWbIEr776Kt5+++2Ie9/YbDakpekPZ1y2bBlOnDiBl156CQDw9NNPo7y8HJMnT4bH48Err7yCn/3sZ3jzzTdx/fXX9/ieDDhDg8+ronZPM1rqO+DzqPB5NPi8Gnxe/7hHher1z/OoXV71Os2XoD8PCd1DlEmG0awEB9ndDlF3HNrRwxBNdVBUtz5oXqSPHomMygtgm3kR0orzYbQoMJr860UJT0TJoqfnZSWKhO6hKl6Aihe6zIoZFoMFaYa0boPVaI06P9aQan/rSRVwYn3zV6xYgcWLFwMAFi9ejCNHjmDdunUAgCeffBLPPfccTpw4gbS0NEyePBnLli3DokWLevWeDDjUW5om9BAUHoDCgpLq0eDtGpT84SgQlNQY4Snwqno1eP115+KvUZYlGC0KDCYlIjCFD4YY8+PVJ/tVbJR8As/L2nh8I7bVb0On2glN06BBgyY0qEKFECLiVROhZYHx8CFabfgzuJJNX8JQcDB2CVWG6KEqEQ9bTaqAkwgMODQYCSGgqaJ7UPJoUL0qvB4NPrcKbw+Dx9EO16lWeBwd8Hk1qIoZqmKGJg/sc75kg6QHnvDgZOky7T8XSpIlSLIEWQYgSZBlvVdJkuGfry+XpC7Tsv6fotC0XhM+LftrQtOh9WJPR9u2/739dTR0CSGiByNo0DR/KIKAqqkR8zX468NCV7wgFRh3+9zo8HWg09fZ8+DtPs+lunr+ovqBSTZ1C0PhQ0l6CZZetLRf37Mvn9+8hSTRICFJEhSDBMUg99tzuLz19WhbvQZtq1bBue0TaLIRqmKGTzFDqRgP88WzYJpaCZFTAJ8nLCi5VHjDpmMGK5cKTdP/j6T5BNw+X8RJ3qkkEHoCgUdWAoMMxaC/yor+8wssC42HahRFguyfr/jXkQ3Ra8O3oSgyZIMUNh5Y3nMtA9rZCRxeUqDAiMH/QGBNaHD5XNFDUpRAFD70Zp1Aj5ZH88Dj9sDutkdtxyjbqH4POH3BHhz24NAQ4WtqQtuaf6Nt1Sq0f/wxoIZOwDZVVCBzwZXIWrgQ5vHj+3TcXvVp3YJPRCDy+ANTYJlHhdAENKFf7h8YNCEgtNA8TRMQout0qEYLrCsQGo82Hf5eXd8jbDqVBQJZt3DlD0fRftyR86Ruo9HXif57E3V22Mzevn/EvD68f096XC3u8tgLz/T0l16v14c36HVlT4UC/p4pDZpQg71OqqZGzFM1DeZMYPE9X+x1G3uDh6h6wIBDQ52vpQXOD9bCsepfaP+oCvCG7iNkHDkSWQuuRObChbBMmZJyJynGEgo8XUJU1FClH04MDKqqQfP5X1UBzadB9Qn90ITPX+PTYtcGtuXTwuZH2Z7ac63q05DEp4xQCklXW7D4Dzf06zYZcHrAgEMUora1wbluHRz/+hfaN26CCLsppqGkGFlXLkDmwgVImzoVkswTiZOBpulhqGvwCYatLuEqyP9xEPGhILqPR5x0K7qXRSyO9hHTfXWEn2Ef9VMpYp3ub3qmn2Rn9REYZ9W4JybHWzQQn8i93KgAIDxeqA4HVLsdmv9Vn3ZAc/jH29oAVetxe6YMM+a+//uzbHwkBpweMOAQRae1t8O5cSMc//oXnOs3QHSEPdU9Px+ZV16BzAULYa2cDsnAU/iIkoUQAmpLC3wNDfA2NMDX0AhfQ33YeAO8jY3Q7NHPp+lGlmHIy4OhsBCGwgIYCwpD40VFMBQUwlhYADm9fx+xwYDTAwYcop5pLhfaN22CY9UqONeug9bWFlymDBuGzCsuR+aChUifOQOScfCfeEmUqjSPB75GPaToAcYfWBrqg+HF19gIEXYoOh4pLQ3GggIYior0wFJYCENBYWi8qAiG3NyE/CeHAacHDDhEfSM8HrRv3qz37Kz5N9Sw/+XJWVnI/MIXkLlgAdLnzIZsNiewpUSpQwgBzeGAt74BvsaGLr0veo+Lr74eaktLr7ep5OZG9LgYiwLhRe9xMRQWQs7MHLTn3jHg9IABh+jMCa8XHVu3wrFqFdrW/BtqU+hOsnJ6OjIuvRSZCxYgY94lkP13IycayoSmQXM6obW1QQ28trVBczr11zYnNKd/nqMNvlOn4G3Ug4xw9e6eNpLJFP1wUWEhDIVFenjJz4dkSu7ndjHg9IABh6h/CFVF5yefwLFqNdpWrYKvoSG4TLJYkDFvnh52+ORzSlJ9CidtTqhO/7w2B9Q2vV5rbz+rNig2mz+whPe4BMKLPijZ2YO216U/MeD0gAGHqP8JTYPrs8+CYSfiyecmk/7k80DYGSI7Y0qsMwonjjb/vP4JJ+EkoxFyZibkzAwoGZmQMzOhZGZAzszSXzP0ZYa8/ODhIkNBAWSLpd/akOwYcHrAgEM0sIQQcO3Zg7ZVq9H2r3/Bc+RIZIHBACUzE0pWFuSsLP9rJpQsG5SsTH1eZhYUW5a+87dlQcnMhGyzQcnI4EnNQ4jw+fRLk1vt+mXKdv/Qag+7hLnVf1lzAsJJRqb+O+sPJ0qmPq4Hl0zIGf55mZk8P60fMOD0gAGH6NwRQsB94EAw7LgPHDjrbcpWa/dglJkJ2RYjGPmDk5KVBclqZe/ROSaEgHC5QuHEP2jhYSVwz5VAWPHP05zOs37/eOFEDyVZDCdJggGnBww4RImjdXToH2YOh34jMUcbVIf+v2/V4dDPXbDrNxPT7HaobW3B2n75H7m/9yhuMMrK8vckhYKR3quUOaR7j4Sm6Yd57Hb9ZxQeSCLm2YPLNP884fGc1XvLGRlQbDYoNpv+87JlB6f1ISt0qIfhJGXxYZu91d4OKFEe964oQPgxz3g7VVkGwq8U6UttR0fsO0xKEmC1nlltZyegxbnLZPiNl/pS63JFPL/orGqt1tBzVNxuwBfnAY19qU1L07/PAODxRDyC4KxqLZbQ70pfar1evT4WsxkI3EuiL7U+n/69iMVkAgIfxH2pVVX9ZxeL0ajX97VW0/TfNQAyADkzE8bMTGD48Li1XQmfD2pnJzS3Ww9G9lZoTU36XVadbcFXra0NqqMNPqcTWnt7sHdA8n+PtdOnoZ0+ja6/SSLsTs1SjL8LKS0Nii0LUlYWJMUAKDJkTQCKDEkx6Hd7lmVIigzICmBQAJNZXy4rkDQVkqwAsqS/+udDkQHFAFgskBT/fJ8aWacogKwEty1Z00LTXp/+UM3AtGII1SkyYE0PTfu8ACR9GoDa5vSHTv/daztDvS1ac3MoqLS1ddsP9eZ7BuiPOBJGYzCQGDIy9F6TLD2syFn+Hjd/z5xSWAg5OxtKdjYUkyn+I5Li7SN8vshp7iN0g3gf0WNtb4khyG63CwDCrv+pdh8WLYpcwWqNXgcIMX9+ZG1eXuzaysrI2rKy2LWTJkXWTpoUu7asLLK2sjJ2bV5eZO38+bFrrdbI2kWLYtd2/VW68cb4tU5nqPb22+PXNjaGapcsiV9bUxOqXbo0fu3u3aHahx+OX7tlS6j2ySfj165dG6p95pn4te+9F6pdsSJ+7RtvhGrfeCN+7YoVodr33otf+8wzodq1a+PXPvlkqHbLlvi1Dz8cqt29O37t0qWh2pqa+LVLloRqGxvj195+e7BUa2uLW+u68EJR//jj4sQPl4ljS+6OW9uWni72jJ8QHFRJilnrTEuLqPUqSszaDoslotZtMMRur8kUUesymWLWug2GiNoOiyVmrVdRImqdaWkxa1VFEUe+fpuovececeLBB4VrwoS43zdN00I/O+4jdNxH6Hq5jwh+ftvtoidDuweHiIaMns67MVdUoHDZstCM3/4mZm3atGkY+eSTEKoKqCqkhQtj/k/VPGo0ih55GFA1CE2FfOedQNhdocMZ8vOR++1vA5oKoWpQfrYcaG2NWitnZiJr0RchVA3QVMh//nPM/93LJhMs558P+HwQmgapvi5me2WzGbl33an3tGTZYP7xo8Cnn8asLXvpxdCMHTuAvXuj1gJn/rRvojMxtM/BOXky+jE8HqKKXstDVH2vZfezPt6X7ueeag0G/XsB6H8TYc/LOqvavvzdcx8RvZb7iL7Xch+hj/dyH8GTjHvAk4yJiIiST18+v+W4S4mIiIiSEAMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSzqAIOL/97W8xatQoWCwWTJ8+HRs3boxbv379ekyfPh0WiwWjR4/Gs88+e45aSkRERMkg4QHn9ddfx3333YcHH3wQO3bswCWXXIIvfvGLOHbsWNT6mpoaLFq0CJdccgl27NiB//7v/8b3vvc9vPnmm+e45URERDRYSUIIkcgGzJgxA9OmTcPvfve74LyJEyfiy1/+MpYvX96t/gc/+AHeeecdVFdXB+fdeeed+PTTT1FVVdWr93Q4HLDZbLDb7cjKyjr7L4KIiIgGXF8+vxPag+PxeLB9+3YsWLAgYv6CBQvw0UcfRV2nqqqqW/3ChQuxbds2eL3eqOu43W44HI6IgYiIiFJXQgNOU1MTVFVFYWFhxPzCwkLU19dHXae+vj5qvc/nQ1NTU9R1li9fDpvNFhxKS0v75wsgIiKiQSnh5+AAgCRJEdNCiG7zeqqPNj9g2bJlsNvtwaG2tvYsW0xERESDmSGRb56XlwdFUbr11jQ2NnbrpQkoKiqKWm8wGJCbmxt1HbPZDLPZ3D+NJiIiokEvoT04JpMJ06dPx+rVqyPmr169GrNnz466zqxZs7rVr1q1CpWVlTAajQPWViIiIkoeCT9E9cADD+CPf/wj/vSnP6G6uhr3338/jh07hjvvvBOAfnjptttuC9bfeeedOHr0KB544AFUV1fjT3/6E55//nksXbo0UV8CERERDTIJPUQFALfccgtOnz6NH//4x6irq8OUKVPw97//HWVlZQCAurq6iHvijBo1Cn//+99x//334ze/+Q1KSkrwv//7v7jhhhsS9SUQERHRIJPw++AkAu+DQ0RElHyS5j44RERERAOBAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRxDohuQUKdOAS5X9/kmE5CdHZpubIy9DYMByMk5s9qmJkDTotfKMpCXd2a1zc2Azxe7HQUFZ1bb2gp4PP1Tm5entxsAHI7oP4czqc3J0b/PAOB0Ah0d/VObna3/XvS1tqNDr48lKwuwWPpe63Lp34tYMjIAq7XvtR6P/rOLxWrV6/ta6/Ppv2v9UWux6N8LQP+baGrqn9q+/N1zHxG9lvuIvtdyH6GP92Uf0VtiCLLb7QKAsANCRBvy8yNXiFUHCGGzRdZKUuxaqzWyVlFi15rNkbVmc+xaRYmstVpj10pSZK3NFv/rC5ef3/va4cPj1zY0hGorKuLX7tkTqp0yJX7txo2h2srK+LUrV4Zq58+PX/vCC6HaRYvi1/7yl6Ham26KX/vww6Hab30rfu3994dq778/fu23vhWqffjh+LU33RSq/eUv49cuWhSqfeGF+LXz54dqV66MX1tZGarduDF+7ZQpodo9e+LXVlSEahsa4tcOHy4ixKvlPiI0hOM+Qsd9hG4A9hHBz2+7XfSEh6iIiIgo5UhCCJHoRpxrDocDNpsN9oMHkZWZ2b2A3c/Ra9n93Pdadj/r4zxEdWa13Efo49xH9L02RfcRwc9vux1Zgb/tGIZ2wOnFN4iIiIgGh758fvMQFREREaUcBhwiIiJKOQkLOEeOHMG3vvUtjBo1CmlpaaioqMDDDz8MT7xjsgAWL14MSZIihpkzZ56jVhMREVEySNh9cPbu3QtN0/D73/8eY8aMwe7du3HHHXegvb0dP//5z+Oue9VVV2HFihXBaVPgZC0iIiIiJDDgXHXVVbjqqquC06NHj8a+ffvwu9/9rseAYzabUVRUNNBNJCIioiQ1qM7BsdvtyAm/RDKGdevWoaCgAOPGjcMdd9yBxniXXQJwu91wOBwRAxEREaWuQRNwDh06hF//+te4884749Z98YtfxJ///Gd88MEH+MUvfoGtW7fiC1/4Atxud8x1li9fDpvNFhxKS0v7u/lEREQ0iPT7fXAeeeQRPProo3Frtm7disrKyuD0yZMnMX/+fMyfPx9//OMf+/R+dXV1KCsrw2uvvYbrr78+ao3b7Y4IQA6HA6WlpbwPDhERURLpy31w+v0cnHvuuQe33npr3Jry8vLg+MmTJ3HZZZdh1qxZeO655/r8fsXFxSgrK8OBAwdi1pjNZpjN5j5vm4iIiJJTvwecvLw85IXfEjyOEydO4LLLLsP06dOxYsUKyHLfj5idPn0atbW1KC4u7vO6RERElJoSdg7OyZMncemll6K0tBQ///nPcerUKdTX16O+vj6ibsKECXjrrbcAAE6nE0uXLkVVVRWOHDmCdevW4ZprrkFeXh6+8pWvJOLLICIiokEoYZeJr1q1CgcPHsTBgwcxYsSIiGXhpwXt27cPdrsdAKAoCnbt2oWXXnoJra2tKC4uxmWXXYbXX38dmdEemklERERDEh+2yZOMiYiIkgIftklERERDGgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5CQ045eXlkCQpYvjhD38Ydx0hBB555BGUlJQgLS0Nl156KT7//PNz1GIiIiJKBgnvwfnxj3+Murq64PDQQw/FrX/yySfx1FNP4ZlnnsHWrVtRVFSEK6+8Em1tbeeoxURERDTYJTzgZGZmoqioKDhkZGTErBVC4Omnn8aDDz6I66+/HlOmTMGLL76Ijo4OvPrqq+ew1URERDSYJTzgPPHEE8jNzcXUqVPx2GOPwePxxKytqalBfX09FixYEJxnNpsxf/58fPTRRzHXc7vdcDgcEQMRERGlLkMi3/zee+/FtGnTMGzYMGzZsgXLli1DTU0N/vjHP0atr6+vBwAUFhZGzC8sLMTRo0djvs/y5cvx6KOP9l/DiYiIaFDr9x6cRx55pNuJw12Hbdu2AQDuv/9+zJ8/H+effz7+z//5P3j22Wfx/PPP4/Tp03HfQ5KkiGkhRLd54ZYtWwa73R4camtrz/4LJSIiokGr33tw7rnnHtx6661xa8rLy6POnzlzJgDg4MGDyM3N7ba8qKgIgN6TU1xcHJzf2NjYrVcnnNlshtls7r6gvR1QlO7zFQWwWCLrYpFlIC3tzGo7OgAhotdKEmC1nlltZyegabHbkZ5+ZrUuF6Cq/VNrtertBgC3G/D5+qc2LU3/PgOAxwN4vf1Ta7GEflf6Uuv16vWxmM2AwdD3Wp9P/17EYjIBRmPfa1VV/9nFYjTq9X2t1TT9d60/ag0G/XsB6H8THR39U9uXv3vuI6LXch/R91ruI/TxvuwjeksMIu+++64AII4ePRp1uaZpoqioSDzxxBPBeW63W9hsNvHss8/2+n3sdrsAIOz67qD7sGhR5ApWa/Q6QIj58yNr8/Ji11ZWRtaWlcWunTQpsnbSpNi1ZWWRtZWVsWvz8iJr58+PXWu1RtYuWhS7tuuv0o03xq91OkO1t98ev7axMVS7ZEn82pqaUO3SpfFrd+8O1T78cPzaLVtCtU8+Gb927dpQ7TPPxK99771Q7YoV8WvfeCNU+8Yb8WtXrAjVvvde/NpnngnVrl0bv/bJJ0O1W7bEr3344VDt7t3xa5cuDdXW1MSvXbIkVNvYGL/29ttDtU5n/NobbxQR4tVyH6EP3EeEBu4j9GGA9xHBz2+7XfQkYScZV1VV4Ze//CV27tyJmpoavPHGG/jOd76Da6+9FiNHjgzWTZgwAW+99RYA/dDUfffdh8cffxxvvfUWdu/ejcWLF8NqteJrX/taor4UIiIiGmQkIYRIxBt/8sknWLJkCfbu3Qu3242ysjLceuut+P73vw9rWFeqJElYsWIFFi9eDAAQQuDRRx/F73//e7S0tGDGjBn4zW9+gylTpvT6vR0OB2w2G+wnTyIrK6t7Abufo9ey+7nvtex+1sd5iOrMarmP0Me5j+h7bYruI4Kf33Z79M/vMAkLOInUl28QERERDQ59+fxO+H1wiIiIiPobAw4RERGlHAYcIiIiSjkMOERERJRyGHCIiIgo5TDgEBERUcphwCEiIqKUw4BDREREKYcBh4iIiFIOAw4RERGlHAYcIiIiSjkMOERERJRyGHCIiIgo5TDgEBERUcphwCEiIqKUw4BDREREKYcBh4iIiFIOAw4RERGlHAYcIiIiSjkMOERERJRyGHCIiIgo5TDgEBERUcphwCEiIqKUw4BDREREKYcBh4iIiFIOAw4RERGlHAYcIiIiSjkMOERERJRyGHCIiIgo5TDgEBERUcphwCEiIqKUw4BDREREKYcBh4iIiFIOAw4RERGlnIQFnHXr1kGSpKjD1q1bY663ePHibvUzZ848hy0nIiKiwc6QqDeePXs26urqIub96Ec/wpo1a1BZWRl33auuugorVqwITptMpgFpIxERESWnhAUck8mEoqKi4LTX68U777yDe+65B5IkxV3XbDZHrEtEREQUbtCcg/POO++gqakJixcv7rF23bp1KCgowLhx43DHHXegsbExbr3b7YbD4YgYiIiIKHVJQgiR6EYAwKJFiwAAf//73+PWvf7668jIyEBZWRlqamrwox/9CD6fD9u3b4fZbI66ziOPPIJHH32023y73Y6srKyzbzwRERENOIfDAZvN1qvP734POLHCRLitW7dGnGdz/PhxlJWV4Y033sANN9zQp/erq6tDWVkZXnvtNVx//fVRa9xuN9xud3Da4XCgtLSUAYeIiCiJ9CXg9Ps5OPfccw9uvfXWuDXl5eUR0ytWrEBubi6uvfbaPr9fcXExysrKcODAgZg1ZrM5Zu8OERERpZ5+Dzh5eXnIy8vrdb0QAitWrMBtt90Go9HY5/c7ffo0amtrUVxc3Od1iYiIKDUl/CTjDz74ADU1NfjWt74VdfmECRPw1ltvAQCcTieWLl2KqqoqHDlyBOvWrcM111yDvLw8fOUrXzmXzSYiIqJBLGGXiQc8//zzmD17NiZOnBh1+b59+2C32wEAiqJg165deOmll9Da2ori4mJcdtlleP3115GZmXkum01ERESD2KC5iupc6stJSkRERDQ49OXzO+GHqIiIiIj6GwMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCnHkOgGJFR7O6Ao3ecrCmCxRNbFIstAWtqZ1XZ0AEJEr5UkwGo9s9rOTkDTYrcjPf3Mal0uQFX7p9Zq1dsNAG434PP1T21amv59BgCPB/B6+6fWYgn9rvSl1uvV62MxmwGDoe+1Pp/+vYjFZAKMxr7Xqqr+s4vFaNTr+1qrafrvWn/UGgz69wLQ/yY6Ovqnti9/99xHRK/lPqLvtdxH6ON92Uf0lhiC7Ha7ACDs+u6g+7BoUeQKVmv0OkCI+fMja/PyYtdWVkbWlpXFrp00KbJ20qTYtWVlkbWVlbFr8/Iia+fPj11rtUbWLloUu7brr9KNN8avdTpDtbffHr+2sTFUu2RJ/NqamlDt0qXxa3fvDtU+/HD82i1bQrVPPhm/du3aUO0zz8Svfe+9UO2KFfFr33gjVPvGG/FrV6wI1b73XvzaZ54J1a5dG7/2ySdDtVu2xK99+OFQ7e7d8WuXLg3V1tTEr12yJFTb2Bi/9vbbQ7VOZ/zaG28UEeLVch+hD9xHhAbuI/RhgPcRwc9vu130hIeoiIiIKOVIQgiR6Eacaw6HAzabDfaTJ5GVldW9gN3P0WvZ/dz3WnY/6+M8RHVmtdxH6OPcR/S9NkX3EcHPb7s9+ud3mKEdcHrxDSIiIqLBoS+f3zxERURERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhERESUchhwiIiIKOUMaMB57LHHMHv2bFitVmRnZ0etOXbsGK655hqkp6cjLy8P3/ve9+CJd50/ALfbje9+97vIy8tDeno6rr32Whw/fnwAvgIiIiJKRgMacDweD2666SbcddddUZerqoqrr74a7e3t2LRpE1577TW8+eab+L//9//G3e59992Ht956C6+99ho2bdoEp9OJL33pS1Dj3TSKiIiIhoxzcqO/F154Affddx9aW1sj5v/jH//Al770JdTW1qKkpAQA8Nprr2Hx4sVobGyMehMfu92O/Px8vPzyy7jlllsAACdPnkRpaSn+/ve/Y+HChT22hzf6IyIiSj5Jc6O/qqoqTJkyJRhuAGDhwoVwu93Yvn171HW2b98Or9eLBQsWBOeVlJRgypQp+Oijj6Ku43a74XA4IgYiIiJKXQkNOPX19SgsLIyYN2zYMJhMJtTX18dcx2QyYdiwYRHzCwsLY66zfPly2Gy24FBaWto/XwARERENSn0OOI888ggkSYo7bNu2rdfbkwIPSAsjhIg6P5546yxbtgx2uz041NbW9mnbRERElFwMfV3hnnvuwa233hq3pry8vFfbKioqwscffxwxr6WlBV6vt1vPTvg6Ho8HLS0tEb04jY2NmD17dtR1zGYzzIEnChMREVHK63PAycvLQ15eXr+8+axZs/DYY4+hrq4OxcXFAIBVq1bBbDZj+vTpUdeZPn06jEYjVq9ejZtvvhkAUFdXh927d+PJJ5/s1fsGzqvmuThERETJI/C53avro8QAOnr0qNixY4d49NFHRUZGhtixY4fYsWOHaGtrE0II4fP5xJQpU8Tll18uPvnkE7FmzRoxYsQIcc899wS3cfz4cTF+/Hjx8ccfB+fdeeedYsSIEWLNmjXik08+EV/4whfEBRdcIHw+X6/aVVtbKwBw4MCBAwcOHJJwqK2t7fGzvs89OH3xP//zP3jxxReD0xdeeCEAYO3atbj00kuhKAref/99LFmyBHPmzEFaWhq+9rWv4ec//3lwHa/Xi3379qGjoyM475e//CUMBgNuvvlmdHZ24vLLL8cLL7wARVF61a6SkhLU1tYiMzOzz+f69MThcKC0tBS1tbW8BH0Q4M9jcOHPY3Dhz2Pw4c8kPiEE2traIq6+juWc3AdnKOE9dgYX/jwGF/48Bhf+PAYf/kz6D59FRURERCmHAYeIiIhSDgNOPzObzXj44Yd5WfogwZ/H4MKfx+DCn8fgw59J/+E5OERERJRy2INDREREKYcBh4iIiFIOAw4RERGlHAYcIiIiSjkMOP3ot7/9LUaNGgWLxYLp06dj48aNiW7SkLV8+XJcdNFFyMzMREFBAb785S9j3759iW4W+S1fvhySJOG+++5LdFOGrBMnTuA///M/kZubC6vViqlTp2L79u2JbtaQ5PP58NBDD2HUqFFIS0vD6NGj8eMf/xiapiW6aUmNAaefvP7667jvvvvw4IMPYseOHbjkkkvwxS9+EceOHUt004ak9evX4+6778bmzZuxevVq+Hw+LFiwAO3t7Ylu2pC3detWPPfcczj//PMT3ZQhq6WlBXPmzIHRaMQ//vEP7NmzB7/4xS+QnZ2d6KYNSU888QSeffZZPPPMM6iursaTTz6J//f//h9+/etfJ7ppSY2XifeTGTNmYNq0afjd734XnDdx4kR8+ctfxvLlyxPYMgKAU6dOoaCgAOvXr8e8efMS3Zwhy+l0Ytq0afjtb3+Ln/70p5g6dSqefvrpRDdryPnhD3+IDz/8kL3Mg8SXvvQlFBYW4vnnnw/Ou+GGG2C1WvHyyy8nsGXJjT04/cDj8WD79u1YsGBBxPwFCxbgo48+SlCrKJzdbgcA5OTkJLglQ9vdd9+Nq6++GldccUWimzKkvfPOO6isrMRNN92EgoICXHjhhfjDH/6Q6GYNWXPnzsW///1v7N+/HwDw6aefYtOmTVi0aFGCW5bcBvRp4kNFU1MTVFVFYWFhxPzCwkLU19cnqFUUIITAAw88gLlz52LKlCmJbs6Q9dprr+GTTz7B1q1bE92UIe/w4cP43e9+hwceeAD//d//jS1btuB73/sezGYzbrvttkQ3b8j5wQ9+ALvdjgkTJkBRFKiqisceewxf/epXE920pMaA048kSYqYFkJ0m0fn3j333IPPPvsMmzZtSnRThqza2lrce++9WLVqFSwWS6KbM+RpmobKyko8/vjjAIALL7wQn3/+OX73u98x4CTA66+/jldeeQWvvvoqJk+ejJ07d+K+++5DSUkJbr/99kQ3L2kx4PSDvLw8KIrSrbemsbGxW68OnVvf/e538c4772DDhg0YMWJEopszZG3fvh2NjY2YPn16cJ6qqtiwYQOeeeYZuN1uKIqSwBYOLcXFxZg0aVLEvIkTJ+LNN99MUIuGtv/6r//CD3/4Q9x6660AgPPOOw9Hjx7F8uXLGXDOAs/B6QcmkwnTp0/H6tWrI+avXr0as2fPTlCrhjYhBO655x787W9/wwcffIBRo0YluklD2uWXX45du3Zh586dwaGyshL/8R//gZ07dzLcnGNz5szpdtuE/fv3o6ysLEEtGto6Ojogy5Efx4qi8DLxs8QenH7ywAMP4Otf/zoqKysxa9YsPPfcczh27BjuvPPORDdtSLr77rvx6quv4u2330ZmZmawd81msyEtLS3BrRt6MjMzu53/lJ6ejtzcXJ4XlQD3338/Zs+ejccffxw333wztmzZgueeew7PPfdcops2JF1zzTV47LHHMHLkSEyePBk7duzAU089hW9+85uJblpyE9RvfvOb34iysjJhMpnEtGnTxPr16xPdpCELQNRhxYoViW4a+c2fP1/ce++9iW7GkPXuu++KKVOmCLPZLCZMmCCee+65RDdpyHI4HOLee+8VI0eOFBaLRYwePVo8+OCDwu12J7ppSY33wSEiIqKUw3NwiIiIKOUw4BAREVHKYcAhIiKilMOAQ0RERCmHAYeIiIhSDgMOERERpRwGHCIiIko5DDhENGSUl5fj6aefTnQziOgcYMAhogGxePFifPnLXwYAXHrppbjvvvvO2Xu/8MILyM7O7jZ/69at+Pa3v33O2kFEicNnURFR0vB4PDCZTGe8fn5+fj+2hogGM/bgENGAWrx4MdavX49f/epXkCQJkiThyJEjAIA9e/Zg0aJFyMjIQGFhIb7+9a+jqakpuO6ll16Ke+65Bw888ADy8vJw5ZVXAgCeeuopnHfeeUhPT0dpaSmWLFkCp9MJAFi3bh2+8Y1vwG63B9/vkUceAdD9ENWxY8dw3XXXISMjA1lZWbj55pvR0NAQXP7II49g6tSpePnll1FeXg6bzYZbb70VbW1twZq//vWvOO+885CWlobc3FxcccUVaG9vH6DvJhH1FgMOEQ2oX/3qV5g1axbuuOMO1NXVoa6uDqWlpairq8P8+fMxdepUbNu2Df/85z/R0NCAm2++OWL9F198EQaDAR9++CF+//vfAwBkWcb//u//Yvfu3XjxxRfxwQcf4Pvf/z4AYPbs2Xj66aeRlZUVfL+lS5d2a5cQAl/+8pfR3NyM9evXY/Xq1Th06BBuueWWiLpDhw5h5cqVeO+99/Dee+9h/fr1+NnPfgYAqKurw1e/+lV885vfRHV1NdatW4frr78efMQfUeLxEBURDSibzQaTyQSr1YqioqLg/N/97neYNm0aHn/88eC8P/3pTygtLcX+/fsxbtw4AMCYMWPw5JNPRmwz/HyeUaNG4Sc/+Qnuuusu/Pa3v4XJZILNZoMkSRHv19WaNWvw2WefoaamBqWlpQCAl19+GZMnT8bWrVtx0UUXAQA0TcMLL7yAzMxMAMDXv/51/Pvf/8Zjjz2Guro6+Hw+XH/99SgrKwMAnHfeeWfx3SKi/sIeHCJKiO3bt2Pt2rXIyMgIDhMmTACg95oEVFZWdlt37dq1uPLKKzF8+HBkZmbitttuw+nTp/t0aKi6uhqlpaXBcAMAkyZNQnZ2Nqqrq4PzysvLg+EGAIqLi9HY2AgAuOCCC3D55ZfjvPPOw0033YQ//OEPaGlp6f03gYgGDAMOESWEpmm45pprsHPnzojhwIEDmDdvXrAuPT09Yr2jR49i0aJFmDJlCt58801s374dv/nNbwAAXq+31+8vhIAkST3ONxqNEcslSYKmaQAARVGwevVq/OMf/8CkSZPw61//GuPHj0dNTU2v20FEA4MBh4gGnMlkgqqqEfOmTZuGzz//HOXl5RgzZkzE0DXUhNu2bRt8Ph9+8YtfYObMmRg3bhxOnjzZ4/t1NWnSJBw7dgy1tbXBeXv27IHdbsfEiRN7/bVJkoQ5c+bg0UcfxY4dO2AymfDWW2/1en0iGhgMOEQ04MrLy/Hxxx/jyJEjaGpqgqZpuPvuu9Hc3IyvfvWr2LJlCw4fPoxVq1bhm9/8ZtxwUlFRAZ/Ph1//+tc4fPgwXn75ZTz77LPd3s/pdOLf//43mpqa0NHR0W07V1xxBc4//3z8x3/8Bz755BNs2bIFt912G+bPnx/1sFg0H3/8MR5//HFs27YNx44dw9/+9jecOnWqTwGJiAYGAw4RDbilS5dCURRMmjQJ+fn5OHbsGEpKSvDhhx9CVVUsXLgQU6ZMwb333gubzQZZjr1rmjp1Kp566ik88cQTmDJlCv785z9j+fLlETWzZ8/GnXfeiVtuuQX5+fndTlIG9J6XlStXYtiwYZg3bx6uuOIKjB49Gq+//nqvv66srCxs2LABixYtwrhx4/DQQw/hF7/4Bb74xS/2/ptDRANCEryekYiIiFIMe3CIiIgo5TDgEBERUcphwCEiIqKUw4BDREREKYcBh4iIiFIOAw4RERGlHAYcIiIiSjkMOERERJRyGHCIiIgo5TDgEBERUcphwCEiIqKUw4BDREREKef/B7lbK+T4XgufAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -476,45 +520,140 @@ ], "source": [ "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", + "print('Eigenvalues:', eigvals )\n", "plt.figure()\n", - "for i in range(2**nqubits):\n", - " plt.plot(range(iters), columnNorm[i], label='State ' + str(i))\n", - " plt.axhline(y=eigvals[i], color='r', linestyle='--')\n", + "for i in range(len(states)):\n", + " plt.plot(range(iters), energy[i,:],'.', label='State ' + str(states[i]))\n", + "for eigvals in eigvals:\n", + " plt.axhline(y=eigvals, color='r', linestyle='--')\n", "plt.xlabel('Iterations')\n" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradients for finding optimal $D$\n", + "\n", + "An advantage of the least-squares cost function is that one can use gradient descent and the learning is more stable than with the off-diagonal cost function." + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[20], line 8\u001b[0m\n\u001b[0;32m 5\u001b[0m step \u001b[39m=\u001b[39m \u001b[39m1e-2\u001b[39m\n\u001b[0;32m 6\u001b[0m iterations \u001b[39m=\u001b[39m \u001b[39m100\u001b[39m\n\u001b[1;32m----> 8\u001b[0m d, loss, grad, diags \u001b[39m=\u001b[39m gradient_ascent(dbi, d,step, iterations)\n\u001b[0;32m 10\u001b[0m n \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:253\u001b[0m, in \u001b[0;36mgradient_ascent\u001b[1;34m(dbi_object, d, step, iterations)\u001b[0m\n\u001b[0;32m 250\u001b[0m diagonals[:,\u001b[39m0\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mdiag(d)\n\u001b[0;32m 252\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(iterations):\n\u001b[1;32m--> 253\u001b[0m grad[i,:] \u001b[39m=\u001b[39m gradientDiagonal(dbi_object, d, H)\n\u001b[0;32m 254\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(d)):\n\u001b[0;32m 255\u001b[0m d[j,j] \u001b[39m=\u001b[39m d[j,j] \u001b[39m+\u001b[39m step\u001b[39m*\u001b[39mgrad[i,j] \u001b[39m# note the plus sign as we maximize the potential\u001b[39;00m\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:237\u001b[0m, in \u001b[0;36mgradientDiagonal\u001b[1;34m(dbi_object, d, H)\u001b[0m\n\u001b[0;32m 235\u001b[0m grad \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros(\u001b[39mlen\u001b[39m(d))\n\u001b[0;32m 236\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(d)):\n\u001b[1;32m--> 237\u001b[0m derivative \u001b[39m=\u001b[39m dpolynomial_diDiagonal(dbi_object,d,H,i)\n\u001b[0;32m 238\u001b[0m grad[i] \u001b[39m=\u001b[39m derivative\u001b[39m-\u001b[39md[i,i]\n\u001b[0;32m 239\u001b[0m \u001b[39mreturn\u001b[39;00m grad\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:224\u001b[0m, in \u001b[0;36mdpolynomial_diDiagonal\u001b[1;34m(dbi_object, d, H, i)\u001b[0m\n\u001b[0;32m 220\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdpolynomial_diDiagonal\u001b[39m(dbi_object, d,H,i):\n\u001b[0;32m 221\u001b[0m \u001b[39m# Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz)\u001b[39;00m\n\u001b[0;32m 222\u001b[0m \u001b[39m# Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation\u001b[39;00m\n\u001b[0;32m 223\u001b[0m derivative \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m--> 224\u001b[0m s \u001b[39m=\u001b[39m polynomial_step(dbi_object, d, H, i)\n\u001b[0;32m 225\u001b[0m A \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros(d\u001b[39m.\u001b[39mshape)\n\u001b[0;32m 226\u001b[0m Gamma_list \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mgenerate_Gamma_list(\u001b[39m4\u001b[39m, d)\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:127\u001b[0m, in \u001b[0;36mpolynomial_step\u001b[1;34m(dbi_object, n, n_max, d, coef, cost)\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[39mif\u001b[39;00m d \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 125\u001b[0m d \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m--> 127\u001b[0m \u001b[39mif\u001b[39;00m n \u001b[39m>\u001b[39;49m n_max:\n\u001b[0;32m 128\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 129\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mNo solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 130\u001b[0m )\n\u001b[0;32m 131\u001b[0m \u001b[39mif\u001b[39;00m coef \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", - "\u001b[1;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" - ] + "data": { + "text/plain": [ + "Text(0, 0.5, 'Diagonal elements')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "cost = DoubleBracketCostFunction.least_squares\n", + "nqubits = 5\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", "\n", "step = 1e-2\n", "iterations = 100\n", - "\n", "d, loss, grad, diags = gradient_ascent(dbi, d,step, iterations)\n", "\n", - "n = 3" + "plt.figure()\n", + "plt.plot(range(iterations+1), loss)\n", + "plt.xlabel('Learning iterations')\n", + "plt.ylabel('Loss: Least squares')\n", + "\n", + "plt.figure()\n", + "for i in range(2**nqubits):\n", + " plt.plot(diags[i,:], label='State ' + str(i))\n", + "plt.xlabel('Learning iterations')\n", + "plt.ylabel('Diagonal elements')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cost = DoubleBracketCostFunction.least_squares\n", + "nqubits = 5\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "d_fixed = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "d_trained = deepcopy(d_fixed)\n", + "dbi_trained = deepcopy(dbi)\n", + "flows = 10\n", + "iterations = 30\n", + "off_diagonal_norm = np.empty((2,flows+1))\n", + "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", + "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", + "\n", + "for i in range(flows):\n", + " d_trained, loss, grad, diags = gradient_ascent(dbi_trained, d_trained,step, iterations)\n", + " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", + " dbi_trained(s,d=d_trained)\n", + " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", + " s = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_fixed, n=3)\n", + " dbi(s,d=d_fixed)\n", + " off_diagonal_norm[1,i+1] = dbi.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(off_diagonal_norm[0,:], label='Trained')\n", + "plt.plot(off_diagonal_norm[1,:], label='Untrained')\n", + "plt.legend()\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n" ] } ], @@ -534,7 +673,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.18 (main, Sep 11 2023, 14:09:26) [MSC v.1916 64 bit (AMD64)]" }, "orig_nbformat": 4, "vscode": { diff --git a/examples/dbi/dbi_misc.ipynb b/examples/dbi/dbi_misc.ipynb new file mode 100644 index 0000000000..1a8a4da3e7 --- /dev/null +++ b/examples/dbi/dbi_misc.ipynb @@ -0,0 +1,944 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_scheduling import *" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normalization of D" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-21 14:14:06]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 7\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCostFunction.least_squares\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "factor = np.array([1,2**nqubits])\n", + "s_space = np.linspace(1e-5, 1.0, 100)\n", + "off_diagonal_norm_diff = np.empty((len(factor)+1,len(s_space)))\n", + "potential = np.empty((len(factor)+1,len(s_space)))\n", + "for i in range(len(factor)):\n", + "# generate data for plotting sigma decrease of the first step\n", + " d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/factor[i]\n", + " for s in range(len(s_space)):\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s_space[s],d=d)\n", + " off_diagonal_norm_diff[i,s] = (dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", + " potential[i,s] = dbi_eval.least_squares(D=d)\n", + " \"\"\"\n", + " # grid_search\n", + " step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", + " print('grid_search step:', step_grid)\n", + " # hyperopt\n", + " step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", + " print('hyperopt_search step:', step_hyperopt)\n", + " # polynomial\n", + " step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + " print('polynomial_approximation step:', step_poly)\n", + " \"\"\"\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "step = 1e-2\n", + "iterations = 200\n", + "d, loss, grad, diags = gradient_ascent(dbi, d,step, iterations)\n", + "for s in range(len(s_space)):\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s_space[s],d=d)\n", + " off_diagonal_norm_diff[2,s] = (dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", + " potential[2,s] = dbi_eval.least_squares(D=d)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Least squares cost function')" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(s_space, off_diagonal_norm_diff[0,:],label= 'No normalization')\n", + "plt.plot(s_space, off_diagonal_norm_diff[1,:],label= 'Normalization')\n", + "plt.plot(s_space, off_diagonal_norm_diff[2,:],label= 'Gradient Ascent')\n", + "plt.xlabel('s')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-||\\sigma(H_k)||$')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.title('D not normalized')\n", + "plt.plot(s_space, potential[0,:],label= 'No normalization')\n", + "plt.xlabel('s')\n", + "plt.ylabel('Least squares cost function')\n", + "\n", + "plt.figure()\n", + "plt.title('D normalized')\n", + "plt.plot(s_space, potential[1,:],label= 'Normalization')\n", + "plt.xlabel('s')\n", + "plt.ylabel('Least squares cost function')\n", + "\n", + "plt.figure()\n", + "plt.title('D optimized')\n", + "plt.plot(s_space, potential[2,:],label= 'Gradient Ascent')\n", + "plt.xlabel('s')\n", + "plt.ylabel('Least squares cost function')" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "dbi_eval = deepcopy(dbi)\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "flows = 50\n", + "off_diagonal_norm = np.empty((flows+1,3))\n", + "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", + " dbi_eval(step_poly,d=d)\n", + " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", + "\n", + "\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", + "dbi_eval = deepcopy(dbi)\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", + " dbi_eval(step_poly,d=d)\n", + " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", + "\n", + "dbi_eval = deepcopy(dbi)\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "step = 1e-2\n", + "iterations = 200\n", + "d, loss, grad, diags = gradient_ascent(dbi_eval, d,step, iterations)\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", + " dbi_eval(step_poly,d=d)\n", + " off_diagonal_norm[i+1,2] = dbi_eval.off_diagonal_norm\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(off_diagonal_norm[:,0],label= 'No normalization')\n", + "plt.plot(off_diagonal_norm[:,1],label= 'Normalization')\n", + "plt.plot(off_diagonal_norm[:,2],label= 'Gradient Ascent')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hyperopt does it get stuck" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-21 15:05:50]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 7\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCostFunction.least_squares\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "dbi_eval = deepcopy(dbi)\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "flows = 50\n", + "off_diagonal_norm = np.empty(flows+1)\n", + "off_diagonal_norm[0] = dbi_eval.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d)\n", + " dbi_eval(step_poly,d=d)\n", + " off_diagonal_norm[i+1] = dbi_eval.off_diagonal_norm\n" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(off_diagonal_norm[:],label= 'Hyperopt')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Difference between numerical gradients and analytic ones\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-21 14:20:15]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 7\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCostFunction.least_squares\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "nqubits = [3,4,5,6,7]\n", + "iterations = 100\n", + "step = 1e-2\n", + "differences = np.empty((len(nqubits),iterations+1))\n", + "loss_max = np.empty(len(nqubits))\n", + "for q in range(len(nqubits)):\n", + " # define the hamiltonian\n", + " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", + "\n", + " # define the least-squares cost function\n", + " cost = DoubleBracketCostFunction.least_squares\n", + " # initialize class\n", + " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + " loss_max [q] = dbi.least_squares(D=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", + " d = np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q]))\n", + " d_analytic, loss_analytic, grad_analytic, diags_analytic = gradient_ascent(dbi, d,step, iterations)\n", + " d = np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q]))\n", + " d_numerical, loss_numerical, grad_numerical, diags_numerical = gradient_ascent(dbi, d,step, iterations, analytic=False)\n", + " differences[q,:] = loss_analytic - loss_numerical\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ic85mcsqdJxvVKrUkhSbRP6I/yeHJJIUmoVc3Lqx5TLUJKvLsYasiD8rzzoSvivwzz2vX1ZUX9z56/5owFVQTrILswcoQBF6BZx4bAs8s0tIlhGiObDYwldcPNQ2GobpLneOslzgZsyEQnspoip/Gwa0Bavfu3UycOJGqqip69eqFQqFgx44d6PV6fv31V7p3735JxbcGlzNAAexcdYw1X6WDAq68ryfxvesP+rZZrZT8+CO5r71O9alTABiS+xH+l6cx9Li4/2c2m43jpcfZlLOJLTlb2JKzhdOVzl24GqWGniE96Rfej+TwZHqF9cJb431R79dsmMrtYaoiD8rz7euK/DNhqyIfKgrObL+YFq5aSrV9MLwhwP5hUftYH2APZHUf6/1q1v6gq3mu0pzn5EKINstqBVNp/WBTLwidZ7FZLr0OharmsyvgzOeX0/OA+p9vdRdd047Jdfs0BpWVlXz++efs378fm81GYmIit99+OwbDZRoL08xd7gBls9n4ff5Bdv92ArVWyfVP9CM0uuFfKmtFBfmffEr+J59gq6oChQL/a68l9LFH0TRwmxpX68gsyWRzzmZST6WyNWcruZW5TseoFCq6BXWjb3hf+oX3o29YXwL0AZf0vs2epdr+11hFQU2gKrA/rrcuPLNUFMBZg/kvitpg/+DR+dWsfWsWf9D5nHmu9bEfo/W2b9d6g9a3Zl2zSBgTonmw2exTxFSV1ASeEvu4z9og5NhW4tzi4whHNfsaM1nyhSjVdcKMX80fdTWPHUEn4BzhyN/+2dKMWtllIk0Pu9wBCsBqsfLDv3dybG8B3v5abvxLf3wCzz0WyXzyJLlvvEnJ998DoDAYCL77boLvvgtlEwVhm83GsdJjpJ5KJTUnlW252zhRdqLecfH+8fQN70vfsL70CetDO592MnkggKnCHrwqC6Gy6Ey4qts8XlnUwF+GRY2ff8sVKh1ovUDjXbP2sn/4abzss81rve3r2udqfc1zgz3IafT2tVp3Zr9ab3/uWNc8buJJVYVoFmw2+yTFpjJ7gDGWnllqQ42x9My6qu7zs0JRU7T+gP1q5XqtOudo7WnouGYWgC5VkweoJUuWcOWVV6LRaFiyZMl5j508ebJr1bZCnghQAMYKM9/+cyuFORWERvty3eN90ejO/0VUuXMnp15+hcrt2wFQh4cT+uij+F9bf/6opnCy7CRbc7ey9dRWtp3axpHiI/WOCTWE0jusN33C+tAnrA9dgrqgUUrrh0ss5poP4OI6H7ylzh/GxjL7c1PZmX2m8prnZfbmfVM5WKsvf/0K1ZlApdKBWluz1tlbwlQ169r9Ko39i0ClrfNYc+axUnPWc/X5Hytrz6E+81qnfWc9V6pa1ZeIqGG12sc8mirs/y7MFWf9G6l57PS89Kx/WzX/low12y913E9dCmWd1mX/Oq3N/s4tzw0Fo9rHmmY+RvUya/IApVQqycnJISwsDOV5vlQVCgUWSxOl4hbMUwEKoPh0Jd+8mkpVmZm4XiFMvK/nBWdrttlslP7yC7n/eg1zdjYAusRuhD/5FN6DBrq13sKqQrbnbmd77na2ndrG3oK9VJ/1ha1X6ekR0oPeYb3pHdqbpNAkAvWBbq1L1FFtqvmSKLcv5nL7F0rtl4m50v7YXGF/bCqH6qqa7TVLddWZbbVri6nmcZX9S8qVOb6aHUWdcKWuE9rUDYQxbZ0AVifo1T3OKfRpnbc7gp6WekGxbsA7V9irF/7UzX8ONJvN/keB1Wz/vbGY7S05FlPN2mj/PbUY7c+rq86sHb9zNb9njnXlmd/Z2t9bc6Xz77c7WnJrac/qQnd0sdcEH51v/W36s9atrPWnOZAuPA/zZIACOHmoiO/e2o612kbvcdEMvSGhUa+zGo0U/ve/5H3wIdYy+73zfEaNIuyJx9F17OjOkh2qqqvYnbfbEarSTqdRYiqpd1yMXwy9Qns5lo4BHVErZZqAFs1SfdYXYFXNl6LJ+Yuy7penYzHXOc7k/EXrWNf9Aq52fmypfU3t9rrHm+vva4qxI82JQlkTpGoXlX2tUNU8Vp15rFA6Lzju/1Pz/Gw19xiqXdts9rBss9jXVov9sdVqb+20Wezruv+9m6q76lJovOxBR+tds/aq89inzthBnzrjCn1rxhr6OT/X+kg3dTPl1gD12Wefccstt6DTOY+vMZlMzJ8/n2nTprlecSvj6QAFcHBLDss+2QvAyNu70H1Yu0a/trqggLzZ/6bwq6/AYgGVioCbbiR0+nTUISHuKrlBVpuVjOIMdpzewY7cHWzP3V5vLiqw38+vR0gPeob0JCk0iaSQpIu/BY0QF2K1nAlm1mrnsOUUvKrrHGeuH9Ycj83OYc9aExJrz+10jrrHmuq8x9lh0Nzw+7bolr46VDp7q5taax83p9I6j6NT62rG3Gmdx+DVrmvH8dWOx3OM5/OqM8avJiipDc2/lU40CbcGKJVKxcmTJwk7a0br/Px8wsLCpAuP5hGgALb8eJTN3x9FoVRwzfRedEgMcun1xiNHyH39DcpWrABA6eVF0D13E5ySgtKD9zwsNhaTdjqNnad3knY6jV15uyg315/ANcI7gp4hPR1LYnAiXhq5V6No46xW54BXG9JqW36slrO21W0pstZZap/Xti5x7nCmqLnLNDVrhdLeoqVQnmnVcmoBq9MK5tQ1WhOaZMyZcBO3BiilUsmpU6cIDXX+6z4tLY1Ro0ZRUFDgesWtTHMJUDabjeVz93Jw0ym0ehXXP9mP4Cgfl89TsWULp/71GlU77RNxqkNDCXl4OgHXX49C7fluM4vVwtHio+zM28nO0zvZmbeTw0WHsZ71Ya5UKOkY0JEewT3oEWJfOgV0QiOX5wshhMBNAapPnz4oFArS0tLo3r076jpfnBaLhaNHjzJx4kQWLFhwadW3As0lQIHzjYd9g/Tc8FQ/vP1dv9WKzWaj9OefyX3jTczHjwOg7diRsJmP4TN6dLObdqDcXM7e/L3sPL2TXXm72JW3i9yK3HrHaZVaugR1oXtwd7qHdKd7cHfi/eNRyfgEIYRoc9wSoJ577jnH+vHHH8fH50xLhlarJTY2lhtuuAGtVnsJpbcOzSlAAVSWmfj21a0Un64kLMaXKY/3RaO9uIBgNZkomj+fvPfex1JUBIChXz/CHn8cr759mrDqppdbkcvuvN2OZU/+ngYHqBvUBroGdaV7cHcSgxNJDE4k1i9WQpUQQrRybu3CmzdvHlOnTq03iFyc0dwCFEDRqQq++WcqxvJq4nuHMvFPPVBcYHqD87GUlpL/8ScUzJtnn9Ec8BkzhrDHHkWX0Lir/jytdqLPPfl7HKFqf8F+KqrrX7psUBvoEtjFEai6BXcj3j9ervwTQohWxK0BasuWLVitVgYOdJ4faNOmTahUKpKTk12vuJVpjgEKIPtQEYtrpzcY24GhN3a65HOaT50ib/Zsir5daB+cqlTif90UQqdPRxMZ2QRVX14Wq4XMkkz25O9hT/4e9ubvZX/BfiobuIGwTqWjc2BnugZ1pVtwN7oFdaNTYCd0KvnjQgghWiK3BqgBAwbw5JNPcuONNzptX7hwIa+++iqbNm1yveJWprkGKHCe3mDErZ3pMaJ9k5zXePgwp996i9JlywFQaLUE3nYbwff9CXVgy570sm6oqg1U+wv2U2Yuq3esSqEizj+OrkFd7cEqqBtdgrrgr/P3QOVCCCFc4dYA5ePjw86dO4mPj3fafvToUZKSkigtLXW94lamOQcogNSfjrJpyVEUCrjqwSRiezbd3E4V27dz+vU3qEhNBUDp7U3QXX8k6M4UVD7eTfY+nma1WTleepy9+XvZV7DPEaoKqhq+CjXKO4rOQfbWqq6BXekc1Jn2Pu2b3eB7IYRoy9waoIKDg/nhhx8YPHiw0/b169czadIkCgsLXa+4lWnuAcpms7Hyv/vZv/4kap2K6x/vS2i0b5Oev3ztWnLffBPj3n0AqIKCCLnvTwRMnYqylY6fs9ls5Fbksr9gP/sK9nGg4AD7CvY1eANlAG+NN50DO9M5sDNdgrrQJbALCQEJMleVEEJ4iFsD1NSpU8nJyWHx4sX4+9u7JYqKipgyZQphYWEyjQHNP0ABWCxWfng3jeP7C/Hy13LjU8n4BjXtTSVtViulv/zC6bffwZSZCYA6IoKQhx4kYMoUFJq2Mf9SiamEAwUHOFh4kP0F+zlQcIBDRYcwN3BTUQUKov2i6RzYmU6BnRwBq51PO5QN3iZDCCFEU3FrgDpx4gTDhw8nPz+fPn3sl63v2LGD8PBwli1bRocOHS6+8laiJQQoAGNlNQv/tZWC7HKCory5/s/90Bma/qoym9lM0aJF5L33PtU5OQBoYqIJnf4wfpOuQtEGb5FgtprJKM7gQOEBDhYc5EDhAQ4UHCC/Kr/B473UXiQEJtiDVUAnR7iSsVVCCNF03H4z4fLycr744gvS0tIwGAwkJSVx6623omkjLQoX0lICFEBpQRXfvJpKRbGJ9l0DuXp6L1Rq9wQaq9Fon0Pqw4+w1MxYr+vUiZAZD+M7dqyMBwLyK/M5WHjQsaQXpp+ztQogzCvMHqYCOpMQmEBCQALx/vHo1U3bmiiEEG2B2wOUOL+WFKAATmeVsuj1bZiNFroOimD0nd3cGmas5eUUfPYZ+Z/OwVpz0YE+MZHQR2bgPXy4BKmzmK1mskqySC9Md4Sqg4UHyS7PbvB4pUJJtG80CQEJjlDVKaATHfw6oFHKHzlCCHEubg9QBw8eZPXq1eTm5mK1Ot9v7Nlnn3X1dK1OSwtQAJm78/nxvZ3YrDaSJ8Uy8Jr4C7/oElmKi8mfO5fCeZ9hrbBPXmno1YuQGQ/jPWSIBKkLKDOVcajoEAcLD3Ko6BDphemkF6VTbCxu8Hi1Uk2cfxwJ/gl0DOhIQoB93cG3g8yyLoQQuDlA/ec//+GBBx4gJCSEiIgIpy85hULBtm3bLq7qVqQlBiiAPWtOsPqLAwCMuqMriUOjLsv7VhcUkP/xJxR+8QU2oxGw3x4m9OGH8R408AKvFnXZbDbyKvM4VHTIsaQXpnO46HCDM6yD/X6Acf5xjlAVHxBPQkAC7X3aS7ASQrQpbg1QMTExPPjggzz11FOXVGRr1lIDFMDGxYfZ+nMmCqWCSQ8lEdM9+LK9tzk3l/yPP6Zo/lfYTCYAvPr3J+Th6XgPGHDZ6miNrDYrJ8tPcrjosD1YFdrD1dHio1RZqhp8jVapJdY/lo7+HYkPiCfeP56OAR2J9o1Go5KuQCFE6+PWAOXn58eOHTvqTaQpzmjJAcpms7Fi7j4ObMpBo1NxXRPPEdUY5lOnyP/oPxQtWIDNbB887TVgACHTH5Ig1cQsVgvZZdkcKjrE4eLDHC6yL+cLVmqFmg5+Hejo35E4/zhHuIr1i5U5rIQQLZpbA9Tdd99N//79uf/++y+pyNasJQcoAEu1lR9m18wR5aflhqf64RdsuOx1mE+eJP8//6Ho628kSF1mtcHqSPERR7A6UnSEI8VHztkVCPYZ1+MC4ojzswerOL844vzjCNIHyZg2IUSz59YA9fLLL/PGG28wadIkevbsWW/qghkzZrhecSvT0gMU2OeIWvTaVvJPlBMY4cX1f+6H3tsz3TbmkyfJ++gjir75FmqDVHIyIQ89iNegQfLFfBnZbDZOVZxyhKnDxfZgdbT4KIXGc9+FwE/rR5x/HLF+scT520NVrH8sHXzlykAhRPPh1gAVFxd37pMpFBw5csSV07VKrSFAAZQVVvHtP7dSVmgksqM/kx/pjVrruUHFtUGq+JtvHS1Shr59CXnwQbyHylV7nlZYVciRYnuwyijO4EixPVhll2Vjo+GPGZVCRQffDsT6xRLrH+tYx/jFEKwPlv+nQojLSuaB8rDWEqAA8rPLWPivbZgqq4nvE8qEe3ugVHr2S82ck0P+x5/Yx0jVDDbXJyURcv/9+IwaKV+6zUxVdRWZJZkcLTlKRnEGR4uPcrT4KBklGVRWV57zdb4aX2L8YojxjyHGL4ZYP3uwivGLwVvTem5MLYRoPiRAeVhrClAAJw4WsuSdHVirbfQc2Z5ht3RqFiHFnJtLwSefUPjVAmxV9gHPuq5dCbn/fnzHj2uTt4hpSWq7AzNKMsgoznBan6/VCiDEEEK0bzSx/rFE+0YT7RftWBvUl3+8nhCidXBrgLrrrrvOu//TTz915XStUmsLUADpqadY+vEeAAZf15G+E2I8XNEZ1Xl5FMybR+EX/3NMyKnt2JGQP92L31VXtZmbFrcmRouRrJIsskqyyCjJILMkk8ySTDJKMiioKjjva8MMYfZA5RdNB98ORPva1x18O+Cj9blMP4EQoiVya4C67rrrnJ6bzWZ2795NUVERo0ePZuHCha5X3Mq0xgAFsGN5Fuu+OQTA2JRudBkU6eGKnFmKiij47+cU/Pe/WEtKANC0a0fwvffgf911KHU6D1comkKpqdQpWGWV2oNWVmnWOWdhrxWkD6K9b3tHoOrg24H2PvbnIYaQZtGyKoTwnMvehWe1WnnwwQeJj4/nySefvNTTtXitNUABrP0mnbTlx1AqFUyankR04uWbaLOxLGVlFP7vSwrmznXctFgdGkpQSgoBt9yCykfGz7RWxcZiMksyOVZ6zBGqskqyOFZ67LxXCQLoVXra+bSjvW972vu2p51PO8fS3re9jLsSog3wyBioAwcOMHLkSE6ePNkUp2vRWnOAslltLJuzl/Qtp1DrVFw3sw9hMc3zZ7RWVlL0zbfkf/IJ1Tk5ACj9/Qm6/TYC77gDdWCghysUl1OZqYxjpcecluNlxzleepyT5Sex2qznfb2/zt8RqKK8o4jyiaKdTzsifSKJ8o6S7kEhWgGPBKiffvqJO++8k9OnTzfF6VyWkZHBCy+8wMqVK8nJySEqKoo//OEP/N///R9ardZxXENN9O+//77TxKC7du1i+vTpbN68maCgIO677z7+9re/Nbp5vzUHKHCeaNPgq+GGJ/vhH9p8Z6C2mUwUf/89+f/5GFNGBgAKvZ6Am24iOOVONO3aebZA4XFmi5mT5ScdgepE2QnH+kTZCYqMRRc8h6/WlyjvKEegivSOJMInwr72iiDEECL3FhSimXPl+1vt6slnzpzp9Nxms3Hy5El+/PFH7rzzTldP12T279+P1Wrlww8/JCEhgd27d3PvvfdSXl7Oa6+95nTsnDlzmDhxouO5v7+/43FJSQnjxo1j1KhRbNmyhYMHD5KSkoK3tzePP/74Zft5mjOVWsmV9/Vk0RvbyDtWxvfvpHH9n/vh5ae98Is9QKHVEnDDDfhPmULpsuXkf/QRVXv3Uvjf/1L4v//hf/Ukgu6+G33nzp4uVXiIRqVxDDxvSLm5nOOlx8kuyya7PJsTZSfsj2ueFxuLKTWVcsB0gAOFBxo8h1qhJtQrlAjvCCK8Igj3DifcK9yxDvMKI8QQglrp8seyEK1GtbWaYmMxRcYiCqoKKDIWUVhVSGFVYb1tPlofPp3guQvXXG6BGjnSeZ4dpVJJaGgoo0eP5q677kKtbj7/+P/1r3/x/vvvO03uqVAoWLRoEVOmTGnwNe+//z5PP/00p06dQlcz6PiVV17h3Xff5fjx441qhWrtLVC1youNfPvPrZTmVxEW48u1j/VBq28+///PxWazUb5+Pfkff0zFho2O7T4jRhB8z90YkpNlMLFwSbm5nOyybE6WnyS7LJuc8hxOlp8kpzyH7PJsTlecxmKzXPA8SoWSYH0wYV5hjiXEEEKYVxihhlBCvUIJMYQQqAuU1izR7JmtZoqNxRQbiymsKnQEo9qlNhTVfV5iKmn0+f11/qydurZJa27yLrwlS5Zw5ZVX1rttS3P317/+lV9++YXU1FTHNoVCQbt27aiqqiIuLo67776bP/3pTyhr5gyaNm0axcXFLF682PGa7du307dvX44cOdLgTOxGoxGj0eh4XlJSQocOHVp9gAIoOlXBt//aSlWZmQ7dApn0UC9U6pYz/1Llrt3kf/IJpb/+CjX/FPS9kgi+6258x45BoZIvKXHpqq3V5FXmkVOeQ05FDqfKT3Gq4tSZdcUp8iryqLZVN+p8KoWKIH0QIYYQgg3BBOuDCTYE25/rgwkyBBGkDyJYH0yALkDClrgkZquZEmMJxaZi+9pYTImphCJjkSMgFZuKHQGpdl1uLr/o9/TX+ROoCyRAF0Cg/sw6SB/k9LhHSI8m/EndEKBUKhU5OTmEhoaiUqk4efIkYWFhTVawOxw+fJi+ffvy+uuvc8899zi2v/jii4wZMwaDwcCKFSt49tlnefrpp/nrX/8KwPjx44mNjeWjjz5yvCY7O5t27dqxfv16Bg8eXO+9Zs2axXPPPVdve1sIUACnMkr47s3tVBstdOofzrg/JqLw8GzlrjJlZpL/6RyKFy1yzG6uiYkmOCUF/ylTUBpkckbhXlablYKqAk5VnCK3PJfcilxOV57mdOVp++OK0+RV5lFQVXDeSUbPpkBBgC6AAH0AgbpAAvWBji+kuou/zh9/nT8BugB8tb7SldiKWG1WysxllJnKKDWVUmYuo8RYQqm5lFJTKSWmEvvaWOJ4XrsUG4vPe8eAC1GgwE/nV+/3rfZ3svZxoD7QHpj0Afhp/Tz2+9fkASoiIoL//Oc/XHPNNSiVSk6dOkVoaGiTFXw+5wondW3ZsoXk5GTH8+zsbEaMGMGIESP4+OOPz/va119/neeff57iYvv8MePHjycuLo4PP/zQccyJEydo3749GzZsYNCgQfXO0ZZboGpl7cnnx3/vxGq10Wt0B4belNAiu8Gq8/Io+OILCv/3Jdaa3wlVQACBt91G4O23oQ5uftM2iLal2lpNQVUBeZV55FXmkV+ZT35VPvmV+fbnVfkUVhVSUFVAYVWhS2GrLl+NL75aX/x0fvhp7YuP1gdfrS++Gl98tD74aHzw0frgrfG2P9b44KXxsi9qLwlhl8BsMVNRXUFldaV9bbavy83lVJgrKK+2r8vMZZSbyx1LmanMsa02NJWZy5qkJl+tL/5af0fY9tP6OR7Xbq8N47XByFfr26JaQJs8QM2aNYvnn3++UV+IFsuF+/ldkZeXR15e3nmPiY2NRa/XA/bwNGrUKAYOHMjcuXMdXXPnsm7dOq644gpycnIIDw+/qC68s7WVMVBnO7Aph+Vz9gLNb7ZyV1krKij6diEF8+ZhPn4csA9G97/2WoL+mIIuPt7DFQpxYdXWaqdBuAXGAoqq6o89qe12qW2VaCo6lQ5vjTcGtaHeolPp0Kv16FV6dGodepUerUqLTqVzrDVKDVqVFq1Si0alQa1Uo1Ha12qFGrVSjUqpQqWoWWoeKxVKFCjsa4UCBYoza+zfY7XB0oYNm83mWFttVmzYsNgsWK1Wqm3VWG1WLDYL1dZqLFYL1bZqqq3VmK1mx9psNWO22NdGixGTxYTZasZkMWG0GB2LyWKiqroKo8VIVXUVVZYqqqqrqKyupLK60vG4sd25rtAqtfhoffDT+tmDcM3io/FxCsqO5azw3BYCcZNfhTdr1iymTp3KoUOHmDx5MnPmzCEgIKApar2gkJAQQkJCGnXsiRMnGDVqFP369WPOnDkXDE9gD0d6vd7x8wwePJhnnnkGk8nkmP5g6dKlREVFERsbe7E/RpvQZWAElaUm1n1ziA2LDqP30ZA4NMrTZV0UpZcXQXf8gcBbp1K6fDn5n86haudOir7+mqKvv8Z7xHCCU1LwGjSoRba0ibZBrVQTYgghxNC4z1Cwh67arhtHd47R3p1TZi6zr032lo0Sc8mZVhCTvcWjwlzh+PKvDQ3i4mmUGrw0XhjUBrzV3k4tfN4a73pLbUtg3RZCH4295VCrap5XSrdULl+F99xzz/HnP/8ZL6/mNe9PbbdddHQ0n332Gao6g38jIiIA+P7778nJyWHw4MEYDAZWrVrF448/TkpKCm+//TZgH7fUpUsXRo8ezTPPPEN6ejopKSk8++yzjZ7GoK22QNVa/+0hti/LQqGAK+/vSVyvy9Pd6042m43KbdvInzOHshUrHQPOdV26EHTnnfhdPQmlVj6chLDZbJitZntXU02XU23rSqW5ppWlptWlylKFsdpIpaXS0VJTt8WmtlXHZDFhspqotlY7ltrWH4vNYm8hqmkZstnsrUc2mw0r1gtOkFqrbouVEiVKhRKV0t6aVduqVdsCplKoHK1fWqUWjVJjbyFTqO0tZiqtU+uZTq1Dp9I5Wtf0Kn29VjiDxt4yV7tPr9bjpfFCo2xZF2+1dB6ZSNPT5s6dyx//+McG99X+iL/88gtPP/00hw4dwmq1Eh8fzz333MNDDz3kNP3Crl27eOihh9i8eTOBgYHcf//9PPvsszKRZiPZbDZW/nc/+9efRKVRMnlGL6I6tZ5Zv02ZmRT893OKFi7EVnPzYlVwMIG33krgrVNlnJQQzUztd0BtN13tZ3ltd560IotabTJANSdtPUABWC1Wfv5wNxk789DqVVz3RF9C2vt6uqwmZSkupnDBAgq/+J/jVjEKjQa/a64haNod6Lt29XCFQgghXCEBysMkQNlVmywseWcHJw8VY/DTcsOf+zbrW75cLJvZTMnSpRTM+4yqnTsd27369ydw2h34jh4t80kJIUQLIAHKwyRAnWGsMLPo9e3knyjDL0TP9X/uh7e/ztNluU3ljh0UfPYZJb8uhZorUjVRUQTefhsBN9yA6jJdfCGEEMJ1lz1AFRUVXbar8loCCVDOyouNLPzXVkryqghu582UmX3Re7fugZHmnBwKv5xP0VdfYSkqAuw3MPa/5hoC//AH9F3kvntCCNHcuPL97fI9N1599VW++uorx/Obb76Z4OBg2rVrR1pamuvVilbP21/H5Ef64OWnJf9EOT/+eydmY9POF9bcaCIiCHvsURJWryLyxRfQde2KraqKoq+/5ui115J5xzRKfl2Krbrp53oRQgjhfi63QMXHx/P5558zZMgQli1bxs0338xXX33FggULyMrKYunSpe6qtcWQFqiG5Z8oY9Hr2zBWVBPdPYirHkhqUffNuxQ2m43KrVsp+PwLSpctc3TvqcPDCbjlZgJvugn1ZZrdXwghRMPc2oVnMBg4ePAgHTp04JFHHqGqqooPP/yQgwcPMnDgQAoLCy+p+NZAAtS5nTxczJK3tlNtttIpOYyxd3VH2cLum3epzDk5FM6fT9HX32DJz7dv1GjwGz+ewNtvw9Cnj1xWLYQQHuDWLrzAwECOHTsG2OdVGjt2LGD/C7upb+MiWp/Ijv5MvL8nSqWC9NRcfp9/kLZ2HYMmIoKwRx8lYdVKov71Lwy9e4PZTMmPP5J52+0cvXYKhfPnYym7+DuZCyGEcC+XA9T111/Pbbfdxrhx48jPz+fKK68EYMeOHSQkJDR5gaL1iekezNi7EkEBe34/wcbFRzxdkkcotVr8r7ma2PlfEvvtN/jfeAMKvR7jwYPkzHqOQyNGkPP881QdOOjpUoUQQpzF5S48s9nM22+/zbFjx0hJSaFPnz4AvPXWW/j4+HDPPfe4pdCWRLrwGmfPmhOs/uIAAIOv70jf8S335sNNxVJcTPF331H4vy8xZWY6thv69iXw1qn4jh+PUtd6p4EQQghPknmgPEwCVONt+zWTDYsOAzDy9i50H9bOwxU1DzarlYqNGymc/xWlK1Y4Bp2rAgLwv+46Am6+CV1cnIerFEKI1sWtY6DmzZvHjz/+6Hj+5JNPEhAQwJAhQ8is8xezEI3Rd0IMfSdEA7D6fwdITz3l4YqaB4VSifeQIbR/520SVq4kZMbDqCMisBQVUTBnDkeuvIrMO1Mo+eknbCaTp8sVQog2x+UWqC5duvD+++8zevRoNmzYwJgxY3jrrbf44YcfUKvVLFy40F21thjSAuUam83Gb/87wJ412SiVCq58oCexPUM8XVazY6uupuz3NRR99RVla9aA1X6XeVVQEP5TphBw043SKiWEEJfArV14Xl5e7N+/n+joaJ566ilOnjzJZ599xp49exg5ciSnT5++pOJbAwlQrrNabSyfs5f0LadQaZRc83Av2nUO9HRZzZY5O5uib76l6JtvqM7NdWz3Sk4m4JabZayUEEJcBLd24fn4+JBfM3fN0qVLHdMY6PV6KisrL6JcIUCpVDAmpRuxSSFYzFZ+/PdOTh0t8XRZzZYmKorQGQ+TsHIF7d97D5+RI0GppCI1lew/P0n68BHkvPgSVQcOeLpUIYRolVxugbr99tvZv38/ffr04csvvyQrK4vg4GCWLFnCM888w+7du91Va4shLVAXr9ps4YfZOzlxoBCdl5rrHu9LcDsfT5fVIphzcihauNDeKpV90rFd37MnATfcgN+kq1D5+nqwQiGEaN7c2gL173//m8GDB3P69Gm+/fZbgoODAdi6dSu33nrrxVUsRA21RsVVD/QkPM4PY0U1i9/eQdGpCk+X1SJoIiIIffBBEpYto8PHH+M7cSJoNFTt2kXOrFmkDxtO9lNPUb55c5ubvFQIIZqaTGPgBtICdemqys1898Z28k+U4ROo47on+uIXbPB0WS1OdUEBxd8tpujbbzEdPuzYromOJuD66/CfMgVNRIQHKxRCiObD7fNAFRUV8cknn7Bv3z4UCgXdunXj7rvvxt/f/6KLbk0kQDWNihITi17fRtGpCvxDDVz3RF+8/WVg9MWw2WxUpaVR9O1CSn76CWt5zW1iFAq8hwwh4Ibr8RkzRgaeCyHaNLcGqNTUVCZMmIDBYGDAgAHYbDZSU1OprKxk6dKl9O3b95KKbw0kQDWdssIqFr2+jZK8KgIjvbnu8T4YfLSeLqtFs1ZUUPLrUooXLqRiyxbHdqWfH36TriLguuvQ9+wpNzQWQrQ5bg1Qw4YNIyEhgf/85z+o1WoAqqurueeeezhy5Ai///77xVfeSkiAaloleZUsfG0b5UVGQjr4MOWxPui8NJ4uq1UwZWVRtGgRxd8tpvrkmYHn2oSOBEyZgt81k9GEh3mwQiGEuHzcGqAMBgPbt2+na9euTtv37t1LcnIyFRUy4FcCVNMrzCln0evbqCw1ExHvxzUzeqPVqz1dVqths1io2LSJokXfUbpsGbaqKvuOmhnR/adMwXfMaJQGGYcmhGi93HoVnp+fH1lZWfW2Hzt2DF+5RFq4SWCEN5Mf6Y3OS03OkRJ+em8nZpPF02W1GgqVCu8hQ2j3r3/Sae0aIl98AUPfvmC1Ur52LdlPPEH6FcPI/utf7Vfx1cyCLoQQbZXLLVAzZsxg0aJFvPbaawwZMgSFQsHatWv585//zA033MBbb73lplJbDmmBcp9TGSUseWs7pioLHRKDmPRAEiqNy38HiEYyZWVR/N1iihcvxnzihGO7JioKv8nX4D/5WnTxcvsYIUTr4NYuPJPJxJ///Gc++OADqqurAdBoNDzwwAO88sor6OQqHglQbnbyUBFL3tlBtclKbFIIE+/rgUolIcqdbFYrFampFC9eTOkvv565ig/QJyXhf801+F11JeqaeeGEEKIlcvs0BgAVFRUcPnwYm81GQkICXl5eF1VsayQByv2O7y/gh3/vxGK20rFvGOPvTkQpIeqysFZVUbZyJUWLF1O+dh1YarpSVSq8rxiK/zWT8R09CqV8JgghWpjLEqDEuUmAujwy9+Tz03s7sVpsdB4QzpiURJRKufT+cqrOz6fkp58pXrKEql27HNsVXl74jh2D/zXX4D14MAq1DPgXQjR/TR6grr/++ka/+cKFCxt9bGslAeryObLjNL9+tBur1UbXIZGM/kNXFBKiPMJ45CglP3xP8ZLvMR8/7tiuCg7G78or8b96EvpevWR+KSFEs9XkAeqPf/xjo998zpw5jT62tZIAdXkd2prL0o93Y7NB92FRjLiti3xJe5DNZqNyxw5Kvv+Bkp9/xlJY6Ninad8ev6sn4T9pErpOnTxYpRBC1CddeB4mAeryO7g5h2Vz9oINeo5qz7CbO0mIagZsZjPl69dT/MOPlK5Yga3OPHG6Ll3wu+oq/CZdhbZ9ew9WKYQQdhKgPEwClGfsW3+SlZ/tA6DX2A4MvSFBQlQzYq2ooHTVKkp++JGytWvBbHbsM/Tqhd+kSfhOnIAmTGY+F0J4hgQoD5MA5Tl71pxg9RcHAOgzPprB13WUENUMWYqKKFm2jJIff6Ji0yao/RhSKPDq3x+/q67Cd8J41IGBni1UCNGmSIDyMAlQnrVr9XF+n38QgH4TYxh4bbyEqGbMnJtL6S+/UvLTT1Tu2HFmh0qF96BB+F11Jb5jx6Ly9/dYjUKItkEClIdJgPK8tJXHWLsgHYDkSbEMvCbewxWJxjCfOEHJL79Q8uNPVO3de2aHRoPPkCH4XXUlPqNHo5LbRgkh3EAClIdJgGoedizPYt03hwAYcE0c/SfJLUdaEuPRo5T++islP/2M8eBBx3aFRoP3sGH4TZxgD1M+Ph6sUgjRmjR5gHrnnXca/eYzZsxo9LGtlQSo5mPb0kw2LDwMwMDJ8SRfFevZgsRFMR4+TMlPP1Pyyy+YDh92bFdotXhfcYU9TI0aJS1TQohL0uQBKi6ucX+5KxQKjhw50rgqWzEJUM3L1l8y2Pid/fdy4LXxJF8Z69mCxCUxpqdT8vMv9jBV5/NGodHgPXQovhMn4Dt6NCr5tyeEcJF04XmYBKjmJ/XnDDYttn/ZDpoST7+JsZ4tSFwym82G8WA6pb/+QskvvzqFKTQavIcMxm/8eHxGj5ar+YQQjSIBysMkQDVPqT9lsGmJ/Ut28HUd6TshxsMViaZkTE+n5JdfKfn1F0yHznTzoVLhPXAAvuPH4zt2LOqQEM8VKYRo1tweoI4fP86SJUvIysrCZDI57XvjjTdcPV2TiY2NJTMz02nbU089xSuvvOJ4npWVxUMPPcTKlSsxGAzcdtttvPbaa2i1Wscxu3btYvr06WzevJmgoCDuu+8+/va3vzX6UngJUM1X6k9H2bTkKCAhqjUzHj5M6dKllPy6FOP+/Wd2KBR49etnD1PjxqKJjPRckUKIZseV72+Xb5G+YsUKJk+eTFxcHAcOHKBHjx5kZGRgs9no27fvRRfdVJ5//nnuvfdex3OfOlfoWCwWJk2aRGhoKGvXriU/P58777wTm83Gu+++C9j/440bN45Ro0axZcsWDh48SEpKCt7e3jz++OOX/ecRTSv5Kvt4vk1LjrJh0WFsNpt057VCuo4d0T3wACEPPIApM5OSpUspXbqMql27qEhNpSI1lVP/+Af6pCR8x43Fb9w4tLGxni5bCNGCuNwCNWDAACZOnMjzzz+Pr68vaWlphIWFcfvttzNx4kQeeOABd9V6QbGxsTz66KM8+uijDe7/+eefufrqqzl27BhRUVEAzJ8/n5SUFHJzc/Hz8+P999/n6aef5tSpU+h0OgBeeeUV3n33XY4fP95gK5TRaMRoNDqel5SU0KFDB2mBasbqtkTJwPK2w5ydTemyZZQsXUbltm1nZkAHdJ064TtuLL7jxqHr2lUmXxWiDXJrF56vry87duygY8eOBAYGsnbtWrp3705aWhrXXnstGRkZl1L7JYmNjcVoNGIymejQoQM33XQTf/7znx3dc88++yyLFy8mLS3N8ZrCwkKCgoJYuXIlo0aNYtq0aRQXF7N48WLHMdu3b6dv374cOXKkwSsSZ82axXPPPVdvuwSo5q3umKiBk+McrVOibag+fZrSFSspXbaM8k2boLrasU/Trh2+Y8fiO24shj59UKhUHqxUCHG5uLULz9vb29HaEhUVxeHDh+nevTsAeXl5F1Fu03nkkUfo27cvgYGBbN68maeffpqjR4/y8ccfA5CTk0N4eLjTawIDA9FqteTk5DiOiT2rKb/2NTk5OQ0GqKeffpqZM2c6nte2QInmLfmqWBRK2PjdETYtOYrNhky22YaoQ0MJnHoLgVNvwVJcTNnq1ZQuX07ZmrWYT5ygYN48CubNQxUUhM/oUfiOHYv34MEoa1qmhRBtm8sBatCgQaxbt47ExEQmTZrE448/zq5du1i4cCGDBg1q8gLP1bpT15YtW0hOTuaxxx5zbEtKSiIwMJAbb7yRV199leDgYIAGm+VtNpvT9rOPqW2kO1eTvk6nc3T3iZaldvzTxu+OsPn7o1itNgZcHSfdN22Myt8f/2uvxf/aa7FWVlK+bh2ly5ZRuvo3LAUFFH/zLcXffIvCywufYcPwHTsGn+HD5f58QrRhLgeoN954g7KyMsAebsrKyvjqq69ISEjgzTffbPICp0+fztSpU897zNktRrVqA92hQ4cIDg4mIiKCTZs2OR1TWFiI2Wx2tDJFREQ4WqNq5ebmAtRrvRKtQ7+JsSiUCjYsPEzqjxnYLDa5AXEbpjQY7N13Y8diM5upSE2ldNlySlesoPrUKUp//ZXSX38FtRqv/sn4jhmL7+hRaGrGVQoh2oZWPQ/UDz/8wDXXXENmZibR0dGOQeTHjx8nsuby5a+++oo777zTaRD5M888w6lTpxxjp1599VXeeeedcw4iP5tMY9Ay1b13Xp9x0Qy+vqOEKOFgs1qp2rOH0hUrKFuxAmP6Iaf9usRu+I4eg++Y0TIIXYgWyq2DyLds2YLVamXgwIFO2zdt2oRKpSI5Odn1ipvAhg0b2LhxI6NGjcLf358tW7bw2GOPkZyc7BgQbrFY6N27N+Hh4fzrX/+ioKCAlJQUpkyZ4pjGoLi4mC5dujB69GieeeYZ0tPTSUlJ4dlnn230NAYSoFqunauOseardAB6jenA0BsT5ItQNMiUmWkfhL5yBZXbtoPV6tinjorEd9RofMeMxis5GUWdeeaEEM2XWwPUgAEDePLJJ7nxxhudti9cuJBXX321XhfZ5bJt2zYefPBB9u/fj9FoJCYmhqlTp/Lkk0/i5eXlOC4rK4sHH3yw3kSadccw7dq1i4ceeojNmzcTGBjI/fffz7PPPisTabYRu38/wW//OwBAz5HtGXZLJwlR4ryqCwooW7Wa0pUrKV+3DltVlWOf0scHn+HD8Bk1Gp/hw2TclBDNmFsDlI+PDzt37iQ+Pt5p+9GjR0lKSqK0tNT1ilsZCVAt39612az6Yj/YIHFYFCNv7YJCKSFKXJi1qory9RsoW7WS0lWrsdS9OlmlwqtfP/tVfaNGoY2RmfCFaE7cOo2BTqfj1KlT9QLUyZMnUatdPp0QzVLiFVEoVQpWfraPvWuysZqtjJrWDaWEKHEBSr0e39Gj8B09igirlcq0NMpWraZs1SqM6elUbN5MxebN5L7yKtr4eHxGjcR31CgMvXujkM9QIVoMl1ugpk6dSk5ODosXL8a/pim6qKiIKVOmEBYWxoIFC9xSaEsiLVCtx8EtOSyfsw+b1Uan/uGMTemGUqX0dFmihTIdO0bZqlWUrlxFRWqq0+SdKn9/vEcMx3fkSLyHDUPl6+vBSoVom9zahXfixAmGDx9Ofn4+ffr0AWDHjh2Eh4ezbNkymUASCVCtzeFtuSz9eA9Wq42OfUIZd3d3VGoJUeLSWEpLKV+zhtJVqyn7/XesxcVndqrV9q6+kSPxGTkCXQMT+Aohmp5bAxRAeXk5X3zxBWlpaRgMBpKSkrj11lvRaDQXXXRrIgGq9Tm6M49fPtqFtdpGbM9gJvypB2qN3N5DNA1bdTWV27dTuno1ZatWYzpyxGm/NibGEaa8+vWTq/qEcBO3ByhxfhKgWqesPfn89MEuLGYr7bsGctUDSWh0EqJE0zNlZlL222+Urf6N8i1bwGx27FN6e+M9ZAg+I0fgM3w46tBQD1YqROvS5AFqyZIlXHnllWg0GpYsWXLeYydPnuxata2QBKjW68SBQn54byfVRguRCf5c/VAvtAYZ+Cvcx1JWTvn6dZSt/o2y3393vqoP0Hfvjs+IEfiMHIG+Rw8USuleFuJiNXmAUiqV5OTkEBYWhvI8/zgVCgUWi8X1ilsZCVCtW86RYr5/Nw1TZTVhMb5cM6M3em/pvhbuZ58NfS9lq1dT9ttvVO3e7bRfFRSEz7Bh+IwYjvfQoTLnlBAuki48D5MA1fqdziplyds7qCo3E9zeh8kzeuPlJ+NSxOVVffo0Zb+voez33ylftw5rzX1KAVCpMPTujc/w4fiMGI6uSxeZEFaIC3BbgDKbzYwfP54PP/yQzp07X3KhrZUEqLYh/0QZi9/eQWWJiYBwL659tDc+gXpPlyXaKJvZTMXWbZSt+Z3y33+vd68+dVgY3sOH4TNsON5DBss0CUI0wK0tUKGhoaxfv55OnTpdUpGtmQSotqPoVAWL39pOWaER32A91z7aB/9Qg6fLEgLziROUrVljH4i+aRO2ysozO9VqvHr3xnv4cHyGD5PWKSFquDVAPf7442g0Gl555ZVLKrI1kwDVtpTkV7LkrR0Un67Ey1/LtY/0ISjK29NlCeFgNRqpSE2l/PffKfvtd0wZGU771WFheA+7Ap9hw/AePFjGTok2y60B6uGHH+azzz4jISGB5ORkvL2dvyjeeOMN1ytuZSRAtT3lxUaWvL2Dguxy9D4aJs/oTWi0dJGI5sl07Jh93NTva+ytU3VufoxKhaFXL3yGXYH3FcPQd0+UK/tEm+HWADVq1Khzn0yhYOXKla6crlWSANU2VZWZ+f7dHeRmlqLVq5g0vRdRCQGeLkuI83K0Tq1ZS9naNZgOHXbarwoMxHvoULyvGIrP0KEy75Ro1eQqPA+TANV2mSqr+fG9nWSnF6HWKJl4f09iugd7uiwhGs2cnU3ZmrWUr11D+YaNzlf2AbquXfG5YijeV1yBoW9flDIrumhFLkuAOnToEIcPH2b48OEYDAZsNpsMQqwhAaptM5ss/PLhbrL25KNUKRh3V3cS+oV5uiwhXGYzm6lMS7MHqjVrqNq712m/wssL7/798b7iCryHDkUbFyvfA6JFc2uAys/P5+abb2bVqlUoFArS09OJj4/n7rvvJiAggNdff/2Sim8NJEAJS7WV5XP3cig1F4UCRv6hK4lDozxdlhCXpDo/n/L16ylfu5aydevrzYquiYqyd/cNHYr34EEyGF20OG4NUNOmTSM3N5ePP/6Ybt26kZaWRnx8PEuXLuWxxx5jz549l1R8ayABSgBYrTZ++98B9q7NBmDojQn0Hhvt4aqEaBo2qxXjgQOUr1tH2bp1VKZuxVbnnn0oleh79rDft2/oUAy9eqGQG86LZs6tASoiIoJff/2VXr164evr6whQR48epWfPnpSd1V/eFkmAErVsNhsbFh5m+7IsAPpNjGHgtfHSzSFaHWtFBRVbtlC+fj1l69bVG4yu9PLCa8AAvIcMxnvIELQdO8q/A9HsuPL97fJdUMvLy/Hy8qq3PS8vD51O5+rphGjVFAoFQ25IQO+jYcOiw2z9JZOqcjPDb+2CUilfHqL1UHp52W9qPGIE4YA5J4fy9RsoX7eO8g0bsBQU2O/ht3o1AOrwcLwHD7YHqsGD5eo+0eK43AI1adIk+vbtywsvvICvry87d+4kJiaGqVOnYrVa+eabb9xVa4shLVCiIXvWnGD1/w6ADRKSwxibkohKLfPriNbPZrVi3L/f0TpVuXUbNpPJ6Rhdp054DxmM1+DBeCX3R+Ujk9GKy8+tXXh79+5l5MiR9OvXj5UrVzJ58mT27NlDQUEB69ato2PHjpdUfGsgAUqcS3rqKZbP2YvVYiM6MYiJ9/VEo1N5uiwhLitrVRUVW7dSsWEDZevXY9y7z/kAtRpDUhLegwbhPXiQffyUTJcgLgO3T2OQk5PD+++/z9atW7FarfTt25eHHnqIyMjIiy66NZEAJc4na08+P3+4i2qTlfA4P66e3gu9twyuFW1XdWEhFZs22bv8NmzAfOyY036FwYBXv354Dx6E18BB6Lt1RaGSPzxE05OJND1MApS4kJwjxfzw7zSM5dUERnozeUYvfAL1ni5LiGbBdPw45Rs2ULFhI+WbNmHJz3far/T3x3tAf7wGDsJ74AC0CQkyIF00CbcHqKqqKnbu3Elubi5Wq9Vp3+TJk109XasjAUo0RkF2OUve2UF5kRGfIB2TZ/QmMELGfQhRl81mw3gwnYqNGyjfuImKLVvqzY6uCglxClSamBgJVOKiuDVA/fLLL0ybNo28syZQA/sVRxaLxbVqWyEJUKKxSvIr+f6dNIpOVaD30XD19F6Ex8rvjBDnYquupmrPHso3bqJ84wYqt23HZjQ6HaMOD8drwAC8BvTHe+BANB06SKASjeLWAJWQkMCECRN49tlnCQ8Pv6RCWysJUMIVlaUmfpidRm5mKWqdiiv/1INouX+eEI1iNZmoSkujfNNmKjZupDItzXlCT0AdEWEPUwMG4NW/P5roaAlUokFuDVB+fn5s375drrY7DwlQwlWmqmp++XAXx/YVolQqGH1nN7oMjPB0WUK0ONaqKip37KB80yYqNm2mctcuODtQhYfjlZyMV//+eA3ojzYuTgKVANwcoO666y6GDh3K3XfffUlFtmYSoMTFsFRbWTFvH+lbTgFy6xchmoK1osIeqLZsoWLzFip37qwXqFQhIXj161cTqpLRdeokV/m1UW4NUBUVFdx0002EhobSs2dPNGfd22jGjBmuV9zKSIASF8tmtbHum0OkrbRfxt1nXDSDr+uIQmYtF6JJWCsrqUzbScWWLVRs2WLv8jtrDJXS1xevvn0x9OuHV3I/9D16oJR5qNoEtwaojz/+mPvvvx+DwUBwcLBTs6dCoeDIkSMXV3UrIgFKXAqbzcb2pVlsWGS/l1jngeGMvqObzFouhBtYTSaqdu6kInUrFampVG7bhrWiwukYhVaLPqknXn374dWvL4bevVH5+3uoYuFObr+Z8IwZM/jLX/6CUikf6A2RACWawv4NJ1n53/3YrDY6JAYx8U890Opdvn2lEMIFtupqqvbtp2JrKpVbt1GxdSuWggLngxQKdAkJGPr2xatvHwx9+siVfq2EWwNUUFAQW7ZskUHk5yEBSjSVzD35/PLRbqqNFkKjfbl6ei+8/KQrQYjLxWazYcrIoHLrViq2bady61ZMmZn1jlOFhODVpzeG3vZF3707Sr1MjtvSuDVAPfbYY4SGhvLMM89cUpGtmQQo0ZROZZTw47/TqCw14xei55qHexMQ7uXpsoRos6rz86nYto3K7Tuo3LaNqj176k2dgEaDvls3DL17YehlXzTt2kkrVTPn1gA1Y8YMPvvsM3r16kVSUlK9QeRvvPGG6xW3MhKgRFMryq3g+3d2UJJXhd5Hw6QHk4iIlzEYQjQHVqORqj17qdy+jcodO6jYvgNLA5NNq4KDMSQlYeiVhCEpCX2PHqjkO6JZcWuAGjVq1LlPplCwcuVKV07XKkmAEu5QUWLix3/XTLipUTL+nu7E9Qr1dFlCiLPYbDbMJ07YW6h27KBy506q9u2D6up6x2rj4hxhytCzB7pu3VDqdB6oWoDcTNjjJEAJdzFVVbP04z1k7s5HoYDhUzvTY0R7T5clhLgAq9FI1d69VKalUbVzF5U7d2I+frz+gWo1us6dMHTvgb57d/Tdu6Pr0lmmUbhMJEB5mAQo4U5Wi5XV/zvAvnUnAeg7MYZB18bL2AohWpjqggKqdu2icucuKnfvomrX7vpX/IF9PFWnTui7J6Lr1g1DYiK6Ll1QGgyXv+hWzu1deOf7oPZUF97q1avP2b24efNm+vfvD9Bg7e+//z7333+/4/muXbuYPn06mzdvJigoiPvuu4+//e1vjf6CkgAl3M1ms5H6Uwabvz8KQKf+4YyZ1g2VRqYWEaKlstlsVJ88SeWu3VTt3k3Vnj1U7dmDpbi4/sFKJdr4OPRduqLv1hVd127ou3ZBHRJy+QtvRVz5/nZ5UpnevXs7PTebzezYsYPdu3dz5513unq6JjNkyBBOnjzptO1vf/sby5cvJzk52Wn7nDlzmDhxouO5f50J0UpKShg3bhyjRo1iy5YtHDx4kJSUFLy9vXn88cfd+0MI0UgKhYL+k+LwCdSx+vMDpG85RUWxkYn39UTvrbnwCYQQzY5CoUATFYUmKgq/CeOB2vFU2fYwtW8vVXv3UrV3H5a8PEyHDmM6dJiSH390nEMVEoK+c2d0nTuj69IFXedO6Dp2lCkV3MDlAPXmm282uH3WrFmUlZVdckEXS6vVEhFx5uarZrOZJUuWMH369HotRwEBAU7H1vXFF19QVVXF3Llz0el09OjRg4MHD/LGG28wc+ZM6SYRzUq3IVF4B+j45aPdnDhYxMJ/beXqh3vhFyxN+0K0BgqFAm37dmjbt3OEKgBzbi7G/fup2refqv37MO4/gCkjA0teHuV5eZSvX3/mJEol2uhodJ062ZeEjmg7JqCNi5WxVZegycZAHTp0iAEDBlDQUP+tB3z77bfcfPPNZGRk0KFDB8d2hUJBu3btqKqqIi4ujrvvvps//elPjlnVp02bRnFxMYsXL3a8Zvv27fTt25cjR44QFxdX772MRiPGOvdSKikpoUOHDtKFJy6bvOOl/DB7J+VFRrz8tEx6KImwGPndE6ItsVZUYDx8GOOBA1QdOIjxwAGMBw403AUIjmCl7dgRXXwc2rj4mnVcm71VjVu78M5lw4YN6JtRE+Enn3zChAkTnMITwAsvvMCYMWMwGAysWLGCxx9/nLy8PP76178CkJOTQ2xsrNNrwsPDHfsaClAvv/wyzz33nHt+ECEaIaS9Lzc+1Y8fZqeRf6KcRa9vY/w9PYhLkvEQQrQVSi8vDD17YujZ07HNZrNhycvDmJ6OMT2dqvR0TIcOYzx8GGtpKaaMDEwZGZStcD6XKigIbWws2pgY+xIbiza6A5roaFQ+Ppf5J2ueXG6Buv76652e22w2Tp48SWpqKn/729/4+9//3qQFzpo164LhZMuWLU7jnI4fP05MTAwLFizghhtuOO9rX3/9dZ5//nmKaxL6+PHjiYuL48MPP3Qcc+LECdq3b8+GDRsYNGhQvXNIC5RoLkyV1fzyn90c21uAQgFX3NyZpFEyzYEQwpnNZqM69zSmI4cxHjqM6ehRjEePYDpylOpTp877WlVgINroaDQdOqBp3w5Nu3Zo27dH0749mvBwFC24W9CtLVD+ZzXrKZVKunTpwvPPP8/48ePP8aqLN336dKZOnXreY85uMZozZw7BwcFMnjz5gucfNGgQJSUlnDp1ivDwcCIiIsjJyXE6Jjc3FzjTEnU2nU6HTiY+E82A1qBm0kNJ/P7lQfauzWbNVwcpOV3JkBsTUCpl/J4Qwk6hUKAJD0MTHob34MFO+yxl5ZizMu2tU5k164xMTMeOYSkowFJYSGVhIZVpaQ2dGHVoaM1g+EjUkZFowsNRh0egiQhHHR6OOiQEhbrl3xjd5Z9gzpw57qjjnEJCQghx4bJMm83GnDlzmDZtWr3bzDRk+/bt6PV6AgICABg8eDDPPPMMJpMJbU2KXrp0KVFRUfWCmhDNkUqlZOTtXfAPNbBh0WHSVh6jJL+ScXd1R6NTebo8IUQzp/LxRpWYiD4xsd4+S1kZ5qwsTFnHMB8/hun4ccwnsjEfP475xAlsJhPVublU5+ZSuWNHw2+gUKAKCkIdGnpmCQ5CFRyMumZRBQbal4CAZnsF4UUPIjeZTOTm5mK1Wp22R0dHN0lhF2vFihWMHTuWvXv30q1bN6d933//PTk5OQwePBiDwcCqVat4/PHHSUlJ4e233waguLiYLl26MHr0aJ555hnS09NJSUnh2WefbfQ0BjIPlGgu0lNPsWLuPizVVkKjfZn0YBLeAdJaKoRoejarFUtBAeaTJzFnn8ScnU11Tg7mU6fOrHNzwWJx6bwKgwFVQAAqPz9Ufn4o/f1Q+fqhDgkh7PGZTfozuHUizYMHD3L33Xezvu4lkthbfhQKBRYX/8M0tdtuu43MzEzWrVtXb98vv/zC008/zaFDh7BarcTHx3PPPffw0EMPoa7TnLhr1y4eeughNm/eTGBgIPfffz/PPvusTKQpWqSTh4v56f2dVJWZ8QnUMemhJELa+3q6LCFEG2SzWLAUFlJ9+nSdJY/qgnwseflU5+dTnZ+HpagYS1FRg/cPrKUODaXTmt+btD63BqihQ4eiVqv5y1/+QmRkZL1Q0atXL9crbmUkQInmpvh0JT/+O43CnAo0OhXj7+lObE+5Qk8I0XzZbDasZWVYCguxFBVhKS7BUlKMtbQUS3EJCrWa4LvvatL3dGuA8vb2ZuvWrXTt2vWSimzNJECJ5shYYebnD3dz4kAhCgUMvakTSaPay+SwQghRw5Xvb5dvnJWYmEheXt5FFyeE8Aydl4ZrZvSi29BIbDZYuyCd3788iMVivfCLhRBCOHE5QL366qs8+eSTrF69mvz8fEpKSpwWIUTzpVIpGfWHrgy+viMoYPfvJ/hxdhrGCrOnSxNCiBbF5S682luenN3s31wGkTcH0oUnWoIjO06z7NM9VJusBEZ4MemhJPxDvTxdlhBCeIxbx0D99ttv590/YsQIV07XKkmAEi3F6axSfnzPfg89vbeGK+/vQVSnQE+XJYQQHuHWACUuTAKUaEnKi4z89P5OcjNLUaoUjLitC4lDozxdlhBCXHZuHUQuhGhdvAN0THm8Lwn9wrBabKz6737Wfp2O1Sp/WwkhxLlIgBJCoNHa54bqf3UcAGkrjvHTezsxVZ57EjshhGjLJEAJIQD7hSEDro5j/D3dUWmUZO7O55tXUynKrfB0aUII0exIgBJCOOmUHM71T/TF219LYU4F37ySyvH9BZ4uSwghmhUJUEKIesJi/Ljp6f6ExfphrKhmyTtp7Fp9HLnmRAgh7Bp1FV6fPn0afbuHbdu2XXJRLZ1chSdai2qzhVWf7+fgplMAdB8WxbBbOqNSy99eQojWx5Xvb3VjTjhlyhTH46qqKt577z0SExMZPHgwABs3bmTPnj08+OCDF1+1EKLZUWtUjE1JJDjKhw3fHWbPmmwKcyqYcG8PvPy0ni5PCCE8xuV5oO655x4iIyN54YUXnLb//e9/59ixY3z66adNWmBLJC1QojXK2JXHsk/2YKqy4BOk46r7kwiN9vV0WUII0WTcOpGmv78/qampdOrUyWl7eno6ycnJFBcXu15xKyMBSrRWBSfL+en9nRTnVqLWKBk9rRud+od7uiwhhGgSbp1I02AwsHbt2nrb165di16vd/V0QogWJCjSm5v+kkx09yCqzVaWfrKHDYsOyaSbQog2p1FjoOp69NFHeeCBB9i6dSuDBg0C7GOgPv30U5599tkmL1AI0bzovDRMeqgXG787zPalWWz7NYu842WMu6s7em+Np8sTQojL4qLuhbdgwQLefvtt9u3bB0C3bt145JFHuPnmm5u8wJZIuvBEW3FwSw6rPttPtdmKX6iBqx7oSXCUj6fLEkKIiyI3E/YwCVCiLTl9rJSf399FaUEVap2KsSnd6NgnzNNlCSGEyy5LgDKZTOTm5mK1Wp22R0dHX8zpWhUJUKKtqSwz8et/dnPiQBEA/SbGMGByPEpl4+aPE0KI5sCtASo9PZ277rqL9evXO2232WwoFAosFovrFbcyEqBEW2S1WFn/7WHSVh4DILp7kIyLEkK0KE0+kWZdKSkpqNVqfvjhByIjIxs9Q7kQonVTqpRccXMnQmN8Wf35frL2FPD1y1u48v4kQtrLuCghROvicguUt7c3W7dupWvXru6qqcWTFijR1p0+VsrPH+yiNL8KtVbJ6DtkvighRPPn1nmgEhMTycvLu+jihBCtX2gHX25+uj8dEoOoNtnni1r7dToWi/XCLxZCiBbA5QD16quv8uSTT7J69Wry8/MpKSlxWoQQAkDvo+Hq6b3oOzEGgLQVx1j85nbKi40erkwIIS6dy114SqU9c5099kkGkZ8hXXhCODuy/TTL5+3FXGXBy1/LxHt7EJkQ4OmyhBDCiVsHka9ateqiCxNCtE3xfUK5Oao/P32wi8KT5Xz3xnaG3JhA0qj2ciGKEKJFkok03UBaoIRomKmqmlWf7+dQai4ACclhjPpDV7R6l/+WE0KIJufWFqhaFRUVZGVlYTKZnLYnJSVd7CmFEK2cVq9m/N3diYjzZ/23hziUmkv+8TIm3teToEhvT5cnhBCN5nIL1OnTp/njH//Izz//3OB+GQMlLVBCNMbJQ0X8+p/dlBebUOtUjL6jK52SZaoDIYTnuHUag0cffZTCwkI2btyIwWDgl19+Yd68eXTq1IklS5ZcdNFCiLYlMiGAm/9vAO26BFBttLD04z2s+eoglmqZ6kAI0fy53AIVGRnJ4sWLGTBgAH5+fqSmptK5c2eWLFnCP//5T9auXeuuWlsMaYESovGsFiublhxl26+ZAITH+THh3h74Buk9XJkQoq1xawtUeXk5YWH2O60HBQVx+vRpAHr27Mm2bdsuolwhRFumVCkZfF1HJj2YhM5LzamjJXz10mYy9+R7ujQhhDgnlwNUly5dOHDgAAC9e/fmww8/5MSJE3zwwQdERkY2eYFCiLYhNimEm5/pT2i0L8byan6YncamJUewWuVCYSFE8+NyF94XX3yB2WwmJSWF7du3M2HCBPLz89FqtcydO5dbbrnFXbW2GNKFJ8TFs5itrP0mnd2/nQCgXecAxt3dHW9/nYcrE0K0dq58f1/yPFAVFRXs37+f6OhoQkJCLuVUrYYEKCEu3cEtOaz+/ABmowWDn5bxdyXSvmuQp8sSQrRilzVAifokQAnRNApzyvn1P7vJP1EOCug/KY7kq2JRKmX2ciFE03PrIHIhhLhcAiO8ueGpZLoNjQQbbPnhKN+/s0NuSCyE8LgWE6BeeuklhgwZgpeXFwEBAQ0ek5WVxTXXXIO3tzchISHMmDGj3kzpu3btYsSIERgMBtq1a8fzzz/P2Y1wv/32G/369UOv1xMfH88HH3zgrh9LCHEBGq2K0Xd0Y0xKN9RaJcf3F/LVS1s4tq/A06UJIdqwFhOgTCYTN910Ew888ECD+y0WC5MmTaK8vJy1a9cyf/58vv32Wx5//HHHMSUlJYwbN46oqCi2bNnCu+++y2uvvcYbb7zhOObo0aNcddVVDBs2jO3bt/PMM88wY8YMvv32W7f/jEKIc+s6KJKbnu5PUJQ3lSUmlryzw36VnkUm3hRCXH4tbgzU3LlzefTRRykqKnLa/vPPP3P11Vdz7NgxoqKiAJg/fz4pKSnk5ubi5+fH+++/z9NPP82pU6fQ6exX9Lzyyiu8++67HD9+HIVCwVNPPcWSJUvYt2+f49z3338/aWlpbNiwoVE1yhgoIdyn2mRhzYJ09q7NBiCqUwDj7krEJ1Am3hRCXBq3joGaM2cOX3/9db3tX3/9NfPmzXP1dE1mw4YN9OjRwxGeACZMmIDRaGTr1q2OY0aMGOEIT7XHZGdnk5GR4Thm/PjxTueeMGECqampmM3mBt/baDRSUlLitAgh3EOtVTHqD10Zd1ciGp2K7PQivnpxCxk78zxdmhCiDXE5QL3yyisNTlcQFhbGP/7xjyYp6mLk5OQQHu58I9LAwEC0Wi05OTnnPKb2+YWOqa6uJi+v4Q/ol19+GX9/f8fSoUOHJvmZhBDn1nlAhGPizapyMz++t5O1C9KxmKVLTwjhfi4HqMzMTOLi4uptj4mJISsry6VzzZo1C4VCcd4lNTW10edTKOpf2myz2Zy2n31MbQ+mq8fU9fTTT1NcXOxYjh071uiahRAXLyDcixv+3I+k0e0BSFt5jG//tZWiUxUerkwI0dqpXX1BWFgYO3fuJDY21ml7WloawcHBLp1r+vTpTJ069bzHnP0+5xIREcGmTZucthUWFmI2mx0tShEREY6Wplq5ubkAFzxGrVaf8+fT6XRO3YJCiMtHpVEy7ObOtO8SyIrP9nE6q5QF/9jCiFs702WQ3F5KCOEeLgeoqVOnMmPGDHx9fRk+fDhgv+z/kUceuWAYOltISEiTzV4+ePBgXnrpJU6ePOm4J9/SpUvR6XT069fPccwzzzyDyWRCq9U6jomKinIEtcGDB/P99987nXvp0qUkJyej0WiapFYhRNOL6xXK1L/6suzTvWSnF7F87j6y9hUw4tYuaPUuf9QJIcR5udyF9+KLLzJw4EDGjBmDwWDAYDAwfvx4Ro8e7dYxUFlZWezYsYOsrCwsFgs7duxgx44dlJWVATB+/HgSExO544472L59OytWrOCJJ57g3nvvdYykv+2229DpdKSkpLB7924WLVrEP/7xD2bOnOnonrv//vvJzMxk5syZ7Nu3j08//ZRPPvmEJ554wm0/mxCiafgE6rn2sT4MuCYOhQIObjrFVy9tITdTLuwQQjSti57G4ODBg6SlpWEwGOjZsycxMTFNXZuTlJSUBq/yW7VqFSNHjgTsIevBBx9k5cqVGAwGbrvtNl577TWn7rVdu3bx0EMPsXnzZgIDA7n//vt59tlnncY3/fbbbzz22GPs2bOHqKgonnrqKe6///5G1yrTGAjheScPFbH00z2UFRhRKhUMvDaePuOiUchtYIQQ5yD3wvMwCVBCNA9V5WZWf7Gfw9tOA9CuSyBjUxLxCZQxi0KI+po8QM2cOZMXXngBb29vZs6ced5j687q3VZJgBKi+bDZbOxbf5I1Xx2k2mRF561m9B3diO8d6unShBDNjCvf340aWbl9+3bHJJLbt2+/9AqFEOIyUSgUJA6NIrKjP8s+3cvprFJ+/mAXicOiuOLGTmh0Kk+XKIRogaQLzw2kBUqI5slSbWXT4iNsX2afsy4g3ItxdyUSFiP/ToUQbr6Vy1133UVpaWm97eXl5dx1112unk4IIS4blVrJkBsSmPxob7wDdBSdquDbV7ey9ZcMrFb5W1II0Xgut0CpVCpOnjxJWFiY0/a8vDwiIiKorq5u0gJbImmBEqL5O3uAeVSnAMakdMMv2ODhyoQQnuKWFqiSkhKKi4ux2WyUlpY63Ti3sLCQn376qV6oEkKI5krvrWHCvT0YPa3bmZsSv7CZA5tykJENQogLafT0vAEBAY7703Xu3LnefoVCwXPPPdekxQkhhDspFAq6DYkkqpM/y+fsJedICcvn7CVjZx4jbuuC3lvuPiCEaFiju/B+++03bDYbo0eP5ttvvyUoKMixT6vVEhMTQ1RUlNsKbUmkC0+IlsdqsbLt10w2/5CBzWrDO0DHmDu70aFb0IVfLIRoFdw6kWZGRgYxMTFOM3cLZxKghGi5TmXYW6GKTlUA0HNUewZf1xGNVqY7EKK1c+tVeKNGjeKFF14gKyvrogsUQojmKjzWj5uf6U+PEe0A2LXqOAte2sKpDLmfnhDiDJcD1MyZM1m8eDHx8fGMGzeO+fPnYzQa3VGbEEJ4hEanYsStXbjm4V54+Wvt0x38cyubfziKxWL1dHlCiGbgoifSTEtL49NPP+XLL7+kurqa2267jbvuuou+ffs2dY0tjnThCdF6VJWb+e3LAxxKzQUgLMaXMXcmEhTl7eHKhBBN7bLeTNhsNvPee+/x1FNPYTab6dGjB4888gh//OMf2+w4KQlQQrQ+B7fk8PuXBzFWVKNSKxk0JZ5eozugULbNzzkhWqPLEqDMZjOLFi1izpw5LFu2jEGDBnH33XeTnZ3N7NmzGTVqFP/73/8u6gdo6SRACdE6lRcZWfnffWTtKQDsk2+OntYN/1CZfFOI1sCtAWrbtm3MmTOHL7/8EpVKxR133ME999xD165dHcds2bKF4cOHU1lZeXE/QQsnAUqI1stms7F3bTZrvzlEtdGCWqdi6A0JdB8W1WZb3YVoLVz5/m70RJq1+vfvz7hx43j//feZMmUKGk39ieYSExOZOnWqq6cWQohmT6FQ0H1YO9p3DWLlZ/vITi/it/8d4PC2XEZP64ZvkN7TJQohLgOXW6AyMzOJiYlxVz2tgrRACdE22Kw2dq46zsbvDlNttqLVqxh6Uye6DYmU1ighWqDLOohc1CcBSoi2pehUBSvm2W8FAxDdPZhRf+iCT6C0RgnRkjR5gAoMDGz0X1MFBQWNq7IVkwAlRNtjtdrYsTyLzUuOYqm2ojWoueKmBLoOltYoIVqKJh8D9dZbbzVFXUII0WoplQr6jo8htmcIKz/bx6mjJaz8bD+Htp6W1ighWiHpwnMDaYESom2r1xqlVzH0xk50GyqtUUI0Z5dtDFRlZSVms9lpmwQGCVBCCLuCk+WO1iiA9l0DGfWHrviFyLxRQjRHbr2ZcHl5OdOnTycsLAwfHx8CAwOdFiGEEHZBkd5c/+d+DLkhAZVGyfH9hcx/YTO7Vh/HZpXGfyFaMpcD1JNPPsnKlSt577330Ol0fPzxxzz33HNERUXx2WefuaNGIYRosZRKBX3GRTP1rwOITPDHbLTw+/yDLHpjG0WnKjxdnhDiIrnchRcdHc1nn33GyJEj8fPzY9u2bSQkJPDf//6XL7/8kp9++sldtbYY0oUnhGiIzWpj128n2PDdYaqNFlQaJQOujqP32A4oVS7/PSuEaGJu7cIrKCggLi4OsI93qp224IorruD333+/iHKFEKJtUCgVJI1qz61/G0CHboFYzFY2LDrMN69uJe94qafLE0K4wOUAFR8fT0ZGBmC/ZcuCBQsA+P777wkICGjK2oQQolXyCzFwzYzejJ7WDZ2XmtNZpXz9j9SaGc0tni5PCNEILnfhvfnmm6hUKmbMmMGqVauYNGkSFouF6upq3njjDR555BF31dpiSBeeEKKxyouN/D7/IEe2nwYgINyLUX/oQlQnuShHiMvtst7KJSsri9TUVDp27EivXr0u5VSthgQoIYSrDm/P5ff5B6koNgHQfXg7Bl/XEZ3B5Xu+CyEuktwLz8MkQAkhLoaxwsz6hYfZuzYbAG9/LcOndiG+T6iHKxOibXB7gFqxYgUrVqwgNzcXq9XqtO/TTz919XStjgQoIcSlOHGgkFVf7Kc4txKAuF4hDJ/aBZ9AnYcrE6J1c+tVeM899xzjx49nxYoV5OXlUVhY6LQIIYS4NO26BDL1rwPoNzEGpVLB0bQ8/vfcRpmAU4hmxOUWqMjISP75z39yxx13uKumFk9aoIQQTSX/RBmrPt/vuB1MeJwfo/7QleB2Ph6uTIjWx60tUCaTiSFDhlx0cUIIIRovuJ0P1/+5H8Nu6YxGr+LU0RIWvLSFDYsOYTbJlAdCeIrLLVBPPfUUPj4+/O1vf3NXTS1eYxOsxWKpdzNm0XZotVqUSpl9WjReWaGRNV8d5MgO+5QHfiF6RtzahejuwR6uTIjWwZUWKJevj62qquKjjz5i+fLlJCUlodFonPa/8cYbrp6yzbHZbOTk5FBUVOTpUoQHKZVK4uLi0Gq1ni5FtBA+gTquvL8nR3acZs1XBynJq+L7d9NISA7jips64e0vg8yFuFxcboEaNWrUuU+mULBy5cpLLqqlu1CCPXnyJEVFRYSFheHl5YVCofBAlcKTrFYr2dnZaDQaoqOj5XdAuMxUVc3mJUfZueoYNhto9SoGTelI9+HtUCrl90mIiyHzQHnY+f4HWCwWDh48SFhYGMHB0uzelhUXF5OdnU1CQkK9llwhGut0Vimrv9hPbqb9XnphMb6MuK0LYTFyAYsQrnLrIHJPeemllxgyZAheXl4N3nMvLS2NW2+9lQ4dOmAwGOjWrRtvv/220zEZGRkoFIp6yy+//OJ03G+//Ua/fv3Q6/XEx8fzwQcfNNnPUTvmycvLq8nOKVqm2q47i0UGAouLFxrtyw1PJTN8ame0ehW5maV880oqv88/iLGy2tPlCdFqXdQ9ArZs2cLXX39NVlYWJpPJad/ChQubpLCzmUwmbrrpJgYPHswnn3xSb//WrVsJDQ3l888/p0OHDqxfv54//elPqFQqpk+f7nTs8uXL6d69u+N5UFCQ4/HRo0e56qqruPfee/n8889Zt24dDz74IKGhodxwww1N9vNIl42Q3wHRVJRKBT1Htie+TyjrvjlE+pZT7Fp9nMPbchl6UwKdksPl902IJuZygJo/fz7Tpk1j/PjxLFu2jPHjx5Oenk5OTg7XXXedO2oE7BN4AsydO7fB/XfddZfT8/j4eDZs2MDChQvrBajg4GAiIiIaPM8HH3xAdHQ0b731FgDdunUjNTWV11577ZwBymg0YjQaHc9LSkoa8yMJIUST8vbXMf7u7nQbEslvXx6gOLeSZZ/sZd+6kwyf2pnACG9PlyhEq+FyF94//vEP3nzzTX744Qe0Wi1vv/02+/bt4+abbyY6OtodNV604uJip9alWpMnTyYsLIyhQ4fyzTffOO3bsGED48ePd9o2YcIEUlNTzznlwMsvv4y/v79j6dChQ9P9EG3Q3LlzG+ymrWvWrFn07t37stQjREvToVsQU/82gAHXxKFSKzm+v5D5L2xmw3eHMRuly1iIpuBygDp8+DCTJk0CQKfTUV5ejkKh4LHHHuOjjz5q8gIv1oYNG1iwYAH33XefY5uPjw9vvPEG33zzDT/99BNjxozhlltu4fPPP3cck5OTQ3h4uNO5wsPDqa6uJi8vr8H3evrppykuLnYsx44dc88PJRyeeOIJVqxY4XiekpLClClTPFcQsHbtWoYOHUpwcDAGg4GuXbvy5ptverQm0XapNSr6T4rj1r8PIKZHMFaLjW2/ZPK/5zZyZMdp5PohIS6Ny114QUFBlJbar/Zo164du3fvpmfPnhQVFVFRUeHSuWbNmuXomjuXLVu2kJyc7NJ59+zZw7XXXsuzzz7LuHHjHNtDQkJ47LHHHM+Tk5MpLCzkn//8J3/4wx8c288eK1D7QXOuMQQ6nQ6dTuZfuZx8fHzw8Wlet7Lw9vZm+vTpJCUl4e3tzdq1a7nvvvvw9vbmT3/6k6fLE22Uf6gXkx5K4mhaHmsWHKSswMjPH+wiunsww27pRECYXNAixMVwuQVq2LBhLFu2DICbb76ZRx55hHvvvZdbb72VMWPGuHSu6dOns2/fvvMuPXr0cOmce/fuZfTo0dx777389a9/veDxgwYNIj093fE8IiKCnJwcp2Nyc3NRq9VtftqBkSNHMmPGDJ588kmCgoKIiIhg1qxZTsekp6czfPhw9Ho9iYmJLFu2DIVCwXfffQfA6tWrUSgUTpOI7tixA4VCQUZGhtO5vvvuOzp37oxer2fcuHFOLXt1u/BmzZrFvHnzWLx4sePKytWrV2MymZg+fTqRkZHo9XpiY2N5+eWX3fBfxq5Pnz7ceuutdO/endjYWP7whz8wYcIE1qxZ47b3FKIxFAoF8b1Due3vg+g7MQalSkHWnny+fH4Tm5YckVvCCHERXG6Bmj17NlVVVYC960qj0bB27Vquv/56l2/vEhISQkhIiKslnNOePXsYPXo0d955Jy+99FKjXrN9+3YiIyMdzwcPHsz333/vdMzSpUtJTk5221w9NpuNSrNnPsAMGpVLV+fMmzePmTNnsmnTJjZs2EBKSgpDhw5l3LhxWK1Wrr/+ekJCQti4cSMlJSU8+uijF1VXRUUFL730EvPmzUOr1fLggw8ydepU1q1bV+/YJ554gn379lFSUsKcOXMAe0vpO++8w5IlS1iwYAHR0dEcO3bsvN2rX3zxhVOXb0M+/PBDbr/99kb9DNu3b2f9+vW8+OKLjTpeCHfT6FQMntKRroMiWLMgnWN7C0j9KYMDG3O44uZOxPUKkav1hGiki+rCq6VUKnnyySd58sknm7SohmRlZVFQUEBWVhYWi4UdO3YAkJCQgI+PD3v27GHUqFGMHz+emTNnOlqRVCoVoaGhgP3LX6PR0KdPH5RKJd9//z3vvPMOr776quN97r//fmbPns3MmTO599572bBhA5988glffvml2362SrOFxGd/ddv5z2fv8xPw0jb+1yApKYm///3vAHTq1InZs2ezYsUKxo0bx/Lly9m3bx8ZGRm0b98esF90cOWVV7pcl9lsZvbs2QwcOBCw/7/r1q0bmzdvZsCAAU7H+vj4YDAYMBqNTldXZmVl0alTJ6644goUCgUxMTHnfc/Jkyc73u9czh4f15D27dtz+vRpqqurmTVrFvfcc88FXyPE5RQY4c01D/fiyI7TrP06ndKCKn7+YBcdEoMYdnMnuVpPiEa4qHmgPOHZZ59l3rx5jud9+vQBYNWqVYwcOZKvv/6a06dP88UXX/DFF184jouJiXHqGnrxxRfJzMxEpVLRuXNnPv30U6fxT3Fxcfz000889thj/Pvf/yYqKop33nmnSeeAasmSkpKcnkdGRpKbmwvAvn37iI6OdoQnsLfoXQy1Wu009q1r164EBASwb9++egHqXFJSUhg3bhxdunRh4sSJXH311fWusKzL19cXX1/fi6q3rjVr1lBWVsbGjRv5y1/+QkJCArfeeusln1eIpqRQKOjYJ4zoxGC2/pLB9mVZHNtbwPwXNtNrdAeSJ8Wi1beYrwghLrsW869j7ty555wDCuzjYM4ej3O2O++8kzvvvPOC7zVixAi2bdvmYoUXz6BRsff5CZft/c5+b1ec3Y2pUCiwWq0ADV7Vc3Z3gFKprHfsuaaHaKgrwZXuhb59+3L06FF+/vlnli9fzs0338zYsWPrTV1Rq6m68OLi4gDo2bMnp06dYtasWRKgRLOl0akYdG1Hug6OZN3X6WTsymf7siwObM5hyPUJdB4gk3AK0ZAWE6BaM4VC4VI3WnOVmJhIVlYW2dnZREVFAfbpJOqq7U49efIkgYGBAI7u2Lqqq6tJTU11tDYdOHCAoqIiunbt2uB7a7XaBm+J4ufnxy233MItt9zCjTfeyMSJEykoKDjn/GBN0YVXl81mc5pkVYjmKiDMi0kP9SJjVx5rFqRTcrqS5XP2svu3Ewyf2pnQ6EtvnRWiNWn539qi2Rg7dixdunRh2rRpvP7665SUlPB///d/TsckJCTQoUMHZs2axYsvvkh6ejqvv/56vXNpNBoefvhh3nnnHTQaDdOnT2fQoEHn7L6LjY3l119/5cCBAwQHB+Pv78/s2bOJjIykd+/eKJVKvv76ayIiIs45SeelduH9+9//Jjo62hHy1q5dy2uvvcbDDz980ecU4nKL7RlCh65B7FiRRepPGeQcKWbBy1tIHBrFoGvjMfhqPV2iEM1Ci7mZsGj+lEolixYtwmg0MmDAAO655556V0NqNBq+/PJL9u/fT69evXj11VcbvErNy8uLp556ittuu43BgwdjMBiYP3/+Od/73nvvpUuXLiQnJxMaGsq6devw8fHh1VdfJTk5mf79+5ORkcFPP/3k6EZsalarlaeffprevXuTnJzMu+++yyuvvMLzzz/vlvcTwl1UGiX9JsZy+3OD6NQ/HGywd202X/x9I2krjmGxWD1dohAep7C5OB1teXk5r7zyCitWrCA3N9cx/qXWkSNHmrTAlqikpAR/f3+Ki4vx8/Nz2ldVVcXRo0eJi4tDr9d7qMLLS6FQsGjRIo/PFN7ctMXfBdEyZR8qYs1XB8k7VgZAYIQXV9zUiejubXtuPNH6nO/7+2wud+Hdc889/Pbbb9xxxx1ERkbK4EIhhGjlohICuOnp/uxbl83GxUcozKng+3fTiO0ZzNAbOxEQLrOZi7bH5QD1888/8+OPPzJ06FB31COEEKIZUioVdB/WjoR+YWz5MYNdq46TsSufrL0F9BzVnv5XxaLzcs9kw0I0Ry4HqMDAwAavYBLiXOSmpUK0HjovDVfc1Inuw6JY980hMnfnk7b8GAc25jBwcjyJV0ShVErPhGj9XB5N+8ILL/Dss8+6fONgIYQQrUdghDdXT+/F1Q/3IjDCi6oyM7/97wALXtrMsX0Fni5PCLdzuQXq9ddf5/Dhw4SHhxMbG1tvYsXLOQGlEEIIz4rpHkz7roHs+f0Em78/Sv6Jcpa8vYPYnsEMuSFBbgsjWi2XA5RcSSWEEKIulUpJ0qgOdB4QwZYfjrL7txP28VF7Cugxoh39r45D7y3jo0Tr4vI0BuLCZBoD0RjyuyBaq8KcctZ/e4iMXfkA6LzU9J8UR48R7VCpZfpB0Xy5Mo2B/CYLIYRoUoER3kx6qBeTZ/QmuJ03xopq1n6dzpfPbeLI9tNyYYloFRrVhRcUFMTBgwcJCQkhMDDwvHM/FRTI4EEhhBDQITGIm7sOYP/6k2xccoTi05X8/OEuojoFMPTGBMJizv8XvhDNWaMC1Jtvvum4R9hbb73lznqEYO7cuTz66KMUFRWd85hZs2bx3XffNXgjYiFE86FUKki8IoqE5DC2/ZrJjuXHyE4v4uuXU+k8IJyB18bjF2zwdJlCuEzGQLmBjIG6NI0JUGVlZRiNRoKD7beSSElJoaioiO++++7yFHkB69atY8SIEfTo0eOcIU9+F0RbVFpQxabFRziwKQcAlVpJrzHt6TsxFp1B7m8vPEvGQIlWz8fHxxGempvi4mKmTZvGmDFjPF2KEM2Ob5CesX9M5OZn+tOucwCWaivbfs3i879uIG3lMSzVcqNi0TJIgBKNNnLkSGbMmMGTTz5JUFAQERERzJo1y+mY9PR0hg8fjl6vJzExkWXLlqFQKBwtQ6tXr0ahUDi1Lu3YsQOFQkFGRobTub777js6d+6MXq9n3LhxHDt2zLFv1qxZ9O7d2/F43rx5LF68GIVCgUKhYPXq1ZhMJqZPn05kZCR6vZ7Y2FhefvllN/yXcXbfffdx2223MXjwYLe/lxAtVWi0L9c+1odJDybZJ+IsN7N2QTr/e24Th7bmykBz0exJe2lzYLOB2UMzu2u8wIUbQs+bN4+ZM2eyadMmNmzYQEpKCkOHDmXcuHFYrVauv/56QkJC2LhxIyUlJTz66KMXVVZFRQUvvfQS8+bNQ6vV8uCDDzJ16lTWrVtX79gnnniCffv2UVJSwpw5cwD7hQ/vvPMOS5YsYcGCBURHR3Ps2DGnEHa2L774gvvuu++8dX344Yfcfvvt59w/Z84cDh8+zOeff86LL77YyJ9WiLZJoVAQmxRCdPcg9q47yeYfjlJyupJf/7Ob8Dg/hlyfQFSnAE+XKUSDJEA1B+YK+EeUZ977mWzQNn6m4KSkJP7+978D0KlTJ2bPns2KFSsYN24cy5cvZ9++fWRkZNC+fXsA/vGPf3DllVe6XJbZbGb27NkMHDgQsAe3bt26sXnzZgYMGOB0rI+PDwaDAaPRSEREhGN7VlYWnTp14oorrkChUBATE3Pe95w8ebLj/c4lPDz8nPvS09P5y1/+wpo1a1Cr5Z+WEI2lVCnpMbwdnQeEs2NZFtuXZXHqaAmLXt9GbFIIg6/rSFCkzGgumpeL/pQ/dOgQhw8fZvjw4RgMBmw223mnNxCtQ1JSktPzyMhIcnNzAdi3bx/R0dGO8ARcdDeWWq0mOTnZ8bxr164EBASwb9++egHqXFJSUhg3bhxdunRh4sSJXH311YwfP/6cx/v6+jquNnWVxWLhtttu47nnnqNz584XdQ4h2jqtXs2Aa+LpPrwdW344yt51J8nYmUfmrjy6DYlkwDXxeAfoPF2mEMBFBKj8/HxuueUWVq5ciUKhID09nfj4eO655x4CAgJ4/fXX3VFn66bxsrcEeeq9XTn8rHsfKhQKrFb7oM+GxiycHaqVSmW9Y81mc4Pv1VAgdyWk9+3bl6NHj/Lzzz+zfPlybr75ZsaOHcs333zT4PGX0oVXWlpKamoq27dvZ/r06QBYrVZsNhtqtZqlS5cyevToRtcuRFvm7a9j5O1d6TWmAxsWHeZoWh57153k4OZTJI3pQN/x0ei85NYwwrNcDlCPPfYYarWarKwsunXr5th+yy238Nhjj0mAuhgKhUvdaM1VYmIiWVlZZGdnExVl75LcsGGD0zGhoaEAnDx5ksDAQIAGL/Ovrq4mNTXV0dp04MABioqK6Nq1a4PvrdVqsVgs9bb7+flxyy23cMstt3DjjTcyceJECgoKCAoKqnfspXTh+fn5sWvXLqdt7733HitXruSbb74hLi7uvOcVQtQXGOHNVQ8kcfJQEesXHibnSDHbfslkz5oTJF8ZS88R7VFp5Foo4RkuB6ilS5fy66+/OnXTgH08TGZmZpMVJlqesWPH0qVLF6ZNm8brr79OSUkJ//d//+d0TEJCAh06dGDWrFm8+OKLpKenNxi6NRoNDz/8MO+88w4ajYbp06czaNCgc3bfxcbG8uuvv3LgwAGCg4Px9/dn9uzZREZG0rt3b5RKJV9//TUREREEBAQ0eI5L6cJTKpX06NHDaVtYWBh6vb7ediGEayITArj+z305mpbHxu8OU5hTwbpvDpG28hgDr4mn88AIlEoZQiIuL5eje3l5OV5e9bt98vLy0Omkb7otUyqVLFq0CKPRyIABA7jnnnt46aWXnI7RaDR8+eWX7N+/n169evHqq682eLWal5cXTz31lGM6AIPBwPz588/53vfeey9dunQhOTmZ0NBQ1q1bh4+PD6+++irJycn079+fjIwMfvrpJ0c3ohCi5VAoFMT3DmXq3wYw6o6ueAfoKCswsmLePr56cTNHd+bJ1AfisnJ5JvJJkybRt29fXnjhBXx9fdm5cycxMTFMnToVq9V6zvElbYnMRO5MoVCwaNEipkyZ4ulSmpW2+LsgRFOpNlnYueo4237NxFhRDUBkR38GXdeRqIQAzxYnWixXZiJ3uQvvX//6FyNHjiQ1NRWTycSTTz7Jnj17KCgoaHCOHiGEEKKpqbUq+k6IIfGKKLYvzWLnymOcPFzMote2EdMzmEHXxhPS/uK65IVoDJf7MhITE9m5cycDBgxg3LhxlJeXc/3117N9+3Y6duzojhqFEEKIBum9NQy+riN/eGEwicOiUCgVZO7K56uXtrD0kz0Un/bQJMWi1ZObCbuBdOGJxpDfBSGaXtGpCjZ/f4T0VPv8dEqlgm5XRNH/qliZQ0pckFtvJjxnzhy+/vrretu//vpr5s2b5+rphBBCiCYTEO7F+Ht6cPMz/YnuHoTVamPP7yf47982sP7bQ1SVNTzvnBCucjlAvfLKK4SEhNTbHhYWxj/+8Y8mKUoIIYS4FKHRvlzzcG+ue7wPkR39sZitbF+WxX//up4tPx7FVFnt6RJFC+dygMrMzGxwUsCYmBiysrKapCghhBCiKUR1CuS6J/oy6aEkgtv7YKqysPn7o/z3rxvYvjSLalP9CXiFaAyXr8ILCwtj586dxMbGOm1PS0sjODi4qeoSQgghmoRCoSC2Zwgx3YM5tC2Xzd8fpehUBesXHmLHiiySr4wl8YooVGqZI040nssBaurUqcyYMQNfX1+GDx8OwG+//cYjjzzC1KlTm7xAIYQQoikolAo6JYfTsU8oBzblsOWHDEoLqvh9/kG2L82i/9WxdBkYgVIlQUpcmMsB6sUXXyQzM5MxY8agVttfbrVamTZtmoyBEkII0ewpVUq6DYmic/8I9q7LJvUne5Ba+dl+tv6SyYCr40hIDpfbw4jzuuhpDA4ePEhaWhoGg4GePXsSExPT1LW1WDKNwaWZO3cujz76KEVFRec8ZtasWXz33XcN3oi4pZDfBSGaB7PJwu7fTrDt10zHVXpBUd4MuDqO+N6hKCRItRlunYm8VufOnencufPFvlyIS/LEE0/w8MMPO56npKRQVFTEd99957GaVq9ezahRo+pt37dvH127dvVARUKIxtBoVfQZF033YVHsXHmcHcuzKMgu55ePdhPSwYcBV8cRmxSCQiFBSpzhcoCyWCzMnTuXFStWkJubi9Vqddq/cuXKJitOiHPx8fHBx8fH02U06MCBA05/uYSGhnqwGiFEY2n1apKviqXnyHbsWH6MtJXHyDtWxk/v7yIsxpcBk+OJTgySICWAi5jG4JFHHuGRRx7BYrHQo0cPevXq5bS4y0svvcSQIUPw8vIiICCgwWMUCkW95YMPPnA6ZteuXYwYMQKDwUC7du14/vnn693B+7fffqNfv37o9Xri4+PrnaOp2Ww2KswVHllc6cEdOXIkM2bM4MknnyQoKIiIiAhmzZrldEx6ejrDhw9Hr9eTmJjIsmXLUCgUjpah1atXo1AonLrnduzYgUKhICMjw+lc3333HZ07d0av1zNu3DiOHTvm2Ddr1ix69+7teDxv3jwWL17s+P++evVqTCYT06dPJzIyEr1eT2xsLC+//LIr/2suSlhYGBEREY5FpVK5/T2FEE1H56Vh4OR4pr04hL4TYlBrleRmlvLDu2ks/NdWju0tcOmzU7ROLrdAzZ8/nwULFnDVVVe5o55zMplM3HTTTQwePJhPPvnknMfNmTOHiRMnOp77+/s7HpeUlDBu3DhGjRrFli1bOHjwICkpKXh7e/P4448DcPToUa666iruvfdePv/8c9atW8eDDz5IaGgoN9xwg1t+tsrqSgb+b6Bbzn0hm27bhJfGq9HHz5s3j5kzZ7Jp0yY2bNhASkoKQ4cOZdy4cVitVq6//npCQkLYuHEjJSUlPProoxdVV0VFBS+99BLz5s1Dq9Xy4IMPMnXq1AZvWP3EE0+wb98+SkpKmDNnDgBBQUG88847LFmyhAULFhAdHc2xY8ecQtjZvvjiC+67777z1vXhhx9y++23n/eYPn36UFVVRWJiIn/9618b7NYTQjR/eh/7ffZ6jenAtqWZ7P7tBDlHSljyzg4iE/wZcE087bsEerpM4SEuByitVktCQoI7ajmv5557DrAPMD6fgIAAIiIiGtz3xRdfUFVVxdy5c9HpdPTo0YODBw/yxhtvMHPmTEeLVXR0NG+99RYA3bp1IzU1lddee81tAaolSUpK4u9//zsAnTp1Yvbs2axYsYJx48axfPly9u3bR0ZGBu3btwfgH//4B1deeaXL72M2m5k9ezYDB9qD5bx58+jWrRubN29mwIABTsf6+PhgMBgwGo1O/++zsrLo1KkTV1xxBQqF4oIXOkyePNnxfucSHh5+zn2RkZF89NFH9OvXD6PRyH//+1/GjBnD6tWrHVN+CCFaHi8/LVfc2Ik+46LZ9msme37P5uShYha/uZ2oTgEMuCaOdp0lSLU1Lgeoxx9/nLfffpvZs2c3y37g6dOnc8899xAXF8fdd9/Nn/70J5RKe0/lhg0bGDFiBDrdmRtKTpgwgaeffpqMjAzi4uLYsGED48ePdzrnhAkT+OSTTzCbzWg0mnrvaTQaMRqNjuclJSUu1WxQG9h02yaXXtNUDGqDS8cnJSU5PY+MjCQ3137Tzn379hEdHe0ITwCDBw++qLrUajXJycmO5127diUgIIB9+/bVC1DnkpKSwrhx4+jSpQsTJ07k6quvrvf/ti5fX198fX0vql6ALl260KVLF8fzwYMHc+zYMV577TUJUEK0At7+Oobd3Jk+42LY9ksGe9Zlk51exHdvbKddlwAGXB1HVCcJUm2FywFq7dq1rFq1ip9//pnu3bvXCxQLFy5ssuJc9cILLzBmzP+3d+dhVdXrAse/a8NmRkZlUMZUFFER0US9agVOldc8pqZp1MmjFaZxbDjZDSrN4R67xrGy07GkMm1wONpRE1AwzRwQzIGQFNgioHJQkVQG97p/eN3XLaB760ZA38/zrOdxr/Xba7383LpffuND2Nvbk5aWxp///GfKysp44403ACgtLa2zgvrVFoXS0lKCgoIoLS2t08rg5eVFbW0tZWVl+Pj41Hnu3LlzDS1kt0JRFLO60ZrS9X/fiqIYJhLUNybg+iT7ajJ7bdmamvo396wvQTcnaY+IiCA/P5+NGzeSmprKmDFjiI6O5rvvvqu3vKW68K7Vp08fvvzyS5PLCyGaPyc3WwY8EUKPIQHs21TI4R3FnMg9y5rcLNqGuP1fIuXa1GGKRmZ2AuXq6spjjz1mkYcnJibeNPHYs2ePUUvEjVxNlADDAOO3337b6Pz1X8BXv8ivPW9KmWv95S9/IT4+3vC6oqICPz8/k2K+m4SGhqLT6SguLsbX1xe40up3rasz0kpKSnBzu/KbWn1rOdXW1rJ3715Da1Nubi5nz55tcDkAGxsbLl+uu6dVq1atGDt2LGPHjmX06NEMHTqU8vJy3N3d65S93S68+mRlZdWbdAshWj5ndzsGjg8hYmgAmZsKydlRzIncM6zJPSOJ1D3A7ATq6iBdS4iLi7vp9i/XtxiZo0+fPlRUVHDy5Em8vLzw9vamtLTUqMzV7qerX4wNlbG2tm5wrz9bW1ujbsF7VXR0NCEhIUyaNImFCxdSUVHBrFmzjMq0b98ePz8/EhMTmT17Nnl5eSxcuLDOvbRaLdOmTSMpKQmtVktcXBx9+vRpsPsuMDCQH374gdzcXDw8PHBxcWHx4sX4+PgQHh6ORqPh22+/xdvbu8FZnLfbhbdo0SICAwPp0qUL1dXVfPnll6xatYpVq1bd8j2FEM2fs7sdg8aH0LPeRMqVXg/LGKm70S0tpFlbW0t6ejpHjx5l/PjxODs7U1xcTKtWrcxam8fT0xNPT89bCcEkWVlZ2NnZGb4wo6KieP3116mursbGxgaAzZs34+vra0jUoqKiWL9+vdF9Nm/eTGRkZL3jn8T/02g0rFmzhj/+8Y/07t2bwMBAkpKSjGZFarVaVqxYwXPPPUf37t3p1asXs2fP5vHHHze6l4ODA6+++irjx4+nqKiI/v378+mnnzb47MmTJ5Oenk5kZCSVlZVs3boVJycn5s+fT15eHlZWVvTq1YsNGzYYuhEtrbq6mpkzZ3LixAns7e3p0qUL//rXv+74jFUhRNOoP5E6y4ncK4PNez0SRNuOrs1y/LAwn9lbuRQWFjJ06FB0Oh1VVVUcOXKE4OBgZsyYwaVLlxptzSSdTkd5eTnr1q3jv//7v/nxxx+BKy0aTk5OrF+/ntLSUqKiorC3t2fr1q38+c9/JjY2lvfffx+Ac+fOERISwoMPPsjrr79OXl4esbGxvPnmm0bLGISFhTFlyhQmT57Mzp07mTp1KitWrDB5Fp5s5WJMURTWrFnDyJEjmzqUZuVe/CwIcS85X37pyhipn4rR1175qvVp70Kvh4No18lNEqlmqFG3cpk+fTqRkZHs37/fqEvrscce49lnnzU/WhO9+eabJCcnG1736NEDgK1btzJo0CC0Wi0ffvgh8fHx6PV6goODefvtt3nhhRcM73FxcSElJYUXXniByMhI3NzciI+PNxq/FBQUxIYNG3jppZf44IMP8PX1JSkpSZYwEEIIYZarY6R6Dgtg3w86Dm+/svzBuvez8Q5uReTDQbKyeQtmdguUp6cnO3bsICQkBGdnZ/bv309wcDAFBQWEhoZy4cKFxoq1xZAWKGPSAlW/e/GzIMS9rPJMFVmbCzm0vZjLNVdmL7cJcKbXw0EEdPWQRKoZaNQWKL1eX+9sp6KiotsagCvuXrLlgRBCXFn+4D/GdiRiaABZKToOZZzgVOF5/vXhL3j6ORE5PJDg7q1RNJJItQRmj6aNiYkxrNINV1oXKisrSUhIkMGyQgghxE04utjSf3QHJs7pS4/B/ljbWlF2vJJNHx9k5ezd5O05iV4vv3g2d2Z34Z04cYIHH3wQKysr8vLyiIyMJC8vD09PT7Zt20abNm0aK9YWQ7rwhCnksyCEALhYWc3+tOMc2FpE9aUrPTyuXg70HBpAh95eWFk1zsxhUZc5XXhmJ1AAFy9eZOXKlWRmZqLX64mIiGDChAnY25u3LcjdShIoYQr5LAghrlV1oYZfthaxP+04VRdqAWjlaUfEkAA69fHBSiuJVGNrtASqpqaGkJAQvv/+e0JDQ2870LuVJFDCFPJZEELUp/pSLQczTpCdquPi+StbXTm52RIe409of1+0NlZNHOHdq9EGkWu1WqqqqmSmgBBCCNFIbOysiRgSQNcH2nH4x2KyNhdSeaaK7d/kkbmxgPBof8IGtMXG/pbWwhYWYnZ74LRp05g/fz61tbWNEY8QQgghAK2NFd0f8mPi7L4MHB+Cs4cdF8/XsHPNUT6f9RO71x/j0u/1b8YuGp/Z6euuXbtIS0tj8+bNdO3aFUdHR6Prq1evtlhw4t60bNkyZsyYwdmzZxssk5iYyNq1a+vdiFgIIe4mVloNYQPa0rmfD3l7TrJvUyFnSi+w518FZKcep8uAtoRH++HoInuy3klmt0C5urryhz/8gSFDhuDr64uLi4vRIcSdMHPmTNLS0gyvY2Njm8VCnVVVVcyaNYuAgABsbW257777briHnxBCmMrKSkOnPj6Me/N+hkwOw9PPiZqqy2Sn6Phi1k4yvsqlouxiU4d5zzC7Beqzzz5rjDiEMIuTk5NZG1ffKWPGjOHkyZMsXbqU9u3bc+rUKenuFkJYlEaj0L5nG+6LaE3hwX+TubGQ0mPnOLjtBIe2F9OxtxcRQwJw93G8+c3ELbulOZG1tbWkpqby8ccfc/78eQCKi4uprKy0aHD3ClVV0V+40CSHOatYDBo0iBdffJFXXnkFd3d3vL29SUxMNCqTl5fHgAEDsLOzIzQ0lJSUFBRFYe3atQCkp6ejKIpR91x2djaKolBQUGB0r7Vr19KxY0fs7OyIiYnh+PHjhmuJiYmEh4cb/pycnMw///lPFEVBURTS09Oprq4mLi4OHx8f7OzsCAwMZO7cueb81Zhl06ZNZGRksGHDBqKjowkMDKR379707du30Z4phLh3KYpCYFdPRr0cwcj4Hvh1dkPVq+T+XMqKt3ex8eMDnCqsaOow71pmt0AVFhYydOhQdDodVVVVxMTE4OzszIIFC7h06RJLlixpjDjvaurFi+RG9GySZ4fsy0RxcDC5fHJyMvHx8ezatYudO3cSGxtLv379iImJQa/XM2rUKDw9Pfn555+pqKhgxowZtxTXhQsXmDNnDsnJydjY2PD8888zbtw4duzYUafszJkzycnJoaKiwtBC6u7uTlJSEuvWreObb77B39+f48ePGyVh11u+fDlTpky5YVwff/wxEyZMqPfaunXriIyMZMGCBXzxxRc4OjoyYsQI3nnnHVkjTQjRaBRFoW1HN9p2dONUYQWZGws5ln2aY1lXjnad3Og5NIC2IW4yi96CzE6gpk+fTmRkJPv378fDw8Nw/rHHHuPZZ5+1aHCi+enWrRsJCQkAdOjQgcWLF5OWlkZMTAypqank5ORQUFBAu3btAHj33XcZNmyY2c+pqalh8eLF3H///cCVxK1z587s3r2b3r17G5V1cnLC3t6eqqoqvL29Ded1Oh0dOnSgf//+KIpCQEDADZ85YsQIw/Ma4uXl1eC1Y8eOsX37duzs7FizZg1lZWU8//zzlJeXyzgoIcQd0SagFcOmdqW8+Hf2/VDIkT0nKfr1DEW/nqFNYCt6Dg0gqJun7LdnAWYnUNu3b2fHjh3Y2NgYnQ8ICODEiRMWC+xeotjbE7Ivs8mebY5u3boZvfbx8eHUqVMA5OTk4O/vb0ieAKKiom4pLmtrayIjIw2vO3XqhKurKzk5OXUSqIbExsYSExNDSEgIQ4cO5ZFHHmHw4MENlnd2dr6tDbH1ej2KorB8+XLDhIr33nuP0aNH88EHH0grlBDijnH3dST66VB6PxpEVoqOnJ9KOFVQwcYlB3DzcSRiiD8desk2MbfD7JrT6/Vcvny5zvmioqLb+vK5lymKgsbBoUkOc5tztVptndj1ej1AveOprr+/RqOpU7ampv51TOqLzZx4IyIiyM/P55133uHixYuMGTOG0aNHN1h++fLlhsHpDR3Lly9v8P0+Pj60bdvWaDZq586dUVWVoqIik+MWQghLaeVpz8AnQpg0py8RQwKwsbPiTMnvpC3LYfl//cwvW4uoqa77nS5uzuwWqJiYGBYtWsTf//534MoXWmVlJQkJCQwfPtziAYqWIzQ0FJ1OR3FxMb6+vgDs3LnTqEzr1q0BKCkpwc3NDaDetZxqa2vZu3evobUpNzeXs2fP0qlTp3qfbWNjU29i36pVK8aOHcvYsWMZPXo0Q4cOpby8HHd39zplb7cLr1+/fnz77bdUVlYaZggeOXIEjUZj1ConhBB3mkMrG6Ieu4+IoQEczLiy39758kv8+PUR9vwrn+4PtiNsYDvsHLU3v5kAbiGB+p//+R8eeOABQkNDuXTpEuPHjycvLw9PT09WrFjRGDGKFiI6OpqQkBAmTZrEwoULqaioYNasWUZl2rdvj5+fH4mJicyePZu8vDwWLlxY515arZZp06aRlJSEVqslLi6OPn36NNh9FxgYyA8//EBubi4eHh64uLiwePFifHx8CA8PR6PR8O233+Lt7Y2rq2u997jdLrzx48fzzjvv8PTTT/PWW29RVlbGyy+/zDPPPCPdd0KIZsHW3pqeQwPp/qAfv+4sIStFR0XZJXaty2ffDzpC/8OX8If8cHKT/TlvxuwuPF9fX7Kzs3n55ZeZMmUKPXr0YN68eWRlZdGmTZvGiFG0EBqNhjVr1lBVVUXv3r159tlnmTNnjlEZrVbLihUr+PXXX+nevTvz589n9uzZde7l4ODAq6++yvjx44mKisLe3p6VK1c2+OzJkycTEhJCZGQkrVu3ZseOHTg5OTF//nwiIyPp1asXBQUFbNiwwdCNaGlOTk6kpKRw9uxZIiMjmTBhAo8++ihJSUmN8jwhhLhV1jZWhA1sx4S3+hDzx1A82l5ZlHN/6nG+eGMnaZ/nUF7ye1OH2awpqgkLAUVERJCWloabmxtvv/02M2fOxMGMqe/3mhvt5nzp0iXy8/MJCgrCzu7eyPAVRWHNmjXNYqXw5uRe/CwIIZonVVXRHSpn3w+FFOedNZwP6u5JxJAAvIPvjZ1GbvT9fT2TfhXPycnh99+vZKJvvfWWLJgphBBC3EUURSEgzIPH/hzBH17pSVB3TwDy95exakEmaxbuo+BAmVmLL9/tTBoDFR4eztNPP03//v1RVZW//vWvDW6j8eabb1o0QCGEEELcOd7BLgx/rhvlJb+TlaLjyK5SivPOUpx3FndfRyIG+9NelkAwrQsvNzeXhIQEjh49yr59+wgNDcXaum7upSgK+/bta5RAWxLpwhOmkM+CEKIlqDxzif1biji07QQ1VVdmOzu52RIe7U/nfj7Y2Jk9H63ZMqcLz6QE6loajYbS0lIZMH4DkkAJU8hnQQjRklRdqOHgthPs31LExYpqAGwdrAkb0JZuD/rh0MrmJndo/iw+BioiIoIzZ84AkJCQ0GD3nRBCCCHuTrYOWnoODWTSnCgGTQjBpY09VRdqydxUyOev/8TW5b9y9uSFpg7zjjGpBcre3p68vDzatWuHlZUVJSUl0gJ1A9ICJUwhnwUhREum16sU7C9j3+ZCTuZXXDmpQHD31vQY7N8iZ+6Z0wIlg8iFEEIIYTaNRiG4R2uCwj0p+e0cWZsLKTjwb45ln+ZY9ml87nMhPMb/rt282KQEatmyZSQkJPD999+jKAobN25scBC5JFBCCCHEvUNRFHw7uOLbwZXy4t/JTtWRu7uUkqPnKDl6AFcvB8Kj/Qjp44211qqpw7UYGUTeCKQLT5hCPgtCiLvV7+eq+GVLEQe3naD6Yi0A9s5auj3QjrAB7bBzap577ll8EPm19Hq9JE+iUS1btqzB/equSkxMJDw8/I7EI4QQwjyOLrZEPXYfT83tS7/R7XFys+Xi+Rp2rcsn+fUdbFt5hHOnLzZ1mLfFpC68devWMWzYMLRaLevWrbth2REjRlgkMCFuZObMmUybNs3wOjY2lrNnz7J27domiyk2Npbk5OQ650NDQzl06FATRCSEEE3Lxs6a8Gh/uj7QjqOZp8hK0VF2vJID6UUczCgiuEdrwmP88Q5qeQPOTUqgRo4caei2u9F+ZoqicPnyZUvFJkSDnJycmt1yGu+//z7z5s0zvK6traV79+48/vjjTRiVEEI0PSsrDR17e9OhlxdFuWfITtGhO1TO0X2nObrvND7tXQiP9iewmyeaFjLg3KQuvGu77fR6fYOHJE+3RlVVaqouN8lhzhC4QYMG8eKLL/LKK6/g7u6Ot7c3iYmJRmXy8vIYMGAAdnZ2hIaGkpKSgqIohpah9PR0FEXh7NmzhvdkZ2ejKAoFBQVG91q7di0dO3bEzs6OmJgYjh8/brh2bRdeYmIiycnJ/POf/0RRFBRFIT09nerqauLi4vDx8cHOzo7AwEDmzp1rzl+NWVxcXPD29jYce/fu5cyZMzz99NON9kwhhGhJFEXBr5M7j04LZ9x/9aZTH280Vgolv51j45IDfJXwMwfSi6ipbv75xN2z/noLVlut5+/TM5rk2X96fyBaW9NnRSQnJxMfH8+uXbvYuXMnsbGx9OvXj5iYGPR6PaNGjcLT05Off/6ZiooKZsyYcUtxXbhwgTlz5pCcnIyNjQ3PP/8848aNY8eOHXXKzpw5k5ycHCoqKvjss88AcHd3JykpiXXr1vHNN9/g7+/P8ePHjZKw6y1fvpwpU6bcMK6PP/6YCRMmmPQzLF26lOjoaAICAkwqL4QQ9xKPtk48FBtKn5H38Uv6la1izp2+yLaVR9i1/hhhA9rSdVA7HF1smzrUepmVQOn1epYtW8bq1aspKChAURSCgoIYPXo0EydORFFaRrObuHXdunUjISEBgA4dOrB48WLS0tKIiYkhNTWVnJwcCgoKaNeuHQDvvvsuw4YNM/s5NTU1LF68mPvvvx+4krh17tyZ3bt307t3b6OyTk5O2NvbU1VVhbe3t+G8TqejQ4cO9O/f/8pO4zdJZEaMGGF4XkO8vLxMir+kpISNGzfy1VdfmVReCCHuVY6utkSNvI+eQwP4dWcJ+9OOU1F2icyNhWSl6OjY25vwh/zwaNu8hm2YnECpqsqIESPYsGED3bt3p2vXrqiqSk5ODrGxsaxevbpJB/C2ZNY2Gv70/sAme7Y5unXrZvTax8eHU6dOAZCTk4O/v78heQKIioq6tbisrYmMjDS87tSpE66uruTk5NRJoBoSGxtLTEwMISEhDB06lEceeYTBgwc3WN7Z2RlnZ+dbivd6V2cS3mjMoBBCiP9nY2dNtwf8CBvYjvzs02Sn6ig9VsGvP5Xw608l+Ie60z3aD7/O7s2iwcbkBGrZsmVs27aNtLQ0HnjgAaNrW7ZsYeTIkXz++edMmjTJ4kHe7RRFMasbrSlptcZrdyiKgl6vB6h3PNX1H3KNRlOnbE1NTb3Pqu8fiDn/aCIiIsjPz2fjxo2kpqYyZswYoqOj+e677+otb6kuPFVV+fTTT5k4cSI2Ni1/c00hhLiTNBqF+yLacF9EG0qPnSM7Rcex7NPoDpejO1yOu68j4dF+dOzljZXW7NWYLMbkBGrFihW8/vrrdZIngAcffJDXXnuN5cuXSwJ1DwsNDUWn01FcXIyvry8AO3fuNCrTunVr4EoXl5ubG3BlEPn1amtr2bt3r6G1KTc3l7Nnz9KpU6d6n21jY1PvJIZWrVoxduxYxo4dy+jRoxk6dCjl5eW4u7vXKWupLryMjAx+++03/vjHP960rBBCiIZ5B7swdEpXzp2+yC9bj5Ozo4Ty4t/Z8vmv7Fx7jPFv3t9ki3KanED98ssvLFiwoMHrw4YNIykpySJBiZYpOjqakJAQJk2axMKFC6moqGDWrFlGZdq3b4+fnx+JiYnMnj2bvLw8Fi5cWOdeWq2WadOmkZSUhFarJS4ujj59+jTYfRcYGMgPP/xAbm4uHh4euLi4sHjxYnx8fAgPD0ej0fDtt9/i7e3d4CKdlurCW7p0Kffffz9hYWG3fS8hhBDg0tqe/xjTkd6PBHFoezEHthbh0sahSVc0N7ntq7y8/Ia/fXt5eXHmzBmLBFWfOXPm0LdvXxwcHOr9Aly2bJlhCvv1x9UxOlcHvl9/bNq0yeheGRkZ9OzZEzs7O4KDg1myZEmj/Vx3E41Gw5o1a6iqqqJ37948++yzzJkzx6iMVqtlxYoV/Prrr3Tv3p358+cze/bsOvdycHDg1VdfZfz48URFRWFvb8/KlSsbfPbkyZMJCQkhMjKS1q1bs2PHDpycnJg/fz6RkZH06tWLgoICNmzYYOhGbAznzp1j1apV0vokhBCNwNZBS8TgAJ6cHUXM06FNGovJe+FZWVlRWlpq6IK53smTJ/H19W20taASEhJwdXWlqKiIpUuXGq0jBHDx4kXOnTtndC42NpZLly6Rnp4OXEmggoKCSE1NpUuXLoZy7u7uhrEq+fn5hIWFMXnyZKZMmcKOHTt4/vnnWbFiBX/4wx9MilX2wjOmKApr1qyRAdXXuRc/C0II0ZyZsxeeWbPwYmNjsbWtfz2Gqqoq86I001tvvQVcaWmqj729Pfb29obXp0+fZsuWLSxdurROWQ8PD6Pp7tdasmQJ/v7+LFq0CIDOnTuzd+9e/vrXvzaYQFVVVRn9/BUVFab8SEIIIYRooUzuy3jqqado06YNLi4u9R5t2rRpVgPIP//8cxwcHBg9enSdayNGjKBNmzb069evzoysnTt31pnqPmTIEPbu3dvgbLG5c+ca1YWfn5/lfhAhhBBCNDsmt0BdXeG5pfj0008ZP368UauUk5MT7733Hv369UOj0bBu3TrGjh1LcnIyTz75JAClpaV1xnp5eXlRW1tLWVkZPj4+dZ71l7/8hfj4eMPriooKSaKuYc52MUIIIURL0KRbuSQmJhq65hqyZ88eowUVTbFz504OHz7M559/bnTe09OTl156yfA6MjKSM2fOsGDBAkMCBXXXGrqaADS0BpGtrW2DXZtCCCGEuPs0aQIVFxfHuHHjblgmMDDQ7Pv+4x//IDw8nJ49e960bJ8+ffjHP/5heO3t7U1paalRmVOnTmFtbY2Hh4fZsTREWmWEfAaEEKLlatIEytPTE09PT4ves7Kykm+++Ya5c+eaVD4rK8uoWy4qKor169cbldm8eTORkZF1VuG+FVfvceHCBaPuRXHvqa6uBq7McBVCCNGyNGkCZQ6dTkd5eTk6nY7Lly8bVq9u3749Tk7/v8Hg119/TW1tbb3bbSQnJ6PVaunRowcajYb169eTlJTE/PnzDWWmTp3K4sWLiY+PZ/LkyezcuZOlS5eyYsUKi/wcVlZWuLq6GtamcnBwaBZ7+og7S6/Xc/r0aRwcHLC2bjH/DIUQQvyfFvM/95tvvklycrLhdY8ePQDYunUrgwYNMpxfunQpo0aNMmwTcr3Zs2dTWFiIlZUVHTt25NNPPzUa/xQUFMSGDRt46aWX+OCDD/D19SUpKcnkNaBMcXUJhatJlLg3aTQa/P39JYEWQogWyOSFNIXpTF2I6/Llyw0ujSDufjY2No26KroQQgjzNMpCmsLyrKysZPyLEEII0QLJr79CCCGEEGaSBEoIIYQQwkySQAkhhBBCmEnGQDWCq+PyZVNhIYQQouW4+r1tyvw6SaAawfnz5wFkPzwhhBCiBTp//jwuLi43LCPLGDQCvV5PcXExzs7Ot7XGz9VNiY8fP37T6ZTi9khd31lS33eO1PWdI3V95zRWXauqyvnz5/H19b3pMjPSAtUINBoN7dq1s9j9WrVqJf8Y7xCp6ztL6vvOkbq+c6Su75zGqOubtTxdJYPIhRBCCCHMJAmUEEIIIYSZJIFqxmxtbUlISMDW1rapQ7nrSV3fWVLfd47U9Z0jdX3nNIe6lkHkQgghhBBmkhYoIYQQQggzSQIlhBBCCGEmSaCEEEIIIcwkCZQQQgghhJkkgWrGPvzwQ4KCgrCzs6Nnz578+OOPTR1Sizd37lx69eqFs7Mzbdq0YeTIkeTm5hqVUVWVxMREfH19sbe3Z9CgQRw6dKiJIr47zJ07F0VRmDFjhuGc1LNlnThxgieffBIPDw8cHBwIDw8nMzPTcF3q2zJqa2t54403CAoKwt7enuDgYN5++230er2hjNT1rdm2bRuPPvoovr6+KIrC2rVrja6bUq9VVVVMmzYNT09PHB0dGTFiBEVFRY0TsCqapZUrV6parVb95JNP1MOHD6vTp09XHR0d1cLCwqYOrUUbMmSI+tlnn6kHDx5Us7Oz1Ycfflj19/dXKysrDWXmzZunOjs7q6tWrVIPHDigjh07VvXx8VErKiqaMPKWa/fu3WpgYKDarVs3dfr06YbzUs+WU15ergYEBKixsbHqrl271Pz8fDU1NVX97bffDGWkvi1j9uzZqoeHh/r999+r+fn56rfffqs6OTmpixYtMpSRur41GzZsUGfNmqWuWrVKBdQ1a9YYXTelXqdOnaq2bdtWTUlJUfft26c+8MADavfu3dXa2lqLxysJVDPVu3dvderUqUbnOnXqpL722mtNFNHd6dSpUyqgZmRkqKqqqnq9XvX29lbnzZtnKHPp0iXVxcVFXbJkSVOF2WKdP39e7dChg5qSkqIOHDjQkEBJPVvWq6++qvbv37/B61LflvPwww+rzzzzjNG5UaNGqU8++aSqqlLXlnJ9AmVKvZ49e1bVarXqypUrDWVOnDihajQaddOmTRaPUbrwmqHq6moyMzMZPHiw0fnBgwfz008/NVFUd6dz584B4O7uDkB+fj6lpaVGdW9ra8vAgQOl7m/BCy+8wMMPP0x0dLTRealny1q3bh2RkZE8/vjjtGnThh49evDJJ58Yrkt9W07//v1JS0vjyJEjAOzfv5/t27czfPhwQOq6sZhSr5mZmdTU1BiV8fX1JSwsrFHqXjYTbobKysq4fPkyXl5eRue9vLwoLS1toqjuPqqqEh8fT//+/QkLCwMw1G99dV9YWHjHY2zJVq5cyb59+9izZ0+da1LPlnXs2DE++ugj4uPjef3119m9ezcvvvgitra2TJo0Serbgl599VXOnTtHp06dsLKy4vLly8yZM4cnnngCkM92YzGlXktLS7GxscHNza1Omcb47pQEqhlTFMXotaqqdc6JWxcXF8cvv/zC9u3b61yTur89x48fZ/r06WzevBk7O7sGy0k9W4ZerycyMpJ3330XgB49enDo0CE++ugjJk2aZCgn9X37vv76a7788ku++uorunTpQnZ2NjNmzMDX15ennnrKUE7qunHcSr02Vt1LF14z5OnpiZWVVZ2M+dSpU3Wyb3Frpk2bxrp169i6dSvt2rUznPf29gaQur9NmZmZnDp1ip49e2JtbY21tTUZGRkkJSVhbW1tqEupZ8vw8fEhNDTU6Fznzp3R6XSAfK4t6eWXX+a1115j3LhxdO3alYkTJ/LSSy8xd+5cQOq6sZhSr97e3lRXV3PmzJkGy1iSJFDNkI2NDT179iQlJcXofEpKCn379m2iqO4OqqoSFxfH6tWr2bJlC0FBQUbXg4KC8Pb2Nqr76upqMjIypO7N8NBDD3HgwAGys7MNR2RkJBMmTCA7O5vg4GCpZwvq169fneU4jhw5QkBAACCfa0u6cOECGo3xV6eVlZVhGQOp68ZhSr327NkTrVZrVKakpISDBw82Tt1bfFi6sIiryxgsXbpUPXz4sDpjxgzV0dFRLSgoaOrQWrTnnntOdXFxUdPT09WSkhLDceHCBUOZefPmqS4uLurq1avVAwcOqE888YRMQbaAa2fhqarUsyXt3r1btba2VufMmaPm5eWpy5cvVx0cHNQvv/zSUEbq2zKeeuoptW3btoZlDFavXq16enqqr7zyiqGM1PWtOX/+vJqVlaVmZWWpgPree++pWVlZhuV7TKnXqVOnqu3atVNTU1PVffv2qQ8++KAsY3Av+uCDD9SAgADVxsZGjYiIMEy1F7cOqPf47LPPDGX0er2akJCgent7q7a2tuqAAQPUAwcONF3Qd4nrEyipZ8tav369GhYWptra2qqdOnVS//73vxtdl/q2jIqKCnX69Omqv7+/amdnpwYHB6uzZs1Sq6qqDGWkrm/N1q1b6/3/+amnnlJV1bR6vXjxohoXF6e6u7ur9vb26iOPPKLqdLpGiVdRVVW1fLuWEEIIIcTdS8ZACSGEEEKYSRIoIYQQQggzSQIlhBBCCGEmSaCEEEIIIcwkCZQQQgghhJkkgRJCCCGEMJMkUEIIIYQQZpIESgghhBDCTJJACSGEhQQGBrJo0aKmDkMIcQdIAiWEaJFiY2MZOXIkAIMGDWLGjBl37NnLli3D1dW1zvk9e/bwpz/96Y7FIYRoOtZNHYAQQjQX1dXV2NjY3PL7W7dubcFohBDNmbRACSFatNjYWDIyMnj//fdRFAVFUSgoKADg8OHDDB8+HCcnJ7y8vJg4cSJlZWWG9w4aNIi4uDji4+Px9PQkJiYGgPfee4+uXbvi6OiIn58fzz//PJWVlQCkp6fz9NNPc+7cOcPzEhMTgbpdeDqdjv/8z//EycmJVq1aMWbMGE6ePGm4npiYSHh4OF988QWBgYG4uLgwbtw4zp8/byjz3Xff0bVrV+zt7fHw8CA6Oprff/+9kWpTCGEqSaCEEC3a+++/T1RUFJMnT6akpISSkhL8/PwoKSlh4MCBhIeHs3fvXjZt2sTJkycZM2aM0fuTk5OxtrZmx44dfPzxxwBoNBqSkpI4ePAgycnJbNmyhVdeeQWAvn37smjRIlq1amV43syZM+vEpaoqI0eOpLy8nIyMDFJSUjh69Chjx441Knf06FHWrl3L999/z/fff09GRgbz5s0DoKSkhCeeeIJnnnmGnJwc0tPTGTVqFLIHvBBNT7rwhBAtmouLCzY2Njg4OODt7W04/9FHHxEREcG7775rOPfpp5/i5+fHkSNH6NixIwDt27dnwYIFRve8djxVUFAQ77zzDs899xwffvghNjY2uLi4oCiK0fOul5qayi+//EJ+fj5+fn4AfPHFF3Tp0oU9e/bQq1cvAPR6PcuWLcPZ2RmAiRMnkpaWxpw5cygpKaG2tpZRo0YREBAAQNeuXW+jtoQQliItUEKIu1JmZiZbt27FycnJcHTq1Am40upzVWRkZJ33bt26lZiYGNq2bYuzszOTJk3i3//+t1ldZzk5Ofj5+RmSJ4DQ0FBcXV3JyckxnAsMDDQkTwA+Pj6cOnUKgO7du/PQQw/RtWtXHn/8cT755BPOnDljeiUIIRqNJFBCiLuSXq/n0UcfJTs72+jIy8tjwIABhnKOjo5G7yssLGT48OGEhYWxatUqMjMz+eCDDwCoqakx+fmqqqIoyk3Pa7Vao+uKoqDX6wGwsrIiJSWFjRs3Ehoayt/+9jdCQkLIz883OQ4hROOQBEoI0eLZ2Nhw+fJlo3MREREcOnSIwMBA2rdvb3RcnzRda+/evdTW1rJw4UL69OlDx44dKS4uvunzrhcaGopOp+P48eOGc4cPH+bcuXN07tzZ5J9NURT69evHW2+9RVZWFjY2NqxZs8bk9wshGockUEKIFi8wMJBdu3ZRUFBAWVkZer2eF154gfLycp544gl2797NsWPH2Lx5M88888wNk5/77ruP2tpa/va3v3Hs2DG++OILlixZUud5lZWVpKWlUVZWxoULF+rcJzo6mm7dujFhwgT27dvH7t27mTRpEgMHDqy327A+u3bt4t1332Xv3r3odDpWr17N6dOnzUrAhBCNQxIoIUSLN3PmTKysrAgNDaV169bodDp8fX3ZsWMHly9fZsiQIYSFhTF9+nRcXFzQaBr+ry88PJz33nuP+fPnExYWxvLly5k7d65Rmb59+zJ16lTGjh1L69at6wxChystR2vXrsXNzY0BAwYQHR1NcHAwX3/9tck/V6tWrdi2bRvDhw+nY8eOvPHGGyxcuJBhw4aZXjlCiEahqDIfVgghhBDCLNICJYQQQghhJkmghBBCCCHMJAmUEEIIIYSZJIESQgghhDCTJFBCCCGEEGaSBEoIIYQQwkySQAkhhBBCmEkSKCGEEEIIM0kCJYQQQghhJkmghBBCCCHMJAmUEEIIIYSZ/heQeT+v0AVbvgAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "for q in range(len(nqubits)):\n", + " plt.plot(differences[q,:],label= 'nqubits = {}'.format(nqubits[q]))\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel('Difference in analytic and numerical loss function')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.title('Normalized difference')\n", + "for q in range(len(nqubits)):\n", + " plt.plot(differences[q,:]/loss_max[q],label= 'nqubits = {}'.format(nqubits[q]))\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel('Difference in analytic and numerical loss function')\n", + "plt.legend()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Difference in optimization moments\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-21 14:44:56]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 7\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCostFunction.least_squares\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "dbi_eval = deepcopy(dbi)\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "step = 1e-2\n", + "iterations = 100\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "flows = 100\n", + "dbi_eval = deepcopy(dbi)\n", + "dbi_eval2 = deepcopy(dbi)\n", + "d_not_optimized = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "step = 1e-2\n", + "iterations = 100\n", + "d_optimized, loss, grad, diags = gradient_ascent(dbi_eval, d,step, iterations,analytic=False)\n", + "off_diagonal_norm = np.empty((flows+1,3))\n", + "off_diagonal_norm[0] = dbi_eval.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", + " dbi_eval(step_poly,d=d)\n", + " step_poly = dbi_eval2.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_not_optimized)\n", + " dbi_eval2(step_poly,d=d_not_optimized)\n", + " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", + " off_diagonal_norm[i+1,1] = dbi_eval2.off_diagonal_norm\n", + "\n", + "dbi_eval3 = deepcopy(dbi)\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "for i in range(flows):\n", + " d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + " d_opt, loss, grad, diags = gradient_ascent(dbi_eval3, d,step, 20,analytic=False)\n", + " step_poly = dbi_eval3.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt)\n", + " dbi_eval3(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,2] = dbi_eval3.off_diagonal_norm " + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(off_diagonal_norm[:,0],label= 'Optimized at beginning')\n", + "plt.plot(off_diagonal_norm[:,1],label= 'Not optimized')\n", + "plt.plot(off_diagonal_norm[:,2],label= 'Optimized at each step')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.5|INFO|2024-03-21 16:37:52]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"qibojit\", \"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 7\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCostFunction.least_squares\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "end = np.linspace(1.1,10*2**nqubits,100)\n", + "loss = np.empty(100)\n", + "spacing = np.empty(100)\n", + "for i in range(100):\n", + " dbi_eval = deepcopy(dbi)\n", + " d = np.diag(np.linspace(1,end[i],2**nqubits))\n", + " spacing[i] = d[1,1] - d[0,0]\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n = 3)\n", + " dbi_eval(step_poly,d=d)\n", + " loss[i] = dbi_eval.off_diagonal_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Loss function')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(spacing,loss)\n", + "plt.xlabel('Spacing')\n", + "plt.ylabel('Loss function')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def d_poly_spacing(nqubits,degree):\n", + " d = np.empty(2**nqubits)\n", + " for i in range(len(d)):\n", + " d[i] = 1 + i**degree\n", + " return np.diag(d)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "degrees = np.linspace(0.1,5,100)\n", + "nqubits = [3,4,5,7]\n", + "h = 3.0\n", + "\n", + "loss = np.empty((100,len(nqubits)))\n", + "best_degree = np.empty(len(nqubits))\n", + "for q in range(len(nqubits)):\n", + " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", + " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + " for i in range(100):\n", + " dbi_eval = deepcopy(dbi)\n", + " d = d_poly_spacing(nqubits[q],degrees[i])\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n = 3)\n", + " dbi_eval(step_poly,d=d)\n", + " loss[i,q] = dbi_eval.off_diagonal_norm\n", + " best_degree[q] = degrees[np.argmin(loss[:,q])]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.18888889 1.13939394 1.13939394 1.13939394]\n" + ] + }, + { + "data": { + "image/png": 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ZgHBfVxzNrcSpAhXLDBER/a3c8jpsOVGIzScKkJZT2eq+CF8XjIjywYhoX0QrXa368tGNYpkxgSi/y2UmX4W7eyqljkNERGboYmktNp0owObjhTh+qfVmqwlBbhgZ7YuR0b4I8TTP1UdSYpkxgZZJwBmcBExERH9ysbQWG48XYOOxglYLRWQC0DfUA6NifDEiyhe+CvPceddcsMyYQMskYK5oIiKi3PI6/HysABuP5+PEpT9+LshlAvp38cBdMX4YHuUDz07SH5psKVhmTCDc1xWCABSp1CitUfN/UCKiDqZI1YCNxwrw07F8HPnTHJiWAnN3Tz+MiPLtUCuQjIllxgQ62dsgxMMZWaW1yChQ4bZuXlJHIiKidlZV34QtJwqwPj0fBy6UoWW/fUEA+oV54O6eStzZwxedWWDajGXGRKL8XJFVWotT+SwzRETWSq3R4peMYqxLv4Rdp0vQqP3jGIGEIDfcE6vEXTF+8HblHBhjYpkxkSilKzYeL+AkYCIiK6PTiUjJrsDaI3nYeKwAqgaN/r7uPp2QGOePe2KVCOzsJGFK68YyYyKRnARMRGRVLpbW4se0PPx45BLyKur1t/spHHBPnBLj4v0R4Wv6A447IpYZE4nyUwAAzpfUoqFJa3WHfBERdQSqhiZsPFaAH1LzkJJdob+9k70NRvXwxbgEf9wa6gEZj64xKZYZE/FxtUdnZzuU1zYis6gaPQPcpI5EREQ3QKcTcfBCGf6XmofNJwrQ0NQ8D0YmAAO7eeHeBH+MiPKFox1/SZUKy4yJCIKAKD9X7DtXiowCFcsMEZGZu1RZj/+l5OL71LxWl5G6enfChF4BGBfvDx9O5DULLDMmFKVsLjOn8jlvhojIHDVqdNiZUYRvD+diz9kS/XJqF3sbjIlT4v7egYgNUPA8JDPDMmNCUX7NE8E4CZiIyLxkldbi20M5+D41D2W1jfrb+4V54ME+gRgZ7cu5jmaMZcaEIv1azmiqhk4ncoIYEZGEGjU6bD1ZiG8O5WD/+TL97d4u9rivdwDu7x2IYA8e6mgJWGZMKMzLGXY2MtSoNcitqONfEiIiCeSW12H1oRz8LyUXpTXNozCCAAwJ98bEPkEYEu4FG7lM4pRkCJYZE7KVyxDu44Ljl6pwKl/FMkNEZCJanYg9mSX46mA2dp0p1s+F8XG1xwO9A/FAnyD4uzlKG5JuGsuMiUX6NZeZjAIVRsX4SR2HiMiqVdY14ruUXHx1MBu55X+sSLqtmycm9Q3GsEhvjsJYAZYZE/tjEnC1xEmIiKzXqXwVvjxwEevSL+n3hXF1sMF9vQMxqW8Qwrw6SZyQjIllxsT+mATMFU1ERMak1YnYfqoIn/2WhUNZ5frbI/1ckdQvGIlx/tzYzkqxzJhYpLK5zFyqrEdVXRMUTrYSJyIismyqhiZ8dzgXX+y/qN/cTi4TcGcPX0zpH4Lewe7cF8bKscyYmKuDLQLcHZFXUY9TBSr06+IhdSQiIouUW16Hz37LwneHc1HbqAUAuDnZ4qE+QZjcLxh+Ck7o7ShYZiQQ5eeKvIp6ZLDMEBEZLDW7Ap/uu4AtJwqhu7wqqZt3J0wdGIqxvJTUIbHMSCDSzxXbThVxJ2Aiohuk04nYeboYn+w+3+q06tu6eeKx28JwezdPXkrqwFhmJBB1ed4Mz2giIro+tUaL9Ufy8cme8zhfUgsAsJPLkBinxKO3hSLC11XihGQOWGYk0LI8+1xxDRo1OtjZcI8DIqI/q1FrsPr3bPx3bxaKq9UAABcHG/zfrcF4pH8IvHlaNf0Jy4wEAtwd4WJvg2q1BudLavTLtYmIOrry2kZ88VsWVh7IRlV9EwDA19UBjw4MxYN9AuHiwBWgdCWWGQkIgoBIP1cculiOjAIVywwRdXhFqgb8e88FrP49B/VNzSuTwjyd8dSgLhgb788RbLoulhmJRCmby8ypfBXGJ0idhohIGpcq67Hi1/NYk5KLRk3zTr09/F0xbXBXjIz2hVzGSb3091hmJBLp5wIAyCjkJGAi6nhyy+vw4a5z+CEtD03a5vXVvYPd8czQrhjU3Ysrk8ggLDMSifJTAGhe0SSKIv/iElGH0FJivk/Ng+byJjH9u3jg2aHdcGtYZ/5bSDeFZUYi3Xw6QS4TUFHXhCKVGr4KzswnIuuVV9FcYv6X8keJua2bJ6bf0Q29QzpLnI4sHcuMRBxs5eji5YzMohqcKqhimSEiq1SkasAHv5zDt4dz9JeTbuvmiRnDuqFXMEsMGQfLjIQi/VyRWVSDjIJqDI3wkToOEZHRlNWosWL3eXx5IBvqyxN7B3T1wPPDunMkhoyOZUZCUX6uWJ+ez52AichqVDc04T97s/Dp3gv6wx97B7vjhRHhPIuO2g3LjIRa9pfJ4BlNRGThGpq0WHUwGx/uOoeKuubN7mL8FXhhRHeuTqJ2xzIjoZYyk1VWi7pGDZzs+MdBRJZFqxPxY1oe/rk9E/lVDQCAMC9nzBoRjjt7+LLEkEnwp6eEvFzs4eVij5JqNU4XViMhyF3qSEREN0QURezOLMFbm0/jdGE1gOZjB54f3g33JgTARs4de8l0WGYkFuXnit3VJTiVr2KZISKLcOJSFZI3Z+C3c2UAAFcHGzwztCse7hcCB1u5xOmoI2KZkViknyt2Z5Zw3gwRmb2Cqnos3XoGP6ZdAgDYyWVI6h+Mp4d0hZuTncTpqCNjmZFYlLJ53sxJrmgiIjNV16jBJ7sv4JM959HQ1LzMOjFOiRdHhCOws5PE6YhYZiQXfbnMnC5UQaPV8TozEZkNnU7E2iOX8PbW0yhSqQE0L7Oed3cUYgPdpA1H9CcsMxIL9XBGJ3sb1Kg1OFdSgwhfV6kjERHhSE4FFvx0CkdzKwEAAe6OmDsqEnfFcIUSmR+WGYnJZAKilK44lFWO43lVLDNEJKliVQOWbDmDH9LyAADOdnI8M7QbHhnAyb1kvlhmzECMvwKHsspx4lIV7usdKHUcIuqAmrQ6fP5bFv6146x+5957EwIw+85weLvy7DgybywzZqBngAIAcOxSlcRJiKgj2n+uFPM3nMS54hoAQGygGxaMiUI8t4sgC8EyYwZ6+DeXmYwCTgImItMprGrA4k0Z+OloPgDAw9kOs0dFYEJCAGQyzoshy8EyYwY4CZiITEmj1WHlgWws23YGtY1ayATg/24NxgvDw6FwspU6HpHBWGbMgEwmIFrpit85CZiI2ll6biVeWXtcv7dVfJAb3kjsoR8hJrJELDNmIsZf0VxmOAmYiNqBqqEJ72w9g68OZkMUm48gmDMqEg/eEshLSmTxWGbMRMzlScDHOQmYiIxsy4lCzF9/AsXVzRvfjYv3xyujI+HZyV7iZETGwTJjJmI4CZiIjKxI1YDX1p/ElpOFAIBQT2csHtsD/bt6SpyMyLhYZsxECCcBE5GR6HQi1qTk4s1NGahu0MBGJuDJQWF4dmg3bnxHVollxkz8eRLwMU4CJqKblFNWh9k/HMOBC2UAgNgABd66tyci/fhvClkvXsswIy2Xmk5w3gwRGUinE/HFb1kYuXwPDlwog4OtDK+OjsSP0wawyJDV48iMGeEkYCK6GVmltZj9/TEculgOAOgb2hlvT+iJYA9niZMRmQbLjBlpGZk5lc9JwET093Q6EV8euIi3tpxGQ5MOTnZyzB0VgUl9g7ncmjoUlhkz8udJwGeLazg0TETXlFdRh5e+P4b955vnxvTv4oEl9/ZEYGcniZMRmR7LjBmRyQT08HfFwQvNm+exzBDRX4miiP+l5mHhT6dQo9bAwVaGuaMiMflWjsZQx8XrGGaGk4CJ6FrKatR44qtUvPT9MdSoNUgIcsPm6bcjqX8Iiwx1aByZMTMt56Mcy2OZIaI//HqmGLO+P4aSajVs5QKeH94dT97eBXKWGCKWGXPTM8ANAHcCJqJmDU1aJG/KwMoD2QCAbt6dsPzBOEQreTAkUQtJf1JqNBq8+uqrCA0NhaOjI8LCwrBw4ULodLpWj8vIyMA999wDhUIBFxcX3HrrrcjJyZEodfsK7uwEF3sbqDU6nC2ukToOEUkoo0CFMe/v0xeZKf1D8NOzA1lkiP5C0pGZJUuWYMWKFVi5ciWio6ORkpKCRx55BAqFAtOnTwcAnD9/HgMHDsSjjz6K119/HQqFAhkZGXBwcJAyeruRyQREt0wCzuMkYKKOSBRFfHUwG4s2ZqBRo4OXiz2WTuiJweHeUkcjMkuSlpkDBw4gMTERo0ePBgCEhITgm2++QUpKiv4xr7zyCu666y68/fbb+tvCwsJMntWUYgPdcPBCOY7kVuL+WwKljkNEJlRR24iXfjiG7aeKAABDwr3wzn2x8OAJ10TXJOllpoEDB2Lnzp3IzMwEABw9ehT79u3DXXfdBQDQ6XTYuHEjunfvjpEjR8Lb2xt9+/bFunXrrvmcarUaKpWq1YeliQ90AwCk51ZKmoOITOv3C2UY9a+92H6qCLZyAfPujsJnU25hkSH6G5KWmdmzZ2PixImIiIiAra0t4uPjMWPGDEycOBEAUFxcjJqaGrz11lu48847sW3bNowbNw7jx4/H7t27r/qcycnJUCgU+o/AQMsb2YgLdAcAnClUoVatkTgNEbU3nU7Eh7vOYeJ/DqJQ1YAwT2esnTYAjw4MhSBwtRLR35H0MtOaNWuwatUqrF69GtHR0UhPT8eMGTOgVCqRlJSknwicmJiI559/HgAQFxeH/fv3Y8WKFRg0aNAVzzl37lzMnDlT/7lKpbK4QuOrcICfwgEFVQ04fqkKt4Z5SB2JiNpJeW0jnl+Tjt2ZJQCAcfH+WDS2B5ztudiU6EZJ+rdl1qxZmDNnDh588EEAQExMDLKzs5GcnIykpCR4enrCxsYGUVFRrb4uMjIS+/btu+pz2tvbw97e8odk4wLdUFBViPTcSpYZIiuVcrEcz6w+gkJVA+xtZFiYGI37ewdyNIbIQJKWmbq6Oshkra90yeVy/YiMnZ0dbrnlFpw5c6bVYzIzMxEcHGyynFKIC3TD5hOFSM+plDoKERmZKIr4dF8WkjefhlYnIszLGR9NSkCEL1cvEt0MScvMmDFjsHjxYgQFBSE6OhpHjhzBsmXLMHXqVP1jZs2ahQceeAC33347hgwZgi1btuCnn37Cr7/+Kl1wE4i7PAn4SG6FtEGIyKhq1BrM/v4YNh4vAADcE6vEm+Nj0ImXlYhumiCKoijVi1dXV2PevHlYu3YtiouLoVQqMXHiRMyfPx92dnb6x3322WdITk5GXl4ewsPD8frrryMxMfGGXkOlUkGhUKCqqgqurpbzW09dowYxC7ZBqxNxYO5Q+CkcpY5ERG10rrgaT36VivMltfrVSpNvDeZlJaKrMOTnt6RlxhQstcwAwF3/2otTBSp8PCkBo2L8pI5DRG2w6XgBZv3vKGobtfB1dcCHkxLQK9hd6lhEZsuQn988+MeMxQW5AeB+M0SWTKsT8faW05j2dRpqG7XoF+aBn58byCJDZEQsM2ZMP2+Gk4CJLFJVfRMeXXkYH/16HgDwxO1h+OrRPvDkJnhERsUZZ2asZSfg45eqeII2kYU5W1SNJ75KRVZpLRxsZVhyb08kxvlLHYvIKrHMmLEuXp3gYm+DarUGZ4qqeVIukYXYmVGE5745gtpGLfzdHPHJ5F7o4c+/v0Tthb/qmzGZTEAsz2kishiiKOKT3efx2JcpqG3Uom9oZ2x4ZgCLDFE7Y5kxc5w3Q2QZ1BotXvzfMSRvPg1RBB7qG4RVj/XlIZFEJsDLTGYujiMzRGavtEaNp75KRUp2BWQCMP/uKCT1D+H+MUQmclNlRqfT4dy5cyguLtYfPdDi9ttvN0owatayPPt8SQ1UDU1wdbCVNhARtXK2qBqPfHEYeRX1cHGwwYcPJeD27l5SxyLqUAwuMwcPHsRDDz2E7Oxs/HW/PUEQoNVqjRaOAM9O9gjs7Ijc8nocy63CwG6eUkciost+O1eKp1alorpBgxAPJ/w36RZ09e4kdSyiDsfgOTNPPfUUevfujRMnTqC8vBwVFRX6j/Ly8vbI2OHFBTZvrnUkh+c0EZmL7w7nIumzQ6hu0KB3sDt+nDaARYZIIgaPzJw9exbff/89unbt2h556CriAt3w09F8zpshMgM6nYh3t5/Bh7uaN8K7J1aJtyf0hIOtXOJkRB2XwSMzffv2xblz59ojC13DnycBW/lRWkRmrVGjw8zv0vVF5pkhXbH8gTgWGSKJGTwy8+yzz+KFF15AYWEhYmJiYGvbekJqz549jRaOmvXwd4WdXIay2kbklNch2MNZ6khEHU6NWoOnvkrFvnOlkMsEJI+Lwf23BEodi4hwE2Xm3nvvBQBMnTpVf5sgCBBFkROA24m9jRzR/q44klOJ1OwKlhkiEyuubsAjnx/GyXwVnOzk+HBSAoaEe0sdi4guM7jMZGVltUcO+hu9gtxxJKcSaTkVGJ8QIHUcog7jfEkNkj47hLyKenh2ssNnU25BzwA3qWMR0Z8YXGaCg4PbIwf9jYRgd2BfFlKzK6WOQtRhHM2txJTPD6GirgkhHk5YObUPR0aJzNBNbZp3/vx5LF++HBkZGRAEAZGRkZg+fTq6dOli7Hx0Wa/g5uXZZwpVqFFr0MmemzcTtaffzpXiictnLPUMUOCzKbfAk0cTEJklg1czbd26FVFRUTh06BB69uyJHj164Pfff0d0dDS2b9/eHhkJgI+rA/zdHKETm39bJKL2s+VEAR75/DBqG7UY0NUDqx+/lUWGyIwZ/Ov9nDlz8Pzzz+Ott9664vbZs2dj+PDhRgtHrSUEu+NSZT3SsiswoCt3AiZqD98eysHLa49DJwKjevhi+YNxsLfh0msic2bwyExGRgYeffTRK26fOnUqTp06ZZRQdHUJl89pSuNOwETt4t97zmPOj81F5sFbAvHBQwksMkQWwOAy4+XlhfT09CtuT09Ph7c3lyq2p5Z5M2k5ldDpuHkekbGIooh/bs/Em5tOAwCeGtQFyeNjIJfx1GsiS2DwZabHH38cTzzxBC5cuID+/ftDEATs27cPS5YswQsvvNAeGemySD9XONjKUFXfhAultTwHhsgIRFFE8ubT+PeeCwCAWSPD8fQQHtdCZEkMLjPz5s2Di4sL3n33XcydOxcAoFQqsWDBAjz33HNGD0h/sJXL0DPADYeyypGWXcEyQ9RGOp2I+RtOYNXBHADAvLuj8OjAUIlTEZGhDL7MJAgCnn/+eeTl5aGqqgpVVVXIy8vD9OnTIQgckm1vCUEtl5o4b4aoLbQ6ES/9cAyrDuZAEIDk8TEsMkQWqk2blbi4uBgrB92glnkzqdksM0Q3S6sT8cJ36ViXng+5TMC798VibLy/1LGI6CbdUJlJSEjAzp074e7ujvj4+OuOwKSlpRktHF0p/vKKprPFNaiqb4LC0fb6X0BErWi0Osz87ig2HM2HjUzA+xPjMSrGT+pYRNQGN1RmEhMTYW9vr/9vXk6SjmcnewR7OCG7rA7puZUY1N1L6khEFkOj1WHGmnT8fKwANjIBHzyUgDt7+Eodi4ja6IbKzGuvvab/7wULFrRXFrpBvYLckV1Wh9TsCpYZohvUpNVhxrfp2Hi8ALZyAR8+lIAR0SwyRNbA4AnAYWFhKCsru+L2yspKhIWFGSUUXV/85XkzRzgJmOiGaP5UZOzkMnw8qReLDJEVMbjMXLx4EVqt9orb1Wo18vLyjBKKrq9XUEuZqYSWm+cRXZdWJ+KF/x3VF5kVkxMwLMpH6lhEZEQ3vJppw4YN+v/eunUrFAqF/nOtVoudO3ciNJTLGk0h3NcFznZy1Kg1OFtcjQhfV6kjEZklnU7E7B+OYX1682TfDyclYGgEiwyRtbnhMjN27FgAzfvMJCUltbrP1tYWISEhePfdd40ajq5OLhMQF+SG386VITW7gmWG6Cp0OhGvrDuO71PzIL+8amk4R2SIrNINX2bS6XTQ6XQICgpCcXGx/nOdTge1Wo0zZ87g7rvvbs+s9Cctl5rSsiulDUJkhkRRxOs/ncQ3h3IhE4Bl98dy+TWRFTN407ysrKz2yEEGSgjmTsBE17JkyxmsPJANQQCWTohFYhw3xCOyZgZPAH7uuefw3nvvXXH7Bx98gBkzZhgjE92A+MDmMpNVWouyGrXEaYjMx4e7zmHF7vMAgMVjY3BvrwCJExFRezO4zPzwww8YMGDAFbf3798f33//vVFC0d9TONmi2+WDJtNyKqUNQ2QmvjxwEUu3ngEAvHJXJB7qGyRxIiIyBYPLTFlZWauVTC1cXV1RWlpqlFB0Y3hOE9EffkjNw/z1JwEAzw3tisdv575XRB2FwWWma9eu2LJlyxW3b968mZvmmZh+3gzLDHVwW08WYtb3RwEAU/qH4Pnh3SVORESmZPAE4JkzZ+KZZ55BSUkJhg4dCgDYuXMn3n33XSxfvtzY+eg6WkZmjuZVolGjg52Nwd2UyOIdvFCGZ785Ap0ITOgVgPl3R/H8OKIOxuAyM3XqVKjVaixevBhvvPEGACAkJAQff/wxHn74YaMHpGsL83SGm5MtKuuacKpAhbhAN6kjEZnUqXwVHl+ZgkaNDiOifPDW+BjIZCwyRB3NTf0q/49//AN5eXkoKiqCSqXChQsXWGQkIAiCfr8ZzpuhjianrA5Jnx9CtVqDPiGd8d7EeNjIOTpJ1BG16W++l5cXOnXqZKwsdBM4b4Y6otIaNR7+7HeUVKsR4euC/yT1hoOtXOpYRCQRg8tMUVERJk+eDKVSCRsbG8jl8lYfZFpc0UQdTY1agymfH8LFsjoEuDti5dQ+UDjaSh2LiCRk8JyZKVOmICcnB/PmzYOfnx8n2kksNsANcpmAQlUD8ivroXRzlDoSUbtp0uow7es0nLikQmdnO3w5tQ98XB2kjkVEEjO4zOzbtw979+5FXFxcO8QhQznayRGtdMWxvCqkZlewzJDVEkURr649gT2ZJXCwleGzKbcgzIuXuYnoJi4zBQYGQhTF9shCNymBk4CpA3j/l3NYk9J8cOQHExO4eo+I9AwuM8uXL8ecOXNw8eLFdohDN6MXD50kK/d9ah6Wbc8EALx+TzSGRflInIiIzInBl5keeOAB1NXVoUuXLnBycoKtbeuJd+Xl5UYLRzemZUXTyXwV6ho1cLIz+I+VyGz9dq4Uc344BgB4clAYJvcLkTYQEZkdg3/qcZdf86NUOMDX1QGFqgYcy6vCrWEeUkciMoqzRdV46qtUaHQixsQqMXtkhNSRiMgMGVxmkpKS2iMHtYEgCOgV7I6NxwuQml3BMkNWoaxGjakrD6NarcEtIe54576e3N2XiK7K4DKTk5Nz3fuDgoJuOgzdvITLZYab55E1aGjS4omvUpFbXo9gDyd8Mrk37G24jxURXZ3BZSYkJOS6e8totdo2BaKbo988L6cCoihy/x+yWKIoYvYPx5CaXQEXBxt8mnQLOjvbSR2LiMyYwWXmyJEjrT5vamrCkSNHsGzZMixevNhowcgwUX6usLeRobKuCRdKa9GF+2+QhXpv5zmsT8+HXCbg40m90NWb/y8T0fUZXGZiY2OvuK13795QKpVYunQpxo8fb5RgZBg7GxliA9xw6GI5UrMrWGbIIv18LB//3NG8BPuNxB4Y2M1T4kREZAmMdsRs9+7dcfjwYWM9Hd2E+GA3AMAR7jdDFujEpSq8+L+jAIBHB4biob6cf0dEN8bgkRmVStXqc1EUUVBQgAULFqBbt25GC0aG68WdgMlClVSr8cSXKWho0mFQdy+8fFek1JGIyIIYXGbc3NyumFwqiiICAwPx7bffGi0YGa5l87zMohpU1TfxJGGyCI0aHf6xKhX5VQ0I83TGexPjIecSbCIygMFlZteuXa0+l8lk8PLyQteuXWFjw51npeTZyR4hHk64WFaHIzkVGBzuLXUkousSRRHz159AyuWVS/9J6s0STkQGu6H2kZCQgJ07d8Ld3R27d+/Giy++CCcnp/bORjchIcgdF8vqkJZTyTJDZu/LA9n49nAuBAF4b2I8J64T0U25oQnAGRkZqK2tBQC8/vrr+v8m89NyqYmb55G5O3ihDAt/PgUAmDsqAkNYvonoJt3QyExcXBweeeQRDBw4EKIoYunSpejU6eq/Qc2fP9+oAckwLZvnHcmpgFYncu4BmaXCqgY8szoNWp2IxDglHr8tTOpIRGTBbqjMfPHFF3jttdfw888/QxAEbN68+arzYwRBYJmRWHcfF3Syt0GNWoMzhdWIUrpKHYmoFbVGi6dWpaK0phERvi54a3xP7lhNRG1yQ2UmPDxcv1JJJpNh586d8PbmkLA5kssExAW6Yd+5UqTlVLDMkNl5/adTSM+thKuDDf49uTcc7XjmEhG1jcGb5ul0OhYZM8d5M2Su1hzOwerfcyAIwL8mxiPIgwsJiKjtjLYDMJmPPx86SWQujuVVYt76kwCAmcO6c8IvERkNy4wVigt0gyAA2WV1KK1RSx2HCJV1jfjHqjQ0anQYFumDp4d0lToSEVkRlhkrpHC0RbfLJw3zUhNJTacTMfO7o7hUWY9gDye8e38sZFxlR0RGJGmZ0Wg0ePXVVxEaGgpHR0eEhYVh4cKF0Ol0V338k08+CUEQsHz5ctMGtUC81ETmYsWe8/jldDHsbGT4aFICd/glIqMz+PyB3NxcCIKAgIAAAMChQ4ewevVqREVF4YknnjDouZYsWYIVK1Zg5cqViI6ORkpKCh555BEoFApMnz691WPXrVuH33//HUql0tDIHVJCkDu+OZTLkRmS1IHzZXhn6xkAwBuJ0YhWKiRORETWyOCRmYceekh/PlNhYSGGDx+OQ4cO4eWXX8bChQsNeq4DBw4gMTERo0ePRkhICCZMmIARI0YgJSWl1eMuXbqEZ555Bl9//TVsbflb3Y1oGZk5lleFRs3VR7qI2lOxqgHPfnMEOhG4NyEA9/cOlDoSEVkpg8vMiRMn0KdPHwDAd999hx49emD//v1YvXo1vvjiC4Oea+DAgdi5cycyMzMBAEePHsW+fftw11136R+j0+kwefJkzJo1C9HR0X/7nGq1GiqVqtVHRxTq6Qx3J1uoNTqcKuiY7wFJR6PV4dlvjqC0Ro0IXxcsGtuDG+MRUbsxuMw0NTXB3t4eALBjxw7cc889AICIiAgUFBQY9FyzZ8/GxIkTERERAVtbW8THx2PGjBmYOHGi/jFLliyBjY0NnnvuuRt6zuTkZCgUCv1HYGDH/G1QEAQkBF2eN8NLTWRi7/1yDr9nlcPZTo6PJiVwYzwialcGl5no6GisWLECe/fuxfbt23HnnXcCAPLz8+Hh4WHQc61ZswarVq3C6tWrkZaWhpUrV+Kdd97BypUrAQCpqan417/+hS+++OKGf6ubO3cuqqqq9B+5ubmGfYNWhJvnkRT2nyvF+7+cBQC8OT4GYTwJm4jamcETgJcsWYJx48Zh6dKlSEpKQmxsLABgw4YN+stPN2rWrFmYM2cOHnzwQQBATEwMsrOzkZycjKSkJOzduxfFxcUICgrSf41Wq8ULL7yA5cuX4+LFi1c8p729vX7kqKNrGZlJ44omMpHSGjWmr0mHKAIP9A5EYpy/1JGIqAMwuMwMHjwYpaWlUKlUcHd319/+xBNPwMnJsK3J6+rqIJO1HhySy+X6pdmTJ0/GsGHDWt0/cuRITJ48GY888oih0Tuc2EAF5DIBBVUNyK+sh9LNUepIZMVa9pMpqVajm3cnLLjn7+e4EREZg8Flpr6+HqIo6otMdnY21q5di8jISIwcOdKg5xozZgwWL16MoKAgREdH48iRI1i2bBmmTp0KAPDw8Lji0pWtrS18fX0RHh5uaPQOx8nOBlF+rjh+qQqp2RUsM9Su/r33AvZklsDeRoYPHuI8GSIyHYPnzCQmJuLLL78EAFRWVqJv37549913MXbsWHz88ccGPdf777+PCRMmYNq0aYiMjMSLL76IJ598Em+88Yahsega9Jvncd4MtaO0nAr9fjIL7olGuK+LxImIqCMxuMykpaXhtttuAwB8//338PHxQXZ2Nr788ku89957Bj2Xi4sLli9fjuzsbNTX1+P8+fNYtGgR7Ozsrvk1Fy9exIwZMwyN3WHFB7kBAI5w3gy1k+qGJkz/9gg0OhF39/TDg7d0zBWERCQdg8tMXV0dXFyaf+vatm0bxo8fD5lMhltvvRXZ2dlGD0ht0zIyczJfhfpGrcRpyBrNX38SueX1CHB3xJvjY7ifDBGZnMFlpmvXrli3bh1yc3OxdetWjBgxAgBQXFwMV1dXowektvF3c4SPqz00OhHH8iqljkNWZn36Jaw9cglymYB/PRgHVwfu0E1EpmdwmZk/fz5efPFFhISEoE+fPujXrx+A5lGa+Ph4owekthEEgYdOUrvILa/Dq2tPAACeHdoVvYI7S5yIiDoqg1czTZgwAQMHDkRBQYF+jxkAuOOOOzBu3DijhiPjSAhyx6bjhUjLrpQ6ClkJjVaHGWvSUa3WoHewO54Z0lXqSETUgRlcZgDA19cXvr6+yMvLgyAI8Pf3N3jDPDId/U7AORUQRZFzGqjNPth1DqnZFXCxt8E/H4iDjdzgQV4iIqMx+F8gnU6HhQsXQqFQIDg4GEFBQXBzc8Mbb7yh3+yOzEu00hV2NjKU1zbiYlmd1HHIwqXlVOC9nc3HFSwa1wOBnQ3bLJOIyNgMHpl55ZVX8Omnn+Ktt97CgAEDIIoifvvtNyxYsAANDQ1YvHhxe+SkNrC3kaOnvwIp2RVIza5AqKez1JHIQtU1ajBzTTp0IpAYp+RxBURkFgwuMytXrsR///tf/WnZABAbGwt/f39MmzaNZcZMJQS7IyW7Amk5FZjQK0DqOGShFm/MwMWyOvgpHLAwsYfUcYiIANzEZaby8nJERERccXtERATKy8uNEoqMT3/oJHcCppu060wxvv49BwDwzn2xUDhyGTYRmQeDy0xsbCw++OCDK27/4IMPWq1uIvOSEOwGADhTVA1VQ5O0YcjilNc24qXvjwEAHhkQggFdPSVORET0B4MvM7399tsYPXo0duzYgX79+kEQBOzfvx+5ubnYtGlTe2QkI/B2cUBQZyfklNchPacSt3f3kjoSWQhRFPHquuMoqVajq3cnzL7zypFZIiIpGTwyM2jQIGRmZmLcuHGorKxEeXk5xo8fjzNnzujPbCLzlHD5nKY0bp5HBliXfgmbjhfCRibgn/fHwcGWp2ETkXm5qX1mlErlFRN9c3NzMXXqVHz22WdGCUbG1yvYHevS83mCNt2wwqoGvLb+JADguTu6ISZAIXEiIqIrGW2nq/LycqxcudJYT0ftoGXzvPScSmh1osRpyNyJooi5Px6DqkGDngEKTBvcRepIRERXxW07O5BwHxc428lRrdbgbHG11HHIzP0vNQ+7zpTATi7Du/fFcpdfIjJb/NepA7GRyxB3ed4MLzXR9eRX1uONn04BAGaO6I5uPi4SJyIiujaWmQ6m1+X9ZlIvsszQ1YmiiNk/HEO1WoP4IDc8fluY1JGIiK7rhicAjx8//rr3V1ZWtjULmUCvkM4AgBSOzNA1fHs4F3vPlsLeRoZ37ouFXMaDSYnIvN1wmVEorr+KQaFQ4OGHH25zIGpf8UFuEAQgp7wOxdUN8HZxkDoSmZG8ijos+rn58tKskeHo4tVJ4kRERH/vhsvM559/3p45yERcHWwR7uOC04XVSMuuwJ09/KSORGaiefXScdQ2atE72B2PDAiVOhIR0Q3hnJkOqNflJdqcBEx/9n1qHvaeLYWdjQxLJvTk5SUishgsMx1QS5nhvBlqUaxqwBuXLy89P6w7Ly8RkUVhmemAegc3TwI+cakKDU1aidOQ1ERRxLz1J6Bq0CDGX4HHb+PlJSKyLCwzHVBgZ0d4udijSSvi+KUqqeOQxDYdL8TWk0WwkQlYcm9Pbo5HRBaH/2p1QIIg6PebSeF+Mx1aRW0jXttwAgAwbXAXRCldJU5ERGQ4lpkOqncIJwET8MbPp1Ba04juPp3w9NCuUschIropLDMdVMuhk2k5FRBFHjrZEe3OLMGPRy5BEIAl9/aEvY1c6khERDeFZaaD6qFUwM5GhvLaRmSV1kodh0ysrlGDV9YeBwBM6R+C+MuXHYmILBHLTAdlZyNDbEDzrs5cot3xLN9xFnkV9fB3c8SLI8KljkNE1CYsMx1Yr8tLtNNYZjqUE5eq8N+9FwAAb4yNhrP9DW8ETkRkllhmOrDe3Dyvw9FodZjz4zHoRODunn4YGuEjdSQiojZjmenAWiYBnyuuQWVdo8RpyBS+2H8RJy6p4Opgg/ljoqSOQ0RkFCwzHVhnZzuEeTkDaF7VRNYtt7wO727LBAC8MjqSJ6YTkdVgmenguHlexyCKIuavP4H6Ji36hnbG/b0DpY5ERGQ0LDMdXMvmeZw3Y922nCjErjMlsJULWDwuBoLAE7GJyHqwzHRwvUOaVzSl51ZCreGhk9aoRq3Bgp9OAgCeGtQFXb15IjYRWReWmQ4uzNMZnp3s0KjR4XgeD520Rsu2ZaJIpUawhxOeHsIjC4jI+rDMdHCCIKD35f1mDnPejNU5cakKX+zPAgC8kdgDDrY8soCIrA/LDOGW0JYyUy5xEjImrU7EK2uPQycCY2KVuL27l9SRiIjaBcsM4ZaWScAXy6HT8dBJa/H179k4mlcFF3sbzBsdKXUcIqJ2wzJDiPJzhbOdHKoGDc4UVUsdh4yguLoBS7ecAQC8dGc4vF25pwwRWS+WGYKNXKbfDZiXmqxD8qbTqFZr0DNAgYf6Bksdh4ioXbHMEADglhBOArYWBy+UYe2RSxAEYNHYHpDLuKcMEVk3lhkC8Kcyk1UOUeS8GUvVpNVh/voTAICH+gShZ4CbtIGIiEyAZYYAAHGBbrCVCyhUNSCvol7qOHSTPv8tC5lFNejsbIdZI8OljkNEZBIsMwQAcLSTo4e/AgBwKIvzZixRQVU9lu84CwCYMyoCbk52EiciIjINlhnS6xPC/WYs2aKfM1DXqEWvYHdMSAiQOg4RkcmwzJDeLSwzFmvv2RJsPF4AmdC806+Mk36JqANhmSG9XpeXZ58vqUVZjVriNHSjGjU6vLah+SDJh/uFIErpKnEiIiLTYpkhPXdnO3T3aT5RmUu0LccX+7NwoaQWnp3sMHNEd6njEBGZHMsMtcJLTZalSNWAf+kn/UbC1cFW4kRERKbHMkOt9OGhkxYleVMGahu1SAhyw/h4f6njEBFJgmWGWmkZmTmZr0KtWiNxGrqe3y+UYV16PgQBWMhJv0TUgbHMUCtKN0f4uzlCqxORms15M+ZKo/1j0u/EPkH6PYKIiDoilhm6Qt+w5tGZ37PKJE5C1/L17zk4XVgNNydbzBrBnX6JqGNjmaEr3BrmAQA4cJ5lxhyV1ajx7rYzAIAXR4TD3Zk7/RJRx8YyQ1fod7nMHMur4rwZM/TOtjNQNWgQrXTFxD5BUschIpIcywxdIbCzEwLcHaHRiUjhvBmzcuJSFb49nAsAeP2eaMg56ZeIiGWGro6XmsyPKIp4bcNJiCKQGKdE78srz4iIOjqWGbqqlktNBy6wzJiL9en5SM2ugJOdHHNHRUodh4jIbLDM0FX169JcZk5cqkJ1Q5PEaahWrUHy5gwAwNNDusJX4SBxIiIi88EyQ1eldHNEsIcTtDoRKTynSXIf7jqHIpUaQZ2d8OjAUKnjEBGZFZYZuqZbQ3mpyRxcLK3Ff/dmAQDm3R0FB1u5xImIiMwLywxdU8ulpoMsM5JatDEDjVodbu/uhWGR3lLHISIyOywzdE0tK5pOXKqCivNmJLEnswQ7MopgIxMw/+5ICAKXYhMR/RXLDF2Tr8IBoZ7O0InAoQs8RdvUmrQ6LPz5FADg4X4h6OrtInEiIiLzxDJD19UyOsNLTaa36mA2zhXXoLOzHaYP6yZ1HCIisyVpmdFoNHj11VcRGhoKR0dHhIWFYeHChdDpdACApqYmzJ49GzExMXB2doZSqcTDDz+M/Px8KWN3KC3zZjgJ2LTKaxvxz+2ZAJrPX1I42kqciIjIfNlI+eJLlizBihUrsHLlSkRHRyMlJQWPPPIIFAoFpk+fjrq6OqSlpWHevHmIjY1FRUUFZsyYgXvuuQcpKSlSRu8wbg1t3mX2VIEKlXWNcHPioYamsGx78/lLkX6ueOCWQKnjEBGZNUnLzIEDB5CYmIjRo0cDAEJCQvDNN9/oi4pCocD27dtbfc3777+PPn36ICcnB0FBVx6yp1aroVar9Z+rVKp2/A6sn7erA7p4OeN8SS0OZZVjRLSv1JGsXkaBCqt/zwEAvDYmiucvERH9DUkvMw0cOBA7d+5EZmbzcPrRo0exb98+3HXXXdf8mqqqKgiCADc3t6ven5ycDIVCof8IDORvtW3FS02mI4oiXv/pJHQiMDrGTz9niYiIrk3SMjN79mxMnDgRERERsLW1RXx8PGbMmIGJEyde9fENDQ2YM2cOHnroIbi6ul71MXPnzkVVVZX+Izc3tz2/hQ6hX5gnAB46aQpbThTi4IVy2NvIMGdUhNRxiIgsgqSXmdasWYNVq1Zh9erViI6ORnp6OmbMmAGlUomkpKRWj21qasKDDz4InU6Hjz766JrPaW9vD3t7+/aO3qH07+IBQQBOF1ajSNUAH1eeC9QeGpq0WLyp+fylJ24PQ2BnJ4kTERFZBklHZmbNmoU5c+bgwQcfRExMDCZPnoznn38eycnJrR7X1NSE+++/H1lZWdi+ffs1R2Wofbg726GnvwJA8yZu1D4+3ZeFvIp6+Lo64B+Du0gdh4jIYkhaZurq6iCTtY4gl8v1S7OBP4rM2bNnsWPHDnh4cA6BFG7v7gUA2HO2VOIk1qlI1YAPd50DAMwZFQEnO0kHTYmILIqkZWbMmDFYvHgxNm7ciIsXL2Lt2rVYtmwZxo0bB6B5H5oJEyYgJSUFX3/9NbRaLQoLC1FYWIjGxkYpo3c4LWVm39kSaHWixGmsz9tbzqCuUYv4IDckximljkNEZFEk/fXv/fffx7x58zBt2jQUFxdDqVTiySefxPz58wEAeXl52LBhAwAgLi6u1dfu2rULgwcPNnHijisu0A0u9jaoqGvCiUtViA10kzqS1UjPrcQPaXkAgNfGRPP8JSIiA0laZlxcXLB8+XIsX778qveHhIRAFDkKYA5s5TL07+qBrSeLsCezhGXGSERRxMKfTgIAxif4I47vKxGRwXg2E92wP+bNcBKwsWw4mo+0nEo42ckx+04uxSYiuhksM3TDbu/WXGbSciqhamiSOI3lq2vUIHnTaQDA00O6csk7EdFNYpmhGxbY2Qlhns7Q6kTsP8cN9Npqxa/nUahqQIC7Ix4dGCp1HCIii8UyQwbhpSbjyKuowyd7LgAAXh0dCQdbucSJiIgsF8sMGeT27s1HG+zJLOHk7DZI3nwaao0Ot4Z1xkge3klE1CYsM2SQW8M8YCeXIa+iHlmltVLHsUi/XyjDxmMFkAnA/Lu5FJuIqK1YZsggTnY26B3iDoBHG9wMrU7E6z+dAgBM7BOEKCWP5iAiaiuWGTIYjza4ed+l5OJUgQquDjaYOby71HGIiKwCywwZrGWJ9oHzZVBrtBKnsRyqhia8s/UMAGDGsO7w6MTT3YmIjIFlhgwW6ecCLxd71DdpkXKxQuo4FuNfO86irLYRXbycMblfsNRxiIisBssMGUwQBAy+fKlp+6kiidNYhnPF1Vi5/yIAYP6YaNjK+VePiMhY+C8q3ZQRl5cTbz9VxCXaf0MUmyf9anQihkX6YNDlIkhERMbBMkM35bZunnC0leNSZT1O5qukjmPWdmQUY+/ZUtjJZZh3d6TUcYiIrA7LDN0UB1u5foRh68lCidOYr4YmLd74uXkp9mO3hSLYw1niRERE1odlhm7ayB4+AFhmrufTfVnIKa+Dj6s9nh7SVeo4RERWiWWGbtrQcB/YyARkFtVwN+CrKKxqwIe7zgEA5o6KhLO9jcSJiIisE8sM3TSFky1uDfMAAGzj6MwV3tqcgbpGLXoFuyMxTil1HCIiq8UyQ20yMpqXmq7m9wtlWJeeD0EAFozh+UtERO2JZYbaZHhU8xLttJxKFKsaJE5jHjRaHV7bcBJA8/lLMQEKiRMREVk3lhlqE1+FA2ID3QAA27iBHgDgywPZOF1YDTcnW8waES51HCIiq8cyQ23GS01/KK5uwD+3ZwIAXhoZAXdnO4kTERFZP5YZarORl3cDPnC+DFX1TRKnkdZbm0+jWq1BzwAFHrglUOo4REQdAssMtVkXr07o6t0JGp2IX88USx1HMikXy/Fj2iUIArAwsQfkMk76JSIyBZYZMooRUR37UpNGq8O89c2Tfh/oHYi4y/OIiIio/bHMkFG0XGradboENWqNxGlM76uD2cgoUEHhaIuX7oyQOg4RUYfCMkNG0TNAgVBPZ9Q3abH5eIHUcUyqsKoB725rnvQ7a2Q4OnPSLxGRSbHMkFEIgoB7E/wBAD+k5UmcxrQW/nwSNWoN4oPc8FCfIKnjEBF1OCwzZDTjEgIgCMDBC+XILa+TOo5J7DpdjE3HCyGXCXhzXAxknPRLRGRyLDNkNP5ujujfpfmspo4wOlPfqMW89ScAAI8ODEWkn6vEiYiIOiaWGTKqCb0CADSXGZ1OlDhN+3rvl7PIq6iHv5sjZgzrJnUcIqIOi2WGjGpktC+c7eTILa/H4YvlUsdpN2cKq/GfPRcAAK/fEw0nOxuJExERdVwsM2RUTnY2GN3TD4D1XmrS6US8svY4NDoRI6J8MOzyHjtERCQNlhkyunsTmi81bTxWgLpG69tz5ssDF5GSXQFnOzkW3BMtdRwiog6PZYaM7paQzgjq7ITaRq3V7QicW16Ht7eeAQDMuSsSSjdHiRMRERHLDBmdTCZg/OU9Z75PtZ5LTaIoYs6Px1DXqEXf0M6YxD1liIjMAssMtYuWS037z5fhUmW9xGmMY83hXPx2rgwOtjIsubcn95QhIjITLDPULgI7O6FvaGeIYnMJsHQFVfVYvDEDAPDiiHCEeDpLnIiIiFqwzFC7+b9bgwEAXx24iPpGrcRpbp4oinj5x+OoVmsQF+iGRwaESh2JiIj+hGWG2s2oHr4I7OyIiromfJdiuaMzP6Rdwq4zJbCTy7B0Qk/IeXmJiMissMxQu7GRy/DEbWEAgP/svQCNVidxIsPllNVhwYaTAIDpw7qhm4+LxImIiOivWGaoXU3oFYjOznbIq6jHxuMFUscxiEarw/Q1R1Cj1uCWEHc8NaiL1JGIiOgqWGaoXTnayTGlfwgAYMXuCxBFyzmv6f1fzuFITiVc7G3wzwfieHmJiMhMscxQu3u4XzAcbeXIKFBh79lSqePckNTscrz/y1kAwKJxPRDg7iRxIiIiuhaWGWp3bk52eLBPIABgxe7zEqf5e9UNTZj+bTp0IjAu3h+Jcf5SRyIioutgmSGTeOy2MNjIBOw/X4ZjeZVSx7kmURQxf/1J5FXUI8DdEa8n8uwlIiJzxzJDJuHv5oh7YpUAgE92X5A4zbV9/XsO1h65BJkA/OvBOLg62EodiYiI/gbLDJnME4Oal2lvOlFglqMzKRfL8fpPzcuwZ42MQK/gzhInIiKiG8EyQyYT4euKsXFKiCLwytoT0OrMZ2VTYVUDnlqVhiatiNE9/fDU5eJFRETmj2WGTOrl0ZFwcbDB8UtV+Pr3bKnjAADUGi2eWpWK0ho1InxdsHRCTwgCl2ETEVkKlhkyKW8XB8waGQ4AWLr1DIqrGyTNI4oiXlt/Eum5lVA42uKTyb3gZGcjaSYiIjIMywyZ3KS+wegZoEB1gwZvXj6JWipf7L+Ibw/nQhCA9ybGI9iDp2ETEVkalhkyOblMwKKxPSAIwLr0fOw/J81Get+n5uH1n04BAGaNDMeg7l6S5CAiorZhmSFJ9Axww+RbgwEAr64/AbVGa9LX33y8AC99fxQAMKV/CP7Bc5eIiCwWywxJ5oUR4fDsZI8LJbVYtj3TZK+7O7MEz317BDoRuK9XAObfHcUJv0REFoxlhiSjcLTF6/c077D7ye4L+Opg+69uOpRVjie/Smlegh3jh7fu7QkZD5AkIrJoLDMkqdE9/TBzeHcAwGvrT2DrycJ2e61fThdh6heH0dCkw5BwL56ETURkJVhmSHLPDu2KiX2CoBOB5745gpSL5UZ9flEU8dGv5/DoyhTUqDUY0NUDH/9fL9jZ8H9/IiJrwH/NSXKCIOCNxGgMi/SBWqPDoytTcK642ijPXd+oxfRv0/H2ljMQRWBS3yB8PqUPHGzlRnl+IiKSHssMmQUbuQzvT4xHfJAbquqb8H//PYTf2rhkO7e8Dvd9sh8bjubD5vJy8MXjYjgiQ0RkZQRRFM3ngJx2oFKpoFAoUFVVBVdXV6nj0N8or23EfSv243xJLYDm1UavjI6Em5PdDT9HaY0aH+46h68P5qBRq0NnZzt8NCkBt4Z5tFdsIiIyMkN+frPMkNmpbmjCO1vP4MuD2RBFwLOTHRbcE43RMX7XXUJd3dCE/+zNwqd7L6C2sXnfmn5hHnh7Qk8EdnYyVXwiIjIClpk/YZmxXKnZ5Zj9w3GcK64BAPi6OqCHvwIx/gr0DFCgs7MdzhRW42R+FU4VqHAyX4W6yyUmxl+Bl+4Mx8CuntxDhojIArHM/AnLjGVTa7T4+Nfz+OjX82jU6P728WFezpg1Ihx39vBliSEismAsM3/CMmMdatUanCpQ4VheFY7nVeL4pSpU1jUh3NcFUX6uiPZ3RZSfAt28O3ETPCIiK2DIz28bE2UiahNnexvcEtIZt4R0ljoKERGZGa5RJSIiIosmaZnRaDR49dVXERoaCkdHR4SFhWHhwoXQ6f6YGyGKIhYsWAClUglHR0cMHjwYJ0+elDA1ERERmRNJy8ySJUuwYsUKfPDBB8jIyMDbb7+NpUuX4v3339c/5u2338ayZcvwwQcf4PDhw/D19cXw4cNRXW2cHWKJiIjIsklaZg4cOIDExESMHj0aISEhmDBhAkaMGIGUlBQAzaMyy5cvxyuvvILx48ejR48eWLlyJerq6rB69WopoxMREZGZkLTMDBw4EDt37kRmZiYA4OjRo9i3bx/uuusuAEBWVhYKCwsxYsQI/dfY29tj0KBB2L9//1WfU61WQ6VStfogIiIi6yXpaqbZs2ejqqoKERERkMvl0Gq1WLx4MSZOnAgAKCwsBAD4+Pi0+jofHx9kZ2df9TmTk5Px+uuvt29wIiIiMhuSjsysWbMGq1atwurVq5GWloaVK1finXfewcqVK1s97q+bn4mieM0N0ebOnYuqqir9R25ubrvlJyIiIulJOjIza9YszJkzBw8++CAAICYmBtnZ2UhOTkZSUhJ8fX0BNI/Q+Pn56b+uuLj4itGaFvb29rC3t2//8ERERGQWJB2Zqaurg0zWOoJcLtcvzQ4NDYWvry+2b9+uv7+xsRG7d+9G//79TZqViIiIzJOkIzNjxozB4sWLERQUhOjoaBw5cgTLli3D1KlTATRfXpoxYwbefPNNdOvWDd26dcObb74JJycnPPTQQ1JGJyIiIjMhaZl5//33MW/ePEybNg3FxcVQKpV48sknMX/+fP1jXnrpJdTX12PatGmoqKhA3759sW3bNri4uEiYnIiIiMwFD5okIiIis2PIz2+ezUREREQWjWWGiIiILJqkc2ZMoeUqGncCJiIishwtP7dvZDaM1ZeZlgMpAwMDJU5CREREhqquroZCobjuY6x+ArBOp0N+fj5cXFyuuWvwX6lUKgQGBiI3N5eThk2A77dp8f02Lb7fpsX327Ta8/0WRRHV1dVQKpVX7En3V1Y/MiOTyRAQEHBTX+vq6sq/DCbE99u0+H6bFt9v0+L7bVrt9X7/3YhMC04AJiIiIovGMkNEREQWjWXmKuzt7fHaa6/xwEoT4fttWny/TYvvt2nx/TYtc3m/rX4CMBEREVk3jswQERGRRWOZISIiIovGMkNEREQWjWWGiIiILBrLzFV89NFHCA0NhYODA3r16oW9e/dKHclq7dmzB2PGjIFSqYQgCFi3bp3UkaxWcnIybrnlFri4uMDb2xtjx47FmTNnpI5ltT7++GP07NlTv5lYv379sHnzZqljdRjJyckQBAEzZsyQOopVWrBgAQRBaPXh6+srWR6Wmb9Ys2YNZsyYgVdeeQVHjhzBbbfdhlGjRiEnJ0fqaFaptrYWsbGx+OCDD6SOYvV2796Np59+GgcPHsT27duh0WgwYsQI1NbWSh3NKgUEBOCtt95CSkoKUlJSMHToUCQmJuLkyZNSR7N6hw8fxr///W/07NlT6ihWLTo6GgUFBfqP48ePS5aFS7P/om/fvkhISMDHH3+svy0yMhJjx45FcnKyhMmsnyAIWLt2LcaOHSt1lA6hpKQE3t7e2L17N26//Xap43QInTt3xtKlS/Hoo49KHcVq1dTUICEhAR999BEWLVqEuLg4LF++XOpYVmfBggVYt24d0tPTpY4CgCMzrTQ2NiI1NRUjRoxodfuIESOwf/9+iVIRtY+qqioAzT9gqX1ptVp8++23qK2tRb9+/aSOY9WefvppjB49GsOGDZM6itU7e/YslEolQkND8eCDD+LChQuSZbH6gyYNUVpaCq1WCx8fn1a3+/j4oLCwUKJURMYniiJmzpyJgQMHokePHlLHsVrHjx9Hv3790NDQgE6dOmHt2rWIioqSOpbV+vbbb5GWlobDhw9LHcXq9e3bF19++SW6d++OoqIiLFq0CP3798fJkyfh4eFh8jwsM1chCEKrz0VRvOI2Ikv2zDPP4NixY9i3b5/UUaxaeHg40tPTUVlZiR9++AFJSUnYvXs3C007yM3NxfTp07Ft2zY4ODhIHcfqjRo1Sv/fMTEx6NevH7p06YKVK1di5syZJs/DMvMnnp6ekMvlV4zCFBcXXzFaQ2Spnn32WWzYsAF79uxBQECA1HGsmp2dHbp27QoA6N27Nw4fPox//etf+OSTTyROZn1SU1NRXFyMXr166W/TarXYs2cPPvjgA6jVasjlcgkTWjdnZ2fExMTg7Nmzkrw+58z8iZ2dHXr16oXt27e3un379u3o37+/RKmIjEMURTzzzDP48ccf8csvvyA0NFTqSB2OKIpQq9VSx7BKd9xxB44fP4709HT9R+/evTFp0iSkp6ezyLQztVqNjIwM+Pn5SfL6HJn5i5kzZ2Ly5Mno3bs3+vXrh3//+9/IycnBU089JXU0q1RTU4Nz587pP8/KykJ6ejo6d+6MoKAgCZNZn6effhqrV6/G+vXr4eLioh+BVCgUcHR0lDid9Xn55ZcxatQoBAYGorq6Gt9++y1+/fVXbNmyRepoVsnFxeWK+V/Ozs7w8PDgvLB28OKLL2LMmDEICgpCcXExFi1aBJVKhaSkJEnysMz8xQMPPICysjIsXLgQBQUF6NGjBzZt2oTg4GCpo1mllJQUDBkyRP95y7XWpKQkfPHFFxKlsk4t2w0MHjy41e2ff/45pkyZYvpAVq6oqAiTJ09GQUEBFAoFevbsiS1btmD48OFSRyNqs7y8PEycOBGlpaXw8vLCrbfeioMHD0r2s5L7zBAREZFF45wZIiIismgsM0RERGTRWGaIiIjIorHMEBERkUVjmSEiIiKLxjJDREREFo1lhoiIiCwaywwRERFZNJYZIiIismgsM0TUrqZMmQJBECAIAmxtbeHj44Phw4fjs88+g06nkzoeEVkBlhkiand33nknCgoKcPHiRWzevBlDhgzB9OnTcffdd0Oj0bTb6zY2NrbbcxOR+WCZIaJ2Z29vD19fX/j7+yMhIQEvv/wy1q9fj82bN+sPFK2qqsITTzwBb29vuLq6YujQoTh69Gir51m0aBG8vb3h4uKCxx57DHPmzEFcXJz+/ilTpmDs2LFITk6GUqlE9+7dAQCXLl3CAw88AHd3d3h4eCAxMREXL15s9dyff/45IiMj4eDggIiICHz00Uft+ZYQkRGxzBCRJIYOHYrY2Fj8+OOPEEURo0ePRmFhITZt2oTU1FQkJCTgjjvuQHl5OQDg66+/xuLFi7FkyRKkpqYiKChIfxL4n+3cuRMZGRnYvn07fv75Z9TV1WHIkCHo1KkT9uzZg3379qFTp06488479SM3//nPf/DKK69g8eLFyMjIwJtvvol58+Zh5cqVJn1PiOgmiURE7SgpKUlMTEy86n0PPPCAGBkZKe7cuVN0dXUVGxoaWt3fpUsX8ZNPPhFFURT79u0rPv30063uHzBggBgbG9vqtXx8fES1Wq2/7dNPPxXDw8NFnU6nv02tVouOjo7i1q1bRVEUxcDAQHH16tWtnvuNN94Q+/XrZ/D3S0SmZyN1mSKijksURQiCgNTUVNTU1MDDw6PV/fX19Th//jwA4MyZM5g2bVqr+/v06YNffvml1W0xMTGws7PTf56amopz587BxcWl1eMaGhpw/vx5lJSUIDc3F48++igef/xx/f0ajQYKhcIo3ycRtS+WGSKSTEZGBkJDQ6HT6eDn54dff/31ise4ubnp/1sQhFb3iaJ4xeOdnZ1bfa7T6dCrVy98/fXXVzzWy8sLDQ0NAJovNfXt27fV/XK5/Ea/FSKSEMsMEUnil19+wfHjx/H8888jICAAhYWFsLGxQUhIyFUfHx4ejkOHDmHy5Mn621JSUv72dRISErBmzRr9xOK/UigU8Pf3x4ULFzBp0qSb/n6ISDosM0TU7tRqNQoLC6HValFUVIQtW7YgOTkZd999Nx5++GHIZDL069cPY8eOxZIlSxAeHo78/Hxs2rQJY8eORe/evfHss8/i8ccfR+/evdG/f3+sWbMGx44dQ1hY2HVfe9KkSVi6dCkSExOxcOFCBAQEICcnBz/++CNmzZqFgIAALFiwAM899xxcXV0xatQoqNVqpKSkoKKiAjNnzjTRu0REN4tlhoja3ZYtW+Dn5wcbGxu4u7sjNjYW7733HpKSkiCTNS+q3LRpE1555RVMnToVJSUl8PX1xe233w4fHx8AzaXkwoULePHFF9HQ0ID7778fU6ZMwaFDh6772k5OTtizZw9mz56N8ePHo7q6Gv7+/rjjjjv0IzWPPfYYnJycsHTpUrz00ktwdnZGTEwMZsyY0a7vCxEZhyBe7aIzEZEFGD58OHx9ffHVV19JHYWIJMSRGSKyCHV1dVixYgVGjhwJuVyOb775Bjt27MD27duljkZEEuPIDBFZhPr6eowZMwZpaWlQq9UIDw/Hq6++ivHjx0sdjYgkxjJDREREFo3HGRAREZFFY5khIiIii8YyQ0RERBaNZYaIiIgsGssMERERWTSWGSIiIrJoLDNERERk0VhmiIiIyKL9P7jgAOwO+CeyAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(degrees,loss[:,3],label = f'{nqubits[q]} qubits')\n", + "plt.xlabel('Degree')\n", + "plt.ylabel('Loss function')\n", + "plt.legend()\n", + "print(best_degree)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_qubits = 7\n", + "d = d_poly_spacing(n_qubits,best_degree[2])\n", + "step = 1e-2\n", + "iterations = 50\n", + "H_TFIM = hamiltonians.TFIM(nqubits=n_qubits, h=h)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "d_optimized, loss, grad, diags = gradient_ascent(dbi, d,step, iterations,analytic=False)\n", + "\n", + "flows = 50\n", + "off_diagonal_norm = np.empty((flows+1,2))\n", + "off_diagonal_norm[0,:] = dbi.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_optimized)\n", + " dbi(step_poly,d=d_optimized)\n", + " off_diagonal_norm[i+1,0] = dbi.off_diagonal_norm\n", + "\n", + "d = d_poly_spacing(n_qubits,1)\n", + "dbi2 = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "d_optimized, loss, grad, diags = gradient_ascent(dbi2, d,step, iterations,analytic=False)\n", + "for i in range(flows):\n", + " step_poly = dbi2.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_optimized)\n", + " dbi2(step_poly,d=d_optimized)\n", + " off_diagonal_norm[i+1,1] = dbi2.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(off_diagonal_norm[:,0],label= 'optimal poly degree')\n", + "plt.plot(off_diagonal_norm[:,1],label= 'linear spacing')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "def delta_to_d(delta):\n", + " d = np.empty(len(delta))\n", + " d[0] = 0\n", + " for i in range(len(d)-1):\n", + " d[i+1] = d[i] + delta[i]\n", + " return np.diag(d)\n", + "\n", + "def d_to_delta(d):\n", + " delta = np.empty(len(d)-1)\n", + " for i in range(len(d)-1):\n", + " delta[i] = d[i+1,i+1]-d[i,i]\n", + " return delta\n", + "\n", + "def polynomial(x,degree,coefficients):\n", + " y = np.empty(len(x))\n", + " for i in range(degree):\n", + " y += coefficients[i]*x**i\n", + " return y\n", + "\n", + "def gradient_delta_polynomial(dbi, degree, coefficients,h=1e-5):\n", + " grad = np.empty(degree)\n", + " dim = dbi.h.matrix.shape[0]\n", + " delta = polynomial(np.linspace(0,1,dim),degree,coefficients)\n", + " d = delta_to_d(delta)\n", + " dbi_eval = deepcopy(dbi)\n", + " angle = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", + " dbi_eval(angle,d=d)\n", + " norm = dbi_eval.off_diagonal_norm\n", + " for i in range(len(grad)):\n", + " new_coeff = np.copy(coefficients)\n", + " new_coeff[i] += h\n", + " new_delta = polynomial(np.linspace(0,1,dim),degree,new_coeff)\n", + " new_d = delta_to_d(new_delta)\n", + " dbi_eval = deepcopy(dbi)\n", + " angle = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=new_d)\n", + " dbi_eval(angle,d=new_d)\n", + " new_norm = dbi_eval.off_diagonal_norm\n", + " grad[i] = (new_norm-norm)/h\n", + "\n", + " return grad\n", + "\n", + "\n", + "def optimize_poly(dbi, step, iterations, degree):\n", + " coefficients = np.random.rand(degree)\n", + " dim = dbi.h.matrix.shape[0]\n", + " delta = polynomial(np.linspace(0,1,dim),degree,coefficients)\n", + " d = delta_to_d(delta)\n", + " loss = np.empty(iterations)\n", + " grad = np.empty(degree)\n", + " for i in range(iterations):\n", + " dbi_eval = deepcopy(dbi)\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", + " dbi_eval(step_poly,d=d)\n", + " loss[i] = dbi_eval.off_diagonal_norm\n", + " grad = gradient_delta_polynomial(dbi_eval, degree, coefficients)\n", + " coefficients -= step*grad\n", + " delta = polynomial(np.linspace(0,1,dim),degree,coefficients)\n", + " d = delta_to_d(delta)\n", + " return coefficients, loss, grad\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "H_TFIM = hamiltonians.TFIM(nqubits=7, h=5.0)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "matrix = dbi.h.matrix\n", + "eigenenergies = np.diag(np.linalg.eigh(matrix)[0])\n", + "delta = d_to_delta(eigenenergies)\n", + "\n", + "plt.figure()\n", + "plt.plot(delta,'.')\n", + "plt.xlabel('Index')\n", + "plt.ylabel(r'$\\Delta$')\n", + "\n", + "d, loss, grad = optimize_poly(dbi, 1e-3, 100, 3)\n", + "plt.figure()\n", + "plt.plot(loss)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "c4f92193806e2908606a5f23edd55a5282f2f433b73b1c504507f9256ed9f0b4" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 3aebfddff2..bb781a50ef 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -153,6 +153,30 @@ def polynomial_step( else: return None +def d_ansatz(params, type = 'Full'): + r""" + Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. + Args: + params(np.array): parameters for the ansatz. + type(str): type of parameterization, 'Full' or 'Pauli' + (Full being each entry parametrized and Pauli being a linear combination of Z_i matrix). + """ + + if type == 'Full': + d = np.zeros((len(params), len(params))) + for i in range(len(params)): + d[i, i] = params[i] + + if type == 'Pauli': + d = np.zeros((2**len(params), 2**len(params))) + Z = np.array([[1, 0], [0, -1]]) + for i in range(len(params)): + I1 = np.eye(2**i) + I2 = np.eye(2**(len(params)-i-1)) + d += params[i]*np.kron(I1,np.kron(Z,I2)) + + return d + def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): if d is None: @@ -203,6 +227,8 @@ def energy_fluctuation_polynomial_expansion_coef(dbi_object, d, n, state): coef = list(reversed(coef)) return coef + +# D GRADIENTS def dGamma_diDiagonal(dbi_object, d, H, n, i,dGamma, Gamma_list): # Derivative of gamma with respect to diagonal elements of D (full-diagonal ansatz) A = np.zeros(d.shape) @@ -227,34 +253,43 @@ def dpolynomial_diDiagonal(dbi_object, d,H,i): return derivative -def gradientDiagonal(dbi_object,d,H): +def gradientDiagonal(dbi_object, params, H, analytic = True, ansatz = 'Full', h = 1e-4): # Gradient of potential function with respect to diagonal elements of D (full-diagonal ansatz) - grad = np.zeros(len(d)) - for i in range(len(d)): - derivative = dpolynomial_diDiagonal(dbi_object,d,H,i) - grad[i] = d[i,i]-derivative + grad = np.zeros(len(params)) + d = d_ansatz(params, ansatz) + if analytic == True: + for i in range(len(params)): + derivative = dpolynomial_diDiagonal(dbi_object,d,H,i) + grad[i] = d[i,i]-derivative + else: + for i in range(len(params)): + params_new = deepcopy(params) + params_new[i] += h + d_new = d_ansatz(params_new, ansatz) + grad[i] = (dbi_object.least_squares(d_new)-dbi_object.least_squares(d))/h return grad -def gradient_ascent(dbi_object, d, step, iterations): +def gradient_ascent(dbi_object, params, step, iterations, analytic = True, ansatz = 'Full'): H = dbi_object.h.matrix + d = d_ansatz(params,ansatz) loss = np.zeros(iterations+1) - grad = np.zeros((iterations,len(d))) + grad = np.zeros((iterations,len(params))) dbi_new = deepcopy(dbi_object) s = polynomial_step(dbi_object, n = 3, d=d) dbi_new(s,d=d) - loss[0] = dbi_new(d) - diagonals = np.empty((len(d),iterations+1)) - diagonals[:,0] = np.diag(d) + loss[0] = dbi_new.least_squares(d) + params_hist = np.empty((len(params),iterations+1)) + params_hist[:,0] = params for i in range(iterations): dbi_new = deepcopy(dbi_object) - grad[i,:] = gradientDiagonal(dbi_object, d, H) - for j in range(len(d)): - d[j,j] = d[j,j] - step*grad[i,j] + grad[i,:] = gradientDiagonal(dbi_object, params, H, analytic=analytic, ansatz=ansatz) + for j in range(len(params)): + params[j] = params[j] - step*grad[i,j] + d = d_ansatz(params,ansatz) s = polynomial_step(dbi_object, n = 3, d=d) dbi_new(s,d=d) loss[i+1] = dbi_new.least_squares(d) - diagonals[:,i+1] = np.diag(d) + params_hist[:,i+1] = params - - return d,loss,grad,diagonals \ No newline at end of file + return d,loss,grad,params_hist \ No newline at end of file From 4f6fe62606ba8db7f457e0f77a4b59eb426a15cd Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 26 Mar 2024 09:48:59 +0000 Subject: [PATCH 049/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/utils_scheduling.py | 65 ++++++++++++++----------- 1 file changed, 36 insertions(+), 29 deletions(-) diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 87ab83a60e..77b05f5900 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -155,7 +155,8 @@ def polynomial_step( else: return None -def d_ansatz(params, type = 'Full'): + +def d_ansatz(params, type="Full"): r""" Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. Args: @@ -164,19 +165,19 @@ def d_ansatz(params, type = 'Full'): (Full being each entry parametrized and Pauli being a linear combination of Z_i matrix). """ - if type == 'Full': + if type == "Full": d = np.zeros((len(params), len(params))) for i in range(len(params)): d[i, i] = params[i] - if type == 'Pauli': - d = np.zeros((2**len(params), 2**len(params))) + if type == "Pauli": + d = np.zeros((2 ** len(params), 2 ** len(params))) Z = np.array([[1, 0], [0, -1]]) for i in range(len(params)): I1 = np.eye(2**i) - I2 = np.eye(2**(len(params)-i-1)) - d += params[i]*np.kron(I1,np.kron(Z,I2)) - + I2 = np.eye(2 ** (len(params) - i - 1)) + d += params[i] * np.kron(I1, np.kron(Z, I2)) + return d @@ -238,7 +239,7 @@ def energy_fluctuation_polynomial_expansion_coef( # D GRADIENTS -def dGamma_diDiagonal(dbi_object, d, H, n, i,dGamma, Gamma_list): +def dGamma_diDiagonal(dbi_object, d, H, n, i, dGamma, Gamma_list): # Derivative of gamma with respect to diagonal elements of D (full-diagonal ansatz) A = np.zeros(d.shape) A[i, i] = 1 @@ -267,43 +268,49 @@ def dpolynomial_diDiagonal(dbi_object, d, H, i): return derivative -def gradientDiagonal(dbi_object, params, H, analytic = True, ansatz = 'Full', h = 1e-4): + +def gradientDiagonal(dbi_object, params, H, analytic=True, ansatz="Full", h=1e-4): # Gradient of potential function with respect to diagonal elements of D (full-diagonal ansatz) grad = np.zeros(len(params)) d = d_ansatz(params, ansatz) if analytic == True: for i in range(len(params)): - derivative = dpolynomial_diDiagonal(dbi_object,d,H,i) - grad[i] = d[i,i]-derivative + derivative = dpolynomial_diDiagonal(dbi_object, d, H, i) + grad[i] = d[i, i] - derivative else: for i in range(len(params)): params_new = deepcopy(params) params_new[i] += h d_new = d_ansatz(params_new, ansatz) - grad[i] = (dbi_object.least_squares(d_new)-dbi_object.least_squares(d))/h + grad[i] = ( + dbi_object.least_squares(d_new) - dbi_object.least_squares(d) + ) / h return grad -def gradient_ascent(dbi_object, params, step, iterations, analytic = True, ansatz = 'Full'): + +def gradient_ascent(dbi_object, params, step, iterations, analytic=True, ansatz="Full"): H = dbi_object.h.matrix - d = d_ansatz(params,ansatz) - loss = np.zeros(iterations+1) - grad = np.zeros((iterations,len(params))) + d = d_ansatz(params, ansatz) + loss = np.zeros(iterations + 1) + grad = np.zeros((iterations, len(params))) dbi_new = deepcopy(dbi_object) - s = polynomial_step(dbi_object, n = 3, d=d) - dbi_new(s,d=d) + s = polynomial_step(dbi_object, n=3, d=d) + dbi_new(s, d=d) loss[0] = dbi_new.least_squares(d) - params_hist = np.empty((len(params),iterations+1)) - params_hist[:,0] = params + params_hist = np.empty((len(params), iterations + 1)) + params_hist[:, 0] = params for i in range(iterations): dbi_new = deepcopy(dbi_object) - grad[i,:] = gradientDiagonal(dbi_object, params, H, analytic=analytic, ansatz=ansatz) + grad[i, :] = gradientDiagonal( + dbi_object, params, H, analytic=analytic, ansatz=ansatz + ) for j in range(len(params)): - params[j] = params[j] - step*grad[i,j] - d = d_ansatz(params,ansatz) - s = polynomial_step(dbi_object, n = 3, d=d) - dbi_new(s,d=d) - loss[i+1] = dbi_new.least_squares(d) - params_hist[:,i+1] = params - - return d,loss,grad,params_hist \ No newline at end of file + params[j] = params[j] - step * grad[i, j] + d = d_ansatz(params, ansatz) + s = polynomial_step(dbi_object, n=3, d=d) + dbi_new(s, d=d) + loss[i + 1] = dbi_new.least_squares(d) + params_hist[:, i + 1] = params + + return d, loss, grad, params_hist From f4ddcfa7d91625da0a3748bd5516bb94d9a54227 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 2 Apr 2024 09:39:11 +0800 Subject: [PATCH 050/154] Update notebook --- examples/dbi/dbi_strategies_compare.ipynb | 215 +++++++++++++++++++++- 1 file changed, 207 insertions(+), 8 deletions(-) diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 0bb5642abf..1160babed3 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -24,7 +24,8 @@ "from qibo.hamiltonians import Hamiltonian, SymbolicHamiltonian\n", "from qibo.quantum_info import random_hermitian\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", - "from qibo.models.dbi.utils import *" + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_scheduling import *" ] }, { @@ -58,7 +59,7 @@ "outputs": [], "source": [ "# backend\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "# initialize dbi object\n", "nqubits = 5\n", "h0 = random_hermitian(2**nqubits, seed=2)\n", @@ -76,7 +77,7 @@ "# iterations steps\n", "NSTEPS = 15\n", "# choose polynomial scheduling\n", - "scheduling = DoubleBracketScheduling.use_hyperopt" + "scheduling = DoubleBracketScheduling.simulated_annealing" ] }, { @@ -107,7 +108,7 @@ "steps_canonical_plot = [0]\n", "for s in range(NSTEPS):\n", " # same settings as iteration from list\n", - " step = dbi_canonical.choose_step(d=dbi.diagonal_h_matrix, max_evals=50)\n", + " step = dbi_canonical.choose_step(d=dbi.diagonal_h_matrix)\n", " dbi_canonical(step=step)\n", " print(f\"New optimized step at iteration {s+1}/{NSTEPS}: {step}, loss {dbi_canonical.off_diagonal_norm}\")\n", " off_diagonal_norm_history_canonical.append(dbi_canonical.off_diagonal_norm)\n", @@ -144,9 +145,9 @@ "# add in initial values for plotting\n", "off_diagonal_norm_history_pauli = [dbi_pauli.off_diagonal_norm]\n", "steps_pauli_plot = [0]\n", - "scheduling = DoubleBracketScheduling.use_hyperopt\n", + "scheduling = DoubleBracketScheduling.simulated_annealing\n", "for _ in range(NSTEPS):\n", - " dbi_pauli, idx, step, flip_sign = select_best_dbr_generator(dbi_pauli, Z_ops, scheduling=scheduling, compare_canonical=False, max_evals=50)\n", + " dbi_pauli, idx, step, flip_sign = select_best_dbr_generator(dbi_pauli, Z_ops, scheduling=scheduling, compare_canonical=False)\n", " off_diagonal_norm_history_pauli.append(dbi_pauli.off_diagonal_norm)\n", " steps_pauli_plot.append(steps_pauli_plot[-1]+step)\n", " if flip_sign < 0:\n", @@ -193,7 +194,7 @@ "off_diagonal_norm_history_gradient = [dbi_gradient.off_diagonal_norm]\n", "steps_gradient_plot= [0]\n", "for _ in range(NSTEPS):\n", - " step, d_coef, d = gradient_descent_onsite_Z(dbi_gradient, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=50, n_taylor=5)\n", + " step, d_coef, d = gradient_descent_onsite_Z(dbi_gradient, d_coef, d, onsite_Z_ops=onsite_Z_ops)\n", " dbi_gradient(d=d,step=step)\n", " off_diagonal_norm_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", @@ -230,12 +231,210 @@ "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test on TFIM\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# backend\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", + "# initialize dbi object\n", + "# hamiltonian parameters\n", + "# define the hamiltonian\n", + "nqubits = 5\n", + "h = 1\n", + "H_TFIM = SymbolicHamiltonian( - h*symbols.Z(nqubits-1), nqubits=nqubits)\n", + "# add linear interaction terms\n", + "for i in range(nqubits-1):\n", + " H_TFIM -= SymbolicHamiltonian(symbols.X(i)*symbols.X(i+1) + h*symbols.Z(i), nqubits=nqubits)\n", + "H_TFIM = H_TFIM.dense\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.canonical)\n", + "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)\n", + "visualize_matrix(dbi.h.matrix, title=f'Random hamiltonian with L={nqubits}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# iterations steps\n", + "NSTEPS = 15\n", + "# choose polynomial scheduling\n", + "scheduling = DoubleBracketScheduling.simulated_annealing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Canonical" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# initialize DBI class for the canonical case\n", + "dbi_canonical = DoubleBracketIteration(deepcopy(H_TFIM), mode=DoubleBracketGeneratorType.canonical, scheduling=scheduling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Canonical\n", + "off_diagonal_norm_history_canonical = [dbi_canonical.off_diagonal_norm]\n", + "steps_canonical_plot = [0]\n", + "for s in range(NSTEPS):\n", + " # same settings as iteration from list\n", + " step = dbi_canonical.choose_step(d=dbi.diagonal_h_matrix)\n", + " dbi_canonical(step=step)\n", + " print(f\"New optimized step at iteration {s+1}/{NSTEPS}: {step}, loss {dbi_canonical.off_diagonal_norm}\")\n", + " off_diagonal_norm_history_canonical.append(dbi_canonical.off_diagonal_norm)\n", + " steps_canonical_plot.append(steps_canonical_plot[-1]+step)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pauli-Z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize DBI class for the Pauli-Z strategy\n", + "dbi_pauli = DoubleBracketIteration(deepcopy(H_TFIM), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generate_local_Z = generate_Z_operators(nqubits)\n", + "Z_ops = list(generate_local_Z.values())\n", + "Z_names = list(generate_local_Z.keys())\n", + "Z_optimal = []\n", + "# add in initial values for plotting\n", + "off_diagonal_norm_history_pauli = [dbi_pauli.off_diagonal_norm]\n", + "steps_pauli_plot = [0]\n", + "scheduling = DoubleBracketScheduling.simulated_annealing\n", + "for _ in range(NSTEPS):\n", + " dbi_pauli, idx, step, flip_sign = select_best_dbr_generator(dbi_pauli, Z_ops, scheduling=scheduling, compare_canonical=False)\n", + " off_diagonal_norm_history_pauli.append(dbi_pauli.off_diagonal_norm)\n", + " steps_pauli_plot.append(steps_pauli_plot[-1]+step)\n", + " if flip_sign < 0:\n", + " Z_optimal.append('-' + Z_names[idx])\n", + " else:\n", + " Z_optimal.append(Z_names[idx])\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with operator {Z_optimal[-1]}, loss {dbi_pauli.off_diagonal_norm}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Magnetic field" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize DBI class for the canonical case\n", + "dbi_gradient = DoubleBracketIteration(deepcopy(H_TFIM), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", + "d_coef = onsite_Z_decomposition(dbi_gradient.h.matrix, onsite_Z_ops)\n", + "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "initial_s = polynomial_step(dbi_object=dbi, d=d, n=4)\n", + "print(initial_s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "off_diagonal_norm_history_gradient = [dbi_gradient.off_diagonal_norm]\n", + "steps_gradient_plot= [0]\n", + "for _ in range(NSTEPS):\n", + " step, d_coef, d = gradient_descent_onsite_Z(dbi_gradient, d_coef, d, onsite_Z_ops=onsite_Z_ops)\n", + " dbi_gradient(d=d,step=step)\n", + " off_diagonal_norm_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", + " steps_gradient_plot.append(steps_gradient_plot[-1]+step)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.title(str(nqubits) + ' random Hamiltonian diagonalization')\n", + "plt.plot(off_diagonal_norm_history_canonical, label='canonical', marker='o')\n", + "plt.plot(off_diagonal_norm_history_pauli, label='Pauli-Z', marker='o')\n", + "plt.plot(off_diagonal_norm_history_gradient, label='gradient', marker='o')\n", + "plt.legend()\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.title(str(nqubits) + ' random Hamiltonian diagonalization')\n", + "plt.plot(steps_canonical_plot, off_diagonal_norm_history_canonical, marker='o', label='canonical')\n", + "plt.plot(steps_pauli_plot, off_diagonal_norm_history_pauli, marker='o', label='Pauli-Z')\n", + "plt.plot(steps_gradient_plot,off_diagonal_norm_history_gradient, marker='o', label='gradient')\n", + "plt.legend()\n", + "plt.xlabel('Duration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] } ], "metadata": { From 13ac2933fb32418eedf5497a22fddcfde6a0e895 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 2 Apr 2024 09:44:29 +0800 Subject: [PATCH 051/154] Fix lint error --- src/qibo/models/dbi/utils_scheduling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 77b05f5900..c9d2da1fe2 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -261,7 +261,7 @@ def dpolynomial_diDiagonal(dbi_object, d, H, i): np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s ) for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) + dGamma.append(dGamma_diDiagonal(dbi_object, d, H, n, i, dGamma, Gamma_list)) derivative += np.real( np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) ) From 8cc73f6066909bc3f0e7d7d09b8d8cf6bac1f3be Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 2 Apr 2024 10:04:10 +0800 Subject: [PATCH 052/154] Fix test error w energy fluctuation --- tests/test_models_dbi.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index b2716e4352..01f2cd7f0c 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -106,12 +106,9 @@ def test_energy_fluctuations(backend): h0 = np.array([[1, 0], [0, -1]]) h0 = backend.cast(h0, dtype=backend.dtype) - state = np.array([1, 0]) - state = backend.cast(state, dtype=backend.dtype) - dbi = DoubleBracketIteration(Hamiltonian(1, matrix=h0, backend=backend)) - energy_fluctuation = dbi.energy_fluctuation(state=state) - assert energy_fluctuation == 1.0 + energy_fluctuation = dbi.energy_fluctuation() + assert energy_fluctuation == 0.0 @pytest.mark.parametrize( From 39dbe4e9e2cd1b5893b912d307a2494f24dd746c Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 2 Apr 2024 10:53:42 +0800 Subject: [PATCH 053/154] Update notebook to run --- examples/dbi/dbi_costs.ipynb | 235 ++++------------------------------- 1 file changed, 27 insertions(+), 208 deletions(-) diff --git a/examples/dbi/dbi_costs.ipynb b/examples/dbi/dbi_costs.ipynb index 558f74cff3..e772b94bce 100644 --- a/examples/dbi/dbi_costs.ipynb +++ b/examples/dbi/dbi_costs.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -39,20 +39,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-15 18:17:05]: Using qibojit (numba) backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -70,19 +62,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.02021181818181818\n", - "hyperopt_search step: 0.2796044748864459\n", - "polynomial_approximation step: 0.016462159944159827\n" - ] - } - ], + "outputs": [], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", @@ -108,37 +90,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.02021181818181818\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -172,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -194,30 +148,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", @@ -239,20 +172,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-15 18:17:12]: Using qibojit (numba) backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 3\n", @@ -271,19 +196,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.8585872727272726\n", - "hyperopt_search step: 0.3413442272248831\n", - "polynomial_approximation step: 0.028303853122485182\n" - ] - } - ], + "outputs": [], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", @@ -309,37 +224,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.8585872727272726\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -365,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -383,40 +270,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Energy fluctuation')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", @@ -431,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -450,30 +306,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Iterations')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", "plt.figure()\n", @@ -485,25 +320,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[20], line 8\u001b[0m\n\u001b[0;32m 5\u001b[0m step \u001b[39m=\u001b[39m \u001b[39m1e-2\u001b[39m\n\u001b[0;32m 6\u001b[0m iterations \u001b[39m=\u001b[39m \u001b[39m100\u001b[39m\n\u001b[1;32m----> 8\u001b[0m d, loss, grad, diags \u001b[39m=\u001b[39m gradient_ascent(dbi, d,step, iterations)\n\u001b[0;32m 10\u001b[0m n \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:253\u001b[0m, in \u001b[0;36mgradient_ascent\u001b[1;34m(dbi_object, d, step, iterations)\u001b[0m\n\u001b[0;32m 250\u001b[0m diagonals[:,\u001b[39m0\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mdiag(d)\n\u001b[0;32m 252\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(iterations):\n\u001b[1;32m--> 253\u001b[0m grad[i,:] \u001b[39m=\u001b[39m gradientDiagonal(dbi_object, d, H)\n\u001b[0;32m 254\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(d)):\n\u001b[0;32m 255\u001b[0m d[j,j] \u001b[39m=\u001b[39m d[j,j] \u001b[39m+\u001b[39m step\u001b[39m*\u001b[39mgrad[i,j] \u001b[39m# note the plus sign as we maximize the potential\u001b[39;00m\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:237\u001b[0m, in \u001b[0;36mgradientDiagonal\u001b[1;34m(dbi_object, d, H)\u001b[0m\n\u001b[0;32m 235\u001b[0m grad \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros(\u001b[39mlen\u001b[39m(d))\n\u001b[0;32m 236\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(d)):\n\u001b[1;32m--> 237\u001b[0m derivative \u001b[39m=\u001b[39m dpolynomial_diDiagonal(dbi_object,d,H,i)\n\u001b[0;32m 238\u001b[0m grad[i] \u001b[39m=\u001b[39m derivative\u001b[39m-\u001b[39md[i,i]\n\u001b[0;32m 239\u001b[0m \u001b[39mreturn\u001b[39;00m grad\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:224\u001b[0m, in \u001b[0;36mdpolynomial_diDiagonal\u001b[1;34m(dbi_object, d, H, i)\u001b[0m\n\u001b[0;32m 220\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdpolynomial_diDiagonal\u001b[39m(dbi_object, d,H,i):\n\u001b[0;32m 221\u001b[0m \u001b[39m# Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz)\u001b[39;00m\n\u001b[0;32m 222\u001b[0m \u001b[39m# Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation\u001b[39;00m\n\u001b[0;32m 223\u001b[0m derivative \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m--> 224\u001b[0m s \u001b[39m=\u001b[39m polynomial_step(dbi_object, d, H, i)\n\u001b[0;32m 225\u001b[0m A \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros(d\u001b[39m.\u001b[39mshape)\n\u001b[0;32m 226\u001b[0m Gamma_list \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mgenerate_Gamma_list(\u001b[39m4\u001b[39m, d)\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\utils_scheduling.py:127\u001b[0m, in \u001b[0;36mpolynomial_step\u001b[1;34m(dbi_object, n, n_max, d, coef, cost)\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[39mif\u001b[39;00m d \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 125\u001b[0m d \u001b[39m=\u001b[39m dbi_object\u001b[39m.\u001b[39mdiagonal_h_matrix\n\u001b[1;32m--> 127\u001b[0m \u001b[39mif\u001b[39;00m n \u001b[39m>\u001b[39;49m n_max:\n\u001b[0;32m 128\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 129\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mNo solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 130\u001b[0m )\n\u001b[0;32m 131\u001b[0m \u001b[39mif\u001b[39;00m coef \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", - "\u001b[1;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" - ] - } - ], + "outputs": [], "source": [ "cost = DoubleBracketCostFunction.least_squares\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", @@ -534,7 +353,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.11.7" }, "orig_nbformat": 4, "vscode": { From 709dcd335416486039e5e22ac15e88ceee5cb8ad Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Tue, 2 Apr 2024 11:24:11 +0200 Subject: [PATCH 054/154] added more comments on functions --- src/qibo/models/dbi/double_bracket.py | 61 ++++----- src/qibo/models/dbi/utils.py | 2 +- src/qibo/models/dbi/utils_scheduling.py | 160 +++++++++++++++--------- 3 files changed, 136 insertions(+), 87 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index b09ca8bbee..0369e0c521 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -6,11 +6,23 @@ import numpy as np from qibo.hamiltonians import Hamiltonian + +class DoubleBracketCost(Enum): + """Define the DBI cost function.""" + + off_diagonal_norm = auto() + """Use off-diagonal norm as cost function.""" + least_squares = auto() + """Use least squares as cost function.""" + energy_fluctuation = auto() + """Use energy fluctuation as cost function.""" + + from qibo.models.dbi.utils_scheduling import ( grid_search_step, hyperopt_step, polynomial_step, - simulated_annealing_step, + #simulated_annealing_step, ) @@ -35,19 +47,11 @@ class DoubleBracketScheduling(Enum): """Use greedy grid search.""" polynomial_approximation = polynomial_step """Use polynomial expansion (analytical) of the loss function.""" - simulated_annealing = simulated_annealing_step + #simulated_annealing = simulated_annealing_step """Use simulated annealing algorithm""" -class DoubleBracketCostFunction(Enum): - """Define the DBI cost function.""" - off_diagonal_norm = auto() - """Use off-diagonal norm as cost function.""" - least_squares = auto() - """Use least squares as cost function.""" - energy_fluctuation = auto() - """Use energy fluctuation as cost function.""" class DoubleBracketIteration: @@ -79,8 +83,8 @@ def __init__( hamiltonian: Hamiltonian, mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.canonical, scheduling: DoubleBracketScheduling = DoubleBracketScheduling.grid_search, - cost: DoubleBracketCostFunction = DoubleBracketCostFunction.off_diagonal_norm, - state: int = 0, + cost: DoubleBracketCost = DoubleBracketCost.off_diagonal_norm, + state: np.array = None, ): self.h = hamiltonian self.h0 = deepcopy(self.h) @@ -151,11 +155,11 @@ def backend(self): """Get Hamiltonian's backend.""" return self.h0.backend - def least_squares(self, D: np.array): - """Least squares cost function.""" - H = self.h.matrix - return -np.real( - np.trace(H @ D) - 0.5 * (np.linalg.norm(H) ** 2 + np.linalg.norm(D) ** 2) + def least_squares(self, d: np.array): + """Least squares cost function. (without the constant term norm(H))""" + h = self.h.matrix + return np.real( + 0.5 * np.linalg.norm(d) ** 2 - np.trace(h @ d) ) def choose_step( @@ -193,11 +197,11 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): self.__call__(mode=self.mode, step=step, d=d) # loss values depending on the cost function - if self.cost == DoubleBracketCostFunction.off_diagonal_norm: + if self.cost == DoubleBracketCost.off_diagonal_norm: loss = self.off_diagonal_norm - elif self.cost == DoubleBracketCostFunction.least_squares: + elif self.cost == DoubleBracketCost.least_squares: loss = self.least_squares(d) - elif self.cost == DoubleBracketCostFunction.energy_fluctuation: + elif self.cost == DoubleBracketCost.energy_fluctuation: loss = self.energy_fluctuation(self.state) # set back the initial configuration @@ -205,7 +209,7 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): return loss - def energy_fluctuation(self, state=None): + def energy_fluctuation(self, state): """ Evaluate energy fluctuation @@ -217,19 +221,16 @@ def energy_fluctuation(self, state=None): Args: state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ - if state is None: - state = self.state - state_vector = np.zeros(len(self.h.matrix)) - state_vector[state] = 1.0 - return np.real(self.h.energy_fluctuation(state_vector)) + + return np.real(self.h.energy_fluctuation(state)) def sigma(self, h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) def generate_Gamma_list(self, n: int, d: np.array): r"""Computes the n-nested Gamma functions, where $\Gamma_k=[W,...,[W,[W,H]]...]$, where we take k nested commutators with $W = [D, H]$""" - W = self.commutator(d, self.sigma(self.h.matrix)) - Gamma_list = [self.h.matrix] + w = self.commutator(d, self.sigma(self.h.matrix)) + gamma_list = [self.h.matrix] for _ in range(n - 1): - Gamma_list.append(self.commutator(W, Gamma_list[-1])) - return Gamma_list + gamma_list.append(self.commutator(w, gamma_list[-1])) + return gamma_list diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index d80d453f7d..3556eb0bd2 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -158,7 +158,7 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) + W = dbi_object.commutator(d, dbi_object.h.matrix) Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 87ab83a60e..cc279cc493 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -2,6 +2,7 @@ from copy import deepcopy from functools import partial from typing import Optional +from qibo.models.dbi.double_bracket import DoubleBracketCost import hyperopt import numpy as np @@ -9,21 +10,21 @@ error = 1e-3 -def commutator(A, B): +def commutator(a, b): """Compute commutator between two arrays.""" - return A @ B - B @ A + return a @ b - b @ a -def variance(A, state): +def variance(a, state): """Calculates the variance of a matrix A with respect to a state: Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" - B = A @ A - return B[state, state] - A[state, state] ** 2 + b = a @ a + return b[state, state] - a[state, state] ** 2 -def covariance(A, B, state): +def covariance(a, b, state): """Calculates the covariance of two matrices A and B with respect to a state: Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$""" - C = A @ B + B @ A - return C[state, state] - 2 * A[state, state] * B[state, state] + c = a @ b + b @ a + return c[state, state] - 2 * a[state, state] * b[state, state] def grid_search_step( @@ -107,7 +108,7 @@ def polynomial_step( n_max: int = 5, d: np.array = None, coef: Optional[list] = None, - cost: str = None, + cost: DoubleBracketCost = None, ): r""" Optimizes iteration step by solving the n_th order polynomial expansion of the loss function. @@ -119,7 +120,7 @@ def polynomial_step( backup_scheduling (`DoubleBracketScheduling`): the scheduling method to use in case no real positive roots are found. """ if cost is None: - cost = dbi_object.cost.name + cost = dbi_object.cost if d is None: d = dbi_object.diagonal_h_matrix @@ -129,11 +130,11 @@ def polynomial_step( "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." ) if coef is None: - if cost == "off_diagonal_norm": + if cost == DoubleBracketCost.off_diagonal_norm: coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n) - elif cost == "least_squares": + elif cost == DoubleBracketCost.least_squares: coef = least_squares_polynomial_expansion_coef(dbi_object, d, n) - elif cost == "energy_fluctuation": + elif cost == DoubleBracketCost.energy_fluctuation: coef = energy_fluctuation_polynomial_expansion_coef( dbi_object, d, n, dbi_object.state ) @@ -171,11 +172,11 @@ def d_ansatz(params, type = 'Full'): if type == 'Pauli': d = np.zeros((2**len(params), 2**len(params))) - Z = np.array([[1, 0], [0, -1]]) + z = np.array([[1, 0], [0, -1]]) for i in range(len(params)): - I1 = np.eye(2**i) - I2 = np.eye(2**(len(params)-i-1)) - d += params[i]*np.kron(I1,np.kron(Z,I2)) + i1 = np.eye(2**i) + i2 = np.eye(2**(len(params)-i-1)) + d += params[i]*np.kron(i1,np.kron(z,i2)) return d @@ -184,9 +185,9 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) - Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) - sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) + + gamma_list = dbi_object.generate_Gamma_list(n + 2, d) + sigma_Gamma_list = list(map(dbi_object.sigma, gamma_list)) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients for rotation with [W,H] and H c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] @@ -207,12 +208,12 @@ def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: i if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + gamma_list = dbi_object.generate_Gamma_list(n + 1, d) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients coef = np.empty(n) for i in range(n): - coef[i] = np.real(exp_list[i] * np.trace(d @ Gamma_list[i + 1])) + coef[i] = np.real(exp_list[i] * np.trace(d @ gamma_list[i + 1])) coef = list(reversed(coef)) return coef @@ -224,67 +225,114 @@ def energy_fluctuation_polynomial_expansion_coef( if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + gamma_list = dbi_object.generate_Gamma_list(n + 1, d) # coefficients coef = np.empty(3) - coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1], state)) - coef[1] = np.real(2 * variance(Gamma_list[1], state)) + coef[0] = np.real(2 * covariance(gamma_list[0], gamma_list[1], state)) + coef[1] = np.real(2 * variance(gamma_list[1], state)) coef[2] = np.real( - covariance(Gamma_list[0], Gamma_list[3], state) - + 3 * covariance(Gamma_list[1], Gamma_list[2], state) + covariance(gamma_list[0], gamma_list[3], state) + + 3 * covariance(gamma_list[1], gamma_list[2], state) ) coef = list(reversed(coef)) return coef # D GRADIENTS -def dGamma_diDiagonal(dbi_object, d, H, n, i,dGamma, Gamma_list): - # Derivative of gamma with respect to diagonal elements of D (full-diagonal ansatz) - A = np.zeros(d.shape) - A[i, i] = 1 - B = commutator(commutator(A, H), Gamma_list[n - 1]) - W = commutator(d, H) - return B + commutator(W, dGamma[-1]) - - -def dpolynomial_diDiagonal(dbi_object, d, H, i): - # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) - # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation +def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): + r""" + Gradient of the nth gamma operator with respect to the ith diagonal elements of D. + Args: + d(np.array): D operator. + h(np.array): Hamiltonian. + n(int): nth Gamma operator. + i(int): Index of the diagonal element of D. + dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). + gamma_list(list): List of the n gamma operators. + Returns: + (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. + """ + dD_di = np.zeros(d.shape) + dD_di[i, i] = 1 + dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) + w = commutator(d, h) + return dW_di + commutator(w, dGamma[-1]) + + +def dpolynomial_diDiagonal(dbi_object, d, h, i): + r""" + Gradient of the polynomial expansion with respect to the ith diagonal elements of D. + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + d(np.array): D operator. + h(np.array): Hamiltonian. + i(int): Index of the diagonal element of D. + Returns: + derivative(float): Derivative of the polynomial expansion with respect to the ith diagonal elements of D. + """ derivative = 0 s = polynomial_step(dbi_object, n=3, d=d) - A = np.zeros(d.shape) - Gamma_list = dbi_object.generate_Gamma_list(4, d) - A[i, i] = 1 - dGamma = [commutator(A, H)] + dD_di = np.zeros(d.shape) + gamma_list = dbi_object.generate_Gamma_list(4, d) + dD_di[i, i] = 1 + dGamma = [commutator(dD_di, h)] derivative += np.real( - np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s + np.trace(gamma_list[0] @ dD_di) + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s ) for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) + dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) derivative += np.real( - np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) + np.trace(dGamma[-1] @ d + gamma_list[n] @ dD_di) * s**n / math.factorial(n) ) return derivative -def gradientDiagonal(dbi_object, params, H, analytic = True, ansatz = 'Full', h = 1e-4): - # Gradient of potential function with respect to diagonal elements of D (full-diagonal ansatz) +def gradientDiagonalEntries(dbi_object, params, h, analytic = True, ansatz = 'Full', delta = 1e-4): + r""" + Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + h(np.array): Hamiltonian. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + ansatz(str): Ansatz used for the D operator. Options are 'Full' and 'Pauli'. + delta(float): Step size for numerical gradient. + Returns: + grad(np.array): Gradient of the D operator. + """ + grad = np.zeros(len(params)) d = d_ansatz(params, ansatz) if analytic == True: for i in range(len(params)): - derivative = dpolynomial_diDiagonal(dbi_object,d,H,i) + derivative = dpolynomial_diDiagonal(dbi_object,d,h,i) grad[i] = d[i,i]-derivative else: for i in range(len(params)): params_new = deepcopy(params) - params_new[i] += h + params_new[i] += delta d_new = d_ansatz(params_new, ansatz) - grad[i] = (dbi_object.least_squares(d_new)-dbi_object.least_squares(d))/h + grad[i] = (dbi_object.least_squares(d_new)-dbi_object.least_squares(d))/delta return grad -def gradient_ascent(dbi_object, params, step, iterations, analytic = True, ansatz = 'Full'): - H = dbi_object.h.matrix +def gradient_descent(dbi_object, params, iterations, lr = 1e-2, analytic = True, ansatz = 'Full'): + r""" + Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + iterations(int): Number of gradient descent iterations. + lr(float): Learning rate. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + ansatz(str): Ansatz used for the D operator. Options are 'Full' and 'Pauli'. + Returns: + d(np.array): Optimized D operator. + loss(np.array): Loss function evaluated at each iteration. + grad(np.array): Gradient evaluated at each iteration. + params_hist(np.array): Parameters evaluated at each iteration. + """ + + h = dbi_object.h.matrix d = d_ansatz(params,ansatz) loss = np.zeros(iterations+1) grad = np.zeros((iterations,len(params))) @@ -297,10 +345,10 @@ def gradient_ascent(dbi_object, params, step, iterations, analytic = True, ansat for i in range(iterations): dbi_new = deepcopy(dbi_object) - grad[i,:] = gradientDiagonal(dbi_object, params, H, analytic=analytic, ansatz=ansatz) + grad[i,:] = gradientDiagonalEntries(dbi_object, params, h, analytic=analytic, ansatz=ansatz) for j in range(len(params)): - params[j] = params[j] - step*grad[i,j] - d = d_ansatz(params,ansatz) + params[j] = params[j] - lr*grad[i,j] + d = d_ansatz(params, ansatz) s = polynomial_step(dbi_object, n = 3, d=d) dbi_new(s,d=d) loss[i+1] = dbi_new.least_squares(d) From 573b6c9abe52dd0924006c755d11d1d2cedbfb89 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Apr 2024 09:26:34 +0000 Subject: [PATCH 055/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/double_bracket.py | 13 ++---- src/qibo/models/dbi/utils_scheduling.py | 62 +++++++++++++++---------- 2 files changed, 41 insertions(+), 34 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 0369e0c521..a2efdc84bc 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -7,6 +7,7 @@ from qibo.hamiltonians import Hamiltonian + class DoubleBracketCost(Enum): """Define the DBI cost function.""" @@ -18,11 +19,10 @@ class DoubleBracketCost(Enum): """Use energy fluctuation as cost function.""" -from qibo.models.dbi.utils_scheduling import ( +from qibo.models.dbi.utils_scheduling import ( # simulated_annealing_step, grid_search_step, hyperopt_step, polynomial_step, - #simulated_annealing_step, ) @@ -47,13 +47,10 @@ class DoubleBracketScheduling(Enum): """Use greedy grid search.""" polynomial_approximation = polynomial_step """Use polynomial expansion (analytical) of the loss function.""" - #simulated_annealing = simulated_annealing_step + # simulated_annealing = simulated_annealing_step """Use simulated annealing algorithm""" - - - class DoubleBracketIteration: """ Class implementing the Double Bracket iteration algorithm. @@ -158,9 +155,7 @@ def backend(self): def least_squares(self, d: np.array): """Least squares cost function. (without the constant term norm(H))""" h = self.h.matrix - return np.real( - 0.5 * np.linalg.norm(d) ** 2 - np.trace(h @ d) - ) + return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h @ d)) def choose_step( self, diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 3addd16b95..357aad16fa 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -2,11 +2,12 @@ from copy import deepcopy from functools import partial from typing import Optional -from qibo.models.dbi.double_bracket import DoubleBracketCost import hyperopt import numpy as np +from qibo.models.dbi.double_bracket import DoubleBracketCost + error = 1e-3 @@ -171,14 +172,14 @@ def d_ansatz(params, type="Full"): for i in range(len(params)): d[i, i] = params[i] - if type == 'Pauli': - d = np.zeros((2**len(params), 2**len(params))) + if type == "Pauli": + d = np.zeros((2 ** len(params), 2 ** len(params))) z = np.array([[1, 0], [0, -1]]) for i in range(len(params)): i1 = np.eye(2**i) - i2 = np.eye(2**(len(params)-i-1)) - d += params[i]*np.kron(i1,np.kron(z,i2)) - + i2 = np.eye(2 ** (len(params) - i - 1)) + d += params[i] * np.kron(i1, np.kron(z, i2)) + return d @@ -186,7 +187,7 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - + gamma_list = dbi_object.generate_Gamma_list(n + 2, d) sigma_Gamma_list = list(map(dbi_object.sigma, gamma_list)) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) @@ -278,7 +279,8 @@ def dpolynomial_diDiagonal(dbi_object, d, h, i): dD_di[i, i] = 1 dGamma = [commutator(dD_di, h)] derivative += np.real( - np.trace(gamma_list[0] @ dD_di) + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s + np.trace(gamma_list[0] @ dD_di) + + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s ) for n in range(2, 4): dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) @@ -288,7 +290,10 @@ def dpolynomial_diDiagonal(dbi_object, d, h, i): return derivative -def gradientDiagonalEntries(dbi_object, params, h, analytic = True, ansatz = 'Full', delta = 1e-4): + +def gradientDiagonalEntries( + dbi_object, params, h, analytic=True, ansatz="Full", delta=1e-4 +): r""" Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. Args: @@ -306,17 +311,22 @@ def gradientDiagonalEntries(dbi_object, params, h, analytic = True, ansatz = 'Fu d = d_ansatz(params, ansatz) if analytic == True: for i in range(len(params)): - derivative = dpolynomial_diDiagonal(dbi_object,d,h,i) - grad[i] = d[i,i]-derivative + derivative = dpolynomial_diDiagonal(dbi_object, d, h, i) + grad[i] = d[i, i] - derivative else: for i in range(len(params)): params_new = deepcopy(params) params_new[i] += delta d_new = d_ansatz(params_new, ansatz) - grad[i] = (dbi_object.least_squares(d_new)-dbi_object.least_squares(d))/delta + grad[i] = ( + dbi_object.least_squares(d_new) - dbi_object.least_squares(d) + ) / delta return grad -def gradient_descent(dbi_object, params, iterations, lr = 1e-2, analytic = True, ansatz = 'Full'): + +def gradient_descent( + dbi_object, params, iterations, lr=1e-2, analytic=True, ansatz="Full" +): r""" Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. Args: @@ -334,9 +344,9 @@ def gradient_descent(dbi_object, params, iterations, lr = 1e-2, analytic = True, """ h = dbi_object.h.matrix - d = d_ansatz(params,ansatz) - loss = np.zeros(iterations+1) - grad = np.zeros((iterations,len(params))) + d = d_ansatz(params, ansatz) + loss = np.zeros(iterations + 1) + grad = np.zeros((iterations, len(params))) dbi_new = deepcopy(dbi_object) s = polynomial_step(dbi_object, n=3, d=d) dbi_new(s, d=d) @@ -346,13 +356,15 @@ def gradient_descent(dbi_object, params, iterations, lr = 1e-2, analytic = True, for i in range(iterations): dbi_new = deepcopy(dbi_object) - grad[i,:] = gradientDiagonalEntries(dbi_object, params, h, analytic=analytic, ansatz=ansatz) + grad[i, :] = gradientDiagonalEntries( + dbi_object, params, h, analytic=analytic, ansatz=ansatz + ) for j in range(len(params)): - params[j] = params[j] - lr*grad[i,j] - d = d_ansatz(params, ansatz) - s = polynomial_step(dbi_object, n = 3, d=d) - dbi_new(s,d=d) - loss[i+1] = dbi_new.least_squares(d) - params_hist[:,i+1] = params - - return d,loss,grad,params_hist \ No newline at end of file + params[j] = params[j] - lr * grad[i, j] + d = d_ansatz(params, ansatz) + s = polynomial_step(dbi_object, n=3, d=d) + dbi_new(s, d=d) + loss[i + 1] = dbi_new.least_squares(d) + params_hist[:, i + 1] = params + + return d, loss, grad, params_hist From 1f22e0eb2f0b4bb4fd2df0c3febc13b68fb34323 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 4 Apr 2024 10:04:33 +0800 Subject: [PATCH 056/154] Code adding Ising model to magnetic field, remaining test --- examples/dbi/dbi_strategy_Ising_model.ipynb | 1177 +++++++++++++++++++ src/qibo/models/dbi/utils.py | 114 +- 2 files changed, 1243 insertions(+), 48 deletions(-) create mode 100644 examples/dbi/dbi_strategy_Ising_model.ipynb diff --git a/examples/dbi/dbi_strategy_Ising_model.ipynb b/examples/dbi/dbi_strategy_Ising_model.ipynb new file mode 100644 index 0000000000..159560f792 --- /dev/null +++ b/examples/dbi/dbi_strategy_Ising_model.ipynb @@ -0,0 +1,1177 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Double-Bracket Iteration Strategy: magnetic field (Ising model)\n", + "This notebook shows the diagonalization process of DBI using the magnetic field strategy, which varies the diagonal operator $D$ by gradient descent. To find the gradient with respect to $D$, parameterization of $D$ is required. For the purpose of this notebook, we represent it by the Ising model, i.e.\n", + "\n", + "$$ D = \\sum \\alpha_i Z_i +\\sum \\beta_{ij}Z_iZ_j$$\n", + "\n", + "\n", + "The gradients are calculated under the premise that the diagonalization gain curve can be fitted by a polynomial, and that the iteration step duration is taken at the first dip of the curve." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.hamiltonians import Hamiltonian, SymbolicHamiltonian\n", + "from qibo.quantum_info import random_hermitian\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", + "from qibo.models.dbi.utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_matrix(matrix, title=\"\"):\n", + " \"\"\"Visualize hamiltonian in a heatmap form.\"\"\"\n", + " fig, ax = plt.subplots(figsize=(5,5))\n", + " ax.set_title(title)\n", + " try:\n", + " im = ax.imshow(np.absolute(matrix), cmap=\"inferno\")\n", + " except TypeError:\n", + " im = ax.imshow(np.absolute(matrix.get()), cmap=\"inferno\")\n", + " fig.colorbar(im, ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test on random Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-04-03 08:21:24]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 31.576176740060667\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# backend\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", + "# initialize dbi object\n", + "nqubits = 5\n", + "h0 = random_hermitian(2**nqubits, seed=2)\n", + "scheduling = DoubleBracketScheduling.hyperopt\n", + "mode = DoubleBracketGeneratorType.single_commutator\n", + "n_taylor = 5\n", + "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", + "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)\n", + "visualize_matrix(dbi.h.matrix, title=f'Random hamiltonian with L={nqubits}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Order 1: $D=\\sum \\alpha_iZ_i$" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 572.27trial/s, best loss: 27.607173414553387]\n", + "The initial D coefficients: [(-0.2980910136757636+0j), (-0.17678355790937256+0j), (0.294550421681131+0j), (-0.2301056409534723+0j), (-0.07297191764284382+0j)]\n", + "Gradient: [-0.20481773 0.41841615 -0.03164361 0.18666951 -0.86436728]\n", + "s: 0.11659660342715238\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pethidine/Documents/GitHub/qibo/src/qibo/models/dbi/utils.py:268: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " grad[i] = (\n" + ] + } + ], + "source": [ + "# generate pauli_operator_dict\n", + "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=1)\n", + "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", + "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", + "grad, s = gradient_Pauli(dbi, d=d, pauli_operator_dict=pauli_operator_dict)\n", + "print('The initial D coefficients:', d_coef)\n", + "print('Gradient:', grad)\n", + "print('s:', s)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 656.84trial/s, best loss: 27.607178280638898]\n", + "100%|██████████| 500/500 [00:00<00:00, 639.38trial/s, best loss: 24.351929237913915]\n", + "100%|██████████| 500/500 [00:00<00:00, 655.61trial/s, best loss: 22.089739447397726]\n", + "100%|██████████| 500/500 [00:00<00:00, 663.89trial/s, best loss: 20.346440770606122]\n", + "100%|██████████| 500/500 [00:00<00:00, 639.24trial/s, best loss: 18.946903760228178]\n", + "100%|██████████| 500/500 [00:00<00:00, 650.95trial/s, best loss: 17.7755177782418] \n", + "100%|██████████| 500/500 [00:00<00:00, 674.60trial/s, best loss: 16.785446486734276]\n", + "100%|██████████| 500/500 [00:00<00:00, 664.40trial/s, best loss: 15.933524819099162] \n", + "100%|██████████| 500/500 [00:00<00:00, 655.76trial/s, best loss: 15.195995775240494]\n", + "100%|██████████| 500/500 [00:00<00:00, 633.46trial/s, best loss: 14.474767679378404]\n", + "100%|██████████| 500/500 [00:00<00:00, 638.92trial/s, best loss: 14.025120975809307]\n", + "100%|██████████| 500/500 [00:00<00:00, 603.17trial/s, best loss: 13.622403395436098]\n", + "100%|██████████| 500/500 [00:00<00:00, 670.03trial/s, best loss: 13.244304046805466]\n", + "100%|██████████| 500/500 [00:00<00:00, 666.31trial/s, best loss: 12.938522658932913] \n", + "100%|██████████| 500/500 [00:00<00:00, 656.53trial/s, best loss: 12.622483913681776]\n", + "100%|██████████| 500/500 [00:00<00:00, 585.38trial/s, best loss: 12.260523262483813]\n", + "100%|██████████| 500/500 [00:00<00:00, 645.94trial/s, best loss: 11.863294969541887]\n", + "100%|██████████| 500/500 [00:00<00:00, 697.70trial/s, best loss: 11.477785002400976] \n", + "100%|██████████| 500/500 [00:00<00:00, 684.35trial/s, best loss: 11.090736331075858] \n", + "100%|██████████| 500/500 [00:00<00:00, 739.76trial/s, best loss: 10.686003198269908] \n", + "100%|██████████| 500/500 [00:00<00:00, 717.78trial/s, best loss: 10.057480301384102] \n", + "100%|██████████| 500/500 [00:00<00:00, 712.94trial/s, best loss: 10.058298967085108] \n", + "100%|██████████| 500/500 [00:00<00:00, 724.09trial/s, best loss: 10.059015434243745] \n", + "100%|██████████| 500/500 [00:00<00:00, 709.43trial/s, best loss: 10.060281060360927] \n", + "100%|██████████| 500/500 [00:00<00:00, 740.79trial/s, best loss: 10.060583338964047]\n", + "100%|██████████| 500/500 [00:00<00:00, 733.24trial/s, best loss: 10.061781462286367] \n", + "100%|██████████| 500/500 [00:00<00:00, 725.24trial/s, best loss: 10.06205592136255]\n", + "100%|██████████| 500/500 [00:00<00:00, 732.59trial/s, best loss: 10.06257005000825] \n", + "100%|██████████| 500/500 [00:00<00:00, 750.26trial/s, best loss: 10.063192908922257] \n", + "100%|██████████| 500/500 [00:00<00:00, 710.47trial/s, best loss: 10.063501140769239] \n" + ] + } + ], + "source": [ + "iters = 30\n", + "off_diagonal_norm_1 = [dbi.off_diagonal_norm]\n", + "s_step = [0]\n", + "for i in range(iters):\n", + " s, d_coef, d = gradient_descent_pauli(dbi, d_coef=d_coef, d=d, pauli_operator_dict=pauli_operator_dict, max_evals=100)\n", + " dbi(step=s, d=d)\n", + " off_diagonal_norm_1.append(dbi.off_diagonal_norm)\n", + " s_step.append(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(str(nqubits) + ' spins random hamiltonian')\n", + "plt.plot(off_diagonal_norm_1)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Order 2: $D=\\sum \\alpha_iZ_i + \\beta_{ij}Z_iZ_j$" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 692.10trial/s, best loss: 27.60718707087908]\n", + "The initial D coefficients: [(-0.2980910136757636+0j), (-0.17678355790937256+0j), (0.294550421681131+0j), (-0.2301056409534723+0j), (-0.07297191764284382+0j), (0.15766557989586075+0j), (0.1267267343988193+0j), (0.10801310050393904+0j), (0.37981790024704787+0j), (0.25748679935414437+0j), (-0.2531727610451514+0j), (0.13345922163435078+0j), (-0.02961684041039575+0j), (0.36206828748686004+0j), (0.12254924877248492+0j)]\n", + "Gradient: [-0.07705712 0.15660998 -0.01151922 0.06967692 -0.32391798 -0.15885245\n", + " 0.16488954 0.11742344 0.44237635 0.39554303 -0.59878233 -0.03326744\n", + " 0.2060505 0.08567309 0.17837982]\n", + "s: 0.11627601978827411\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pethidine/Documents/GitHub/qibo/src/qibo/models/dbi/utils.py:268: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " grad[i] = (\n" + ] + } + ], + "source": [ + "# generate pauli_operator_dict\n", + "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", + "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", + "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", + "grad, s = gradient_Pauli(dbi, d=d, pauli_operator_dict=pauli_operator_dict)\n", + "print('The initial D coefficients:', d_coef)\n", + "print('Gradient:', grad)\n", + "print('s:', s)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 693.59trial/s, best loss: 27.60716505293791] \n", + "100%|██████████| 500/500 [00:00<00:00, 739.77trial/s, best loss: 24.351930977389397] \n", + "100%|██████████| 500/500 [00:00<00:00, 685.07trial/s, best loss: 22.089870557907307] \n", + "100%|██████████| 500/500 [00:00<00:00, 674.17trial/s, best loss: 20.346996185055133]\n", + "100%|██████████| 500/500 [00:00<00:00, 717.27trial/s, best loss: 18.942039981805472]\n", + "100%|██████████| 500/500 [00:00<00:00, 747.61trial/s, best loss: 17.152907499929672] \n", + "100%|██████████| 500/500 [00:00<00:00, 738.25trial/s, best loss: 16.019645236154144]\n", + "100%|██████████| 500/500 [00:00<00:00, 744.74trial/s, best loss: 15.2613302219324] \n", + "100%|██████████| 500/500 [00:00<00:00, 742.43trial/s, best loss: 14.603236439507265] \n", + "100%|██████████| 500/500 [00:00<00:00, 747.77trial/s, best loss: 14.050749361766682] \n", + "100%|██████████| 500/500 [00:00<00:00, 665.47trial/s, best loss: 13.555811973998546] \n", + "100%|██████████| 500/500 [00:00<00:00, 741.86trial/s, best loss: 13.11437793825614] \n", + "100%|██████████| 500/500 [00:00<00:00, 730.33trial/s, best loss: 12.708028521672343] \n", + "100%|██████████| 500/500 [00:00<00:00, 696.14trial/s, best loss: 12.338996561474936] \n", + "100%|██████████| 500/500 [00:00<00:00, 745.68trial/s, best loss: 11.96195414708825] \n", + "100%|██████████| 500/500 [00:00<00:00, 729.21trial/s, best loss: 11.962452492607511] \n", + "100%|██████████| 500/500 [00:00<00:00, 730.13trial/s, best loss: 11.963468727156927] \n", + "100%|██████████| 500/500 [00:00<00:00, 735.49trial/s, best loss: 11.965058898358617] \n", + "100%|██████████| 500/500 [00:00<00:00, 735.24trial/s, best loss: 11.965394318636747] \n", + "100%|██████████| 500/500 [00:00<00:00, 729.17trial/s, best loss: 11.966117166464612] \n", + "100%|██████████| 500/500 [00:00<00:00, 728.15trial/s, best loss: 11.966425772210973] \n", + "100%|██████████| 500/500 [00:00<00:00, 723.59trial/s, best loss: 11.966970026858228] \n", + "100%|██████████| 500/500 [00:00<00:00, 732.31trial/s, best loss: 11.967330716148668] \n", + "100%|██████████| 500/500 [00:00<00:00, 734.26trial/s, best loss: 11.96780433370658] \n", + "100%|██████████| 500/500 [00:00<00:00, 733.15trial/s, best loss: 11.969207838189973] \n", + "100%|██████████| 500/500 [00:00<00:00, 732.58trial/s, best loss: 11.969502660368933] \n", + "100%|██████████| 500/500 [00:00<00:00, 696.01trial/s, best loss: 11.969798703907593] \n", + "100%|██████████| 500/500 [00:00<00:00, 734.39trial/s, best loss: 11.970102117939215] \n", + "100%|██████████| 500/500 [00:00<00:00, 733.83trial/s, best loss: 11.971312239622172] \n", + "100%|██████████| 500/500 [00:00<00:00, 737.13trial/s, best loss: 11.97187547672911] \n" + ] + } + ], + "source": [ + "iters = 30\n", + "off_diagonal_norm_2 = [dbi.off_diagonal_norm]\n", + "s_step = [0]\n", + "for i in range(iters):\n", + " s, d_coef, d = gradient_descent_pauli(dbi, d_coef=d_coef, d=d, pauli_operator_dict=pauli_operator_dict, max_evals=100)\n", + " dbi(step=s, d=d)\n", + " off_diagonal_norm_2.append(dbi.off_diagonal_norm)\n", + " s_step.append(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(str(nqubits) + ' spins random hamiltonian')\n", + "plt.plot(off_diagonal_norm_1, label='order 1')\n", + "plt.plot(off_diagonal_norm_2, label='order 2')\n", + "plt.legend()\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test on TFIM\n", + "Here we choose to customize our TFIM in the X axis using `SymbolicHamiltonian`. It is also possible to use Hadamard gate to rotate the TFIM inbuilt in `qibo`.\n", + "\n", + "$$ H = -(\\sum X_i X_{i+1} + \\sum hZ_i)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-04-03 08:14:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "data": { + "image/png": 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BAHTBuDCP55saT01NjZKTk4OHr1Tbqamp0c9//nPt3LlTAwYM6NYlCTwAYCsXJ5AmJyeHBJ4rOXjwoOrq6jRhwoTgsZaWFu3du1dr165Vc3Oz+vbt22keBB4AgGN33XWXjhw5EnLsoYce0o033qgnn3yyy6AjEXgAwF5RWDInKSlJOTk5IceuuuoqXXPNNe2OXwmBBwAsZQKR71wdhZ2vCTwAgMiUl5eHdT6BBwBsZenq1L0+8Lg96czpxNBYnfQZV5PrEJfiaqJpQC4EHjcKEh4mkAIAPOV64CkuLpbP5wtJaWlpbl8GABBwKXmsR5rabr75Zu3atSv42sm4bgBAmMw3KdI8PNYjgadfv36OaznNzc1qbm4Ovr58oToAQO/SI308VVVVysjIUHZ2tubNm6fPPvvsiueWlJSELEyXmZnZE0UCgF7HBHyuJK+5HngmTZqk1157TTt27NCLL76o2tpaTZkyRadPn+7w/KVLl6q+vj6Yampq3C4SAPRO9PG0KigoCP597Nixys3N1fXXX6+NGzeqqKio3fld7fkAAOhdenwez1VXXaWxY8eqqqqqpy8FAPHF+KRIm8qiMLigx+fxNDc36+OPP1Z6enpPXwoA4oqtfTyu13h++ctfatasWRoxYoTq6ur0zDPPqKGhQYWFhWHlM3VAofr5Ejo9x80ZxTE9O9lFcTWrG+iEm78L/B6Ex/XA88UXX+j+++/XqVOnNHToUE2ePFn79+9XVlaW25cCgPgWcKGprTcMLti8ebPbWQIAOmJ8rSmiPNwpSjhYqw0A4Klevzo1APRWbgwOYCM4AIBzgT4u9PF439ZGUxsAwFPUeADAVoxqAwB4yRifTISj2gyj2gAAvV3M1njev7BRUueRPBoziqMxoz8a12SFA6CVk5/drn4PLpmLev/CBreK9FeWDi6I2cADAOicCciF4dSMagMA9HLUeADAVq5si9ALVqcGAHjDnVFtvWDrawAAOkONBwBsFejTmiLKw52ihIPAAwCWcmeRUJraAAC9nNU1nniZlOj0Pj+dfVuX54zc9l+RFifq3Jy0ygRYuKHrn4+emStj6+ACqwMPAMQ1S/t4aGoDAHiKGg8AWMrWwQUEHgCwlK19PDS1AQA8RY0HAGxl6eACAg8AWMrWPh6a2gAAnqLGAwCWsnVwAYEnTG5uCe32bHgnqxI4nanvVDRm9EfjGbDCAWKScaGPx/sNSGlqAwB4ixoPAFjK1sEFBB4AsJQxkffRGJraAAC9HTUeALCVC01toqkNAOCUMX1kTGQNVyYKbW00tQEAPEWNBwBsFfBF3lRGUxsAwClWLkAIJzPY42k2fDRWcnAzP1Y4ANwTdh/P3r17NWvWLGVkZMjn82nbtm0h7xtjVFxcrIyMDCUmJiovL09Hjx51q7wAgG+0TSCNNHkt7MDT1NSk8ePHa+3atR2+/+yzz2r16tVau3atKisrlZaWppkzZ6qxsTHiwgIA/qptVFukyWthN7UVFBSooKCgw/eMMVqzZo2WL1+uOXPmSJI2btyo1NRUbdq0ST/72c8iKy0AwHquhrrq6mrV1tYqPz8/eMzv9+vOO+/Uvn37OvxMc3OzGhoaQhIAoGtx09TWmdraWklSampqyPHU1NTge5crKSlRSkpKMGVmZrpZJADotdpGtUWavNYjjXs+X+iNGGPaHWuzdOlS1dfXB1NNTU1PFAkAECNcHU6dlpYmqbXmk56eHjxeV1fXrhbUxu/3y+/3u1kMAIgLts7jcbXGk52drbS0NJWVlQWPXbx4URUVFZoyZYqblwKAuGeMC308NkwgPXv2rD799NPg6+rqah0+fFiDBw/WiBEjtGTJEq1cuVKjRo3SqFGjtHLlSg0cOFAPPPCAqwUHANgp7MBz4MABTZs2Lfi6qKhIklRYWKhXX31VTzzxhM6fP69HH31UX331lSZNmqSdO3cqKSnJvVL3Em7Phnfzmk65OVO/N8z6d/OZxvJ9IjbYujp12IEnLy+v04L6fD4VFxeruLg4knIBALpg69bXbIsAAPAUi4QCgKVsHdVG4AEAS9kaeGhqAwB4isADAJYyATfWawvvmqWlpRo3bpySk5OVnJys3Nxcvffee2HlQVMbAFgqGk1tw4cP16pVqzRy5EhJrTsQ3HvvvTp06JBuvvlmR3kQeAAAjs2aNSvk9a9//WuVlpZq//799geeqQMK1c+X0Ok58TLBLhr36fZkzmhMmIzlSZrxMqEWPcudCaStn798Sxon62i2tLTod7/7nZqampSbm+v4mvTxAIClAsbnSpKkzMzMkC1qSkpKrnjdI0eOaNCgQfL7/Vq4cKG2bt2qMWPGOC53zNZ4AADeqampUXJycvB1Z7WdG264QYcPH9aZM2f05ptvqrCwUBUVFY6DD4EHAGzlxg6i33y+bZSaEwkJCcHBBRMnTlRlZaWef/55vfDCC44+T+ABAEvFygRSY4yam5sdn0/gAQA4tmzZMhUUFCgzM1ONjY3avHmzysvLtX37dsd5EHgAwFLRqPF8+eWXevDBB3Xy5EmlpKRo3Lhx2r59u2bOnOk4DwIPAFgqGoHn5Zdfjuh6EsOpAQAeo8YDAJYKmD4KRDiBNNLPd0fMBp73L2yU1HkVMJZnptvO7W253Zyp75Ttzz4azwB2McaFHUjZFgEA0NvFbI0HANC5WJnHEy4CDwBYytbAQ1MbAMBT1HgAwFLfXl06kjy8RuABAEvR1AYAgAPUeADAUrbWeAg8AGAp+niigH3rQ0VjJYfe8L3ZjhUOYBurAw8AxDNjIm8qM8alwoSBwAMAlrK1j4dRbQAAT1HjAQBLGRcGFzCqDQDgGE1tAAA4QI0HACxla42HwAMAlmICaYyKp8l1sTyhNhqTWz+dfVuX54zc9l+uXjOWufm7EMu/B4h9vT7wAEBvZWtTW9iDC/bu3atZs2YpIyNDPp9P27ZtC3l//vz58vl8IWny5MlulRcA8I22prZIk9fCDjxNTU0aP3681q5de8Vz7r77bp08eTKY3n333YgKCQDoPcJuaisoKFBBQUGn5/j9fqWlpTnKr7m5Wc3NzcHXDQ0N4RYJAOKSkU9GETa1Rfj57uiReTzl5eUaNmyYRo8erQULFqiuru6K55aUlCglJSWYMjMze6JIANDrtPXxRJq85nrgKSgo0Ouvv67du3frueeeU2VlpaZPnx5Sq/m2pUuXqr6+PphqamrcLhIAIIa4Pqpt7ty5wb/n5ORo4sSJysrK0jvvvKM5c+a0O9/v98vv97tdDADo9ZjHcwXp6enKyspSVVVVT18KAOJK3AynDtfp06dVU1Oj9PT0nr4UAMACYdd4zp49q08//TT4urq6WocPH9bgwYM1ePBgFRcX67777lN6erqOHz+uZcuWaciQIfrRj37kasHd1htmYsfyjPNoXNfJqgROVjdwmlc4ovGsesPqHE7Ey31KUkAuNLVFYVRb2IHnwIEDmjZtWvB1UVGRJKmwsFClpaU6cuSIXnvtNZ05c0bp6emaNm2atmzZoqSkJPdKDQCwVtiBJy8vT6aTTbp37NgRUYEAAM7Y2sfDWm0AYKmAfBE3lUWjqY2N4AAAnqLGAwC2cmPlAZraAABO2TqBlKY2AICnqPEAgKUY1QYA8FTgmxRpHl4j8PQiTmZix9Osbiecrkjg9HtzKhrfr9NrxvIKGE64eZ/h5AfnCDwAYCma2gAAngqYyEelBa68EE2PYVQbAMBT1HgAwFJGPpkIl7yJ9PPdQeABAEsxgRQAAAeo8QCApVoHF0Seh9cIPABgKfp4YAUm19kjWs8gXp5pvEyojUUEHgCwlK2DCwg8AGApY1pTpHl4jVFtAABPUeMBAEsZ+RRgcAEAwCu2LhJKUxsAwFPUeADAUoxqAwB4ynyTIs3DazS1AQA8RY0HHYqXFQ6iVX43Z8Pb/gxinRtbyl8yF/X+hQ1uFSmIpjYAgKcC36RI8/AaTW0AAE9R4wEAS9k6j4fAAwCWsrWPh6Y2AICnCDwAYCnjUgpHSUmJbr31ViUlJWnYsGGaPXu2Pvnkk7DyIPAAgKXamtoiTeGoqKjQokWLtH//fpWVlenSpUvKz89XU1OT4zzo4wEAOLZ9+/aQ1xs2bNCwYcN08OBB3XHHHY7yIPAAgKXcnMfT0NAQctzv98vv93f5+fr6eknS4MGDHV+TwIOI2L5vfTRWJHB63VheVcGpeFktoev77JkV0dwcTp2ZmRlyfMWKFSouLu7is0ZFRUW6/fbblZOT4/iaYfXxOOlUMsaouLhYGRkZSkxMVF5eno4ePRrOZQAAHqupqVF9fX0wLV26tMvPPPbYY/roo4/0xhtvhHWtsAKPk06lZ599VqtXr9batWtVWVmptLQ0zZw5U42NjWEVDADQOaO/Nrd1N7XVxZKTk0NSV81sixcv1ttvv609e/Zo+PDhYZU7rKa2rjqVjDFas2aNli9frjlz5kiSNm7cqNTUVG3atEk/+9nPwiocAODKjFxoagtz62tjjBYvXqytW7eqvLxc2dnZYV8zouHUl3cqVVdXq7a2Vvn5+cFz/H6/7rzzTu3bt6/DPJqbm9XQ0BCSAACxadGiRfrNb36jTZs2KSkpSbW1taqtrdX58+cd59HtwNNRp1Jtba0kKTU1NeTc1NTU4HuXKykpUUpKSjBd3sEFAOhYwLiTwlFaWqr6+nrl5eUpPT09mLZs2eI4j26PamvrVPrggw/avefzhVbdjDHtjrVZunSpioqKgq8bGhoIPgDgQDR2IDUm8hF63Qo8bZ1Ke/fuDelUSktLk9Ra80lPTw8er6ura1cLauN0rDgAoHcIq6nNGKPHHntMb731lnbv3t2uUyk7O1tpaWkqKysLHrt48aIqKio0ZcoUd0oMAJAUnSVz3BBWjWfRokXatGmTfv/73wc7lSQpJSVFiYmJ8vl8WrJkiVauXKlRo0Zp1KhRWrlypQYOHKgHHnigR24AdojlCZNuisaEWjcnfIZzXSd6wzONZbbuQBpW4CktLZUk5eXlhRzfsGGD5s+fL0l64okndP78eT366KP66quvNGnSJO3cuVNJSUmuFBgAYLewAo+TTiWfz6fi4uIul1oAAESGHUgBAJ6ytamN/XgAAJ6ixgMAljKmNUWah9cIPABgqYB8CoS51lpHeXiNpjYAgKeo8QCApbqz1lpHeXiNwAMAtnKhj6eHNkftFIEHMcPtmfqxvFpCvMzUt31rdPQMAg8AWMrWwQUEHgCwlK3DqRnVBgDwFDUeALCUrUvmEHgAwFK2DqemqQ0A4ClqPABgKaPIp+FEocJD4AEAW7U2tUU4nJqmNgBAb0eNB9aJxmz4aKxwEK1VFT6dfVuX54zc9l+uXjOWV5mIZbbO4yHwAIClbB1OTVMbAMBT1HgAwFI0tQEAPEVTGwAADlDjAQBLGReWzKGpDQDgmK0rF9DUBgDwVMzWeKYOKFQ/X0Kn58TTRDGEz82fj3j6WXMyOTSeJnPG8rbctq5OHbOBBwDQOVuHU9PUBgDwFDUeALCUrfN4CDwAYClb+3hoagMAeIoaDwBYytZ5PAQeALAUTW0AADhAjQcALGXrPJ6YDTzvX9goydfpObE8oxiIVCxvy+122dy8ptvc2Jb7krmo9y9scKtIQbYOp6apDQDgqbACT0lJiW699VYlJSVp2LBhmj17tj755JOQc+bPny+fzxeSJk+e7GqhAQDf1HhMhCkK5Q4r8FRUVGjRokXav3+/ysrKdOnSJeXn56upqSnkvLvvvlsnT54MpnfffdfVQgMA/jqcOtLktbD6eLZv3x7yesOGDRo2bJgOHjyoO+64I3jc7/crLS3NUZ7Nzc1qbm4Ovm5oaAinSAAAy0TUx1NfXy9JGjx4cMjx8vJyDRs2TKNHj9aCBQtUV1d3xTxKSkqUkpISTJmZmZEUCQDihulu89q3klWrUxtjVFRUpNtvv105OTnB4wUFBXr99de1e/duPffcc6qsrNT06dNDajXftnTpUtXX1wdTTU1Nd4sEAHGlbTh1pMlr3R5O/dhjj+mjjz7SBx98EHJ87ty5wb/n5ORo4sSJysrK0jvvvKM5c+a0y8fv98vv93e3GAAAy3Qr8CxevFhvv/229u7dq+HDh3d6bnp6urKyslRVVdWtAgIAOmbrPJ6wAo8xRosXL9bWrVtVXl6u7OzsLj9z+vRp1dTUKD09vduFBAC01zocOrK2spjf+nrRokXatGmTfv/73yspKUm1tbWSpJSUFCUmJurs2bMqLi7Wfffdp/T0dB0/flzLli3TkCFD9KMf/cj1wrsxozicvIBYFMsrHERDbK7kEI1By7ErrMBTWloqScrLyws5vmHDBs2fP199+/bVkSNH9Nprr+nMmTNKT0/XtGnTtGXLFiUlJblWaABAnGyLYLoY/pCYmKgdO3ZEVCAAgDNurDzAtggAgF4vZlenBgB0znzzJ9I8vEbgAQBL0dQGAIAD1HgAwFJxMYEUABA7jHGhjycKi7XR1AYA8FSvr/Ewqxto5fbvgpvXdOLT2bc5Om/ktvj5naepDQDgKZraAABwgBoPAFjKKPKmsphfqw0AEDsCxriwLQJNbQCAGLZ3717NmjVLGRkZ8vl82rZtW9h5EHgAwFLGpT/haGpq0vjx47V27dpul5umNgCwVDSGUxcUFKigoCCiaxJ4AABqaGgIee33++X3+3vkWgSebzDRND7xPNuLxr06eQ6xPDG0q2teMhf1/oUNrl2vTUAuDC745vOZmZkhx1esWKHi4uKI8r4SAg8AWMrNUW01NTVKTk4OHu+p2o5E4AEASEpOTg4JPD2JwAMAlmIHUgCAp9zs43Hq7Nmz+vTTT4Ovq6urdfjwYQ0ePFgjRoxwlAeBBwDg2IEDBzRt2rTg66KiIklSYWGhXn31VUd5EHgAwFLRqPHk5eVFvKI1gQcALGVrHw9L5gAAPEWNBwAsZVxoamNUmwXcXOEgnmbDxypWrIgNTr43t5+B0620F+wY58I1e+Yf94AvIJ8vstXaAlHY/JqmNgCAp6jxAIClAjLyeTyqzQ0EHgCwlPlmQHWkeXiNpjYAgKeo8QCApQKSC01t3iPwAIClGNUGAIAD1HgAwFIBBeSLsMYSjRoPgQcALEXgQYhozMRGz2GFg1DRuE+3vzMnKxJI0ov/56Muzxm5LcLCxJmw+nhKS0s1bty44Bapubm5eu+994LvG2NUXFysjIwMJSYmKi8vT0ePHnW90ACAv87jiTR5LazAM3z4cK1atUoHDhzQgQMHNH36dN17773B4PLss89q9erVWrt2rSorK5WWlqaZM2eqsbGxRwoPAPEs4Au4krwWVuCZNWuW7rnnHo0ePVqjR4/Wr3/9aw0aNEj79++XMUZr1qzR8uXLNWfOHOXk5Gjjxo06d+6cNm3adMU8m5ub1dDQEJIAAL1Xt4dTt7S0aPPmzWpqalJubq6qq6tVW1ur/Pz84Dl+v1933nmn9u3bd8V8SkpKlJKSEkyZmZndLRIAxBWjQMR/Yr6pTZKOHDmiQYMGye/3a+HChdq6davGjBmj2tpaSVJqamrI+ampqcH3OrJ06VLV19cHU01NTbhFAoC4ZNTiSvJa2KPabrjhBh0+fFhnzpzRm2++qcLCQlVUVATf9/l8IecbY9od+za/3y+/3x9uMQAAlgo78CQkJGjkyJGSpIkTJ6qyslLPP/+8nnzySUlSbW2t0tPTg+fX1dW1qwUBACLXOgfHvnk8ES+ZY4xRc3OzsrOzlZaWprKysuB7Fy9eVEVFhaZMmRLpZQAAlwm41MvjtbBqPMuWLVNBQYEyMzPV2NiozZs3q7y8XNu3b5fP59OSJUu0cuVKjRo1SqNGjdLKlSs1cOBAPfDAAz1VfqsxKbH3iZet0WP5Z9ftazqZHHopsLHT9xsazmnwd/6vo+vFg7ACz5dffqkHH3xQJ0+eVEpKisaNG6ft27dr5syZkqQnnnhC58+f16OPPqqvvvpKkyZN0s6dO5WUlNQjhQeAeNY6OODKfehO8/BaWIHn5Zdf7vR9n8+n4uJiFRcXR1ImAIADcdvHAwBAOFgkFAAs5cZaa9GYQErgAQBLBdQiRdjHE4hCHw9NbQAAT1HjAQBL0dQGAPBUwLjQ1GZifDi1F4xpm0Xr/WzaWHXJXHR4Jt+ZLZw9U/ufZzR+dqNxzYaGc128f771isb+Z+oGn4mxb+KLL75gawQAvVJNTY2GDx8ecT4NDQ1KSUnRNQMnqI8vsvpDwFzS6XMHVV9fr+Tk5IjL5kTM1XgyMjJUU1OjpKSk4KrWDQ0NyszMVE1NjWdfjNtsvwfbyy/Zfw+2l1+y/x66W35jjBobG5WRkeFqeVr7eCJrKqOPR1KfPn2u+D+C5ORkK39Yv832e7C9/JL992B7+SX776E75U9JSemh0tgn5gIPAMAZYwIKRLpWm6HGAwBwqLWZLNJFQlmrrUN+v18rVqyweqdS2+/B9vJL9t+D7eWX7L8H28sfK2JuVBsAoHNto9pSBoyRz9c3oryMaVH9hWPxPaoNAOBMaw8PTW0AAHSKGg8AWKp1RBqj2gAAHnFj2+pobH1NUxsAwFNWBJ5169YpOztbAwYM0IQJE/T+++9Hu0iOFBcXy+fzhaS0tLRoF6tTe/fu1axZs5SRkSGfz6dt27aFvG+MUXFxsTIyMpSYmKi8vDwdPXo0OoXtQFflnz9/frtnMnny5OgUtgMlJSW69dZblZSUpGHDhmn27Nn65JNPQs6J9Wfg5B5i+TmUlpZq3LhxwdUJcnNz9d577wXfj6Xv3xgjYwIRJu8HNsd84NmyZYuWLFmi5cuX69ChQ5o6daoKCgp04sSJaBfNkZtvvlknT54MpiNHjkS7SJ1qamrS+PHjtXbt2g7ff/bZZ7V69WqtXbtWlZWVSktL08yZM9XY2OhxSTvWVfkl6e677w55Ju+++66HJexcRUWFFi1apP3796usrEyXLl1Sfn6+mpqagufE+jNwcg9S7D6H4cOHa9WqVTpw4IAOHDig6dOn69577w0Gl1j6/tv244k0eV/wGHfbbbeZhQsXhhy78cYbzVNPPRWlEjm3YsUKM378+GgXo9skma1btwZfBwIBk5aWZlatWhU8duHCBZOSkmL+/d//PQol7Nzl5TfGmMLCQnPvvfdGpTzdUVdXZySZiooKY4x9z8CY9vdgjH3P4eqrrzYvvfRSzHz/9fX1RpJJTLjWDPRfF1FKTLjWSDL19fWelT+mazwXL17UwYMHlZ+fH3I8Pz9f+/bti1KpwlNVVaWMjAxlZ2dr3rx5+uyzz6JdpG6rrq5WbW1tyPPw+/268847rXkeklReXq5hw4Zp9OjRWrBggerq6qJdpCuqr6+XJA0ePFiSnc/g8ntoY8NzaGlp0ebNm9XU1KTc3NyY+/6NaXEleS2mA8+pU6fU0tKi1NTUkOOpqamqra2NUqmcmzRpkl577TXt2LFDL774omprazVlyhSdPn062kXrlrbv3NbnIUkFBQV6/fXXtXv3bj333HOqrKzU9OnT1dzcHO2itWOMUVFRkW6//Xbl5ORIsu8ZdHQPUuw/hyNHjmjQoEHy+/1auHChtm7dqjFjxsTc9x95/06A4dRX0rYvTxtjTLtjsaigoCD497Fjxyo3N1fXX3+9Nm7cqKKioiiWLDK2Pg9Jmjt3bvDvOTk5mjhxorKysvTOO+9ozpw5USxZe4899pg++ugjffDBB+3es+UZXOkeYv053HDDDTp8+LDOnDmjN998U4WFhaqoqAi+b8v3H6tiusYzZMgQ9e3bt93/JOrq6tr9j8MGV111lcaOHauqqqpoF6Vb2kbk9ZbnIUnp6enKysqKuWeyePFivf3229qzZ0/I/lQ2PYMr3UNHYu05JCQkaOTIkZo4caJKSko0fvx4Pf/88zH3/ds6uCCmA09CQoImTJigsrKykONlZWWaMmVKlErVfc3Nzfr444+Vnp4e7aJ0S3Z2ttLS0kKex8WLF1VRUWHl85Ck06dPq6amJmaeiTFGjz32mN566y3t3r1b2dnZIe/b8Ay6uoeOxNpzuJwxRs3NzTH3/dva1Bbzo9o2b95s+vfvb15++WVz7Ngxs2TJEnPVVVeZ48ePR7toXXr88cdNeXm5+eyzz8z+/fvND3/4Q5OUlBTTZW9sbDSHDh0yhw4dMpLM6tWrzaFDh8znn39ujDFm1apVJiUlxbz11lvmyJEj5v777zfp6emmoaEhyiVv1Vn5GxsbzeOPP2727dtnqqurzZ49e0xubq757ne/GzPl/4d/+AeTkpJiysvLzcmTJ4Pp3LlzwXNi/Rl0dQ+x/hyWLl1q9u7da6qrq81HH31kli1bZvr06WN27txpjImN779tVFv/vqkmoV96RKl/31TPR7XFfOAxxph/+7d/M1lZWSYhIcF8//vfDxmWGcvmzp1r0tPTTf/+/U1GRoaZM2eOOXr0aLSL1ak9e/YYSe1SYWGhMaZ1OO+KFStMWlqa8fv95o477jBHjhyJbqG/pbPynzt3zuTn55uhQ4ea/v37mxEjRpjCwkJz4sSJaBc7qKOySzIbNmwInhPrz6Cre4j15/Dwww8H/70ZOnSoueuuu4JBx5jY+P7bAk+/vkNN/36pEaV+fYd6HnjYjwcALNO2H0/fPoPl80XWY2JMQC2Bv3i6H09M9/EAAHofK4ZTAwA6YqSIR6V53+hF4AEAS7mzHw+LhAIAejlqPABgqdbJnxHWeGhqAwA4F3ngiUYfD01tAABPUeMBAFu5MLhAURhcQOABAEvZ2sdDUxsAwFPUeADAWgwuAAB4yrT20USSuhl41q1bp+zsbA0YMEATJkzQ+++/7/izBB4AQFi2bNmiJUuWaPny5Tp06JCmTp2qgoICnThxwtHnWZ0aACzTtjq11FfuNLW1hLU69aRJk/T9739fpaWlwWM33XSTZs+erZKSki4/T40HAKx2xS2QHKZWDQ0NIam5ubnDq128eFEHDx5Ufn5+yPH8/Hzt27fPUYkJPABgmYSEBKWlpUlqcSUNGjRImZmZSklJCaYr1VxOnTqllpYWpaamhhxPTU1VbW2to/Izqg0ALDNgwABVV1fr4sWLruRnjJHPF9pk5/f7O/3M5ed3lMeVEHgAwEIDBgzQgAEDPL/ukCFD1Ldv33a1m7q6una1oCuhqQ0A4FhCQoImTJigsrKykONlZWWaMmWKozyo8QAAwlJUVKQHH3xQEydOVG5urtavX68TJ05o4cKFjj5P4AEAhGXu3Lk6ffq0fvWrX+nkyZPKycnRu+++q6ysLEefZx4PAMBT9PEAADxF4AEAeIrAAwDwFIEHAOApAg8AwFMEHgCApwg8AABPEXgAAJ4i8AAAPEXgAQB4isADAPDU/wcM7YOESDzkTQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# generate the Hamiltonian\n", + "nqubits = 5\n", + "h = 1\n", + "H_TFIM = SymbolicHamiltonian( - h*symbols.Z(nqubits-1), nqubits=nqubits)\n", + "# add linear interaction terms\n", + "for i in range(nqubits-1):\n", + " H_TFIM -= SymbolicHamiltonian(symbols.X(i)*symbols.X(i+1) + h*symbols.Z(i), nqubits=nqubits)\n", + "H_TFIM = H_TFIM.dense\n", + "visualize_matrix(H_TFIM.matrix, title=f'TFIM with L={nqubits} h={h}')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-04-03 08:14:05]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], + "source": [ + "# backend\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", + "# initialize dbi object\n", + "dbi_TFIM = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Order 1: $D=\\sum \\alpha_iZ_i$" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 621.13trial/s, best loss: 8.145448855938055]\n", + "The initial D coefficients: [(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j)]\n", + "Gradient: [-0.22522735 -0.52101222 -0.59635378 -0.52101222 -0.22522735]\n", + "s: 0.053751929537431395\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pethidine/Documents/GitHub/qibo/src/qibo/models/dbi/utils.py:268: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " grad[i] = (\n" + ] + } + ], + "source": [ + "dbi_TFIM_1 = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)\n", + "# generate pauli_operator_dict\n", + "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=1)\n", + "d_coef = decompose_into_Pauli_basis(dbi_TFIM_1.h.matrix, list(pauli_operator_dict.values()))\n", + "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", + "grad, s = gradient_Pauli(dbi_TFIM_1, d=d, pauli_operator_dict=pauli_operator_dict)\n", + "print('The initial D coefficients:', d_coef)\n", + "print('Gradient:', grad)\n", + "print('s:', s)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 707.38trial/s, best loss: 8.144345846120405] \n", + "100%|██████████| 500/500 [00:00<00:00, 735.41trial/s, best loss: 7.604689171773317] \n", + "100%|██████████| 500/500 [00:00<00:00, 735.65trial/s, best loss: 7.4176027901243495]\n", + "100%|██████████| 500/500 [00:00<00:00, 755.09trial/s, best loss: 7.008137714268] \n", + "100%|██████████| 500/500 [00:00<00:00, 745.89trial/s, best loss: 5.961852278701001] \n", + "100%|██████████| 500/500 [00:00<00:00, 703.36trial/s, best loss: 5.293878968775635] \n", + "100%|██████████| 500/500 [00:00<00:00, 749.97trial/s, best loss: 4.82113561785614] \n", + "100%|██████████| 500/500 [00:00<00:00, 749.13trial/s, best loss: 4.326041032299924] \n", + "100%|██████████| 500/500 [00:00<00:00, 749.48trial/s, best loss: 3.608232108710637] \n", + "100%|██████████| 500/500 [00:00<00:00, 750.07trial/s, best loss: 2.9342878051864014] \n", + "100%|██████████| 500/500 [00:00<00:00, 749.53trial/s, best loss: 2.619767373811081] \n", + "100%|██████████| 500/500 [00:00<00:00, 757.24trial/s, best loss: 2.553595125371688] \n", + "100%|██████████| 500/500 [00:00<00:00, 757.78trial/s, best loss: 2.5450651469655488] \n", + "100%|██████████| 500/500 [00:00<00:00, 758.64trial/s, best loss: 2.5411137289688988] \n", + "100%|██████████| 500/500 [00:00<00:00, 758.68trial/s, best loss: 2.5071534830104416] \n" + ] + } + ], + "source": [ + "NSTEPS = 15\n", + "off_diagonal_norm_1 = [dbi_TFIM_1.off_diagonal_norm]\n", + "s_step = [0]\n", + "for i in range(NSTEPS):\n", + " s, d_coef, d = gradient_descent_pauli(dbi_TFIM_1, d_coef=d_coef, d=d, pauli_operator_dict=pauli_operator_dict, max_evals=100)\n", + " dbi_TFIM_1(step=s, d=d)\n", + " off_diagonal_norm_1.append(dbi_TFIM_1.off_diagonal_norm)\n", + " s_step.append(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(f'n={nqubits} h={h} TFIM, order=1')\n", + "plt.plot(off_diagonal_norm_1)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# the final matrix\n", + "visualize_matrix(dbi_TFIM.h.matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Order 2" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 722.72trial/s, best loss: 8.144335598357657]\n", + "The initial D coefficients: [(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j), 0j, 0j, 0j, 0j, 0j, 0j, 0j, 0j, 0j, 0j]\n", + "Gradient: [-0.22563381 -0.52082675 -0.5963705 -0.52082675 -0.22563381 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. ]\n", + "s: 0.052813741396418624\n" + ] + } + ], + "source": [ + "dbi_TFIM_2 = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)\n", + "# generate pauli_operator_dict\n", + "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", + "d_coef = decompose_into_Pauli_basis(dbi_TFIM_2.h.matrix, list(pauli_operator_dict.values()))\n", + "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", + "grad, s = gradient_Pauli(dbi_TFIM_2, d=d, pauli_operator_dict=pauli_operator_dict)\n", + "print('The initial D coefficients:', d_coef)\n", + "print('Gradient:', grad)\n", + "print('s:', s)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 744.95trial/s, best loss: 8.144331648273518] \n", + "100%|██████████| 500/500 [00:00<00:00, 744.80trial/s, best loss: 7.604748514246744] \n", + "100%|██████████| 500/500 [00:00<00:00, 756.14trial/s, best loss: 7.420954791755261] \n", + "100%|██████████| 500/500 [00:00<00:00, 759.14trial/s, best loss: 7.016113259744297] \n", + "100%|██████████| 500/500 [00:00<00:00, 744.68trial/s, best loss: 5.989940711739217]\n", + "100%|██████████| 500/500 [00:00<00:00, 750.40trial/s, best loss: 5.307608116085456]\n", + "100%|██████████| 500/500 [00:00<00:00, 749.79trial/s, best loss: 4.822327887100122] \n", + "100%|██████████| 500/500 [00:00<00:00, 661.39trial/s, best loss: 4.30794767213952] \n", + "100%|██████████| 500/500 [00:00<00:00, 744.71trial/s, best loss: 3.5737486865622783]\n", + "100%|██████████| 500/500 [00:00<00:00, 742.92trial/s, best loss: 2.899168952313776] \n", + "100%|██████████| 500/500 [00:00<00:00, 749.93trial/s, best loss: 2.6126410200674473] \n", + "100%|██████████| 500/500 [00:00<00:00, 754.15trial/s, best loss: 2.5428845456217983] \n", + "100%|██████████| 500/500 [00:00<00:00, 761.81trial/s, best loss: 2.520820402680462] \n", + "100%|██████████| 500/500 [00:00<00:00, 700.04trial/s, best loss: 2.4881828281706038] \n", + "100%|██████████| 500/500 [00:00<00:00, 736.96trial/s, best loss: 2.4545260560650717] \n" + ] + } + ], + "source": [ + "NSTEPS = 15\n", + "off_diagonal_norm_2 = [dbi_TFIM_2.off_diagonal_norm]\n", + "s_step = [0]\n", + "for i in range(NSTEPS):\n", + " s, d_coef, d = gradient_descent_pauli(dbi_TFIM_2, d_coef=d_coef, d=d, pauli_operator_dict=pauli_operator_dict, max_evals=100)\n", + " dbi_TFIM_2(step=s, d=d)\n", + " off_diagonal_norm_2.append(dbi_TFIM_2.off_diagonal_norm)\n", + " s_step.append(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(f'n={nqubits} h={h} TFIM')\n", + "plt.plot(off_diagonal_norm_1, label='order 1')\n", + "plt.plot(off_diagonal_norm_2, label='order 2')\n", + "plt.legend()\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Different initial `d`\n", + "Next, we show the effect of different choices of the initial direction of the gradient descent method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(-2.580645161290323+0j), (-1.2903225806451613+0j), (-0.6451612903225807+0j), (-0.32258064516129037+0j), (-0.16129032258064518+0j)]\n" + ] + } + ], + "source": [ + "H = H_TFIM.matrix\n", + "L = int(np.log2(H.shape[0]))\n", + "N = np.diag(np.linspace(np.min(np.diag(H)),np.max(np.diag(H)),2**L))\n", + "d_coef = onsite_Z_decomposition(N, onsite_Z_ops)\n", + "print(d_coef)\n", + "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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CDwCgU8rLy7Vy5UoNH97xatDfReABALfyG3tSJ5w5c0Y//vGP9eqrr+qyyy4L6diofcez+/waSWG2Xcr+0c5Hp95sab+rN9k3etrqNVgRS6O6Y+laY4GdM1aEkl9UM37JhPl70jQHntra2qDNXq9XXq/3kofNmTNHd955p37wgx/o+eefD+mU1HgAAMrIyJDP5wuk4uLiS+67bt06/eEPf2h3n/ZEbY0HANABY8M4nm9rPBUVFUpKSgpsvlRtp6KiQj/72c+0fft29enTp1OnJPAAgFvZOIA0KSkpKPBcyv79+1VVVaWRI0cGtjU1NWnXrl1avny5Ghoa1LNnz3bzIPAAACy77bbbdOjQoaBtDz74oK699lo9+eSTHQYdicADAO4VgSlzEhMTlZ2dHbStX79+uvzyy1ttvxQCDwC4lPGHv3J1BFa+JvAAAMJTWloa0v4EHgBwK5fOTt3tA4/dg86sDgyN1kGfMTW4DjEppgaa+mVD4LGjIKFhACkAwFG2B56ioiJ5PJ6glJqaavdpAAB+m5LDuqSp7YYbbtCHH34Y+GylXzcAIETm2xRuHg7rksDTq1cvy7WchoYGNTQ0BD5fPFEdAKB76ZJ3PEeOHFF6erqysrL0ox/9SF988cUl9y0uLg6amC4jI6MrigQA3Y7xe2xJTrM98IwZM0avv/66PvjgA7366quqrKzUuHHjVF1d3eb+CxYsUE1NTSBVVFTYXSQA6J54x9MsPz8/8Pdhw4YpJydHgwcP1po1a1RYWNhq/47WfAAAdC9dPo6nX79+GjZsmI4cOdLVpwKA2GI8UrhNZRHoXNDl43gaGhr02WefKS0tratPBQAxxa3veGyv8Tz++OOaMmWKBg0apKqqKj3//POqra1VQUFBSPlM6FOgXp64dvexc0RxVI9OtlFMjeoG2mHnvwX+HYTG9sDz1Vdf6f7779c333yj/v37a+zYsdq7d68yMzPtPhUAxDa/DU1t3aFzwbp16+zOEgDQFuNpTmHlYU9RQsFcbQAAR3X72akBoLuyo3MAC8EBAKzz97DhHY/zbW00tQEAHEWNBwDcil5tAAAnGeORCbNXm6FXGwCgu4vaGs/u82sktR/JIzGiOBIj+iNxTmY4AJpZ+dnt6N/BBdOo3edX21WkP3Np54KoDTwAgPYZv2zoTk2vNgBAN0eNBwDcypZlEbrB7NQAAGfY06utGyx9DQBAe6jxAIBb+Xs0p7DysKcooSDwAIBL2TNJKE1tAIBuztU1nlgZlGj1Oo9OvbnDfa7e9G/hFifi7By0ygBY2KHjn4+uGSvj1s4Frg48ABDTXPqOh6Y2AICjqPEAgEu5tXMBgQcAXMqt73hoagMAOIoaDwC4lUs7FxB4AMCl3PqOh6Y2AICjqPEAgEu5tXMBgSdEdi4JbfdoeCuzElgdqW9VJEb0R+IZMMMBopKx4R2P8wuQ0tQGAHAWNR4AcCm3di4g8ACASxkT/jsaQ1MbAKC7o8YDAG5lQ1ObaGoDAFhlTA8ZE17DlYlAWxtNbQAAR1HjAQC38nvCbyqjqQ0AYBUzFyCIlRHssTQaPhIzOdiZHzMcAPYJ+R3Prl27NGXKFKWnp8vj8WjTpk1B3xtjVFRUpPT0dMXHxys3N1eHDx+2q7wAgG+1DCANNzkt5MBTX1+vESNGaPny5W1+v2TJEi1dulTLly9XeXm5UlNTNXnyZNXV1YVdWADAn7X0ags3OS3kprb8/Hzl5+e3+Z0xRsuWLdMzzzyjadOmSZLWrFmjlJQUrV27Vo8++mh4pQUAuJ6toe7YsWOqrKxUXl5eYJvX69Wtt96qPXv2tHlMQ0ODamtrgxIAoGMx09TWnsrKSklSSkpK0PaUlJTAdxcrLi6Wz+cLpIyMDDuLBADdVkuvtnCT07qkcc/jCb4QY0yrbS0WLFigmpqaQKqoqOiKIgEAooSt3alTU1MlNdd80tLSAturqqpa1YJaeL1eeb1eO4sBADHBreN4bK3xZGVlKTU1VSUlJYFtjY2NKisr07hx4+w8FQDEPGNseMfjhgGkZ86c0dGjRwOfjx07poMHDyo5OVmDBg3S/PnztWjRIg0ZMkRDhgzRokWL1LdvX82YMcPWggMA3CnkwLNv3z5NnDgx8LmwsFCSVFBQoNdee01PPPGEzp07p9mzZ+vUqVMaM2aMtm/frsTERPtK3U3YPRreznNaZedI/e4w6t/OZxrN14no4NbZqUMOPLm5ue0W1OPxqKioSEVFReGUCwDQAbcufc2yCAAARzFJKAC4lFt7tRF4AMCl3Bp4aGoDADiKwAMALmX8dszXFto5V6xYoeHDhyspKUlJSUnKycnR1q1bQ8qDpjYAcKlINLUNHDhQixcv1tVXXy2peQWCe+65RwcOHNANN9xgKQ8CDwDAsilTpgR9/sUvfqEVK1Zo79697g88E/oUqJcnrt19YmWAXSSu0+7BnJEYMBnNgzRjZUAtupY9A0ibj794SRor82g2NTXp7bffVn19vXJyciyfk3c8AOBSfuOxJUlSRkZG0BI1xcXFlzzvoUOHlJCQIK/Xq1mzZmnjxo26/vrrLZc7ams8AADnVFRUKCkpKfC5vdrONddco4MHD+r06dPasGGDCgoKVFZWZjn4EHgAwK3sWEH02+NbeqlZERcXF+hcMGrUKJWXl+ull17Sr3/9a0vHE3gAwKWiZQCpMUYNDQ2W9yfwAAAse/rpp5Wfn6+MjAzV1dVp3bp1Ki0t1bZt2yznQeABAJeKRI3n66+/1gMPPKCTJ0/K5/Np+PDh2rZtmyZPnmw5DwIPALhUJALPqlWrwjqfRHdqAIDDqPEAgEv5TQ/5wxxAGu7xnRG1gWf3+TWS2q8CRvPIdLeze1luO0fqW+X2Zx+JZwB3McaGFUhZFgEA0N1FbY0HANC+aBnHEyoCDwC4lFsDD01tAABHUeMBAJf67uzS4eThNAIPALgUTW0AAFhAjQcAXMqtNR4CDwC4FO94IoB164NFYiaH7nDf3I4ZDuA2rg48ABDLjAm/qcwYmwoTAgIPALiUW9/x0KsNAOAoajwA4FLGhs4F9GoDAFhGUxsAABZQ4wEAl3JrjYfAAwAuxQDSKBVLg+uieUBtJAa3Hp16c4f7XL3p32w9ZzSz899CNP87QPTr9oEHALortza1hdy5YNeuXZoyZYrS09Pl8Xi0adOmoO9nzpwpj8cTlMaOHWtXeQEA32ppags3OS3kwFNfX68RI0Zo+fLll9zn9ttv18mTJwNpy5YtYRUSANB9hNzUlp+fr/z8/Hb38Xq9Sk1NtZRfQ0ODGhoaAp9ra2tDLRIAxCQjj4zCbGoL8/jO6JJxPKWlpRowYICGDh2qRx55RFVVVZfct7i4WD6fL5AyMjK6okgA0O20vOMJNznN9sCTn5+vN998Uzt27NCLL76o8vJyTZo0KahW810LFixQTU1NIFVUVNhdJABAFLG9V9v06dMDf8/OztaoUaOUmZmp999/X9OmTWu1v9frldfrtbsYANDtMY7nEtLS0pSZmakjR4509akAIKbETHfqUFVXV6uiokJpaWldfSoAgAuEXOM5c+aMjh49Gvh87NgxHTx4UMnJyUpOTlZRUZHuu+8+paWl6csvv9TTTz+tK664Qvfee6+tBbdbdxiJHc0jziNxXiuzEliZ3cBqXqGIxLPqDrNzWBEr1ylJftnQ1BaBXm0hB559+/Zp4sSJgc+FhYWSpIKCAq1YsUKHDh3S66+/rtOnTystLU0TJ07U+vXrlZiYaF+pAQCuFXLgyc3NlWlnke4PPvggrAIBAKxx6zse5moDAJfyyxN2U1kkmtpYCA4A4ChqPADgVnbMPEBTGwDAKrcOIKWpDQDgKGo8AOBS9GoDADjK/20KNw+nEXi6ESsjsWNpVLcVVmcksHrfrIrE/bV6zmieAcMKO68zlPxgHYEHAFyKpjYAgKP8Jvxeaf5LT0TTZejVBgBwFDUeAHApI49MmFPehHt8ZxB4AMClGEAKAIAF1HgAwKWaOxeEn4fTCDwA4FK844ErMLjOPSL1DGLlmcbKgNpoROABAJdya+cCAg8AuJQxzSncPJxGrzYAgKOo8QCASxl55KdzAQDAKW6dJJSmNgCAo6jxAIBL0asNAOAo820KNw+n0dQGAHAUNR60KVZmOIhU+e0cDe/2ZxDt7FhS/oJp1O7zq+0qUgBNbQAAR/m/TeHm4TSa2gAAjqLGAwAu5dZxPAQeAHApt77joakNAOAoAg8AuJSxKYWiuLhYo0ePVmJiogYMGKCpU6fq888/DykPAg8AuFRLU1u4KRRlZWWaM2eO9u7dq5KSEl24cEF5eXmqr6+3nAfveAAAlm3bti3o8+rVqzVgwADt379ft9xyi6U8CDwA4FJ2juOpra0N2u71euX1ejs8vqamRpKUnJxs+ZwEHoTF7evWR2JGAqvnjeZZFayKldkSOr7OrpkRzc7u1BkZGUHbFy5cqKKiog6ONSosLNT48eOVnZ1t+ZwhveOx8lLJGKOioiKlp6crPj5eubm5Onz4cCinAQA4rKKiQjU1NYG0YMGCDo+ZO3euPvnkE7311lshnSukwGPlpdKSJUu0dOlSLV++XOXl5UpNTdXkyZNVV1cXUsEAAO0z+nNzW2dTS10sKSkpKHXUzDZv3jxt3rxZO3fu1MCBA0Mqd0hNbR29VDLGaNmyZXrmmWc0bdo0SdKaNWuUkpKitWvX6tFHHw2pcACASzOyoaktxKWvjTGaN2+eNm7cqNLSUmVlZYV8zrC6U1/8UunYsWOqrKxUXl5eYB+v16tbb71Ve/bsaTOPhoYG1dbWBiUAQHSaM2eO3njjDa1du1aJiYmqrKxUZWWlzp07ZzmPTgeetl4qVVZWSpJSUlKC9k1JSQl8d7Hi4mL5fL5AuvgFFwCgbX5jTwrFihUrVFNTo9zcXKWlpQXS+vXrLefR6V5tLS+VPvroo1bfeTzBVTdjTKttLRYsWKDCwsLA59raWoIPAFgQiRVIjQm/h16nAk/LS6Vdu3YFvVRKTU2V1FzzSUtLC2yvqqpqVQtqYbWvOACgewipqc0Yo7lz5+qdd97Rjh07Wr1UysrKUmpqqkpKSgLbGhsbVVZWpnHjxtlTYgCApMhMmWOHkGo8c+bM0dq1a/Xuu+8GXipJks/nU3x8vDwej+bPn69FixZpyJAhGjJkiBYtWqS+fftqxowZXXIBcIdoHjBpp0gMqLVzwGco57WiOzzTaObWFUhDCjwrVqyQJOXm5gZtX716tWbOnClJeuKJJ3Tu3DnNnj1bp06d0pgxY7R9+3YlJibaUmAAgLuFFHisvFTyeDwqKirqcKoFAEB4WIEUAOAotza1sR4PAMBR1HgAwKWMaU7h5uE0Ag8AuJRfHvlDnGutrTycRlMbAMBR1HgAwKU6M9daW3k4jcADAG5lwzueLloctV0EHkQNu0fqR/NsCbEyUt/tS6OjaxB4AMCl3Nq5gMADAC7l1u7U9GoDADiKGg8AuJRbp8wh8ACAS7m1OzVNbQAAR1HjAQCXMgp/GE4EKjwEHgBwq+amtjC7U9PUBgDo7qjxwHUiMRo+EjMcRGpWhaNTb+5wn6s3/Zut54zmWSaimVvH8RB4AMCl3NqdmqY2AICjqPEAgEvR1AYAcBRNbQAAWECNBwBcytgwZQ5NbQAAy9w6cwFNbQAAR0VtjWdCnwL18sS1u08sDRRD6Oz8+YilnzUrg0NjaTBnNC/L7dbZqaM28AAA2ufW7tQ0tQEAHEWNBwBcyq3jeAg8AOBSbn3HQ1MbAMBR1HgAwKXcOo6HwAMALkVTGwAAFlDjAQCXcus4nqgNPLvPr5HkaXefaB5RDIQrmpfltrtsdp7TbnYsy33BNGr3+dV2FSnArd2paWoDADgqpMBTXFys0aNHKzExUQMGDNDUqVP1+eefB+0zc+ZMeTyeoDR27FhbCw0A+LbGY8JMESh3SIGnrKxMc+bM0d69e1VSUqILFy4oLy9P9fX1QfvdfvvtOnnyZCBt2bLF1kIDAP7cnTrc5LSQ3vFs27Yt6PPq1as1YMAA7d+/X7fccktgu9frVWpqqqU8Gxoa1NDQEPhcW1sbSpEAAC4T1juempoaSVJycnLQ9tLSUg0YMEBDhw7VI488oqqqqkvmUVxcLJ/PF0gZGRnhFAkAYobpbPPad5KrZqc2xqiwsFDjx49XdnZ2YHt+fr7efPNN7dixQy+++KLKy8s1adKkoFrNdy1YsEA1NTWBVFFR0dkiAUBMaelOHW5yWqe7U8+dO1effPKJPvroo6Dt06dPD/w9Oztbo0aNUmZmpt5//31NmzatVT5er1der7ezxQAAuEynAs+8efO0efNm7dq1SwMHDmx337S0NGVmZurIkSOdKiAAoG1uHccTUuAxxmjevHnauHGjSktLlZWV1eEx1dXVqqioUFpaWqcLCQBorbk7dHhtZVG/9PWcOXO0du1avfvuu0pMTFRlZaUkyefzKT4+XmfOnFFRUZHuu+8+paWl6csvv9TTTz+tK664Qvfee6/thbdjRHEoeQHRKJpnOIiE6JzJIRKdlqNXSIFnxYoVkqTc3Nyg7atXr9bMmTPVs2dPHTp0SK+//rpOnz6ttLQ0TZw4UevXr1diYqJthQYAxMiyCKaD7g/x8fH64IMPwioQAMAaO2YeYFkEAEC3F7WzUwMA2me+/RNuHk4j8ACAS9HUBgCABdR4AMClYmIAKQAgehhjwzueCEzWRlMbAMBR3b7Gw6huoJnd/xbsPKcVR6febGm/qzfFzr95mtoAAI6iqQ0AAAuo8QCASxmF31QW9XO1AQCih98YG5ZFoKkNABDFdu3apSlTpig9PV0ej0ebNm0KOQ8CDwC4lLHpTyjq6+s1YsQILV++vNPlpqkNAFwqEt2p8/PzlZ+fH9Y5CTwAANXW1gZ99nq98nq9XXIuAs+3GGgam3ierUXiWq08h2geGNrROS+YRu0+v9q287Xwy4bOBd8en5GREbR94cKFKioqCivvSyHwAIBL2dmrraKiQklJSYHtXVXbkQg8AABJSUlJQYGnKxF4AMClWIEUAOAoO9/xWHXmzBkdPXo08PnYsWM6ePCgkpOTNWjQIEt5EHgAAJbt27dPEydODHwuLCyUJBUUFOi1116zlAeBBwBcKhI1ntzc3LBntCbwAIBLufUdD1PmAAAcRY0HAFzK2NDURq82F7BzhoNYGg0frZixIjpYuW92PwOrS2k/8sFwG87ZNb/c/R6/PJ7wZmvzR2Dxa5raAACOosYDAC7ll5HH4V5tdiDwAIBLmW87VIebh9NoagMAOIoaDwC4lF+yoanNeQQeAHAperUBAGABNR4AcCm//PKEWWOJRI2HwAMALkXgQZBIjMRG12GGg2CRuE6775mVGQkk6dUfftLhPldvCrMwMSakdzwrVqzQ8OHDA0uk5uTkaOvWrYHvjTEqKipSenq64uPjlZubq8OHD9teaADAn8fxhJucFlLgGThwoBYvXqx9+/Zp3759mjRpku65555AcFmyZImWLl2q5cuXq7y8XKmpqZo8ebLq6uq6pPAAEMv8Hr8tyWkhBZ4pU6bojjvu0NChQzV06FD94he/UEJCgvbu3StjjJYtW6ZnnnlG06ZNU3Z2ttasWaOzZ89q7dq1l8yzoaFBtbW1QQkA0H11ujt1U1OT1q1bp/r6euXk5OjYsWOqrKxUXl5eYB+v16tbb71Ve/bsuWQ+xcXF8vl8gZSRkdHZIgFATDHyh/0n6pvaJOnQoUNKSEiQ1+vVrFmztHHjRl1//fWqrKyUJKWkpATtn5KSEviuLQsWLFBNTU0gVVRUhFokAIhJRk22JKeF3Kvtmmuu0cGDB3X69Glt2LBBBQUFKisrC3zv8XiC9jfGtNr2XV6vV16vN9RiAABcKuTAExcXp6uvvlqSNGrUKJWXl+ull17Sk08+KUmqrKxUWlpaYP+qqqpWtSAAQPiax+C4bxxP2FPmGGPU0NCgrKwspaamqqSkJPBdY2OjysrKNG7cuHBPAwC4iN+mtzxOC6nG8/TTTys/P18ZGRmqq6vTunXrVFpaqm3btsnj8Wj+/PlatGiRhgwZoiFDhmjRokXq27evZsyY0VXldzUGJXY/sbI0ejT/7Np9TiuDQy/417T7fW3tWSX/xf+zdL5YEFLg+frrr/XAAw/o5MmT8vl8Gj58uLZt26bJkydLkp544gmdO3dOs2fP1qlTpzRmzBht375diYmJXVJ4AIhlzZ0DLv0O3WoeTgsp8Kxatard7z0ej4qKilRUVBROmQAAFsTsOx4AAELBJKEA4FJ2zLUWiQGkBB4AcCm/mqQw3/H4I/COh6Y2AICjqPEAgEvR1AYAcJTf2NDUZqK8O7UTjGkZRev8aNpodcE0WtyTe+YW1p6p+59nJH52I3HO2tqzHXx/rvmMxv3P1A4eE2V34quvvmJpBADdUkVFhQYOHBh2PrW1tfL5fLq870j18IRXf/CbC6o+u181NTVKSkoKu2xWRF2NJz09XRUVFUpMTAzMal1bW6uMjAxVVFQ4dmPs5vZrcHv5Jfdfg9vLL7n/GjpbfmOM6urqlJ6ebmt5mt/xhNdUxjseST169Ljk/wiSkpJc+cP6XW6/BreXX3L/Nbi9/JL7r6Ez5ff5fF1UGveJusADALDGGL/84c7VZqjxAAAsam4mC3eSUOZqa5PX69XChQtdvVKp26/B7eWX3H8Nbi+/5P5rcHv5o0XU9WoDALSvpVebr8/18nh6hpWXMU2qOf9pbPdqAwBY0/yGh6Y2AADaRY0HAFyquUcavdoAAA6xY9nqSCx9TVMbAMBRrgg8r7zyirKystSnTx+NHDlSu3fvjnSRLCkqKpLH4wlKqampkS5Wu3bt2qUpU6YoPT1dHo9HmzZtCvreGKOioiKlp6crPj5eubm5Onz4cGQK24aOyj9z5sxWz2Ts2LGRKWwbiouLNXr0aCUmJmrAgAGaOnWqPv/886B9ov0ZWLmGaH4OK1as0PDhwwOzE+Tk5Gjr1q2B76Pp/htjZIw/zOR8x+aoDzzr16/X/Pnz9cwzz+jAgQOaMGGC8vPzdfz48UgXzZIbbrhBJ0+eDKRDhw5Fukjtqq+v14gRI7R8+fI2v1+yZImWLl2q5cuXq7y8XKmpqZo8ebLq6uocLmnbOiq/JN1+++1Bz2TLli0OlrB9ZWVlmjNnjvbu3auSkhJduHBBeXl5qq+vD+wT7c/AyjVI0fscBg4cqMWLF2vfvn3at2+fJk2apHvuuScQXKLp/resxxNucr7gUe7mm282s2bNCtp27bXXmqeeeipCJbJu4cKFZsSIEZEuRqdJMhs3bgx89vv9JjU11SxevDiw7fz588bn85l//ud/jkAJ23dx+Y0xpqCgwNxzzz0RKU9nVFVVGUmmrKzMGOO+Z2BM62swxn3P4bLLLjO/+c1voub+19TUGEkmPu4q09f7vbBSfNxVRpKpqalxrPxRXeNpbGzU/v37lZeXF7Q9Ly9Pe/bsiVCpQnPkyBGlp6crKytLP/rRj/TFF19EukidduzYMVVWVgY9D6/Xq1tvvdU1z0OSSktLNWDAAA0dOlSPPPKIqqqqIl2kS6qpqZEkJScnS3LnM7j4Glq44Tk0NTVp3bp1qq+vV05OTtTdf2OabElOi+rA880336ipqUkpKSlB21NSUlRZWRmhUlk3ZswYvf766/rggw/06quvqrKyUuPGjVN1dXWki9YpLffcrc9DkvLz8/Xmm29qx44devHFF1VeXq5JkyapoaEh0kVrxRijwsJCjR8/XtnZ2ZLc9wzaugYp+p/DoUOHlJCQIK/Xq1mzZmnjxo26/vrro+7+h/9+x0936ktpWZenhTGm1bZolJ+fH/j7sGHDlJOTo8GDB2vNmjUqLCyMYMnC49bnIUnTp08P/D07O1ujRo1SZmam3n//fU2bNi2CJWtt7ty5+uSTT/TRRx+1+s4tz+BS1xDtz+Gaa67RwYMHdfr0aW3YsEEFBQUqKysLfO+W+x+torrGc8UVV6hnz56t/idRVVXV6n8cbtCvXz8NGzZMR44ciXRROqWlR153eR6SlJaWpszMzKh7JvPmzdPmzZu1c+fOoPWp3PQMLnUNbYm25xAXF6err75ao0aNUnFxsUaMGKGXXnop6u6/WzsXRHXgiYuL08iRI1VSUhK0vaSkROPGjYtQqTqvoaFBn332mdLS0iJdlE7JyspSampq0PNobGxUWVmZK5+HJFVXV6uioiJqnokxRnPnztU777yjHTt2KCsrK+h7NzyDjq6hLdH2HC5mjFFDQ0PU3X+3NrVFfa+2devWmd69e5tVq1aZTz/91MyfP9/069fPfPnll5EuWocee+wxU1paar744guzd+9ec9ddd5nExMSoLntdXZ05cOCAOXDggJFkli5dag4cOGD++Mc/GmOMWbx4sfH5fOadd94xhw4dMvfff79JS0sztbW1ES55s/bKX1dXZx577DGzZ88ec+zYMbNz506Tk5Njrrzyyqgp/9/+7d8an89nSktLzcmTJwPp7NmzgX2i/Rl0dA3R/hwWLFhgdu3aZY4dO2Y++eQT8/TTT5sePXqY7du3G2Oi4/639Grr3TPFxPVKCyv17pnieK+2qA88xhjz8ssvm8zMTBMXF2duuummoG6Z0Wz69OkmLS3N9O7d26Snp5tp06aZw4cPR7pY7dq5c6eR1CoVFBQYY5q78y5cuNCkpqYar9drbrnlFnPo0KHIFvo72iv/2bNnTV5enunfv7/p3bu3GTRokCkoKDDHjx+PdLED2iq7JLN69erAPtH+DDq6hmh/Dg899FDg903//v3NbbfdFgg6xkTH/W8JPL169je9e6WElXr17O944GE9HgBwmZb1eHr2SJbHE94bE2P8avL/n6Pr8UT1Ox4AQPfjiu7UAIC2GCnsXmnON3oReADApexZj4dJQgEA3Rw1HgBwqebBn2HWeGhqAwBYF37gicQ7HpraAACOosYDAG5lQ+cCRaBzAYEHAFzKre94aGoDADiKGg8AuBadCwAAjjLN72jCSZ0MPK+88oqysrLUp08fjRw5Urt377Z8LIEHABCS9evXa/78+XrmmWd04MABTZgwQfn5+Tp+/Lil45mdGgBcpmV2aqmn7GlqawppduoxY8bopptu0ooVKwLbrrvuOk2dOlXFxcUdHk+NBwBc7ZJLIFlMzWpra4NSQ0NDm2drbGzU/v37lZeXF7Q9Ly9Pe/bssVRiAg8AuExcXJxSU1MlNdmSEhISlJGRIZ/PF0iXqrl88803ampqUkpKStD2lJQUVVZWWio/vdoAwGX69OmjY8eOqbGx0Zb8jDHyeIKb7Lxeb7vHXLx/W3lcCoEHAFyoT58+6tOnj+PnveKKK9SzZ89WtZuqqqpWtaBLoakNAGBZXFycRo4cqZKSkqDtJSUlGjdunKU8qPEAAEJSWFioBx54QKNGjVJOTo5Wrlyp48ePa9asWZaOJ/AAAEIyffp0VVdX67nnntPJkyeVnZ2tLVu2KDMz09LxjOMBADiKdzwAAEcReAAAjiLwAAAcReABADiKwAMAcBSBBwDgKAIPAMBRBB4AgKMIPAAARxF4AACOIvAAABz1/wGHOV5CNV4bwgAAAABJRU5ErkJggg==", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_matrix(H, 'Initial hamiltonian')\n", + "visualize_matrix(N, 'Min-max diagonal matrix')\n", + "visualize_matrix(d, 'Min-max projection onsite-Z')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we see that the min-max diagonal operator can be correctly decomposed into onsite-Z operators. Then we generate the diagonalization curve and compare with other initializations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-03-26 16:08:28]: Using qibojit (numba) backend on /CPU:0\n" + ] + } + ], + "source": [ + "# backend\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", + "# initialize dbi object\n", + "dbi_TFIM_MMH = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 688.27trial/s, best loss: 9.336239342915379]\n", + "New optimized step at iteration 1/15: 0.039240166337035656 with d_coef [(-2.3180340693309422+0j), (-0.9042157574954297+0j), (-0.6267094129284807+0j), (-0.37510402952816974+0j), (-0.16137910360026844+0j)], loss 9.263805656974093\n", + "100%|██████████| 500/500 [00:00<00:00, 654.86trial/s, best loss: 8.253271106315344] \n", + "New optimized step at iteration 2/15: 0.0636971166898561 with d_coef [(-2.8893154826347565+0j), (-1.3328071932958503+0j), (-0.5996311871447069+0j), (-0.38812640871658144+0j), (-0.16592899239661785+0j)], loss 8.248988639626276\n", + "100%|██████████| 500/500 [00:00<00:00, 705.90trial/s, best loss: 7.820911729728226] \n", + "New optimized step at iteration 3/15: 0.026774099108320803 with d_coef [(-3.9047191557345737+0j), (-1.3620955366051533+0j), (-1.094932722170599+0j), (-0.5744178736473565+0j), (-0.04727696085745736+0j)], loss 7.79237041903216\n", + "100%|██████████| 500/500 [00:00<00:00, 522.76trial/s, best loss: 7.506187222292692]\n", + "New optimized step at iteration 4/15: 0.029295596624437686 with d_coef [(-3.894655859483571+0j), (-1.866243073713661+0j), (-0.7092145648013096+0j), (-0.7039608847825699+0j), (-0.023283739763302808+0j)], loss 7.4962199726801755\n", + "100%|██████████| 500/500 [00:00<00:00, 576.24trial/s, best loss: 7.262455656549131]\n", + "New optimized step at iteration 5/15: 0.02836170693029348 with d_coef [(-3.9671435812064013+0j), (-1.748374386604198+0j), (-0.9350901630745362+0j), (-0.6281543245247632+0j), (-0.021512156595171472+0j)], loss 7.242940534826334\n", + "100%|██████████| 500/500 [00:00<00:00, 671.15trial/s, best loss: 7.044926289914773]\n", + "New optimized step at iteration 6/15: 0.027897015043668715 with d_coef [(-3.948390041694754+0j), (-1.8847156433519916+0j), (-0.8262997874928508+0j), (-0.6276868981090158+0j), (-0.03885078124692265+0j)], loss 7.026681601151195\n", + "100%|██████████| 500/500 [00:00<00:00, 690.94trial/s, best loss: 6.855318614477858] \n", + "New optimized step at iteration 7/15: 0.027095778110113995 with d_coef [(-3.9519653973013913+0j), (-1.911636257457286+0j), (-0.8907292589911223+0j), (-0.6344354980656255+0j), (-0.0239873577390306+0j)], loss 6.826359605831807\n", + "100%|██████████| 500/500 [00:00<00:00, 520.20trial/s, best loss: 6.6782408641935875]\n", + "New optimized step at iteration 8/15: 0.027670995126608866 with d_coef [(-3.9302491674477538+0j), (-1.9666365073691627+0j), (-0.8561543524586357+0j), (-0.6383800207112388+0j), (-0.004655769048021813+0j)], loss 6.636290444352086\n", + "100%|██████████| 500/500 [00:00<00:00, 576.02trial/s, best loss: 6.500633770102917]\n", + "New optimized step at iteration 9/15: 0.027675484066740867 with d_coef [(-3.910374644169554+0j), (-1.9831418560231258+0j), (-0.9056736621483122+0j), (-0.6540987589359828+0j), (0.02406147464053876+0j)], loss 6.447464047229631\n", + "100%|██████████| 500/500 [00:00<00:00, 686.99trial/s, best loss: 6.319748615035787]\n", + "New optimized step at iteration 10/15: 0.026930095210157 with d_coef [(-3.886824281463916+0j), (-1.99625546879924+0j), (-0.9414450075378732+0j), (-0.6563760409606512+0j), (0.03970055245590362+0j)], loss 6.2592485638502575\n", + "100%|██████████| 500/500 [00:00<00:00, 678.32trial/s, best loss: 6.1400705423264075]\n", + "New optimized step at iteration 11/15: 0.027416250931757133 with d_coef [(-3.8475665420145373+0j), (-2.037392997099672+0j), (-0.9264643353804642+0j), (-0.6830139042784837+0j), (0.08321313069136971+0j)], loss 6.056764516965165\n", + "100%|██████████| 500/500 [00:00<00:00, 729.97trial/s, best loss: 5.940597947808348] \n", + "New optimized step at iteration 12/15: 0.028200202317592835 with d_coef [(-3.82460449840812+0j), (-2.035906559623582+0j), (-0.9702033338205296+0j), (-0.6848609304443387+0j), (0.11118456157172787+0j)], loss 5.848596463276441\n", + "100%|██████████| 500/500 [00:00<00:00, 743.16trial/s, best loss: 5.72938416138] \n", + "New optimized step at iteration 13/15: 0.025525484486623708 with d_coef [(-3.8277137978993436+0j), (-2.0402358325723036+0j), (-0.9967614632890175+0j), (-0.6822006377994072+0j), (0.09661303923602668+0j)], loss 5.643243093952352\n", + "100%|██████████| 500/500 [00:00<00:00, 722.17trial/s, best loss: 5.532276668994669] \n", + "New optimized step at iteration 14/15: 0.028418788139760974 with d_coef [(-3.7489089984244486+0j), (-2.114017010442895+0j), (-0.9183197191620466+0j), (-0.7036125621442609+0j), (0.16285610695072883+0j)], loss 5.4057916657046725\n", + "100%|██████████| 500/500 [00:00<00:00, 739.76trial/s, best loss: 5.288910417094644] \n", + "New optimized step at iteration 15/15: 0.02625000676004677 with d_coef [(-3.789232226109539+0j), (-2.092494639505251+0j), (-1.0022140546781002+0j), (-0.6714823814533052+0j), (0.13551681944910254+0j)], loss 5.191808803025761\n" + ] + } + ], + "source": [ + "NSTEPS = 15\n", + "off_diagonal_norm_MMH = [dbi_TFIM_MMH.off_diagonal_norm]\n", + "s_step_MMH = [0]\n", + "# d = np.diag(np.linspace(np.min(np.diag(dbi_TFIM_MMH.h.matrix)),np.max(np.diag(dbi_TFIM_MMH.h.matrix)),2**nqubits))\n", + "# d_coef = onsite_Z_decomposition(d, onsite_Z_ops)\n", + "for _ in range(NSTEPS):\n", + " d = np.diag(np.linspace(np.min(np.diag(dbi_TFIM_MMH.h.matrix)),np.max(np.diag(dbi_TFIM_MMH.h.matrix)),2**nqubits))\n", + " d_coef = onsite_Z_decomposition(d, onsite_Z_ops)\n", + " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM_MMH, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", + " dbi_TFIM_MMH(d=d, step=s)\n", + " off_diagonal_norm_MMH.append(dbi_TFIM_MMH.off_diagonal_norm)\n", + " s_step_MMH.append(s)\n", + " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {s} with d_coef {d_coef}, loss {dbi_TFIM_MMH.off_diagonal_norm}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", + "plt.plot(off_diagonal_norm_MMH, label='MMH')\n", + "plt.plot(off_diagonal_norm_delta, label='delta')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect of `n`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-03-26 16:08:41]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 31.576176740060667\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# backend\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", + "# initialize dbi object\n", + "nqubits = 5\n", + "h0 = random_hermitian(2**nqubits, seed=2)\n", + "scheduling = DoubleBracketScheduling.hyperopt\n", + "mode = DoubleBracketGeneratorType.single_commutator\n", + "n_1 = 5\n", + "n_2 = 3\n", + "dbi_1 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", + "dbi_2 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", + "print(\"Initial off diagonal norm\", dbi_1.off_diagonal_norm)\n", + "visualize_matrix(dbi_1.h.matrix, title=f'Random hamiltonian with L={nqubits}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 697.70trial/s, best loss: 9.558186537868679] \n", + "The initial D coefficients: [(-3.321354431855655-1.7961649980378765e-16j), (-0.7143725995296772+3.608986798092513e-17j), (0.472710854506152+9.347215093087467e-17j), (-0.5707798509274735-1.3813111045761499e-17j), (0.34536980200226214-1.1499770144849896e-16j)]\n", + "Gradient: [ 0.65534217 0.16603388 -0.31270245 0.27247095 0.60904527]\n", + "s: 0.024282460160549718\n" + ] + } + ], + "source": [ + "# generate the onsite Z operators\n", + "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", + "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", + "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", + "grad, s = gradient_onsite_Z(dbi,d,n=5, onsite_Z_ops=onsite_Z_ops)\n", + "print('The initial D coefficients:', d_coef)\n", + "print('Gradient:', grad)\n", + "print('s:', s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 596.09trial/s, best loss: 27.467491165569765]\n", + "100%|██████████| 500/500 [00:00<00:00, 689.73trial/s, best loss: 27.469650148347917] \n", + "100%|██████████| 500/500 [00:00<00:00, 684.87trial/s, best loss: 23.138955844687388] \n", + "100%|██████████| 500/500 [00:00<00:00, 680.99trial/s, best loss: 23.147351603039073]\n", + "100%|██████████| 500/500 [00:00<00:00, 709.00trial/s, best loss: 20.03558303567074] \n", + "100%|██████████| 500/500 [00:00<00:00, 638.55trial/s, best loss: 20.01446017712839] \n", + "100%|██████████| 500/500 [00:00<00:00, 659.72trial/s, best loss: 18.534762036988734]\n", + "100%|██████████| 500/500 [00:00<00:00, 702.62trial/s, best loss: 18.511235299525854]\n", + "100%|██████████| 500/500 [00:00<00:00, 716.91trial/s, best loss: 17.667631299146947] \n", + "100%|██████████| 500/500 [00:00<00:00, 717.85trial/s, best loss: 17.623675374778802] \n", + "100%|██████████| 500/500 [00:00<00:00, 626.07trial/s, best loss: 17.043867777621116]\n", + "100%|██████████| 500/500 [00:00<00:00, 662.03trial/s, best loss: 16.99411319096819] \n", + "100%|██████████| 500/500 [00:00<00:00, 694.96trial/s, best loss: 16.644107561332255]\n", + "100%|██████████| 500/500 [00:00<00:00, 712.76trial/s, best loss: 16.467189206952877] \n", + "100%|██████████| 500/500 [00:00<00:00, 713.76trial/s, best loss: 16.046314213095602]\n", + "100%|██████████| 500/500 [00:00<00:00, 699.95trial/s, best loss: 15.878928292681035] \n", + "100%|██████████| 500/500 [00:00<00:00, 691.24trial/s, best loss: 15.62141427589626] \n", + "100%|██████████| 500/500 [00:00<00:00, 667.73trial/s, best loss: 15.306325490043811] \n", + "100%|██████████| 500/500 [00:00<00:00, 694.90trial/s, best loss: 15.001484687547642] \n", + "100%|██████████| 500/500 [00:00<00:00, 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= [0]\n", + "for i in range(iters):\n", + " s_1, d_coef_1, d_1 = gradient_descent_onsite_Z(dbi_1, d_coef_1, d_1, onsite_Z_ops=onsite_Z_ops, n=n_1, max_evals=100)\n", + " s_2, d_coef_2, d_2 = gradient_descent_onsite_Z(dbi_2, d_coef_2, d_2, onsite_Z_ops=onsite_Z_ops, n=n_2, max_evals=100)\n", + " dbi_1(step=s_1, d=d_1)\n", + " dbi_2(step=s_2, d=d_2)\n", + " off_diagonal_norm_1.append(dbi_1.off_diagonal_norm)\n", + " off_diagonal_norm_2.append(dbi_2.off_diagonal_norm)\n", + " s_step_1.append(s_1)\n", + " s_step_2.append(s_2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(str(nqubits) + ' spins magnetic field diagonalization')\n", + "plt.plot(off_diagonal_norm_1, label=f'n_taylor={n_1}')\n", + "plt.plot(off_diagonal_norm_2, label=f'n_taylor={n_2}')\n", + "plt.legend()\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "DBF_qibo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index f478777e93..0aeb738cf0 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -1,6 +1,6 @@ import math from copy import deepcopy -from itertools import product +from itertools import combinations, product from typing import Optional import hyperopt @@ -152,26 +152,21 @@ def cs_angle_sgn(dbi_object, d): return np.sign(norm) -def dGamma_di_onsite_Z( - dbi_object: DoubleBracketIteration, n: int, i: int, d: np.array, onsite_Z_ops=None +def dGamma_di_Pauli( + dbi_object: DoubleBracketIteration, n: int, Z_i: np.array, d: np.array ): """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. Args: dbi_object (DoubleBracketIteration): the target dbi object n (int): the number of nested commutators in `Gamma` - i (int): the index of onsite-Z coefficient + i (int/tupple): the index of onsite-Z coefficient d (np.array): the diagonal operator Returns: (list): [dGamma_0_di, dGamma_1_di, ..., dGamma_n_di] """ nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) - if onsite_Z_ops is None: - Z_i_str = "I" * (i) + "Z" + "I" * (nqubits - i - 1) - Z_i = SymbolicHamiltonian(str_to_symbolic(Z_i_str)).dense.matrix - else: - Z_i = onsite_Z_ops[i] dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) W = dbi_object.commutator(d, dbi_object.h.matrix) @@ -188,13 +183,11 @@ def dGamma_di_onsite_Z( return dGamma_di -def ds_di_onsite_Z( +def ds_di_Pauli( dbi_object: DoubleBracketIteration, d: np.array, - i: int, - n: int = 3, + Z_i: np.array, taylor_coef: Optional[list] = None, - onsite_Z_ops: Optional[list] = None, ): r"""Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients\ Args: @@ -207,10 +200,7 @@ def ds_di_onsite_Z( floats da, db, dc, ds """ # generate the list of derivatives w.r.t ith Z operator coefficient - nqubits = int(np.log2(d.shape[0])) - if onsite_Z_ops is None: - onsite_Z_ops = generate_onsite_Z_ops(nqubits) - dGamma_di = dGamma_di_onsite_Z(dbi_object, n=4, i=i, d=d, onsite_Z_ops=onsite_Z_ops) + dGamma_di = dGamma_di_Pauli(dbi_object, n=4, Z_i=Z_i, d=d) Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) def derivative_product(k1, k2): @@ -238,10 +228,10 @@ def derivative_product(k1, k2): return da, db, dc, ds -def gradient_onsite_Z( +def gradient_Pauli( dbi_object: DoubleBracketIteration, d: np.array, - onsite_Z_ops: list, + pauli_operator_dict: dict, use_ds=False, n=3, **kwargs, @@ -257,11 +247,10 @@ def gradient_onsite_Z( """ # n is the highest order for calculating s - # initialize gradient - nqubits = int(np.log2(d.shape[0])) - if onsite_Z_ops is None: - onsite_Z_ops = generate_onsite_Z_ops(nqubits) - grad = np.zeros(nqubits) + # pauli_index is the list of positions \mu + pauli_operators = list(pauli_operator_dict.values()) + num_paul = len(pauli_operators) + grad = np.zeros(num_paul) coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) s = dbi_object.choose_step( d=d, @@ -269,9 +258,10 @@ def gradient_onsite_Z( ) a, b, c = coef[len(coef) - 3 :] - for i in range(nqubits): - da, db, dc, ds = ds_di_onsite_Z( - dbi_object, d=d, i=i, n=n, taylor_coef=[a, b, c], onsite_Z_ops=onsite_Z_ops + + for i, operator in enumerate(pauli_operators): + da, db, dc, ds = ds_di_Pauli( + dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] ) if use_ds is True: ds = 0 @@ -288,33 +278,44 @@ def gradient_onsite_Z( return grad, s -def onsite_Z_decomposition(h_matrix: np.array, onsite_Z_ops=None): +def decompose_into_Pauli_basis(h_matrix: np.array, pauli_operators: list): """finds the decomposition of hamiltonian `h_matrix` into Pauli-Z operators""" nqubits = int(np.log2(h_matrix.shape[0])) - if onsite_Z_ops is None: - onsite_Z_ops = generate_onsite_Z_ops(nqubits) + decomposition = [] - for Z_i in onsite_Z_ops: + for Z_i in pauli_operators: expect = np.trace(h_matrix @ Z_i) / 2**nqubits decomposition.append(expect) return decomposition -def generate_onsite_Z_ops(nqubits): - """generate the list of Pauli-Z operators of an `nqubit` system in the form of np.array""" - onsite_Z_str = ["I" * (i) + "Z" + "I" * (nqubits - i - 1) for i in range(nqubits)] - onsite_Z_ops = [ - SymbolicHamiltonian(str_to_symbolic(Z_i_str)).dense.matrix - for Z_i_str in onsite_Z_str +def generate_pauli_index(nqubits, order): + if order == 1: + return list(range(nqubits)) + elif order > 1: + indices = list(range(nqubits)) + return indices + [ + comb for i in range(2, order + 1) for comb in combinations(indices, i) + ] + else: + raise ValueError("Order must be a positive integer") + + +def generate_pauli_operator_dict(nqubits: int, parameterization_order: int): + pauli_index = generate_pauli_index(nqubits, order=parameterization_order) + pauli_operators = [ + generate_Pauli_operators(nqubits, symbols.Z, index) for index in pauli_index ] - return onsite_Z_ops + return {index: operator for index, operator in zip(pauli_index, pauli_operators)} -def gradient_descent_onsite_Z( +def gradient_descent_pauli( dbi_object: DoubleBracketIteration, d_coef: list, d: Optional[np.array] = None, - n: int = 2, + pauli_operator_dict: dict = None, + parameterization_order: int = 1, + n: int = 3, onsite_Z_ops: Optional[list] = None, lr_min: float = 1e-5, lr_max: float = 1, @@ -345,12 +346,13 @@ def gradient_descent_onsite_Z( """ nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) - if onsite_Z_ops is None: - onsite_Z_ops = generate_onsite_Z_ops(nqubits) - if d is None: - d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) - grad, s = gradient_onsite_Z( - dbi_object, d, n=n, onsite_Z_ops=onsite_Z_ops, use_ds=use_ds + if pauli_operator_dict is None: + pauli_operator_dict = generate_pauli_operator_dict( + nqubits, parameterization_order + ) + + grad, s = gradient_Pauli( + dbi_object, d, n=n, pauli_operator_dict=pauli_operator_dict, use_ds=use_ds ) # optimize gradient descent step with hyperopt if space is None: @@ -360,7 +362,12 @@ def gradient_descent_onsite_Z( def func_loss_to_lr(lr): d_coef_eval = [d_coef[j] - grad[j] * lr for j in range(nqubits)] - d_eval = sum([d_coef_eval[i] * onsite_Z_ops[i] for i in range(nqubits)]) + d_eval = sum( + [ + d_coef_eval[i] * list(pauli_operator_dict.values())[i] + for i in range(nqubits) + ] + ) return dbi_object.loss(step=s, d=d_eval) best = hyperopt.fmin( @@ -373,7 +380,7 @@ def func_loss_to_lr(lr): lr = best["lr"] d_coef = [d_coef[j] - grad[j] * lr for j in range(nqubits)] - d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)]) + d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) return s, d_coef, d @@ -405,3 +412,14 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): # coefficients from high to low (n:0) coef = list(reversed(trace_coefficients[: n + 1])) return coef + + +def generate_Pauli_operators(nqubits, symbols_pauli, positions): + # generate matrix of an nqubit-pauli operator with `symbols_pauli` at `positions` + if isinstance(positions, int): + return SymbolicHamiltonian( + symbols_pauli(positions), nqubits=nqubits + ).dense.matrix + else: + terms = [symbols_pauli(pos) for pos in positions] + return SymbolicHamiltonian(math.prod(terms), nqubits=nqubits).dense.matrix From ca0b77b09c655135ed17fc19f648d30e7f673399 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Thu, 4 Apr 2024 22:33:10 +0200 Subject: [PATCH 057/154] did most of Marek's comments --- src/qibo/models/dbi/utils_gradients.py | 175 +++++++++++++++++++++++++ 1 file changed, 175 insertions(+) create mode 100644 src/qibo/models/dbi/utils_gradients.py diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py new file mode 100644 index 0000000000..dcb1d68e09 --- /dev/null +++ b/src/qibo/models/dbi/utils_gradients.py @@ -0,0 +1,175 @@ +import numpy as np +import math + +from copy import deepcopy +from enum import Enum, auto +from qibo.models.dbi.utils import commutator +from qibo.models.dbi.utils_scheduling import polynomial_step +from qibo.hamiltonians import SymbolicHamiltonian +from qibo import symbols + +class d_ansatz_type(Enum): + + element_wise = auto() + local_1 = auto() + #local_2 = auto() # for future implementation + #ising = auto() # for future implementation + +def d_ansatz(params: np.array, d_type: d_ansatz_type == d_ansatz_type.element_wise): + r""" + Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. + If $\alpha_i$ are our parameters and d the number of qubits then: + + element_wise: $D = \sum_{i=0}^{2^d} \alpha_i |i\rangle \langle i|$ + local_1: $D = \sum_{i=1}^{d} \alpha_i Z_i$ + Args: + params(np.array): parameters for the ansatz. + d_type(d_ansatz type): type of parameterization for the ansatz. + """ + + if d_type is d_ansatz_type.element_wise: + d = np.zeros((len(params), len(params))) + for i in range(len(params)): + d[i, i] = params[i] + + elif d_type is d_ansatz_type.local_1: + + op_list = [params[i]*symbols.Z(i) for i in len(params)] + from functools import reduce + d = SymbolicHamiltonian(reduce(symbols.Z.add, op_list) , nqubits = len(params)) + d.dense.matrix + else: + raise ValueError(f"Parameterization type {type} not recognized.") + + return d + +def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): + r""" + Gradient of the nth gamma operator with respect to the ith diagonal elements of D. + $Gamma_{n} = [W,[W,...,[W,H]]...]]$, + $\frac{\partial Gamma_{n}}{\partial D_{ii}} = \partial_{D_{ii}} W\Gamma_{n-1}-\partial_{D_{ii}}\Gamma_{n-1} W$. + and thus is can be computed recursively. + Args: + d(np.array): D operator. + h(np.array): Hamiltonian. + n(int): nth Gamma operator. + i(int): Index of the diagonal element of D. + dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). + gamma_list(list): List of the n gamma operators. + Returns: + (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. + """ + dD_di = np.zeros(d.shape) + dD_di[i, i] = 1 + dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) + w = commutator(d, h) + return dW_di + commutator(w, dGamma[-1]) + +#def dpolynomial_diDiagonal(dbi_object, d, h, i): #element_wise_ansatz +def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): + + r""" + TODO: add formula and explain terms + Gradient wrt the ith diagonal elements of D. + We make Double_bracket rotation with duration given by the minimzer of the ´polynomial_step´ function. + Gradient of the Taylor expansion of the least squares loss function as a function of $s$ the duration of Double-Bracket rotation element-wise ansatz: + $\partial_{D_{ii}} \text{Tr}(H_k@D) \approx \sum_{k=0}^{n} \frac{1}{k!!} \partial_{D_ii}\text{Tr}(\Gamma_{k}D)$. + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + d(np.array): D operator. + h(np.array): Hamiltonian. + i(int): Index of the diagonal element of D. + Returns: + derivative(float): Derivative of the polynomial expansion with respect to the ith diagonal elements of D. + """ + derivative = 0 + s = polynomial_step(dbi_object, n=3, d=d) + dD_di = np.zeros(d.shape) + gamma_list = dbi_object.generate_Gamma_list(4, d) + dD_di[i, i] = 1 + dGamma = [commutator(dD_di, h)] + derivative += np.real( + np.trace(gamma_list[0] @ dD_di) + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s + ) + for n in range(2, 4): + dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) + derivative += np.real( + np.trace(dGamma[-1] @ d + gamma_list[n] @ dD_di) * s**n / math.factorial(n) + ) + + return derivative + +def gradientDiagonalEntries(dbi_object, params, h, analytic = True, d_type = d_ansatz_type.element_wise, delta = 1e-4): + r""" + Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. + As the analytical gradient is applied on the polynomial expansion of the cost function, the numerical gradients may be more accurate. + + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + h(np.array): Hamiltonian. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + d_type(d_ansatz_type): Ansatz used for the D operator. Options are 'Full' and '1-local'. + delta(float): Step size for numerical gradient. + Returns: + grad(np.array): Gradient of the D operator. + """ + + grad = np.zeros(len(params)) + d = d_ansatz(params, d_type) + if analytic == True: + for i in range(len(params)): + derivative = derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object,d,h,i) + grad[i] = d[i,i]-derivative + else: + for i in range(len(params)): + params_new = deepcopy(params) + params_new[i] += delta + d_new = d_ansatz(params_new, d_type) + grad[i] = (dbi_object.cost(d_new)-dbi_object.cost(d))/delta + return grad + +def gradient_descent_dbr_d_ansatz(dbi_object, params, nmb_iterations, lr = 1e-2, analytic = True, d_type = d_ansatz_type.element_wise): + r""" + Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. + - Declare variables + - Calculate initial loss + - Iterate, learning at each the optimal D and measure loss + - Return values + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + nmb_iterations(int): Number of gradient descent iterations. + lr(float): Learning rate. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + d_type(d_ansatz_type): Ansatz used for the D operator. + Returns: + d(np.array): Optimized D operator. + loss(np.array): Loss function evaluated at each iteration. + grad(np.array): Gradient evaluated at each iteration. + params_hist(np.array): Parameters evaluated at each iteration. + """ + + h = dbi_object.h.matrix + d = d_ansatz(params,d_type) + loss = np.zeros(nmb_iterations+1) + grad = np.zeros((nmb_iterations,len(params))) + dbi_new = deepcopy(dbi_object) + s = polynomial_step(dbi_object, n=3, d=d) + dbi_new(s, d=d) + loss[0] = dbi_new.least_squares(d) + params_hist = np.empty((len(params), nmb_iterations + 1)) + params_hist[:, 0] = params + + for i in range(nmb_iterations): + dbi_new = deepcopy(dbi_object) + grad[i,:] = gradientDiagonalEntries(dbi_object, params, h, analytic=analytic, ansatz=d_type) + for j in range(len(params)): + params[j] = params[j] - lr*grad[i,j] + d = d_ansatz(params, d_type) + s = polynomial_step(dbi_new, n = 5, d=d) + dbi_new(s,d=d) + loss[i+1] = dbi_new.cost(d) + params_hist[:,i+1] = params + + return d,loss,grad,params_hist \ No newline at end of file From a8bef3e4cc983e57de2cb2a40d2ea68d16b929a3 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Thu, 4 Apr 2024 22:34:24 +0200 Subject: [PATCH 058/154] did most of Marek's comments --- examples/dbi/dbi_misc.ipynb | 858 +++++++++++++++++++++--- src/qibo/models/dbi/double_bracket.py | 15 +- src/qibo/models/dbi/utils.py | 52 ++ src/qibo/models/dbi/utils_scheduling.py | 225 +------ 4 files changed, 821 insertions(+), 329 deletions(-) diff --git a/examples/dbi/dbi_misc.ipynb b/examples/dbi/dbi_misc.ipynb index 1a8a4da3e7..05f8f81117 100644 --- a/examples/dbi/dbi_misc.ipynb +++ b/examples/dbi/dbi_misc.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "\n", "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCost\n", "from qibo.models.dbi.utils import *\n", "from qibo.models.dbi.utils_scheduling import *" ] @@ -34,14 +34,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.5|INFO|2024-03-21 14:14:06]: Using qibojit (numba) backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-01 22:40:49]: Using numpy backend on /CPU:0\n" ] } ], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", - "\n", + "set_backend(\"numpy\")\n", "# hamiltonian parameters\n", "nqubits = 7\n", "h = 3.0\n", @@ -50,21 +49,21 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", + "cost = DoubleBracketCost.least_squares\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "factor = np.array([1,2**nqubits])\n", "s_space = np.linspace(1e-5, 1.0, 100)\n", "off_diagonal_norm_diff = np.empty((len(factor)+1,len(s_space)))\n", - "potential = np.empty((len(factor)+1,len(s_space)))\n", + "potential = np.empty((len(factor),len(s_space)))\n", "for i in range(len(factor)):\n", "# generate data for plotting sigma decrease of the first step\n", " d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/factor[i]\n", @@ -72,7 +71,7 @@ " dbi_eval = deepcopy(dbi)\n", " dbi_eval(s_space[s],d=d)\n", " off_diagonal_norm_diff[i,s] = (dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", - " potential[i,s] = dbi_eval.least_squares(D=d)\n", + " potential[i,s] = dbi_eval.least_squares(d=d)\n", " \"\"\"\n", " # grid_search\n", " step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", @@ -87,17 +86,12 @@ "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", "step = 1e-2\n", "iterations = 200\n", - "d, loss, grad, diags = gradient_ascent(dbi, d,step, iterations)\n", - "for s in range(len(s_space)):\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval(s_space[s],d=d)\n", - " off_diagonal_norm_diff[2,s] = (dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", - " potential[2,s] = dbi_eval.least_squares(D=d)\n" + "\n" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -106,13 +100,13 @@ "Text(0, 0.5, 'Least squares cost function')" ] }, - "execution_count": 40, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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ejB07dmDUqFEAgAceeADz5s3DXXfdhbq6OixduhRdXV146qmnEIvFMHnyZHzwwQd48MEHsXDhQgQCgWL9LAZlhKQJrPol0gxjZa6tAQC8+ekB/KOpA5ccd0ipD8Wgj6NsNFaZTAbLli1De3s7ZsyY4Xp/9+7dWLFiBa666irXe0uXLkVDQwOOPvpo3HjjjWhtbaXvvfHGG4jH4zSoAoCTTjoJ8Xgcr7/+Ot1m8uTJNKgCgLPPPhuJRAJvv/023eb0009HLBZzbPP5559j27Zt3T5/g/IEYTbM5Nu/YDRWBjyuX/Yubvjde/i8qbPUh2LQx1FSxgrI6ZZmzJiBrq4u1NTUYPny5Zg0aZJru1/96leora3F7NmzHa9ffvnlOOywwzBixAhs2rQJN998M9577z2sXr0aALBr1y4MGzbM9XnDhg3Drl276DbDhw93vF9fX49oNOrYZty4cY5tyD67du3CYYcdJjy/RCKBRCJB/25paVH9HAZlhlR6YFUFZrIWQsH+z74m06Yq0MCJps6c7UZrV7rER9Lz+LypE+s+3Y8Lpo5CJFQ2/Eq/QckDqwkTJmDDhg1oamrCM888gyuuuAIvv/yyK7j6xS9+gcsvvxwVFRWO1+fPn0//f/LkyTjyyCMxffp0vPPOO5g2bRoACNN0lmU5Xi9kGyJcV6UBFy9ejDvuuEP6fl9ENmth58FOHDqkqtSH0uMYSBqr5o4UZj70Ms6cMAw//aeppT6cHgWrqxooQbOBGqSIYSBo7u7+8xY8+7dG1MQimDlpuPcOBr5Q8lA1Go1i/PjxmD59OhYvXoxjjjkGDz/8sGObV199FVu3bsW3vvUtz8+bNm0aIpEIPvzwQwDAiBEjsHv3btd2e/fupYzTiBEjKDNFcPDgQaRSKeU2e/bsAQAX28Xi5ptvRnNzM/23Y8cOz3Mod/zsLx/gtPtexOrN7t+1vyGVHTipwA/2tGJPawJ//XhfqQ+lx5FyaKz6/0RqoIZlWQNKT3mgPVfxfrDdtHTqCZQ8sOJhWZYjdQYATz75JI4//ngcc8wxnvu///77SKVSGDlyJABgxowZaG5uxptvvkm3WbduHZqbm3HyySfTbTZt2uSoQly1ahVisRiOP/54us0rr7zisGBYtWoVRo0a5UoRsojFYtROgvzr6/h4XzsAYFv+v/0ZJBU4EAZbEmwMBM8uNhVo7BYMWNZyIDCYNjvX/8+1FChpYHXLLbfg1VdfxbZt27Bx40b86Ec/wksvvYTLL7+cbtPS0oL/+Z//EbJVH3/8Me6880689dZb2LZtG/785z/jn//5n3HcccfhlFNOAQBMnDgR55xzDubPn4+1a9di7dq1mD9/Pi644AJMmDABADBr1ixMmjQJc+fOxbvvvos1a9bgxhtvxPz582kgNGfOHMRiMcybNw+bNm3C8uXLcffddw/IisCBpDuiqcAB4FeWGkCDLZvuGQhBs4EaqQHma2azc4at7QmUVGO1e/duzJ07F42NjYjH45g6dSpWrlyJmTNn0m2WLVsGy7LwL//yL679o9Eo1qxZg4cffhhtbW0YM2YMzj//fNx2220IhUJ0u6VLl2LBggWYNWsWAOCiiy7Co48+St8PhUJYsWIFrr32WpxyyimorKzEnDlzcP/999Nt4vE4Vq9ejeuuuw7Tp09HfX09Fi5ciIULF/bET1PWIA9ldkAEG/kgcgAMtgORnQMGRiBpoMZA8zUj52ju/Z5BSQOrJ5980nObq6++GldffbXwvTFjxuDll1/2/IzBgwfj6aefVm5z6KGH4tlnn1VuM2XKFLzyyiue39ffMZCCjeQAslsYSH0RTVWgAYvUAPM1o6nAATCGlwJlp7EyKH+k0iTY6P8ru4EUbAwkob5hrAxYDLT7ITmAxrVSwARWBr6RHFC6owGU9qTauf4fMDs1Vv3/fA3USLEM5gBgcdKZgbM4LgVMYGXgG8kBJF63KfP+PwDZVYElPpBeADuRDoT72EANR1PuAfAApA1j1aMwgZWBb9BKuQGwshtI3jZ22rP/TyzJAaapMVBjoBnGJo3GqkdhAisD3xhYFgQD6Vxz55i1+r+XVXqAldcbqJEaYMUMpiqwZ2ECKwPfGEiNiQeqBUF/DyTZ8vqBcG0N1EgOsEDbaKx6FiawMvCNgZUeGzimmQOp5Dw5wKrADNQYSPc+YKoCexomsDLwjeQAYXHY/mHWAEiPOU0S+/u5Fl4VuKelq9/f+wMN7P2QGgAsTnoAeRGWAiawMvCNgeLtxJ9f/0+PDZxVe7rAIPJvO5twwt1rcNsfN/XEYRkUAZ3JDF75YC9dAOogPYBSw5msBXKK/X0MLxVMYGXgGwOlWW+Ks1jo7wPuQAqsCj3XD3e3Of5rUH5Y8vLH+OYv3sT/99YO7X0GksaqO2ytgR5MYGXgC5ZlDRjdUYobYPt/sDFw+qUVqrEaSPrCvopdzZ0AcilbXQykRQV7v/f3MbxUMIGVgS8kB1TlmDO46O+D0ICaXNgg0of5ayKVAWC3/+kr2HmwA6s374bVz59ZwF4g+LlGA6mlTXoAPeelggmsDHzBUaY+gChzoP8PQqkBmg4pjLHqW4zeTc9sxPxfv4X3djb72u/37+zEXz/a10NH1TOgFW8+AmaHE38/77IwkNKepYIJrAx8IZUeOCs7drAFBkJgNXAEvIU6r9N2Tn1sQtrbmgAA7Mv/Vwe7W7qw8P97D99btqGHjqpnQMYoPpWv3GcAOa+nB1DKv1QwgZWBL7Ar/f7emDg5gBmr/p7mLbQqMNFH+2QWUsnb0pkCALR2pXrkmHoKhbRmYheM/f05H0gVkKWCCawMfCExkBgrl8aqf6/uBpLGqtBz7aseboWI7hN99FxTBfTBG0gebsYct+dhAisDX3AwVv38oeQDq34eVzknlz6W6vKLQvVkCZpm6ls3Q0EsDsNy9SXRe7IAVtGZGu5b19YvHA2n+/lzXiqYwMrAFwZSST6v0ej/5ztwGKukIx3ioyqwj7I46W6yOH3pfAsSr7PO6/082DAaq56HCawMfGEgTb4Duiqwnw+46UKrAgsQRpcDkgUwVsk+mvYn97Efu4WBpDsqtHDDQB8msDLwhcQAEnkOPB8r+/z6e2FCoQuERDqT36dvBZ6FiNf7qrdTqpuMVV8610JQaOGGgT5MYGXgC4ax6r8YSD5WyQInl0L0O+UAEjQXIl4H+pZnXSHi9QGlsRpAz3mpYAIrA18YSCu75ADzsUoOIDayUPdpW7/Td36fTNai5+jL26mPpoZpunYAsHOFwFQF9jxMYGXgCwOZservg9BA6iFW6ESaSPU98XqhTXf76rNeUCrQ4bzed861EKQLLNww0IcJrAx8gWVx+tJgWwhcdgtGd9RvkCpwcklSYXTfmZAKrXhjGcy+1BuxEFaRvZ79/d5PDyCX+VLBBFYGvjCQmjDzA3N/X8kOpHZFjoo3Xz5WOfG6ZfUdH7dCK94cgXYfuvdpSxtf7NzAsSBw6At9Xte3PzuAd7YfLPYh9TuYwMrAFwZS64cB19ImO3DYyHSBDIWTxekbE3DhDaf7ZrBRiFB/II1rheoLO5MZXP7zdZj783X9vlF1dxEu9QEY9C0MpMoxV1VgP2foBmoqsJCqQKDv/EYOsbKPCbEv+lhZlmWnawsW6veNcy0UhRqE7m1NoCuvMUykswiHDC8jg/llDHxhIDVhdtst9O9VmjMVOHDOtVALgr5iElqog3pfXEQ5CjD8BJF98FwLRaEGofvaE/T/+3vw2V2YwMrAFwZWE+YBprEaQO7TqWxhQWRfZKyKIV7vi+fqZ3waSG1e0gXeD/tamcDKpAKVMIGVgS843Ln7yGBbKNiJBejfDJ1lWVyw0X/PFSg8iEz2QVbPGSAV2D+vj5wra5vgp1H2QE2D+znX/e3JgvYbiDCBlYEvDCQtwkDyscpkLbBxY38eOLOMYSbg08eqwGrCUqJw8XrfCzYKTekNqHGtQLZ2f5vNWPUl+41SwARWBr4wsFZ2A6cqkE8J+DnXt7YdwFf/86/YsKOpyEfVM+DZF91zZYXRfvYrNZy6owLZuT4SRDpdxf1orAZOGrxQ+419bQxj1Ufuh1LBBFYGvtAXdReFojvBRl9DocEGADz7t0Zs2NGElZt2FfuwegSFaucSXGrYT6qplCjUn6wvLqJSBRYXFKpDKzWaO1P49z9sxNuf6XtL8edqaUoc2FRgX0kNlwomsDLwhb64Yi8UAykVmEoXfq5dqZxpZl8RtLrPVe+4+6qvWaENhtkAo69MpM5KRv1jThf4G5Uaf9m8G0+v3Y7HXv5Yex8+cNS9jdlUYF+590sFE1gZ+IJjFduPxdzAQE8F6k8uhMnpK4FnoQEzX8zQV5gNZ4BUYFVgHznXpENw74exKkxzV2p0JNMA7MWNDviAU3dhsZ9JBfYVtrZUKGlgtWTJEkydOhV1dXWoq6vDjBkz8Nxzz9H3A4GA8N99991Ht0kkEvjud7+LhoYGVFdX46KLLsLOnTsd33Pw4EHMnTsX8Xgc8Xgcc+fORVNTk2Ob7du348ILL0R1dTUaGhqwYMECJJNJxzYbN27E6aefjsrKSowePRp33nmnNo3aX5DiegX25/MfUKnAbrBzZFDvK4MtP+HqXlc+FdhX7odCW9Mk+6Cgu+Bqzz7KxBNtGB/0q1DognF/u2GsdFHSwOqQQw7BPffcg7feegtvvfUWvvzlL+Piiy/G+++/DwBobGx0/PvFL36BQCCAr33ta/Qzrr/+eixfvhzLli3Da6+9hra2NlxwwQXIZOwIfs6cOdiwYQNWrlyJlStXYsOGDZg7dy59P5PJ4Pzzz0d7eztee+01LFu2DM888wwWLVpEt2lpacHMmTMxatQorF+/Ho888gjuv/9+PPjgg73wS5UPBhKL09Opn03/aMa//2Gjg2IvFVzX1ccEbKcC+8a94EoFah43P3n1FbuFVKGC7j5oLcHrwnQXfn3RDBWwr5EvawluHNMJmjNZCwdYjVUJfqOuVAbn/OwV3Pz7jb3+3X5R0pY2F154oePvu+66C0uWLMHatWtx9NFHY8SIEY73/+///g9nnnkmDj/8cABAc3MznnzySfzmN7/BV77yFQDA008/jTFjxuAvf/kLzj77bGzZsgUrV67E2rVrceKJJwIAnnjiCcyYMQNbt27FhAkTsGrVKmzevBk7duzAqFGjAAAPPPAA5s2bh7vuugt1dXVYunQpurq68NRTTyEWi2Hy5Mn44IMP8OCDD2LhwoUIBAI9/XOVBRKCNi/9tS9Sd3RHOvj5q5/gDxs+x5HDanHFyeOK+tl+4WLnfDCRhMnpizocwA9j5Uy39BUWp9DWNH1dvA7k7uto2Hts7qtdB5IFpOFdqUCNIKmpI+nQYpXifvhkbzv+vqsVu1q6sHj2lF7/fj8oG41VJpPBsmXL0N7ejhkzZrje3717N1asWIGrrrqKvvb2228jlUph1qxZ9LVRo0Zh8uTJeP311wEAb7zxBuLxOA2qAOCkk05CPB53bDN58mQaVAHA2WefjUQigbfffptuc/rppyMWizm2+fzzz7Ft27bi/Ah9APzA1VcG3ELQ0y1tOpK5iZpdCZYK3WEiCWPVV+4FV1VggRqrvsJssOfX31va8CyzbpDUVxuQk2vkLxXI3//e++7nxqhSFKqQhY2fcy0VSh5Ybdy4ETU1NYjFYrjmmmuwfPlyTJo0ybXdr371K9TW1mL27Nn0tV27diEajaK+vt6x7fDhw7Fr1y66zbBhw1yfN2zYMMc2w4cPd7xfX1+PaDSq3Ib8TbYRIZFIoKWlxfGvL2MgVcqRc4vmm436GUt+/85OfOPn69DUIQ+ayOe3dqULP8giwT0h+Qms8qvmPjL5Fhow94dUoJ+UUV9MBRZSYGBZVp81CCXPbaFMJKAXSO7j5Aql+I0KSXuWCiUPrCZMmIANGzZg7dq1+M53voMrrrgCmzdvdm33i1/8ApdffjkqKio8P9OyLEdqTpSmK8Y2JH+vSgMuXryYiubj8TjGjBnjefzlDFepbh8ahPyCPMgVERJY6T/Qv1n7GV77aB/WfrJfug0ZIFq6Ut04yuKAD4r8NSbuY+L1AoNIXrzeVybgQr3nkn2wUs7tUeZ9T7q6DvhcIOw40OGrKq+YKCTY4J91nQURaw4KlCbQppKDjFX2807JA6toNIrx48dj+vTpWLx4MY455hg8/PDDjm1effVVbN26Fd/61rccr48YMQLJZBIHDzrN0fbs2UPZpBEjRmD37t2u7927d69jG551OnjwIFKplHKbPXv2AICLyWJx8803o7m5mf7bsWOHdNu+APeqvbxv8O6ADFYVkRAAf+fansixUEnFoEUGtJbO0gdWrmDDl3i9b9ktkFV+LEwC5v6dCnTYLfhyXreDhb6SHiskaC40NQwAH+5uxen3vYh/++272vsUEzTY6EZVoM758gU2pbj32YUNz7CXG0oeWPGwLAuJhPMiPvnkkzj++ONxzDHHOF4//vjjEYlEsHr1avpaY2MjNm3ahJNPPhkAMGPGDDQ3N+PNN9+k26xbtw7Nzc2ObTZt2oTGxka6zapVqxCLxXD88cfTbV555RWHBcOqVaswatQojBs3Tno+sViM2kmQf30Z/A1d7JXDp/va8eLWPUX9zEJBBlwSWPk5V6KfUq2YyaqvHFOB/nys8ueq+H3aE2k8vfYz7G7pKuwAiwgyKVRF/QXMbruF8h7cCQoVoTu8nfpIEMnfxzpMTqG6LAD460f7kLWAbfvbtfcpJsj5+fLsctmNaGisXIxV6VKBQPmz4yUNrG655Ra8+uqr2LZtGzZu3Igf/ehHeOmll3D55ZfTbVpaWvA///M/LrYKAOLxOK666iosWrQIa9aswbvvvotvfOMbmDJlCq0SnDhxIs455xzMnz8fa9euxdq1azF//nxccMEFmDBhAgBg1qxZmDRpEubOnYt3330Xa9aswY033oj58+fTQGjOnDmIxWKYN28eNm3ahOXLl+Puu+8eUBWBQM9rrBb897v411+ux8d724r6uYWAnGtlAYxVZ9LbgoCwWWWZCvRTFUg1VvLB7o/vfY5//8Mm/OwvHxZ2gEUEz0T296rAQvVDhdo0lBKFMK9uw0z932jjP1qEn9FbKCwVWABj1V56jRX7/JW7gL2klfK7d+/G3Llz0djYiHg8jqlTp2LlypWYOXMm3WbZsmWwLAv/8i//IvyMhx56COFwGJdeeik6Oztx1lln4amnnkIoFKLbLF26FAsWLKDVgxdddBEeffRR+n4oFMKKFStw7bXX4pRTTkFlZSXmzJmD+++/n24Tj8exevVqXHfddZg+fTrq6+uxcOFCLFy4sNg/S1mjp32s9rbmHuBdzV04YmhNUT/bL5J0AvaXMgJsxkplQUAGuHJgrLpVFZj2DiIP5kX8Ow92FHB0xQUfMBO/I68Fkp9U4Osf7cOhQ6pwSH1VN4+2+3AYfRYsXu8jQWQBUgU+FWhZOXY6GPReMG/6R7PwM3oLyWKkAgvRWJUgkEz2oVRgSQOrJ5980nObq6++GldffbX0/YqKCjzyyCN45JFHpNsMHjwYTz/9tPJ7Dj30UDz77LPKbaZMmYJXXnlFfcD9HPzkUuzAiqyM2xLlE2z4ZTayWQudxI1cMeCly4ixKlRjlcladFJRB5G5bUjgXErwKV4gdx7hkHoi1RWvf7SnFXN+vg7HHBLH//3bqd082u6DvZYFu5H3kVRgIVYCIrYnnbUQ9QisOpMZfLinVft7egIFpQIL0JSVncaqzBmrstNYGZQ3uiP09PP57WUQWJHBo9KnFqeLoaxV+6QYjVWpWwMVGjCz9LxqsCUrXL5suxSgjFXUDqx0rq2bsRIP7ntacue4ubGlLETfDrsFX02Y2f1Kfx46cOmlNAIAsk+ECax1rtvmxhZqmlkqDRo5dl+pwCz/rOv7WA2ujuY/w2isVDCBlYEvuMTrRQ4IyGRVDoEVZazC/hgrkgbMfYZ3VWAmazn2KQX4gVI7iEzppYvIxHygPVnyYCPFpXgBvWur6/VlT3ZWWaU+AZ+9Ah02DeU9kRG4fay8jzstYDB1AtCNO5t8fU9PgLA4lqU/PrH9XgG9oJCI14fV5gyyS2O3kGH+v7zvRxNYGfhCd8ry9T4/93ltidIGGoDAx0oziOxMsiyOt8YKKH060KWx0jxX1r9H51yzVumd5sk9Vhnxx1glUvy9Lz5f9pn4ZF9pqsVYJJmJVDdgzmQtRwuTPqOxKshuwZnyB/QCUCJc1/2enkAh5q980OgVkHWlMlSaMSKe85EsBUOXNKlAg/6KntZYkYe+PBgrZypQd7XvYKw0WBwAaOks7fm6rqvmuSY0Bc4sc1fqdCCZgGKcxsoLyYxeVSC7mv9kb+kDq0KqAvuuZ1fhdgvE1wzQ+52IcB0oYSqwAEE3f6xeaV6SBoyGgqivIqnAUjBWbBBZ3vejCawMfIEMVKG8sNNPWb4XWAfkchSv605KHUn72FUDO8t4tJacsSo0FcimPRXnygzEZRNYhYIg+mSdiULXHJf9LT/dVz62IYB+NZfb16y8JzICt/O6BmOVv67RcJDqrLzOlxWuA6VrQO6o3NQMNsj9QIpgvdK8RLg+pCaKcP6BKY3dgmGsDPohcj21cg9UFa2UK94Nzk4ApWasslmLDh7UILTIqUB2Eih1KpAcpz2x6F1XNrBSTUbpsmKscscSCQURDupbabgNQr0Zq0/LIBVYiEGoO6VW3hMZQSFmn+R+iIaCdMHotR8RrhOZgB+NUzFRUCqQS4V7BWREXzWkJkorZ0teFZgpvVREBRNYGWiDDQRiNLAq5uczgVWytIEVuwL1axCqK15nz7fUqcBC2Tldet6RCmwttcYqH0SGA/ZEqlM9ppkeY8+1PFKB9vHoVvf1dMq/p8Dbm+ikjMizzgbaXvcDSQNOHT2I+a7Sejvpfj8JGnVtZPYSxqo6Zv8+JTcILe/70QRWBtpgH9zKKHnAijeYsINZqcXr7IBc6ZOdY4NC1e/DDk6lTgUmuVWs7kTqEK8rz7X8UoHhYJCmNgphrGTnyz4njc1djtRwKVAMxqrcNS0EhRjdkmAsHGICbY/9NuYDq+MOHURfK4kFQUZvYcOCnG9FXlPmqbFiGCt7IVLaILLcDUJNYGWgDfbG9jsB64BliUqdCmQHDkL369LfzlSgeB/Lshy/XUuJ3dfTGX+rWAKH3YKGtQRQepNQmvoJBxEK6aV+APv+j4TUky//O5Q6HcgHVjqeaT3dYaGnUEivQGdqWC/Q3iQKrPoIY0UCqYqo3oKRaKyG1sS0NWg9AaOxMuiXIA9uMJCblIDiPmDshFTqwCopOFddjZVOKpB/vaWz1OJ1zlqiAINQ1cDOvre3TBirSCjgS4xLBnZbm+LNWAGlD6yS3L3m51z97FMO4FNEWuL1/PWKhoK2hkgRbOSE67mihGPGDGI+p9SMlW5VIH8f61UF5hirPMtVYrsFYxBq0G9gOxQH6QNWVMaKeVhKXRVIBo4wc666k0unRqUcP3CXmrHiU4HaGquUkw2Rgf08vu9YbyPluI/1NVYkiKyO5TqBye0WOMaqxDorXneklR5zVdeV90RGUIjoPukItL3HNeKo31ATw4i6CobBLDVj5X1dWX8yXXZ6H6Ox8lvcUkz0+ybM2WwWH330Efbs2YMs9wOfdtppRTkwg/KDo3qGlqkXM7AqH8aKlmCHggjllx/6zuveGisXY1Vyu4UCU4Fc+x5ZM2N2wiu5xipdWFUgGcyrouqVPh+ElNokVOTtxJphiqBrLVFuKEQblmZSgSTQVu1H0oBTRtchEMgFY6lMptcr5dKZrMPEVS/t6ZZz+NFY7ch3EihFi6O+lAr0HVitXbsWc+bMwWeffebK1QcCAWTKvAzSoHBQjUnYnpCyxUwFOjRWpRav26tYv+ycTiqQn3xby0xjVQhjRfaLCJoZs5POgfYkslkLQY8mtz0FuwpMX6wM2MyGF2NFrnlDTRT72pJlF1jpMVZ9U2PF+uxlspYW08YymDoaq400sIoDyIneker99BQ/tmi172HOi6b9Pfbb355bCDXUxBAJkX1Kmwrsd+L1a665BtOnT8emTZtw4MABHDx4kP47cOBATxyjQQ9hT2sXvnz/S3j85Y+1tme1CH4mJF2wk28yky3pqiQpGGy1U4EaPlblp7HiqwL1fnu2KhBQsDjM52WyFg52lC4dWIhYGbCDyCoP0S8516OG1wIAPtnbVtIm2+4JWD+ItPcp74mMgKS0q3wsEJyLKO+0HmGsJucDKxJs9Dar5+6LqF8BCegtoizLElYFlsIQtS8xVr4Dqw8//BB33303Jk6ciEGDBiEejzv+GfQdvPNZEz7Z144/b2zU2p4GG4z/TzGbMPODeSnTgezkGwz4q4RhGSs5q8FrrEptt1CYeJ1NBQLyAZcf9Eups7LL6/UNIQGGsYrmGSuPwoTxw2oQCOTYyP0l7I9YCPvU132sqmL5NJePIDISCiIcUt//XSlbuD7lkDxjRdOHvTvZJ/gWSzrsXP4+D2gWILV0pukYNrg66mshUmz0a8bqxBNPxEcffdQTx2LQyyADgW6n8FTaHoD8iH51wX9WKQXsZJCKhv2xGoAzsJKtrPiAq9xSgdpVgVwqUJYi4AOXUuqsbObVXiDo+VjlrmuVZyrQThmOilcCKG1lYCGC7kKaGZcDyHFX0eBX41yJ5i7szU7bwvUoRtTlGhJTxqqX02MFpQLJgjEYRESjKGdfPg1YWxFGLByyf5+SOK/3Y/H6d7/7XSxatAi7du3ClClTEIlEHO9PnTq1aAdn0LMgD6Ju9J8UpAKLa7fAMVYlNFZkK4VCPr1bOlOseF1PY1UuqUC/GitdxooMxMEAkLVKHFhlbTbSTxqHitfpb6Q+10gwgMOHVuMfTZ34ZG8bvjhucLePvRAU1D/PB8v1/721A5s/b8EJhw3GiYcNxpCaWGEHWgSQ59ZPdSu5jlGNBSObBiRFGjoWDT0BPrjgbTVEoOa4zLimuh9IGrAhf03DoeIbQ+uiL9kt+A6svva1rwEArrzySvpaIBCg1UBGvN53QB4o3eg/xbA4PdGEma80KYdUYDgYRCjgT2PV4UNjFQ0HkUxnkUhnkUhnEAurq7V6CnRCihZuEAqo0mO57YbWxrC7JVFSk1AR86ojxk1yaSYvPVkkFMThDdV49cN9JRWwu/vn+U8Fyu7jRDqDW36/Eemshade3wYAmDC8FjOOGILvnHEEhudZnd5Ciq/c9JHiDQcDDDst3m/jTqdwnewH9L63k+41YiHWF8r3ow2Yq6MAUGLGqu9orHwHVp9++mlPHIdBCUAGFN2bNJl2P5RFtVvgjqOUbW1SjgrIvJ6sAPG6tCowP5jVV0WwuyU3eLV2pRGrKU1glSpgpQ8IjCSl55t7fWS8MhdYlUEqsFCDUKqx8qgKDIeCOKyhGkDpegbmGqf7TwWyvmadqYz0XJPpLH3vyGE1+HBPG7bubsXW3a0AgNsvOro7h+8b9Lg9LDFYsKlAr6Iccl5Hj6qjr5UuFei/wCCdZe99jVQgCaxq8oFViYT6AKex6m+B1dixY3viOAxKAL+pQHZCCvoMNnTAD/ilZazcOhxd+tspXlczVhWREGpiYbQl0mjpTFHKvbeRpsfjU7zOVwV6pMdGxiuwYUdpGzGzqUA/KW0XY+WR5o2EAjh8aA2A0mmsci1scv9P2FE/DaerornASvb7sJ/13Pe+hJauNO57fiv++83tJUlv8wsErUo5Rrwe8RCvkzFpUFWUvkZSgb1dKccvarRSgWmbiQ9rSBz20YrAfCrQ51hYLKQzWcfzVu7i9YIMQj/++GP87Gc/w5YtWxAIBDBx4kR873vfwxFHHFHs4zPoQfhNBSaZFEqPMFZlJF53uswXLl738rEKBwOoq8gHViUUsBdsEOoKrNSpwBHxXGqopBor9j7W1MdkshY9tyoPSwo7jRygjNVn+9uRyVr0Xuot8M3Ek+msLx+rymgIaJezIWyVWSgYwODqKI4angsmSzH52eJ1/VSgcBHlwbyyXm2U+ekLqUDCWDGV3argk3pYkVSghi6rJ8DfS+XOWPmuCnz++ecxadIkvPnmm5g6dSomT56MdevW4eijj8bq1at74hgNegh+U4EOH6uAd36ex4e7W/Hu9oPS93uasdqwowlbd7VqbctqEfzqyToZ0b10QmI+v64yVwDSWkLLhWSBgRW/apa38LEZK6A8qgLDPsxf2WeEVAXK+0CSzw9i9KBKRMNBpDIWduZdq3sTLIviK9jg+iLKfh96HweDVMxNWJ9SCIztgFB9jZz7iHRHkvNlgnIC2tKm1w1CC0gFMtdLT2MlY6xKG0T2O8bqpptuwg033IB77rnH9foPf/hDzJw5s2gHZ9CzIA9iOmtpOWELxeua97dlWfiXJ9ahpTOFt279CuoqIq5tUlwD1WIGVm2JNC59/A3UVYTx1r9736NphrGibU80BmnLstCRYsXrkgE6a0/uZPJq6SwfxkqX6k9oGoTajFXOfqAcAquoD+aVHdiJxkqaHmNYjWAwgMOGVGPr7lZ8sq8dY4dUd/v4/YDVLeo23QXsicsOxjyYV4bBidLAqncn35yeLPed1VRjpc9Ysb5msrRekgnCCMj40NttXgoyCGWul47GijUHBaC1T09AdwFXLvDNWG3ZsgVXXXWV6/Urr7wSmzdvLspBGfQOeKdzL7CDSthnM85EOot9bQkkM1k0d4iZGX4wK6Z4vbkzhWQ6i31tSV89taLhAPJjibbAmSW2vDRH4WCQBpmlZKzI8XgxFDxc4nUNjRWQG7CLqc/zAyEb6XEsxEMnGABiYZL6kbGRTlbj8KGlE7CzaUkv/RALnsH0MkMNM4uySLg0hpnsGOYVELJgU4FeuiNW/E0QLhFjVYg7vqN9j47GimlnA5TwXHnGqr+lAocOHYoNGza4Xt+wYQOGDRtWjGMy6CWwD6JOYMU+lLYbud53sc7i0nQRN3gXk7FiV+68LkgERxBJ+iJqpAJZfRXgrbGKhAI0FVhK9/VCqwL9trQZXltBP7+5RN5dzlW7HmNFAshomNVlqQXdpIKK6Kw+3dfWzSP3D/aZtdkYfZGzV4DCstgEJIDr7cnPoSejqUCfwYaHXkqcCuxDVYHMvamlsaI+Vk7Gqred1xOcX165B1a+U4Hz58/H1VdfjU8++QQnn3wyAoEAXnvtNfz0pz/FokWLeuIYDXoIbDClc6Mm6eTi7ffCg3UW99KmEBQ1sGI+O5HOolZz+3DQX1/EDs7UVC76tRmr2orcY1jKVCC5tpXR3MBpWdBKD7t8rAS/EZuiqYyGEK+MoLkzhX1tCdRXR13b9zREPS+9GtGy5rheEyllNfKfbQdWvc9YsUa3fljmZN6PsIqmPb2ZV4JSaazYxVOVD7sF5yLKQ2MlTAWWR1WgH+PXaMh7DE+ms3TxM6TayVj1evueAiogSwnfgdWtt96K2tpaPPDAA7j55psBAKNGjcLtt9+OBQsWFP0ADXoOjlSgRmDFTkhBnyJGZ2ClFnQTFLMqkA0idRirlCiI1HiYecZKOvkyrElZpALz15E1KM1YFoJQB1ZkJUlK+UXXlp2kIqEAhtbG0NyZwt7WBI4c7hXiFh9pwUTqyVjlA8hYJORpv5HiGCtiuVCaVCDLxugbWRLGysuJn9UKEpRKY0XO1ZGu1XFeJ7+Rh4+VZVnC8y0VY1WIoJtd0Hnd+6RReigYQDzPqpeqV6ArsEqXtxG578AqEAjghhtuwA033IDW1lyFVW1t7w+OBt1Hyi9jxQzS+UygdqVcq1Yq0E5HdaYyRW1pww7yPMsi3t6dQtFjrPKBRiiIZCbr2eIlGgqirjLPWJWR3QKQGzyZP4Ugv2VtLIz96aRwcmF/t3AoiIaaKD7ag5KZhCYdE6lmVaCDsfJiNZyT7+F5xqqxuQsdyTRlgXoDKYepr3+NlRfzw97HBKVirETNlP3YLUSCAaWGyOEJxjJWJWJx3OJ1jXNN2/dmyENzRwpMBldH6UI6XKKgua9VBfrWWLGora01QVUfhiMVqPVQ5gdpdkLSfMC0UoH5B3xQVW51VEzxerIbGis/dgskFUiCJcsSD1zsyrc2z1iVql8gn6oj8AokLcuivQJrKogbufs+Ygf8cDBAhbDEfLC34ZhItRmr3HnGmHtfrp+zS9oBoL46ivr8Pd3b6UDWt8hLG8aCD6x0g0jAFnb39uSXYoI8P61XRM+66Ddir7eoKrC3K+Vcjvoa58r2RfRiMGlFIJOuLxfGiq8gLzdoLZ2mTZuGNWvWoL6+Hscddxz1KxHhnXfeKdrBGfQs/KYCie4iEgrSvLx+KlCfsRpUFUVjc1cPaqw0UoGCFIrOYELa2dRVRmjgkMpkEQo6qR975chWBZaGsWIH1gpGhOwVNKcy9gpe1eYlzU1IdmDV+4xVJmuBHGIkFNRusE0Zq7CGDkeQLjqsoRoHtzfhk73tOHpUvFvn4AeivohaFgRUc6e232C1iASRcIk0VgwT6YdFcravkrM4LPssCiRLXimnJdQnaeoAUxEr3m8/VxFI9gN633mdLGxIl4pyZ6y0AquLL74YsViM/r8qsDLoO+AF3Z7bp4kOJwgS8+hUygHOoEH2UJDjGZTP5xczsHJqrHSqZ2yRZ5CZSEmzcRlIKpD16RIGG8TrKBhgUoGlYazYQZJNBXoNnl1MgFqTN80UrZp5d+6htfnAqgSNmNl7nu0D6TUpkkksFvZ2a2c1XASkGTHRrfQWhCxOAYxVVlLMYJ+rQGPVy6wCLa5hq/t82C149Y5kxfGRoCgV2MvnWwBjxXp2ed0PpO0U8bACSmgQmj9uGlj1h6rA2267jf7/7bff3lPHYtDL8KuxYgegdMbfA8bqh/hmy/bn86nAnrFb0GGsRJVCQG6CCSnWFSxj5fhurgUgK3AudSqQnQBj4SCCgdx5erE4JKUaCNjMhogh4FNjpHS7FIwVn5bU1c8laGAV8pxcWCsNAmJH0PsWBALdkY8JuDLiTA1H+cAqa38+Qck1VuGALxbJNnRlGUxRSpsIvwOOANMO4kpzvhWRILpS4sIRHqwmLuylscozVqQiEChd+x5SPFJTEQZayt9uwbfG6vDDD8f+/ftdrzc1NeHwww8vykEZ9A7YFZaeQSg7SPtrwuxMBarL1Elg1Z5Iw9JkxLxQsHidqRRij1EGorGqjdlrFpGA3eFjldcnlSoVSK4rYZR0NWVksKsIh+yJTKhNcabGSqmx4nUyumle22pEw+uIqbwiICyODjNcTCQFbIyO3QJfFZjbz32+SSa1RFAyjVXaHUTqsEiiHqii/UR6stx+JWKs8sdd7cezi6SpWX2h5Lib2nNjNtEHAvDdkL5YIPcSsaZJZrJFmxt6Ar4Dq23btiGTca/4E4kEdu7cWZSDMugd+K4KFOk1imi3kKaMVY7RyFrFm4jY79SyW8jYAxAbWHlNwKSdTWU0RCdTcXrMnnxpr8BEutdFoQDru5Tr9+bViJaAMH+xiDr1wrtzl1JjRQJaEkCGNFNGbGDlbbdQfoxV2Oczm6CpQN0FQukZKzbtGfERALDXS1UlyjvqE4RLxdDl76WqGGGLNZjItM2Ue93HvPs+UDprCVZjRdDbgawfaNf9/vGPf6T///zzzyMetwWYmUwGa9aswWGHHVbcozPoURSaCoyGgwgl/VWHsIyVbCVLXmf1SW2JtOPBLhR+NVaivoiA9/mSVGBVNIRwKIBkRjwIsT5WZBUGAG1dacSr3H0UexJ2SX6+pDoYBJDVSAXajJWqTJ1PFzXU2oGVl2at2EgyATP7X92WNrGwhkEo52MFMIFViQTdOsamjv3IpM1UiYqKGdJc0AzY51oqH6tC055eOjQ2cGMR0VyIFBvkuP0wVnZVYMDz3k8KFghscN6bzy7PWJHXWMf/coL2UX31q1/FV7/6VQQCAVxxxRX0769+9av4+te/jtWrV+OBBx7w9eVLlizB1KlTUVdXh7q6OsyYMQPPPfecY5stW7bgoosuQjweR21tLU466SRs376dvn/GGWcgEAg4/n396193fMbBgwcxd+5cxONxxONxzJ07F01NTY5ttm/fjgsvvBDV1dVoaGjAggULkEw6UxUbN27E6aefjsrKSowePRp33nlnWdORXnCmAnVYHHtg0e2xRuCHsYqEArSJarEE7H4Zq6TA/wfQYKySNmOlcmQmA3c0FEQsHKKGhqUQsLO+TgB86I5y51oRUZe3p7l0EdFYpTK939aGnxx1UxsJIWNlCZ//FBe8AbbxaqnavERC+noywL4nYhH73hftl1QwVpms1asMrMhlXk+8zjzrSo2VOBXoxzOrmKCpwJif9j120G8ftySlnXaOC4AzyOrNa0s1VixjVcY6K23GKpu/aQ477DCsX78eDQ0N3f7yQw45BPfccw/Gjx8PAPjVr36Fiy++GO+++y6OPvpofPzxxzj11FNx1VVX4Y477kA8HseWLVtQUVHh+Jz58+fjzjvvpH9XVlY63p8zZw527tyJlStXAgCuvvpqzJ07F3/6058A5Bi3888/H0OHDsVrr72G/fv344orroBlWXjkkUcAAC0tLZg5cybOPPNMrF+/Hh988AHmzZuH6urqPtvKpzsGod0KrCTfRVdT4SCqY2G0JzNFE7A7xet+0gM5Mbd9jHqBVVUkrEyJ8IN0bUUEibZESQIrPsWhy+IQxioWDtnpENWElA9QY+EQ6irCaOlKY19bgqZ+ewNpLoj0MvskSDAVZ+zkIipmoEFzWMBYlVC87kfQTfaL5Ssn05IgSZwKtH8QkdVIT8FxrlQHp3+uXr0j5anA0lYFVkV9pAJF5+rRYow9X6fe1EK4dy4tc65hhIIBZLJWWVsu+LYA/vTTT4v25RdeeKHj77vuugtLlizB2rVrcfTRR+NHP/oRzjvvPNx77710G5FAvqqqCiNGjBB+x5YtW7By5UqsXbsWJ554IgDgiSeewIwZM7B161ZMmDABq1atwubNm7Fjxw6MGjUKAPDAAw9g3rx5uOuuu1BXV4elS5eiq6sLTz31FGKxGCZPnowPPvgADz74IBYuXNgnLSi6UxXoP7DyFq/bWpwgamJh7GlNoL1IJqFO8br3Z7Jd7InuKJO1PMX6nXnxenWMTY+pWI3coFVXGca+tkRJBOx21V7ueHWvLfkdKyLspC2YfLM2a0LQUBtDS1cae1uTGN+Lvdv59EZIs8qJ2i1EnKlhPnCwLDsAcTJWJQqsGNbBD2PlFILnAithxSc5V0GLFyD3+xQjla8DUfpeq+E0kwpU3Q/yVKB+EFdMEFa9xgdjlXacq5dW0H2+7LXtTcsFljGOhPKBVRkzVr4TlAsWLMB//Md/uF5/9NFHcf311xd8IJlMBsuWLUN7eztmzJiBbDaLFStW4KijjsLZZ5+NYcOG4cQTT8Qf/vAH175Lly5FQ0MDjj76aNx444201Q4AvPHGG4jH4zSoAoCTTjoJ8Xgcr7/+Ot1m8uTJNKgCgLPPPhuJRAJvv/023eb000+nfl5km88//xzbtm0r+LxLCYdBqJ/qmbA/TxxAz8eK1R0RertYqUCHxkrHIDQtSxnppwJVjBXv/1NXQssFWSpQl8WJRUIe4nVbQE1QKgE7G7zn/uvTIDQUUqaG2QDeobGiVYG92+PM6WPlv6VN1Ms0U9GEmX2/N8A+s7rsHG8Yq7of2PGJBWWsSuTtRAoMtPRkgl6B3horMWOl23WjGGA7H5BnqZwZK9+B1TPPPINTTjnF9frJJ5+M//3f//V9ABs3bkRNTQ1isRiuueYaLF++HJMmTcKePXvQ1taGe+65B+eccw5WrVqFSy65BLNnz8bLL79M97/88svx3//933jppZdw66234plnnsHs2bPp+7t27cKwYe4l8bBhw7Br1y66zfDhwx3v19fXIxqNKrchf5NtREgkEmhpaXH8KxckC2SsYkwTZp3SbcAZWMmpZ0Zjla90KVYqkD2/hIZ4nR9UQgG9CbgzZYvXIwoNA9+ol4gyS9EvkKf8dXVHXY42LwrxukDgPLRkgZUdMAA+9GS0CXPQMbHy97LDgFRUFVgqbycm9SPrX+nYT7MC2P49nQJnyhj14vkmmPtYV7zOG8aq7n1RoAEwGqteZ6xyz191TO4hx4PtFeipsRKI19lnWOc+KhbYXp3REukV/cB3KnD//v2OikCCuro67Nu3z/cBTJgwARs2bEBTUxOeeeYZXHHFFXj55ZcxaNAgADmn9xtuuAEAcOyxx+L111/HY489htNPPx1ATl9FMHnyZBx55JGYPn063nnnHUybNg0AhGk6vqKhkG2IcFWVBly8eDHuuOMO5W9QKvhPBeYDH7ath8aipSuVcUwosgGAneBJpUupxOu8BkqX2aCMVSTMeOKIUihOgTO1XCihxspvpRytCoyofaxEJpKlMgnlJ4uwpsaKFHewlWOAewJmJ3KHj1UZaKyo+aVW/zy3pkwUbFAWJ+gMNki6pjcDq5SAUfea/JNcIKzS3LEidxalqwrMfR9hrHSCdrZoxrM1E8dkA3DIIootXhc5+xOwC5so8Ukr48DKN2M1fvx4KgJn8dxzzxVkEBqNRjF+/HhMnz4dixcvxjHHHIOHH34YDQ0NCIfDmDRpkmP7iRMnOqoCeUybNg2RSAQffvghAGDEiBHYvXu3a7u9e/dSxmnEiBEu1ungwYNIpVLKbfbs2QMALiaLxc0334zm5mb6b8eOHdJtexvOVKBOpZxIvO59c/O6IWlVIENTk1Rg0cTrBQZWNBWoWWXU4bBbkJed821P7FRg6TRWvlkcWhXInqtIvO7W4dBUYGtpWryQQECXnWN9rBxeX3wqMOucqAlKZRAqFHT7qJRzVEGq/Nh400yPRtU9ATZo1rWWcPSxDDIaK6VQX1wV2OupQGoQmu/n6FO8brOKsqpAsaZMtWAsFLtbuvDFu/6Cu1ZsFr5P2MhYOMTYefSjwGrhwoX4wQ9+gNtuuw0vv/wyXn75Zfz4xz/GTTfdRJml7sCyLCQSCUSjUXzxi1/E1q1bHe9/8MEHGDt2rHT/999/H6lUCiNHjgQAzJgxA83NzXjzzTfpNuvWrUNzczNOPvlkus2mTZvQ2NhIt1m1ahVisRiOP/54us0rr7zisGBYtWoVRo0ahXHjxkmPJxaLUTsJ8q9c4DcVyKYV/IjXeRbGS2MVcWisii9e16sKtNk5wEcqMC9ez6UC5ekxnhGrqyhdv0A+xaGrxbGrAoPKVTvf0gZweln1JvgScn0fK/tcAXnwyaY9WSa7VIwVq+XTDSJZNoK1G1HpjlyC7hJMfg4/KmYhpLLEIfuE8m1qVJVyslRgqZsw+7NbsPWOXk78fNqcwE9Tel28t6MJ+9uTePmDvcL3CWMVDQdL9iz5ge9U4JVXXolEIoG77roLP/nJTwAA48aNw5IlS/DNb37T12fdcsstOPfcczFmzBi0trZi2bJleOmllygj9v3vfx+XXXYZTjvtNJx55plYuXIl/vSnP+Gll14CAHz88cdYunQpzjvvPDQ0NGDz5s1YtGgRjjvuOKoDmzhxIs455xzMnz8fjz/+OICc3cIFF1yACRMmAABmzZqFSZMmYe7cubjvvvtw4MAB3HjjjZg/fz4NhObMmYM77rgD8+bNwy233IIPP/wQd999N3784x/3yYpAoPCqwFhYvxUIoM9YJRndUU1eN9Ce7AHxupaPVWG6I7F4XVEpR6sCi5sK7Eim0dyZwsh4pee2fHqskKpAlV6Dpj1FjFUvB1asQSIAJUPBIskFVpFgAEm402oyr6NYyTVW+s8s+3x6Oc2L2Mjc9/V+uobt78kG8ems5WKZ6D5p8b2vOld3YFUaN3JynUhVoFYqkBYzeGus1JqyTFHZSDLOk/FTdiysQW+ijBkr34EVAHznO9/Bd77zHezduxeVlZWoqakp6Mt3796NuXPnorGxEfF4HFOnTsXKlSsxc+ZMAMAll1yCxx57DIsXL8aCBQswYcIEPPPMMzj11FMB5NKIa9aswcMPP4y2tjaMGTMG559/Pm677TaEQnaJ79KlS7FgwQLMmjULAHDRRRfh0Ucfpe+HQiGsWLEC1157LU455RRUVlZizpw5uP/+++k28Xgcq1evxnXXXYfp06ejvr4eCxcuxMKFCws693KAMxWoL3zMeTvpVwW6AitJ13sRY9UTPlY6zuus3QJgr9K8Mp+287qtsRIP0oQ54RirIqUC//WX6/HO9oP4601fxrDaCuW2fFrSb1VgRTikFK/zQn0AGJpnrPa29m5gleTO1S9jxadLeQ0PHzATlIPGSrd/Hq878mMbYu9XOsaKNfoEctdW5vjgx8NNmgr0URRQTJB7krS0KXZVoEi8ntu3+IxVWz4zIQusSFUgy1j1C4NQEYYOHdqtL3/yySc9t7nyyitx5ZVXCt8bM2aMo0JQhsGDB+Ppp59WbnPooYfi2WefVW4zZcoUvPLKK57f1xeQzVqOoMhXesxnE2aehfHSWEVCQboK6xHxuo7dAu/QrRDwEliWRVdejqpAZSsQUhWY11gVibH6eG87UhkL2/d3eAZWrgpITY0VrQpkfaxUE1KQZayIeD3Zq60x7Koo7lx1fazyFUmsuzgLWUl+yQIropPxwTKzE1bEYwLmbUMIoppBXDGRYoJf9vdXeWmxYm4AShZHZhBaKsaKPLe+mjAzVYFe9z7VFUoNUYt3L5NxvkOSoUgwz1+/tFsgLNOoUaMQDocRCoUc/wz6BvjVldeAb1mWYwLWTaEAbsZK9kCw1WnF9rFyGoT6Y+cAPY1VIp2lnjiVUdsgVHS+/GqwrjJ3vsUyCCWl2O2SFaDqWLy0FwRUvB62vZ2E4nWBwJmkApOZbK9aTPCpQG0fKxljxZ0v7UXITUYkFVg68bp+mxdWSxn0aFSdElR8sn/3JmNlVzIGHAyaKuBJMoEGoL4feLaToCcCDR3QJsxEvO6hJ8ttw+jQNA1CXefrww9NF2Sc70qJe5Syz1+/1FjNmzcP27dvx6233oqRI0f2WX3RQAc/2HjdpGxgEg0HtcXcgJuF0bJbKHIqkJ3QEjoaqwJMMzuZIKaKNc1UVVMRjVWRGSty/B0av5+UxdH0dmLtFtTGivYAXREJoTYWRmsi19YmXtk7jaf5yVG/V6Dt2QXIJ2DexZ4gGiqN945IY+Ulsk6lnb+Rym5BlOYF7BR3b7IKDmuJYACBAGBZ6hSdzMNNXMkrZiNV5rg9CdqEme2fl7EcnmLufezrRc41a4mtDvgxkCCswd77BTvOd6Yyjp6AgPP5o4xVfwqsXnvtNbz66qs49thje+BwDHoLspW2zvZ+mzATFiYaCiKZyUrTA6zWh4rXi1YVyARWftr3UG8n71VaR4rxOtL1/2F6BQLFc14ng44OY+VOh2iK19N2KlDLWoIbuOsqI2hNpHu1jQ8fROqeK6+xkp2vLdSXaKx6mdVgq8BCmrpIYr3iCj4VwQafCqSMVW+K10lASAsMcuONirHiU/4qtpa3YCEol6pAIHeMfBUfC0evQOY8MpaFIOxraFmWi8km0E2f+wGbmehIpl2BFdsJoF/aLYwZM8aTbjQof/ADvDdj5RS0FhJY1VdHXJ8l+o5wKFBSg1C+zQUAuppTTUrEaqEy6tThaPlYManA7j5f6YydkpRpFljI+udpG4Sy4nXRhCQJNioiwfzn9F6bF3cFpGZVIDepyhirlExzVGK7hahDhK4+hiSjywLU6TFRSxtAfe/3FFxCdIXoXraPiq2VpwJ7/1xZjSzxsQL0fbtcRrfcfpmsBTIMSXsjFjUVaI8BnYLFYIKxdomWKK3uB74Dq5/97Ge46aab+mx/PIMc/KYCyfuBgLNlRUYjCCDi9cHVMeV3sRVVpTQI5dtcAMzkojhf1hwUgHIio8FG0MlYpbMWbYtTKNigWYfx41M/JCbQNQiNeTVhlgQbJADt7vkue3M7Trz7L9jS2OK5rYud060KpM7P3LWVOK/zk2+pAithKlDTbsH+jfKBg1LQLROv934qkNfPaaUC8+kzEiT56hWomU4uJthnvCIaAlHkeGYemHGH7fvHHzsbJPL3sp9m3rpoczBW7vHAtluwi4LKWbzuOxV42WWXoaOjA0cccQSqqqoQiTi1EQcOHCjawRn0HPymAtkVeyCgNtLjQRirIdW5SjCv3lThUKDoVYFsk+kuryCSY+cARmOlOF/WwwpQr+zSjNYByK06g4Gc3qG1K03bVBQCdvLWYazcq3Z/jFUsHKKfIW44TQZzjrHKV9jpaN5U+MuWPdjdksBfP9qHiSPrlNvyYmXdSYJnrEIS/VxK4NkFOH2serUKkrm22fyiQLvhdNjJ/IjSY6J2Rbm/S6uxYv+rkwrkm3KrrCXcqcDerwpkf9do3rcrmcl6Bncig1DAfU84x8CeT33yqUAeIsZKZttTDvA9ev/sZz/rgcMw6G24AitN8XqUo8y1UoEJwlhFhd8NkJy+vdonD3N7MqPsIaULVutBepjxAwYB3+YC0JuAOyWMlei35R2rA4EA6iojaOpIoaUzheF1aosEFZyBlQZjJfHs0rVbyKX05JM2YTr437tYjBVhzg60e7fHkfkWeU0SrI9Obn9JKjAtDiJZ3Usyk6W2DT0N9pkigZVnY2KZaaYw2HBXfJLvy73fmywOl8LUqNZzta4qwCC0FFWB7DNO+jkmM97BBpsKdDJWfErbvbgkKC1jxYjXNdqwlQq+A6srrriiJ47DoJfB6wF0xeuuKjmtVGDuoSGBlSjQYCeoSCjgmHg6BFUifsEPeom0PLBibR9IQGdX0GikAiO5Y6UrWaHuyD0h1VaEc4FVNysDE34ZKz4VSJv1egQbjLcMuZ9E6SKZwJl4C+nYX+gcx8EO78CKTwX6Zaz4ljb8fWV7sYlTY0Du/u+9wIqwTwGQ+NWzL6Is+FReW74qsPfF67xFik7BiTsVqNKTidnIntAceYFNwQaDRIie0RjH7XGHZB7SWcvNvHIZChbhHmDo2A4bfGCVztgWDP3WbkHVABnIGW0alD/8Mlay1g9+xOtDFIwVOyiFQ0FURII0NdaecFeJ+AX/nV2KYI1vZwPosTgdLvG6d1qBnYBzlgud3fZ18q2x4uwQCmGsutIKPZmE1SCBlUis6gc0sGr3Dkj5a6sz+VqW5WppIxevSxgrLrDqLTjPN/f/uu17bPG6PHCQivVLIOjmA0JVVS7dx3U/yO99mUGojki+2OCPW7WIYyFqX5XOWm6NVVp8XYGe0ZSx4xS/GGQXiqxBaG/eW37he7YaN26cUh+QKWN6zsCG78BKUj3jpwnz4BoSWIkqbuzvJw1sq2NhtHal0ZZIY7jnt6jBf6dKwC4Keuzzlf9OJKVlpwK9qwLZCbiuSJYLfjVW7v553uwcwFQFRkKI5AdGoXg9Kw42KklVoIYTvgokTXdAg7HiWQcdxirNVIkSpkkm6Kbida7kPRgMIBIKIJWxerWaiQ30yKF6pgJ5ry8FgylvaVM6jRXxcVJ5UhGw3R4AJtBWPLNSJ/LeFK9zwS95dr1Tge5AMgG5xoq/jwFvxuqlrXuwq7kLXz9Bn2RRpQIdac8+UhXoO7B69913HX+nUim8++67ePDBB3HXXXcV7cAMehZksCEmep4UsmsVqx9YtegwVoIqlJp8YFUMATt/fqr0k0hLYQdW8u9wi9dVeg13WqGW9AvsLmPFDDg6jFXSVRWox1ixpn0q00BZVSBNBRaNsfKvsVIZmxLwAzsgF3RTMbdAExgNBZHKZHqVsaKpz3AAWUvvmSXaFb5yUtxgu4w0VjIWR8tuwTvQTgqeWcBOBVpW7rcNCa59seGy/9CslLMlCOx+Gdf5ilh7Aq+x/8b/+Rv2tSXwpaOGYvQgvSbw7DPBM9jk+SaVjLQqsD8FVsccc4zrtenTp2PUqFG47777MHv27KIcmEHPggwoVZEQ2pPegz0vXrebMHvpcOzPJnYLqsqxYMAe3IppucCfnx5j5U4FqhgrElgRD66wYnLhV8pAzjATcPdW9At2cC2kKpAGDR7Mhsh5XZUu4n2sKiNFEq/n9z/Y4f278ayDqnk0gTCwkgi6ZWlPsm970lsHU0ywuiNyrF7MSsrlY6WqbnVO8ASlbsIM6DFJrlSg0mVenQok24SCPa+f4xkr3Uo9t/Gx7D4WX1d2H9nvShj3/W0JrcCKXzjLGCvKzvVHg1AZjjrqKKxfv75YH2fQw6CBVT540TUI5QcgL/abddUenDcIFX0Xv5ICwPQL7H56mRw/yWInFOknmwa3B8ygBovTyTRgBthqIec+lmXR1R5b8mynArsXSCZSbGDlv1egTnosk7V7R1ZEQlL7gdzn2CtOFrEiideJfcbBjqSnuSrPOuhorPgVMyA3FhW17yEoheiWNbXUdZnnDWO1DEIlvl29axAqTuvp2C3oGITKUoFsoNVbAna+QbKOIStr+unV0knmTwaoBf5sT1ndjgr8wrndpbFytpPql+L1lpYWx9+WZaGxsRG33347jjzyyKIdmEHPgjyANbEw9rYmcjoSha1BIs1NvpqMFXm4qqMhqk8RPfx0Zc18v93WpvuMFRkoSHoxoZjMRSaPdiDpXRVIUoFk0ONXkez5sxOSnQrsLmPFCkF1AiuJzkQzPRYL2544qjSvy26hyIxVJmuhpSut7Dvo8uzSaEzMr5hz+4v3o7+l4Dki938pNFY5g1C9ai77fHPHS38jRREGHzRTjVUJzjXmg8Vx22/YaT1+PJSlAp0O5r1zvjSDEOYCKw0zVIBdWIiDJF5Ty8JuuC5PDQP6zDu/cJalAsnzE+uPBqGDBg1yidcty8KYMWOwbNmyoh2YQc/CbuBp09bJTBYVEhrbFoY6VzpZC0rDQ/Jw1VZElOkBqk1hJi+SUutuKpD1yKqriKC1K60UTItocJ3WJy4fK0qZc6wGM/ix32GnAouosSrIINQ72GBTqblUoKpyTDwhFaulDRuoNHUklYEVzzroaAX5FTMgTyHK2vcApVlps/dyl2Y1F89WqLSCopQ2+3dvupHLgiSRBQiBncp0srVA7tyizN+yVGDIsU0vMVZEB8cFkSp7C6c3lVObJV0gKDVW7u9i721drSg/vvOLQb5PJ8kklHMq0Hdg9eKLLzr+DgaDGDp0KMaPH49wuHsl8Qa9BxpYMQ7fiXSWCopl2/ODFpCblESaEsAOEmorwo5VPr8aFPUcK5b7OjvYEVZIlX4SrdbI6SmbMFPGyqmxUjNWbCowz1h1syrQ4WPlw26Bb9+jqgokgSnpG6lq38O2KmJRSVOBhQdW6UzWMSkcaE9i7JBq6fY868AGkbIFAj+wA/KATCbUB8AYG5Yg2AgHtFOBbtNMRdDMMdkEdhPmUlgQ5NkYDcYqyaUy2fOQX1vnfRwI2BWfskDysZc/xtjBVTh3ykjt81HBrQ3zTgWKCoSkGqu0c0xgIZM45F6zz79NM7DidaD837zVSTTU+8yvX2hFQtOmTcOaNWtQX1+Pl19+GTfeeCOqqqp6+tgMehDkoahiGniqVtL8YMvOkemsBZnfoc1YhR0PaSqbRYxhx0QTUnXRAiv7vOzASsFYCSYLHcaqg9gtRJw+VvwA5KDkmeCS9AvsdiqQuY7JfMWNquN9mktfBSWDLYsE084mt69/xoqkTLujseIHVy+TUJnzOpBjX0XrA77FCyCfyGSGmez+vcVYsUxtOBhUpnBYuHysVKaZkpY2pegV6Pax8lMV6GRrgdy5VSIk2FagOwrmKj5F37V9fwfuee7vGFIdLVpgleA0VlENhpBcK9LvFfDWWEUF56pqecXe27rMu5d4nTDGvHi9nDVWWuL1LVu2oL29HQBwxx130P836LtgU3s6K2mZsSKgZjZaKGMVcaS9XE2gBZOvXRXYvVSRM7DKBS86dgtsOkcnZeQSrwfFk4vtYRVwMCR1lbnz7XYqkPs+LwNOmdO2qgKSMFYknafS4fB9EQlIUNYdjZUrsPIwCeVZB1UjWvodXBAJyH8jWoQRlAdWqsKJYoINoKKhoNY9DDgF74DcZR5gry2nO/LwsUplsvjft3di58EOz/PQBa870jG6dTduZph41xilSI8p2tqQll7NnSnP4gpduDVW3po2W//nTmnLNFaiBZlKu8Zeb12NFT++y1KBvHaunAMrLcbq2GOPxb/+67/i1FNPhWVZuO+++1BTUyPc9sc//nFRD9CgZ0B7moVyhmuE2ZCB78PFM1YyOFOBDGPlCjbcK99iidfJwx4M2EGPanITaqw00ihEhEl9rMLiFbOMwekJg1Agp7OKV2kIujn9nFpjxTNW8hJsmbcT7RXYDR8r/jrqMlbERJJPaYvAewYB8nSIrH0PwDRi7qUJgQ0UI+GAlJ3g4Srll0y+lmV5aqxkjNWLf9+DG//nPZw/ZST+8/JpWuejQiZrV9rqWCcQ8IwVe4vKKj5FgZVKY0h+z3S+krYY7YwKSwW6703Zcas0Vqrxgf1+v4xVZSSEzlTGNR70RbsFrcDqqaeewm233YZnn30WgUAAzz33nFBPFQgETGDVR8D2TIuGg0BCLxUo1FgpHmZWvB4KBmibGn4lK9LhUMZKQ4CtAjtI6PSn48vNAT3TTNt5Pd8rUDKRyTRHdT2QCgS8KwNl3jZKQXf+XGMR58AuMkmUleRX5AfI7jiv89WdXo2Yk0xqDHCLlYX7CDVW4nSI0seql40NWX0T29jc23mdT4+pJ1/AfS97tR3Zn79Oe1q71CehCVHTYJ3UJx0b8teW7Z8na1ekavMimuwdnRASmSIFVpwFgU4qUPAcUsZKsvgrNIgEbKbOC0S8Pqwuhs/2dwjsFpyLOLpA6euB1YQJE2jFXzAYxJo1azBs2LAePTCDngUbPOgM+Lbzel6Hw4wtqkbMZNVChNmRUBCJdFaqOxKlArvNWDF6BJ1KNJFIlVZBavQK9Gppk5YwVqywPp3JCivLdOAOrNS/H9+GRaWhICDeURWkxUuIDVCcJonUx0qmseoGY8UHZV4moSqNlWyBIKoKlKWZZC1eAEYb0ksTAt8mSkeoDwjSYxLDWHYSd7mRe1RukcC8u1YbBOKKN327BfY+CIck/fMULI4qaHX07kymUZ/vQNEduNKeWqlAd2Ao6/unMgi1K2LV4nW/jNXQmlxgJWOs7FRgP9FYschmsyao6gcgq1mSCgTUAz4/IQUCAWl+ngUrXgeYlWxaPGixwUTxqgLtVBdZ9ejYLUQ0JlIWvI+VTHchY3CqmabQ3TFF5a+j12fJNVbedgskUHV6+Uiqqbhgg7KH3RggecbKq60NLa8nC4RggJrG+mGsQpJJW1UV2NupQHZyJJVrBDoeZbxWiU/zyqpbAe/Jj7AQOj5rOuD1ZIC6oMLeT85GylL4Ko2ViDFy9u4szvnyrLqKRSJQLRjlLW3c97HKfoPVPPoOrGpznTl0xevlnAosbEls0OfBDro6VRYJobeTTmBli9cBO1iRaayiAsaqu+J1dpAggYDKIFTsY+Ut6OZ9rCKSAdpuesylT5gGo7o0ugh+GSt+JRvUCCJ5ep5laHQ1ZdQgtFsaKy4V6KmxcovLvQJJ/lzZfVyMlaRKDmDF670bWPGO+oCHIaqkUs5tP8CwRK4mzOrJj/wG3bn2LFjmidy/KkE5Ad8nE5AHG3qpQHV6zM8i8W87m3Duw6/ilQ/2ut5zeTtJFqwsUgLmWFbxqTIIVdpvdEO8PowGVjK7BafxcjnbLZjAaoDCngACWlWBhOESsTh6gRVJBYqrhUTVVMUSr7MrTZK6UqUChRorjybMybTtp1QVIT5W6pW+SIdTU4Q2PvyA0+6pseJEvwUwVuxvxZ+vLXB2ni/RZ3WlMwVXS/Hi9SaPwEroUeYh6uZTEQDDavDnKmEjgd4vE7cDWoEuUoPFcXkdSTRWISaYIfDSWCWLzFiJmgYXYrcAyO//YqQC/Zzvc5t2YUtjC5792+fS4yaeThGNIDIl+I2k7JxgvKf7KFKs3UoF1pJespbjc2RBZL9KBRr0DwhTgT7E64CeoJsVrwNyQalId1Q8HytbqE/ST6rVTkqwivWyIGBX3rQqUDLYytqAAHZg1dYdxoob8Do8fj+587qCwaSBVe5cVanhtIAlAmzGyrLk1+PNTw/g3pV/l96bdnVi7rMPeNotOFOB7HFJqwL9GIQyCxYeZBKULWDe+Hg/LnzkNby3o0l5Drrg2RiHDk4RbLh9rMh9LNbhiO5jb8aquBor0WLIq1kwIL5env3zVKaZHukxP4HV/rYEAPEiy92EOf97q1KBgqKZQjRWKllEQT5WSWdgBTh/J35hEzOpQINyBTuZ+gms2FSdjgVBa8LJWMny46LArVgtbVgdhY54XXQsXumxjlTuGMPBgGfHeZmDM1Cc9GfBVYEaTXcJeG8Zdj/ZtXW3tLFTa7LU7L0r/47/99LHeP3jfZLjyJ3byHgFgBxjpWK/RKyDl72EULwuYWRIMKMUr0uesxUbP8fGfzTj+fd3SY/fD+yUs/O6su+JwC5E2P3cQaSKwREz0wTk/kmms56+WjoQaqV0GCvBIor8v7QqUBBI0gWj4No6NVb6Y9n+thz7KmpLZRfkcGlPjTE8LAwifaQ9Fb8r++x3pjJawQ8Z3wdVRem9xi5UZU2Ys5a6MKGU8B1YHX744di/f7/r9aamJhx++OFFOSiDngc76NqpQEV6TCTgDRSeCpSJ19kHmbA3iXS2Ww8Qm/6JabRRUVXPyMxQeeF6bh/xKlKkdSCoJYFVN0xC/WusnJOLjnaui2OsAHl7DJXXER1IJdejKe/pJes7RgKyEfnAKp21aDAvgm1Eq5/SLoSxEtoteBiEdibzuqMiV8qxVgI6HmX8+cr2UXl2ybSUBGwg7SfYkEEcIHnfx6rUMBs0Z7IWyMeoAkkvFsdPin9fvhBDxNjzgaSO071oUVGIxkr1vLhkCBqLYrJNTSxMx082mHQbhNrHVK6WC74Dq23btiEjmIATiQT+8Y9/FOWgDHoeSZ+pQNUApFMVSDyaaH5cIl5nJ7xiVcmxLWrIw+m7V6BEi0DAC9fJ9wEiob6c1agugq6MnxhVGquswFjRj0GoI7CSeOmoUkZUwC4JJkgaUxYIk0G3riJCf3tVZaAoaFaVj7PfITIIdQcbhVcFknPsblNqAj/CbMd+vMZK4qovClIJ7OpfSbDKMhtF0FmpAyQdiYN7EcWOaw47B1EqUNJlgT02wC9jVeRUoPBc1XIFvy7zPIOrkw4k51cdC9NnuFOQCuSrAgH5/VVqaHdN/uMf/0j///nnn0c8Hqd/ZzIZrFmzBuPGjSvqwRn0HBypQB0fKx8iT3YfMgHXMj5WufckrAYz+ZJ2O8lMFm0e7uEqCA1CFXYL4rLk3H9l59pBAyv7kZJR5qqVfk0+AFWxLl4gA3l9VQS7WxJKjRWrCeGrx9SpQHd6TNYbUZX6jEVCaE2kpcEECQpl73dRo9IQ6qui6Eh2ShsxZ7IWCOEYFdzH0pY2ZMUcEewj1R3Jgw3vwKo4q3DZM5uE2tTXnRpWC/VFqTFdjRVQHAG7KBXoR7wuqgBmz1fW35O+pmjpxLJzfhaIqlRgQhL8qlKBScG9KdVY5QMWUUsb1bjPX28ds+M2yliF8vKPhOOecFcg5yxSLAtIZDIACpsXehLagdVXv/pVADk6+YorrnC8F4lEMG7cODzwwANFPTiDnoMjFahRBs7rLgBWdyTej12tkLQedQjmHkDZ6rc6FkKyI9s9Bidj+6BQ8bqO3QKb9iQCZ2kq0G7LQCBjrFT95IpRCUkm7vqqKHa3JJSMlcM9m6se02ppE3GnPv2kxyqjuX2kjFX+d5WxGmyaoL46gn80daJJYhLqmBzZiVSRxgHYljb2ucpKzlW6IxKYydIX5DcotgVBVMDGqATdMh8rqVBf0U9OqrFinr9ipD5VCz+leJ0zxwXECwvRc8LC9pESMVZsEKn3XHck0/R3EaYCeRbHj4+V4FxlGiuxeF3OjvGLBi/GyrIsen7VklRgklvYBAI5+UoirW7DVkpoB1bZ/A1z2GGHYf369WhoaOixgzLoeQhTgUq/FzKI6uuOSBqwKhqiExnZ350KFE9I1bEwDnakuiVgZ/UXOm1UVBVGsoFLnAqUTL6K6iK7KrD7gVW8MreSUw3m7ArXj8YqwTVhBsQpAsuyhN5RBCr7iyTj0C9jcdjjqK/KOVrL2tqw95yjX5pHVSBtwiw4V3dhgpyN1GWsiq6xcqQw1efK7hfl7ge3iSS5roUwVmx6rIiMlcOjyZuxslkc5n4QPLfkuoYYB3sW2j5WmoEVYasASSqQu0YyiQULUc9Omau+aAx07SMIIvnr7RVYJRibGlkqkBqEMvcxCaxU7YpKCd8aq08//dQVVDU1NRXreAx6CX6rAkWr9qCHNoUXrpPvy32/XnqsGO7r7CChx1i5gzxyrrIUili8bgcoWccgLU+hVBchsCJpAhJoqNIPZDUfDNgTqI7LPN+EWbYfO4GLBmna1kYQTLCDqyzYYI9jcL5ViKwRM3ufsiXnXhorYRNmD28nlY+VjBnuzJ9L8SwIyLEIFgg6/fO4SVtmdKsyQ5VNfGwq0A9Dt3LTLnzj5+vQ2NzpeF3sY6Vm1HPHJ9dmsecrCsBY2L+RgLHiegXqYF9eXwXk7geXuNxliaGTChTdD+LFn9JaQtGDkb+3vUxC2XG9OhpGZV5KIUwFMgub3vaE8wvfgdVPf/pT/O53v6N///M//zMGDx6M0aNH47333ivqwRn0HFj/Fn8aK4HIU8JYtXAeVrn9xStZWXqsGF5WDoNQnarAtDst6RVsdOQ/r1qgsQKc6Qg77SnQWBWxKrC+Ove7qyYuof1A/rhUfRF5g1CAtSCwz5X9vUTBhqopNru6l4vXba0XCSRlgVWKYR1YQ0svhi7JtdRgz0Uq+hVMwF6TAfEGSxQpsEqLGCsdjzLtqkD3RE3AWhaIftdCLQj+Y82HeO2jfXjh73ucnyeqePPRhFlc8Zl1bye4hwF5MUPu2PyzcyxjldtP7EbOM1bqVKAiiPTRF1H1vPDjutcCkSz6KiMhhIIBVOcXWh2CqkB2Ud/vAqvHH38cY8aMAQCsXr0af/nLX7By5Uqce+65+P73v1/0AzToGbDpMZ0eZqIVjFezXhFjJSsLZiv3WFQXwYmcTW3YVYHedgsOzy6PtGcn14AZcJ4LOwGLKiAJisLQ5QOBQYSx0kgF+jV+VbZ5Yc7VS/RboWhr06EVWGXp59ipQPEqWdb/TNXnjf0OkWeXzNtJVSnnqbEqciowKgiadfrnuZ34+clXzlg5nPgF58syG7rne6A9ic2NLQCAlk7nPU3vY0fwK07XCvfzqJxUpe8BfRZHOxXYnnD8zY9/vA5Ux3ldJLeQLRj5wI2FzJ+P3Y/AKxXYxuirAJvBVhmEsuegsggqJbQ1VgSNjY00sHr22Wdx6aWXYtasWRg3bhxOPPHEoh+gQc+ALU/2o7FyVs/k/isbpPk+gbnvCzg+j0Am+q0WiBn9gl2V6jT+FZZuewQbolQgu78jsBJUQBLU5IPQYlUFsscmgsh52stlHhAzViJjRUf6TTBIV0bk4nV2MpFNvgnGed3LbkF2j3ktEFQ+VlJrCZWPlSQNXWyNlYjFkfWvdOzHTWRydo7cx3IxNyB+ZhyBlSaLs/YT2z+RTzGJFkMqbym6HymsEOmOmP28UoHEyd8zFah5rvs4xopnfhJcQKjT5kV0DiFPjZWcsfLqFQh4VwWScZ0U7VQJAiueQWX/P1mmdgu+Gav6+nrs2LEDALBy5Up85StfAZATqYr8rVRYsmQJpk6dirq6OtTV1WHGjBl47rnnHNts2bIFF110EeLxOGpra3HSSSdh+/bt9P1EIoHvfve7aGhoQHV1NS666CLs3LnT8RkHDx7E3LlzEY/HEY/HMXfuXJcubPv27bjwwgtRXV2NhoYGLFiwAMmk8+beuHEjTj/9dFRWVmL06NG48847C+5xVmr4TwW6KXMyIclSRnY7G3d6jF/ZySakYmiOWP0FCQQyWUu6khWu7BSCTUAsXmcHsJQgrSBiNYqR+iTnSxkrlWFmgV5HJDCtCLOVcu5VMznvAKPhYqFKzbLBtGzy7WJSgYM0U4H8KtwrzStkrDx8rEQrfa8FTGex7RYELI5OuyJ5iyP9ak/2fhLpfth0p26w8dePbPd9ngkROq9rBJHiVKB7P1VqjN3Hq1JO97n2TAW6GCudVKC7KjDipbFSXFux87rzNb+MFZFSdDpSgW5rF53+tqWE78Bq9uzZmDNnDmbOnIn9+/fj3HPPBQBs2LAB48eP9/VZhxxyCO655x689dZbeOutt/DlL38ZF198Md5//30AwMcff4xTTz0VX/jCF/DSSy/hvffew6233oqKigr6Gddffz2WL1+OZcuW4bXXXkNbWxsuuOACR5A3Z84cbNiwAStXrsTKlSuxYcMGzJ07l76fyWRw/vnno729Ha+99hqWLVuGZ555BosWLaLbtLS0YObMmRg1ahTWr1+PRx55BPfffz8efPBBvz9hWYBNBfqqCnSwOLn/ejFWdULxOsdYSQauYqTGRBorQM5aqQStXj5WlYzGKhAICNNjqsqx2iIGkiQ1ppq4xJVjGlWB1D9KJBYWCfXFQ02lIrBigynZtUowtg9e4nXyu/CBgLfGyp32lBnGqoINsr9oAWNZFg2oujwCDd3FnMoMVXau2azlaprt1ZRYtEBgq+eKlQp842ObseKZELHGSh1Esvovr/3SgsCNhSrtWJDGiksF8uNBQalAQVVgIX0RVcE5ua6kItkrsGqXpALbFQah7HGVq8bKdyrwoYcewrhx47Bjxw7ce++9qKmpAZBLEV577bW+PuvCCy90/H3XXXdhyZIlWLt2LY4++mj86Ec/wnnnnYd7772XbsO2zWlubsaTTz6J3/zmN5Q5e/rppzFmzBj85S9/wdlnn40tW7Zg5cqVWLt2LU1VPvHEE5gxYwa2bt2KCRMmYNWqVdi8eTN27NiBUaNGAQAeeOABzJs3D3fddRfq6uqwdOlSdHV14amnnkIsFsPkyZPxwQcf4MEHH8TChQsRCIgp4nJFob0CRa69co2VW7welQwAMndu24m8+xqrSDjgWPV0pTI0cBNtzzbq9Wrf0yFgrIDcgJvOWhyLI7cfKC5jlfvd25NpWJYlvEfFk6/3Sp/VNhGI3KdVAmd2f7F4nQmspD5WIrsFtY8VH7x7MVbCgd1D9Cu6tioto26gse6T/fjWr9/Cv58/EZd98VDpdrljcTN0XqaZbBDAi9fdiyF3+o1FJBRAJmsJF2x+xeuNzZ34ZF87/VvGWImsJWTidYebukf/PO2qwCIJ9V2MFTf+ycTrKqG+n3ZO1CBUpbFSpAKH1ETR3JlCm2ZVIBmHxXYL7oVNzMPOo9TwzVhFIhHceOONePjhh3HcccfR16+//np861vfKvhAMpkMli1bhvb2dsyYMQPZbBYrVqzAUUcdhbPPPhvDhg3DiSeeiD/84Q90n7fffhupVAqzZs2ir40aNQqTJ0/G66+/DgB44403EI/HHfqvk046CfF43LHN5MmTaVAFAGeffTYSiQTefvttus3pp5+OWCzm2Obzzz/Htm3bpOeVSCTQ0tLi+FcOSLGpQF9NmPVZHKqxirkZK36wTQlWjkBxUoFsRU8gYJ+vTBAtWv16pcc6BOJ1wGZqnMGGXL9ABhjdzvAi8Bory5KX+Av7h3kI9QFGY8WK1wVMl0jDwqJC0dKGdYyXaqyYQZcwVrJGzOTa8ZOFfR9LWDFBKqKQ5rUqZpi9F/lAnMW6Tw+gtSuNP773ufB9FqLyeq/72BlsOCdtWVNiURDJ7ucl6CY9ElV4/aP9jr9bOjmNlYBR9ypKEJ0ru5/IIFSeCpQzRoX0CiR2C+Sx4TWm0pY2PsXrtJhBIs0QMXSqhRc5riH5Z9E7FWi3swHA2C3IDULZ4ypXxsp3YAXkUnTf/e538ZWvfAUzZ87EggUL8MknnxR0ABs3bkRNTQ1isRiuueYaLF++HJMmTcKePXvQ1taGe+65B+eccw5WrVqFSy65BLNnz8bLL78MANi1axei0Sjq6+sdnzl8+HDs2rWLbjNs2DDX9w4bNsyxzfDhwx3v19fXIxqNKrchf5NtRFi8eDHVdsXjcSr8LzUcqUCNfLWwCbNuYMWmAom/DSc67EkfK17oSU1CJVoWVXpMpiejqcAIF1iF3StZPtXCgvxWOfM7/4NGNmsbcsYro/R12e8nOtegn6pAgXjdmfZUT0hE8ybWWDGMlVS8zmqsIvS4ReL/lCQV6OXtJBSvy7ydVD5WCi0jHzjKzpfcZ5v+0eKZEhQF8KqKLv7YvAxCUx5spGyyz3IsVmfK+9l+PZ8GPOaQOAD3hJ0UsGdeQn1ZYYUocPCsClQwgc5zdXtSiUDE6yPjlQBEqUDnIkGvKlCUdRCP4TpNmEUBK2WsqnMEhG4qkIjXq4XidbdBqKwIqlzgO7B6/vnnMWnSJLz55puYOnUqJk+ejHXr1mHSpElYvXq17wOYMGECNmzYgLVr1+I73/kOrrjiCmzevJk6vV988cW44YYbcOyxx+Kmm27CBRdcgMcee0z5mXzaQ5QCKcY2ZGBTpQFvvvlmNDc3039E+F9q+E8Fylkc2UDhy8dKMiERMWP3GCsusCImoRL3dWV6zNN53ZlaFK1kZVofgG88LT7ndCarYNvs76mMhmigJ9N2iLQUOhorEWNFU0aCHmuyyVfVhNkvY1URCSkrA2WThWdVoFAYLUmhCHQsBKrnjA/yZedLRL3NnSnsPNgp3IYei4JlljNWJJVpe315tbSReTvJJj9+AeelO7IsC69/nBOunz15BACRxkq+GJIFG+R11hwXEJ+vyp+MfV3Y0iatd20JslkLB/Iaq7FDqgAoUoG8eF2ZCnSnqWWiexXzquqLSPYbXEMYK71UIBnn+apAy7LUjFV/SQXedNNNuOGGG7Bu3To8+OCDeOihh7Bu3Tpcf/31+OEPf+j7AKLRKMaPH4/p06dj8eLFOOaYY/Dwww+joaEB4XAYkyZNcmw/ceJEWhU4YsQIJJNJHDx40LHNnj17KJs0YsQI7N692/W9e/fudWzDs04HDx5EKpVSbrNnT86kjmeyWMRiMVr1SP6VA9gHx6sq0LIs4cBVCGMVldDzMo0V2belG6kxfpBQ6XoAtbeTVGOVkqQCRSwOTY+J9Au215YomLQsCxf/519x1gMvC6+XQyMTCtoaNYm2QzRh2M7T8vtBbLcgEOor9GSA2nldi7GiWq/c59smoe4BXcaeeVYFilzmBfcx2+RZ5UaezGRdbBNf9SizZGAn5fc/bxZuQyBO86qDSBVbK18M+WOs+HPzCqy27e9AY3MXoqEgzpyQyz64NFaEgRc0BffqASm7H5ymvh6pQEXak39OvXRWTZ0pkEMeU58LrNixQDQek+uqbGnDaE3t4xanwVWGqKpeouR5HFyllwok45I7FZih30G+JuYwCJUXgpQDfAdWW7ZswVVXXeV6/corr8TmzZu7fUCWZSGRSCAajeKLX/witm7d6nj/gw8+wNixYwEAxx9/PCKRiIMpa2xsxKZNm3DyyScDAGbMmIHm5ma8+eabdJt169ahubnZsc2mTZvQ2NhIt1m1ahVisRiOP/54us0rr7zisGBYtWoVRo0ahXHjxnX7vHsb7KDrFf2zD5Cf1a9IvG77rUhWvxzVPrQ2RynvbelSnY4SvF6ATMIyh2uR3ku/KtAtXmePAWAnePGEpOoX2JnK4P3PW/CPpk5H2wsCdqCJhAKUQZNpO1QaK9m5Oga7sJd4Xb7yBdhegYL0mMMgVKJ/oqnA3OcQt3kRYyWzWwh5MHQJIWPlDpidTZ7ljBXg1rzxvSvlTant1zf9Q63XLITFEXkGyYIxm40UTyNRSbCR4Gx5VGa9gG2zcNyhgzAsPx60JdJiRkkQRMpYHFnwIOqf55UKVKVY+Wvt1dZmf/65jldGaHqbDcbY35M2YVb4aNFzoP55gjGc11gJFpcEYcXCixWvA0BbMq3s4NBONVbOVCB59tnfzsFY9Te7haFDh2LDhg2u1zds2CDUMqlwyy234NVXX8W2bduwceNG/OhHP8JLL72Eyy+/HADw/e9/H7/73e/wxBNP4KOPPsKjjz6KP/3pT7T6MB6P46qrrsKiRYuwZs0avPvuu/jGN76BKVOm0CrBiRMn4pxzzsH8+fOxdu1arF27FvPnz8cFF1yACRMmAABmzZqFSZMmYe7cuXj33XexZs0a3HjjjZg/fz5lmObMmYNYLIZ58+Zh06ZNWL58Oe6+++4+WREI+EsFOnQXonSItAmzqlegZPXLMRvD63LWGntaE8oHVIUkV+Fim4R6pQL102MiHyuATQW6hbCyCYmYhIpSgU0MEyOakNiJMRAIMNS6B2OlIcwWfa+oMbFI9Cs71wpBFRABy1h1pjJCTVEXlyZQNWKWtRJSMVZsKiIquB9E2jlAwlgxr/ETAl/1KPPtYl/f5MVYKRqn+2Gs2PuBvQaqnpfsZ3SXsSI2C6eMb3As0ti2T+Q7RF5jXqlAPlgS3f9eqUCb5VJrrABvs2OirxpSE6ULozYmGGM/L8alAvWqAn1orES9AlXtewhjlRevW5b6fGXO6+TZF2n+ADuQVPVGLCV82y3Mnz8fV199NT755BOcfPLJCAQCeO211/DTn/7U4fukg927d2Pu3LlobGxEPB7H1KlTsXLlSsycORMAcMkll+Cxxx7D4sWLsWDBAkyYMAHPPPMMTj31VPoZDz30EMLhMC699FJ0dnbirLPOwlNPPYUQQxsuXboUCxYsoNWDF110ER599FH6figUwooVK3DttdfilFNOQWVlJebMmYP777+fbhOPx7F69Wpcd911mD59Ourr67Fw4UIsXLjQ709YcliW06vGKxUoK0u2GxOL97N9rBjGKiwebNOSSW9obQyBQO4hPtCRRENNDH7BTxYxL/G6YCL1EnTL7BbsMmyGxfGolCN6AxGNzgZWIlaD6hHy30uOR85YuScMEtzKqgLZ383RZkKQIvA6V1pIIAhy2WAwkxflsxYYgFO8DkDpZSWr7FJVBTomMTaIFKza2f8XnS/7W/HPGn/+XuJ1ANj0j2apjQYgZuhURpbOfRgROPP/maxlByyKJsyAHdDxgYWLwVEEVtmsra86+Ygh+e4JQXSlsmjpSiGeZ3TYRuv2ceulPflrJWLodFOBOm1evAJJ4mHVUB1j7GbcVXLs8YQ1GBxx70j3M+tMNbrvLZW1BHnG6ioiiIQCSGUstHalHQExC7fdAjEIzf1GRAcbCTn7e5Y7Y+U7sLr11ltRW1uLBx54ADfffDOAnMXB7bffjgULFvj6rCeffNJzmyuvvBJXXnml9P2Kigo88sgjeOSRR6TbDB48GE8//bTyew499FA8++yzym2mTJmCV155RX3AfQDsqibMMFayknxy8/Lu2TZjJfqOLJ34RRorV2AlqZSLhIIYUh3DvrYEdjV3dS+woqlAdSNmOgH7WOl30lSg85EqpFLOZqzcx9fUaQcMosCQF7WSlaCs8koUbBDSUO5Ebgcz7KQeUqY91RorIWPFnX9XOuNKFfN+WqpGzDKbCxVjJVsxq7yO2PdZBAK5RUwyk3ULmjnLAWkqkHl9X1sSe1oTlNXlwQrR6XHRNJeanRZV/wK58yXZX6JrkrGRlEVJ84EVz87JGY2/72rFwY4UqqIhHDNmEICctKArlXAI2FXFNXLGSh1o++kVKNIXEpDftLYijNautGeF836GsSIBhzMVmKXHSY7Vq9qTPTZHVaCHVlCksWJlEXxgz8ouaisiONCeVOqs5OL1tJQtJp8P9CONVSAQwA033ICdO3fSSredO3fie9/7Xp9MiQ1EsANNVENjxQ5AjomUCrrd+7E0fY0gFZjkBiCRszvB8LpcMLWntTCdFb8KJ3ocUSBZiFA/ncnSfaoi3horr0o5W2PlFmA3e6QC+YlRm7ESaGosS2wvQQK6Ct5aQqDXkKXfCOwKTfe14NOXfLosncnSyY+wQSqTUFmlE60K9PBbEjWBFbrMhwLSsVA2IfDXUhb080HIpn/I04Hia+tf0M2m5zMCNlKmn5Olp/wwOIStOuGwwfTzyEKNnbCTlNlQM6gsZF5NIhd171SgnAm0feW8OyEAtsZqSE0UVQK9pSjYiFCW2bu6NeyhsWKvl7DBNrO/rHlzJBRkrpO8MpBPBZLxKmvlnj3b1kWcCShXxsp3YPXpp5/iww8/BADU1taitrYWAPDhhx8qjTINygdO/5aA0hGafV1mrCgauMigVxkJid2QJU2YRdVjI/Ir8t0tbrG2DngaX+WdJNPJqM6VZRF48bptEOrW4kgZq5hbV0HQ1OkRWOWFwZSxEhjusVC1PWGPlYWoIhBg0iEFtLQRMVb8BMSzOM6gJ/c5g/Pi9SYBYyVL5+gwVlF+UcGt2gHGw0pyroC8TJw/Ny/x+uEN1QDUAnaloNuHy7zjfhBpBSXna4vXxalA8rkq+wHiX3XyEUPoa0RawJqEioToKlsAgBVoe2vuPA1CJYxROpOlgQ4x7PVirPbl9YENNTHq7yRqSuxIezLXS8bQiXoFiph49t4UNmHmUsMsWGsSHbNjsuDjU4FA7pz5huAE/Y6xmjdvHnUsZ7Fu3TrMmzevGMdk0MPgUxbRkLp01WtlJ2I1WgQNmAFFKlCR0x9GA6vCGCueDVPZLcj0ZCo3chIUBAPuAUBEtas8YgDGbV4wILEpLtGElOCC4CqPlkApQeDDpo5Eq19RiwlAPLmoeucBTJAr1FjxLI5cq0N+90EK8bosFahiI0WBBiBetac8GBxAbhLqZqzEzyLZbvq4egBqAbsw2PAUr8urRAFOKygo32dBfawkgdWgfD85mVA/lcli3ScksGqgr4sYq5TgWHgtmOvzJQscka+Z6PNZyHyk2HOvr/bLWMUY8bqAsWKeP/aekwZWAo81UaAtGwPtfeTflWSCPnqdFIGk3Sswdy6hoN0ZoyOZts1B+3tg9e677+KUU05xvX7SSScJqwUNyg+sqJVt8SKjVZOSlZ1K0C2qCMx9hngVq6oeI6nAQgMruXjdPcCxjvBCxkrwG9nC9bArBSTyt/FqBUJ+M+9UoIbGSpexYvsisoGVIJBMyBgrxUpfWhWoYKz4lb2bsbIdmYmwVS1eVzMUogk4IVkxi1btXnoyAFI9I38vyoINcq99cdxgAMD7ilSgMKXtxeIIBO/BYIC2VhGbZnporFxVgblziDNO+aIJcvPnLWhPZhCvjGDSyDr6eh1t8Gs/CyIpQYRJaYsCSduJX+c+Vj+zsnuIPS/9VGCesaqOCjtPiCog2WsgqwxMCp7FkGLhx7O0BKqFF7svEazLUoGWZdGKQbZnazWju5Q9f+UuXi9IY9Xa2up6vbm5GZmM+oYxKA/wIkYy2LPd3ll4Na8VMVYiDyv2M3iNlYrF6W4qkG+qrLJbYAdGh+hXsdInQQufBgQYQ1ShoFtdFShMBWpWBZJrKuoWz0KcLmIGTsEg3UUbH/PWEu6JlLIaEm1KJaOx4u8jMgGR34kPNrpS7kFXZRAqTQWS6jGF8NjF1grSpV7aOfZzvNy4RdfWsiz6+vR8YPV5cxdlOHiInimVQzh7XO7zFTAbJH0v01jR9lVqxgoQB5J7W3PnNW5IlaMirE5gGiysgPRgcUQVkIC4SpQ8s6LeeYCCscqfayBgH7eXQej+diJej1H2ukNgt8AeS5ARsssE7KqWNo4Ub1o9PoUcjBU/jtu/k4hZZNGZylA/PLbjBPXeYwIrGWPVb5owf+lLX8LixYsdQVQmk8HixYsdNggG5Qu+y3nUIz8v11jJ9Rp+GSuV7mh4N1OBvNmdbRCqGmzFmhrRuco8rACxqJVOSJLVb02Fm/4nYKsCRQanSW6CoYyVtFegvJoKEE/ACUFAk/sMwSDtMfmywRnL4rCrWRIs8YEwrU5kmDPWIJT3vSqkKlCWinAEVvnP9WI1APu68JVxrjSn4Np2pbK0WmtobQyH5XVW738u1lmJPco8NFYyVk/kqi/5PQlkBqFkPKmpiNDfsUNQtdqaEC/O6gRMSEqgH2KPS6ifkxy/6D5W2Q8Acs8sNjVfRdknNQGxjxGvV9OFUZrez7IMgiz1SiBswqzQWMkqIAOBgNz/ihlrRdeJBRnfAgHn2FnJVAaKuh4A6r6b5QDfdgv33nsvTjvtNEyYMAFf+tKXAACvvvoqWlpa8MILLxT9AA2KD56lYAOmRDrrYiJSktUa2U3E4pCHqY4bFKmxmysVSCYlkcZKnQpsT6Qx54m1mHFEA2469wuu95Pc8VfQqkB5KtCdLsrtK9JYsalAHkLdkWdVoNu7hsDBWAlW+a6qQIH4VbQ9e76BQC71I6swkjJWQvG6ON1CwH5GZyrDtLixg4ghNTHsaU24qgJFgy4JwkgjZvb+k+lkdDRWrlSggLFSNdcmIEFgIYwVy3RURkI4elQdPt3Xjk2fN+O0o4a6tqf3cpANNtSshoydDgmYrpQH8+qlsYqFg6iMhtDalRbey7LFGW1z1enBWAmCXxZyuwW57kieChQHrCy7VO1h1gvkxiRy3g3VMVvHauWeicpoSMhYAbnr3IWsNBUoGmNFXl+ye8BxvqEA0lnLMY6zFdU64nXqus5JKKqYVCC/UOSPW2YRVGr4ZqwmTZqEv/3tb7j00kuxZ88etLa24pvf/Cb+/ve/Y/LkyT1xjAZFBqW18w8tOzCKVgDywdb9UBJ4MVY8Za7SpxDGal9bUsiovf3ZQby3sxm/f2en6z3R8avE67LVmsrbibazibiPXcTQefpYxXLBgEi83sxWBQoCQz4QqOZ6b/FIS0weVdVjXZJVpKjrvZc7d654wq15YycfUuknqwpkGSu2EXMTZ7kgEuo7j1vAWEkmMXbVTs7Rq8ULINeGkHNXNqVO2v5hoWAAk0fHAQDvSyoDRWJ6L1d9r9SnaAKWBRtSjRXjg8Y33WVBqv7cgVWeCWE0iCqLlNwxuM/Xi8F0WEt4pgLFASv7PLIpLhlI0UU4GEBdZZjeD4DN8MgyCGTMkgbNWffYpmqcLmuunTs+UUDm1Kd6pQJ54TpBFSNfoAbA3Nha7qlA34wVkDMEvfvuu4t9LAa9BD4V6DAuFKUCpf4/uf+KBuk2rrkmgcx/RLVKGlwVpS6+e1sTGDWo0vH+Z/vbAagMP50DhcpuQa4ns4Ww2azl0HwkJAxObj/C0LHpMTk7B9gDjTAV6GCs5NfK5WMlE6+nZav2AJCRVAVKBjuhXiPrHWxURHL3XqcjsLLTq5WR3D3EB8Lk+vEBXn1VFB3JThzoSOLQIVX0dVnqx+4VqEp7uq9tKJhbtZNA0o94XVYVWF8VQWdzRtw7MeVMOU8elQusZJWBKh8rLzdyd/88d1qP2kt4+FjxiyhWN5MLNhLCQNJenHGpwEo3YyVjXsP5a6TWjooZTHaB4J0KVKc92YbosrQ84DQHDQQCCARyYu72ZAbtiTSG1saknn/k2nqmAgUVwKy+0KtqOXe+ctE7OV9bvC4+X97DisB2X0/TuaXfG4Qa9H2IHhzVjVoIYyXX4bhXGmyLHdEgHQwGMKw2x1rtEqQDP9vfAUDuh+OuCpQ7r8uMAFWVcrLKFYBdRbonJJmGoVahsWKr3VSMFdVYCcSvLLwq5cSpwNw+FS67BRU7Jx+kRU74JBCsioZtd3YZY8X9jrJGzDJ7AKWPlWL1zgcOXoaZ7LG6A6vc38QuQnQvd3Ip56NH1QHI3f8sk0mQSrsnJVkAQOCHsaKN02UaKwmrwAar5NoLGStBSywAqI2JNFaSYEPRL9DL10zY89KjKpDXJLILHZuxkgdWe4m+qtruMEG1Wfn9ZPY3Xv0CRcUVIgYzIQncWIieGb6nrJdBKN/OhqCSYTHpvRKRaKzKlLEygVU/QDKdxc9f/QTX/OZtYaNaHqKVtSqw8hxsBd8pe/ipoJX5Hocpp2TgIjqrPYLAals+sEplLNcAalmWSwsSo4yVIohUaGpkgk1RmkBUheVVpl4tKLHOHW/GoSngNUcAXFU0noyVzMtH0WhVZhAaEexDNVYKQbetq2ICqwTLWIkZRpsp5AIriZeVLBWoWiCori0/KYlayPCQGoTmryUJCkXXlqac879XfXUUo/Ps7WaBgF3VUFnEzuWOS8FggmdxyGJInR7jz5WY2LKpQLHGSpwKtO0WWI2VOFWncl+XB2Midk48LtDv8agKjIYZxkqRCmQZK4IaTvTuZZop18+5r61YY+XNvFKpgIDpCgZy94tXKrCNa2dDUM0EVuTekZ0ra49TTjCBVT/AntYu3Pv8Vqx8fxdWbd7tub1oQFFVWcjSAyEBjUwgNVak4nU3gwPI0woqywWSCgTck6/DRZjrFSgUr3usYgH3IC0zzARkPlbqFAoVfXKBVRNnIaBkrGhgpdZY8RWTBHa7IlGlnExj5Z7EvKoCAZv5YlObHZSxCkl7O8rSdLJ+gdKKN42qQCEbGXL+Rr40VpImzLIKSMDu98hWUE0enWOt3hekA8WNid3pWhZe1iriwgR1KlDKWEWYwEpUFeglXmcZK4nuSOaI7thHwmCK9GS8NQP/PTLDTAdjpUwF5hswMz1RaSPm/DMhS2l7pwLd94OonyIdEyRBJLufqFcmGXtsg1AZY5UXr0tSgWxVoNQg1DBWBj2FQ+qrcPWXDgcA3P3nLcKAgYVowLVvVFEJv3oVK2KsZCkUMvkmM1m7fJgNfiSTEhGw86nAbNbC9gMd9G8+hcIGNLbGytt5XXaugDvYUE2+ogHXK2VEAqtk2tmsl7VaACRVgfR3z51jlUclkh+dCYG8pY28AlK1+q0QMFYkEKyOhaVtb7okv7vMJFTGOhSyQGD3I7+hTtpTZhBKGSuSClQwVqyW7+i8zkpkuSBmrOQMDqBgpwWMjFe7IrsJs3whUqlIBcq88AhjxfpYyYpOVKlPufO6gp2Tnavkd3XYLSjYOQLqYVVtM1Z8QCZdtEpYMwKRWW8hQWTuu9zPOq/98tJY2alA58Ko0sFYiZ/xcrdb8B1YdXZ2oqPDnsg+++wz/OxnP8OqVauKemAG/vCdM47AsNoYPtvfgV/+dZtyW1UqUFS+KlvBqFgN2cPPBlq0TN0RWIkfZpnlwu7WLi495jx+NuVIqwKJ87qQsRIPKqGAPLBSpwLd9LxXTzl2Bceubl2MlY7zeswWfguvk4/KKP57pYyVsJ+cfJAmqb5OQVWgg7Hifawk+gtZI+aCqgIV15avjNLysZI8Z+Rv0k+uU3BtWUE/AWGsRM2YVe1pvMXrGiyOxwJB3ivQniwrlalAorESM1bJdJYG47IFgsoQVXa9xFWBahZH1pdQrLGSB1b7mHY2BDWcTlJuQSDXkwH6VaKyMYGFaD++hRLVinalhRIVqXidWUjJGKt+14T54osvxq9//WsAQFNTE0488UQ88MADuPjii7FkyZKiH6CBHqpjYfzwnJyH06MvfERdi0XwmwqUPciq0m1Z+pAVDtOVfn7/UDAgbKEAAMPz4vU9XCqQCNcJ3IyVM+8P2BOxyCA0KamSCwZzFTq54xVPiqJUIF21i8rUFSkUskJrUwRWOs7r7CQsYq1klWwqjZVM2yTqzZbWqgqUa6yqo2Gm7Y34d69wMVbiRszePlbyQFWc5nVO2lo+VmFxX04SWBDxusggVGRESyoDP97b5ri+WaYSzuG07TX5ejC2Iq2gb42Vw4JAJV4XM1Y10TB9Flu70shkLergrVPNSI8/rb4fRD5WnqlASUubaDjEaKz0qgIJyG/E2y3wQZ4s9QqQZuH57RxVgXKNlcxagv0uh4kqN/aQ65bOWsJFoEy8bov1M9Kxtd9VBb7zzjvUGPR///d/MXz4cHz22Wf49a9/jf/4j/8o+gEa6OOS40bjmEPiaEuk8cCqrdLtlKlABWPl0iIomjDLVvrsgE1SBCpzUIIRcbH7OquvAtyDlmj1VYjdAnt8/PyrYqzEjYm9xaGiykASKJDvER0/L2wlnkeAePLyspcQXtv8IMnbS/CaI8DbxwqwvZtEPlY64nXe9kHWiFlapq4IImVCYYDtH+lkXguyWyAaK4lnF8D6pdkT0bC6CgyujiJrAZ/us58FdoL3Y7cge26VqUBPCwLZQiRIz0Vtt+CceIPBAJ2MW7pSjs+XXluRxkrK/AiCBs1UIN+XkK3SJYxVKiPujQgA+9uJxkokXueqAiXHLQwimfMPCwLtdNairJKOQajakiLPlkdDtMekqDKwXWLJY6dM09LnT1ZdWy7wHVh1dHSgtrYWALBq1SrMnj0bwWAQJ510Ej777LOiH6CBPoLBAH584SQAwO/e2iFMDwAeVYGKAcjF4gTcDxeBTo818l08hSwCacTMa6y2eTJW7tVXhY7dgrIKTJ7a4MGLhS3LZhJUgaSoMrApX1JPhPzCwIobdAOBgF0ZKBDNemus5FWB/PnavQJFQn0dxsr+XWlVYCwkrBpkt+dXs1KNlVRT4161EyivLaer0TlXu0ycc1rnGCuh3ULKzVgBwNB82uhgu7vFC/udgLjFEguv4g1R6sdvSxuWhZDpjtKZLA0k6yqdjBXAtrVJO9KqrvY0Ck2ZdwWk/1Qg4AxikgIzVEDOWlHGqpoVrztTiLJgQ8VGyoJPUUNlLYNQQfDJV4IHAmwA7D7fNql4nbFbkDx/KnauHOA7sBo/fjz+8Ic/YMeOHXj++ecxa9YsAMCePXtQV1dX9AM08Ifjxw7GRceMgmUBdz67WZjbFgVKqhWAt9+L+zhkeXpiRsoeh1d1EZBbmQO5gZQdlLZzgRU/+YoGiQqm8S8PmY8VINeUKRkrRqyf+3x7X1XVjagykKQCCXuno7EC1O7r0rYeGlWB7ibMInZOrcNhP0eksXKkAmWMFe9jJWnE7OXZpWKsdOwW6Lnq2C0w9142a9HfdLBCvN7JsHgsCMt1gAkkRdpCwH7GpHYLkt6IorY/Xtc2ImlfxTKNlZLiCpap5Rkr9rVWF2Olv0Dwdl53pz3l7Xvsz5BVykVCQfq7inRWlmUJU4HV3MLIS78qCjbYAEjeF9RyfL7SIFThY8V+fq2iX6BMvM5WMnvZLaSzlpBVLzV8B1Y//vGPceONN2LcuHE44YQTMGPGDAA59uq4444r+gEa+MdN534BFZEg3vz0AFZu2uV6X2gQqtJYSVIoQYU2ReZjxX5vigs2VCv92liYTiiszmoblwrkdTiiYyepwHTWcqUIVKtwWU85pUEox1g5JgGFyJmn/wGgOV8VSBgrEashSuXoMVbeq3YCeVWgmx3QcSOvEIrXbTNMeWAlDvBIGuVAe5JjD9TnqgoixQahzvvfy0YDEC9g2ACfBIWJdNY1YfA+VgSUoWNSnyJtIeBOX/KQsce2RxkTbKRJCl+msRKPKVSQHApKqwJJGrAiEhTeO4SxaulMO8YzXqOp6o3oh631YudY5ocNal2GvSSQFDyLrYk0DSTEjBWxWxAfi04qMMDdD+y1y3DMq55BqDv4ZHVoKi8rufO6uwmzzG4BKE8Bu+/A6p/+6Z+wfft2vPXWW3j++efp62eddRYeeuihoh6cQWEYNagS804+DADw7N8aXe+LHhxV7yVZkKSz0o+JJqSwcwDQWekHAgGX5YJlWVS8fkh9zihRJl5nBarsRNzFDfqqVCAJHFyBlaQ6jd2Hb3uSe09+viSwYvsFEsZqZFw/FQgwjZgVqU+Zfk4UNNs2B7zzupuh8BLqAzKNFUkThJj3xRM0H9A21MQQCweRyVpobLJTx7JUoNZ9LOgDadstcFpBHY0V8xux5z2o2k57ySwZKqVVkHZgJa/2lKfG2HNwC7oFqR8PjzJZuoayEIyPFX8vy4TrBA7GSuAwT6AUr8uYeIFW0Et35OhLyOqOJL5yIsaKsFXV0ZAjeK7iFllySwxFKlB27zPXjl/8qRh10W8kOi5VF4l2SWDltFsQp/vZYLgcGzH7DqwAYMSIEaitrcXq1avR2dkJAPjiF7+IL3zhC0U9OIPCMX5YDQC3ySQgngCUdgsyCwIi5hb5WKksCLgBV+dBBoBhtU7LhQPtSbQl0ggEgKOG53R/nVxKQRhEMv8vSx2KaHBbUyYuqRYGkdzAzg66Ko1VjVC8nptsSIApYjVEqRxKrQva2shadaiYDdbgkUVEoFWiwYyCnRNXBdotbWQGoTIfq2AwgDGDcz0CWY8zL4ZC2CtQoTfhK6p0hPoiZpgsBqKhIC01Z18nENktAGJNmYx5UgWRAMvQidO8IgsCaUsbmcaK0cZVSqoCZcJ1AtvLKiX1sGKPW6gD9Qo+ffTPCwQCQvPVBA0MSGAlrwyk5qC1McfrJFXW7mG3QP4WPbM07cm36mIYvnTWOR7rNGFmr63o91SlAsk156sCq2mvQLndAnts5aiz8h1Y7d+/H2eddRaOOuoonHfeeWhszDEi3/rWt7Bo0aKiH6BBYaArQUUlWFSUChTcpNLmtYrJV5Z2Yb/LlQpUTEiAHVCQVCARro+sq8CgSnE1lagTfDAYkFbWqYT0sooqWXUa4E6hpJlzlVlLAPYqrk0gXieMVe67xalPp8ZK3tbGy2lbFDR3SZpO2ytm94Skx1jZ5+JswiwWOKuYwrH5wOqzA0ylnKwqUDCR8t8RVdgt0Ps4K7/nCUQLmE4mtRoOBem9x9+bhHGs5FqAiBgr2e+uajjN7icLPsk5shYHcrsFMWOVcAi6xfq/lk5dxiqtZJNUppl+zHELbfPCBwZVMfkiZ1+b2xwUsAMNmgrM/35uM9TccSsLkASLEDLsZgrQWGUEv5GIsVKmAqPiVGB7Mu0KTAlYrW45Vgb6DqxuuOEGRCIRbN++HVVVduf4yy67DCtXrizqwRkUDroSFLSKEPaMUtkteIjXhYyVwguF11jp6HAAW7RNUoHb85Pm2CHV1L2b11jJBt0KCUOnCghlWhxR8EZAV3a8tYRi0AJymjKANwjNa6yYwMoVSArStvZgLg+s+FSgWmMlHuxE7AANJFUaK0FlWDtrtxDNBxq8QajETwuAmLHycNpWGaiq7BZ4xkp1riSl4TC1TTkD1ZhAcwbIxeuEsRIFVtIqOa+WNq4qLOe5ysr3RfvwE73IeZ0PmmXmoARsVaCKYVGlx9KSMaqQVKDjuwQWBNSwV7HIIVYLrDko4K4QJuMxz5CrKuVUTaT59LCOQahoESUaO1VVgXYqUOy83pXKUmJANY+UY2AlvmsVWLVqFZ5//nkccsghjtePPPJIY7dQRlC1ihCmApXidfHNLUuNqfYBWFGrMz3mFWzwqcBt+3KT5tghVfYALQk0+NVXRSSElq60IhUon0ilvQIFEzw/2OqkxgAJY5VPBQ6pjiEaCiKZyUoZOnbQtQdz/6lAoaA7JWOs3JO2jn5O5ITfwZRix8IejJWATRo7JB9Y7RelAr0nUgKd+9jtY+WvKrCLMlG586iMhNDalXadbye3HUG9MLDStxJgIdNG8horncbptpZSHFhFGed1fgFIUkd1HoxVS2dKybCoNGVkbJA5r4sMQlXXVsc0U9W7k2isWA8rgA2snHYLflraqDzHwqEAkhl30KzDzglb4TgYK3EqMJu1HG2rWLALB9LGS/SMR8NBtCcz/SMV2N7e7mCqCPbt24dYLCbYw6AUUPWlEqUCVXYLUr2GakJSriK5VKCCJWLBpwKJOejYIdVSEayUsZIIolUDqDQVqJjged2FjrUEwIjX84NpVypDJ9Z4VYQGca6m08KqQLupKQ+ZPkUkViYggn+Z3YJTvO7N4ojamnSkWMbKZnlYTZnKY+pQEWMlmYB1WtooDULpSl/OChCIA6v875m/f+zzlWisuN99sKDptKzgRFUll9svPwFL2EibnbP392xpw/UKFDmv80yzvsYqrWRYCqsKzAcNAj82nUo5USUqrQqkeim5xoqtCATcLJeMVVe1tFH9RnzPS1oMoCFeV50r4Gxrw4Jl7HiNVUU4RJ31iWWK6FhUuuBSw3dgddppp9GWNkAu15nNZnHffffhzDPPLOrBGRQOu2u8nneRlkGopFLIT69AwA7o+JY2XiwOnwr87IDNWFVQho4Xr4uPg0yUfOuQQuwWROk3ArdQXy/taVcF5gYWojkJBnJpQlkKRRxYydlLGcsiCyKzWcsxMbIg5yTyOlIFktSwlRkgWcaKrYJjB1FVKyEaWO3vYBylZSltEkSK2FrVteX1c97nKtIykmtIUqIVYXGw0clYULAgPlYH21P2uUqCSF3GSiZ6J8wre/whCRvpqbGKsIEVx1gl1IEVZay6UspnSl0VKF5U8IG2sz2Q/LmljJGgUi6mwVjta3d7WAHuVKBsvFH1z1Pdm/yzriNeF42FouxAnURjRdi3UDAgLD4hz7yq84Fqzio1fKcC77vvPpxxxhl46623kEwm8YMf/ADvv/8+Dhw4gL/+9a89cYwGBaBSaQrpNxUoXu3ITCRVvbvYz+GrAr1YHNIvcHdLl8NqYeyQKuxqzgVbfPNaWSm2rLGvTirQzVjJmRM+FaFDswN2VSAZgIhwPV4ZQTAYYExOxalPNtiolghmVddJNgGzg5hMvE7aYwQCAaZSzpux6hJorCojIcf3dKYyDgYrdxxyjVVrIo2mjhQGVUWk4nJVsKFqwixLj/lOBRKtWP69CsmiyPaxch4L0VglM1m0JzOoiYU9K95kLW2k+xF2mjKv9rnKijBEGqtM1rI1QuEQHUM6Uhl6zwB26kguXmc0VkqDXjmrLhsb+B6QjvZAylSgmx3zpbESNGDO7eNshSO3W5CzzLIG5ABzH7s0VopzFaRYlanAhDMVaAvXQ8L7pyoacsxdYo9AwoiWX2Dlm7GaNGkS/va3v+GEE07AzJkz0d7ejtmzZ+Pdd9/FEUcc0RPHaFAA2IifH1SEBqFK53XxCkY6+TKfodRYcYO0Kl0EAMPybW0S6Sx2HuykmpKxQ6qF6ST22N2pQJJKk6QCw+6HXdbSRs1YOQdbHQYHsIMhsnIn+irS8sRmrOT6FYIqyWDuFCBLUj9cYQKbeuSbH7NVnXwgqWSsOLF2hmnaWh0LIxS0K4DYYMNureNmrCoiIdoGafuBDmcTWp6dU2qs5KyYbKWvSgWKUu7Un4pqrMRpXns7zvcnEqKfS0xCvTyaRAwOew5exQw6BScixoo97xijsbIsJxvZ4pUKZDRWSvE6x7Sx0A20Hd0SlIJuNzsma4quqgps4KsCGXF3eyItDXz4TAALeq6CcU1WmKCyv6FN2j3E67KqQFkDZgKelRVnPvoJY5VKpTBr1iw8/vjjuOOOO3rqmAyKAL4vFbvyE1XD0Hy1j1Jd6mPlN7Aiota0k7GSdY4nqIiEEK+MoLkzhfXbDgDICT1rmHSRVGMV5gMrMeOjY7fAV0GqNFb8YKtrLcE7rxP9zKCq3HWskGjKRIGVrKWNTiuQDHc/kIAnFAy4gzHm73TGQiSkx+LwPlVsOpfcxxWRnFifPV9V0QAAjB1cjd0tCXx2oIP6nOWORZ+xUqUi+Ek7LdEnsRAahBLmjWisBIUY6UyW7sNrrAKBAAZXR9HY3IUD7UmMGVwl1Q+JyuRF5+v2sSIMhZPFUd3HtO0Iaz/APG/RcJAWwAC5wJHcC952C7YoOqlYDKlZHHHQT87VsnJjm1NP5q2xSosMQvO/J63QFYrXxYxVOBRELBxEIp1Fe9KugpT3z5OL10VBv0tjpRM0q5zXmeOirbkkgRUvXCfgK19FY2s5N2L2xVhFIhFs2rRJ6b9jUB6IhYNUACivlBNorETidQllLq2SyzeYDQTEAy+/sqIOzh4aK8Bu5/Lmp7nAauyQagDifnPsd/ATTIw2YhazT6JBhbTw4QfphGBAIeAFrbrWEjVcVWAzYazyol3CFrHna1mWUCNTJRHMOlbiMoNQiWcXz1YBPGPlPF/VteWvHWFmWP1FhUBTRqsTBYMuYKcDdxzo4NI5EnZOEFixHlM8XOkxnV6BgpR7F8dYiQxRWdd8vioQYLysOghjJUvpyQMNx368E7+LxfFOabOaI7L4IucdzI8NIcZTjj1HT7uFSvv5SAiYEvsYFOJ1j3GNHDsZE/j2QNLzFRiE8qlAXguazmSpUJvXWAHOykBp8Ku0W5Cn9wrTWMn1ZE7xurgqUNbOhoC/x3X0q+UE36nAb37zm3jyySd74lgMiohAIEBXtnx6zE5ZMKlAhcbKy0RS5usUCQWFQTj/QOhWygF2OtAOrHKTp0ygLRPjVkjSLaoKRdH5ssGMSmBJJyTNVCDvvE7KjmkqUMBYsYGSH8YqFAzQoJFApiejFWwCU84Ix1ix36FjEEqYv3Yq0rb1F6Lz9WKsiID9s/3tXFNiWZrLfe/L0m8A41HGpYyUPlb5Y2WZG773ooh9JcFXMCC+z/h+gV7ecyJ2zrIsKWMb4oNIjb6I7O9M7nu24IBeWzpO2cGGl8aK2DBkLXvRIX5mndeIhde4Bjg1YV5SBWWlnEdLmz2tCfrdJEhmQdKBbYm0ogmzIhXow0ZG5eVHINaTue85aSow6ZUK5Bkr+dhajlWBvsXryWQSP//5z7F69WpMnz4d1dXVjvcffPDBoh2cQfdQGQ2jPZlxTaiqVKAosJIJeGWTr8zAjoCnrHVZHMC2XPhkX95qYXDu/hNNvIC8YamMsVKt7Oj5MqlA9qFWpos4xsqLnavJD8BErEo0VnHKWLnPl00vscci01ip/X/EEzBvZsmCjc1cvl0quwWiB8zk9IB2O5uQaxtyvdKZLP1s0e8OMF5WBzocKVg+2JcJulXpN8DdCkfH64ht85LNWggGAwwrlmesBBYEbFNq0WKFNwn1aq6tskgB3OlMXqzsh7HKbW8hFhZ3KaiKhtDcmXKMU152C7FwkHq57csba6oaZQsrPmXpUi4g9GrdQ79LyOJkHPuSAIk36/28KdcabkS8QsiKVTOWKbLj1koFKoJPt4+V/D4WMdqigI8EwIn8OEbea6NVv2K22aWxEhy3as4qNXwHVps2bcK0adMAAB988IHjPZMiLC+Q6iEXiyMYFGm+2pffi1q8LvNB4RkrnSoUApIKJBjXkJs8ZfYDch8rCWOlKt0WTMDs71UsE0nALVglVYFEY8W6E9NjYbVtbCqQ6b3FQhX0EKqf18/ZgnH3PqRfWipjuRkrlUEo2xQ7ZS8E2FYXMS5d6Axo1anA7fs7CjJ+9Uq/uT3KvINm9h5JZrKoCIZcLCC1WxA0pRYdB+DuF+jVXFso5mYZT2na34cOhw2s0lkgZt+v7OeL+gXaqUAxYxUIBFBbEcb+9iQ11pSZX/LnRiBb0LHXL5OxtAIN53exLI4eY/WPfGA1elCl8LNZywWvqkAlYyV4DvnjVvVe5L9L3E+R0VgxgXFrV4rqx/xorKKhoItRZ79HVohRSvgOrF588cWeOA6DHkBVRDyhClOBEpdk9jWZtw0v5vYKrMhg72JxtBgrp7DTU2NFWBlugrHF6zLGSk9jlUiJgxkCdz85PXYuHAqiIhJEVyqLtkTarbEStD0hv3uYS+3JNVbylbiUsaLpN/EEHw4GkcpkXMGG6nzZIK0zlbHb2cRYxsp5vl5MIWCnAhtbuuhnqtg5IlYmv12nR/qN15nopHldgVUkZDuqR4hBqDvo70ylHdvwsPsF5u4TWTqHF2az90nSkS5Vp/3TGgFzKK+hyqXTnJM2z1jlzjF3vqmM3VFAxliR9/a3JylLJ9Y4kmuknx5jTymdtWiHCO9UoDvY4GUClLFK8oxVzi7GK7BqS2QUPlaqqkDVgpHTWEm0Z6J9vOwWQsEAtU5o7Uq7AiudVKB8HiGMlbsQoNTwrbEy6DuojIqDDVH1EhFC+ukVaAcazn2SefG6bDJ12S1oiH4JhnGMFWm2KztXWkGjzVhpiDyZQJId5ESMLetjZVkWDfR0gsiaWC6IakukqcaKtC8RCZxlAS2rsbIs96AvCgS8PLtEYm72s6h4XSPYCDIi9c5khpais+kAXneUYFIsotUskKsYrYqGYFnAtnzqWMhYMcfGThRe6Te+TJ2m2BXXln2P/P78byrSWLFNqUUYTE1C1RorNs3Es1as5o5PR/Hl9bTgxCs9xnlZiSpoebaZdemuUQRWxH2dVNOpxevO+zibtaQVq4FAwBFskHvYOxWoqAoMk2vr9KcjIKnAUbLAKmpXS5JHmD8edUsb72fdT5o3LEix2npW53eIdFZe4nX22ZctnGJ0Him/VGBBgdX69evxgx/8AF//+tcxe/Zsxz8/WLJkCaZOnYq6ujrU1dVhxowZeO655+j78+bNQyAQcPw76aSTHJ9xxhlnuLb5+te/7tjm4MGDmDt3LuLxOOLxOObOnYumpibHNtu3b8eFF16I6upqNDQ0YMGCBUgmk45tNm7ciNNPPx2VlZUYPXo07rzzTsdEVW6oklSgiJgKpY8VZX1kjBW/vVvD5dzPyY7pikMBZyqwriJMU2NEA8P7dsk0VhW0Ga4kFahoYcJ+vsocFHD+xmlmkNYJImsYwerBdk5jJWDoSEDL/+6E+WErnMjfgL/0GG8NwIOtBGPF0F6aMraNC7lfq5kggg8kZY2gWQQCAcpafbxXHljxYmUCchyy9Ju7TN07iAwEAq5CEZ6xEl1br1Qg7RfoqgrkdTjic2WPR7So4PVDtu5IfR/z6RpRGyLezJhMwJWRkHJyJxP2/naSCvRmFQkcVaLKdkXZAlKB8ko5GWP1D6/AKh+ANDFti/w4r6uCJV7ioPLls/dxj4WyimqRSagfxkr2jEdC8jmr1PAdWC1btgynnHIKNm/ejOXLlyOVSmHz5s144YUXEI/HfX3WIYccgnvuuQdvvfUW3nrrLXz5y1/GxRdfjPfff59uc84556CxsZH++/Of/+z6nPnz5zu2efzxxx3vz5kzBxs2bMDKlSuxcuVKbNiwAXPnzqXvZzIZnH/++Whvb8drr72GZcuW4ZlnnsGiRYvoNi0tLZg5cyZGjRqF9evX45FHHsH9999f1mJ9adsTAY1PBg0+NWZZ9mTMr5DsJsw8Y6Ve4ZG0HO9j5Ue8DgDjGqpdVWOAc0KS+VjFPAxCRcceCriDDVVbFcA5yaYzbIWRRmDFVAY2d4oNQtnjT6TFx84Kr1ljQp1UIO93RDVWEsaKDTbYQddrUmLbuFB2JuZmrMi9LBJBi0ADqz1tueNQGL8Cznu504MlcqXHNG1D+EWM/ZtygRXz3HodC+0XmA8y0pJnircSYKF6bmWshtdiyBbr5xkrQQVtFVcV2EIaMFeqlSpEf6VKBcrE6yo9WW4/m/3xnwr01lh1JDMO/aLNWDkZeQKyyDjABlYuxkpe3WozmN4aK51AUlRtKWPMRYwVYeyqJfdzpZ9UYH/QWN1999146KGHcN1116G2thYPP/wwDjvsMHz729/GyJEjfX3WhRde6Pj7rrvuwpIlS7B27VocffTRAIBYLIYRI0YoP6eqqkq6zZYtW7By5UqsXbsWJ554IgDgiSeewIwZM7B161ZMmDABq1atwubNm7Fjxw6MGjUKAPDAAw9g3rx5uOuuu1BXV4elS5eiq6sLTz31FGKxGCZPnowPPvgADz74IBYuXFiWwn2RKBSQpAIl4nV28NVtwuypsZLYLeiI1xtqoggEchoRMmkCnE4n39aD/Q55E2Y9p3bATodkBYOJbFUlqzDSYedICq+tK01XqrzGSicVyBsMEnYjpZEKdPlYeXhH0XRIxnLs63W+VIyfZjRWTEBYwYn1VaasLMg98tHefGAlCHocYmXmmHkmybUfx8boMhuxcBBtCftec2msaLslJsij24iH7HpOvC5jatnz51NGsr6aAPuscyyzB/NK9qNpT41eli0eVgsEZMIm+6nS97zdAhv8iM6Bvf8py6ydCsx9V5axaqA+VoxusCudoYEWYawOqVczVsTrKhBwe2rZLV78VQXyTLzKJJmAvx9y+3kwVn5SgRGWsRI/f+VcFeibsfr4449x/vnnA8gFPe3t7QgEArjhhhvwX//1XwUfSCaTwbJly9De3o4ZM2bQ11966SUMGzYMRx11FObPn489e/a49l26dCkaGhpw9NFH48Ybb0Rrayt974033kA8HqdBFQCcdNJJiMfjeP311+k2kydPpkEVAJx99tlIJBJ4++236Tann346YrGYY5vPP/8c27Ztk55XIpFAS0uL419vQdaIWcRUyFxsHe7ckjYX0sBKU2PlxyA0HAqiIS+AHDfEtvoIBAJCbYrM1V1kV8Bur2tBIFqBs2AnslTatgjQSQWSiaOpI0mriGhVoA+NFWAPYCwLogoiaZrXZRCaTwVKNVYk9ZJ13DteE7BIY8UOunylnJeHFcGhQzjGSlSUwImVCbx0TTyrp1uEQf13UoSxIr8pEa/nry1zrbw1ViSwSuUndIkwOxigxsEuplnx3Nrpe04X6amxci6iRC2CeH2kl9UCAV8xqGrC7NaB5v4WBSiAk43UTwU6fyNRxXBFOER/f8LatHSl6DmPjOulAqMht6ZTZZiprArk2UgNHyv+fmD/X85Y+UgFMq97VZf3i8Bq8ODBNHAZPXo0Nm3aBABoampCR0eH7wPYuHEjampqEIvFcM0112D58uWYNGkSAODcc8/F0qVL8cILL+CBBx7A+vXr8eUvfxmJRILuf/nll+O///u/8dJLL+HWW2/FM88849B67dq1C8OGDXN977Bhw7Br1y66zfDhwx3v19fXIxqNKrchf5NtRFi8eDHVdsXjcYwZM8bPz9MtyMrsRVoQmXidXf3ImjBnLTi0ZqqVL/s5hRiEArbOikyaBCIBu6w6yjZqFJ+vkLEKuFdpXuxcMBigE3fah9kgYA+mO/Or2UDAXv3x9gOAOpVDAjG2zFtVsSfTpqh8rABW1Go5GBGvCZj1IaOMFetjxVXKqfoEsiCMFTlvUSqQFysT8D38ePBsiI61BMCyw85zsTVW+SCTrQoU/CYsSMCdyVq5xsQKDZTM2FfVI87tzi1PLbGIcsGGUGPFpT7twMqLsfIOrGTidVbcL8o2ODVWepW8fNpR5CsXDNrGzURn1ZivCBxUFZEyOCRlRrSWKlbRT19EwH7WeY2V0m5BpLGSBOa1XFubLY0t+DRfTKJjtyDVryoq2UsN36nAL33pS1i9ejWmTJmCSy+9FN/73vfwwgsvYPXq1TjrrLN8H8CECROwYcMGNDU14ZlnnsEVV1yBl19+GZMmTcJll11Gt5s8eTKmT5+OsWPHYsWKFTR4mj9/vmObI488EtOnT8c777xD/bZEDw7bSb3QbUgwoUoD3nzzzVi4cCH9u6WlpdeCqwr6AHt7NclSgar2NHwKhaf95YEVn9P3pp5ZXHXqYfjft3di5kRnoCvSlEnF65JUoGolLkqPiSYKHpFQLg2XymR9pT3Jau4fB3OBVV1FhB6DirESHYvImFCpsZKkeVXO64BT5EwGcxkrwILtjydirNxVgd7idcCZLgbkrGgoGMinfdyMlSz9Fsr/bnZLG70JmPze5Bz4tjnCljZJdUAbC4dQEwujLZHGgY6kp1iZtcQgSKoWFQVqrPhFFE3hMufBpwJt13X19MS/LzbodWuBAJuVkY05BTFW3HfJrFiqYjnjZsJY/aMpR0iMkrBVAJsKtBkrHjSI9ZkK5ANCLY2VwNpBngq0G2b//NVPcO/KrUhmshgZr8C0sfXCz9exWyjnXoG+A6tHH30UXV25CPvmm29GJBLBa6+9htmzZ+PWW2/1fQDRaBTjx48HAEyfPh3r16/Hww8/7BKgA8DIkSMxduxYfPjhh9LPmzZtGiKRCD788ENMmzYNI0aMwO7du13b7d27lzJOI0aMwLp16xzvHzx4EKlUyrENz0yRtCTPZLGIxWKO9GFvwk4F6lcFZrIWMlmLESHLV3bsHJXOWiDkQUIxYbPf5aeaisVXjxuNrx432vW6SFNm+1jJAitxOkQl8hRrrOTMCQms0hlL28cKsMXrO/OBVX2VvUK3K8f02DORMWFS8bsHBUJ9QG0QCjjF65QR00jxssEEMeasElQF2qlAte0DwSH1VVSTB6gn0gTsIAmwGQUZS8RranQnYJl4vYLTWImqAmXHAgD11ZFcYNWetIMkBfvkR7zOl9enNRkrwhBSuwVBQCyrCpT1CSQgdgv0uzSOm8DrWrFpPd3iGv672N+THTuroyHshX1//SPPWMkqAgFBYKUyI1Z6dqn1ZJmsRau81b0C3QsvWYcOwiz++o3P6G/ylYnDcM/XpiqqAr3tFmh1bRkyVgWlAokWKRgM4gc/+AH++Mc/4sEHH0R9vTj69APLshypPhb79+/Hjh07lCL5999/H6lUim4zY8YMNDc3480336TbrFu3Ds3NzTj55JPpNps2bUJjYyPdZtWqVYjFYjj++OPpNq+88orDgmHVqlUYNWoUxo0bV/D59iRoYKWTCmSNC5kVgGplJxP9yuwZCOzcuNP/Ryc9poIfjRV5WLtkdgtCLY5cY6UsTWaqdex0kUZgFXUGVnGmh5jdX0+QClQxVkmWsZKfqyxd1EWr8WR2C+5UoE7AzFY5dgha2vCVcl2a4vVoOOhgAkSpQEDcL9CrEo/fR7c1Ex9YdXJslIix8joWwFkZqEpLSoMNxXMb5s1QNYMNW1BNAiu3JUhV1Hm+Xn0CCXjGSpwKJBorcSpQNuY4GSvdVKDzN5ItdCq5Rc7nHsJ1wA6sqHO9IhXox4sQELNzsm3598TO6857jlynZCaLykgId18yBU98czrVyoqgw1jxHmnlBN+M1fbt25XvH3roodqfdcstt+Dcc8/FmDFj0NraimXLluGll17CypUr0dbWhttvvx1f+9rXMHLkSGzbtg233HILGhoacMkllwDICemXLl2K8847Dw0NDdi8eTMWLVqE4447DqeccgoAYOLEiTjnnHMwf/58yoJdffXVuOCCCzBhwgQAwKxZszBp0iTMnTsX9913Hw4cOIAbb7wR8+fPR11dHYCcZcMdd9yBefPm4ZZbbsGHH36Iu+++Gz/+8Y/LsiIQEDM4rLeQIxXI/H8ynaX76qx0AKdppld3dH5l5cfbSQXRSl+m96LO6wzjw/aGE7s4i1Zp3qlAMikl05a/VGB+QNqXN0AcVMkyVm4djsxuAWAYK8ZuQVaSD6iqAnXF63Yq0EtzBNiaN4fzusgglJugvVKBADBmcCWtupJNFuS4HT5WRPsk01hx6VJd5pVfaZNzIt8jstKwj0U+ZLNeVtTUUsMxnkCly+IZCl1rCd7HSpSuruQ0Ry2dmoyVS2OlqgoUM1ayMaogHyvO10xu2OtMy3tZLQC2px2BMhXo2yDUvh+SmoGVaCFCzZi58z16VByBADBldBw/u+xYHD60Rvq5BE6NlawqUKwLLgf4DqzGjRunDCQyGX17+d27d2Pu3LlobGxEPB7H1KlTsXLlSsycOROdnZ3YuHEjfv3rX6OpqQkjR47EmWeeid/97neora0FkEsjrlmzBg8//DDa2towZswYnH/++bjtttsQCtkXY+nSpViwYAFmzZoFALjooovw6KOP0vdDoRBWrFiBa6+9FqeccgoqKysxZ84c3H///XSbeDyO1atX47rrrsP06dNRX1+PhQsXOvRT5QZRoMEOpuyDww4aOV2V3TyT35bAEViJTPE0NVYyHZRfVAgYOrnGiuhc7G03N7Ygk7VQVxHGUMFqihd5At7nmvtutxBWh8XhhZ2DBKlA7apAgVmsrJ8ce3yuXoEeBqFhZsD101ybZRtpr8CYm7EilXIirY4MYwdXY+0nB/LHp2Yo2OfD28fKTmlblqUdbLCMVSZr0etWQdy5mSIMovP0y1ip9FIyNlJnUeGuHNMVr/OpQHdVIE0FJgpjrNQ2ERKhvuT42UBSt1sC39JGJuYmFW+EsSIaSlUqkG9KrGTnVKlAwb3JaqycbY3k19Ye0wSZCu7YTjhsMN760VdQXxWVdkngUamTCuxPGqt3333X8XcqlcK7776LBx98EHfddZevz3ryySel71VWVuL5559X7j9mzBi8/PLLnt8zePBgPP3008ptDj30UDz77LPKbaZMmYJXXnnF8/vKBbwoFODsE5gHJxAIIBoOIpl2PlxNHc7mvyxkZepe3k68yNJvVaAMfD85QOFjFXazAm9+mpt8p48bLBwAyEeIdAVKxopxZKYtXjRSgbV8YFUp0FglRYGVe/Ilg3mHIOgUHYtoRQp4G4SyVYF+tHOshqo9IWCsos5rm+CCERXY6lFREAmIg40OAXMm28e5YFGfb4xZabOBPQkwyG9B0lDRcIDqJNUaK5uxUorXJT3lZBMjuw8VOGf1FgiuljYCppG3hdG1W4jraKwEtgDs8cgZTDtwINfWb0sbWSeEaroA5BkreWDFa5GUVYEZy1V4ZWs71RorlslTESiU5RLYLYh+0yGKtJ8I1T5Sgf2iKvCYY45xvTZ9+nSMGjUK9913n++2NgY9B14UCqjtE2Ihd2C1uyUnrBxW66apAwG7yWpW0D/PSxhKtvPDbKggspfwYxC6flsusPriuMHCzxelUHQ0VrYeIast+gXcjJVDY0UNNRnxuiK9QUq82xnGSpUKlGqsPAxCWRZHl8EBWM1Yll6/aiagcbe00XNeB4AxTGWg7B4LCVbgdlWgt0EoO8F4MRu0mimTddyr5Ddl06xd6Qyi4aDnsQCMl1V7UsnIeDJWigCFb8Ls18fKZhqZVKDLIFTXbsGbxZHpybzGnDATOHiNZ/Z3OYM42djAFpKkM1nsyo+xhygZKy4VqBCvk2NgFxEqY2KHxiqtNz7xQaSjQ4fGYscLlRqpwHKuCuz+L5DHUUcdhfXr1xfr4wyKAF4UCji1Brz2RWS5sKc1p+8ZVidecYi0OL7tFnxocVQQ9ViTie/JQ5nO5nRPlmXhrW0HAQAnHCYuwhBrrLxF1A4LAh9Cfb4BraMqkEsnkf8HJFWBhLEStbQRsDiyqsCEl90Cu9L3oSejmrFkhqZIqgSpQJdBqId4HbAbdQOqVKAd/BL4aWnj6D2nWRWYSGVpYBwN282ko6EgZYNJ6tPLUwsA6vOB94H2lIeVhpjFUYnXC2nUy36WLV53H5fbx0rPboFncYQ9DjlBOYFnVSBj/prSbGnj8rGSjD2s9cnu1gSyVm5flZibX2SJ2/AwgZRLU6ZirNwaK5WHVW4f/n7Q96zTQTQUpN/h3dKm/AIr34wV7xxuWRYaGxtx++2348gjjyzagRl0H7woFHAOKDzVS20QGFZLxVgBuQEoCc6CgA7qEtEht4pVGRP6gUhTRjVWXPDABgZd6Sx2NXdhf3sSsXAQU0YPEn5+UBhYaYjXmUAy7SOI5CcONh3LTrCJfGsMpY9V1M1YqVKBMm2Kl81BuMAgkjJSTBNmh0Goy8dKz24BcHpZyVKBogVCp6Z4PRdEMhOLl8aKYWxpRSBzzUgXgfZkhh6Djt3C4Orc/XGwI6n01JIbhMpTXm6vI3Lv6Gms+Eo5p49Vnmn2mQoMh4KojoZoIK7ShvHpIq/AUNTSxisVyBvGyhY6LGNF0oAj45VK/VEsHKTZAdFn8ueSSluATXBrpYadnl3+9GTs76tTUOKFQCCAqmgIrV3pPtmE2XdgNWjQIKFR5pgxY7Bs2bKiHZhB9yH2dZIPuLwjNADsac0FVsNljJWA2fBkrKiPldNuQcfvSAWqw3HojvKO2xLGCshN1iQNeOyYQdLjFgmcdcTropSRDl3uCqwq7ZGSTcV1JvOBlYKKVzFWSud1jtXwMghlUy9+gkhyr7Z0puh9IawKTDmZDx3GalBVBLUVYbR2pRWpH5HGSp1+o5MvY/waDMBToEsZq3TWdl3nAqaKfGBFztfLrBSwGauD7Ul6fdR9IDmNlYLl4gNPem01WRwdjRUJqAljxVf9iVBbEbEDKx92C14FM2yw4TcV6PKxcgVW9vnawnV5RSCQCzSqoyGaJlU1TgfcVZAqSxs2+PSqluT34dlyoDiMFQAaWHn1nO0XjNWLL77o+DsYDGLo0KEYP348wmHfH2fQgxD5WKlaG5DX2DYve1pyqcDhdeIHP0QHIEbr41ExxK80/BqEyiD2sRKvwoNBW6yfSGexPi9cP+Ewsb4KsCeXrO9UIFN148PHyq2xsica9vhJOknV603EWNFUnagqkAycFhdYeTB0NPhkgkidgZYEivvbbZ84mUGoZVmeRqUsAoEADh1chfc/b9FiKAjsVKBMvG7rjmyzVe/jYauZZC2C+NSnV0sbwNZYHehIYlhtbiGkSgW6nddV9wOvsSLPlZd43cmCiw1C7aC5K2UHkzqBVV1lGLtanN/lOO6QOIhUpT0B58LCfypQbi0B2NewPZGhNiAq4TpBTSxMAyvRcQcCAURCAYepKYFOr0B/fRE5OUf+v6FgwLPLgi5yz11iYFQFnn766T1xHAY9gKr86pZUe0RCQeWDExVQq7tbSSpQzVixz7HMz4SA11jZaYtuBlYisb6ClakggUkqgzc9hOtAYXoy9rtTjO5IJ4h0M1bOiYYcPwkAVEL6Q+pz6bC3PzuIpo4kBlVFlakfWYNt716B5FyzvgJmMrnub7OdpdlrVslVyum2tCEggZXsWPiGygDQka/E80oFplhrCY1JxRlY5b6PZ8UqmUWRZVk0wNKpCmzuTNE0mh/ndZutcH8HW3EGQDuQdInXBQsR9pz2ttrm0LzGUARW4O6nKtBO70kWf6xtiGbDab6ljVxjRcaptG0OqhFYORoTKzwCU5mMq62NquODQ2OlyGiw4APthMdiuhCQZ0JuRtyPAqs//vGP2ttedNFFfj/eoIhgJ4SOZAbxyqBeKjBtV3p4MlaCtIJXsMFrrIpVFchrrLJsqbTgWCoiOWp927527DzYiWAA0t5VgHjy9WO3kGYq5XQGoFAwp7Uh5zOIqQoEcte3pStNgx3V7/7FcfWYNLIOmxtb8Mu/bsMNM49SsmeyyZfaHEi0TeS8Mhn7t9draZPb5kCesarmU2NRJnWbzniK6HkcP7Yez23ahcOGVAvfDwWdbAPgQ7zO2mjoMFY0hWFrqPjJg/weXakMEuksbTOiEq+TwNuy7ABFbf7KpQKVjJXz3k/raqy49lUiFodNa5NimepoSIv5YE1ERfo5XhtGoNIXAjILAj0WRzcV2J7IaFktELAMtpdcwZ0KlC9ywswz67d9j0snW6Q0IGCL/GOSz4xx91Y5wXdg9dWvfhWBQIA2ICbgXwsEAr7MQg2Kj0jItkPoTGYQr4woU4F8lUVLZ5pOpEMljFWYpsfs17zKbl1uzD7SYyoQjRUJNLwqtUjJ96sf7gOQcwiW9a4CGBaHufWTtMWLN2Pl8HbSPNeaijCdfHnfHt6CQKWRCQQCuO7M8bjut+/gqde3Yf5phzPpEEFVoCCIZL9LlvpkV+2F+lgB7vRbzlcnFzR0JTO2VkdDvA4AV55yGM6aOBzjhlQJ3+dX4IC3YNx5rvqLA3LMbCqwkjsPNq3NpvJlaUkgF9TFKyNo7kxRJkxcKScTr/vXWHlpBXU0VsFgABWRILpSWezJF8t4WS0QeDFWtHelpAmzLBXo0FhppwKdKVa587rNWB1oz22jFVix3k6SZ4oPZAnUTbntYEy3kMilsfLIUhSCMYOrsH7bQYyWtPop51Sg719h1apVOPbYY/Hcc8+hqakJzc3NeO655zBt2jQ8//zzyGazyGazJqgqAwQCAepfRIShtouwINDgblSSBoxXRqTMQFCw+lW5tQP2Q2v7WBWHRubLtr1KgMlK+dUP9wJQpwEBNWOlEnuS/ZI+fawAOx1YVxF2reB5QXdS0IeNxTmTR+Dwhmo0d6bw23WfKY0PwwIGJ6dt8iFeL6AqkIAPZkilHJALvhKavQIJgsEADmuolpoe8oEDm37TaWnj57qyKfdODY0VaWcTDQc9WZwh1U5WUxwkSTRWPnysUh6MD4E0FcgFkiRg3E0DK701P7udSjfq33nd/o30U4FOtkg2NhAbkfakP42VDmPFa9oIVFkBh8u8NjvnTHuqMiGF4s6LJ+P3156Mk48YIny/nO0WfP8K119/PR5++GGcffbZqKurQ21tLc4++2w8+OCDWLBgQU8co0E3wLbHANQl1bzGyk4Dyv1VRNVU3r0CnZR5sZow86xHilnJCAOr/PYf720HIPevIggKgg2d1iqsQSh1rNZlrPKDKZ8GZL+zUyMVCOQG0GvOOAIA8MSrn9KydlE6R9W9Pvfd6lQEW6auozviNUZVAuaQDST99ArUAW8v0ZXKghDwXs7rqUzW9mLTCKzIMSfSWdpE26WxYq4tEa6rzEEJ6rnAShhsSFOBxJpEEFhxwmxdNtIlXpcExOTcdudTgbqBVR3D4nr192QzKqpCD34/3WDDL2O1u7kLbfkuA6M1xesE0lRgyBncEajuT/aZ9VoU8/vwjFUxzEEJamJhTDu0XroYYjMffAat1PD9K3z88ceIx+Ou1+PxOLZt21aMYzIoIvjKQD+pQC8PK0Ds7aSrscpaTnPFojFWNIhUV6rwOqHp2oyVyLNLlQpkgg2fFZBEZyBqKVTJ6HDYY1EFG5ccNxqjB1Vib2sCL3+wJ3d8wgkpv9K33EEkoHBeZxgKP30R+aCB11gBzsDZ1nrpMVZe4Bkr1vtN6rzOsDg0taQRMLMpDC/GqiuV1fKwIqjnAnC13YKMxVFp7nhdpIfGysVYiVlVsgAkY05dpW4qUM1YsYs1lsFu6sxZOvDpdYIQwz7ppgJ53ZEs2CDPdGs+qBpcHVVq5wiqHKlAtaA7xaXHKKMkal9FWL2MndL2CpD4gFXVDqmnwB5jubFWvn+FL37xi7j++uvR2NhIX9u1axcWLVqEE044oagHZ9B9VNBUoJPFEQ24fM7ay3UdkAQbns7r7GCX9aVPUYFvaePlP8NOZocPrVY6HwNME2Y22NDQ+oh8rHTPtSaWG/hFEwDP0Hn1aCTfe/Vph9PjkR2LmLHKfU8woLDSYPvn+bAgcKcC3YwF687utyrQC3yalzwvMUX6jW2DQ9v36KQCmQWMLLUqakqtM/kSk1D6XR6GkCxUzy21GrFyRSHazuucxsrLgoCw5LoaK9aSwY8bOSmSGFztZoLZ/TK+UoHOINI+V77i03lve3lYEbCMlUgXCbgNWQnoOYgE/kzQrC1eZwK0tI/7oZhgr3e56ax8/wq/+MUvsGfPHowdOxbjx4/H+PHjceihh6KxsVHZVNmgNOAbMasmU3KjkkmLrB5lFYGAONjwYk7YySfBtGTpbksbvlGv1yqKHfBO8GCrAHETZjp46jBWGf2Bi6CGMlbuCcDur+dtt8Disi+OQUON/XlKsTKzymeDABk97wgiaVVgAYxVzB1E2P0RM54ier/gWRwdewPWn8xPUQJhGxIMY6WyW9A5FgKdVKDMgoAuEjyYH3Yi9dRYcWJqme8bTQV2S2MlSnOJGat9bbkATtYcWFQV6Nk/j+s3KU8FOs99VNw7DQg4FxvSFCYXyBKoNHGyJswqsGN4OmN5LqZ7AtGQ+NqWA3xXBY4fPx5/+9vfsHr1avz973+HZVmYNGkSvvKVryi7YRuUBna7iBztrGrP4Gas1B5WgB1ssCuklEdAw9LRbMVT0TRWSW89GeBkmbyE64BY9CsT47JgV7J+tDiA7eVTL0gFyhgrWZqA3e+qUw/HT1f+XXosIibSyxwUcLqR+2lp4xYzC1KB+cm4i2WsNKsCvcCLs3WaHrMsjqoXGw9HKpC0tOHOI8akeantg8J1nWAwF4CrfKz4ik+i9xEFNeyiJ8MwdN7Nelm21rYbkTFWfgMrkjIMBCBkFh2MVcbNWPFifwJnmlfT20misXKfK89Y6QVW7GLDq82LKxWoCA6dFZB6QST7W7NMl5cOrZgIBgMIBwN5/63yYqwKskoPBAKYNWsWZs2aBQBoamoyQVWZwl75OulpZSoww4vXNRirjJuxkq1e2AeC1bJ4rZK8QBmcdFYrXcHqhFSO6wQiN3Kd6jRbUMqkAjXF61NGxx3/ZcHqcAA9s1KCb5x0KP7fSx+htSstDB5EOhwvc1DAuWrXXekDuYmC2CkA4lQgW4hBWDqZ1ssveIZOp+kxGzCS30aHiWQDK8ISqcTrflKBbsZKPpHyq3xSzCAy5nRPpJoWBGFbd8SyKHxATFsa5Y9Bx3U9t13eCDUUFM5BgYBtOcPey8SIdkiNOLBysDh+qwJ5PRm3X878NkB/w0MkdgI8HKlAj0WrKxWoTPvbAaGuLIN9P52xekS8roNoOIh0MlN2gZXvX+GnP/0pfve739G/L730UgwZMgSjR4/Ge++9V9SDM+g++EbMylSgxG5BxVgJgw0NCwLy/R0Oxqq7qUB74ulKZ5hO7TKNVe4YRtRVaA1uwYC9siPQGVCo7sGnoBsALvvioXj737+Cf54+Rnr8nT5TgUBOw/Kzy47FpdMPwSnjG1zvU38ywXVVBVYsO5f2kR4LBAKOIEklXu9K2W18isdYOa8tYXiVvlFMsEGugZ+qQEcTZo2qQJ1UoIuxEqZ+nOwcAWGsRF5u/ESqa5HCumOzxQ/82MD3QNRmrPIBmCoVzwc8lmV5aqzCgjSv7rnSVKBibGDvK13GqkrHbiHsPFcgd74qplyosfIYQ1hyMJ0tjXid/b4+L15//PHHMWZMbpBfvXo1Vq9ejeeeew7nnnsuvv/97xf9AA26B74qUJUKjDGDoI7rOsC2tNEXrwP2IOUIrLqpsWIn5o5khtLhsgCPTF5fPGywFuMqciNPpLzTYyxDkC6gAlKmA+F7I+pUKLI4a+Jw3PtPxwgDJTalR6DTn481n6TWEprnygbGIrsFcpxtiRS934olXndXBeowVvZ5FcpYUd2aoAkzkGNEddKSBCxjFQ4GhA2hZa76hLESBTX8RKovXrcDbXJ/hoIBF9PFB426gdURQ2tw/pSRuPLUw6Tb8J5sbYk0PZYh1RLjY5Z51U4FclWBinGQXTjoBlY1TCrQqzCIZXBylXv594uksSJ9CXP7Fa8AyS/K1STUdyqwsbGRBlbPPvssLr30UsyaNQvjxo3DiSeeWPQDNOgeyMTQwelwVKnAVCar5boOiFNGOg8n+S7CpIWDgW6nk1kH585kxvNhP3fKCLy57QC+OWOs1ueTKrCsT8YqTOl5xjSzmy7zgMB5vYgCUpETuZc5KMBXQPpbxVYwxy1irIi9RFNHir5WLPG6rTNxVgWqWKKwQCuosziwi0RsYXoFd80KTQWyDIzsd+dbrwA5VsNmrNxpuEDATt+Tfo2A9/lGmfvBTpuLGBznuemmAoPBAP7z8mnKbXL3ZIaOUSQNWBUNSX9TltXTTQVGOA2mirlnFw66VYEsyyW9toJUIPv/Ko8ytpm4bsstUunsZU7cU4j2F8aqvr4eO3bsAACsXLkSX/nKVwDkHkzjtl5+cPlYKYINdrWzR8N1HWAEvLTrvd3XTKcxMTmuYq10WBYn5RH0HD92MP74b6dqCdcBm50jA1WWmWBUEzxtOp321+bFC27n9eJZEJCYQaSx0hKvszocTSaSZW34knTAPt/mTjawKq54nVYFagRW7GmRa6Al1HcwVuKgqaLAqkA2FejdcNoZNJO/ZWwRe211LQhYxkpl6sqPMbp2CzpgmRUA2O+RBgScrJ52KjDs/B7VQodcy2goiAYJa8ZDxyA0KkgFsv8vNgNmNFY+HNRZPZdXoVBPQdbCp9TwzVjNnj0bc+bMwZFHHon9+/fj3HPPBQBs2LAB48ePL/oBGnQPvLeTikUgN2kik8VuDdd1wM1YsSsHncCqPamvTdFBZSSEg0ihM8VorIr0sPPVVA4xrjIVmH/4s2wT5mIwVs7gtJgCUjFj5UO87rMxMeBMdak0VsTcMRoKClNdhYD37bLTb/IhkqRDUhmLVkz6rQrskojwadCczjBCeu/hujbf+iiTtaSTXEhgt9CaSOXPSR7ARUJBJNLZvMZKV+Rsl//LrBaAwlOBOghzTNJ+D6sFgL0fsvqpQOZ3tSy1oJuc76hBFdr3MNvSRqYp4/uw8v/v5cTvxw4mxLC8fmUIxQLfLaRc4PvufeihhzBu3Djs2LED9957L2pqagDkUoTXXntt0Q/QoHugBqGcG7loAmAHfB3XdYAROXPeLYCeG3mnD22KDtiVvp+qNB3wk69DjKtMBeYnF8azqziBlT35ZvIpGqA4g5soxWuL1/2lPXV8rHKfq6exas6nAovZ8JXXHekKxkk6xA/zKjIIdTFWTNDsx3k9GAygviqCfW1JeeUYl/YEmIrAWFirn2JSk3llfaxUxRU9Glhx2icvqwXAqcvSZ+eclhRqjVXu/HT1Vbl97N9I2jyaMykF7MV0UGJJ4biuPuQEbCBJxeuSQqGeQr/RWEUiEdx4442u16+//vpiHI9BkWGnAnMDZ1LDIDSXCvR2XQfYJsxOxkrmK0NgpwLl/eoKQRWjKSu2G7ArsGKcyFXHTx7+LiYQKwZDx/p2OQLaojBW9vFlsxaCwYBvxsqPjxXgzViR4KOpMzcxqvoz+kUo5Ly2usFMJBhEF+w0l55BqD0hdeSrD/lglU1p2xWKeudbXxXNBVaSSU4UNLcR4bogoCVgU4i6tiH0XNOWMhXIs3HFTQU607z7dQIrZh9dbyfeRFVlHkwWDr4CKzYVKHmmaCqQGQ/sIhK15s7ZF1EjFSjYr/erAnPHkCizwKp3fwWDXgcNrFL6qUCWsVJVBAKC9Bgj2FSJ0W3xeg9prJhgo1j0ND8hsStw1bmSyZYEJoC+j5UKdrooW/TAKsRMIvz5KtOeZBWb9V8ByQYXwpY2Yad4vScYKzJBdEi0TzzI72QzVvqpQABo6SSBlbgq0CFe1wwkSWWgnLFypsYA1hxUHtCwzI+qRYrwu7JZpakrHzSKLB8KBX9tiXh9sMTDit2HFerr+liR71KlAonh76GDq7TOAXBef2kTZub5o8fiMQ6yGiu/4nUgf64lcF5nv6/Pa6wM+hZsHyvvVCDrr7OXMFaKikDA3YRZ9wEjg1CHjwlJB+yEpGLnCgEZtLKuQEM94ZEJiTVDLSZj1ZXMIJEvHAl4sGe64J22AT2NFe2fl2ErIP2nAlUtbWgqsEgeVoDb26lTM5gh94Q/Hyv7M1u7cuei04RZpyoQsAXs/397Zx8eVXXt/++8kwQyDQkhBEKAgkh4sUquEKiXohBEXqzeq7Z4U+hV1B9atAFa0V69+FTxXahWrtdr1V5p8blafOpbCOUpbwoRECoQK1RFQRJAyBshJGFyfn9M9pl9zsxk9jnZZyZnZn2eJw9k5sxk731mzl5nre9aK/pGGu6x6qo4KMMdQawcs6UN01hxdawifV/4de7tc3fp7TaKviL66ebgta0r0Xh3hPrsb3V1Lbz1+8PQp5cb8yYMFp0GnE4H+vRyRy3qy49BEwqM0e2Bb7AtWseK/1tGPV0y8XZ+lmwfCiTsRZouK7DLUGA3PFb6UGAsb0KoQGhnKFCyx6qlPVTHSuQiIYI+U040Cy+kJ+s6O8cofG9EUU+hKE7uPYIXZpdYKFBTxdmYeJ1/30hGBHu+qdO7IqvUAhD+OT4nqLFir2PnViQUyN9EMNsmaq9AvqWNaCgwhseK30gZXRUHZTBjo5X3vAoWzWwPxAoFhuaWKVFfFRyDdr6xioMCWo2VaLsil9Ohdg9o7+jaizM4Ox3LZlxscCbAL66+GJ+fPIvC7MieLk+EUGDbha6NYG0oUPxmNOSxUtR9JWHidfJYEfFE34S5y1CgK2T9Mx2LaFYgq9AtmkGjDwXK0ljxhqRMMTcQninXKli7RR8KlFGzCwgZFud5w0pa+YFwj5UqXhcohnpBkwEpNletxiq6eD30u0yPVefmwlraqKLyri+RbL6hAqGx5+pwOOB1OzV32dEqr7dd6EBzp9HTVYYiT98Mj2ZsYWOO6LEKes669FixuXLjjmU086EakfIDgFx9FaA1AADg2xjtbPjXsGxPQOwGweN0oq3TWytbigAAZRO7rrkXqaVNqCh07M+DEc8THy5tk3wTK0qk8hI9AcOrMGzYMJw+fTrs8fr6egwbNkzKoAh5sIuxSCiQz1ZiVddjZQXqazu1BcSMDX0dK1kGQRon6A7VsZKcFaiIa46A8LlKKy3BeTVEPYWiaHvDaUOBXYnGI/UKFC2GyhtKkUId+sd6QlagW+fFEfXO6QXN0XoFAkDduVBBSxGy0rv2WLlYCIfXWKk9+mKL1/nG6UbavHRVB01rWEn2WOluiM50hgKjVV0Hwr+zgKCxwSVvtCagf16kFi+xkkj4fq9GWtOEeqB2JC4U2Pn3Nn16EmurvsJf/34Sn9U2qTcKicLwJ/jIkSMRC4G2trbim2++kTIoQh7sgnVeLbcQOxR4qqlVqOo6wLmRBaoN87ALcrNVHqv2gHohkV7HSte93hsjJOXRaaxkCNcBbdsT2XfHfPPaDtWwEhev87WOhFvadM4n3euKWNtHHx6UGQrk6xYBYi1t+NcZLRvidTuB1tDv+jXlf2d1u0QNqwH+YKZZtOrlHtOhQONJGLzhdbaVGVbRw7yAfMOKF91r+gQKeKz48L2IN1IVyscIBVpFpKr6sYoSmymGGnxdBIMszh4rVotsy6FT2HLolOa5Pb+a1mWtMisR/gT/+c9/Vv+/YcMG+P1+9fdAIIBNmzZhyJAhUgdHdB+1pU3bhWAzzi5DgcHH2EU2VtV1gGtMrBgTr4eVW7BAY8V8bbLLLRjJkgNCc1M3X8neOb6Zr8yLODOs9B4rkUr8fOaYqLHHapBFa3ysL6JprceqU9cU4/Pv0Z9bwRsE/jz53OGFTp1OB3zuYEFO1udNVLw+rSgXy2dejCsvzo34fETxehftbBhsjdjnwOlAzOKW/HfvbGv0bE7+nMsOBfLi9cbzF9SbS5E6VrwRKSKoj+Tp8rnk3QDEgi/lwYhVGoMP36s33kJ1rMJF79HCjVbxsyuHY4C/F74+cw7H61twvP48jje0oKUt0KWGzmqEDasf/vCHAIJ3svPnz9c85/F4MGTIEDz11FNSB0d0H3Yx7lCChkBXdy/6TTmWvgoIb48hKn5kFwDZWYF8/R8WprSqjpWwEanTeMjyzvGhs8bOUI5Mw8rtdKANETRWXYYCI2SOCfcKDHmsIsHE+urxUutYacNFoTpWXV8iVc9GmzGhPn+eos0jzevS1OeJNRaGz+3C7VO+G/V5XqzMEMoKDNOTiWeOAaFwY6xyC/JDgSEDgFVdz/C6hG4Q2FxFk0JCGcBcCDGuocDOLEzOY1XT0AIguljfTBNmgDfIuq4ybyXfSffi1ivCJUgtbQEpOlazCH+COzrvPocOHYpdu3YhJyfHskER8uDvuPnGxJG+OPo7yVgZgUB4mrpxj5XkOlaceJ3NUdZdlGpEqhqr2L3zgPDNVlqVec6D08javMj0WOn0cyGPlYB4nfNYCYcCvV0bVvqN0EqPFQvbxvISsXPbakC8Dmi/f9FS54Pntz3mcUaJVJ37bKcmpSujRp8BKZo5xjyfLBTojeDB8bmdakadfI9V6MZGrboeI0TEXtNi8LyytW3mSqvENxTIvHOhc/uPk2cBABf17x35NZ1jVpTQzZOQxoqvf2XgdfFA1LtrFYZX4csvvwwzqurr62WNh5CM2+VUL+ItMaqR6y8AsfRVAMDeJqATr8cUdLuZxopVXperO7KiVyBfs0tRuMrKghorhizxutPpUM9ZA9c/TxZ8LzAglA2mD8nx8FlJotW5Gcxgy4ii8wkzrKzICmShQMHGxyFjw5hWkB97NEOV3xwcDnlZkJGaMKsFQgXKLRip2QWEPv9qKDDCPBwOh2o4ZqbJ1liFjA2RBswAV59MTTgRW3tVT9ka+jzIrMkVi0ihwMOdhtXw3MiGFT8+I5ICXruWKPF6T8XwKjz22GN4/fXX1d9vuOEG9O3bFwMHDsTf/vY3qYMj5JDGlVzoKhSoN0CMeKz0tZ1ii9f1His5Fx++vASr3yJL06QvQWA0KzDS+3QXthk1npfvsdJ7cVrVrMDofyNUIFSJKZrVc9ngLGRneDF1ZL+Iz4dnBVpTxyq4UQTnLGpYsTUSDgW6eMMqtocuzeOSFtpwR9h8RUKBHp13TvRmiL3urFp/LPLr2FrL9lh5uHPEqq7ndCFcB7hsT4OeGLa2rERGvENjngjlB/6hGlZ9Ir+G+36qCTYGhPoBLhQYb/F6T8XwKrzwwgsoKCgAAGzcuBF/+ctfUFFRgZkzZ2LZsmXSB0h0H20JguihwDCNlSmPlTGNVYvBNPVY8Borq3oFAsFwoHgoUHuRkukuZ14M1WNlSaacuHhd1ZOZqGNVmJ2B3b+ahruuHBHx+bjUsero0OhjYum49OdW9I5dRGOlbfEj34iMqLEy4LESDbGrSTHnuy7sym4AZRcI5Q1JVmohlsdK72USnStbW+aJj7dhpQ/ztrQFcLTuHABgRJRQoMZjxWnKhP9Wh6LWLySPVRDDn+CamhrVsHrnnXdw4403orS0FEOGDMGECROkD5DoPulcZqBIViAj10KNFa97CI5HkqCbK7fQLllQ6dJ5rMTb9+g8VhIzZ1SPVWfPOZkXtvCCqLFDgWwTU5TQZ8FImLcrr4zL6YDX5eRqdsk0NkJ6EeZFdTpiG80uk+eWN4Cjaaf4x2VqRiI2YRbpFagTdIuHx4LHNcXwWLGisNHKRJglVEU9FAoU1ViFfjc2V2acx9vQ8Oi8kZ+fOgtFCfYmjJYFyX8/DVVeV0vtdN0XMRUxvApZWVk4evQoAKCiogLTpk0DACiKErG+VVesWbMG48aNQ2ZmJjIzM1FSUoL3339ffX7BggVwOByan4kTJ2reo7W1FT/72c+Qk5ODjIwMzJ07F8eOHdMcU1dXh7KyMvj9fvj9fpSVlYXpwr7++mvMmTMHGRkZyMnJweLFi9HW1qY5Zv/+/ZgyZQrS0tIwcOBAPPTQQ1AUBT2dtAjGRuRQoPYxQ1mBJsstqL9L0lhFLBAqyZDRF80UDwXqLtKS5gqEvB3MYyVT0K3fgI2I1/njZRqS/N+2Yq6BDoVrZ+OOGX7Tl1cwFwqMorHiDKt0warrIuhrHSmKwhlWsXsFqu17RL04rGZdjPDYwiuGobSoPyYM6yv0vqLw2WuqeD2mx0qfcCJ3rlbh0Z1bFgYckdsn6mc5kjJBqFcgd33oaeL1RGP423r99ddj3rx5GDFiBE6fPo2ZM2cCAPbt24fhw4cbeq9Bgwbh0UcfVV/36quv4tprr8XevXsxevRoAMDVV1+Nl19+WX2N16v9Qtxzzz14++23sW7dOmRnZ2PJkiWYPXs29uzZA1dn9sm8efNw7NgxVFRUAABuu+02lJWV4e233wYQrMM1a9Ys9OvXD9u3b8fp06cxf/58KIqCZ599FgDQ2NiI6dOnY+rUqdi1axcOHTqEBQsWICMjA0uWLDG6jHEl3RseCoz0BdC32ohVdR0IbwVi1rCSVo2ca15rVRNmINiIWRWvC9Y6Ysi8i+1locYqPBQY21PEG8hGi2aKkOZ1qaUlrMoKZOMW8RLpQ0aidaz4sUf7O7281nis9J7I8+0d6v+7DAWyTDmmixS8QQgPBUZ+3b+MH4R/GT9I6D2NEGoe3aE2YO6qnU3wNebC92xNzllQV07o76uV14Pn8/DJJgDA8ChhQCB43fe4HBrNnYghyetrSbyuxbBh9cwzz2DIkCE4evQoHn/8cfTuHTxhNTU1WLRokaH3mjNnjub3hx9+GGvWrMHOnTtVw8rn8yEvLy/i6xsaGvDSSy/hf//3f1XP2WuvvYaCggL85S9/wYwZM/Dpp5+ioqICO3fuVEOVL774IkpKSvDZZ59h5MiRqKysRHV1NY4ePYr8/HwAwFNPPYUFCxbg4YcfRmZmJtauXYvz58/jlVdegc/nw5gxY3Do0CE8/fTTKC8vT2jNjFiwzfdcW0C9k4n2BfC5QoaVWFag1qsh+gXTPy+73MK5tguhJsyysgK5U8x7rGLNNTysYEUo0IKsQK4RraIoat+0rjxWvKFxXm1MLNNj5Yr4/+4SyoBUDDU9Dr9BMKGximKo9hIIF5rBxengAKCpM1vP4eh6ziFBNyt0K5oV2CnobovdEskKQl6ckHi9bxftbADzusgwj1XCQoF6j1V0wwoIfiZ4w0pk3Lx3LBQK7Ln7YDwxfNY9Hg+WLl2K1atX49JLL1Ufv+eee3DrrbeaHkggEMC6devQ3NyMkpIS9fHNmzcjNzcXF110ERYuXIiTJ0+qz+3Zswft7e0oLS1VH8vPz8eYMWPw4YcfAgB27NgBv9+v0X9NnDgRfr9fc8yYMWNUowoAZsyYgdbWVuzZs0c9ZsqUKfD5fJpjjh8/jiNHjkSdV2trKxobGzU/8UbVWLUH1DuZaJs7u+B/Jz121XUgVOuINWEWjbXr74hkFwiNVVrCDKzNC8CyAgVLS4TpcOSL162oY6XP+mFRb5FegdrHJHqsuL8ts9wCXyZCbWcj8vkPCwUa91j1EiiIKlO87lG1McETygvXY2ncAK4EgWhWoG6zlelpFIEXr58WDAWGe6xEQ4Faj1W856oPBcYqtcDQn0vRGmVAp8eKQoEaTAfuq6ur8fXXX4fpkObOnWvoffbv34+SkhKcP38evXv3xvr161FUVAQAmDlzJm644QYUFhbiyy+/xH/8x3/gyiuvxJ49e+Dz+VBbWwuv14usrCzNe/bv3x+1tbUAgNraWuTmhrd2yM3N1RzTv39/zfNZWVnwer2aY/Qte9hramtrMXTo0IjzW7lyJVasWGFoTWTDKja3xBCvA6GNOVfAWwWEe6yEyy24rTE2mMfqfHtHyKMk8S6KFTvkyy2ICvUZouEiEdh8G6woEMrp5/gK4F15rPgegwy5GivOsLKoV+A5Ax6r8HMrz2MVL/F6qAFz16JxNjfmuRQ1NsJC4fE2Nrg2S3WqeN2oxko0FJjYrEBevN56IYCvTndmBEYptcAIC2kbqKp/IZC4yus9FcOG1RdffIHrrrsO+/fvh8PhUMXb7E7HqIB95MiR2LdvH+rr6/Hmm29i/vz52LJlC4qKinDTTTepx40ZMwbFxcUoLCzEu+++i+uvvz7qeyqKornzinQXJuMY/dwjsXz5cpSXl6u/NzY2qlmV8SJUjbwjZiiQfTFEalgBfHuM4PuKGhvh4nW5HisAaodzmXdRzEPXrTpWMg2Nzk252QJNh5szNpgQ3eEQCH3qDSupYv3Yom8z8ALnlvaQeD3m60x6NjSV172R58EbkXI9ViHvHCDWgBmIoLEymCnHSJTH6kxzmzrn2AVCuxcKZAVC415ugSvaeeTbcwh0KOjjc8dMRDLzOeZDykayCVMBw6tw9913Y+jQoThx4gTS09Nx8OBBbN26FcXFxdi8ebPhAXi9XgwfPhzFxcVYuXIlLrnkEqxevTrisQMGDEBhYSEOHz4MAMjLy0NbWxvq6uo0x508eVL1JuXl5eHEiRNh73Xq1CnNMcwzxairq0N7e3uXx7CwpN7bxePz+dSsR/YTb5ixca491IA0aiiw84shoq8CuCbMeo1VjAuKfnOW5bHiNyMmcpb5ZedFziGhvlgRSfV3maFA3YZrlcaqtT1kRMbMlAvTz8nXlAXHYk3NLjUUKCRet1BjpTGs5GUFhs5rp8ZKoDgowJdbMNgXMcywiq/Gil3rahvPAwhWl481hnAPjrFQoOqxirOhEaq83qERrsf6zvL7gcflMNQX8XyC+iL2ZAyvwo4dO/DQQw+hX79+cDqdcDqd+P73v4+VK1di8eLF3R6QoihobW2N+Nzp06dx9OhRDBgwAAAwfvx4eDwebNy4UT2mpqYGBw4cwKRJkwAAJSUlaGhowEcffaQeU1VVhYaGBs0xBw4cQE1NjXpMZWUlfD4fxo8frx6zdetWTeizsrIS+fn5YSHCngafFRirzYthj1WUxsSxjJnwzVfOF9LFtXlhuiOpHitO5CzqsXI5HeCvUzJDgfpN2aqsQJHioPrXMWQakryxY0lWYMCoeN2kx0ogK9CqUKC+Wjzz7MbyWDHPo9qEWfBzrF+TuOuOOsd9otOw6hsjDAiYF6+zNUl0VmB7QBEWrgNar7KoMejWzdXIa5Mdw6sQCATUTMCcnBwcP34cAFBYWIjPPvvM0Hvdd9992LZtG44cOYL9+/fj/vvvx+bNm3HzzTfj7NmzWLp0KXbs2IEjR45g8+bNmDNnDnJycnDdddcBAPx+P2655RYsWbIEmzZtwt69e/Fv//ZvGDt2rJolOGrUKFx99dVYuHAhdu7ciZ07d2LhwoWYPXs2Ro4cCQAoLS1FUVERysrKsHfvXmzatAlLly7FwoULVQ/TvHnz4PP5sGDBAhw4cADr16/HI4880uMzAoHQRZndmQICoUBRj1UUwypR4nUgtCGyjUNqvSPOQ9fGxOsxQlIOh0OjvZFraFgXZuG1OCLFQRnhdbusMSQt91iZMSJNaKyiJQNoPFYSM+ncUUKBXdWwCr6uMxRouFegdk1khnBFYOfoZGNnqYUYYUDAnJgbiNTSJjHeuaDHKlTDKhb851i0LQ1bo3PtIcOKQoFBDPuXx4wZg08++QTDhg3DhAkT8Pjjj8Pr9eK///u/MWzYMEPvdeLECZSVlaGmpgZ+vx/jxo1DRUUFpk+fjpaWFuzfvx+///3vUV9fjwEDBmDq1Kl4/fXX0adP6IPyzDPPwO1248Ybb0RLSwuuuuoqvPLKK2oNKwBYu3YtFi9erGYPzp07F88995z6vMvlwrvvvotFixZh8uTJSEtLw7x58/Dkk0+qx/j9fmzcuBF33nkniouLkZWVhfLyco1+qqeSrkvJB6KnSvfrrEg8tF/suxwg/O5XrYptVLwuuX9ePbi5WhEe6+gQLrcABC947MZOaksb3YVbrhcn+F5aj5XAXE1uSiLwoU+pGiuu1pGRUKDZsKdGYxWt8jpnNFvhsQroxOuxDCuXGgo0qLHSfSa9rvgaG+ycsGtTrFILgPlQoNqEOUGV1/lQ4D9OiGUEAtrrr7ARqcsSdcW54XRPxrBh9atf/QrNzc0AgF//+teYPXs2rrjiCmRnZ2uaM4vw0ksvRX0uLS0NGzZsiPkevXr1wrPPPqsW8oxE37598dprr3X5PoMHD8Y777zT5TFjx47F1q1bY46pp8EuyqyIJBD9znrFtaNx3aUDccXwHKH31heRTLTGCgjfqGR6w/hNSdUdCXgTtBcu+VmBDOtCgeJtZHhPhsMRvkl1B6s1Vhc6FLSoldetC3tqyi0IVF63yrDiq67HCgWyMBfLSxDdgMM0VnH2WOnPSawGzEA3xOtOncYqQaHADgX44lsDhhX3nRUOBaqlJcQbN6cKhg2rGTNmqP8fNmwYqqurcebMGWRlZfX4kFiqktYpfG3gPVZRvgQD/GkYMDZN+L31hpVoN3j9hcuKauQMqzRWbQaqDfNjkJklpzfqrKpjZcxjxRmREueq//syN2h1rgGFK7cQ+/Ko1xmJbi4+geKfVmUF8p+/Cx2KmuTR29d1uYUwob5NNFb6ccbKCATMG8zMQFFrviUoKxAI6qzSPC4M/E7s6zl/boWF+mpTbqq6rsf0SvzjH//Ahg0b0NLSgr59+8ocEyEZFgpsUMXcYlkfIkQTr8fUWIXVsbKJF0dTbkFMYwVoDSursuQAuWEW3otzXp2riMeK15PJvdnSeqys0ZOpLW2ENFbmwp6arEABwypNYq9AF3dOAh1ifQKB7jcmZsQ7K1D/90UMq/A2VOb0ZInyWDGG5/ZWdbBdYSoUqJbfSIx3ridjeCVOnz6Nq666ChdddBGuueYaNZPu1ltv7fE981IVdrfb2BL8Asj0mLCNxWgTZitDgfq7e2s0VlyvQIELCr8pWdWUGLAqFNihhj1FsgL5i7RM7Zz+78tsaePmPJFGsgLDjA3B+YoYVmmWeax4r0YHzrKsQEGNFcMuxob+HOX0jq2x0p9Go7ojRvwrr2v/nkhGIKATr5vMCiSPVQjDK/Hzn/8cHo8HX3/9NdLT09XHb7rpJrXJMdGzUAuEqqJTeZsd+y6FaayMlluwqJ9c8G/J1Fh1ahgMlFsIjsGaUGCYx8oiL47aJ9DgXGVnCfHn1pKaXQbF62ZrlPFjj6qx8lpvWGk8VjHLLZicq5v/7Mdf4Kz/vol4rBwOh/YGwaDuiBFvY8PldGiMwq6aL/Pw10jhrMDOuakFY8ljpWLYv1xZWYkNGzZg0CBtF/IRI0bgq6++kjYwQh7WCpw7s6kC+l6BXV88w8stWClel/feTs7YaDUi6LZIvK43ImVeyLUaKwNCfYu8c0Do3HpdTqEQhyiqwayExLgiGiuz2WOaOlbRQoG8DsuCljZAUIdjtECo+rvBTDkg/h4c/d8HYrezYbicDjXbWdg7p/fqJWS+TvWmb7hgdjf/mRCdq0vnsaJSCyEMr0Rzc7PGU8X49ttvNQ2KiZ5Duk6fITUUqG/CrJYg6HojsLLNS7juyBpjw0h/LK3uyDqhvhVGs0a8LjJXTShQ7sWWGRiyN2h+Y2GGhliBUL2gW6LGyhvb+DID740JdIQMqz4xegW6THpj+DUSMcxlo/++ZQuUWwDM6o4SG/YEtGMd0T92DStA+7kVb1WkrWtGocAQhlfin//5n/H73/9e/d3hcKCjowNPPPEEpk6dKnVwhBz0d7vRaliZIWoT5lgaK93zUj1W3HzdTodUzwabb+uFDjX8KbLJ83eBUus66cJIllQj71DQaqDyuttEhpEobL6yN2h+E2VZcmItbazzWGk1VvLE64A29Gm03AJD1IjUGFaJMDRMZAUC5m6G9Oc/MYaVQ/3bBVliGd4aj5XgmMMSNygUqGL42/rEE0/gBz/4AXbv3o22tjb84he/wMGDB3HmzBl88MEHVoyR6CZWirmZp6nDcOV1/Z2+NeEx2e5pdgFijXoB0dpOccoKlOmxitC+R6jcgiYUaI3GylqPVVDMLeIl4s+l2ymebavVWEU3rNK9LlwIKMhMk2tYsXDRhYB4VmB4CQLjc02EYcWPu08vt/B3xEz43soyMqKw79ywnAxTBqHR9j2MWEWhUwnD39aioiJ88sknWLNmDVwuF5qbm3H99dfjzjvvVHv4ET2LMDG3xPAMa8Ksr7we60IUprGSeMHlDUnZHhO9rgAQDAVaFB6zUmPl4s6tkV6BWqG+3PUfntsbvTxOjM6X28ycHyczIsUKhPJeDfG5aupxRfn8uF1OvDT/n9Ae6LDMY3W29YLqeY3ZK9BkGyr+uER4cHjjQiQjkGHGi9MTQoHsGiAaBgT0GitRj5X+Gh7fpISejKlva15eHlasWKF57OjRo/j3f/93/O53v5MyMEIeLqcDPndI0CjzC6Cv4iyqOwrPCrRGvC77wqZv4yCa5aQxNuziseLKLYQqrxszImV7DHP79MJH909DhkWGBo/RrEAjn2FmKPX2ubsMVZd8N1v4PY3Axl13LthU3umIbUiabVXE3zTFu4YVoDXsRMOAgLmbobAq8wkMBYqWWgDMhe/D2zmRx4ohbSXOnDmDV199VdbbEZLRenGs2HwVXOhQQhWHEyhe72XRXIHQfJtbjXWvd2tc7RZmBVpSeR2hcgsJzgoEgMxeHukp+w5HuIEs4iUyO9f876Rh8VUj8B+zR4kPUiJsrHXnOmtY+dwxw5hmyy0kWmPFGw0iDZgZLhPfWf1nIJHidZFWNgwzdazC65qRYcWQe9tH9FjSvW71IirTOxSpYCYQ+4LC6q2E+o5Z48WxyrA616mxEt0oLGtpo29wa4nGqsNgX0Tu7ldyVqCVuJwONSwGiGmszNQ6YpRPv8jQ8TJh56ih02MVKyMQiBD6MdHSJt59AvV/X7TUAqD97JoOBca54TQA3DxhMDYcPIErRoj1ewV0N34mbhaNvC4VIMMqReA1HdaEAjvU4qCAmKHE11uxqo6VbI2VPhQoesH1WOSxcurCvFaUljBaINRqj5VVeJwOtHX+3+d2CnnFtEakfeYayWMl+prQ72Y8Voktt2AkFOgyEQrsCXWsFkweigWThxp6DX+DINyEWbcmJF4PQSuRIvBhDSsKZgY4j5XTIXbR9Wp0R1aJ1+V+xJlYn4UCRTcK/iJkVW0nQHY18uB78XWsRDxWHqc159Vq+I1UtNK5y8Sdfk/ApdNYxSoOCkTSWBnX4iQkS447r6I1rAB9eEw0FJh48boZTDVhtrDIs90R9lhdf/31XT5fX1/f3bEQFqKt7WSFDieUki/sxXE7gdbO/1tUbkG6eF0timcsFGilFydYobsdHpfkml2arMDOcgsC89UYG7by4oTmJpqFpzEi7TTXzrHWd3qsYpVa4F/DEG44rSkQmjjNEWAsFCgjPGYX3ZGpYqiUFRgVYcPK7/fHfP4nP/lJtwdEWAN/Bx6r3YwRmAcnwGcEmqidItOzkWapeD34fueMhgKd1t21s/nKfl+1MXHAYLkFp3VGpJXwHgrRFjJmRL89AXZzVc88VgKhwLA6VsIaqwSL111mPVbGtYL64+zjsTJjWCVeT9ZTETasXn75ZSvHQViMVYJudfPtUEINmE0Iuq0Sr8s2Ntgwz6mhQFHvnDVGJD8G2RdxNeNTCYV5xbIC7RkKdJsIBdpVT8bGWt9iwGOlT683oS9MiMaKO69GNFYeU6FA7XGJMCTNYKbWWLh43T6ff6uxx1knuo1VocBIWYGixoxXY1hZJF6Xbmx0eqw6Q4HiVZx5Y0PuBUj1WFlUs0vTK9Bg5XU7hQL5u3YRAxKwrvCr1ehDgULi9bCsQAMh/04S47EK/c0cA6FAjRfHhBEJ2MljZdyjrv88kHg9BGUFpghWhQKZDkdRYFxjZVGFbr55rWindlHYOFkoUPQOXJMVKHkD7uW2xrDijebzF1iB0OQVr5vyWFnYF9FK2LjrulFuwUxLm0QYGhleF0bnZyLQoSDbQOV1t4nvbHh4zB6ff3Pte/TJDPaYazwgwypFsCorkP9yGS5BwBl4Mgs+WjVXICTMbmkzFgrUhscs8ljJ1lhpKq+Le6zMZFP1BMxkBWoLv9pnY2FzbWgR91iFV9o2o7GKfyjQ4XDgz3d9H4Cx64xGYyV4M9oTCoSawYx3jupYRYcMqxSBD4/JDFnwb9XSufkKt7roPM7rcgo3rxWBz1yTblip5RaMhQLN6DVEYcaOV/KmxTIM242K1zWNie1zseXHmuYRuzR2p0BoImGbIuuUIFJuIaxAqIkklURkBQLmbtzMZMrpj7OLYWWmCXNPaDjdU6GVSBE0mXJSC4R2w2PV+UWU7cFxu5zql9yqyuvMiBSuY2VR5XUgZOxYpbFqvdChVsjvJTBfK71zVmLOY8VnjtlnrvpNsY8JjZVw0cwEZwWaxUwfSP41Tod9SnCY0liZTGZIBWglUoR0y4pIhi4czNgwWm7BiotPyItjjcaqPaB0vr8JPZl0j1Xw3MoWj6pC/U7vHCDmcbCyCbOV8OdFXGNlz6xAl85QMNXSRlRjxX1H7OLBAfR1rIyHPb1uuZ54KzHzndV/Hki8HoJWIkWwKhTIf7kM13ayyKsEhDx0VnmsGOK9Arl0Ztl1rCz2WJ3lDSuBv2FVUoLVmKljpS23YJ/Lqd4oEgkFhjdON+Oxsk+tI201cuO6IzuFxszoIvXHUbmFEPY580S30Ai6pRYIDf1fbXvSAwwrNt+eYlhZqcUJeeesmWtzW6jKvMgduMvCuVqJmaxAzYZkUyMSMFcg1MwGnPyhQN5jZR8jUqOxEjxHZjV3qQCtRIqQpilBIO+0OxyOkO6ozZh4nY3DihCK1bojRo/ICvRYmxXIiqGK1nbSlpawp7GRJtjSxm3T0hL676iVLW3sqrHSZsoltxFpRmMVVmXeRp9/q6GVSBH4LCfZ4Rl2ATIeCnR0/mtBKNBjjTdM34tPpClxcBzW1bHypweLHmamyU3ydepCgSKlFgD7Ghv8uNNFC4TavNwCQ6zyul68bqLcguC69gRM9c9LcM0us5iZq9PpAO/AJvF6CCq3kCKka7IC5Xs22mBGvO5UXy+bUG0na8TrDKNzBeR7rOZeko8zZ9sw93v5Ut+XzwoEjGRAplAdK5vW7NJnr6WJtCrS3RCIGs0uZ9CrHehQbOXV0OjnBK9R/HF2mqvZ2nMep1PtEUvi9RBkWKUI6VY2Ju68bWEaK/ECoRaK1z1Wide17ydal0dbbkHuBuxP8+DuaSOkvicQ7tUw5bGyUSiQH2svE5XXbVWzi9s8e/vchrVzgDHDweMKGlaJqmNlBnY+PS6HcHafPivQLmhE9wbG7XI6gOBlnzxWHLQSKUIvTRNmyaFAFwsFXuh8f2MaKyvu9LM6w2MiaeRG0E9NXI8QKi1hnxRs7dyE++fZNFNO47Ey0SvQXh6r0HkR/Y6E1bEy4tno/BzYS3dkXKrg4sJjtjKsTGisAPtmQVoNeaxSBCs9VuyC29LOQkbGNFZWbL53Th2Owux0zL5kgNT37a7Hyl61jnQeKzN9EW00X20dK7FLo9PpgNMBdCj2Ore8kSSSEQho56p/j1gM/E4a/nHyLPpn9jI0zkTi5m6GjMDCY3YyNLShQAOGlU1r1lkNGVYpgpX985wOlhVosM2LhRqrITkZuOtK+eGx8KxAY14cO1189IaCsBFp0/AYbzSL1rECgnNsC3TYa67cuRURrjPYXI2ExwDg97dcjrrmduQYaIKcaJixYdTz5HY50Bawm8fKeLkFQC/Wt8+NhdWQYZUi9PI44XAEe4PJ9iKEPFbmxOt2ugDpswJF52pVix0rcTq6Z0Tq/9/T4UtDiIrXgZDOxE6fY43HyoBhxeZq1IjM7dMLuX3s460CQkaD0e+s26RBlkhcJr3MWrG+fTI+rcY+Z57oFg6HwzJBNzM21DpWghcUduGxq8AZMOLFMRdWSCT6sZoRr9vJkDSTFQiEjEd7ndvQeRENBQLcXG1kMJtF/c4anKsdbxg9EjRWVHk9hH3OPNFtrDKs3DrDSriJp3rhss/H0GxmlJUZkFYRnhVowmNlJ2ODG7exUKD9Psf8eTEWCrRfSNssZsTrQOhzZKfyA+Y1VuYMsmQnoSuxZs0ajBs3DpmZmcjMzERJSQnef//9iMfefvvtcDgcWLVqlebxH/zgB3A4HJqfH/3oR5pj6urqUFZWBr/fD7/fj7KyMtTX12uO+frrrzFnzhxkZGQgJycHixcvRltbm+aY/fv3Y8qUKUhLS8PAgQPx0EMPQVGUbq9DvMhMC2b/GLkbF8GlDwUa9FjZ6Qvp0ofHBI2NUXmZ+F7Bd/Avlw20YliWoL9TF/VYeWzusXI5HQbv2q3LbrUK3gg0kjnrctpvrmZRQ3qGQ4H281iZLXRrVpuV7CRUYzVo0CA8+uijGD58OADg1VdfxbXXXou9e/di9OjR6nFvvfUWqqqqkJ8fuQDiwoUL8dBDD6m/p6WlaZ6fN28ejh07hoqKCgDAbbfdhrKyMrz99tsAgEAggFmzZqFfv37Yvn07Tp8+jfnz50NRFDz77LMAgMbGRkyfPh1Tp07Frl27cOjQISxYsAAZGRlYsmSJvEWxkOUzL8aer+owJt8v9X3NGlZTR+bi3U9qcL2NjQ3RDMg0rwtv3TnZiiFZRneF+vr/93TYhpjucRkSZofCvPbZWMxkBQJcJq+N5moWl+lQoA01VibLhrg0Giv7zNdqEmpYzZkzR/P7ww8/jDVr1mDnzp2qYfXNN9/grrvuwoYNGzBr1qyI75Oeno68vLyIz3366aeoqKjAzp07MWHCBADAiy++iJKSEnz22WcYOXIkKisrUV1djaNHj6rG21NPPYUFCxbg4YcfRmZmJtauXYvz58/jlVdegc/nw5gxY3Do0CE8/fTTKC8vt0VtotLReSgdHXmdugO7iz3fWW5B9As2JCcDb/y/SdLHYyVhoUAbXTyNoi8tIeyx0oQC7bM+7NwaCQMC9tQduUwaVqHwmH3mahazTeKZN9BOhobHRDHU4PH29E5bTY9ZiUAggHXr1qG5uRklJSUAgI6ODpSVlWHZsmUaD5aetWvXIicnB6NHj8bSpUvR1NSkPrdjxw74/X7VqAKAiRMnwu/348MPP1SPGTNmjMYjNmPGDLS2tmLPnj3qMVOmTIHP59Mcc/z4cRw5ciTq2FpbW9HY2Kj5STbCimYms7ERlimXOnMVrWPl0oQC7bMBu80aVjYMj/FjNZIVaEc9mVlMa6zsmBVoMuzJh8/1N52pTMLLLezfvx8lJSU4f/48evfujfXr16OoqAgA8Nhjj8HtdmPx4sVRX3/zzTdj6NChyMvLw4EDB7B8+XL87W9/w8aNGwEAtbW1yM3NDXtdbm4uamtr1WP69++veT4rKwter1dzzJAhQzTHsNfU1tZi6NChEce3cuVKrFixQmAl7Ives2GnOzWjpJTHKkxjZaYauX3WR/VYGWwUbHYDTiT8dzbTiGFlw7IhZnGb9M7ZMSuQ1aAyOmY17JkCnwcjJNywGjlyJPbt24f6+nq8+eabmD9/PrZs2YKWlhasXr0aH3/8cZeuyYULF6r/HzNmDEaMGIHi4mJ8/PHHuOyyywAg4usVRdE8buYYJlzvanzLly9HeXm5+ntjYyMKCgqiHm9HwhoT2+iCYpRwjVXy1m4xW27ByobTVsLmazS5w/4aK3Hxulljw44wT14fA+sDcFmB61OKrwAAF0dJREFUNro2DMvpjR9+Lx9F+ZmGXpdKoWEjJNyw8nq9qni9uLgYu3btwurVqzFq1CicPHkSgwcPVo8NBAJYsmQJVq1aFTX8dtlll8Hj8eDw4cO47LLLkJeXhxMnToQdd+rUKdXjlJeXh6qqKs3zdXV1aG9v1xzDvFeMkydPAkCYt4vH5/NpwofJiD5klMx3s2EtbZLYiNR758yI1+30WWDnVrSdDYNlhooanj0Bt8lQoCroToGwz7RR/fHLqy/GtFHhEY+u8NgwK9DpdGDVjy41/Do7eufiQY9bDUVR0NrairKyMnzyySfYt2+f+pOfn49ly5Zhw4YNUV9/8OBBtLe3Y8CAYI+4kpISNDQ04KOPPlKPqaqqQkNDAyZNmqQec+DAAdTU1KjHVFZWwufzYfz48eoxW7du1ZRgqKysRH5+fliIMNVIqfCYzohMZhd4d4uhRnqPngwzNoxqrO6aOhw3jB+EicOyrRiWJZgVr6dSKLCXx4X/94PvYkT/PoZe57ZhVqBZzJakSHYS6rG67777MHPmTBQUFKCpqQnr1q3D5s2bUVFRgezsbGRnay9UHo8HeXl5GDlyJADg888/x9q1a3HNNdcgJycH1dXVWLJkCS699FJMnhxMbR81ahSuvvpqLFy4EC+88AKAYLmF2bNnq+9TWlqKoqIilJWV4YknnsCZM2ewdOlSLFy4EJmZQdfovHnzsGLFCixYsAD33XcfDh8+jEceeQQPPPCALTICrSTcs5G8XzJ9erG+xU0yoZ+beIFQe2YKmQ0FTi/qj+lF0b3WPRH+vBjSWNlQTxZvmEGVzNdBhlrXLAXmaoSEGlYnTpxAWVkZampq4Pf7MW7cOFRUVGD69OlCr/d6vdi0aRNWr16Ns2fPoqCgALNmzcKDDz4IF9e3aO3atVi8eDFKS0sBAHPnzsVzzz2nPu9yufDuu+9i0aJFmDx5MtLS0jBv3jw8+eST6jF+vx8bN27EnXfeieLiYmRlZaG8vFyjn0pVUsljxYdQknmeQCSNlXHxup00VleNysWGg7W4/rJBiR6K5Wg8VmZCgTY6r/Hmx5cPxoWAgikX9Uv0UCyHxOuRSahh9dJLLxk6Xq+rKigowJYtW2K+rm/fvnjttde6PGbw4MF45513ujxm7Nix2Lp1a8y/l2roN+BkvpvlGxMn+x2pWU+kRrxuI0H38Nw++NMiexVxNQv7zjodxrIg7SjUjzczRudhhgX1AnsidsyIjQcJF68T9kcfMkpmT46mm3sSzxMI3zxFPVYupwPTi/qj4Vw7sjO8VgyN6CYsXNvb5zZWZV4VK5PHiuCKqCb5tdAoZFgR3Sas3EIS373wXpxk91jp5WNGst5e/Emx5NEQMmHfWSN9AvnXkceKAELXQzs1nI4HtBpEt0klj5XWsLJPnRozOBwOjdEsWnmd6PkM7psOpwMY0b+3odeRxorgYRorD3kwNZDHiug2qeSxSqVQINBpNHcEC+GKhgKJnk9B33Rs++WVhkO16kZKHisC5lvhJDtkWBHdJpWyAlMpFAgEDUlWuS0V5ptKDPxOmuHXhNLryUNB8H0y6drAQ6tBdBu+aGayN+N0pZjHip8veawI0lgRPHZsOB0PaDWIbqOp7ZTkdy6p6LFipMJ8ia5JpV6BRGzULNEkv+4bhVaD6DZ8badkv3Ph79STfa5AKPTjdSd3lXlCDHYTRaEfAqBK/NGg1SC6De/VSPYvGB8BSfasQCB0bslbRQCh8gxGyzQQyUl272DyQ04fqlfHQ+J1otu4OGsj2TdgdwrNFQiFPklfRQDATycPgT/Ng5v+qSDRQyF6ADf9UwGye/tSon2PEciwIroN76RK9vBYqorXjRQHJZKXAf403Dl1eKKHQfQQ0r1uzL0kP9HD6HHQ1ZLoNrzHKtlFjKlUIBQIhQKpOChBEIQYyb0LEnFBo7FK8vo2qVYglEKBBEEQxkj+nYGwHD5bLLU8Vsk9V4DrBZYCcyUIgpABXS2JbpNKXhy+GKovBXRHLL2ePFYEQRBiJP/OQFiOVtCd3Buw0+kAs62S3TsHhAxJEq8TBEGIQVdLottoDKsUqMis1nZKAS+OK4XmShAEIQMyrIhuk0qhQIAzNlLAY8XqdlFWIEEQhBjJvzMQlqNpaZMCxgYLj6WCxirksUr+uRIEQciArpZEt9E0YU4lj1UKzFUVr5PHiiAIQojk3xkIy+E9VsneKxDgOrqngGFFldcJgiCMQVdLotukmsaKGZKpUHk9lBWY/HMlCIKQQfLvgoTlpFr/PGZIpsJcUynsSRAEIQO6WhLdRlONPAVCgdm9vQCAfr19CR6J9fT2Bfu0+9M8CR4JQRCEPXAnegCE/eENq1TQWD3740vx1ZlzGJKTkeihWM6iqcNR0Dcd14wbkOihEARB2AIyrIhuk2qhwGH9emNYv96JHkZcGJ7bGz+fflGih0EQBGEbkn8XJCwn1cTrBEEQBBEN2gWJbuNyhj5GZFgRBEEQqQztgkS34WVVqaCxIgiCIIho0C5IdBveY0Vp+QRBEEQqQ7sg0W1cKdYrkCAIgiCiQbsg0W1SLSuQIAiCIKJBuyDRbfgmzKSxIgiCIFIZ2gWJbsM3YSaPFUEQBJHK0C5IdBuqY0UQBEEQQRK6C65Zswbjxo1DZmYmMjMzUVJSgvfffz/isbfffjscDgdWrVqleby1tRU/+9nPkJOTg4yMDMydOxfHjh3THFNXV4eysjL4/X74/X6UlZWhvr5ec8zXX3+NOXPmICMjAzk5OVi8eDHa2to0x+zfvx9TpkxBWloaBg4ciIceegiKonR7HeyORmNFoUCCIAgihUnoLjho0CA8+uij2L17N3bv3o0rr7wS1157LQ4ePKg57q233kJVVRXy8/PD3uOee+7B+vXrsW7dOmzfvh1nz57F7NmzEQgE1GPmzZuHffv2oaKiAhUVFdi3bx/KysrU5wOBAGbNmoXm5mZs374d69atw5tvvoklS5aoxzQ2NmL69OnIz8/Hrl278Oyzz+LJJ5/E008/bcHK2AtNE2byWBEEQRCpjNLDyMrKUv7nf/5H/f3YsWPKwIEDlQMHDiiFhYXKM888oz5XX1+veDweZd26depj33zzjeJ0OpWKigpFURSlurpaAaDs3LlTPWbHjh0KAOXvf/+7oiiK8t577ylOp1P55ptv1GP++Mc/Kj6fT2loaFAURVGef/55xe/3K+fPn1ePWblypZKfn690dHQIz6+hoUEBoL5vMnCotlEp/OU7SuEv31G+PHU20cMhCIIgCOmI7t89xr0QCASwbt06NDc3o6SkBADQ0dGBsrIyLFu2DKNHjw57zZ49e9De3o7S0lL1sfz8fIwZMwYffvghAGDHjh3w+/2YMGGCeszEiRPh9/s1x4wZM0bjEZsxYwZaW1uxZ88e9ZgpU6bA5/Npjjl+/DiOHDkSdV6tra1obGzU/CQbTtJYEQRBEASAHiBe379/P3r37g2fz4c77rgD69evR1FREQDgscceg9vtxuLFiyO+tra2Fl6vF1lZWZrH+/fvj9raWvWY3NzcsNfm5uZqjunfv7/m+aysLHi93i6PYb+zYyKxcuVKVdvl9/tRUFAQ9Vi7QuJ1giAIggjiTvQARo4ciX379qG+vh5vvvkm5s+fjy1btqClpQWrV6/Gxx9/DAeXzi+Coiia10R6vYxjlE7helfjW758OcrLy9XfGxsbk864ogKhBEEQBBEk4YaV1+vF8OHDAQDFxcXYtWsXVq9ejVGjRuHkyZMYPHiwemwgEMCSJUuwatUqHDlyBHl5eWhra0NdXZ3Ga3Xy5ElMmjQJAJCXl4cTJ06E/d1Tp06pHqe8vDxUVVVpnq+rq0N7e7vmGL1n6uTJkwAQ5sni8fl8mvBhMkJZgQRBEAQRpMftgoqioLW1FWVlZfjkk0+wb98+9Sc/Px/Lli3Dhg0bAADjx4+Hx+PBxo0b1dfX1NTgwIEDqmFVUlKChoYGfPTRR+oxVVVVaGho0Bxz4MAB1NTUqMdUVlbC5/Nh/Pjx6jFbt27VlGCorKxEfn4+hgwZYtl62AEyrAiCIAgiSEI9Vvfddx9mzpyJgoICNDU1Yd26ddi8eTMqKiqQnZ2N7OxszfEejwd5eXkYOXIkAMDv9+OWW27BkiVLkJ2djb59+2Lp0qUYO3Yspk2bBgAYNWoUrr76aixcuBAvvPACAOC2227D7Nmz1fcpLS1FUVERysrK8MQTT+DMmTNYunQpFi5ciMzMTADBkg0rVqzAggULcN999+Hw4cN45JFH8MADDxgOVSYbORk+fH94DvpmeDVCdoIgCIJINRJqWJ04cQJlZWWoqamB3+/HuHHjUFFRgenTpwu/xzPPPAO3240bb7wRLS0tuOqqq/DKK6/A5XKpx6xduxaLFy9Wswfnzp2L5557Tn3e5XLh3XffxaJFizB58mSkpaVh3rx5ePLJJ9Vj/H4/Nm7ciDvvvBPFxcXIyspCeXm5Rj+VqjidDrx264TYBxIEQRBEkuNQFCodHk8aGxvh9/vR0NCgesMIgiAIgujZiO7fJIghCIIgCIKQBBlWBEEQBEEQkiDDiiAIgiAIQhJkWBEEQRAEQUiCDCuCIAiCIAhJkGFFEARBEAQhCTKsCIIgCIIgJEGGFUEQBEEQhCTIsCIIgiAIgpAEGVYEQRAEQRCSIMOKIAiCIAhCEmRYEQRBEARBSIIMK4IgCIIgCEm4Ez2AVENRFADBLtkEQRAEQdgDtm+zfTwaZFjFmaamJgBAQUFBgkdCEARBEIRRmpqa4Pf7oz7vUGKZXoRUOjo6cPz4cfTp0wcOh0Pa+zY2NqKgoABHjx5FZmamtPcltNA6xwda5/hBax0faJ3jg5XrrCgKmpqakJ+fD6czupKKPFZxxul0YtCgQZa9f2ZmJn1p4wCtc3ygdY4ftNbxgdY5Pli1zl15qhgkXicIgiAIgpAEGVYEQRAEQRCSIMMqSfD5fHjwwQfh8/kSPZSkhtY5PtA6xw9a6/hA6xwfesI6k3idIAiCIAhCEuSxIgiCIAiCkAQZVgRBEARBEJIgw4ogCIIgCEISZFgRBEEQBEFIggwrG/H8889j6NCh6NWrF8aPH49t27Z1efyWLVswfvx49OrVC8OGDcN//dd/xWmk9sbIOv/pT3/C9OnT0a9fP2RmZqKkpAQbNmyI42jti9HPM+ODDz6A2+3G9773PWsHmCQYXefW1lbcf//9KCwshM/nw3e/+1387ne/i9No7Y3RtV67di0uueQSpKenY8CAAfjpT3+K06dPx2m09mPr1q2YM2cO8vPz4XA48NZbb8V8TUL2QYWwBevWrVM8Ho/y4osvKtXV1crdd9+tZGRkKF999VXE47/44gslPT1dufvuu5Xq6mrlxRdfVDwej/LGG2/EeeT2wug633333cpjjz2mfPTRR8qhQ4eU5cuXKx6PR/n444/jPHJ7YXSdGfX19cqwYcOU0tJS5ZJLLonPYG2MmXWeO3euMmHCBGXjxo3Kl19+qVRVVSkffPBBHEdtT4yu9bZt2xSn06msXr1a+eKLL5Rt27Ypo0ePVn74wx/GeeT24b333lPuv/9+5c0331QAKOvXr+/y+ETtg2RY2YTLL79cueOOOzSPXXzxxcq9994b8fhf/OIXysUXX6x57Pbbb1cmTpxo2RiTAaPrHImioiJlxYoVsoeWVJhd55tuukn51a9+pTz44INkWAlgdJ3ff/99xe/3K6dPn47H8JIKo2v9xBNPKMOGDdM89pvf/EYZNGiQZWNMJkQMq0TtgxQKtAFtbW3Ys2cPSktLNY+Xlpbiww8/jPiaHTt2hB0/Y8YM7N69G+3t7ZaN1c6YWWc9HR0daGpqQt++fa0YYlJgdp1ffvllfP7553jwwQetHmJSYGad//znP6O4uBiPP/44Bg4ciIsuughLly5FS0tLPIZsW8ys9aRJk3Ds2DG89957UBQFJ06cwBtvvIFZs2bFY8gpQaL2QWrCbAO+/fZbBAIB9O/fX/N4//79UVtbG/E1tbW1EY+/cOECvv32WwwYMMCy8doVM+us56mnnkJzczNuvPFGK4aYFJhZ58OHD+Pee+/Ftm3b4HbTZUsEM+v8xRdfYPv27ejVqxfWr1+Pb7/9FosWLcKZM2dIZ9UFZtZ60qRJWLt2LW666SacP38eFy5cwNy5c/Hss8/GY8gpQaL2QfJY2QiHw6H5XVGUsMdiHR/pcUKL0XVm/PGPf8R//ud/4vXXX0dubq5Vw0saRNc5EAhg3rx5WLFiBS666KJ4DS9pMPJ57ujogMPhwNq1a3H55ZfjmmuuwdNPP41XXnmFvFYCGFnr6upqLF68GA888AD27NmDiooKfPnll7jjjjviMdSUIRH7IN362YCcnBy4XK6wO5+TJ0+GWeOMvLy8iMe73W5kZ2dbNlY7Y2adGa+//jpuueUW/N///R+mTZtm5TBtj9F1bmpqwu7du7F3717cddddAIIGgKIocLvdqKysxJVXXhmXsdsJM5/nAQMGYODAgfD7/epjo0aNgqIoOHbsGEaMGGHpmO2KmbVeuXIlJk+ejGXLlgEAxo0bh4yMDFxxxRX49a9/TVEFCSRqHySPlQ3wer0YP348Nm7cqHl848aNmDRpUsTXlJSUhB1fWVmJ4uJieDwey8ZqZ8ysMxD0VC1YsAB/+MMfSB8hgNF1zszMxP79+7Fv3z7154477sDIkSOxb98+TJgwIV5DtxVmPs+TJ0/G8ePHcfbsWfWxQ4cOwel0YtCgQZaO186YWetz587B6dRuwS6XC0DIq0J0j4Ttg5ZK4wlpsFTel156SamurlbuueceJSMjQzly5IiiKIpy7733KmVlZerxLM305z//uVJdXa289NJLVG5BAKPr/Ic//EFxu93Kb3/7W6Wmpkb9qa+vT9QUbIHRddZDWYFiGF3npqYmZdCgQcq//uu/KgcPHlS2bNmijBgxQrn11lsTNQXbYHStX375ZcXtdivPP/+88vnnnyvbt29XiouLlcsvvzxRU+jxNDU1KXv37lX27t2rAFCefvppZe/evWpJi56yD5JhZSN++9vfKoWFhYrX61Uuu+wyZcuWLepz8+fPV6ZMmaI5fvPmzcqll16qeL1eZciQIcqaNWviPGJ7YmSdp0yZogAI+5k/f378B24zjH6eeciwEsfoOn/66afKtGnTlLS0NGXQoEFKeXm5cu7cuTiP2p4YXevf/OY3SlFRkZKWlqYMGDBAufnmm5Vjx47FedT24a9//WuX19uesg86FIV8jgRBEARBEDIgjRVBEARBEIQkyLAiCIIgCIKQBBlWBEEQBEEQkiDDiiAIgiAIQhJkWBEEQRAEQUiCDCuCIAiCIAhJkGFFEARBEAQhCTKsCIIgCIIgJEGGFUEQBEEQhCTIsCIIgiAIgpAEGVYEQRASeOONNzB27FikpaUhOzsb06ZNQ3Nzc6KHRRBEnHEnegAEQRB2p6amBj/+8Y/x+OOP47rrrkNTUxO2bdsGasVKEKkHNWEmCILoJh9//DHGjx+PI0eOoLCwMNHDIQgigVAokCAIoptccskluOqqqzB27FjccMMNePHFF1FXV5foYREEkQDIY0UQBCEBRVHw4YcforKyEuvXr0dtbS2qqqowdOjQRA+NIIg4QoYVQRCEZAKBAAoLC1FeXo7y8vJED4cgiDhC4nWCIIhuUlVVhU2bNqG0tBS5ubmoqqrCqVOnMGrUqEQPjSCIOEOGFUEQRDfJzMzE1q1bsWrVKjQ2NqKwsBBPPfUUZs6cmeihEQQRZygUSBAEQRAEIQnKCiQIgiAIgpAEGVYEQRAEQRCSIMOKIAiCIAhCEmRYEQRBEARBSIIMK4IgCIIgCEmQYUUQBEEQBCEJMqwIgiAIgiAkQYYVQRAEQRCEJMiwIgiCIAiCkAQZVgRBEARBEJIgw4ogCIIgCEISZFgRBEEQBEFI4v8Dui0lHS1/IVMAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", "text/plain": [ "
" ] @@ -156,7 +150,6 @@ "plt.figure()\n", "plt.plot(s_space, off_diagonal_norm_diff[0,:],label= 'No normalization')\n", "plt.plot(s_space, off_diagonal_norm_diff[1,:],label= 'Normalization')\n", - "plt.plot(s_space, off_diagonal_norm_diff[2,:],label= 'Gradient Ascent')\n", "plt.xlabel('s')\n", "plt.ylabel(r'$||\\sigma(H_0)||-||\\sigma(H_k)||$')\n", "plt.legend()\n", @@ -171,13 +164,7 @@ "plt.title('D normalized')\n", "plt.plot(s_space, potential[1,:],label= 'Normalization')\n", "plt.xlabel('s')\n", - "plt.ylabel('Least squares cost function')\n", - "\n", - "plt.figure()\n", - "plt.title('D optimized')\n", - "plt.plot(s_space, potential[2,:],label= 'Gradient Ascent')\n", - "plt.xlabel('s')\n", - "plt.ylabel('Least squares cost function')" + "plt.ylabel('Least squares cost function')\n" ] }, { @@ -262,20 +249,20 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.5|INFO|2024-03-21 15:05:50]: Using qibojit (numba) backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-01 22:42:14]: Using numpy backend on /CPU:0\n" ] } ], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 7\n", @@ -285,21 +272,38 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", + "cost = DoubleBracketCost.least_squares\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" ] }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:14<00:00, 35.54trial/s, best loss: 334743.9033516353]\n", + "100%|██████████| 500/500 [00:14<00:00, 34.22trial/s, best loss: 332247.1783501899] \n", + "100%|██████████| 500/500 [00:10<00:00, 48.63trial/s, best loss: 330843.2174669805] \n", + "100%|██████████| 500/500 [00:12<00:00, 40.89trial/s, best loss: 329875.86478824326]\n", + "100%|██████████| 500/500 [00:09<00:00, 52.81trial/s, best loss: 329131.1483035261] \n", + "100%|██████████| 500/500 [00:12<00:00, 41.02trial/s, best loss: 328494.15894863644]\n", + "100%|██████████| 500/500 [00:12<00:00, 41.42trial/s, best loss: 328037.3084475153]\n", + "100%|██████████| 500/500 [00:31<00:00, 15.94trial/s, best loss: 327764.40651092166]\n", + "100%|██████████| 500/500 [00:26<00:00, 18.99trial/s, best loss: 327524.34657881147]\n", + "100%|██████████| 500/500 [00:30<00:00, 16.47trial/s, best loss: 327316.4158512455]\n" + ] + } + ], "source": [ "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", "dbi_eval = deepcopy(dbi)\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "flows = 50\n", + "flows = 10\n", "off_diagonal_norm = np.empty(flows+1)\n", "off_diagonal_norm[0] = dbi_eval.off_diagonal_norm\n", "for i in range(flows):\n", @@ -310,22 +314,22 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 81, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -352,20 +356,20 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.5|INFO|2024-03-21 14:20:15]: Using qibojit (numba) backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-01 22:50:13]: Using numpy backend on /CPU:0\n" ] } ], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 7\n", @@ -375,19 +379,19 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", + "cost = DoubleBracketCost.least_squares\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "nqubits = [3,4,5,6,7]\n", - "iterations = 100\n", + "iterations = 50\n", "step = 1e-2\n", "differences = np.empty((len(nqubits),iterations+1))\n", "loss_max = np.empty(len(nqubits))\n", @@ -396,35 +400,35 @@ " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", "\n", " # define the least-squares cost function\n", - " cost = DoubleBracketCostFunction.least_squares\n", + " cost = DoubleBracketCost.least_squares\n", " # initialize class\n", " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - " loss_max [q] = dbi.least_squares(D=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", - " d = np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q]))\n", - " d_analytic, loss_analytic, grad_analytic, diags_analytic = gradient_ascent(dbi, d,step, iterations)\n", - " d = np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q]))\n", - " d_numerical, loss_numerical, grad_numerical, diags_numerical = gradient_ascent(dbi, d,step, iterations, analytic=False)\n", + " loss_max [q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", + " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", + " d_analytic, loss_analytic, grad_analytic, diags_analytic = gradient_descent(dbi, params, iterations, step)\n", + " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", + " d_numerical, loss_numerical, grad_numerical, diags_numerical = gradient_descent(dbi, params,iterations,step, analytic=False)\n", " differences[q,:] = loss_analytic - loss_numerical\n" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 40, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -493,25 +497,11 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", + "cost = DoubleBracketCost.least_squares\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n" ] }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "dbi_eval = deepcopy(dbi)\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "step = 1e-2\n", - "iterations = 100\n", - "\n", - "\n" - ] - }, { "cell_type": "code", "execution_count": 75, @@ -525,7 +515,7 @@ "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", "step = 1e-2\n", "iterations = 100\n", - "d_optimized, loss, grad, diags = gradient_ascent(dbi_eval, d,step, iterations,analytic=False)\n", + "d_optimized, loss, grad, diags = gradient_descen(dbi_eval, d,step, iterations,analytic=False)\n", "off_diagonal_norm = np.empty((flows+1,3))\n", "off_diagonal_norm[0] = dbi_eval.off_diagonal_norm\n", "for i in range(flows):\n", @@ -584,20 +574,20 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.5|INFO|2024-03-21 16:37:52]: Using qibojit (numba) backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-01 23:01:13]: Using numpy backend on /CPU:0\n" ] } ], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 7\n", @@ -607,14 +597,14 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", + "cost = DoubleBracketCost.least_squares\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -632,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -641,13 +631,13 @@ "Text(0, 0.5, 'Loss function')" ] }, - "execution_count": 7, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -794,13 +784,13 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 239, "metadata": {}, "outputs": [], "source": [ "def delta_to_d(delta):\n", - " d = np.empty(len(delta))\n", - " d[0] = 0\n", + " d = np.empty(len(delta)+1)\n", + " d[0] = 1\n", " for i in range(len(d)-1):\n", " d[i+1] = d[i] + delta[i]\n", " return np.diag(d)\n", @@ -811,16 +801,16 @@ " delta[i] = d[i+1,i+1]-d[i,i]\n", " return delta\n", "\n", - "def polynomial(x,degree,coefficients):\n", + "def polynomial(x, coefficients):\n", " y = np.empty(len(x))\n", - " for i in range(degree):\n", + " for i in range(len(coefficients)):\n", " y += coefficients[i]*x**i\n", " return y\n", "\n", - "def gradient_delta_polynomial(dbi, degree, coefficients,h=1e-5):\n", - " grad = np.empty(degree)\n", + "def gradient_delta_polynomial(dbi, coefficients,h=1e-5):\n", + " grad = np.empty(len(coefficients))\n", " dim = dbi.h.matrix.shape[0]\n", - " delta = polynomial(np.linspace(0,1,dim),degree,coefficients)\n", + " delta = polynomial(np.linspace(0,1,dim),coefficients)\n", " d = delta_to_d(delta)\n", " dbi_eval = deepcopy(dbi)\n", " angle = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", @@ -829,7 +819,7 @@ " for i in range(len(grad)):\n", " new_coeff = np.copy(coefficients)\n", " new_coeff[i] += h\n", - " new_delta = polynomial(np.linspace(0,1,dim),degree,new_coeff)\n", + " new_delta = polynomial(np.linspace(0,1,dim), new_coeff)\n", " new_d = delta_to_d(new_delta)\n", " dbi_eval = deepcopy(dbi)\n", " angle = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=new_d)\n", @@ -843,7 +833,7 @@ "def optimize_poly(dbi, step, iterations, degree):\n", " coefficients = np.random.rand(degree)\n", " dim = dbi.h.matrix.shape[0]\n", - " delta = polynomial(np.linspace(0,1,dim),degree,coefficients)\n", + " delta = polynomial(np.linspace(0,1,dim),coefficients)\n", " d = delta_to_d(delta)\n", " loss = np.empty(iterations)\n", " grad = np.empty(degree)\n", @@ -852,9 +842,9 @@ " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", " dbi_eval(step_poly,d=d)\n", " loss[i] = dbi_eval.off_diagonal_norm\n", - " grad = gradient_delta_polynomial(dbi_eval, degree, coefficients)\n", + " grad = gradient_delta_polynomial(dbi_eval, coefficients)\n", " coefficients -= step*grad\n", - " delta = polynomial(np.linspace(0,1,dim),degree,coefficients)\n", + " delta = polynomial(np.linspace(0,1,dim),coefficients)\n", " d = delta_to_d(delta)\n", " return coefficients, loss, grad\n", "\n" @@ -862,16 +852,16 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 106, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" }, @@ -887,7 +877,17 @@ }, { "data": { - "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -908,9 +908,665 @@ "plt.xlabel('Index')\n", "plt.ylabel(r'$\\Delta$')\n", "\n", - "d, loss, grad = optimize_poly(dbi, 1e-3, 100, 3)\n", + "coefficients, loss, grad = optimize_poly(dbi, 1e-3, 100, 2)\n", + "plt.figure()\n", + "plt.plot(loss)\n", + "poly = polynomial(np.linspace(0,1,2**7),coefficients)\n", + "plt.figure()\n", + "plt.plot(poly)" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 286, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nqubits = 7\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=10.0)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "matrix = dbi.h.matrix\n", + "eigenenergies = np.diag(np.linalg.eigh(matrix)[0])\n", + "delta = d_to_delta(eigenenergies)\n", + "def remove_zeros(vector,epsilon = 3):\n", + " return vector[vector>epsilon]\n", + "delta_plus = remove_zeros(delta)\n", + "x = np.linspace(0,1,len(delta))\n", + "p1 = np.polynomial.Polynomial.fit(x,delta,4)\n", + "plt.figure()\n", + "plt.plot(x,delta,'.')\n", + "plt.plot(x,p1(x))\n", + "x = np.linspace(0,1,len(delta_plus))\n", + "p2 = np.polynomial.Polynomial.fit(x,delta_plus,4)\n", + "x = np.linspace(0,1,100)\n", + "plt.plot(x,p2(x))\n", + "plt.xlabel('Index')\n", + "plt.ylabel(r'$\\Delta$')\n", + "\n", + "flows = 200\n", + "off_diagonal_norm = np.empty((flows+1,2))\n", + "x = np.linspace(0,1,2**nqubits-1)\n", + "delta = p2(x)\n", + "d = delta_to_d(delta)\n", + "d /= d[-1,-1] \n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "off_diagonal_norm[0,:] = dbi.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", + " dbi(step_poly,d=d)\n", + " off_diagonal_norm[i+1,0] = dbi.off_diagonal_norm\n", + "\n", + "delta = p1(x)\n", + "d = delta_to_d(delta)\n", + "d /= d[-1,-1]\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "for i in range(flows):\n", + " step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", + " dbi(step_poly,d=d)\n", + " off_diagonal_norm[i+1,1] = dbi.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(off_diagonal_norm[:,0],label='Polynomial over great differences')\n", + "plt.plot(off_diagonal_norm[:,1],label='Polynomial over small differences')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = np.linspace(-10,10,100)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pauli Ansatz" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-04-02 09:51:24]: Using numpy backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 7\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCost.least_squares\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def Z_decomposition(matrix):\n", + " Z = np.array([[1, 0], [0, -1]])\n", + " params = np.empty(int(np.log2(matrix.shape[0])))\n", + " for i in range(len(params)):\n", + " Z_i = np.kron(np.eye(2**(i)), np.kron(Z, np.eye(2**(len(params)-i-1))))\n", + " params[i] = np.trace(matrix@Z_i) \n", + " return params\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "17920.0\n" + ] + } + ], + "source": [ + "nqubits = [7]\n", + "iterations = 100\n", + "step = 1e-3\n", + "differences = np.empty((len(nqubits),iterations+1))\n", + "loss_pauli = np.empty((len(nqubits),iterations+1))\n", + "loss_full = np.empty((len(nqubits),iterations+1))\n", + "loss_max = np.empty(len(nqubits))\n", + "for q in range(len(nqubits)):\n", + " # define the hamiltonian\n", + " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", + "\n", + " # define the least-squares cost function\n", + " cost = DoubleBracketCost.least_squares\n", + " # initialize class\n", + " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + " loss_max[q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", + " params = np.linspace(2**nqubits[q],1,2**nqubits[q])\n", + " d_full, loss_full[q,:], grad_full, diags_full = gradient_descent(dbi, params, iterations, step,analytic=False, ansatz = 'Full')\n", + " params = np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])/2**nqubits[q])\n", + " params = Z_decomposition(params)\n", + " params = np.linspace(nqubits[q],1,nqubits[q])\n", + " d = d_ansatz(params, type='Pauli')\n", + " print(np.linalg.norm(d)**2)\n", + " d_pauli, loss_pauli[q,:], grad_pauli, diags_pauli = gradient_descent(dbi, params, iterations, step, analytic=False, ansatz = 'Pauli')\n", + " differences[q,:] = loss_full[q,:] - loss_pauli[q,:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Parameters')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.title('Full ansatz')\n", + "plt.plot(loss_full[0,:],label='nqubits = '+str(nqubits[0]))\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel('Loss for Full ansatz')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.title('Pauli ansatz')\n", + "plt.plot(loss_pauli[0,:],label='nqubits = '+str(nqubits[0]))\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel('Loss for Pauli ansatz')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.title('Evolution of parameters')\n", + "for i in range(nqubits[0]):\n", + " plt.plot(diags_pauli[i,:])\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel('Parameters')" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "nqubits = 7\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent(dbi, params, 100,1e-2, analytic=False, ansatz = 'Full')\n", + "flows = 50\n", + "off_diagonal_norm = np.empty((flows+1,2))\n", + "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", + "\n", + "\n", + "\n", + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(7,1,7)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent(dbi, params, 30, 1e-3, analytic=False, ansatz = 'Pauli')\n", + "best = np.argmin(loss_opt)\n", + "d_opt = d_ansatz(diags_opt[:,best], type='Pauli')\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ExES8vb258sorLbfHLF/TvfUdBJPJRGNjIwBDhw4lIyODb7/9lqVLl3L11VczefJkPv7441P6Xidy2223kZWVxbp16yzXyloUbNqThy+YXMFooF+oQfI+9bMRETllJtMZ3Q6yB19fXxITE49Zn5KSQmNjI88++ywuLuabIh9++GGrfcLDw8nNzcUwDEwmEwCpqalnVc/KlSu54YYbuPzyywFzC86+fftO+zgBAQHMnDmTmTNncuWVV3LRRRdRVFR0St/Lw8ODhoaGY4753HPP8eGHH7Jq1SpCQ0NP/8udJgWb9mQymVttqoroGWD+h7s7X8FGRKSzSExMpK6ujpdeeolp06axcuVKXnvttVb7TJw4kYKCAhYsWMCVV17Jd999x7fffktAQMAZn7dnz558+umnTJs2DZPJxCOPPGJpiTlVzz33HNHR0QwZMgQXFxc++ugjoqKiCAoKOqXvFR8fT3l5OcuWLWPQoEH4+PiwatUq7r//fl5++WXCwsLIzc0FzJ2tAwMDz/j7noj62LS3pttRPfzMAzWpxUZEpPMYNGgQzz33HE899RQDBgzg3//+d6tHpgH69u3LK6+8wssvv8ygQYNYt24d995771md97nnniM4OJgxY8Ywbdo0pkyZwtChQ0/rGP7+/ixYsIDhw4czYsQI9u3bxzfffIOLi8spfa8xY8bwpz/9iZkzZxIeHs6CBQtYsWIFDQ0N/OlPfyI6Otqy3HXXXWf1fU/EZHSymRpLS0sJDAykpKTkrNJxm16fADmpFE7/F8P+44qbi4mdf7sId1dlSBGRlqqrq8nIyCAhIQEvLy97lyMO4ES/E6f691t/bdtb0wzfwS5V+Hi4Ut9okFlkvZ7wIiIicoSCTXtrfuS7ppTu4eZOcOpnIyIiYhsKNu2t+ZHv6mISw/0A9bMRERGxFQWb9mYJNiX0aA42+RqkT0RExBYUbNpby2AToRYbEZGT6WTPsMgJtMfvgoJNe2uaL4qq4hYtNuX6F1dE5Ciurq4Ax4yOK51X8/QMR4+QfDo0QF97a9FiEx/mg4sJymrqKSirISJAjzOKiDRzc3PDx8eHgoIC3N3dLSPaSudjGAaVlZXk5+cTFBRkCb1nQsGmvbUINp5urnQL8WFfYSW7C8oVbEREWjCZTERHR5ORkcH+/fvtXY44gKCgoGNmBz9dCjbtrflWVHUJAD3C/dhXWMmeggrG9AizX10iIg7Iw8ODnj176naU4O7uflYtNc0UbNpbixYbgMQIP5b9ls8ejWUjInJcLi4uGnlY2o1uaLa3lsHGMI50INaTUSIiIlanYNPemoON0QC1FfSIMI8+rBYbERER61OwaW/u3uDS9Jha9ZFHvrNLqqmoqbdjYSIiIs5Pwaa9mUytbkcF+XgQ5ucBwN4CjUAsIiJiTQo21nBUB+Lu6mcjIiJiEwo21uAdZP7Z4pFvULARERGxNgUbaziqxaZHeFMHYgUbERERq1KwsYbjjGUDsFtPRomIiFiVQwWbhoYGHnnkERISEvD29qZHjx787W9/azWBpGEYPProo0RHR+Pt7c3kyZNJT0+3Y9XHcUyLjTnY7DtUSX1Do72qEhERcXoOFWyeeuopXn31Vf7v//6PnTt38tRTT7FgwQJeeuklyz4LFixg4cKFvPbaa6xduxZfX1+mTJlCdXW1HSs/SnOwqSoGoEuQN55uLtQ2NHLgcJX96hIREXFyDhVsVq1axfTp07nkkkuIj4/nyiuv5MILL2TdunWAubXmhRde4OGHH2b69OkMHDiQd999l+zsbD7//HP7Ft/SUS02Li4my5NRuh0lIiJiPQ4VbMaMGcOyZcvYtWsXAJs3b2bFihVMnToVgIyMDHJzc5k8ebLlM4GBgYwaNYrVq1cf95g1NTWUlpa2WqzOEmyKLaua+9moA7GIiIj1ONQkmA8++CClpaX06dMHV1dXGhoaeOKJJ7juuusAyM3NBSAyMrLV5yIjIy3bjjZ//nwef/xx6xZ+tKNm+AY9GSUiImILDtVi8+GHH/Lvf/+b999/n40bN/LOO+/wzDPP8M4775zxMR966CFKSkosS1ZWVjtW3IbjBpvmFhuNPiwiImItDtVic9999/Hggw9yzTXXAHDOOeewf/9+5s+fz6xZs4iKigIgLy+P6Ohoy+fy8vIYPHjwcY/p6emJp6en1Wtv5ag+NnAk2OzOL8cwDEwmk21rEhER6QQcqsWmsrISF5fWJbm6utLYaH5EOiEhgaioKJYtW2bZXlpaytq1a0lKSrJprSd0nGDTPdwXkwlKquoorKi1U2EiIiLOzaFabKZNm8YTTzxBt27d6N+/P5s2beK5557jpptuAsBkMjFnzhz+/ve/07NnTxISEnjkkUeIiYnhsssus2/xLbUMNo2N4OKCl7srXYO9ySqqYk9+OWF+Nm5FEhER6QQcKti89NJLPPLII9x+++3k5+cTExPD//7v//Loo49a9rn//vupqKjgj3/8I8XFxYwbN47vvvsOLy8vO1Z+lOZggwG1ZZb3PcL9zMGmoIJR3UPtV5+IiIiTMhkth/XtBEpLSwkMDKSkpISAgADrnehvEdBQA3O2QlA3AJ767jdeXb6HSwdG83+/H2q9c4uIiDiZU/377VB9bJzKUTN8A0wdYO78vHRnHuU19XYoSkRExLkp2FjLcToQn9MlkIQwX6rrGvlxx/HH3REREZEzp2BjLccJNiaTiemDYwD4b2q2PaoSERFxago21nKcYAPwu0HmYPNr+iEOldfYuioRERGnpmBjLUfN8N2se7gfA7sG0tBo8M3WHNvXJSIi4sQUbKyljRYbgOmDuwC6HSUiItLeFGys5QTBZtrAaEwmSNl/mKyiShsXJiIi4rwUbKzlOBNhNosI8GJMD/MAfV9sVquNiIhIe1GwsZYTtNjAkdtRn286SCcbI1FERMRqFGys5STB5qIBUXi4uZCeX87OnDIbFiYiIuK8FGys5STBJsDLnfN6RwDw380HbVWViIiIU1OwsRZLH5viNndpHqzvy9RsGht1O0pERORsKdhYy0labAAm9YnA39ON7JJq1u8rslFhIiIizkvBxlqag01NKTQ2HH8Xd1cuapoY8796OkpEROSsKdhYS3OwAXO4aUPz01HfbM2htr7R2lWJiIg4NQUba3HzAHcf8+sT3I5K6hFKuL8nxZV1/JpeYKPiREREnJOCjTWdQj8bVxcT0waaOxF/rikWREREzoqCjTWdQrCBI09H/bgjl4qaemtXJSIi4rQUbKypjRm+jzawayAJYb5U1zXy444869clIiLipBRsrOkUW2xMJhO/G9R8O0qD9YmIiJwpBRtrOsVgA/C7pttRv6YforC8xppViYiIOC0FG2s6jWDTI9yPc7oE0tBo8M3WHCsXJiIi4pwUbKzJMq3CyYMNHOlErKejREREzoyCjTWdRosNwLRBMZhMkLL/MFlFlVYsTERExDkp2FjTaQabyAAvkrqHAvDpRnUiFhEROV0KNtZkCTbFp/yRq4fHAvCvNfuprjv+HFMiIiJyfAo21nSaLTYAlwyMJibQi0PlNXy2Sa02IiIip0PBxprOINi4u7pw07gEAN78dS+NjYY1KhMREXFKCjbWdAbBBmDmiFj8Pd3YW1DBst/yrVCYiIiIc1KwsSbvYPPP2nJoOPU5oPy93Pn96G4AvPHLHmtUJiIi4pQUbKzJM+DI65rS0/roTWMTcHc1sX7fYTZmHm7nwkRERJyTgo01ubqBh5/5ddXphZPIAC+mD+4CwBvJe9u7MhEREaekYGNtZ9jPBuCP47sD8P2OXPYdqmjPqkRERJySgo21nUWw6RXpz6Te4RgGvLVCrTYiIiIno2BjbWcRbABubWq1+WjDAc36LSIichIKNtZ2lsEmqXso53QJpKa+kXdX72/HwkRERJyPgo21neYM30czmUyWvjbvrt5HVa2mWRAREWmLgo21nWWLDcDUAVF0DfbmcGUdH2880E6FiYiIOB8FG2s7g4kwj+bm6sItTdMsvPXrXho0zYKIiMhxKdhYWzu02ABcPSKWIB939hdW8sP23HYoTERExPko2FhbOwUbHw83rh8dB8Drv+zFMNRqIyIicjSHCjbx8fGYTKZjltmzZwNQXV3N7NmzCQ0Nxc/PjxkzZpCXl2fnqk+inYINwB+S4vFwcyE1q5gN+zXNgoiIyNEcKtisX7+enJwcy/Ljjz8CcNVVVwEwd+5cvvzySz766COSk5PJzs7miiuusGfJJ9eOwSbc35MZQ83TLLyuaRZERESO4WbvAloKDw9v9f7JJ5+kR48eTJgwgZKSEt5++23ef/99zjvvPAAWLVpE3759WbNmDaNHjz7uMWtqaqipOTKwXWnp6U1Geda8g8w/2yHYANxybneWrM9i6c48dueXkxjh1y7HFRERcQYO1WLTUm1tLe+99x433XQTJpOJlJQU6urqmDx5smWfPn360K1bN1avXt3mcebPn09gYKBliY2NtUX5R7Rjiw1Aj3A/JveNBGDBd7+pr42IiEgLDhtsPv/8c4qLi7nhhhsAyM3NxcPDg6CgoFb7RUZGkpvb9lNCDz30ECUlJZYlKyvLilUfR3OwqauE+tp2OeS9F/bGzcXEDzvy+HabnpASERFp5rDB5u2332bq1KnExMSc1XE8PT0JCAhotdiUZ4vztVOrTe8of26f2AOAR/+7neLK9glMIiIiHZ1DBpv9+/ezdOlSbrnlFsu6qKgoamtrKS4ubrVvXl4eUVFRNq7wNLi4Hgk37RRsAGafl0hihB+Hymv4+9c72+24IiIiHZlDBptFixYRERHBJZdcYlk3bNgw3N3dWbZsmWVdWloamZmZJCUl2aPMU9fO/WwAPN1ceWrGOZhM8HHKAX5NL2i3Y4uIiHRUDhdsGhsbWbRoEbNmzcLN7chDW4GBgdx8883cfffd/Pzzz6SkpHDjjTeSlJTU5hNRDqMdplU4nmFxIcxKigfgoU+3Ullb367HFxER6WgcLtgsXbqUzMxMbrrppmO2Pf/881x66aXMmDGD8ePHExUVxaeffmqHKk/TWc7wfSL3TelNlyBvDhyu4pnvd7X78UVERDoShws2F154IYZh0KtXr2O2eXl58fLLL1NUVERFRQWffvqpY/evaWaFW1HNfD3deOLyAQAsWpXBpkyNSCwiIp2XwwUbp2SlW1HNJvaO4PIhXTAMeOCTLdTWN1rlPCIiIo5OwcYWrNhi0+yRS/sR6uvBrrxyXlm+22rnERERcWQKNrZgg2AT4uvBY7/rD8DLP+9mV16Z1c4lIiLiqBRsbMEGwQZg2sBozu8TQV2DwQOfbKGhUdMtiIhI56JgYws2CjYmk4m/Xz4AP083NmUW886qfVY9n4iIiKNRsLGFdp7h+0SiA715cGofAJ7+Po2sokqrn1NERMRRKNjYgo1abJr9fmQ3RiaEUFXXwOz3N1Jd12CT84qIiNibgo0tNAebqmKbnM7FxcSzVw0i2MedLQdKuO/jLRiG+tuIiIjzU7CxBRu32ADEhvjwynXDcHMx8eXmbF5Zvsdm5xYREbEXBRtbaA42DTVQV22z0yb1COWv082jEj/9fRrfb8+12blFRETsQcHGFjz8AZP5tQ1bbQB+P6obs5LiAJj7n1R25pTa9PwiIiK2pGBjCy4u4BVgfm3jYAPmUYnHJYZRWdvALe9s4FB5jc1rEBERsQUFG1ux4gzfJ+Pm6sLLvx9KQpgvB4uruO29FM0nJSIiTknBxlbs0IG4pUAfd978w3D8vdxYv+8wD3++VU9KiYiI01GwsRUrz/B9KhIj/Hjp2iG4mODDDQf458p9dqtFRETEGhRsbMUBgg3AxN4R/OWSfgA88fUOkncV2LUeERGR9qRgYyt27GNztJvGxnP18K40GnDH+xvZna+ZwEVExDko2NiKnfvYtGQymfjbZQMYER9MWXU91765lt355fYuS0RE5Kwp2NiKAwUbAE83V16/fjh9ovwpKKvhmjfWqOVGREQ6PAUbW3GwYAMQ4uvB+7eOpm90AIfKzeFmV57CjYiIdFwKNrbiHWT+6UDBBprCzS2j6BcdwKHyWq59Yw1puQo3IiLSMSnY2IqNZ/g+HcG+Hrx/6yj6xwRQWFHLtW+u0dQLIiLSISnY2IoD3opqKcjHg/dvGc05XQIpqqjl92+uYUe2wo2IiHQsCja24uDBBsyjE793yygGdQ3kcGUdv39rDduzHbdeERGRoynY2ErLYOPAUxkEervz7s2jGBQbRHFlHb9/cy3bDirciIhIx6BgYyvNwaaxDuqq7FvLSQR6u/Ovm0cypFsQJVV1/P7NNaTsP2zvskRERE5KwcZWPPzA5Gp+7cC3o5oFeLnz7k0jGdotiNLqeq59Yw0frMu0d1kiIiInpGBjKyZTh+hn05K/l/m21JT+kdQ2NPLQp1t58JMt1NQ32Ls0ERGR41KwsSUHmQjzdPh5uvHqdcO4b0pvTCZYsj6Lq19fQ06JY99OExGRzknBxpY6WItNMxcXE7MnJbL4xpEEeruzOauYaS+tYM3eQnuXJiIi0oqCjS110GDTbEKvcL68Y1zTFAy1XPfWWv65IgPDgZ/yEhGRzkXBxpY6eLAB6Bbqw6e3jWH64BgaGg3++tUO5vwnlapa9bsRERH7U7CxJcu0Ch370WlvD1demDmYRy7th6uLif+mZnPFq6vYU1Bu79JERKSTczudnRMSEjCZTKd9kjlz5nDnnXee9uecTkh388+czfatox2YTCZuHpdAv+gA7nh/IztzSrnohV+4aVwCfz6vJ36ep/WrJSIi0i5Mxml0kEhOTj6jk8THxxMXF3dGn21vpaWlBAYGUlJSQkBAgG1PnrUe3p4M3iFw3x5wcY4Gs5ySKv7fp1v5Oa0AgMgAT/7fxX353aCYMwrCIiIiRzvVv9+nFWycgV2DTUMdPBkHdRVw2yqI7G/b81vZsp15/PWrHewvrARgZHwI837Xn34xNr7OIiLidE7177dzNBl0FK7u0G2U+fW+FfatxQrO7xvJ93PGc++FvfByd2HdviIufelXHv3vNoora+1dnoiIdAIKNrYWP8780wmDDYCXuyt3nNeTZfdM5JJzomk04N3V+5n0zHI+WJdJQ2OnaiAUEREbO61bUc7Qediut6IAMtfCPy8En1C4d7fT9LNpy6rdh5j35XZ25ZmfmOod6c/dF/biwn6R6n8jIiKnzCp9bNR5uB3U18JTcVBXCbethsh+tq/BxuoaGvnX6v28sHQXpdX1AAzqGsg9F/bm3J5hCjgiInJSHbbz8MGDB3nggQf49ttvqaysJDExkUWLFjF8+HAADMPgscce480336S4uJixY8fy6quv0rNnz1M6vt2DDcC702Hvcrj4GRh5q31qsIOSyjre+HUPi1buo7JpQL9RCSHcN6U3w+ND7FydiIg4Mpt0Hq6rqyMrK4u0tDSKiorO5lAAHD58mLFjx+Lu7s63337Ljh07ePbZZwkODrbss2DBAhYuXMhrr73G2rVr8fX1ZcqUKVRXV5/1+W3G0s/mV/vWYWOBPu7cN6UPv9w/iZvGJuDh6sLajCKufG01Nyxax7aDHXdEZhERcQyn3WJTVlbGe++9x5IlS1i3bh21tbUYhoHJZKJr165ceOGF/PGPf2TEiBGnXcyDDz7IypUr+fXX4//BNwyDmJgY7rnnHu69914ASkpKiIyMZPHixVxzzTUnPYdDtNhkroF/TgGfMLhvN3TSWzHZxVW89FM6H244YOlUPHVAFHMm96J3lL+dqxMREUdilRab5557jvj4eBYtWsTkyZP5/PPPSU1NZdeuXaxevZrHHnuM+vp6LrzwQi666CLS09NPq+gvvviC4cOHc9VVVxEREcGQIUN48803LdszMjLIzc1l8uTJlnWBgYGMGjWK1atXH/eYNTU1lJaWtlrsLmYouHlD5SEoSLN3NXYTE+TN/CsGsuzuCVw2OAaTCb7dlsuUF37hlnc2sDGzY089ISIitndaLTbXXnstDz/8MP37n3hguZqaGhYtWoSHhwc33XTTKRfj5eUFwN13381VV13F+vXrueuuu3jttdeYNWsWq1atYuzYsWRnZxMdHW353NVXX43JZOI///nPMcecN28ejz/++DHr7dpiA/DO7yAjudP1szmRtNwyXly2i2+35dL8WzmmRyizJyUypkeoOhmLiHRiHbLzsIeHB8OHD2fVqlWWdXfeeSfr169n9erVZxRsampqqKmpsbwvLS0lNjbW/sEmeQH8/AT0vxyuWmy/OhzQnoJyXlu+h882HaS+6RbVoNggbp/Ygwv6RuLiooAjItLZdMiRh6Ojo+nXr/Xjz3379iUzMxOAqKgoAPLy8lrtk5eXZ9l2NE9PTwICAlotDqHlQH2Oky0dQo9wP56+ahDJ90/ihjHxeLm7sDmrmP/9VwpTXviFzzYdoK6h0d5lioiIA2q3YPPWW2+d9THGjh1LWlrrPie7du2yjIGTkJBAVFQUy5Yts2wvLS1l7dq1JCUlnfX5barLMHDzgooCOLTL3tU4pC5B3sz7XX9WPHAesyf1wN/TjfT8cub+ZzMTFvzMa8l7KKmss3eZIiLiQNot2Hz11Vf89NNPlveVlZWn9JRSS3PnzmXNmjX84x//YPfu3bz//vu88cYbzJ49GwCTycScOXP4+9//zhdffMHWrVv5wx/+QExMDJdddll7fRXbcPOErk1Pjjnp9ArtJczPk/um9GHlQ+dx35TehPl5kF1SzZPf/kbSk8t49L/byDhUYe8yRUTEAbRbH5vS0lIuvvhi3nrrLVxcXPj973/P7NmzufHGG0/rOF999RUPPfQQ6enpJCQkcPfdd3PrrUc61zYP0PfGG29QXFzMuHHjeOWVV+jVq9cp12n3x72bLX8Klv8D+l8BVy2yby0dSHVdA19szuafKzL4LbcMMD8xf36fCG4am0CSOhqLiDgdm3Uenjt3LoMHD2bQoEF4eXlx3XXX0djYyKJFixg8ePDZHNoqHCrY7FsJiy8G3wi4d1enHc/mTBmGwao9hby9IoOffsu3rO8bHcBNY+OZNigGL3dXO1YoIiLtxWbB5osvvmDLli1s2bKFnTt3cvDgQZKSkhg/fjwDBgzgkksuOZvDtzuHCjZ11fBkN2iogTs2QNipTQshx9pbUM6ilfv4OOUAVXXm6RoCvd25fEgXrhkZS58oB+k0LiIiZ8Ruj3tXV1ezbds2tmzZwtatW3n++efb8/BnzaGCDcDiS81TK1z6PAw/9TF/5PiKK2tZsj6Ld1ftI7vkyDQbg2ODuGZELJcOisHP082OFYqIyJmwWrDx9/dnyJAhDBs2jKFDhzJ06FD69evXYfo0OFyw+Xk+JD8JA66EK9+2dzVOo6HR4Nf0Apasy2LpzjzLeDi+Hq5MGxTDzBGxDI4N6jC/tyIinZ3Vgs0rr7xCSkoKGzZsYMeOHTQ2NuLt7c3AgQNbhZ1Bgwad9ZewBocLNhm/wjuXgl8U3POb+tlYQUFZDZ9uPMB/1mext8XTU70j/blyWFemDYohKtDLjhWKiMjJ2ORWVFVVFb6+vjzwwAMUFRWxceNGtm3bRm1tLQ0NDWd6WKtyuGDTqp9NCoQl2rsip2UYBusyivjP+iy+3ppDTb15kD+TCUYnhDJ9cAxTB0QT6ONu50pFRORoNutj4+LiQmpqKgMHDgSgvr6eHTt2WN47GocLNgCLLob9K2HaizDsBntX0ymUVNXx5eZs/pt6kPX7jky26eHqwsTe4Vw2pAvn9YnQU1UiIg7iVP9+t3svSjc3N4cNNQ4rfpw52OxboWBjI4He7vzP6Dj+Z3QcBw5X8sXmbL5Izea33DJ+2JHHDzvy8PN0Y0r/KC4dFM3YHmF4uDnUDCQiInIcejzEEcSNNf9snjdK/WxsqmuwD7dPTOT2iYn8llvKf1PNIedgcRWfbDzAJxsP4O/lxgV9I7loQBTje4WrJUdExEGddrC55ZZbGDZsGMOHD7e0zOjJkrPUdQS4ekBZDhTthdAe9q6o0+oTFUCfiwK478LepGQe5ovUbL7bnmvugLzpIJ9uOoivhyuT+kQwdUA0k/qE4+Oh/z8QEXEUp93HZsKECaSmplJWVoabmxv19fVcccUVTJw4kaFDhzJ48GB8fHysVe9Zc8g+NgD/nAqZq2DaQhg2y97VSAsNjQYbMw/z7dZcvtuW02p8HC93Fyb0CueiAVGc1ztSHY9FRKzE6p2H09PTSUlJYePGjZaluLgYV1dXevXqxfbt28+4eGty2GDz09/hl6fhnKthxpv2rkbaYBgGmw+U8O22HL7dmktmUaVlm6uLidHdQ7iwXxQX9IskJsjbjpWKiDgXu4w8nJGRwYYNG9i0aRP/+Mc/2uuw7cphg83e5fDudAjoAnO3q59NB2AYBjtySvluWy4/bM8jLa+s1fZzugRyYb9ILugfSe9If92yFRE5C1YJNpmZmXTr1u2Uizh48CBdunQ55f1twWGDTW2leTybxjq4cxOEdLd3RXKa9h2q4McdefywI5cN+w/T8t+sbiE+TO4byeS+EYxICMHdVU9YiYicDqsEm8jISC677DJuueUWRowYcdx9SkpK+PDDD3nxxRf54x//yJ133nn61VuRwwYbgLenQNYa+N1LMPQP9q5GzsKh8hqW7czjh+15/Lr7ELVNgwEC+Hu5MaFXOJP7RjKxdzhBPh52rFREpGOwSrApLCzkiSee4J///CdeXl4MGzaMmJgYvLy8OHz4MDt27GD79u0MHTqURx55hIsvvrhdvkx7cuhgs+xv8OszMPAauOJ1e1cj7aSipp5fdhWw7Ld8fv4tn8KKWss2FxMMjwvh/L4RnN83kh7hvrplJSJyHFbtY1NVVcXXX3/NihUr2L9/P1VVVYSFhTFkyBCmTJnCgAEDzqp4a3LoYLPnJ/jX5RDQFeZuUz8bJ9TQaJCaVcyynXks25l/TL+cuFAfzusTwXl9IhiZEIKnm8bLEREBG3QeLi8vx8/P74wLtBeHDja1FU39bOrhrs0QHG/visTKsooq+em3fJbuzGPt3iJqG47csvL1cGVczzDO6xPBpN4RRARook4R6bysHmxcXV358MMPmTFjxhkXaQ8OHWwA3roADqyD3/0fDL3e3tWIDZXX1LMi/RA//5bPT2n5FJTVtNp+TpdAJvWJYFLvcAZ2DcLVRS16ItJ5WD3YuLi4MHnyZCoqKjCZTAwfPpzrrruuzU7FjsLhg03zeDa9L4ZrP7B3NWInjY0G27NL+akp5GzOKm61PdjHnfG9wpnYO5zxPcMJ9fO0T6EiIjZik2ATGhrKNddcg6+vLykpKfz666/ccccdPPPMM2dcuLU5fLDJ3wmvjAYXN7hnF/iG2rsicQAFZTUsT8vn57R8fk0/RFl1vWWbyQQDuwQyoXcEE3uHM0itOSLihGwSbL7//nsuuOACy7otW7Ywffp07rzzTubOnXsmh7U6hw82AK+Ng9ytcMmzMOIWe1cjDqauoZGN+w+zfFcBy9MK2JlT2mp7sI875/YMZ0KvcMb3CifcX605ItLxWT3YhIWFsWLFCvr06dNq/ddff83cuXPZtWvXmRzW6jpEsFn1EvzwMMSOhpu/t3c14uDySqtJTitg+a5jW3MA+scEMKGXOegMjQvW4IAi0iFZPdhMnjyZYcOG8dRTT7Van5aWxqBBg6iurm7jk/bVIYJNaQ481xcw4M5UCEmwd0XSQdQ3NLIxs5hfdhWQvKuArQdLWm3393RjTGIoE3pFML5XGF2DHXfCWhGRlqwebNasWcOkSZO48soruf322xk4cCAVFRXcd999rFu3jp07d55x8dbUIYINmOeN2rscJj0ME+6zdzXSQRWU1bBidwHJaQX8kn6IohaDAwJ0D/NlfK9wzu0Zxujuofh6utmpUhGRE7PJJJibN2/mrrvuYsWKFTQfxsvLi48++sghRx2GDhRsUt+Hz2+D0J5wx3oN1idnrbHRYFt2SdNtqwJSs4ppaDzyr7+7q4mh3YItQWdATCAu6oQsIg7CprN75+fnk5KSQmNjI6NGjSIsLOxsD2k1HSbYVJfCM72gvgpu/Rm6DLV3ReJkSqrqWL2nkF/TC/glvYCsoqpW24N93BmbGMa5PcMY1zOcLkHedqpURMTGwaYj6TDBBuCjG2H7pzDqNpj6pL2rESe3v7CCX9IP8euuAlbvKaSspnUn5O7hvpybGMa5PcMZ3SMUP922EhEbUrBpQ4cKNmnfwQczwTcC7t4JrvpDIrZR19BIalYxv6YfYkW6+bZVi7tWuLmYGNItiHN7hjOuZxgDuwTipqetRMSKrBJsEhISzmjm4Tlz5nDnnXee9uesoUMFm4Y68+2oqiL4n08gcbK9K5JOqvm21YrdBfyafoj9hZWttvt7ujGqeyhjE0MZlxhGYoSfZikXkXZllWCTnJx8RsXEx8cTFxd3Rp9tbx0q2AB8fS+sfxMGzoQr3rB3NSKAefLOX9MP8Wt6Aav2FFJSVddqe4S/J2MTw5qWUKID1T9HRM6ObkW1ocMFm6z18PZkcPeBe9PBs+PNqC7OraHRYEd2KSt2H2LVnkOsyyiipr6x1T7dw3wZkxjK2B5hJPUIJcjHw07VikhHpWDThg4XbAwDFg6BwxlwxZsw8Gp7VyRyQtV1DWzMPMzK3YdYsbuQrQda988xmcyjIY/tEcaYxDBGxAfj46H+YyJyYupj04YOF2wAfp4PyU9C4gXwPx/buxqR01JSVcfavYWs2lPIyt2HSM8vb7Xd3dXEkG7BltacwbFBeLipI7KItKY+Nm3okMGmcA+8NBRMrnDPb+AXYe+KRM5Yfmm1JeSs2lPIweLW4+d4u7syPD6YpB6hJHUP5Rw9cSUi6FZUmzpksAF48zw4mAIXPQWj/2TvakTahWEY7C+sZOWeQ6zeU8jqPYUUHjXtg5+nGyMTQkjqHkpSj1D6RgfgqhGRRTodBZs2dNhgs/Z1+PZ+iBkKf/zZ3tWIWIVhGKTnl7Nq9yFW7y1kzd6iY564CvR2bxV0ekf6a+oHkU5AwaYNHTbYlBfAs73BaIA7UiAs0d4ViVhdY6PBjpxS1jT10VmXUUT5USMiB/u4MyrBHHKSeoTSU2PoiDglBZs2dNhgA/DvqyD9B5jwAEz6f/auRsTm6hsa2Z5dyuq95ttW6/cVUVnb0GqfUF8PRnUPYXT3UEYlmIOOWnREOj4FmzZ06GCz9WP45GYIjoc7UzXjt3R6dQ2NbDlQwpqmoLNhfxHVda3H0Anx9WBUQgijEkIY3SOUXhG6dSXSESnYtKFDB5vaCni6J9RVwM0/QuxIe1ck4lBq6xvZcqCYtRlFrNlbyIZ9h6mqa92iE+xj7qMzMiGUUQkh6ows0kEo2LShQwcbgE//F7YsgRG3wCXP2rsaEYdWW9/I1oPmFp01ewtJ2X/4mFtX/l5ujIwPYWRCCKO6h9I/JgB3PV4u4nA6ZLCZN28ejz/+eKt1vXv35rfffgOgurqae+65hyVLllBTU8OUKVN45ZVXiIyMPOVzdPhgs3sZvHcFeIfAvbvA1d3eFYl0GHUN5qCzdm8RazPMLTpHd0b28XBlWFwwo7uHMjIhhIFdA/F0c7VTxSLS7FT/fjvcOOb9+/dn6dKllvdubkdKnDt3Ll9//TUfffQRgYGB3HHHHVxxxRWsXLnSHqXaR8IE8IuE8jzYvRR6T7V3RSIdhrurC0O7BTO0WzC3TexBfUMjO3PKWJthfrR8/T7z4+XmCT4PAeDh5sKQ2CBGNd2+GhoXpCkgRByYw/3b6ebmRlRU1DHrS0pKePvtt3n//fc577zzAFi0aBF9+/ZlzZo1jB492tal2oerGwy4Eta8DBvfVbAROQturi6c0zWQc7oGcsu53WlsNEjLK2Pt3kLW7StiXUYRh8prWZtRxNqMImA3bi4mBnQJZFRCCCPiQxgeH6xJPUUciMMFm/T0dGJiYvDy8iIpKYn58+fTrVs3UlJSqKurY/LkyZZ9+/TpQ7du3Vi9enWbwaampoaamhrL+9LSUqt/B6sbdoM52Oz6Dg7vh2DHmK5CpKNzcTHRNzqAvtEB3DA2AcMw2HuognUZ5pCzdm8h2SXVpGYVk5pVzOu/7AWgZ4Qfw+NDGBEfzIj4ELoGe2ssHRE7cahgM2rUKBYvXkzv3r3Jycnh8ccf59xzz2Xbtm3k5ubi4eFBUFBQq89ERkaSm5vb5jHnz59/TL+dDi+8F3SfCHuXw4Z/wgVO9v1EHITJZKJHuB89wv24dmQ3AA4crmwKOUWs31/E3oIK0vPLSc8v54N1mQBEBniag05cMMPj9eSViC05VOfhoxUXFxMXF8dzzz2Ht7c3N954Y6vWF4CRI0cyadIknnrqqeMe43gtNrGxsR2383Cz376GJb83dyK+eye4e9m7IpFOqbC8hpT9h9mw/zDr9xWx7WAJdQ2t/7Pq5+nGkG5BjIg3374aHBuEt4c6JIucjg7bebiloKAgevXqxe7du7nggguora2luLi4VatNXl7ecfvkNPP09MTT09MG1dpYr4sgMBZKsmD7pzD49/auSKRTCvXz5ML+UVzY3/zfoaraBjYfKGbDviLW7zvMxv2HKaupb9UhubmfzsiEEIbHBTMsLphQPyf875SIHTh0sCkvL2fPnj1cf/31DBs2DHd3d5YtW8aMGTMASEtLIzMzk6SkJDtXagcurjD8Jlj2uHmCzEHXaiRiEQfg7eHK6O6hjO4eCkBDo0Fabhkb9puDzvqMInJLj/TTeaPpc91CfBjSLYjBsUEM6RZM32h/PWYucgYc6lbUvffey7Rp04iLiyM7O5vHHnuM1NRUduzYQXh4OLfddhvffPMNixcvJiAggD//+c8ArFq16pTP0eHHsWmp4hA81w8aauCWZdB1uL0rEpGTMAyDA4erWgWd9PzyY/bzcHWhX0yAJewM7RasTsnSqXXIW1EHDhzg2muvpbCwkPDwcMaNG8eaNWsIDw8H4Pnnn8fFxYUZM2a0GqCv0/INgwEzYPP7sO5NBRuRDsBkMhEb4kNsiA+XD+kKQElVHVsOFLMps9jSklNUUWt53Sw60MvcTychhJHxIZrgU+Q4HKrFxhacqsUG4GAKvHkeuHrA3B3gF27vikTkLBmGQWZRJalZ5rCzKfMw27NLqW9s/Z/rIB93hscFW8LOOV0CNR2EOK0OOaWCLThdsAFzsDmYAuc9AuPvtXc1ImIFlbX1pGYWs26feYTkjfuLj5ng08vdhYFdghgSF2QZYTncX52SxTko2LTBKYPN5iXw2f9CQFe4a7N5dGIRcWp1DY1sO1jC+n1FrMs4zIb9RRRX1h2zX2yIN0NigxnaLYihccH0jdYkn9IxKdi0wSmDTV01PN8PKgth5nvQd5q9KxIRG2tsNI+SvDHzMJsyD7NxfzG78ss4+r/wfp5uJPUIZXyvcCb0DKdbqI99ChY5TQo2bXDKYAOw9HFY8RwkjIdZX9q7GhFxAKXVdWzOKmbj/mJL4Cmtbj2beUKYL+N7hjG+Vziju4fi66kWX3FMCjZtcNpgU5wFLw4EoxFmr4Pw3vauSEQcTGOjwfbsUn5JLyA5rYCNmYdbdUh2dzUxIj6Ec3uGk9QjlAExAbjptpU4CAWbNjhtsAFYch389hWMuBUuecbe1YiIgyurrmPVnkJ+2VVA8q4CDhyuarXdz9ONkQkhJHUPJalHqOa8ErtSsGmDUwebvcvh3eng4WeeP8rLyb6fiFiNYRhkHKrgl10FrNxTyNq9hcfctgrwcmNkgjnkjE0MpXekvwYMFJtRsGmDUwcbw4CXR8KhXXDxMzDyVntXJCIdVEOjwc6cUlbvKWT13kLWZRRRXtM66EQHejGxdzgTekUwrmcYfuqfI1akYNMGpw42AGvfgG/vg7Be5r42+r8pEWkH9Q2NbMtuGXQKqa5rtGx3dzUxPC6ESX3Cmdg7gp4RfmrNkXalYNMGpw821aXwXF+oLYc//Be6T7R3RSLihKrrGlizt5DlaQUsT8tnX2Flq+1dgrwZ3yvMPCpyfIjmuZKzpmDTBqcPNgBf3wvr34Q+l8I1/7Z3NSLSCWQcqmB5Wj7L0wpYvbeQ2vrGVtsjAzwZHh/CiLhghseHqCOynDYFmzZ0imCT/xu8MgpMLnDXFgiKtXdFItKJVNWaW3PW7C1k/b4ith4soa6h9Z8aP083hnQLYmR8CEk9QhkUG6QRkeWEFGza0CmCDcA70yDjF0i6A6Y8Ye9qRKQTq6ptYPOBYjbsK2L9vsNs3H+YsqM6Ivt4uDI8PoQxPUJJ6h7KgC6BatGRVhRs2tBpgs2uH+D9q8DNG+5KBf8oe1ckIgKYn7hKyy1j/b4i1mYUsnpPIYePmufK39ONUd1DGN09lNHdQ+kV6Y+Hm1p0OjMFmzZ0mmBjGPD2hXBgnQbsExGH1thokJZXxqo95pCzNqOQsqPG0PFwdaFPtD8DugRyTtOisNO5KNi0odMEG4CMX+GdS8HFHf6cAsFx9q5IROSkGhoNtmeXWILOxszDxwQdaB12BnUNZER8CAlhvnr6ykkp2LShUwUbMI9EvHc5DL4OLnvF3tWIiJw2wzDILKpky4ESth0sYetB88+jR0YGCPPzZGRCMCPjQxiZEErvKH/11XESCjZt6HTB5kAKvHWe+Qmp29dCeC97VyQictaaw87WpqCzaX8xqQeKj3nM3N/LzTKWzvD4YPpGB2iE5A5KwaYNnS7YAHxwLaR9A/0vh6sW27saERGrqK5rYMuBkqZOyUWk7CuiorbhmP26hfjQN9qfvtEB9I0OoF90gAYQ7AAUbNrQKYNN7jZ4bRxgwP/+AtGD7F2RiIjV1Tc0sjOnjLUZ5rmuthwoIbe0+rj7+nu60Sfan/4xgYzubr6NFeLrYeOK5UQUbNrQKYMNwMc3w7aPoecUuO5De1cjImIXRRW17MwpZWdOKTtyStmZU8bu/LJjBhAE6BPlb3ncfFRCCMEKOnalYNOGThtsCvfA/40AowFu+gG6jbJ3RSIiDqG2vpE9BeXszCklNauYNXsL2ZVXfsx+zUFnVEIIA7oE6vaVjSnYtKHTBhuAL/4MG9+F+HNh1pea+VtEpA2HymtYu7fIMjVEev6xQcffy41+0QH0iwmgf0wg/WMCSIzw09QQVqJg04ZOHWyKs+ClodBQC9d/Dj0m2bsiEZEOoaCshnUZRazee4hNmcXsyjv+7SsPVxd6RflxTpdAxvQIY2ximPrqtBMFmzZ06mAD8O0DsPY16DIMblmmVhsRkTNQW9/I7vxyduSUsj27hB3Z5j47Rw8kaDLBgJhAxvUM49yeYQyLC8bTzdVOVXdsCjZt6PTBpjwfXhwEdZVwzfvQ5xJ7VyQi4hQMw+DA4Sq2Z5ewYd9hVuw+xG+5Za328XZ3ZWRCCOf2NLfm9Izww023rk6Jgk0bOn2wAVj6OKx4DiL6w59WgIv+pRIRsYb80mpW7D7EivRD/Lr7EAVlNa22e7u70j8mgHO6BjKwayDndAmie5gvLhot+RgKNm1QsAGqDsMLg6CmBGa8Dedcae+KREScnmGYJ/tckX6IX9IPtTmAoK+HK/27BDKwSyCDYoPUT6eJgk0bFGyaJD8NP/8dQrrD7HXg6m7vikREOpXGRoO9hyrYerCYLQdK2HqghO3ZpVTVtQ47JhMM7BrEhF7hTOgVzuDYoE45/5WCTRsUbJrUlMGLg6HyEFz6Agy/0d4ViYh0evUNjewpqGDLgWK2HixhXUbRMf10Ar3dObdnmCXoRAR42ala21KwaYOCTQtrXoXvHgTvEPhzCviE2LsiERE5Sm5JNb/sKiB5VwG/phccM6t5nyh/hnQLYmDXIAZ1DaJXpHN2SFawaYOCTQsNdfD6BMjfDkOuh+n/Z++KRETkBOobGtl8oJjkNHPQ2XKwhKP/inu5u9A/JpBBXYMYFBvIwK5BxIf6dPhRkhVs2qBgc5TMNfDPKebXN34HcUn2rUdERE5ZYXkN6/cVsflACVsOFLMlq4Symvpj9gvycWdcYtPtq97hRPh3vNtXCjZtULA5juapFsL7wp9+VUdiEZEOqrHRIKPQ3Ednc1YJmw8Usz27lNr6xlb79YsOYELvcCb2CmdoXHCHmAZCwaYNCjbHUVkE/zccKgth8uMwbo69KxIRkXZS19DI5qxiftlVwPJdBWw5UNJqu5+nG2MTQzm3ZzhDuwU7bB8dBZs2KNi0IfV9+Pw2cPeB2WshqJu9KxIRESs4VF7DivRDJO8q4JddBRRW1Lba7uXuwoAYc9+cQbHmvjpxDtBHR8GmDQo2bTAMWHwJ7F8JvS+Gaz+wd0UiImJljY0G27JLSE4rYPXeQrYeOH4fnUBvdwZ2DWRwbBDjEsPscvtKwaYNCjYnkP8bvDYWGus1j5SISCfU3Ednc5Z50MDUrGJ25BzbR8fP040xPUKZ0Duc8T3DiQ3xsXptCjZtULA5ieZ5pAK6mm9JefrZuyIREbGj2vpG0nLL2HygmHUZRazYfYiio25f9Qj3ZXzTgIGju4fi5d7+M5gr2LRBweYkaivhlVFQnAlj7oQL/2bvikRExIG0vH31S3oBGzOLaWg8EiU83Vx4cGofbhyb0K7nPdW/327telbp+Dx84OJn4P2rYfXLMOgaiOxv76pERMRBuLiYGNjVPNLxn8/vSUlVHat2H+KX9AKS0wrILqmmS5C3/eqz25lP4sknn8RkMjFnzhzLuurqambPnk1oaCh+fn7MmDGDvLw8+xXprHpNgb7TwGiAr+ZCY+PJPyMiIp1SoLc7U8+JZv4VA1n54HksvXs85/YMt1s9Dhls1q9fz+uvv87AgQNbrZ87dy5ffvklH330EcnJyWRnZ3PFFVfYqUond9FT4OEHWWsh9T17VyMiIh2AyWQiMcIfb4/272Nzqhwu2JSXl3Pdddfx5ptvEhwcbFlfUlLC22+/zXPPPcd5553HsGHDWLRoEatWrWLNmjVtHq+mpobS0tJWi5yCwC4w6f+ZX//4KFQcsm89IiIip8Dhgs3s2bO55JJLmDx5cqv1KSkp1NXVtVrfp08funXrxurVq9s83vz58wkMDLQssbGxVqvd6Yz8X4g8B6oOw6e3Qn3tyT8jIiJiRw4VbJYsWcLGjRuZP3/+Mdtyc3Px8PAgKCio1frIyEhyc3PbPOZDDz1ESUmJZcnKymrvsp2XqxtMf8k8GvGen+CzP0Jjg72rEhERaZPDPBWVlZXFXXfdxY8//oiXV/vNOurp6Ymnp2e7Ha/TiRkCM/8F718D2z8DzwCY9iLYeWhtERGR43GYFpuUlBTy8/MZOnQobm5uuLm5kZyczMKFC3FzcyMyMpLa2lqKi4tbfS4vL4+oqCj7FN1ZJE6GGW+CyQU2vgNL59m7IhERkeNymBab888/n61bt7Zad+ONN9KnTx8eeOABYmNjcXd3Z9myZcyYMQOAtLQ0MjMzSUpKskfJnUv/y6G6FL68E1a+AN5BMG6uvasSERFpxWGCjb+/PwMGDGi1ztfXl9DQUMv6m2++mbvvvpuQkBACAgL485//TFJSEqNHj7ZHyZ3PsFlQXWx+SmrpPPAKguE32rkoERGRIxwm2JyK559/HhcXF2bMmEFNTQ1TpkzhlVdesXdZncvYu6Cq2Dyf1FdzwSsABsywd1UiIiKA5oqydzkdk2GYQ03KInBxh2uXQM/JJ/+ciIjIGTrVv98O03lYOhCTCS55FvpfAY118J//gcy2B0kUERGxFQUbOTMurnD565B4AdRXwb+vhoMp9q5KREQ6OQUbOXNuHnD1uxA7GmpK4J9TYf3b5ltVIiIidqBgI2fHwweu+xB6XwwNNfD13fDJLVBTZu/KRESkE1KwkbPnFQjXvA8X/h1MrrDtY3hjIuRtt3dlIiLSySjYSPswmWDMn+HGbyGgCxTuhjfPh03v2bsyERHpRBRspH11GwX/+6t5Gob6KvjvbPjsNqitsHdlIiLSCSjYSPvzDYXffwTnP2qeX2rz++bWm4I0e1cmIiJOTsFGrMPFBc69B2Z9CX6RULAT3pgEG9/VU1MiImI1CjZiXfHj4E8rIGEC1FXAF3+G92ZAyQF7VyYiIk5IwUaszy8Crv/M/NSUmxfsWQYvj4aUd9R6IyIi7UrBRmzDxdX81NSfVkDXkVBbBl/eCe9dAcVZ9q5ORESchIKN2FZYT7jpuxatNz/BK0mQslitNyIictYUbMT2WrbexI5qar25C/51GRRn2rs6ERHpwBRsxH7CepoH9JvyD3Przd7l5tabX56GqsP2rk5ERDogBRuxLxdXSJoNf1ppnkyzthx++js8PwC+/wuUHLR3hSIi0oEo2IhjCEuEG7+By9+AiP7mgLP6/+DFQfD57ZD/m70rFBGRDsBkGJ2rx2ZpaSmBgYGUlJQQEBBg73LkeAwDdi+FFS/A/hVH1veaCuPmQLfR9qpMRETs5FT/fivYiGM7sAFWvgA7vwKaflVjR8Hg30PiBRDYxZ7ViYiIjSjYtEHBpoM6lA6rXoLNH0BD7ZH1Ef2h5wXmJXYUuLrbr0YREbEaBZs2KNh0cGW5sPFfkP69uTWHFr++ngHQfSL0vNA8u3hAtL2qFBGRdqZg0wYFGydSUWge4G/3j+Y+OZWFrbdH9IfuE8xhJ24MePrbpUwRETl7CjZtULBxUo0NkJ1qDjnpP8DBjbRqzXFxg64jzCGn+0ToMky3rUREOhAFmzYo2HQSFYWw7xfzoH97fobi/a23e/iZZx6PHwfdxkD0QAUdEREHpmDTBgWbTqoowxxy9i6HjORjRzZ294XYEeaQE5cEXYaDh489KhURkeNQsGmDgo3Q2Ai5W8wBZ/8qyFwN1SWt93Fxh5jB5r45saMhdiT4htmlXBERUbBpk4KNHKOxEQp2Hgk5+1dDWfax+4V0h64jzSEndiRE9DNPCSEiIlanYNMGBRs5KcMw98nZvxoyV0HWenPwOZqHP3QZah4/p+twiBkCfhG2r1dEpBNQsGmDgo2ckapiOLgBstZB1lo4kAK1ZcfuF9DVfAsrZog59EQPBp8QGxcrIuJ8FGzaoGAj7aKxAfJ3woF15rCTvQkK0mj1iHmz4ARz0IkZbA460QPBO9jGBYuIdGwKNm1QsBGrqSmDnC2QvdEcdA5uhMMZx983KA6iBzWFnUHmwKPOySIibVKwaYOCjdhUZRHkbDaHnZzN5uXwvuPvG9AVos5pWgaYfwbFg4uLLSsWEXFICjZtULARu6s6bG7ZydkMOanmn4W7j7+vhz9E9m8deCL6gbu3TUsWEbE3BZs2KNiIQ6ouhdytkLfNPMZO7jZzH56GmmP3NblAaCJEDjgSeiIHQEAMmEy2r11ExAYUbNqgYCMdRkM9FKabA0/LpfLQ8ff3Dm4KOwMgsp95EtCIPuDha9u6RUSsQMGmDQo20qEZBpTnQ95Wc6tO3jbI225+IstoOM4HTBAcb759FdnP/DOin7nFx9XN1tWLiJwxBZs2KNiIU6qvgYLfjoSd/B2QtwMq8o+/v6sHhPWCiL7mJbzpZ1CcOiuLiEM61b/f+l82EWfg5tn02Pig1usrDh0JOfnbzf128ndCbXlTa8+21vu7+0B4b3OrTnifptDT2/zElgKPiHQAarER6WwaG6EkE/J/M4ee/J3mKSMKdh2/szKYA09YL3PYCe/dtPQx3+bSfFkiYgO6FdUGBRuRNjTUmwcUbG7Vyd9h7rtTuBsa647/GVdPc3+dsJ5Nwae3+XVoojoti0i7UrBpg4KNyGlqqDMPKljwW9OSZl4O7YL66rY/FxhrDjthvSAsEUJ7mkOPf7QeSxeR09Yhg82rr77Kq6++yr59+wDo378/jz76KFOnTgWgurqae+65hyVLllBTU8OUKVN45ZVXiIyMPOVzKNiItJPGBijOhEPp5pBzKO3I68rCtj/n7guhPZpadppad8ISzT89/W1Xv4h0KB0y2Hz55Ze4urrSs2dPDMPgnXfe4emnn2bTpk3079+f2267ja+//prFixcTGBjIHXfcgYuLCytXrjzlcyjYiNhARWFT2GlaCnebQ8/hfW08lt7EN8IccEK7Q0iPptc9IKS7RlsW6eQ6ZLA5npCQEJ5++mmuvPJKwsPDef/997nyyisB+O233+jbty+rV69m9OjRx/18TU0NNTVHOkSWlpYSGxurYCNiD/W1ULzfHHIK05sCz27z64qCE382oIs54DQHnZCm8BMcDx4+NilfROynwz/u3dDQwEcffURFRQVJSUmkpKRQV1fH5MmTLfv06dOHbt26nTDYzJ8/n8cff9xWZYvIibh5NHU07nnstuoSKNwDRXvNPwt3Q1HTz+oSKD1oXvb9euxn/WOaQk93CE6AkATz++AE8NL/wIh0Jg4XbLZu3UpSUhLV1dX4+fnx2Wef0a9fP1JTU/Hw8CAoKKjV/pGRkeTm5rZ5vIceeoi7777b8r65xUZEHIxXIHQZal5aMgzzLOlFLUJP0d6mZY859JRlm5f9K449rk/okZATktD6p1+EOjKLOBmHCza9e/cmNTWVkpISPv74Y2bNmkVycvIZH8/T0xNPT892rFBEbMpkAt9Q8xI78tjtlUVHgk7hHvMj60UZ5veVh8wdmSsL4cD6Yz/r7mO+lRUcbw46wfFNoScegrqZBz4UkQ7F4YKNh4cHiYmJAAwbNoz169fz4osvMnPmTGpraykuLm7VapOXl0dUVJSdqhURu/MJMS9dhx+7rbrU3GH5cFPQKcpoer0PSg9AXWXTIIU7jnNgk3nGdEvwiTdPOdH8Wq09Ig7J4YLN0RobG6mpqWHYsGG4u7uzbNkyZsyYAUBaWhqZmZkkJSXZuUoRcUheARA90Lwcrb4WSrKOhJ3D+8xLUdPruooj/Xr2H+fJSzdvCI5rCjtxLYJP0zr17RGxC4cKNg899BBTp06lW7dulJWV8f7777N8+XK+//57AgMDufnmm7n77rsJCQkhICCAP//5zyQlJbXZcVhEpE1uHuYnrEJ7HLvNMMzzbDWHneKmn4f3m5fSA1BfdWTQwuPxDjkScoK6tXgdB0GxenxdxEocKtjk5+fzhz/8gZycHAIDAxk4cCDff/89F1xwAQDPP/88Li4uzJgxo9UAfSIi7cpkAr9w8xI74tjtza09xfuPBJ7iptBzeB9UFR1Zsjcd/xx+kebA0yr4NL0P7Kr+PSJnyOHHsWlvGqBPRKyupuxIyCnONIee4swjAai2/CQHMIF/VOvgExRr/hnYzRx83L1s8U1EHEaHH8dGRKTD8vSHqAHm5WiGAVWHjwo7ma0DUF0llOWYl6y1xz+HX6R5Pq6jQ09QrHm9p591v6OIg1KwERGxJZPpyJNcMUOO3W4Y5sfTm1t3LIEnq+n2V1PwKc8zLwc3HP883sFNYSe2xc/YI++9g/VUlzglBRsREUdiMoFvmHnpOuzY7c0DFpY0t/K0CDzFWeb11SXmVqGqw5Cz+fjncfcx39KyLE2hp/l9QBdzB2uRDkbBRkSkI2k5YOHxWnzAPH5PSdZRoSfT/LrkgLmlp67yyCSlxz9RUwfnFmGnuX9P86JWH3FACjYiIs7GKwC8+kNk/+Nvr68xB5xWS6b5Z3GWeeye+moozzUvxxu1GcytPgFdILALBDQHni5N65pafdTXR2xMwUZEpLNx82x7DB84Mo5PcwvP0T+Ls8zTVdRVNs3Snt72ubyCWt/eCuxivuXV/No/Rre8pF0p2IiISGstx/E5elLSZnVVUJptDjqlB6HkoHngwpKDR9bVlEJ1sXnJ29bWyczTUwTEmMNOQJcWr2OOLBrXR06Rgo2IiJw+d+8Tt/qAua9P6cHWt7xavi/NhoaaI094tTWYIYBveFPI6Wr+2XzLq2UAUvgRFGxERMRavALMS0Tf429vvuVVetAcciw/s4/M01Wabe7vU1FgXtp6ygvAJwwCos23t5rDjn+0eV1AF/Nrr0B1eHZyCjYiImIfLW95xQw+/j7Nj7dbQk9TS0/JwWPDT+Uh85K7te1zuvs2hZ4WYaflLS//GHPrkIuLVb6yWJ+CjYiIOK6Wj7cfb5Z2OBJ+yrKhNOfIz9KD5tGbm19XF5tnbT9Zh2cXN/Oj7v7R5qktmlt9/FssAdHgGaDWHwekYCMiIh1by/ATdU7b+9U2TVXR8naX5f1BcwAqz4PG+iOtQSfi7nsk8Fhue8W0CEBR5oCkp75sSsFGREQ6Bw+fk3d4bqiD8nwoyzW3/JTlmoNPWe6R+btKc6CmpKn1Z7d5ORGfUPCLOtL649/UGtTcKhTQ9NrVvX2/byelYCMiItLM1b1prJ0uwHGmtGhWW9Ei9OQc+dkcfMpyzNsb68xzf1UWQv72E5y4aSoNS/iJMvf38Y9sCkVNP/0iFIBOQsFGRETkdHn4nrz1p7nvT3lza0/ekcBTnnvk1ldZjvn2V/OTXyfq/AwtWoAizS09lv5AkS1ahqLMj+R3Qgo2IiIi1tCy709b01sANDZCVdFRt7xyW4eg8vwj/X9OqQUI86PtLVt7WgYhv4gjr72CnOopMAUbERERe3JxOTKje1tPfsGRAFSe1xR48ptag/KafrZY6qvMs7xXl8ChtJOc3938iLtfxJEOz36RrVuAmoNQBxgEUcFGRESkI2gZgE7UAmQY5uksWgadlq0+5XlQXmD+WVVk7gdUlm1eck5Sg3cw+EaYg05zGPINP9IKZFkXYbenwRRsREREnInJZL4N5RUI4b1PvG99rblfjyXw5LVoAcprvb6hFqoOm5eTtQJN+QckzW6/73QaFGxEREQ6KzePFk+BnYBhmANNeZ655aei4EgLUPPrinxzS1BFvrnFxk4UbEREROTETCbwCTEvbc391ayxEYxG29R1HAo2IiIi0n5cXAD7PWXlPM93iYiISKenYCMiIiJOQ8FGREREnIaCjYiIiDgNBRsRERFxGgo2IiIi4jQUbERERMRpKNiIiIiI01CwEREREaehYCMiIiJOQ8FGREREnIaCjYiIiDgNBRsRERFxGp1udm/DMAAoLS21cyUiIiJyqpr/bjf/HW9Lpws2ZWVlAMTGxtq5EhERETldZWVlBAYGtrndZJws+jiZxsZGsrOz8ff3x2QytdtxS0tLiY2NJSsri4CAgHY7rhxL19o2dJ1tQ9fZNnSdbcOa19kwDMrKyoiJicHFpe2eNJ2uxcbFxYWuXbta7fgBAQH6l8ZGdK1tQ9fZNnSdbUPX2TasdZ1P1FLTTJ2HRURExGko2IiIiIjTULBpJ56enjz22GN4enrauxSnp2ttG7rOtqHrbBu6zrbhCNe503UeFhEREeelFhsRERFxGgo2IiIi4jQUbERERMRpKNiIiIiI01CwaScvv/wy8fHxeHl5MWrUKNatW2fvkjq0X375hWnTphETE4PJZOLzzz9vtd0wDB599FGio6Px9vZm8uTJpKen26fYDmz+/PmMGDECf39/IiIiuOyyy0hLS2u1T3V1NbNnzyY0NBQ/Pz9mzJhBXl6enSrumF599VUGDhxoGbQsKSmJb7/91rJd19g6nnzySUwmE3PmzLGs07VuH/PmzcNkMrVa+vTpY9luz+usYNMO/vOf/3D33Xfz2GOPsXHjRgYNGsSUKVPIz8+3d2kdVkVFBYMGDeLll18+7vYFCxawcOFCXnvtNdauXYuvry9TpkyhurraxpV2bMnJycyePZs1a9bw448/UldXx4UXXkhFRYVln7lz5/Lll1/y0UcfkZycTHZ2NldccYUdq+54unbtypNPPklKSgobNmzgvPPOY/r06Wzfvh3QNbaG9evX8/rrrzNw4MBW63Wt20///v3JycmxLCtWrLBss+t1NuSsjRw50pg9e7blfUNDgxETE2PMnz/fjlU5D8D47LPPLO8bGxuNqKgo4+mnn7asKy4uNjw9PY0PPvjADhU6j/z8fAMwkpOTDcMwX1d3d3fjo48+suyzc+dOAzBWr15trzKdQnBwsPHWW2/pGltBWVmZ0bNnT+PHH380JkyYYNx1112GYej3uT099thjxqBBg467zd7XWS02Z6m2tpaUlBQmT55sWefi4sLkyZNZvXq1HStzXhkZGeTm5ra65oGBgYwaNUrX/CyVlJQAEBISAkBKSgp1dXWtrnWfPn3o1q2brvUZamhoYMmSJVRUVJCUlKRrbAWzZ8/mkksuaXVNQb/P7S09PZ2YmBi6d+/OddddR2ZmJmD/69zpJsFsb4cOHaKhoYHIyMhW6yMjI/ntt9/sVJVzy83NBTjuNW/eJqevsbGROXPmMHbsWAYMGACYr7WHhwdBQUGt9tW1Pn1bt24lKSmJ6upq/Pz8+Oyzz+jXrx+pqam6xu1oyZIlbNy4kfXr1x+zTb/P7WfUqFEsXryY3r17k5OTw+OPP865557Ltm3b7H6dFWxEBDD/X+62bdta3SeX9tO7d29SU1MpKSnh448/ZtasWSQnJ9u7LKeSlZXFXXfdxY8//oiXl5e9y3FqU6dOtbweOHAgo0aNIi4ujg8//BBvb287VqbOw2ctLCwMV1fXY3p75+XlERUVZaeqnFvzddU1bz933HEHX331FT///DNdu3a1rI+KiqK2tpbi4uJW++tanz4PDw8SExMZNmwY8+fPZ9CgQbz44ou6xu0oJSWF/Px8hg4dipubG25ubiQnJ7Nw4ULc3NyIjIzUtbaSoKAgevXqxe7du+3+O61gc5Y8PDwYNmwYy5Yts6xrbGxk2bJlJCUl2bEy55WQkEBUVFSra15aWsratWt1zU+TYRjccccdfPbZZ/z0008kJCS02j5s2DDc3d1bXeu0tDQyMzN1rc9SY2MjNTU1usbt6Pzzz2fr1q2kpqZaluHDh3PddddZXutaW0d5eTl79uwhOjra/r/TVu+e3AksWbLE8PT0NBYvXmzs2LHD+OMf/2gEBQUZubm59i6twyorKzM2bdpkbNq0yQCM5557zti0aZOxf/9+wzAM48knnzSCgoKM//73v8aWLVuM6dOnGwkJCUZVVZWdK+9YbrvtNiMwMNBYvny5kZOTY1kqKyst+/zpT38yunXrZvz000/Ghg0bjKSkJCMpKcmOVXc8Dz74oJGcnGxkZGQYW7ZsMR588EHDZDIZP/zwg2EYusbW1PKpKMPQtW4v99xzj7F8+XIjIyPDWLlypTF58mQjLCzMyM/PNwzDvtdZwaadvPTSS0a3bt0MDw8PY+TIkcaaNWvsXVKH9vPPPxvAMcusWbMMwzA/8v3II48YkZGRhqenp3H++ecbaWlp9i26AzreNQaMRYsWWfapqqoybr/9diM4ONjw8fExLr/8ciMnJ8d+RXdAN910kxEXF2d4eHgY4eHhxvnnn28JNYaha2xNRwcbXev2MXPmTCM6Otrw8PAwunTpYsycOdPYvXu3Zbs9r7PJMAzD+u1CIiIiItanPjYiIiLiNBRsRERExGko2IiIiIjTULARERERp6FgIyIiIk5DwUZERESchoKNiIiIOA0FGxEREXEaCjYiclwTJ05kzpw59i7Dod1www1cdtll9i5DRFpQsBHppG644QZMJtMxy+7du+1d2jEWL15MUFCQ5f28efMYPHiwzc6/b98+TCYTqamprda/+OKLLF682GZ1iMjJudm7ABGxn4suuohFixa1WhceHm6namyvtrYWDw+PM/58YGBgO1YjIu1BLTYinZinpydRUVGtFldX1+Pue/jwYf7whz8QHByMj48PU6dOJT09HQDDMAgPD+fjjz+27D948GCio6Mt71esWIGnpyeVlZUYhsG8efPo1q0bnp6exMTEcOedd55SzYsXL+bxxx9n8+bNllam5laT4uJibrnlFsLDwwkICOC8885j8+bNls82t/S89dZbJCQk4OXlBcB3333HuHHjCAoKIjQ0lEsvvZQ9e/ZYPpeQkADAkCFDMJlMTJw4ETj2VlRNTQ133nknEREReHl5MW7cONavX2/Zvnz5ckwmE8uWLWP48OH4+PgwZswY0tLSLPts3ryZSZMm4e/vT0BAAMOGDWPDhg2ndG1ERMFGRE7RDTfcwIYNG/jiiy9YvXo1hmFw8cUXU1dXh8lkYvz48Sxfvhwwh6CdO3dSVVXFb7/9BkBycjIjRozAx8eHTz75hOeff57XX3+d9PR0Pv/8c84555xTqmPmzJncc8899O/fn5ycHHJycpg5cyYAV111Ffn5+Xz77bekpKQwdOhQzj//fIqKiiyf3717N5988gmffvqp5dZSRUUFd999Nxs2bGDZsmW4uLhw+eWX09jYCMC6desAWLp0KTk5OXz66afHre3+++/nk08+4Z133mHjxo0kJiYyZcqUVucH+Mtf/sKzzz7Lhg0bcHNz46abbrJsu+666+jatSvr168nJSWFBx98EHd391O6NiIC2GQOcRFxOLNmzTJcXV0NX19fy3LllVdatk+YMMG46667DMMwjF27dhmAsXLlSsv2Q4cOGd7e3saHH35oGIZhLFy40Ojfv79hGIbx+eefG6NGjTKmT59uvPrqq4ZhGMbkyZON//f//p9hGIbx7LPPGr169TJqa2tPqdZFixYZgYGBlvePPfaYMWjQoFb7/Prrr0ZAQIBRXV3dan2PHj2M119/3fI5d3d3Iz8//4TnKygoMABj69athmEYRkZGhgEYmzZtarXfrFmzjOnTpxuGYRjl5eWGu7u78e9//9uyvba21oiJiTEWLFhgGIZh/PzzzwZgLF261LLP119/bQBGVVWVYRiG4e/vbyxevPjEF0RE2qQWG5FObNKkSaSmplqWhQsXHne/nTt34ubmxqhRoyzrQkND6d27Nzt37gRgwoQJ7Nixg4KCApKTk5k4cSITJ05k+fLl1NXVsWrVKsstnKuuuoqqqiq6d+/OrbfeymeffUZ9ff1ZfZfNmzdTXl5OaGgofn5+liUjI6PVbaW4uLhj+hGlp6dz7bXX0r17dwICAoiPjwcgMzPzlM+/Z88e6urqGDt2rGWdu7s7I0eOtFyjZgMHDrS8br5dl5+fD8Ddd9/NLbfcwuTJk3nyySdb1S4iJ6dgI9KJ+fr6kpiYaFla9ok5Xeeccw4hISEkJye3CjbJycmsX7+euro6xowZA0BsbCxpaWm88soreHt7c/vttzN+/Hjq6urO+Pzl5eVER0e3CmqpqamkpaVx3333tfrOR5s2bRpFRUW8+eabrF27lrVr1wLmzsXW0PLWkslkArDc9po3bx7bt2/nkksu4aeffqJfv3589tlnVqlDxBkp2IjISfXt25f6+nrLH3yAwsJC0tLS6NevH2D+A33uuefy3//+l+3btzNu3DgGDhxITU0Nr7/+OsOHD28VKry9vZk2bRoLFy5k+fLlrF69mq1bt55SPR4eHjQ0NLRaN3ToUHJzc3Fzc2sV1hITEwkLC2vzWM3f4+GHH+b888+nb9++HD58+JjzAcecs6UePXrg4eHBypUrLevq6upYv3695Rqdql69ejF37lx++OEHrrjiimOeXBORtinYiMhJ9ezZk+nTp3PrrbeyYsUKNm/ezP/8z//QpUsXpk+fbtlv4sSJfPDBBwwePBg/Pz9cXFwYP348//73v5kwYYJlv8WLF/P222+zbds29u7dy3vvvYe3tzdxcXGnVE98fDwZGRmkpqZy6NAhampqmDx5MklJSVx22WX88MMP7Nu3j1WrVvGXv/zlhE8VBQcHExoayhtvvMHu3bv56aefuPvuu1vtExERgbe3N9999x15eXmUlJQccxxfX19uu+027rvvPr777jt27NjBrbfeSmVlJTfffPMpfa+qqiruuOMOli9fzv79+1m5ciXr16+nb9++p/R5EVGwEZFTtGjRIoYNG8all15KUlIShmHwzTfftLqtMmHCBBoaGix9acAcdo5eFxQUxJtvvsnYsWMZOHAgS5cu5csvvyQ0NPSUapkxYwYXXXQRkyZNIjw8nA8++ACTycQ333zD+PHjufHGG+nVqxfXXHMN+/fvJzIyss1jubi4sGTJElJSUhgwYABz587l6aefbrWPm5sbCxcu5PXXXycmJqZVmGvpySefZMaMGVx//fUMHTqU3bt38/333xMcHHxK38vV1ZXCwkL+8Ic/0KtXL66++mqmTp3K448/fkqfFxEwGYZh2LsIERERkfagFhsRERFxGgo2IiIi4jQUbERERMRpKNiIiIiI01CwEREREaehYCMiIiJOQ8FGREREnIaCjYiIiDgNBRsRERFxGgo2IiIi4jQUbERERMRp/H+Jh6J/qVEQogAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(off_diagonal_norm[:,0],label='Full ansatz')\n", + "plt.plot(off_diagonal_norm[:,1],label='Pauli ansatz')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "def random_hamiltonian(nqubits):\n", + " n = 2**nqubits\n", + " h = np.random.rand(n,n)\n", + " h = h + h.T\n", + " return h" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "tests = 100\n", + "nqubits = 5\n", + "flows = 50\n", + "losses = np.empty((tests,flows))\n", + "losses_pauli = np.empty((tests,flows))\n", + "for i in range(tests):\n", + " h = random_hamiltonian(nqubits)\n", + " H = hamiltonians.Hamiltonian(nqubits,h)\n", + " dbi = DoubleBracketIteration(H,mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + " dbi_eval = deepcopy(dbi)\n", + " params = np.linspace(1,2**nqubits,2**nqubits)\n", + " d_opt, loss_opt, grad_opt, diags_opt = gradient_descent(dbi, params, 100,1e-2, analytic=False, ansatz = 'Full')\n", + "\n", + " for j in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " losses[i,j] = dbi_eval.off_diagonal_norm\n", + " \n", + " H = hamiltonians.Hamiltonian(nqubits,h)\n", + " dbi = DoubleBracketIteration(H,mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + " dbi_eval = deepcopy(dbi)\n", + " params = np.linspace(nqubits,1,nqubits)\n", + " d_opt, loss_opt, grad_opt, diags_opt = gradient_descent(dbi, params, 5, 1e-3, analytic=False, ansatz = 'Pauli')\n", + " for j in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " losses_pauli[i,j] = dbi_eval.off_diagonal_norm\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(np.mean(losses,axis=0),label='Full ansatz')\n", + "plt.plot(np.mean(losses_pauli,axis=0),label='Pauli ansatz')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'Average $||\\sigma(H_k)||$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-04-02 09:27:21]: Using numpy backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 2\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCost.least_squares\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Step')" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evals = 300\n", + "par1 = np.linspace(-10,10,evals)\n", + "par2 = 1\n", + "par3 = 2\n", + "par4 = 4\n", + "steps = np.empty(evals)\n", + "loss = np.empty(evals)\n", + "off_diagonal_norm = np.empty(evals)\n", + "for i in range(evals):\n", + " dbi_eval = deepcopy(dbi)\n", + " d = d_ansatz([par1[i],par2,par3,par4],type='Full')\n", + " poly_step = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", + " dbi_eval(poly_step,d=d)\n", + " steps[i] = poly_step\n", + " loss[i] = dbi_eval.least_squares(d)\n", + " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(par1,loss)\n", + "plt.xlabel('Parameter')\n", + "plt.ylabel('Loss')\n", + "\n", + "plt.figure()\n", + "plt.plot(par1,off_diagonal_norm)\n", + "plt.xlabel('Parameter')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n", + "plt.figure()\n", + "plt.plot(par1,steps)\n", + "plt.xlabel('Parameter')\n", + "plt.ylabel('Step')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Step size')" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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pbfh6Fl/5A7j7zCWUanUSpyEiIlt0PLMQF4vKjftFRvlIHafeWM109m7dumHlypWYNm0aZs6cicjISHz88ccYO3as+ZiXX34ZJSUlmDRpEgoKCtC7d2+sW7cOzs7OEiavf80D3BHq44L0/CvYcToPg9tU35VHRERUV6Zurr4t/KB0dJA4Tf2RCSG4El4ljUYDlUqFwsJCix/v8+aqJCzdfR73d22K2fd1kDoOERHZmLs+2YET2Rp8NKYD7u3SVOo4N1Wbz2+r6eqiqkzdXVuSuYozERHVr6yCKziRrYFcBgyw8t3Y/42Fj5XqHukDD6Uj8orLcTSjQOo4RERkQ0zdXJ3DvG1iteZrsfCxUgpHOfrGGFdx5rR2IiKqT5tsbLXma7HwsWLxnNZORET1rKRch4QzlwBc/ZyxJSx8rFj/FgGQy4BkdREyLpdKHYeIiGzAjtN50OoNCPNxRXSAu9Rx6h0LHyvm7aZA13Dj2grs7iIiovpgGt8z0IZWa74WCx8rx1WciYiovugNAltTjH9Ix9vg+B6AhY/Vi29t/MHcc/YSNGUVEqchIiJrdiT9MvKKtfBwdkS3CNtZrflaLHysXDN/d0T5u6FCL7A95aLUcYiIyIptOGHsPRgQEwCFo22WCLb5quzMoNbGLStMP7BERER1sTHJ+DkyqI1tdnMBLHxswp2V3V3bknOh1RkkTkNERNYoNbcYZ/NK4OQgQ78W/lLHaTAsfGxAp1Av+LkrUVSuw95zl6SOQ0REVmhjZa9BXDM/eDg7SZym4bDwsQFyucy8yNRGdncREVEdbDihBgAMam273VwACx+bYeqP3XgiB0Jw01IiIqq5XE0ZjqQXALg6fMJWsfCxET2b+cFV4YDswjIkZmqkjkNERFZk08lcCAF0aKpCoKez1HEaFAsfG+Hs5IC+zY2D0TZWNlcSERHVhOlzY1CbIImTNDwWPjbE1DzJae1ERFRTxeU67KrclNTWu7kAFj425Y6WAXCQy5CsLkJ6PjctJSKiW/vn1EVodQaE+7qiuQ1uSvpvLHxsiLebAt0ivAGw1YeIiGrGNBt4UOtAm9yU9N9Y+NiYOytXceY4HyIiupUKvQFbko2bkpo+P2wdCx8bY1p/Yd+5fFwu0UqchoiILNn+c/kovFIBHzcFuoR7Sx2nUbDwsTGhPq5oGeQBgwA2V1bxRERE1TENizCNEbUHLHxs0ODK6Yjrk9jdRURE1RNCmD8nhtjBNHYTFj42yFT4/HPqIkq1OonTEBGRJTqWUYjswjK4KhzQu7mf1HEaDQsfG9Qq2ANhPq4o1xmwPeWi1HGIiMgCmVp7BsQEwNnJQeI0jYeFjw2SyWQY0tbY6rOO3V1ERFQN0+fD4Lb2080FsPCxWYMrNy3dcjIXWp1B4jRERGRJUnOLcPZiCRQOcgyI8Zc6TqNi4WOjOoV6w99DiaJyHXafyZM6DhERWZB1icbWnl7RvvBwdpI4TeNi4WOj5HKZudWHs7uIiOhapm6uIXbWzQWw8LFpptldG0/kQG8QEqchIiJLkHG5FImZGshlQHwr29+U9N9Y+NiwHlG+8HR2RF6xFgcvXJY6DhERWYD1ScZFC7tF+MDXXSlxmsbHwseGOTnIzdU8u7uIiAi4+nkw2I4WLbwWCx8bZ5qmuC5RDSHY3UVEZM/yisux/3w+APubxm7CwsfG9W3uDxcnB2QWXEFSlkbqOEREJKFNJ3IgBNC+qQpNvFykjiMJFj42zkXhgH4tjGs0sLuLiMi+rbPzbi6AhY9dME1XXJvIwoeIyF5pyiqwK9W4rhsLH7Jpd7QKgJODDKm5xTidUyR1HCIiksDmkzmo0AtEB7gjOsBd6jiSYeFjBzydndCnubG7i60+RET26e/jxvf/u9oFS5xEWix87MTQyu6uv49nS5yEiIgaW3G5DttPXQQA3NXOfru5ABY+duPO1oFwlMuQrC7C2YvFUschIqJGtCXZuGF1lJ8bYgI9pI4jKRY+dsLLVYGe0X4A2N1FRGRv1la29g9tFwSZTCZxGmmx8LEjd5lnd7G7i4jIXpRqddiakgsAGNrWvsf3ACx87MqgNkFwkMuQmKlB2qVSqeMQEVEj2JZyEWUVBoT5uKJNiKfUcSTHwseO+Lgp0CPKBwBbfYiI7MXf7OaqgoWPnTE1c/7NcT5ERDavrEKPLcnGbq672M0FgIWP3RncJggyGXA0vQAZl9ndRURky7afuohSrR5NvFzQvqlK6jgWwWoLn/fffx8ymQxTpkwx31dWVobJkyfD19cX7u7uuPfee5GTkyNdSAvk76FE9whjd9c6tvoQEdk082yutuzmMrHKwmf//v1YtGgR2rdvX+X+559/HqtXr8Yvv/yC7du3IysrC6NHj5YopeUa1r6yu4uLGRIR2axynR6bTlbO5rLz1ZqvZXWFT3FxMcaOHYuvvvoK3t7e5vsLCwuxePFizJ07F3fccQe6dOmCJUuWYPfu3dizZ4+EiS2PqbvrUFoBsguvSB2HiIgawM7TeSgu1yHI0xmdQr2kjmMxrK7wmTx5MoYNG4b4+Pgq9x88eBAVFRVV7m/ZsiXCwsKQkJBQ7XOVl5dDo9FUudmDQE9ndA03Fo1rj7O7i4jIFv1V2ao/pG0Q5HJ2c5lYVeHz008/4dChQ5g1a9Z1j6nVaigUCnh5eVW5PzAwEGp19R/us2bNgkqlMt9CQ0MbIrZFMm1S9xe7u4iIbE65To+NScYxrqbhDWRkNYVPeno6nnvuOSxfvhzOzs718pzTpk1DYWGh+Zaenl4vz2sN7moXDJkMOHjhMrIK2N1FRGRL/jmVh6LKbq4uYd63PsGOWE3hc/DgQeTm5qJz585wdHSEo6Mjtm/fjk8//RSOjo4IDAyEVqtFQUFBlfNycnIQFFT9TrRKpRKenp5VbvYi0NMZ3Spnd/11jK0+RES2ZM2xLADGP3LZzVWV1RQ+AwcOxPHjx3HkyBHzrWvXrhg7dqz5305OTti8ebP5nJSUFKSlpSEuLk7C5Jbr7srmzzXs7iIishllFXpsOmHs5hregd1c/+YodYCa8vDwQNu2bavc5+bmBl9fX/P9EyZMwNSpU+Hj4wNPT088++yziIuLQ48ePaSIbPGGtA3GjFVJOJpegPT8UoT6uEodiYiIbtO2lFyUVC5ayNlc17OaFp+amDdvHoYPH457770Xffv2RVBQEH7//XepY1ksfw8lekT5AuAgZyIiW7G6cvjCsPbBXLSwGjIhhJA6hKXQaDRQqVQoLCy0m/E+y/dewGsrE9G2iSfWPNtH6jhERHQbSrU6dHl7E65U6LHqmV5o39RL6kiNojaf3zbV4kO1N6RNEBzkMiRmanA+r0TqOEREdBu2JOfiSoUeYT6uaNeEe3NVh4WPnfN1V6JnM3Z3ERHZgjVH2c11Kyx8CMMrZ3etPpolcRIiIqqr4nIdtqYY9+YazkULb4iFD2FwmyA4ymVIVhchNbdY6jhERFQHm0/moFxnQKSfG1oH28c41bpg4UPwclWgd3M/AFzMkIjIWq2u7OYazm6um2LhQwCA4e1DAFxd7ZOIiKyHpqwC/5y6CODq+zlVj4UPAQDubB0IhYMcp3OLkay2j13qiYhsxfpENbR6A6ID3NEi0F3qOBaNhQ8BAFQuTugf4w8A+PMIW32IiKzJqsrJKSM6hLCb6xZY+JDZPR2NzaOrj2aB61oSEVmHi0Xl2JWaBwC4uwO7uW6FhQ+ZDWwZCDeFAzIuX8GhtAKp4xARUQ38fTwbBgF0aKpChJ+b1HEsHgsfMnNROGBQmyAAwKojmRKnISKimviz8v36no5NJE5iHVj4UBX3VDaT/nU8Gzq9QeI0RER0M+n5pTiUVgCZjIsW1hQLH6qid3M/eLs6Ia9Yi4Szl6SOQ0REN7G6cgmSHpG+CPR0ljiNdWDhQ1U4OchxVzvjXw2c3UVEZNlWVb5Pj+jIQc01xcKHrmPq7lqfqEZZhV7iNEREVJ0UdRGS1UVwcpBhaFt2c9UUCx+6TrcIHwSrnFFUrsO2yg3viIjIsqw6ahzU3K+FP1SuThKnsR4sfOg6crnMvBbEKu7YTkRkcYQQ5r25OJurdlj4ULVM3V2bT+aiqKxC4jRERHStI+kFSMsvhYuTA+JbBUgdx6qw8KFqtQnxRJSfG8p1BmxIypE6DhERXcM0+eTO1oFwVThKnMa6sPChaslkMvMWFn9wMUMiIouh0xuw5pixm4uzuWqPhQ/d0MjKfuNdqXnI1ZRJnIaIiABgZ2oe8orL4e3qhL4t/KWOY3VY+NANRfi5oVOYFwyCg5yJiCzFH4eNrfB3dwiBkwM/xmuL3zG6qdGdjK0+7O4iIpJeSbkO6yvHXY7qxNlcdcHCh25qWPsQOMplSMzU4HROkdRxiIjs2vokNa5U6BHh64qOoV5Sx7FKLHzopnzcFOgfY+xDXnmYrT5ERFIyvQ+P7NQEMplM4jTWiYUP3dLIyubUP49kwWAQEqchIrJPuZoy7ErNA3B18gnVHgsfuqX4VoHwUDois+AK9p/PlzoOEZFdWnU0CwYBdA7zQoSfm9RxrFadCp/09HRkZGSYv963bx+mTJmCL7/8st6CkeVwdnLA0HZBADjImYhIKqb3Xw5qvj11KnwefvhhbN26FQCgVqtx5513Yt++fXjttdcwc+bMeg1IlsHU3bXmWDZ3bCciamSnc4qQmKmBo1yGYe25aOHtqFPhk5iYiO7duwMAVqxYgbZt22L37t1Yvnw5li5dWp/5yEL0iPQ17thepsPWZO7YTkTUmEyDmvvH+MPHTSFxGutWp8KnoqICSqUSALBp0ybcc889AICWLVsiOzu7/tKRxZDLZRhROZiOs7uIiBqPwSDMe3ON6tRU4jTWr06FT5s2bbBw4ULs2LEDGzduxJAhQwAAWVlZ8PX1rdeAZDlM/cpbU3JxuUQrcRoiIvuw73w+MguuwEPpiIHcif221anw+eCDD7Bo0SL0798fDz30EDp06AAAWLVqlbkLjGxPTJAHWgd7okIvsPoYt7AgImoMvx00Tia6q10wnJ0cJE5j/eq0l33//v2Rl5cHjUYDb29v8/2TJk2Cq6trvYUjy3Nvl6Y4seYEfjuYgUfjIqSOQ0Rk00q1Ovx93DiE5N4u7OaqD3Vq8bly5QrKy8vNRc+FCxfw8ccfIyUlBQEBbIazZSM6GrewOJpRyC0siIga2LpENUq0eoT5uKJbhPetT6BbqlPhM2LECHz77bcAgIKCAsTGxuKjjz7CyJEjsWDBgnoNSJbFz12J/jHG4vbXQxm3OJqIiG7Hb5Xvs/d2bsotKupJnQqfQ4cOoU+fPgCAX3/9FYGBgbhw4QK+/fZbfPrpp/UakCzPfV0qd2w/nAk9t7AgImoQWQVXsPvMJQDA6M5ctLC+1KnwKS0thYeHBwBgw4YNGD16NORyOXr06IELFy7Ua0CyPANaBsDL1Qk5mnLsrNw3hoiI6tfKw5kQAoiN9EGoD8fP1pc6FT7R0dH4448/kJ6ejvXr12PQoEEAgNzcXHh6etZrQLI8SkcHjOhgXDn014Ps7iIiqm9CCPP7630c1Fyv6lT4TJ8+HS+++CIiIiLQvXt3xMXFATC2/nTq1KleA5JlMs0u2JCkhqasQuI0RES25VBaAc7llcDFyQFD2wVLHcem1Knwue+++5CWloYDBw5g/fr15vsHDhyIefPm1Vs4slztmqjQPMAd5ToD/jrG1bqJiOqTaVDz0LZBcFfWaeUZuoE6FT4AEBQUhE6dOiErK8u8U3v37t3RsmXLegtHlksmk5lbfdjdRURUf8oq9Fh91LhILNfuqX91KnwMBgNmzpwJlUqF8PBwhIeHw8vLC2+//TYMBkN9ZyQLNapTE8hlwMELl3Eur0TqOERENmHjiRwUlekQonJGXBS3gapvdSp8XnvtNXz22Wd4//33cfjwYRw+fBjvvfce5s+fjzfeeKO+M5KFCvR0Rp/m/gCuLqlORES3x9TNNbpzU8jlXLunvtWp43DZsmX4+uuvzbuyA0D79u3RpEkTPP3003j33XfrLSBZtvu6NMX2Uxfx26EMPH9nCzjwl5SIqM7UhWX459RFAOzmaih1avHJz8+vdixPy5YtkZ+ff9uhyHrc2ToQKhcnZBeWcU0fIqLb9NuhDBgE0D3CB5F+blLHsUl1Knw6dOiAzz777Lr7P/vsM7Rv3/62Q1Vn1qxZ6NatGzw8PBAQEICRI0ciJSWlyjFlZWWYPHkyfH194e7ujnvvvRc5OTkNkoeMnJ0cMKqTcUXRFfvTJU5DRGS9DAaBFQeM76P3dwuVOI3tqlPhM3v2bHzzzTdo3bo1JkyYgAkTJqB169ZYunQp5syZU98ZAQDbt2/H5MmTsWfPHmzcuBEVFRUYNGgQSkquDqp9/vnnsXr1avzyyy/Yvn07srKyMHr06AbJQ1eN6Vq5ps8JNfJLtBKnISKyTvvO5+PCpVK4Kx1xV7sgqePYrDoVPv369cOpU6cwatQoFBQUoKCgAKNHj0ZSUhK+++67+s4IAFi3bh3Gjx+PNm3aoEOHDli6dCnS0tJw8OBBAEBhYSEWL16MuXPn4o477kCXLl2wZMkS7N69G3v27Kn2OcvLy6HRaKrcqPbahKjQtoknKvQCfxzOlDoOEZFVMrWa390hGK4Krt3TUOq8jk9ISAjeffdd/Pbbb/jtt9/wzjvv4PLly1i8eHF95ruhwsJCAICPjw8A4ODBg6ioqEB8fLz5mJYtWyIsLAwJCQnVPsesWbOgUqnMt9BQNi3W1f1djd+7FQfSIQQ3LiUiqg1NWQX+TjQuBjumKz+LGlKdCx8pGQwGTJkyBb169ULbtm0BAGq1GgqFAl5eXlWODQwMhFqtrvZ5pk2bhsLCQvMtPZ1jVOpqRIcmUDjKkawuwvHMQqnjEBFZldVHs1BWYUDzAHd0CvWSOo5Ns8rCZ/LkyUhMTMRPP/10W8+jVCrh6elZ5UZ1o3J1wpA2xj5p0+A8IiKqmRUHjGv33N81FDIZlwVpSFZX+DzzzDNYs2YNtm7diqZNr65xEBQUBK1Wi4KCgirH5+TkICiIg8QawwOVsxD+PJKFsgq9xGmIiKxDiroIR9ML4CiXYVTnJlLHsXm1Gj11qxlS/y466pMQAs8++yxWrlyJbdu2ITIyssrjXbp0gZOTEzZv3ox7770XAJCSkoK0tDTz7vHUsOKifNHU2wUZl69gXaIaIzvxF5iI6FZMreQDWwXAz10pcRrbV6vCR6VS3fLxRx999LYC3cjkyZPxww8/4M8//4SHh4d53I5KpYKLiwtUKhUmTJiAqVOnwsfHB56ennj22WcRFxeHHj16NEgmqkoul2FMl1DM23QKP+9PZ+FDRHQLWp0BKytnwz7AtXsaRa0KnyVLljRUjltasGABAKB///5V7l+yZAnGjx8PAJg3bx7kcjnuvfdelJeXY/Dgwfjiiy8aOal9u69rU3y8+RQSzl7ChUslCPflyqNERDey6WQO8ku0CPBQom/l3ofUsKxmoYCaTJF2dnbG559/js8//7wRElF1mni5oE9zf/xz6iJ+3p+Ol4dcv7UJEREZ/bgvDYBx30NHB6sbdmuV+F2mevdwd9OaPhmo0BskTkNEZJnS80ux47Rxj8OHuodJnMZ+sPChejewVSD83JXIKy7H5pPcK42IqDo/7Te29vRp7odQH1eJ09gPFj5U75wc5Ob9u37YxzV9iIj+rUJvMK/dw9aexsXChxrEg5WzE3acvoj0/FKJ0xARWZbNJ3Nxsagcfu4KxLcKlDqOXWHhQw0i3NcNvaP9IATw8362+hARXcvUzXVfl1AoHPlR3Jj43aYGY2q+XXEgnYOciYgqZVwuxfZTFwFcbR2nxsPChxrMna0D4eumQG5RObYk50odh4jIIqzYnw4hgF7Rvojw41pnjY2FDzUYhaMc91UOcjatVUFEZM90egN+rtyigoOapcHChxrUg92Mv9jbT11ExmUOciYi+7Y15SJyNOXwdVNgUGtuoC0FFj7UoCL93NCzmS8HORMRoepKzRzULA1+16nBmZpzf97PQc5EZL/S80uxNcU43pEbkkqHhQ81uMFtguDnbhzkvOkEV3ImIvv0474086DmKH93qePYLRY+1OAUjnLzXzff7bkgcRoiosZXrtNjReWg5kd6hEucxr6x8KFG8VD3MMhlwO4zl5CaWyx1HCKiRrUuUY28Yi0CPZVcqVliLHyoUTT1dsUdLQMAAMv3stWHiOzL8j3GQc0PdQ+DowM/eqXE7z41mv9UNu/+ejADpVqdxGmIiBpHslqDfefz4SCXmZf4IOmw8KFG07e5P8J8XFFUpsPqo1lSxyEiahTfV45tHNQ6EEEqZ4nTEAsfajRyuQxjY41/7Xy35wKEEBInIiJqWMXlOqw8lAmAg5otBQsfalRjuhp3Ik7M1OBoRqHUcYiIGtTKw5ko0eoR5e+GuGa+UschsPChRubjpsDw9sEAgO8SOMiZiGyXEALLK7u5/hMbDplMJnEiAlj4kARMg5zXHMvC5RKtxGmIiBrGgQuXkawugrOTHPd2aSp1HKrEwocaXadQL7QJ8US5zmBe0IuIyNYs230eAHBPhxCoXJykDUNmLHyo0clkMoyLiwAAfJtwAXoDBzkTkW1RF5ZhXaIaADCuZ4S0YagKFj4kiXs6hsDb1QmZBVew6ST37yIi27J87wXoDALdI3zQJkQldRy6BgsfkoSzkwMerNy13dQcTERkC8p1evyw17hSM1t7LA8LH5LMf3qEw0Euw+4zl5CiLpI6DhFRvVhzNBuXSrQIVjljUBvuy2VpWPiQZJp4uWBQa+ObwrKE89KGISKqB0IILK1sxf5Pj3A4cV8ui8MrQpIaX9kMvPJQJgpLK6QNQ0R0mw6lFeB4ZiEUjnI81J37clkiFj4kqe6RPmgZ5IErFXpObSciq2casziiQwh83BTShqFqsfAhSclkMjzWKwKAsbuLU9uJyFrlaMrw9/FsABzUbMlY+JDkRnRsAi9XJ2RcvoLNnNpORFZq+R7jFPZuEd5o24RT2C0VCx+SnLOTAx7oFgoA5kGBRETWpFynxw/7OIXdGrDwIYvwyDVT209ma6SOQ0RUK6uOZCGvWIsgT2cMbhMkdRy6CRY+ZBGaertiSFvjm8XineckTkNEVHNCCPP71vheEZzCbuF4dchi/Ld3JADjX065RWUSpyEiqpndZy4hWV0EFycHPNSNU9gtHQsfshidwrzROcwLWr0B3ydckDoOEVGNfL3jLADg/q5NoXLlLuyWjoUPWZT/9okCAHy/Nw1lFXqJ0xAR3VxqbhG2plyETAY81itS6jhUAyx8yKIMah2Ipt4uyC/RYuXhTKnjEBHd1De7zgMA4lsFIsLPTdowVCMsfMiiODrIzdtYLN55DkJwQUMiskz5JVr8djADwNUximT5WPiQxXmgWyjclY5IzS3G9lMXpY5DRFStH/ZeQLnOgLZNPNE90kfqOFRDLHzI4ng4O+HBygUNObWdiCxRuU6PZZWTMP7bOwoymUziRFRTLHzIIo3vFQG5DNhxOg/Jai5oSESWZfXRbFwsKkeQpzPuahcsdRyqBRY+ZJGaertiaOWbyVf/sNWHiCyHEAJf/WOcwv5oz3AoHPlRak14tchiTaqc2v7nkUxkF16ROA0RkdG2UxeRklMEN4UDxsaGSx2HaomFD1msDqFeiIvyhc4gsHgHW32IyDIs3HYGAPBwbBhULlyw0Nqw8CGL9kQ/Y6vPj/vSUFhaIXEaIrJ3h9MuY++5fDjKZXicU9itkk0WPp9//jkiIiLg7OyM2NhY7Nu3T+pIVEf9WvijZZAHSrR6fL+X21gQkbS+rBzbM6JjEwSrXCROQ3Vhc4XPzz//jKlTp2LGjBk4dOgQOnTogMGDByM3N1fqaFQHMpnM3OqzZNd5bmNBRJI5l1eCdUlqAFdbo8n62FzhM3fuXEycOBGPPfYYWrdujYULF8LV1RXffPPNdceWl5dDo9FUuZHlGd4+BCEqZ+QVl3MbCyKSzFc7zkIIYGDLALQI9JA6DtWRTRU+Wq0WBw8eRHx8vPk+uVyO+Ph4JCQkXHf8rFmzoFKpzLfQ0NDGjEs15OQgx4TKGV5f/nMWegO3sSCixpVbVIZfK7eneKJfM4nT0O2wqcInLy8Per0egYGBVe4PDAyEWq2+7vhp06ahsLDQfEtPT2+sqFRLD3YLhcrFCefySrDxxPXXkoioIS3bfR5anQGdwrzQLcJb6jh0G2yq8KktpVIJT0/PKjeyTG5KRzzSw7hexoLtZ7l5KRE1muJyHb6r3J7iib7NuD2FlbOpwsfPzw8ODg7Iycmpcn9OTg6CgoIkSkX1ZVzPCCgd5TiaXoCEM5ekjkNEdmL5ngvQlOkQ5eeGO1sH3voEsmg2VfgoFAp06dIFmzdvNt9nMBiwefNmxMXFSZiM6oO/hxIPVG5e+tnWVInTEJE9KKvQ46vKBVSf7N8MDnK29lg7myp8AGDq1Kn46quvsGzZMpw8eRJPPfUUSkpK8Nhjj0kdjerBE/2awVEuw+4zl3Ao7bLUcYjIxq04kI684nI08XLBqE5NpI5D9cDmCp8HHngAc+bMwfTp09GxY0ccOXIE69atu27AM1mna998Pt/CVh8iajgVegMWbTcuWPhEvyg4OdjcR6Zdssmr+Mwzz+DChQsoLy/H3r17ERsbK3UkqkdP9W8GuQzYnJyLE1lce4mIGsbKw5nILLgCP3cl7u/K5U5shU0WPmTbovzdcVe7YADA59vY6kNE9U9vEFhQuRnpxD6RcHZykDgR1RcWPmSVJg+IBgD8fTwbZy4WS5yGiGzN38ezcS6vBCoXJ4ytXEqDbAMLH7JKrYI9Ed8qAELA/FcZEVF9EELg88qZo4/1ioC70lHiRFSfWPiQ1TK1+vxxOBMZl0slTkNEtmLzyVwkq4vgpnDA+J4RUsehesbCh6xWpzBv9Ir2he6avngiotshhMD8LacBAP+JC4eXq0LiRFTfWPiQVfu/O5oDMK61kVlwReI0RGTttqVcxNGMQjg7yfHf3lFSx6EGwMKHrFpslC/ionxRoRf4gqs5E9FtEELg402nAACPxkXA30MpcSJqCCx8yOpNiWerDxHdPlNrj4uTAyb1ZWuPrWLhQ1YvNsoXPZsZW30+Z6sPEdWBEALzzK094fBzZ2uPrWLhQzbhuYHGVp9fDqRzhhcR1drWlFwcq2ztmcjWHpvGwodswrWtPl9whhcR1YJxbI9xJhdbe2wfCx+yGVPiWwBgqw8R1Q5be+wLCx+yGd0jfdArmq0+RFRzVVp7erK1xx6w8CGb8tzAq60+6fls9SGim9t88mprz6Q+bO2xByx8yKZ0j/RBn+Z+qNBf/SuOiKg6BoPAnA0pAIBxPSPgy9Yeu8DCh2zOi4NiAAArD2fgdE6RxGmIyFKtPpaFZHURPJwd8WQ/tvbYCxY+ZHM6hHphcJtAGAQwd+MpqeMQkQWq0Bswr/L94Ym+UdyTy46w8CGb9MKgGMhkwNpENY5lFEgdh4gszK8HM3D+Uil83RR4rFek1HGoEbHwIZvUItADIzs2AQDM2cBWHyK6qqxCj083G8cAPj0gGm5KR4kTUWNi4UM2a0p8czjKZfjn1EXsOXtJ6jhEZCGW701DdmEZglXOGBsbJnUcamQsfMhmhfu64YFuoQCAOetTIISQOBERSa24XIcvKvf0e25gczg7OUiciBobCx+yac/e0RxKRzkOXLiMrSm5UschIokt2XkOl0q0iPRzw71dmkodhyTAwodsWpDKGeN6RgAAZq9Lgd7AVh8ie3WpuByL/jkLwNgV7uTAj0B7xKtONu/p/s3g6eyIZHURfj+UIXUcIpLI/C2pKC7XoW0TT9zdPkTqOCQRFj5k87xcFZg8IBoA8NGGUyir0EuciIga2/m8Eny/5wIA4H9DW0Eul0mciKTCwofswrieEWji5QK1pgzf7DondRwiamSz1ydDZxDoH+OPntF+UschCbHwIbvg7OSAFwcbNzBdsPUMLhWXS5yIiBrLobTL+Pu4GnIZMG1oK6njkMRY+JDdGNGhCVoHe6KoXIf5W1KljkNEjUAIgff+OgkAuK9LU8QEeUiciKTGwofshlwuw//uMv619/2eCzifVyJxIiJqaBtO5ODAhctwdpJj6p0xUschC8DCh+xK7+Z+6NfCHzqDwIcbUqSOQ0QNqEJvwAdrkwEA/+0dhSCVs8SJyBKw8CG78+rQlpDJgL+OZePghctSxyGiBvLjvjSczSuBr5sCT/SLkjoOWQgWPmR3WgV7Ykzliq0zVyfBwEUNiWxOQakWczcaNyieEt8cHs5OEiciS8HCh+zSi4Nj4K50xNGMQqw8nCl1HCKqZx9vOo2C0grEBHrgoe7ciJSuYuFDdinAwxnP3GFc1PCDdckoKddJnIiI6svpnCJ8V7lY4fS7W8ORW1PQNfjTQHbrsV4RCPd1RW5ROb7YxuntRLZACIGZa05AbxC4s3UgenGxQvoXFj5kt5SODnitcnr7VzvOIT2/VOJERHS7tiTnYsfpPCgc5Obfb6JrsfAhu2b8i9AXWp0B7/19Uuo4RHQbtDoD3qlcrPCx3hGI8HOTOBFZIhY+ZNdkMhneGN4achmwNlGNhDOXpI5ERHX0bcJ5nMsrgZ+7Es9UbkxM9G8sfMjutQzyxNjYcADAW6uToNMbJE5ERLWVW1SGTzadBgC8PDiG09fphlj4EAGYemcLeLs6IVldhKW7z0sdh4hq6b2/TqKoXIf2TVW4t3KdLqLqsPAhAuDtpsArQ1oCAOZtPAV1YZnEiYiophLOXMIfR7IgkwHvjGwLB7lM6khkwRylDkBkKe7vGoqfD6TjcFoB3vnrBD57uLPUkaiOcovKcCy9EMcyCnAkoxC5musL2QhfN7QPVaFjUy+0baqCJ7tGrJJWZ8AbfyYCAMbGhqF9Uy9pA5HFY+FDVEkul+HtEW1xz2c7seZYNh7slofezbkGiLXILLiCH/emYeXhTGQWXLnl8cnqIqxLUpu/bhnkgTFdQ3Ffl6ZQubAIshbf7DqH1Nxi+Lop8NKgllLHISsgE0Jwo6JKGo0GKpUKhYWF8PT0lDoOSeTNVUlYuvs8ovzcsHZKHygdHaSORDdgMAjsSM3DdwkXsCU5B6Zt12QyoHmAO9o39UKHpipE+LlBLrva/VGhN+BUThGOphfiaEYBMi5fLZScneQY2bEJ/tMjHG2bqBr7JVEtZBVcwcCPtuNKhR5zxnTAfRzbY7dq8/nNFh+if5k6qAXWHMvG2bwSfL3jHCZzWqxF2nP2Eqb/mYhTOcXm+3o288V/eoSjbwt/uCtv/vbWPybA/O9LxeVYl6TGdwkXkKwuwk/70/HT/nT0ae6Ht+5pgyh/9wZ7HVR3b685gSsVenSL8Ma9nZtIHYeshFUMbj5//jwmTJiAyMhIuLi4oFmzZpgxYwa0Wm2V444dO4Y+ffrA2dkZoaGhmD17tkSJyZp5OjvhtWHGJvP5W05zRWcLc6m4HC+sOIoHv9yDUznF8FA6YnzPCGya2g8/TOyBu9oF37Lo+TdfdyXGxoZj7XN98MuTcRjRMQRODjLsOJ2HIR/vwNyNp1BWoW+gV0R1sTUlF2sT1XCQy/D2yLaQyTigmWrGKlp8kpOTYTAYsGjRIkRHRyMxMRETJ05ESUkJ5syZA8DYzDVo0CDEx8dj4cKFOH78OB5//HF4eXlh0qRJEr8CsjYjOzbBz/vTsedsPv638ji+fbw731glZjAIrDiQjllrk1F4pQIyGfBQ9zC8MrglVK71MyZHJpOhW4QPukX4YOqdLTD9zyRsP3URn24+jVVHMjFzRFv0beFfL/8X1V1JuQ6vrzQOaB7fMwItgzg0gWrOasf4fPjhh1iwYAHOnj0LAFiwYAFee+01qNVqKBQKAMCrr76KP/74A8nJyTV6To7xoWudvViMIZ/sgFZnwEdjOnBtEAkVXqnA8z8fwZbkXABA62BPvDuqLTqFeTfo/yuEwNpENd5anYQcTTkAYELvSEwb2pI7fkvordVJWLLrPJp4uWDD833hVssWPrI9tfn8ttrf3MLCQvj4+Ji/TkhIQN++fc1FDwAMHjwYKSkpuHz5crXPUV5eDo1GU+VGZBLl747nBjYHALz91wnkFZdLnMg+peYWYeTnu7AlORdKRzneGN4aq57p1eBFD2BsAbqrXTA2Te2H8T0jAACLd57DuCX7kF+ivfnJ1CAOpV02LzI6a3Q7Fj1Ua1ZZ+KSmpmL+/Pl44oknzPep1WoEBgZWOc70tVqtRnVmzZoFlUplvoWGhjZcaLJKk/pGoVWwJwpKK/DmqiSp49idDUlqjPx8N87llSBE5YzfnuqJCb0jG721xcPZCW/e0wYLxnaGq8IBu1Iv4Z7PduJEFv9YakxanQGv/nYMQgCjOzdhtyPViaSFz6uvvgqZTHbT27+7qTIzMzFkyBCMGTMGEydOvK3/f9q0aSgsLDTf0tPTb+v5yPY4Ocjxwb3tIJcBa45lY9OJHKkj2QUhBD7edAqTvjuI4nIdYiN9sOrZ3pJPLx/aLhgrn+6FcF9XZFy+gtELdmHNsSxJM9mTBdvO4FSOcc2eN4a1ljoOWSlJ2whfeOEFjB8//qbHREVFmf+dlZWFAQMGoGfPnvjyyy+rHBcUFIScnKofSqavg4KCqn1upVIJpVJZh+RkT9o39cJ/+0Thy3/O4o0/ExEb5cMNEBuQwSDw5uokfJtwAYBx8Oprw1rByULG1MQEeWDV5N549qfD+OfURTz742EUl+nwYPcwqaPZtNM5Rfhsq3ET0hn3tIG3m+IWZxBVT9LCx9/fH/7+NWuqzMzMxIABA9ClSxcsWbIEcnnVN8G4uDi89tprqKiogJOT8UNp48aNiImJgbd3w48FINv2fHwLrEtUIy2/FO+vTca7o9pJHckmGQwC/1t5HD/tT4dMBrw9oi3+0yNc6ljXUbk6Ycn4bpixKhHf70nDq78fh1ZvwKNxEVJHs0l6g8Arvx1DhV5gYMsA3N0+WOpIZMUs40+oW8jMzET//v0RFhaGOXPm4OLFi1Cr1VXG7jz88MNQKBSYMGECkpKS8PPPP+OTTz7B1KlTJUxOtsJF4YBZo43FzvK9adhx+qLEiWyP3iDw0q/H8NP+dMhlwIf3dbDIosfEoXKLkwm9IwEA0/9Mwtc7zkqcyjZ9teMsDqUVwF3pyDV76LZZReGzceNGpKamYvPmzWjatCmCg4PNNxOVSoUNGzbg3Llz6NKlC1544QVMnz6da/hQvekV7YdHKj+IX/rlGApLKyROZDsq9AZM+fkIfjuUAQe5DB8/2Mkqth+QyWR4fVgrPN2/GQDgnb9O4ottqRKnsi3Jag3mbjgFAJg+vDVCvFwkTkTWzmrX8WkIXMeHbqVUq8Ndn+zA+UulGNWpCeY90FHqSFbPYBB47ucjWH00C04OMsx/qBOGtLWurgwhBD7dnIp5m4wf0K8ObYkn+zWTOJX10+oMGPH5LpzM1iC+VQC+erQrW3uoWnaxjg+RFFwVjvjo/o6Qy4CVhzOx9ni21JGsmhACM9ecMBc9C8Z2sbqiBzC2/DwX3xwvDY4BALy/Nhm/HOAs0dv16ebTOJmtgberE94b3Y5FD9ULFj5EtdQl3Nv81/z/Vh7HxSIubFhXX2w7Y16Mbs6YDohvHXjzEyzc5AHReKKvcSbqq78fx5ZkLn9QV4fSLpu7Dd8b1Q4BHs4SJyJbwcKHqA6mxLdAq2BPXC6twLTfj4E9xrW34kA6PlyfAsA4dmNER9vYXfuVIS0xunMT6A0CTy8/hENp1a8cTzd2RavHCyuOwiCAUZ2aYGg762sFJMvFwoeoDhSOcsy9vwOcHGTYdDIXP+1nt0ZtbDqRg2m/HwcAPNW/GR6vnBllC+RyGT64tz36x/ijrMKAx5fuR2pukdSxrMq7f5/AubwSBHk648172kgdh2wMCx+iOmoV7IkXBxnHdLy1OgmncvjhVhNH0gvwzI+HoDcI3NelKV6uHBdjS5wc5PhibGd0DPVCQWkFHl28D7lFZVLHsgprj2fj+z1pAIAPx7SHyoWLhVL9YuFDdBsm9olC3xbGv+wnLz+EK1q91JEsWlbBFfx32QGUVRgwIMYfs2x4wKqrwhHfjO+GKD83ZBWWYeK3B1FWwZ+Pm0nPL8XLvx0DYGwJ7NOce3FR/WPhQ3Qb5HIZ5t7fAf4eSpzOLcZbq7mR6Y2UlOswYdkB5BWXo2WQB+Y/3NlitqFoKD5uCiwe3w1erk44ml6AF385yvFgN1ChN+DZHw+jqEyHzmFemHpnC6kjkY2y7Xcdokbg567Exw90hEwG/LQ/HauOctPKf9MbBJ776QhOZmvg567A1+O6wl0p6Y45jSbSzw0LxnaBo1yGNcey8fGm01JHskhz1qfgSHoBPJ0d8elDnWy+KCbp8CeLqB70ivbDMwOiAQD/+/04zueVSJzIssxel4xNJ3OgcJTjy0e7oqm3q9SRGlVcM1+8O6otAOCTzadZHP/L1pRcLPrHuN3Hh2M62N3PBzUuFj5E9eS5gc3RPcIHxeU6PPPjIY7nqLTiQPrVD7X72qNzmH1uGvxAtzBM7GOcvfbiL0dxmNPcARjHfb2w4igAYFxcOAa3CZI4Edk6Fj5E9cTRQY5PHuoIb1cnJGZq8L/fj9v9eI795/Px2krjtPX/G9jcZtbqqatXh7ZCfKsAaHUGTPruILILr0gdSVJlFXo88d1B5Jdo0SbEE9PuaiV1JLIDLHyI6lGwygWfPdwZDnIZfj+ciSW7zksdSTIZl0vx5HcHUaEXuKtdEKYMbC51JMmZNmCNCfTAxaJyTPr2oN3OBBRCYNrvx3E8sxDerk5Y+J8ucHZykDoW2QEWPkT1rFe0H/5X+Zfru3+fxO7UPIkTNb6Sch0mfnsQl0q0aB3siTljOkAut81p67XlrnTE1+O6wsdNgeOZhXj5N/tc+fubXeex8nAmHOQyfP5wZ4T6cFwPNQ4WPkQN4PFeERjdybhtweQfDiE9v1TqSI3GYBB48Zej5hlcX43rCleFfczgqqlQH1d8MbYzHOUyrD6ahS+2nZE6UqPalZqH9/4+CQB47a5W6BntJ3EisicsfIgagEwmw3uj26FdExUul1Zg0nf206XxyebTWJuohsJBjkWPdEETLxepI1mkHlG+mDnCONPrw/Up2JCkljhR40jPL8UzPxhX7h7duQke6xUhdSSyMyx8iBqIs5MDFj3SBb5uCpzM1uC5nw5Db7DtLo3VR7PwyWbjOjXvjmqLLuE+EieybA/HhmFcXDgAYMrPR5CUVShxooZVWFqBCcv243JpBdo3VeG9Uba7cjdZLhY+RA0oxMsFCx/pAoWjHBtO5ODNVUk2O57j4IV8vPCLcVryf3tHYkzXUIkTWYc3hrdG72g/lGr1mLD0ANSFtrmnV1mFHhO/O4BTOcUI9FRyMDNJhoUPUQPrFuFjXtn5uz0XbHI8R9qlUkz89iC0OgPiWwVyWnItODrI8fnYzogOcIdaU4YJy/ajpFwndax6ZTAITF1xBPvO5cND6Yilj3VHCLtASSIsfIgawV3tgjFjeGsAxvEcvx7MkDhR/SksrcBjS/chv0SLtk088elDHeHAGVy1onJxwpLx3eDrpkBSlm11iwohMHPNCfx9XA0nBxkWPdoFrYI9pY5FdoyFD1EjGd8rEk/0iwIAvPrbMWw/dVHiRLdPqzPgqeUHceZiCYJVzlg8rhtncNVRqI8rvhrXFUpHOTadzMW7f52UOlK9+GrHWSzdfR4A8NH9HdGzGWdwkbRY+BA1olcGt8TIjiHQGQSe/O4g9py9JHWkOhNC4H8rj2P3mUtwUzjgm/HdEOjpLHUsq9Y5zBtz7+8IAPhm1zks3XVO2kC3afneC3jv72QAwOvDWuGeDiESJyJi4UPUqORyGWbf1wF9W/jjSoUejy3Zj4Qz1lf8CCHw9pqT+PVgBuQy4LOHO7P7op4Max+MlwbHAADeXH0Cv1lpt+h3Cefx2spEAMCkvlH4b58oiRMRGbHwIWpkCkc5vnykC/qZip+l+7DLylZ3nrfxFL6pbI2YfV8HDGgZIHEi2/J0/2bm9W1e+vUo1h7PljZQLS3bfR5v/JkEwFj0TBvaUuJERFex8CGSgGmNnwEx/iirMODxpfux47R1jPlZtP0MPt2SCgCYOaIN7uvSVOJEtkcmk+GNYa1xf9emMAjg/346jK0puVLHqpFvdp7DjFXGoufJfs0wbWhLrtVDFoWFD5FEnJ0csPCRLohvFYBynQETlh3A5pM5Use6qe/3XMCstcYxGy8PicGjcRHSBrJhcrkMs0a3x7D2wajQW/6YMCEEFm4/g5lrTgAwtlq9MiSGRQ9ZHBY+RBJSOjrgi7FdcGfrQGh1Bkz89gC+3nHWIhc5/H7PBbzxp3HMxtP9m+Hp/tESJ7J9DnIZ5t3fEXe0rCyOl+63yE1vtToDpv1+HO9XFsXP3hGNlwaz6CHLxMKHSGIKRzm+GNsZD3UPhUEA7/x1Ev9beRwVeoPU0QAY/5KfvS4Zr/+RCCGA8T0jzINvqeGZfj56NvNFiVaPcUv24c8jmVLHMrtcosWj3+zFT/vTIZcZZ29NvbMFix6yWCx8iCyAk4Mc741qh9eHtYJMBvy4Lx2PLt6HglKtpLm0OgOmrjhqXm36+fgWmHF3a36oNTJnJ+NyAcPaGbu9nvvpCBZsOyN5y2BqbjFGfbELe87mw13piK/HdcV/+0Tx54MsmkxI/ZtjQTQaDVQqFQoLC+Hpyam5JI0tyTl49ofDKNHqEe7rinkPdETnMO9Gz6Epq8BT3x/ErtRLcJDLMGt0O9zP/bckZTAIvPv3SSzeaZxR90iPcLx5TxtJVspecywL//v9ODRlOjTxcsE347shJsij0XMQAbX7/Gbhcw0WPmQpktUaTFh6AJkFVyCXARP7RuH5+BaNtqnj8YxCTF1xBKdzi+GqcMAXYzujfwynrFuKr3ecxbt/n4QQQJ/mfph9X3sEqxpn76tLxeWY/mcS/qqcYt8l3BuLHukCP3dlo/z/RNVh4VNHLHzIkhSWVuCt1Un4/bBxPEd0gDs+GtMBHUK9Guz/1OoMmL/lNL7YdgZ6g4C/hxJLxndD2yaqBvs/qW7+Pp6N538+gnKdAR7OjnhjeGuM6dK0QbuZ1h7Pxut/JOJSiRYOchkmD4jGMwOioXDkqAmSFgufOmLhQ5ZoQ5Ia/1t5HHnFxg+bsbFheLJfs3rf3ToxsxAv/nIUyeoiAMCwdsGYOaINfPmXvMVKzS3Ci78cw5H0AgDAgBh/zBrdHkGq+t065ESWBh9vOoUNJ4zLLcQEeuCj+zuwICaLwcKnjlj4kKXKL9FixqokrD6aBQBwcpDhvi5N8VS/aIT5ut7Wc5/I0mDp7nP4/VAmdAYBHzcF3h7RFsPaB9dHdGpgOr0BX+88h7kbTkGrN7b+/KdHOB7pEX7bxfGR9AJ8tuU0Np00Lp4olwFP9W+G/xvYHErHxul2JaoJFj51xMKHLN2u1DzM33Iae87mAzCu8zK8fTCGtAlCz2g/qFycavQ8eoPAxhM5WLLrHPaeyzffP7RtEN4e2ZbjNazQ6ZwivPjLURzNKARg/NkY0iYIj/WKQJdw7xp3gakLy/DP6YtYfTQLO04b1wySy4Dh7UMweUA0BzCTRWLhU0csfMha7D+fj/lbUvHPqavbXDjIZegY6oW+zf0RHeAOd2dHuCuNNwGBFHURTmRpcCJbg6QsDfJLtObzhrQNwuO9ItAl3Eeql0T1QG8Q2HTSWNCaimMAaOLlgtYhnmgV7InWwZ6IDnBDhV6guFyH4nIdisp0SMwsxPaUi0jJKTKf5yCXYVSnJni6fzNE+btL8ZKIaoSFTx2x8CFrczS9AH8eycL2U7k4c7GkVud6uzrhoe5heCQuvNFmBFHjOZmtwdJd5/HHkUyU62q+GKZMBrRv6oV+zf0wpmsoQn1uryuVqDGw8KkjFj5kzTIul+KfU3nYdSYPFzXlKCrXoaTyL3qd3oAWgR7Gv/gr//JvFezBcRp2oKisAklZGpzM1phb/C5cKoWzkwM8nB3hpnSAu9IRod6u6NvCH72j/eDtppA6NlGtsPCpIxY+RERE1qc2n99cfIGIiIjsBgsfIiIishssfIiIiMhusPAhIiIiu8HCh4iIiOwGCx8iIiKyGyx8iIiIyG6w8CEiIiK7YXWFT3l5OTp27AiZTIYjR45UeezYsWPo06cPnJ2dERoaitmzZ0sTkoiIiCyS1RU+L7/8MkJCQq67X6PRYNCgQQgPD8fBgwfx4Ycf4s0338SXX34pQUoiIiKyRI5SB6iNtWvXYsOGDfjtt9+wdu3aKo8tX74cWq0W33zzDRQKBdq0aYMjR45g7ty5mDRpkkSJiYiIyJJYTYtPTk4OJk6ciO+++w6urtfvFpyQkIC+fftCobi6ud7gwYORkpKCy5cvV/uc5eXl0Gg0VW5ERERku6yi8BFCYPz48XjyySfRtWvXao9Rq9UIDAyscp/pa7VaXe05s2bNgkqlMt9CQ0PrNzgRERFZFEkLn1dffRUymeymt+TkZMyfPx9FRUWYNm1avf7/06ZNQ2FhofmWnp5er89PRERElkXSMT4vvPACxo8ff9NjoqKisGXLFiQkJECpVFZ5rGvXrhg7diyWLVuGoKAg5OTkVHnc9HVQUFC1z61UKqs8pxACANjlRUREZEVMn9umz/GbkbTw8ff3h7+//y2P+/TTT/HOO++Yv87KysLgwYPx888/IzY2FgAQFxeH1157DRUVFXBycgIAbNy4ETExMfD29q5RnqKiIgBglxcREZEVKioqgkqluukxMlGT8sjCnD9/HpGRkTh8+DA6duwIACgsLERMTAwGDRqEV155BYmJiXj88ccxb968Gs/qMhgMyMrKgoeHB2QyWb1m1mg0CA0NRXp6Ojw9Pev1uS2Brb8+wPZfI1+f9bP118jXZ/0a6jUKIVBUVISQkBDI5TcfxWNV09lvRqVSYcOGDZg8eTK6dOkCPz8/TJ8+vVZT2eVyOZo2bdqAKQFPT0+b/YEGbP/1Abb/Gvn6rJ+tv0a+PuvXEK/xVi09JlZZ+ERERFTbj9e+fXvs2LFDgkRERERkDaxiOjsRERFRfWDh00iUSiVmzJhx3cw0W2Hrrw+w/dfI12f9bP018vVZP0t4jVY5uJmIiIioLtjiQ0RERHaDhQ8RERHZDRY+REREZDdY+BAREZHdYOFTT95991307NkTrq6u8PLyqvaYtLQ0DBs2DK6urggICMBLL70EnU530+fNz8/H2LFj4enpCS8vL0yYMAHFxcUN8ApqZ9u2bTfcWHb//v03PK9///7XHf/kk082YvKai4iIuC7r+++/f9NzysrKMHnyZPj6+sLd3R333nvvdXvIWYrz589jwoQJiIyMhIuLC5o1a4YZM2ZAq9Xe9DxLvoaff/45IiIi4OzsjNjYWOzbt++mx//yyy9o2bIlnJ2d0a5dO/z999+NlLT2Zs2ahW7dusHDwwMBAQEYOXIkUlJSbnrO0qVLr7tWzs7OjZS4dt58883rsrZs2fKm51jT9QOqf0+RyWSYPHlytcdb+vX7559/cPfddyMkJAQymQx//PFHlceFEJg+fTqCg4Ph4uKC+Ph4nD59+pbPW9vf49pi4VNPtFotxowZg6eeeqrax/V6PYYNGwatVovdu3dj2bJlWLp0KaZPn37T5x07diySkpKwceNGrFmzBv/880+tVqNuKD179kR2dnaV23//+19ERkaia9euNz134sSJVc6bPXt2I6WuvZkzZ1bJ+uyzz970+Oeffx6rV6/GL7/8gu3btyMrKwujR49upLS1k5ycDIPBgEWLFiEpKQnz5s3DwoUL8b///e+W51riNfz5558xdepUzJgxA4cOHUKHDh0wePBg5ObmVnv87t278dBDD2HChAk4fPgwRo4ciZEjRyIxMbGRk9fM9u3bMXnyZOzZswcbN25ERUUFBg0ahJKSkpue5+npWeVaXbhwoZES116bNm2qZN25c+cNj7W26wcA+/fvr/L6Nm7cCAAYM2bMDc+x5OtXUlKCDh064PPPP6/28dmzZ+PTTz/FwoULsXfvXri5uWHw4MEoKyu74XPW9ve4TgTVqyVLlgiVSnXd/X///beQy+VCrVab71uwYIHw9PQU5eXl1T7XiRMnBACxf/9+831r164VMplMZGZm1nv226HVaoW/v7+YOXPmTY/r16+feO655xon1G0KDw8X8+bNq/HxBQUFwsnJSfzyyy/m+06ePCkAiISEhAZIWP9mz54tIiMjb3qMpV7D7t27i8mTJ5u/1uv1IiQkRMyaNava4++//34xbNiwKvfFxsaKJ554okFz1pfc3FwBQGzfvv2Gx9zo/cgSzZgxQ3To0KHGx1v79RNCiOeee040a9ZMGAyGah+3pusHQKxcudL8tcFgEEFBQeLDDz8031dQUCCUSqX48ccfb/g8tf09rgu2+DSShIQEtGvXDoGBgeb7Bg8eDI1Gg6SkpBue4+XlVaUFJT4+HnK5HHv37m3wzLWxatUqXLp0CY899tgtj12+fDn8/PzQtm1bTJs2DaWlpY2QsG7ef/99+Pr6olOnTvjwww9v2jV58OBBVFRUID4+3nxfy5YtERYWhoSEhMaIe9sKCwvh4+Nzy+Ms7RpqtVocPHiwyvdeLpcjPj7+ht/7hISEKscDxt9Ja7pWAG55vYqLixEeHo7Q0FCMGDHihu83luD06dMICQlBVFQUxo4di7S0tBsea+3XT6vV4vvvv8fjjz9+002xren6XevcuXNQq9VVrpFKpUJsbOwNr1Fdfo/rwir36rJGarW6StEDwPy1Wq2+4TkBAQFV7nN0dISPj88Nz5HK4sWLMXjw4Ftu8vrwww8jPDwcISEhOHbsGF555RWkpKTg999/b6SkNfd///d/6Ny5M3x8fLB7925MmzYN2dnZmDt3brXHq9VqKBSK68Z4BQYGWtz1qk5qairmz5+POXPm3PQ4S7yGeXl50Ov11f6OJScnV3vOjX4nreFaGQwGTJkyBb169ULbtm1veFxMTAy++eYbtG/fHoWFhZgzZw569uyJpKSkBt+QubZiY2OxdOlSxMTEIDs7G2+99Rb69OmDxMREeHh4XHe8NV8/APjjjz9QUFCA8ePH3/AYa7p+/2a6DrW5RnX5Pa4LFj438eqrr+KDDz646TEnT5685QA8a1KX15yRkYH169djxYoVt3z+a8cntWvXDsHBwRg4cCDOnDmDZs2a1T14DdXm9U2dOtV8X/v27aFQKPDEE09g1qxZFr2kfF2uYWZmJoYMGYIxY8Zg4sSJNz1X6mtIwOTJk5GYmHjTMTAAEBcXh7i4OPPXPXv2RKtWrbBo0SK8/fbbDR2zVoYOHWr+d/v27REbG4vw8HCsWLECEyZMkDBZw1i8eDGGDh2KkJCQGx5jTdfPmrDwuYkXXnjhptU4AERFRdXouYKCgq4bmW6a7RMUFHTDc/49oEun0yE/P/+G59yuurzmJUuWwNfXF/fcc0+t/7/Y2FgAxtaGxvjQvJ1rGhsbC51Oh/PnzyMmJua6x4OCgqDValFQUFCl1ScnJ6fBrld1avsas7KyMGDAAPTs2RNffvllrf+/xr6G1fHz84ODg8N1M+hu9r0PCgqq1fGW4plnnjFPdKjtX/1OTk7o1KkTUlNTGyhd/fHy8kKLFi1umNVarx8AXLhwAZs2bap1K6k1XT/TdcjJyUFwcLD5/pycHHTs2LHac+rye1wn9TZaiIQQtx7cnJOTY75v0aJFwtPTU5SVlVX7XKbBzQcOHDDft379eosa3GwwGERkZKR44YUX6nT+zp07BQBx9OjRek5W/77//nshl8tFfn5+tY+bBjf/+uuv5vuSk5MtenBzRkaGaN68uXjwwQeFTqer03NYyjXs3r27eOaZZ8xf6/V60aRJk5sObh4+fHiV++Li4ix2cKzBYBCTJ08WISEh4tSpU3V6Dp1OJ2JiYsTzzz9fz+nqX1FRkfD29haffPJJtY9b2/W71owZM0RQUJCoqKio1XmWfP1wg8HNc+bMMd9XWFhYo8HNtfk9rlPWensmO3fhwgVx+PBh8dZbbwl3d3dx+PBhcfjwYVFUVCSEMP7Atm3bVgwaNEgcOXJErFu3Tvj7+4tp06aZn2Pv3r0iJiZGZGRkmO8bMmSI6NSpk9i7d6/YuXOnaN68uXjooYca/fXdyKZNmwQAcfLkyesey8jIEDExMWLv3r1CCCFSU1PFzJkzxYEDB8S5c+fEn3/+KaKiokTfvn0bO/Yt7d69W8ybN08cOXJEnDlzRnz//ffC399fPProo+Zj/v36hBDiySefFGFhYWLLli3iwIEDIi4uTsTFxUnxEm4pIyNDREdHi4EDB4qMjAyRnZ1tvl17jLVcw59++kkolUqxdOlSceLECTFp0iTh5eVlnkn5yCOPiFdffdV8/K5du4Sjo6OYM2eOOHnypJgxY4ZwcnISx48fl+ol3NRTTz0lVCqV2LZtW5VrVVpaaj7m36/xrbfeEuvXrxdnzpwRBw8eFA8++KBwdnYWSUlJUryEm3rhhRfEtm3bxLlz58SuXbtEfHy88PPzE7m5uUII679+Jnq9XoSFhYlXXnnluses7foVFRWZP+sAiLlz54rDhw+LCxcuCCGEeP/994WXl5f4888/xbFjx8SIESNEZGSkuHLlivk57rjjDjF//nzz17f6Pa4PLHzqybhx4wSA625bt241H3P+/HkxdOhQ4eLiIvz8/MQLL7xQpeLfunWrACDOnTtnvu/SpUvioYceEu7u7sLT01M89thj5mLKEjz00EOiZ8+e1T527ty5Kt+DtLQ00bdvX+Hj4yOUSqWIjo4WL730kigsLGzExDVz8OBBERsbK1QqlXB2dhatWrUS7733XpXWuX+/PiGEuHLlinj66aeFt7e3cHV1FaNGjapSSFiSJUuWVPsze21DsLVdw/nz54uwsDChUChE9+7dxZ49e8yP9evXT4wbN67K8StWrBAtWrQQCoVCtGnTRvz111+NnLjmbnStlixZYj7m369xypQp5u9HYGCguOuuu8ShQ4caP3wNPPDAAyI4OFgoFArRpEkT8cADD4jU1FTz49Z+/UzWr18vAIiUlJTrHrO262f6zPr3zfQaDAaDeOONN0RgYKBQKpVi4MCB173u8PBwMWPGjCr33ez3uD7IhBCi/jrOiIiIiCwX1/EhIiIiu8HCh4iIiOwGCx8iIiKyGyx8iIiIyG6w8CEiIiK7wcKHiIiI7AYLHyIiIrIbLHyIiIjIbrDwISIiIrvBwoeIGtz48eMhk8kgk8mgUCgQHR2NmTNnQqfTSR2tTmQyGf744w+pYxBRHThKHYCI7MOQIUOwZMkSlJeX4++//8bkyZPh5OSEadOm1ep59Ho9ZDIZ5HLr/7utoqICTk5OUscgsivW/85BRFZBqVQiKCgI4eHheOqppxAfH49Vq1Zh7ty5aNeuHdzc3BAaGoqnn34axcXF5vOWLl0KLy8vrFq1Cq1bt4ZSqURaWhr279+PO++8E35+flCpVOjXrx8OHTpU5f+UyWRYtGgRhg8fDldXV7Rq1QoJCQlITU1F//794ebmhp49e+LMmTNVzvvzzz/RuXNnODs7IyoqCm+99Za5dSoiIgIAMGrUKMhkMvPXtzrPlGfBggW455574Obmhnfffbeev8tEdCssfIhIEi4uLtBqtZDL5fj000+RlJSEZcuWYcuWLXj55ZerHFtaWooPPvgAX3/9NZKSkhAQEICioiKMGzcOO3fuxJ49e9C8eXPcddddKCoqqnLu22+/jUcffRRHjhxBy5Yt8fDDD+OJJ57AtGnTcODAAQgh8Mwzz5iP37FjBx599FE899xzOHHiBBYtWoSlS5eai5T9+/cDAJYsWYLs7Gzz17c6z+TNN9/EqFGjcPz4cTz++OP1/n0loluo173eiYiqMW7cODFixAghhBAGg0Fs3LhRKJVK8eKLL1537C+//CJ8fX3NXy9ZskQAEEeOHLnp/6HX64WHh4dYvXq1+T4A4vXXXzd/nZCQIACIxYsXm+/78ccfhbOzs/nrgQMHivfee6/Kc3/33XciODi4yvOuXLmyyjE1PW/KlCk3fR1E1LA4xoeIGsWaNWvg7u6OiooKGAwGPPzww3jzzTexadMmzJo1C8nJydBoNNDpdCgrK0NpaSlcXV0BAAqFAu3bt6/yfDk5OXj99dexbds25ObmQq/Xo7S0FGlpaVWOu/a8wMBAAEC7du2q3FdWVgaNRgNPT08cPXoUu3btqtJSo9frr8v0bzU9r2vXrnX59hFRPWHhQ0SNYsCAAViwYAEUCgVCQkLg6OiI8+fPY/jw4Xjqqafw7rvvwsfHBzt37sSECROg1WrNxYKLiwtkMlmV5xs3bhwuXbqETz75BOHh4VAqlYiLi4NWq61y3LWDh03PUd19BoMBAFBcXIy33noLo0ePvu41ODs73/D11fQ8Nze3Gz4HETU8Fj5E1Cjc3NwQHR1d5b6DBw/CYDDgo48+Ms/SWrFiRY2eb9euXfjiiy9w1113AQDS09ORl5d32zk7d+6MlJSU67Jey8nJCXq9vtbnEZH0WPgQkWSio6NRUVGB+fPn4+6778auXbuwcOHCGp3bvHlzfPfdd+jatSs0Gg1eeukluLi43Ham6dOnY/jw4QgLC8N9990HuVyOo0ePIjExEe+88w4A48yuzZs3o1evXlAqlfD29q7ReUQkPc7qIiLJdOjQAXPnzsUHH3yAtm3bYvny5Zg1a1aNzl28eDEuX76Mzp0745FHHsH//d//ISAg4LYzDR48GGvWrMGGDRvQrVs39OjRA/PmzUN4eLj5mI8++ggbN25EaGgoOnXqVOPziEh6MiGEkDoEERERUWNgiw8RERHZDRY+REREZDdY+BAREZHdYOFDREREdoOFDxEREdkNFj5ERERkN1j4EBERkd1g4UNERER2g4UPERER2Q0WPkRERGQ3WPgQERGR3fh/ExPBX2q/ePIAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evals = 100\n", + "par1 = np.linspace(-10,10,evals)\n", + "par2 = -3\n", + "steps = np.empty(evals)\n", + "loss = np.empty(evals)\n", + "off_diagonal_norm = np.empty(evals)\n", + "for i in range(evals):\n", + " dbi_eval = deepcopy(dbi)\n", + " d = d_ansatz([par1[i],par2],type='Pauli')\n", + " poly_step = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", + " steps[i] = poly_step\n", + " dbi_eval(poly_step,d=d)\n", + " loss[i] = dbi_eval.least_squares(d)\n", + " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(par1,loss)\n", + "plt.xlabel('Parameter')\n", + "plt.ylabel('Loss')\n", + "\n", + "plt.figure()\n", + "plt.plot(par1,off_diagonal_norm)\n", + "plt.xlabel('Parameter')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n", + "plt.figure()\n", + "plt.plot(par1,np.log(steps))\n", + "plt.xlabel('Parameter')\n", + "plt.ylabel('Step size')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "par1 = np.linspace(-10,10,100)\n", + "par2 = np.linspace(-10,10,100)\n", + "\n", + "loss = np.empty((100,100))\n", + "\n", + "for i in range(100):\n", + " for j in range(100):\n", + " dbi_eval = deepcopy(dbi)\n", + " d = d_ansatz([par1[i],par2[j]],type='Pauli')\n", + " poly_step = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", + " dbi_eval(poly_step,d=d)\n", + " loss[i,j] = dbi_eval.least_squares(d)\n", + "\n", "plt.figure()\n", - "plt.plot(loss)\n" + "plt.contourf(par1,par2,loss)\n", + "plt.xlabel('Parameter 1')\n", + "plt.ylabel('Parameter 2')\n", + "plt.colorbar()" ] } ], @@ -930,12 +1586,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.19 (main, Mar 21 2024, 17:21:27) [MSC v.1916 64 bit (AMD64)]" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "c4f92193806e2908606a5f23edd55a5282f2f433b73b1c504507f9256ed9f0b4" + "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" } } }, diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 0369e0c521..2c32fe3085 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -2,7 +2,6 @@ from enum import Enum, auto from typing import Optional -import hyperopt import numpy as np from qibo.hamiltonians import Hamiltonian @@ -17,7 +16,7 @@ class DoubleBracketCost(Enum): energy_fluctuation = auto() """Use energy fluctuation as cost function.""" - +# This is needed due to mutual dependencies between double_bracket.py and utils_scheduling.py from qibo.models.dbi.utils_scheduling import ( grid_search_step, hyperopt_step, @@ -84,14 +83,14 @@ def __init__( mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.canonical, scheduling: DoubleBracketScheduling = DoubleBracketScheduling.grid_search, cost: DoubleBracketCost = DoubleBracketCost.off_diagonal_norm, - state: np.array = None, + ref_state: np.array = None, ): self.h = hamiltonian self.h0 = deepcopy(self.h) self.mode = mode self.scheduling = scheduling self.cost = cost - self.state = state + self.ref_state = ref_state def __call__( self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None @@ -197,12 +196,12 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): self.__call__(mode=self.mode, step=step, d=d) # loss values depending on the cost function - if self.cost == DoubleBracketCost.off_diagonal_norm: + if self.cost is DoubleBracketCost.off_diagonal_norm: loss = self.off_diagonal_norm - elif self.cost == DoubleBracketCost.least_squares: + elif self.cost is DoubleBracketCost.least_squares: loss = self.least_squares(d) - elif self.cost == DoubleBracketCost.energy_fluctuation: - loss = self.energy_fluctuation(self.state) + elif self.cost is DoubleBracketCost.energy_fluctuation: + loss = self.energy_fluctuation(self.ref_state) # set back the initial configuration self.h = h_copy diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 3556eb0bd2..96d9cd7a5b 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -175,3 +175,55 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): # coefficients from high to low (n:0) coef = list(reversed(trace_coefficients[: n + 1])) return coef + +def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: int = 3): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients + coef = np.empty(n) + for i in range(n): + coef[i] = np.real(exp_list[i] * np.trace(d @ gamma_list[i + 1])) + coef = list(reversed(coef)) + return coef + + +# TODO: add a general expansion formula not stopping at 3rd order +def energy_fluctuation_polynomial_expansion_coef( + dbi_object, d: np.array = None, n: int = 3, state=0 +): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + # coefficients + coef = np.empty(3) + coef[0] = np.real(2 * covariance(gamma_list[0], gamma_list[1], state)) + coef[1] = np.real(2 * variance(gamma_list[1], state)) + coef[2] = np.real( + covariance(gamma_list[0], gamma_list[3], state) + + 3 * covariance(gamma_list[1], gamma_list[2], state) + ) + coef = list(reversed(coef)) + return coef + +def commutator(a, b): + """Compute commutator between two arrays.""" + return a @ b - b @ a + + +def variance(a, state): + """Calculates the variance of a matrix A with respect to a state: + Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" + b = a @ a + return state.conj().T @ b @state - (state.conj().T @ a @state)**2 + + +def covariance(a, b, state): + """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, + applied to two operators A and B with respect to a state: + Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$""" + c = a @ b + b @ a + return state.conj().T @ c @state - 2 * state.conj().T @ a @state * state.conj().T @ b @state \ No newline at end of file diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 3addd16b95..08c0c9d281 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -2,7 +2,9 @@ from copy import deepcopy from functools import partial from typing import Optional +from enum import Enum, auto from qibo.models.dbi.double_bracket import DoubleBracketCost +from qibo.models.dbi.utils import off_diagonal_norm_polynomial_expansion_coef, least_squares_polynomial_expansion_coef, energy_fluctuation_polynomial_expansion_coef import hyperopt import numpy as np @@ -10,23 +12,6 @@ error = 1e-3 -def commutator(a, b): - """Compute commutator between two arrays.""" - return a @ b - b @ a - - -def variance(a, state): - """Calculates the variance of a matrix A with respect to a state: Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" - b = a @ a - return b[state, state] - a[state, state] ** 2 - - -def covariance(a, b, state): - """Calculates the covariance of two matrices A and B with respect to a state: Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$""" - c = a @ b + b @ a - return c[state, state] - 2 * a[state, state] * b[state, state] - - def grid_search_step( dbi_object, step_min: float = 1e-5, @@ -130,11 +115,11 @@ def polynomial_step( "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." ) if coef is None: - if cost == DoubleBracketCost.off_diagonal_norm: + if cost is DoubleBracketCost.off_diagonal_norm: coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n) - elif cost == DoubleBracketCost.least_squares: + elif cost is DoubleBracketCost.least_squares: coef = least_squares_polynomial_expansion_coef(dbi_object, d, n) - elif cost == DoubleBracketCost.energy_fluctuation: + elif cost is DoubleBracketCost.energy_fluctuation: coef = energy_fluctuation_polynomial_expansion_coef( dbi_object, d, n, dbi_object.state ) @@ -156,203 +141,3 @@ def polynomial_step( else: return None - -def d_ansatz(params, type="Full"): - r""" - Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. - Args: - params(np.array): parameters for the ansatz. - type(str): type of parameterization, 'Full' or 'Pauli' - (Full being each entry parametrized and Pauli being a linear combination of Z_i matrix). - """ - - if type == "Full": - d = np.zeros((len(params), len(params))) - for i in range(len(params)): - d[i, i] = params[i] - - if type == 'Pauli': - d = np.zeros((2**len(params), 2**len(params))) - z = np.array([[1, 0], [0, -1]]) - for i in range(len(params)): - i1 = np.eye(2**i) - i2 = np.eye(2**(len(params)-i-1)) - d += params[i]*np.kron(i1,np.kron(z,i2)) - - return d - - -def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - - gamma_list = dbi_object.generate_Gamma_list(n + 2, d) - sigma_Gamma_list = list(map(dbi_object.sigma, gamma_list)) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients for rotation with [W,H] and H - c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] - c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] - # product coefficient - trace_coefficients = [0] * (2 * n + 1) - for k in range(n + 1): - for j in range(n + 1): - power = k + j - product_matrix = c1[k] @ c2[j] - trace_coefficients[power] += 2 * np.trace(product_matrix) - # coefficients from high to low (n:0) - coef = list(reversed(trace_coefficients[: n + 1])) - return coef - - -def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: int = 3): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients - coef = np.empty(n) - for i in range(n): - coef[i] = np.real(exp_list[i] * np.trace(d @ gamma_list[i + 1])) - coef = list(reversed(coef)) - return coef - - -# TODO: add a general expansion formula not stopping at 3rd order -def energy_fluctuation_polynomial_expansion_coef( - dbi_object, d: np.array = None, n: int = 3, state=0 -): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - # coefficients - coef = np.empty(3) - coef[0] = np.real(2 * covariance(gamma_list[0], gamma_list[1], state)) - coef[1] = np.real(2 * variance(gamma_list[1], state)) - coef[2] = np.real( - covariance(gamma_list[0], gamma_list[3], state) - + 3 * covariance(gamma_list[1], gamma_list[2], state) - ) - coef = list(reversed(coef)) - return coef - - -# D GRADIENTS -def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): - r""" - Gradient of the nth gamma operator with respect to the ith diagonal elements of D. - Args: - d(np.array): D operator. - h(np.array): Hamiltonian. - n(int): nth Gamma operator. - i(int): Index of the diagonal element of D. - dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). - gamma_list(list): List of the n gamma operators. - Returns: - (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. - """ - dD_di = np.zeros(d.shape) - dD_di[i, i] = 1 - dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) - w = commutator(d, h) - return dW_di + commutator(w, dGamma[-1]) - - -def dpolynomial_diDiagonal(dbi_object, d, h, i): - r""" - Gradient of the polynomial expansion with respect to the ith diagonal elements of D. - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - d(np.array): D operator. - h(np.array): Hamiltonian. - i(int): Index of the diagonal element of D. - Returns: - derivative(float): Derivative of the polynomial expansion with respect to the ith diagonal elements of D. - """ - derivative = 0 - s = polynomial_step(dbi_object, n=3, d=d) - dD_di = np.zeros(d.shape) - gamma_list = dbi_object.generate_Gamma_list(4, d) - dD_di[i, i] = 1 - dGamma = [commutator(dD_di, h)] - derivative += np.real( - np.trace(gamma_list[0] @ dD_di) + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s - ) - for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) - derivative += np.real( - np.trace(dGamma[-1] @ d + gamma_list[n] @ dD_di) * s**n / math.factorial(n) - ) - - return derivative - -def gradientDiagonalEntries(dbi_object, params, h, analytic = True, ansatz = 'Full', delta = 1e-4): - r""" - Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). - h(np.array): Hamiltonian. - analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. - ansatz(str): Ansatz used for the D operator. Options are 'Full' and 'Pauli'. - delta(float): Step size for numerical gradient. - Returns: - grad(np.array): Gradient of the D operator. - """ - - grad = np.zeros(len(params)) - d = d_ansatz(params, ansatz) - if analytic == True: - for i in range(len(params)): - derivative = dpolynomial_diDiagonal(dbi_object,d,h,i) - grad[i] = d[i,i]-derivative - else: - for i in range(len(params)): - params_new = deepcopy(params) - params_new[i] += delta - d_new = d_ansatz(params_new, ansatz) - grad[i] = (dbi_object.least_squares(d_new)-dbi_object.least_squares(d))/delta - return grad - -def gradient_descent(dbi_object, params, iterations, lr = 1e-2, analytic = True, ansatz = 'Full'): - r""" - Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). - iterations(int): Number of gradient descent iterations. - lr(float): Learning rate. - analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. - ansatz(str): Ansatz used for the D operator. Options are 'Full' and 'Pauli'. - Returns: - d(np.array): Optimized D operator. - loss(np.array): Loss function evaluated at each iteration. - grad(np.array): Gradient evaluated at each iteration. - params_hist(np.array): Parameters evaluated at each iteration. - """ - - h = dbi_object.h.matrix - d = d_ansatz(params,ansatz) - loss = np.zeros(iterations+1) - grad = np.zeros((iterations,len(params))) - dbi_new = deepcopy(dbi_object) - s = polynomial_step(dbi_object, n=3, d=d) - dbi_new(s, d=d) - loss[0] = dbi_new.least_squares(d) - params_hist = np.empty((len(params), iterations + 1)) - params_hist[:, 0] = params - - for i in range(iterations): - dbi_new = deepcopy(dbi_object) - grad[i,:] = gradientDiagonalEntries(dbi_object, params, h, analytic=analytic, ansatz=ansatz) - for j in range(len(params)): - params[j] = params[j] - lr*grad[i,j] - d = d_ansatz(params, ansatz) - s = polynomial_step(dbi_object, n = 3, d=d) - dbi_new(s,d=d) - loss[i+1] = dbi_new.least_squares(d) - params_hist[:,i+1] = params - - return d,loss,grad,params_hist \ No newline at end of file From d0c0ba2c67350b634b7f419f640dbcea9cf732a1 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 4 Apr 2024 20:37:28 +0000 Subject: [PATCH 059/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/double_bracket.py | 1 + src/qibo/models/dbi/utils.py | 16 +++-- src/qibo/models/dbi/utils_gradients.py | 87 +++++++++++++++---------- src/qibo/models/dbi/utils_scheduling.py | 11 ++-- 4 files changed, 71 insertions(+), 44 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 23c9de2bb0..0017355c11 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -17,6 +17,7 @@ class DoubleBracketCost(Enum): energy_fluctuation = auto() """Use energy fluctuation as cost function.""" + # This is needed due to mutual dependencies between double_bracket.py and utils_scheduling.py from qibo.models.dbi.utils_scheduling import ( grid_search_step, diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 96d9cd7a5b..f8b15029d6 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -176,6 +176,7 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): coef = list(reversed(trace_coefficients[: n + 1])) return coef + def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: int = 3): if d is None: d = dbi_object.diagonal_h_matrix @@ -209,6 +210,7 @@ def energy_fluctuation_polynomial_expansion_coef( coef = list(reversed(coef)) return coef + def commutator(a, b): """Compute commutator between two arrays.""" return a @ b - b @ a @@ -216,14 +218,18 @@ def commutator(a, b): def variance(a, state): """Calculates the variance of a matrix A with respect to a state: - Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" + Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" b = a @ a - return state.conj().T @ b @state - (state.conj().T @ a @state)**2 + return state.conj().T @ b @ state - (state.conj().T @ a @ state) ** 2 def covariance(a, b, state): """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, - applied to two operators A and B with respect to a state: - Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$""" + applied to two operators A and B with respect to a state: + Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ + """ c = a @ b + b @ a - return state.conj().T @ c @state - 2 * state.conj().T @ a @state * state.conj().T @ b @state \ No newline at end of file + return ( + state.conj().T @ c @ state + - 2 * state.conj().T @ a @ state * state.conj().T @ b @ state + ) diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index dcb1d68e09..adb78919f5 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -1,19 +1,22 @@ -import numpy as np import math - from copy import deepcopy from enum import Enum, auto + +import numpy as np + +from qibo import symbols +from qibo.hamiltonians import SymbolicHamiltonian from qibo.models.dbi.utils import commutator from qibo.models.dbi.utils_scheduling import polynomial_step -from qibo.hamiltonians import SymbolicHamiltonian -from qibo import symbols + class d_ansatz_type(Enum): element_wise = auto() local_1 = auto() - #local_2 = auto() # for future implementation - #ising = auto() # for future implementation + # local_2 = auto() # for future implementation + # ising = auto() # for future implementation + def d_ansatz(params: np.array, d_type: d_ansatz_type == d_ansatz_type.element_wise): r""" @@ -33,20 +36,22 @@ def d_ansatz(params: np.array, d_type: d_ansatz_type == d_ansatz_type.element_wi d[i, i] = params[i] elif d_type is d_ansatz_type.local_1: - - op_list = [params[i]*symbols.Z(i) for i in len(params)] + + op_list = [params[i] * symbols.Z(i) for i in len(params)] from functools import reduce - d = SymbolicHamiltonian(reduce(symbols.Z.add, op_list) , nqubits = len(params)) + + d = SymbolicHamiltonian(reduce(symbols.Z.add, op_list), nqubits=len(params)) d.dense.matrix else: raise ValueError(f"Parameterization type {type} not recognized.") - + return d + def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): r""" Gradient of the nth gamma operator with respect to the ith diagonal elements of D. - $Gamma_{n} = [W,[W,...,[W,H]]...]]$, + $Gamma_{n} = [W,[W,...,[W,H]]...]]$, $\frac{\partial Gamma_{n}}{\partial D_{ii}} = \partial_{D_{ii}} W\Gamma_{n-1}-\partial_{D_{ii}}\Gamma_{n-1} W$. and thus is can be computed recursively. Args: @@ -65,9 +70,9 @@ def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): w = commutator(d, h) return dW_di + commutator(w, dGamma[-1]) -#def dpolynomial_diDiagonal(dbi_object, d, h, i): #element_wise_ansatz -def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): +# def dpolynomial_diDiagonal(dbi_object, d, h, i): #element_wise_ansatz +def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): r""" TODO: add formula and explain terms Gradient wrt the ith diagonal elements of D. @@ -89,7 +94,8 @@ def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i dD_di[i, i] = 1 dGamma = [commutator(dD_di, h)] derivative += np.real( - np.trace(gamma_list[0] @ dD_di) + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s + np.trace(gamma_list[0] @ dD_di) + + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s ) for n in range(2, 4): dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) @@ -99,11 +105,14 @@ def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i return derivative -def gradientDiagonalEntries(dbi_object, params, h, analytic = True, d_type = d_ansatz_type.element_wise, delta = 1e-4): + +def gradientDiagonalEntries( + dbi_object, params, h, analytic=True, d_type=d_ansatz_type.element_wise, delta=1e-4 +): r""" Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. As the analytical gradient is applied on the polynomial expansion of the cost function, the numerical gradients may be more accurate. - + Args: dbi_object(DoubleBracketIteration): DoubleBracketIteration object. params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). @@ -119,17 +128,27 @@ def gradientDiagonalEntries(dbi_object, params, h, analytic = True, d_type = d_a d = d_ansatz(params, d_type) if analytic == True: for i in range(len(params)): - derivative = derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object,d,h,i) - grad[i] = d[i,i]-derivative + derivative = derivative_scalar_product_dbr_approx_element_wise_ansatz( + dbi_object, d, h, i + ) + grad[i] = d[i, i] - derivative else: for i in range(len(params)): params_new = deepcopy(params) params_new[i] += delta d_new = d_ansatz(params_new, d_type) - grad[i] = (dbi_object.cost(d_new)-dbi_object.cost(d))/delta + grad[i] = (dbi_object.cost(d_new) - dbi_object.cost(d)) / delta return grad -def gradient_descent_dbr_d_ansatz(dbi_object, params, nmb_iterations, lr = 1e-2, analytic = True, d_type = d_ansatz_type.element_wise): + +def gradient_descent_dbr_d_ansatz( + dbi_object, + params, + nmb_iterations, + lr=1e-2, + analytic=True, + d_type=d_ansatz_type.element_wise, +): r""" Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. - Declare variables @@ -142,7 +161,7 @@ def gradient_descent_dbr_d_ansatz(dbi_object, params, nmb_iterations, lr = 1e-2, nmb_iterations(int): Number of gradient descent iterations. lr(float): Learning rate. analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. - d_type(d_ansatz_type): Ansatz used for the D operator. + d_type(d_ansatz_type): Ansatz used for the D operator. Returns: d(np.array): Optimized D operator. loss(np.array): Loss function evaluated at each iteration. @@ -151,9 +170,9 @@ def gradient_descent_dbr_d_ansatz(dbi_object, params, nmb_iterations, lr = 1e-2, """ h = dbi_object.h.matrix - d = d_ansatz(params,d_type) - loss = np.zeros(nmb_iterations+1) - grad = np.zeros((nmb_iterations,len(params))) + d = d_ansatz(params, d_type) + loss = np.zeros(nmb_iterations + 1) + grad = np.zeros((nmb_iterations, len(params))) dbi_new = deepcopy(dbi_object) s = polynomial_step(dbi_object, n=3, d=d) dbi_new(s, d=d) @@ -163,13 +182,15 @@ def gradient_descent_dbr_d_ansatz(dbi_object, params, nmb_iterations, lr = 1e-2, for i in range(nmb_iterations): dbi_new = deepcopy(dbi_object) - grad[i,:] = gradientDiagonalEntries(dbi_object, params, h, analytic=analytic, ansatz=d_type) + grad[i, :] = gradientDiagonalEntries( + dbi_object, params, h, analytic=analytic, ansatz=d_type + ) for j in range(len(params)): - params[j] = params[j] - lr*grad[i,j] - d = d_ansatz(params, d_type) - s = polynomial_step(dbi_new, n = 5, d=d) - dbi_new(s,d=d) - loss[i+1] = dbi_new.cost(d) - params_hist[:,i+1] = params - - return d,loss,grad,params_hist \ No newline at end of file + params[j] = params[j] - lr * grad[i, j] + d = d_ansatz(params, d_type) + s = polynomial_step(dbi_new, n=5, d=d) + dbi_new(s, d=d) + loss[i + 1] = dbi_new.cost(d) + params_hist[:, i + 1] = params + + return d, loss, grad, params_hist diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 1f2f425703..d8ba5c9d80 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -1,15 +1,15 @@ -import math -from copy import deepcopy from functools import partial from typing import Optional -from enum import Enum, auto -from qibo.models.dbi.double_bracket import DoubleBracketCost -from qibo.models.dbi.utils import off_diagonal_norm_polynomial_expansion_coef, least_squares_polynomial_expansion_coef, energy_fluctuation_polynomial_expansion_coef import hyperopt import numpy as np from qibo.models.dbi.double_bracket import DoubleBracketCost +from qibo.models.dbi.utils import ( + energy_fluctuation_polynomial_expansion_coef, + least_squares_polynomial_expansion_coef, + off_diagonal_norm_polynomial_expansion_coef, +) error = 1e-3 @@ -142,4 +142,3 @@ def polynomial_step( # solution does not exist, return None else: return None - From 6de60038c03f624a69ab4e1a67a430af975c0f97 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 8 Apr 2024 14:18:17 +0800 Subject: [PATCH 060/154] Update notebooks to function --- examples/dbi/dbi_scheduling.ipynb | 25 +- examples/dbi/dbi_strategies_compare.ipynb | 55 +- examples/dbi/dbi_strategy_Ising_model.ipynb | 880 +----------------- .../dbi/dbi_strategy_magnetic_field.ipynb | 872 ----------------- 4 files changed, 82 insertions(+), 1750 deletions(-) delete mode 100644 examples/dbi/dbi_strategy_magnetic_field.ipynb diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index a7a813fcba..fdb087871b 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -105,7 +105,9 @@ "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt, max_evals=100, step_max=0.6)\n", "print('hyperopt_search step:', step_hyperopt)\n", "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, n=5)\n", - "print('polynomial_approximation step:', step_poly)" + "print('polynomial_approximation step:', step_poly)\n", + "step_sa = dbi.choose_step(scheduling=DoubleBracketScheduling.simulated_annealing)\n", + "print('simulated_annealing step:', step_sa)" ] }, { @@ -119,6 +121,7 @@ "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.axvline(x=step_sa, color='b', linestyle=':',label='simulated annealing')\n", "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", "plt.xlabel('s')\n", "plt.title('First DBI step')\n", @@ -184,7 +187,11 @@ "# polynomial expansion\n", "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=5)\n", "poly_min = dbi.loss(step=step_poly, d=d)-dbi.off_diagonal_norm\n", - "print('polynomial_approximation step:', step_poly, 'loss', poly_min)" + "print('polynomial_approximation step:', step_poly, 'loss', poly_min)\n", + "# simulated annealing\n", + "step_sa = dbi.choose_step(scheduling=DoubleBracketScheduling.simulated_annealing, d=d)\n", + "sa_min = dbi.loss(step=step_sa, d=d)-dbi.off_diagonal_norm\n", + "print('simulated_annealing step:', step_sa, 'loss', sa_min)" ] }, { @@ -197,9 +204,11 @@ "plt.plot(s_space, off_diagonal_norm_diff)\n", "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", "plt.text(x=step_grid, y=grid_min, s=f'grid min \\n{round(grid_min,3)}')\n", - "plt.text(x=step_poly, y=poly_min, s=f'grid min \\n{round(poly_min,3)}')\n", + "plt.text(x=step_poly, y=poly_min, s=f'poly min \\n{round(poly_min,3)}')\n", + "plt.text(x=step_sa, y=sa_min, s=f'sa min \\n{round(sa_min,3)}')\n", "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.axvline(x=step_sa, color='b', linestyle=':',label='simulated annealing')\n", "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", "plt.xlabel('s')\n", "plt.title(f'First DBI step with D={d_str}')\n", @@ -320,10 +329,12 @@ "NSTEPS = 8\n", "scheduling_list = [DoubleBracketScheduling.grid_search,\n", " DoubleBracketScheduling.hyperopt,\n", - " DoubleBracketScheduling.polynomial_approximation,]\n", + " DoubleBracketScheduling.polynomial_approximation,\n", + " DoubleBracketScheduling.simulated_annealing,]\n", "scheduling_labels = ['grid search',\n", " 'hyperopt',\n", - " 'polynomial',]\n", + " 'polynomial',\n", + " 'simulated_annealing']\n", "Z_optimal_scheduling = []\n", "s_scheduling = []\n", "off_norm_scheduling =[]\n", @@ -380,7 +391,7 @@ "outputs": [], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -431,7 +442,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 1160babed3..7b422cf05d 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -23,7 +23,7 @@ "from qibo import hamiltonians, set_backend\n", "from qibo.hamiltonians import Hamiltonian, SymbolicHamiltonian\n", "from qibo.quantum_info import random_hermitian\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", "from qibo.models.dbi.utils import *\n", "from qibo.models.dbi.utils_scheduling import *" ] @@ -64,7 +64,8 @@ "nqubits = 5\n", "h0 = random_hermitian(2**nqubits, seed=2)\n", "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0))\n", - "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)\n", + "cost = DoubleBracketCostFunction.off_diagonal_norm\n", + "print(\"Initial loss\", dbi.least_squares(D=dbi.diagonal_h_matrix))\n", "visualize_matrix(dbi.h.matrix, title=f'Random hamiltonian with L={nqubits}')" ] }, @@ -94,7 +95,7 @@ "outputs": [], "source": [ "# initialize DBI class for the canonical case\n", - "dbi_canonical = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.canonical, scheduling=scheduling)" + "dbi_canonical = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.canonical, scheduling=scheduling, cost=cost)" ] }, { @@ -104,14 +105,15 @@ "outputs": [], "source": [ "# Canonical\n", - "off_diagonal_norm_history_canonical = [dbi_canonical.off_diagonal_norm]\n", + "loss_history_canonical = [dbi_canonical.off_diagonal_norm]\n", "steps_canonical_plot = [0]\n", "for s in range(NSTEPS):\n", " # same settings as iteration from list\n", - " step = dbi_canonical.choose_step(d=dbi.diagonal_h_matrix)\n", + " d = dbi.diagonal_h_matrix\n", + " step = dbi_canonical.choose_step(d=d)\n", " dbi_canonical(step=step)\n", " print(f\"New optimized step at iteration {s+1}/{NSTEPS}: {step}, loss {dbi_canonical.off_diagonal_norm}\")\n", - " off_diagonal_norm_history_canonical.append(dbi_canonical.off_diagonal_norm)\n", + " loss_history_canonical.append(dbi_canonical.off_diagonal_norm)\n", " steps_canonical_plot.append(steps_canonical_plot[-1]+step)" ] }, @@ -129,7 +131,7 @@ "outputs": [], "source": [ "# initialize DBI class for the Pauli-Z strategy\n", - "dbi_pauli = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling)" + "dbi_pauli = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling, cost=cost)" ] }, { @@ -143,12 +145,13 @@ "Z_names = list(generate_local_Z.keys())\n", "Z_optimal = []\n", "# add in initial values for plotting\n", - "off_diagonal_norm_history_pauli = [dbi_pauli.off_diagonal_norm]\n", + "loss_history_pauli = [dbi_pauli.off_diagonal_norm]\n", "steps_pauli_plot = [0]\n", "scheduling = DoubleBracketScheduling.simulated_annealing\n", "for _ in range(NSTEPS):\n", " dbi_pauli, idx, step, flip_sign = select_best_dbr_generator(dbi_pauli, Z_ops, scheduling=scheduling, compare_canonical=False)\n", - " off_diagonal_norm_history_pauli.append(dbi_pauli.off_diagonal_norm)\n", + " d = Z_ops[idx]\n", + " loss_history_pauli.append(dbi_pauli.off_diagonal_norm)\n", " steps_pauli_plot.append(steps_pauli_plot[-1]+step)\n", " if flip_sign < 0:\n", " Z_optimal.append('-' + Z_names[idx])\n", @@ -171,7 +174,7 @@ "outputs": [], "source": [ "# initialize DBI class for the canonical case\n", - "dbi_gradient = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling)" + "dbi_gradient = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling, cost=cost)" ] }, { @@ -180,9 +183,9 @@ "metadata": {}, "outputs": [], "source": [ - "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", - "d_coef = onsite_Z_decomposition(dbi_gradient.h.matrix, onsite_Z_ops)\n", - "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])" + "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", + "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", + "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])" ] }, { @@ -191,12 +194,12 @@ "metadata": {}, "outputs": [], "source": [ - "off_diagonal_norm_history_gradient = [dbi_gradient.off_diagonal_norm]\n", + "loss_history_gradient = [dbi_gradient.off_diagonal_norm]\n", "steps_gradient_plot= [0]\n", "for _ in range(NSTEPS):\n", - " step, d_coef, d = gradient_descent_onsite_Z(dbi_gradient, d_coef, d, onsite_Z_ops=onsite_Z_ops)\n", + " step, d_coef, d = gradient_descent_pauli(dbi_gradient, d_coef, d, pauli_operator_dict=pauli_operator_dict)\n", " dbi_gradient(d=d,step=step)\n", - " off_diagonal_norm_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", + " loss_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", " steps_gradient_plot.append(steps_gradient_plot[-1]+step)" ] @@ -208,9 +211,9 @@ "outputs": [], "source": [ "plt.title(str(nqubits) + ' random Hamiltonian diagonalization')\n", - "plt.plot(off_diagonal_norm_history_canonical, label='canonical')\n", - "plt.plot(off_diagonal_norm_history_pauli, label='Pauli-Z')\n", - "plt.plot(off_diagonal_norm_history_gradient, label='gradient')\n", + "plt.plot(loss_history_canonical, label='canonical')\n", + "plt.plot(loss_history_pauli, label='Pauli-Z')\n", + "plt.plot(loss_history_gradient, label='gradient')\n", "plt.legend()\n", "plt.xlabel('Iteration')\n", "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" @@ -223,9 +226,9 @@ "outputs": [], "source": [ "plt.title(str(nqubits) + ' random Hamiltonian diagonalization')\n", - "plt.plot(steps_canonical_plot, off_diagonal_norm_history_canonical, marker='o', label='canonical')\n", - "plt.plot(steps_pauli_plot, off_diagonal_norm_history_pauli, marker='o', label='Pauli-Z')\n", - "plt.plot(steps_gradient_plot,off_diagonal_norm_history_gradient, marker='o', label='gradient')\n", + "plt.plot(steps_canonical_plot, loss_history_canonical, marker='o', label='canonical')\n", + "plt.plot(steps_pauli_plot, loss_history_pauli, marker='o', label='Pauli-Z')\n", + "plt.plot(steps_gradient_plot,loss_history_gradient, marker='o', label='gradient')\n", "plt.legend()\n", "plt.xlabel('Iteration')\n", "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" @@ -375,9 +378,9 @@ "metadata": {}, "outputs": [], "source": [ - "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", - "d_coef = onsite_Z_decomposition(dbi_gradient.h.matrix, onsite_Z_ops)\n", - "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])" + "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", + "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", + "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])" ] }, { @@ -399,7 +402,7 @@ "off_diagonal_norm_history_gradient = [dbi_gradient.off_diagonal_norm]\n", "steps_gradient_plot= [0]\n", "for _ in range(NSTEPS):\n", - " step, d_coef, d = gradient_descent_onsite_Z(dbi_gradient, d_coef, d, onsite_Z_ops=onsite_Z_ops)\n", + " step, d_coef, d = gradient_descent_pauli(dbi_gradient, d_coef, d, pauli_operator_dict=pauli_operator_dict)\n", " dbi_gradient(d=d,step=step)\n", " off_diagonal_norm_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", diff --git a/examples/dbi/dbi_strategy_Ising_model.ipynb b/examples/dbi/dbi_strategy_Ising_model.ipynb index 159560f792..a6c84b0747 100644 --- a/examples/dbi/dbi_strategy_Ising_model.ipynb +++ b/examples/dbi/dbi_strategy_Ising_model.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -57,34 +57,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-04-03 08:21:24]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 31.576176740060667\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# backend\n", "set_backend(\"qibojit\", platform=\"numba\")\n", @@ -108,39 +83,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:21:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 572.27trial/s, best loss: 27.607173414553387]\n", - "The initial D coefficients: [(-0.2980910136757636+0j), (-0.17678355790937256+0j), (0.294550421681131+0j), (-0.2301056409534723+0j), (-0.07297191764284382+0j)]\n", - "Gradient: [-0.20481773 0.41841615 -0.03164361 0.18666951 -0.86436728]\n", - "s: 0.11659660342715238\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/pethidine/Documents/GitHub/qibo/src/qibo/models/dbi/utils.py:268: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " grad[i] = (\n" - ] - } - ], + "outputs": [], "source": [ "# generate pauli_operator_dict\n", "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=1)\n", @@ -154,46 +99,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 656.84trial/s, best loss: 27.607178280638898]\n", - "100%|██████████| 500/500 [00:00<00:00, 639.38trial/s, best loss: 24.351929237913915]\n", - "100%|██████████| 500/500 [00:00<00:00, 655.61trial/s, best loss: 22.089739447397726]\n", - "100%|██████████| 500/500 [00:00<00:00, 663.89trial/s, best loss: 20.346440770606122]\n", - "100%|██████████| 500/500 [00:00<00:00, 639.24trial/s, best loss: 18.946903760228178]\n", - "100%|██████████| 500/500 [00:00<00:00, 650.95trial/s, best loss: 17.7755177782418] \n", - "100%|██████████| 500/500 [00:00<00:00, 674.60trial/s, best loss: 16.785446486734276]\n", - "100%|██████████| 500/500 [00:00<00:00, 664.40trial/s, best loss: 15.933524819099162] \n", - "100%|██████████| 500/500 [00:00<00:00, 655.76trial/s, best loss: 15.195995775240494]\n", - "100%|██████████| 500/500 [00:00<00:00, 633.46trial/s, best loss: 14.474767679378404]\n", - "100%|██████████| 500/500 [00:00<00:00, 638.92trial/s, best loss: 14.025120975809307]\n", - "100%|██████████| 500/500 [00:00<00:00, 603.17trial/s, best loss: 13.622403395436098]\n", - "100%|██████████| 500/500 [00:00<00:00, 670.03trial/s, best loss: 13.244304046805466]\n", - "100%|██████████| 500/500 [00:00<00:00, 666.31trial/s, best loss: 12.938522658932913] \n", - "100%|██████████| 500/500 [00:00<00:00, 656.53trial/s, best loss: 12.622483913681776]\n", - "100%|██████████| 500/500 [00:00<00:00, 585.38trial/s, best loss: 12.260523262483813]\n", - "100%|██████████| 500/500 [00:00<00:00, 645.94trial/s, best loss: 11.863294969541887]\n", - "100%|██████████| 500/500 [00:00<00:00, 697.70trial/s, best loss: 11.477785002400976] \n", - "100%|██████████| 500/500 [00:00<00:00, 684.35trial/s, best loss: 11.090736331075858] \n", - "100%|██████████| 500/500 [00:00<00:00, 739.76trial/s, best loss: 10.686003198269908] \n", - "100%|██████████| 500/500 [00:00<00:00, 717.78trial/s, best loss: 10.057480301384102] \n", - "100%|██████████| 500/500 [00:00<00:00, 712.94trial/s, best loss: 10.058298967085108] \n", - "100%|██████████| 500/500 [00:00<00:00, 724.09trial/s, best loss: 10.059015434243745] \n", - "100%|██████████| 500/500 [00:00<00:00, 709.43trial/s, best loss: 10.060281060360927] \n", - "100%|██████████| 500/500 [00:00<00:00, 740.79trial/s, best loss: 10.060583338964047]\n", - "100%|██████████| 500/500 [00:00<00:00, 733.24trial/s, best loss: 10.061781462286367] \n", - "100%|██████████| 500/500 [00:00<00:00, 725.24trial/s, best loss: 10.06205592136255]\n", - "100%|██████████| 500/500 [00:00<00:00, 732.59trial/s, best loss: 10.06257005000825] \n", - "100%|██████████| 500/500 [00:00<00:00, 750.26trial/s, best loss: 10.063192908922257] \n", - "100%|██████████| 500/500 [00:00<00:00, 710.47trial/s, best loss: 10.063501140769239] \n" - ] - } - ], + "outputs": [], "source": [ "iters = 30\n", "off_diagonal_norm_1 = [dbi.off_diagonal_norm]\n", @@ -207,30 +115,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.title(str(nqubits) + ' spins random hamiltonian')\n", "plt.plot(off_diagonal_norm_1)\n", @@ -247,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -256,51 +143,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:22:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 692.10trial/s, best loss: 27.60718707087908]\n", - "The initial D coefficients: [(-0.2980910136757636+0j), (-0.17678355790937256+0j), (0.294550421681131+0j), (-0.2301056409534723+0j), (-0.07297191764284382+0j), (0.15766557989586075+0j), (0.1267267343988193+0j), (0.10801310050393904+0j), (0.37981790024704787+0j), (0.25748679935414437+0j), (-0.2531727610451514+0j), (0.13345922163435078+0j), (-0.02961684041039575+0j), (0.36206828748686004+0j), (0.12254924877248492+0j)]\n", - "Gradient: [-0.07705712 0.15660998 -0.01151922 0.06967692 -0.32391798 -0.15885245\n", - " 0.16488954 0.11742344 0.44237635 0.39554303 -0.59878233 -0.03326744\n", - " 0.2060505 0.08567309 0.17837982]\n", - "s: 0.11627601978827411\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/pethidine/Documents/GitHub/qibo/src/qibo/models/dbi/utils.py:268: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " grad[i] = (\n" - ] - } - ], + "outputs": [], "source": [ "# generate pauli_operator_dict\n", "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", @@ -314,46 +159,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 693.59trial/s, best loss: 27.60716505293791] \n", - "100%|██████████| 500/500 [00:00<00:00, 739.77trial/s, best loss: 24.351930977389397] \n", - "100%|██████████| 500/500 [00:00<00:00, 685.07trial/s, best loss: 22.089870557907307] \n", - "100%|██████████| 500/500 [00:00<00:00, 674.17trial/s, best loss: 20.346996185055133]\n", - "100%|██████████| 500/500 [00:00<00:00, 717.27trial/s, best loss: 18.942039981805472]\n", - "100%|██████████| 500/500 [00:00<00:00, 747.61trial/s, best loss: 17.152907499929672] \n", - "100%|██████████| 500/500 [00:00<00:00, 738.25trial/s, best loss: 16.019645236154144]\n", - "100%|██████████| 500/500 [00:00<00:00, 744.74trial/s, best loss: 15.2613302219324] \n", - "100%|██████████| 500/500 [00:00<00:00, 742.43trial/s, best loss: 14.603236439507265] \n", - "100%|██████████| 500/500 [00:00<00:00, 747.77trial/s, best loss: 14.050749361766682] \n", - "100%|██████████| 500/500 [00:00<00:00, 665.47trial/s, best loss: 13.555811973998546] \n", - "100%|██████████| 500/500 [00:00<00:00, 741.86trial/s, best loss: 13.11437793825614] \n", - "100%|██████████| 500/500 [00:00<00:00, 730.33trial/s, best loss: 12.708028521672343] \n", - "100%|██████████| 500/500 [00:00<00:00, 696.14trial/s, best loss: 12.338996561474936] \n", - "100%|██████████| 500/500 [00:00<00:00, 745.68trial/s, best loss: 11.96195414708825] \n", - "100%|██████████| 500/500 [00:00<00:00, 729.21trial/s, best loss: 11.962452492607511] \n", - "100%|██████████| 500/500 [00:00<00:00, 730.13trial/s, best loss: 11.963468727156927] \n", - "100%|██████████| 500/500 [00:00<00:00, 735.49trial/s, best loss: 11.965058898358617] \n", - "100%|██████████| 500/500 [00:00<00:00, 735.24trial/s, best loss: 11.965394318636747] \n", - "100%|██████████| 500/500 [00:00<00:00, 729.17trial/s, best loss: 11.966117166464612] \n", - "100%|██████████| 500/500 [00:00<00:00, 728.15trial/s, best loss: 11.966425772210973] \n", - "100%|██████████| 500/500 [00:00<00:00, 723.59trial/s, best loss: 11.966970026858228] \n", - "100%|██████████| 500/500 [00:00<00:00, 732.31trial/s, best loss: 11.967330716148668] \n", - "100%|██████████| 500/500 [00:00<00:00, 734.26trial/s, best loss: 11.96780433370658] \n", - "100%|██████████| 500/500 [00:00<00:00, 733.15trial/s, best loss: 11.969207838189973] \n", - "100%|██████████| 500/500 [00:00<00:00, 732.58trial/s, best loss: 11.969502660368933] \n", - "100%|██████████| 500/500 [00:00<00:00, 696.01trial/s, best loss: 11.969798703907593] \n", - "100%|██████████| 500/500 [00:00<00:00, 734.39trial/s, best loss: 11.970102117939215] \n", - "100%|██████████| 500/500 [00:00<00:00, 733.83trial/s, best loss: 11.971312239622172] \n", - "100%|██████████| 500/500 [00:00<00:00, 737.13trial/s, best loss: 11.97187547672911] \n" - ] - } - ], + "outputs": [], "source": [ "iters = 30\n", "off_diagonal_norm_2 = [dbi.off_diagonal_norm]\n", @@ -367,30 +175,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.title(str(nqubits) + ' spins random hamiltonian')\n", "plt.plot(off_diagonal_norm_1, label='order 1')\n", @@ -412,27 +199,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-04-03 08:14:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# generate the Hamiltonian\n", "nqubits = 5\n", @@ -447,17 +216,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-04-03 08:14:05]: Using qibojit (numba) backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# backend\n", "set_backend(\"qibojit\", platform=\"numba\")\n", @@ -474,39 +235,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:25]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 621.13trial/s, best loss: 8.145448855938055]\n", - "The initial D coefficients: [(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j)]\n", - "Gradient: [-0.22522735 -0.52101222 -0.59635378 -0.52101222 -0.22522735]\n", - "s: 0.053751929537431395\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/pethidine/Documents/GitHub/qibo/src/qibo/models/dbi/utils.py:268: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " grad[i] = (\n" - ] - } - ], + "outputs": [], "source": [ "dbi_TFIM_1 = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)\n", "# generate pauli_operator_dict\n", @@ -521,31 +252,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 707.38trial/s, best loss: 8.144345846120405] \n", - "100%|██████████| 500/500 [00:00<00:00, 735.41trial/s, best loss: 7.604689171773317] \n", - "100%|██████████| 500/500 [00:00<00:00, 735.65trial/s, best loss: 7.4176027901243495]\n", - "100%|██████████| 500/500 [00:00<00:00, 755.09trial/s, best loss: 7.008137714268] \n", - "100%|██████████| 500/500 [00:00<00:00, 745.89trial/s, best loss: 5.961852278701001] \n", - "100%|██████████| 500/500 [00:00<00:00, 703.36trial/s, best loss: 5.293878968775635] \n", - "100%|██████████| 500/500 [00:00<00:00, 749.97trial/s, best loss: 4.82113561785614] \n", - "100%|██████████| 500/500 [00:00<00:00, 749.13trial/s, best loss: 4.326041032299924] \n", - "100%|██████████| 500/500 [00:00<00:00, 749.48trial/s, best loss: 3.608232108710637] \n", - "100%|██████████| 500/500 [00:00<00:00, 750.07trial/s, best loss: 2.9342878051864014] \n", - "100%|██████████| 500/500 [00:00<00:00, 749.53trial/s, best loss: 2.619767373811081] \n", - "100%|██████████| 500/500 [00:00<00:00, 757.24trial/s, best loss: 2.553595125371688] \n", - "100%|██████████| 500/500 [00:00<00:00, 757.78trial/s, best loss: 2.5450651469655488] \n", - "100%|██████████| 500/500 [00:00<00:00, 758.64trial/s, best loss: 2.5411137289688988] \n", - "100%|██████████| 500/500 [00:00<00:00, 758.68trial/s, best loss: 2.5071534830104416] \n" - ] - } - ], + "outputs": [], "source": [ "NSTEPS = 15\n", "off_diagonal_norm_1 = [dbi_TFIM_1.off_diagonal_norm]\n", @@ -559,30 +268,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.title(f'n={nqubits} h={h} TFIM, order=1')\n", "plt.plot(off_diagonal_norm_1)\n", @@ -592,20 +280,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# the final matrix\n", "visualize_matrix(dbi_TFIM.h.matrix)" @@ -620,43 +297,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-03 08:18:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 722.72trial/s, best loss: 8.144335598357657]\n", - "The initial D coefficients: [(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j), 0j, 0j, 0j, 0j, 0j, 0j, 0j, 0j, 0j, 0j]\n", - "Gradient: [-0.22563381 -0.52082675 -0.5963705 -0.52082675 -0.22563381 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. ]\n", - "s: 0.052813741396418624\n" - ] - } - ], + "outputs": [], "source": [ "dbi_TFIM_2 = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)\n", "# generate pauli_operator_dict\n", @@ -671,31 +314,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 744.95trial/s, best loss: 8.144331648273518] \n", - "100%|██████████| 500/500 [00:00<00:00, 744.80trial/s, best loss: 7.604748514246744] \n", - "100%|██████████| 500/500 [00:00<00:00, 756.14trial/s, best loss: 7.420954791755261] \n", - "100%|██████████| 500/500 [00:00<00:00, 759.14trial/s, best loss: 7.016113259744297] \n", - "100%|██████████| 500/500 [00:00<00:00, 744.68trial/s, best loss: 5.989940711739217]\n", - "100%|██████████| 500/500 [00:00<00:00, 750.40trial/s, best loss: 5.307608116085456]\n", - "100%|██████████| 500/500 [00:00<00:00, 749.79trial/s, best loss: 4.822327887100122] \n", - "100%|██████████| 500/500 [00:00<00:00, 661.39trial/s, best loss: 4.30794767213952] \n", - "100%|██████████| 500/500 [00:00<00:00, 744.71trial/s, best loss: 3.5737486865622783]\n", - "100%|██████████| 500/500 [00:00<00:00, 742.92trial/s, best loss: 2.899168952313776] \n", - "100%|██████████| 500/500 [00:00<00:00, 749.93trial/s, best loss: 2.6126410200674473] \n", - "100%|██████████| 500/500 [00:00<00:00, 754.15trial/s, best loss: 2.5428845456217983] \n", - "100%|██████████| 500/500 [00:00<00:00, 761.81trial/s, best loss: 2.520820402680462] \n", - "100%|██████████| 500/500 [00:00<00:00, 700.04trial/s, best loss: 2.4881828281706038] \n", - "100%|██████████| 500/500 [00:00<00:00, 736.96trial/s, best loss: 2.4545260560650717] \n" - ] - } - ], + "outputs": [], "source": [ "NSTEPS = 15\n", "off_diagonal_norm_2 = [dbi_TFIM_2.off_diagonal_norm]\n", @@ -709,30 +330,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.title(f'n={nqubits} h={h} TFIM')\n", "plt.plot(off_diagonal_norm_1, label='order 1')\n", @@ -741,416 +341,6 @@ "plt.xlabel('Iteration')\n", "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Different initial `d`\n", - "Next, we show the effect of different choices of the initial direction of the gradient descent method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(-2.580645161290323+0j), (-1.2903225806451613+0j), (-0.6451612903225807+0j), (-0.32258064516129037+0j), (-0.16129032258064518+0j)]\n" - ] - } - ], - "source": [ - "H = H_TFIM.matrix\n", - "L = int(np.log2(H.shape[0]))\n", - "N = np.diag(np.linspace(np.min(np.diag(H)),np.max(np.diag(H)),2**L))\n", - "d_coef = onsite_Z_decomposition(N, onsite_Z_ops)\n", - "print(d_coef)\n", - "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize_matrix(H, 'Initial hamiltonian')\n", - "visualize_matrix(N, 'Min-max diagonal matrix')\n", - "visualize_matrix(d, 'Min-max projection onsite-Z')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we see that the min-max diagonal operator can be correctly decomposed into onsite-Z operators. Then we generate the diagonalization curve and compare with other initializations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-03-26 16:08:28]: Using qibojit (numba) backend on /CPU:0\n" - ] - } - ], - "source": [ - "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", - "# initialize dbi object\n", - "dbi_TFIM_MMH = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 688.27trial/s, best loss: 9.336239342915379]\n", - "New optimized step at iteration 1/15: 0.039240166337035656 with d_coef [(-2.3180340693309422+0j), (-0.9042157574954297+0j), (-0.6267094129284807+0j), (-0.37510402952816974+0j), (-0.16137910360026844+0j)], loss 9.263805656974093\n", - "100%|██████████| 500/500 [00:00<00:00, 654.86trial/s, best loss: 8.253271106315344] \n", - "New optimized step at iteration 2/15: 0.0636971166898561 with d_coef [(-2.8893154826347565+0j), (-1.3328071932958503+0j), (-0.5996311871447069+0j), (-0.38812640871658144+0j), (-0.16592899239661785+0j)], loss 8.248988639626276\n", - "100%|██████████| 500/500 [00:00<00:00, 705.90trial/s, best loss: 7.820911729728226] \n", - "New optimized step at iteration 3/15: 0.026774099108320803 with d_coef [(-3.9047191557345737+0j), (-1.3620955366051533+0j), (-1.094932722170599+0j), (-0.5744178736473565+0j), (-0.04727696085745736+0j)], loss 7.79237041903216\n", - "100%|██████████| 500/500 [00:00<00:00, 522.76trial/s, best loss: 7.506187222292692]\n", - "New optimized step at iteration 4/15: 0.029295596624437686 with d_coef [(-3.894655859483571+0j), (-1.866243073713661+0j), (-0.7092145648013096+0j), (-0.7039608847825699+0j), (-0.023283739763302808+0j)], loss 7.4962199726801755\n", - "100%|██████████| 500/500 [00:00<00:00, 576.24trial/s, best loss: 7.262455656549131]\n", - "New optimized step at iteration 5/15: 0.02836170693029348 with d_coef [(-3.9671435812064013+0j), (-1.748374386604198+0j), (-0.9350901630745362+0j), (-0.6281543245247632+0j), (-0.021512156595171472+0j)], loss 7.242940534826334\n", - "100%|██████████| 500/500 [00:00<00:00, 671.15trial/s, best loss: 7.044926289914773]\n", - "New optimized step at iteration 6/15: 0.027897015043668715 with d_coef [(-3.948390041694754+0j), (-1.8847156433519916+0j), (-0.8262997874928508+0j), (-0.6276868981090158+0j), (-0.03885078124692265+0j)], loss 7.026681601151195\n", - "100%|██████████| 500/500 [00:00<00:00, 690.94trial/s, best loss: 6.855318614477858] \n", - "New optimized step at iteration 7/15: 0.027095778110113995 with d_coef [(-3.9519653973013913+0j), (-1.911636257457286+0j), (-0.8907292589911223+0j), (-0.6344354980656255+0j), (-0.0239873577390306+0j)], loss 6.826359605831807\n", - "100%|██████████| 500/500 [00:00<00:00, 520.20trial/s, best loss: 6.6782408641935875]\n", - "New optimized step at iteration 8/15: 0.027670995126608866 with d_coef [(-3.9302491674477538+0j), (-1.9666365073691627+0j), (-0.8561543524586357+0j), (-0.6383800207112388+0j), (-0.004655769048021813+0j)], loss 6.636290444352086\n", - "100%|██████████| 500/500 [00:00<00:00, 576.02trial/s, best loss: 6.500633770102917]\n", - "New optimized step at iteration 9/15: 0.027675484066740867 with d_coef [(-3.910374644169554+0j), (-1.9831418560231258+0j), (-0.9056736621483122+0j), (-0.6540987589359828+0j), (0.02406147464053876+0j)], loss 6.447464047229631\n", - "100%|██████████| 500/500 [00:00<00:00, 686.99trial/s, best loss: 6.319748615035787]\n", - "New optimized step at iteration 10/15: 0.026930095210157 with d_coef [(-3.886824281463916+0j), (-1.99625546879924+0j), (-0.9414450075378732+0j), (-0.6563760409606512+0j), (0.03970055245590362+0j)], loss 6.2592485638502575\n", - "100%|██████████| 500/500 [00:00<00:00, 678.32trial/s, best loss: 6.1400705423264075]\n", - "New optimized step at iteration 11/15: 0.027416250931757133 with d_coef [(-3.8475665420145373+0j), (-2.037392997099672+0j), (-0.9264643353804642+0j), (-0.6830139042784837+0j), (0.08321313069136971+0j)], loss 6.056764516965165\n", - "100%|██████████| 500/500 [00:00<00:00, 729.97trial/s, best loss: 5.940597947808348] \n", - "New optimized step at iteration 12/15: 0.028200202317592835 with d_coef [(-3.82460449840812+0j), (-2.035906559623582+0j), (-0.9702033338205296+0j), (-0.6848609304443387+0j), (0.11118456157172787+0j)], loss 5.848596463276441\n", - "100%|██████████| 500/500 [00:00<00:00, 743.16trial/s, best loss: 5.72938416138] \n", - "New optimized step at iteration 13/15: 0.025525484486623708 with d_coef [(-3.8277137978993436+0j), (-2.0402358325723036+0j), (-0.9967614632890175+0j), (-0.6822006377994072+0j), (0.09661303923602668+0j)], loss 5.643243093952352\n", - "100%|██████████| 500/500 [00:00<00:00, 722.17trial/s, best loss: 5.532276668994669] \n", - "New optimized step at iteration 14/15: 0.028418788139760974 with d_coef [(-3.7489089984244486+0j), (-2.114017010442895+0j), (-0.9183197191620466+0j), (-0.7036125621442609+0j), (0.16285610695072883+0j)], loss 5.4057916657046725\n", - "100%|██████████| 500/500 [00:00<00:00, 739.76trial/s, best loss: 5.288910417094644] \n", - "New optimized step at iteration 15/15: 0.02625000676004677 with d_coef [(-3.789232226109539+0j), (-2.092494639505251+0j), (-1.0022140546781002+0j), (-0.6714823814533052+0j), (0.13551681944910254+0j)], loss 5.191808803025761\n" - ] - } - ], - "source": [ - "NSTEPS = 15\n", - "off_diagonal_norm_MMH = [dbi_TFIM_MMH.off_diagonal_norm]\n", - "s_step_MMH = [0]\n", - "# d = np.diag(np.linspace(np.min(np.diag(dbi_TFIM_MMH.h.matrix)),np.max(np.diag(dbi_TFIM_MMH.h.matrix)),2**nqubits))\n", - "# d_coef = onsite_Z_decomposition(d, onsite_Z_ops)\n", - "for _ in range(NSTEPS):\n", - " d = np.diag(np.linspace(np.min(np.diag(dbi_TFIM_MMH.h.matrix)),np.max(np.diag(dbi_TFIM_MMH.h.matrix)),2**nqubits))\n", - " d_coef = onsite_Z_decomposition(d, onsite_Z_ops)\n", - " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM_MMH, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", - " dbi_TFIM_MMH(d=d, step=s)\n", - " off_diagonal_norm_MMH.append(dbi_TFIM_MMH.off_diagonal_norm)\n", - " s_step_MMH.append(s)\n", - " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {s} with d_coef {d_coef}, loss {dbi_TFIM_MMH.off_diagonal_norm}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", - "plt.plot(off_diagonal_norm_MMH, label='MMH')\n", - "plt.plot(off_diagonal_norm_delta, label='delta')\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Effect of `n`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-03-26 16:08:41]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 31.576176740060667\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", - "# initialize dbi object\n", - "nqubits = 5\n", - "h0 = random_hermitian(2**nqubits, seed=2)\n", - "scheduling = DoubleBracketScheduling.hyperopt\n", - "mode = DoubleBracketGeneratorType.single_commutator\n", - "n_1 = 5\n", - "n_2 = 3\n", - "dbi_1 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", - "dbi_2 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", - "print(\"Initial off diagonal norm\", dbi_1.off_diagonal_norm)\n", - "visualize_matrix(dbi_1.h.matrix, title=f'Random hamiltonian with L={nqubits}')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 697.70trial/s, best loss: 9.558186537868679] \n", - "The initial D coefficients: [(-3.321354431855655-1.7961649980378765e-16j), (-0.7143725995296772+3.608986798092513e-17j), (0.472710854506152+9.347215093087467e-17j), (-0.5707798509274735-1.3813111045761499e-17j), (0.34536980200226214-1.1499770144849896e-16j)]\n", - "Gradient: [ 0.65534217 0.16603388 -0.31270245 0.27247095 0.60904527]\n", - "s: 0.024282460160549718\n" - ] - } - ], - "source": [ - "# generate the onsite Z operators\n", - "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", - "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", - "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi,d,n=5, onsite_Z_ops=onsite_Z_ops)\n", - "print('The initial D coefficients:', d_coef)\n", - "print('Gradient:', grad)\n", - "print('s:', s)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 596.09trial/s, best loss: 27.467491165569765]\n", - "100%|██████████| 500/500 [00:00<00:00, 689.73trial/s, best loss: 27.469650148347917] \n", - "100%|██████████| 500/500 [00:00<00:00, 684.87trial/s, best loss: 23.138955844687388] \n", - "100%|██████████| 500/500 [00:00<00:00, 680.99trial/s, best loss: 23.147351603039073]\n", - "100%|██████████| 500/500 [00:00<00:00, 709.00trial/s, best loss: 20.03558303567074] \n", - "100%|██████████| 500/500 [00:00<00:00, 638.55trial/s, best loss: 20.01446017712839] \n", - "100%|██████████| 500/500 [00:00<00:00, 659.72trial/s, best loss: 18.534762036988734]\n", - "100%|██████████| 500/500 [00:00<00:00, 702.62trial/s, best loss: 18.511235299525854]\n", - "100%|██████████| 500/500 [00:00<00:00, 716.91trial/s, best loss: 17.667631299146947] \n", - "100%|██████████| 500/500 [00:00<00:00, 717.85trial/s, best loss: 17.623675374778802] \n", - "100%|██████████| 500/500 [00:00<00:00, 626.07trial/s, best loss: 17.043867777621116]\n", - "100%|██████████| 500/500 [00:00<00:00, 662.03trial/s, best loss: 16.99411319096819] \n", - "100%|██████████| 500/500 [00:00<00:00, 694.96trial/s, best loss: 16.644107561332255]\n", - "100%|██████████| 500/500 [00:00<00:00, 712.76trial/s, best loss: 16.467189206952877] \n", - "100%|██████████| 500/500 [00:00<00:00, 713.76trial/s, best loss: 16.046314213095602]\n", - "100%|██████████| 500/500 [00:00<00:00, 699.95trial/s, best loss: 15.878928292681035] \n", - "100%|██████████| 500/500 [00:00<00:00, 691.24trial/s, best loss: 15.62141427589626] \n", - "100%|██████████| 500/500 [00:00<00:00, 667.73trial/s, best loss: 15.306325490043811] \n", - "100%|██████████| 500/500 [00:00<00:00, 694.90trial/s, best loss: 15.001484687547642] \n", - "100%|██████████| 500/500 [00:00<00:00, 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= [0]\n", - "for i in range(iters):\n", - " s_1, d_coef_1, d_1 = gradient_descent_onsite_Z(dbi_1, d_coef_1, d_1, onsite_Z_ops=onsite_Z_ops, n=n_1, max_evals=100)\n", - " s_2, d_coef_2, d_2 = gradient_descent_onsite_Z(dbi_2, d_coef_2, d_2, onsite_Z_ops=onsite_Z_ops, n=n_2, max_evals=100)\n", - " dbi_1(step=s_1, d=d_1)\n", - " dbi_2(step=s_2, d=d_2)\n", - " off_diagonal_norm_1.append(dbi_1.off_diagonal_norm)\n", - " off_diagonal_norm_2.append(dbi_2.off_diagonal_norm)\n", - " s_step_1.append(s_1)\n", - " s_step_2.append(s_2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.title(str(nqubits) + ' spins magnetic field diagonalization')\n", - "plt.plot(off_diagonal_norm_1, label=f'n_taylor={n_1}')\n", - "plt.plot(off_diagonal_norm_2, label=f'n_taylor={n_2}')\n", - "plt.legend()\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" - ] } ], "metadata": { diff --git a/examples/dbi/dbi_strategy_magnetic_field.ipynb b/examples/dbi/dbi_strategy_magnetic_field.ipynb deleted file mode 100644 index fc1c51bd40..0000000000 --- a/examples/dbi/dbi_strategy_magnetic_field.ipynb +++ /dev/null @@ -1,872 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Double-Bracket Iteration Strategy: magnetic field (onsite Z)\n", - "This notebook shows the diagonalization process of DBI using the magnetic field strategy, which varies the diagonal operator $D$ by gradient descent. To find the gradient with respect to $D$, parameterization of $D$ is required. For the purpose of this notebook, we represent it by onsite Pauli-Z operators, i.e.\n", - "\n", - "$$ D = \\sum \\alpha_i Z_i $$\n", - "\n", - "Note that it is also possible to have higher-order terms, such as $ D = \\sum \\alpha_i Z_i + \\sum \\beta_{i,j}Z_iZ_j+...$\n", - "\n", - "The gradients are calculated under the premise that the diagonalization gain curve can be fitted by a polynomial, and that the iteration step duration is taken at the first dip of the curve." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from qibo import hamiltonians, set_backend\n", - "from qibo.hamiltonians import Hamiltonian, SymbolicHamiltonian\n", - "from qibo.quantum_info import random_hermitian\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", - "from qibo.models.dbi.utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "def visualize_matrix(matrix, title=\"\"):\n", - " \"\"\"Visualize hamiltonian in a heatmap form.\"\"\"\n", - " fig, ax = plt.subplots(figsize=(5,5))\n", - " ax.set_title(title)\n", - " try:\n", - " im = ax.imshow(np.absolute(matrix), cmap=\"inferno\")\n", - " except TypeError:\n", - " im = ax.imshow(np.absolute(matrix.get()), cmap=\"inferno\")\n", - " fig.colorbar(im, ax=ax)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test on random Hamiltonian" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-03-26 16:07:47]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 31.576176740060667\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", - "# initialize dbi object\n", - "nqubits = 5\n", - "h0 = random_hermitian(2**nqubits, seed=2)\n", - "scheduling = DoubleBracketScheduling.hyperopt\n", - "mode = DoubleBracketGeneratorType.single_commutator\n", - "n_taylor = 5\n", - "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", - "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)\n", - "visualize_matrix(dbi.h.matrix, title=f'Random hamiltonian with L={nqubits}')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:07:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 691.96trial/s, best loss: 27.607175404720753]\n", - "The initial D coefficients: [(-0.2980910136757636+0j), (-0.17678355790937256+0j), (0.294550421681131+0j), (-0.2301056409534723+0j), (-0.07297191764284382+0j)]\n", - "Gradient: [-0.20478337 0.418433 -0.03167988 0.18669773 -0.86435984]\n", - "s: 0.11660954506915275\n" - ] - } - ], - "source": [ - "# generate the onsite Z operators\n", - "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", - "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", - "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi, d=d, onsite_Z_ops=onsite_Z_ops)\n", - "print('The initial D coefficients:', d_coef)\n", - "print('Gradient:', grad)\n", - "print('s:', s)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 703.22trial/s, best loss: 27.607182422340095] \n", - "100%|██████████| 500/500 [00:00<00:00, 729.43trial/s, best loss: 24.35179754917795] \n", - "100%|██████████| 500/500 [00:00<00:00, 729.08trial/s, best loss: 22.089444283591433] \n", - "100%|██████████| 500/500 [00:00<00:00, 698.65trial/s, best loss: 20.347120765676763]\n", - "100%|██████████| 500/500 [00:00<00:00, 623.16trial/s, best loss: 18.94635121785982]\n", - "100%|██████████| 500/500 [00:00<00:00, 605.13trial/s, best loss: 17.773702241529776] \n", - "100%|██████████| 500/500 [00:00<00:00, 687.10trial/s, best loss: 16.784805711373227] \n", - "100%|██████████| 500/500 [00:00<00:00, 695.51trial/s, best loss: 15.934402363491223] \n", - "100%|██████████| 500/500 [00:00<00:00, 652.85trial/s, best loss: 15.197822552085507] \n", - "100%|██████████| 500/500 [00:00<00:00, 685.77trial/s, best loss: 14.481250187299748] \n", - "100%|██████████| 500/500 [00:00<00:00, 692.66trial/s, best loss: 14.044172334074341] \n", - "100%|██████████| 500/500 [00:00<00:00, 692.91trial/s, best loss: 13.670766358199891] \n", - "100%|██████████| 500/500 [00:00<00:00, 684.26trial/s, best loss: 13.325331286760488] \n", - "100%|██████████| 500/500 [00:00<00:00, 636.50trial/s, best loss: 13.01668686825596] \n", - "100%|██████████| 500/500 [00:00<00:00, 612.11trial/s, best loss: 12.711623339299685] \n", - "100%|██████████| 500/500 [00:00<00:00, 697.36trial/s, best loss: 12.409020875491057] \n", - "100%|██████████| 500/500 [00:00<00:00, 683.72trial/s, best loss: 12.08748982503799] \n", - "100%|██████████| 500/500 [00:00<00:00, 738.27trial/s, best loss: 11.75348065601818]\n", - "100%|██████████| 500/500 [00:00<00:00, 724.55trial/s, best loss: 11.410208539441799] \n", - "100%|██████████| 500/500 [00:00<00:00, 729.97trial/s, best loss: 11.06582875641592] \n", - "100%|██████████| 500/500 [00:00<00:00, 723.17trial/s, best loss: 10.734278849532725] \n", - "100%|██████████| 500/500 [00:00<00:00, 736.09trial/s, best loss: 10.391106227243483] \n", - "100%|██████████| 500/500 [00:00<00:00, 695.73trial/s, best loss: 9.835687097799866] \n", - "100%|██████████| 500/500 [00:00<00:00, 645.00trial/s, best loss: 9.836151362023536]\n", - "100%|██████████| 500/500 [00:00<00:00, 662.94trial/s, best loss: 9.83679254247866] \n", - "100%|██████████| 500/500 [00:00<00:00, 692.56trial/s, best loss: 9.83815541734947] \n", - "100%|██████████| 500/500 [00:00<00:00, 683.98trial/s, best loss: 9.838623865790995] \n", - "100%|██████████| 500/500 [00:00<00:00, 691.24trial/s, best loss: 9.83915601848446] \n", - "100%|██████████| 500/500 [00:00<00:00, 697.64trial/s, best loss: 9.843010172903082] \n", - "100%|██████████| 500/500 [00:00<00:00, 669.37trial/s, best loss: 9.844996826247685] \n" - ] - } - ], - "source": [ - "iters = 30\n", - "off_diagonal_norm = [dbi.off_diagonal_norm]\n", - "s_step = [0]\n", - "for i in range(iters):\n", - " s, d_coef, d = gradient_descent_onsite_Z(dbi, d_coef=d_coef, d=d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", - " dbi(step=s, d=d)\n", - " off_diagonal_norm.append(dbi.off_diagonal_norm)\n", - " s_step.append(s)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.title(str(nqubits) + ' spins random hamiltonian')\n", - "plt.plot(off_diagonal_norm)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test on TFIM\n", - "Here we choose to customize our TFIM in the X axis using `SymbolicHamiltonian`. It is also possible to use Hadamard gate to rotate the TFIM inbuilt in `qibo`.\n", - "\n", - "$$ H = -(\\sum X_i X_{i+1} + \\sum hZ_i)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# generate the Hamiltonian\n", - "nqubits = 5\n", - "h = 1\n", - "H_TFIM = SymbolicHamiltonian( - h*symbols.Z(nqubits-1), nqubits=nqubits)\n", - "# add linear interaction terms\n", - "for i in range(nqubits-1):\n", - " H_TFIM -= SymbolicHamiltonian(symbols.X(i)*symbols.X(i+1) + h*symbols.Z(i), nqubits=nqubits)\n", - "H_TFIM = H_TFIM.dense\n", - "visualize_matrix(H_TFIM.matrix, title=f'TFIM with L={nqubits} h={h}')" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-03-26 16:08:14]: Using qibojit (numba) backend on /CPU:0\n" - ] - } - ], - "source": [ - "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", - "# initialize dbi object\n", - "dbi_TFIM = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:01<00:00, 383.66trial/s, best loss: 8.1443761719701] \n", - "Initial off-diagonal norm: 9.844996868109437\n", - "The initial D coefficients: [(-1+0j), (-1+0j), (-1+0j), (-1+0j), (-1+0j)]\n", - "Gradient: [-0.22567346 -0.52080864 -0.59637211 -0.52080864 -0.22567346]\n", - "s: 0.05271207518843116\n" - ] - } - ], - "source": [ - "# generate the onsite Z operators\n", - "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", - "d_coef = onsite_Z_decomposition(dbi_TFIM.h.matrix, onsite_Z_ops)\n", - "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi_TFIM, d, n=5, onsite_Z_ops=onsite_Z_ops)\n", - "print('Initial off-diagonal norm:', dbi.off_diagonal_norm)\n", - "print('The initial D coefficients:', d_coef)\n", - "print('Gradient:', grad)\n", - "print('s:', s)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 591.53trial/s, best loss: 8.145383187499851]\n", - "New optimized step at iteration 1/15: 0.05372645731587117 with d_coef [(-0.9894224152921011+0j), (-0.9787695132043422+0j), (-0.9750406784474285+0j), (-0.9787695132043422+0j), (-0.9894224152921011+0j)], loss 8.143621474679835\n", - "100%|██████████| 500/500 [00:00<00:00, 645.70trial/s, best loss: 7.605789256028495]\n", - "New optimized step at iteration 2/15: 0.05650469375482817 with d_coef [(-0.967659680502992+0j), (-1.1697984701193866+0j), (-1.1812229848159992+0j), (-1.169798470119386+0j), (-0.9676596805029926+0j)], loss 7.597613753384701\n", - "100%|██████████| 500/500 [00:00<00:00, 655.89trial/s, best loss: 7.428351470243482] \n", - "New optimized step at iteration 3/15: 0.0398775478554277 with d_coef [(-0.9211510601202757+0j), (-1.8583923563596692+0j), (-1.3920649030243233+0j), (-1.8583923563596683+0j), (-0.9211510601202774+0j)], loss 7.340829616091421\n", - "100%|██████████| 500/500 [00:00<00:00, 662.71trial/s, best loss: 7.044497387511533] \n", - "New optimized step at iteration 4/15: 0.04899248221924902 with d_coef [(-0.6972643143931548+0j), (-2.4626900070115862+0j), (-0.9844514837970455+0j), (-2.462690007011585+0j), (-0.6972643143931571+0j)], loss 6.642213913279594\n", - "100%|██████████| 500/500 [00:00<00:00, 657.76trial/s, best loss: 5.999678025090855] \n", - "New optimized step at iteration 5/15: 0.022874588399740523 with d_coef [(-0.5466790556822081+0j), (-2.999350700331548+0j), (-0.3794556667684317+0j), (-2.9993507003315476+0j), (-0.5466790556822098+0j)], loss 5.886447625252318\n", - "100%|██████████| 500/500 [00:00<00:00, 574.02trial/s, best loss: 5.3112300777947405]\n", - "New optimized step at iteration 6/15: 0.019141369630992236 with d_coef [(-0.7333858133569751+0j), (-3.0535952858417934+0j), (0.5756460221651358+0j), (-3.053595285841793+0j), (-0.7333858133569763+0j)], loss 5.250048883689106\n", - "100%|██████████| 500/500 [00:00<00:00, 675.11trial/s, best loss: 4.816608426854996] \n", - "New optimized step at iteration 7/15: 0.023987091082236008 with d_coef [(-1.0481221272508368+0j), (-3.1124649418400363+0j), (1.4655494332027308+0j), (-3.1124649418400367+0j), (-1.048122127250837+0j)], loss 4.718211391122002\n", - "100%|██████████| 500/500 [00:00<00:00, 679.58trial/s, best loss: 4.284286113986318] \n", - "New optimized step at iteration 8/15: 0.02139251957239659 with d_coef [(-1.4568864129920867+0j), (-3.060562777832911+0j), (2.2749298849030803+0j), (-3.0605627778329114+0j), (-1.4568864129920873+0j)], loss 4.141138157155743\n", - "100%|██████████| 500/500 [00:00<00:00, 707.05trial/s, best loss: 3.539292516453598] \n", - "New optimized step at iteration 9/15: 0.02678154139520766 with d_coef [(-1.8702833982016478+0j), (-2.970343142930349+0j), (3.048114960756174+0j), (-2.9703431429303495+0j), (-1.8702833982016478+0j)], loss 3.3574911798518396\n", - "100%|██████████| 500/500 [00:00<00:00, 712.38trial/s, best loss: 2.874231151326864] \n", - "New optimized step at iteration 10/15: 0.01690916984115942 with d_coef [(-2.3030995103215814+0j), (-2.7605226880132614+0j), (3.777805532801257+0j), (-2.7605226880132614+0j), (-2.3030995103215792+0j)], loss 2.8230284010126816\n", - "100%|██████████| 500/500 [00:00<00:00, 666.99trial/s, best loss: 2.6088111056187437]\n", - "New optimized step at iteration 11/15: 0.026384092579150507 with d_coef [(-2.303103545745607+0j), (-2.76052331639202+0j), (3.7778137111811922+0j), (-2.76052331639202+0j), (-2.303103545745605+0j)], loss 2.6088111361606745\n", - "100%|██████████| 500/500 [00:00<00:00, 698.11trial/s, best loss: 2.546900361123962] \n", - "New optimized step at iteration 12/15: 0.015856203984303208 with d_coef [(-2.4537053596179947+0j), (-2.9677391380139637+0j), (4.069082377286002+0j), (-2.9677391380139513+0j), (-2.453705359617996+0j)], loss 2.546553818422246\n", - "100%|██████████| 500/500 [00:00<00:00, 742.58trial/s, best loss: 2.5253329418331236] \n", - "New optimized step at iteration 13/15: 0.022393088088665674 with d_coef [(-2.3237315151526348+0j), (-3.577847645999822+0j), (4.518564738562181+0j), (-3.577847645999736+0j), (-2.323731515152562+0j)], loss 2.500500419765173\n", - "100%|██████████| 500/500 [00:00<00:00, 746.30trial/s, best loss: 2.477865397687387] \n", - "New optimized step at iteration 14/15: 0.015967541185165194 with d_coef [(-1.9364597181792154+0j), (-4.149207093868703+0j), (4.441437397182407+0j), (-4.149207093868369+0j), (-1.936459718178632+0j)], loss 2.4579020061866172\n", - "100%|██████████| 500/500 [00:00<00:00, 742.62trial/s, best loss: 2.435713071167654] \n", - "New optimized step at iteration 15/15: 0.01431296703708781 with d_coef [(-1.5093021425133906+0j), (-4.700001486852238+0j), (4.295827265099874+0j), (-4.700001486851165+0j), (-1.5093021425092228+0j)], loss 2.422561227610107\n" - ] - } - ], - "source": [ - "NSTEPS = 15\n", - "off_diagonal_norm_delta = [dbi_TFIM.off_diagonal_norm]\n", - "s_step_delta = [0]\n", - "for _ in range(NSTEPS):\n", - " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100, n=5, use_ds=True)\n", - " dbi_TFIM(step=s, d=d)\n", - " off_diagonal_norm_delta.append(dbi_TFIM.off_diagonal_norm)\n", - " s_step_delta.append(s)\n", - " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {s} with d_coef {d_coef}, loss {dbi_TFIM.off_diagonal_norm}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", - "plt.plot(off_diagonal_norm_delta)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# the final matrix\n", - "visualize_matrix(dbi_TFIM.h.matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Different initial `d`\n", - "Next, we show the effect of different choices of the initial direction of the gradient descent method." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(-2.580645161290323+0j), (-1.2903225806451613+0j), (-0.6451612903225807+0j), (-0.32258064516129037+0j), (-0.16129032258064518+0j)]\n" - ] - } - ], - "source": [ - "H = H_TFIM.matrix\n", - "L = int(np.log2(H.shape[0]))\n", - "N = np.diag(np.linspace(np.min(np.diag(H)),np.max(np.diag(H)),2**L))\n", - "d_coef = onsite_Z_decomposition(N, onsite_Z_ops)\n", - "print(d_coef)\n", - "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize_matrix(H, 'Initial hamiltonian')\n", - "visualize_matrix(N, 'Min-max diagonal matrix')\n", - "visualize_matrix(d, 'Min-max projection onsite-Z')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we see that the min-max diagonal operator can be correctly decomposed into onsite-Z operators. Then we generate the diagonalization curve and compare with other initializations." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-03-26 16:08:28]: Using qibojit (numba) backend on /CPU:0\n" - ] - } - ], - "source": [ - "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", - "# initialize dbi object\n", - "dbi_TFIM_MMH = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 688.27trial/s, best loss: 9.336239342915379]\n", - "New optimized step at iteration 1/15: 0.039240166337035656 with d_coef [(-2.3180340693309422+0j), (-0.9042157574954297+0j), (-0.6267094129284807+0j), (-0.37510402952816974+0j), (-0.16137910360026844+0j)], loss 9.263805656974093\n", - "100%|██████████| 500/500 [00:00<00:00, 654.86trial/s, best loss: 8.253271106315344] \n", - "New optimized step at iteration 2/15: 0.0636971166898561 with d_coef [(-2.8893154826347565+0j), (-1.3328071932958503+0j), (-0.5996311871447069+0j), (-0.38812640871658144+0j), (-0.16592899239661785+0j)], loss 8.248988639626276\n", - "100%|██████████| 500/500 [00:00<00:00, 705.90trial/s, best loss: 7.820911729728226] \n", - "New optimized step at iteration 3/15: 0.026774099108320803 with d_coef [(-3.9047191557345737+0j), (-1.3620955366051533+0j), (-1.094932722170599+0j), (-0.5744178736473565+0j), (-0.04727696085745736+0j)], loss 7.79237041903216\n", - "100%|██████████| 500/500 [00:00<00:00, 522.76trial/s, best loss: 7.506187222292692]\n", - "New optimized step at iteration 4/15: 0.029295596624437686 with d_coef [(-3.894655859483571+0j), (-1.866243073713661+0j), (-0.7092145648013096+0j), (-0.7039608847825699+0j), (-0.023283739763302808+0j)], loss 7.4962199726801755\n", - "100%|██████████| 500/500 [00:00<00:00, 576.24trial/s, best loss: 7.262455656549131]\n", - "New optimized step at iteration 5/15: 0.02836170693029348 with d_coef [(-3.9671435812064013+0j), (-1.748374386604198+0j), (-0.9350901630745362+0j), (-0.6281543245247632+0j), (-0.021512156595171472+0j)], loss 7.242940534826334\n", - "100%|██████████| 500/500 [00:00<00:00, 671.15trial/s, best loss: 7.044926289914773]\n", - "New optimized step at iteration 6/15: 0.027897015043668715 with d_coef [(-3.948390041694754+0j), (-1.8847156433519916+0j), (-0.8262997874928508+0j), (-0.6276868981090158+0j), (-0.03885078124692265+0j)], loss 7.026681601151195\n", - "100%|██████████| 500/500 [00:00<00:00, 690.94trial/s, best loss: 6.855318614477858] \n", - "New optimized step at iteration 7/15: 0.027095778110113995 with d_coef [(-3.9519653973013913+0j), (-1.911636257457286+0j), (-0.8907292589911223+0j), (-0.6344354980656255+0j), (-0.0239873577390306+0j)], loss 6.826359605831807\n", - "100%|██████████| 500/500 [00:00<00:00, 520.20trial/s, best loss: 6.6782408641935875]\n", - "New optimized step at iteration 8/15: 0.027670995126608866 with d_coef [(-3.9302491674477538+0j), (-1.9666365073691627+0j), (-0.8561543524586357+0j), (-0.6383800207112388+0j), (-0.004655769048021813+0j)], loss 6.636290444352086\n", - "100%|██████████| 500/500 [00:00<00:00, 576.02trial/s, best loss: 6.500633770102917]\n", - "New optimized step at iteration 9/15: 0.027675484066740867 with d_coef [(-3.910374644169554+0j), (-1.9831418560231258+0j), (-0.9056736621483122+0j), (-0.6540987589359828+0j), (0.02406147464053876+0j)], loss 6.447464047229631\n", - "100%|██████████| 500/500 [00:00<00:00, 686.99trial/s, best loss: 6.319748615035787]\n", - "New optimized step at iteration 10/15: 0.026930095210157 with d_coef [(-3.886824281463916+0j), (-1.99625546879924+0j), (-0.9414450075378732+0j), (-0.6563760409606512+0j), (0.03970055245590362+0j)], loss 6.2592485638502575\n", - "100%|██████████| 500/500 [00:00<00:00, 678.32trial/s, best loss: 6.1400705423264075]\n", - "New optimized step at iteration 11/15: 0.027416250931757133 with d_coef [(-3.8475665420145373+0j), (-2.037392997099672+0j), (-0.9264643353804642+0j), (-0.6830139042784837+0j), (0.08321313069136971+0j)], loss 6.056764516965165\n", - "100%|██████████| 500/500 [00:00<00:00, 729.97trial/s, best loss: 5.940597947808348] \n", - "New optimized step at iteration 12/15: 0.028200202317592835 with d_coef [(-3.82460449840812+0j), (-2.035906559623582+0j), (-0.9702033338205296+0j), (-0.6848609304443387+0j), (0.11118456157172787+0j)], loss 5.848596463276441\n", - "100%|██████████| 500/500 [00:00<00:00, 743.16trial/s, best loss: 5.72938416138] \n", - "New optimized step at iteration 13/15: 0.025525484486623708 with d_coef [(-3.8277137978993436+0j), (-2.0402358325723036+0j), (-0.9967614632890175+0j), (-0.6822006377994072+0j), (0.09661303923602668+0j)], loss 5.643243093952352\n", - "100%|██████████| 500/500 [00:00<00:00, 722.17trial/s, best loss: 5.532276668994669] \n", - "New optimized step at iteration 14/15: 0.028418788139760974 with d_coef [(-3.7489089984244486+0j), (-2.114017010442895+0j), (-0.9183197191620466+0j), (-0.7036125621442609+0j), (0.16285610695072883+0j)], loss 5.4057916657046725\n", - "100%|██████████| 500/500 [00:00<00:00, 739.76trial/s, best loss: 5.288910417094644] \n", - "New optimized step at iteration 15/15: 0.02625000676004677 with d_coef [(-3.789232226109539+0j), (-2.092494639505251+0j), (-1.0022140546781002+0j), (-0.6714823814533052+0j), (0.13551681944910254+0j)], loss 5.191808803025761\n" - ] - } - ], - "source": [ - "NSTEPS = 15\n", - "off_diagonal_norm_MMH = [dbi_TFIM_MMH.off_diagonal_norm]\n", - "s_step_MMH = [0]\n", - "# d = np.diag(np.linspace(np.min(np.diag(dbi_TFIM_MMH.h.matrix)),np.max(np.diag(dbi_TFIM_MMH.h.matrix)),2**nqubits))\n", - "# d_coef = onsite_Z_decomposition(d, onsite_Z_ops)\n", - "for _ in range(NSTEPS):\n", - " d = np.diag(np.linspace(np.min(np.diag(dbi_TFIM_MMH.h.matrix)),np.max(np.diag(dbi_TFIM_MMH.h.matrix)),2**nqubits))\n", - " d_coef = onsite_Z_decomposition(d, onsite_Z_ops)\n", - " s, d_coef, d = gradient_descent_onsite_Z(dbi_TFIM_MMH, d_coef, d, onsite_Z_ops=onsite_Z_ops, max_evals=100)\n", - " dbi_TFIM_MMH(d=d, step=s)\n", - " off_diagonal_norm_MMH.append(dbi_TFIM_MMH.off_diagonal_norm)\n", - " s_step_MMH.append(s)\n", - " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {s} with d_coef {d_coef}, loss {dbi_TFIM_MMH.off_diagonal_norm}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.title(str(nqubits) + ' spins TFIM magnetic field diagonalization')\n", - "plt.plot(off_diagonal_norm_MMH, label='MMH')\n", - "plt.plot(off_diagonal_norm_delta, label='delta')\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Effect of `n`" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-03-26 16:08:41]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 31.576176740060667\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", - "# initialize dbi object\n", - "nqubits = 5\n", - "h0 = random_hermitian(2**nqubits, seed=2)\n", - "scheduling = DoubleBracketScheduling.hyperopt\n", - "mode = DoubleBracketGeneratorType.single_commutator\n", - "n_1 = 5\n", - "n_2 = 3\n", - "dbi_1 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", - "dbi_2 = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), scheduling=scheduling, mode=mode)\n", - "print(\"Initial off diagonal norm\", dbi_1.off_diagonal_norm)\n", - "visualize_matrix(dbi_1.h.matrix, title=f'Random hamiltonian with L={nqubits}')" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-03-26 16:08:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 697.70trial/s, best loss: 9.558186537868679] \n", - "The initial D coefficients: [(-3.321354431855655-1.7961649980378765e-16j), (-0.7143725995296772+3.608986798092513e-17j), (0.472710854506152+9.347215093087467e-17j), (-0.5707798509274735-1.3813111045761499e-17j), (0.34536980200226214-1.1499770144849896e-16j)]\n", - "Gradient: [ 0.65534217 0.16603388 -0.31270245 0.27247095 0.60904527]\n", - "s: 0.024282460160549718\n" - ] - } - ], - "source": [ - "# generate the onsite Z operators\n", - "onsite_Z_ops = generate_onsite_Z_ops(nqubits)\n", - "d_coef = onsite_Z_decomposition(dbi.h.matrix, onsite_Z_ops)\n", - "d = sum([d_coef[i] * onsite_Z_ops[i] for i in range(nqubits)])\n", - "grad, s = gradient_onsite_Z(dbi,d,n=5, onsite_Z_ops=onsite_Z_ops)\n", - "print('The initial D coefficients:', d_coef)\n", - "print('Gradient:', grad)\n", - "print('s:', s)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:00<00:00, 596.09trial/s, best loss: 27.467491165569765]\n", - "100%|██████████| 500/500 [00:00<00:00, 689.73trial/s, best loss: 27.469650148347917] \n", - "100%|██████████| 500/500 [00:00<00:00, 684.87trial/s, best loss: 23.138955844687388] \n", - "100%|██████████| 500/500 [00:00<00:00, 680.99trial/s, best loss: 23.147351603039073]\n", - "100%|██████████| 500/500 [00:00<00:00, 709.00trial/s, best loss: 20.03558303567074] \n", - "100%|██████████| 500/500 [00:00<00:00, 638.55trial/s, best loss: 20.01446017712839] \n", - "100%|██████████| 500/500 [00:00<00:00, 659.72trial/s, best loss: 18.534762036988734]\n", - "100%|██████████| 500/500 [00:00<00:00, 702.62trial/s, best loss: 18.511235299525854]\n", - "100%|██████████| 500/500 [00:00<00:00, 716.91trial/s, best loss: 17.667631299146947] 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range(iters):\n", - " s_1, d_coef_1, d_1 = gradient_descent_onsite_Z(dbi_1, d_coef_1, d_1, onsite_Z_ops=onsite_Z_ops, n=n_1, max_evals=100)\n", - " s_2, d_coef_2, d_2 = gradient_descent_onsite_Z(dbi_2, d_coef_2, d_2, onsite_Z_ops=onsite_Z_ops, n=n_2, max_evals=100)\n", - " dbi_1(step=s_1, d=d_1)\n", - " dbi_2(step=s_2, d=d_2)\n", - " off_diagonal_norm_1.append(dbi_1.off_diagonal_norm)\n", - " off_diagonal_norm_2.append(dbi_2.off_diagonal_norm)\n", - " s_step_1.append(s_1)\n", - " s_step_2.append(s_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$|| \\\\sigma(e^{sW}He^{-sW}) || $')" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.title(str(nqubits) + ' spins magnetic field diagonalization')\n", - "plt.plot(off_diagonal_norm_1, label=f'n_taylor={n_1}')\n", - "plt.plot(off_diagonal_norm_2, label=f'n_taylor={n_2}')\n", - "plt.legend()\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "DBF_qibo", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 5c391b2b123e072d174e4592e2cf0d38f83e0564 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 8 Apr 2024 15:32:28 +0800 Subject: [PATCH 061/154] Update code strucutre and dependencies --- examples/dbi/dbi_scheduling.ipynb | 3 +- examples/dbi/dbi_strategies_compare.ipynb | 4 +- examples/dbi/dbi_strategy_Ising_model.ipynb | 7 +- examples/dbi/dbi_strategy_Pauli-Z.ipynb | 4 +- src/qibo/models/dbi/double_bracket.py | 1 - src/qibo/models/dbi/utils.py | 321 ++------------------ src/qibo/models/dbi/utils_analytical.py | 210 +++++++++++++ src/qibo/models/dbi/utils_scheduling.py | 179 +++-------- src/qibo/models/dbi/utils_strategies.py | 181 +++++++++++ 9 files changed, 465 insertions(+), 445 deletions(-) create mode 100644 src/qibo/models/dbi/utils_analytical.py create mode 100644 src/qibo/models/dbi/utils_strategies.py diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index fdb087871b..a0ac88e6a6 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -30,7 +30,8 @@ "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" + "from qibo.models.dbi.utils_scheduling import *\n", + "from qibo.models.dbi.utils_strategies import *" ] }, { diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 7b422cf05d..7dfe2c5c60 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -15,6 +15,7 @@ "metadata": {}, "outputs": [], "source": [ + "\n", "from copy import deepcopy\n", "\n", "import numpy as np\n", @@ -25,7 +26,8 @@ "from qibo.quantum_info import random_hermitian\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" + "from qibo.models.dbi.utils_scheduling import *\n", + "from qibo.models.dbi.utils_strategies import *" ] }, { diff --git a/examples/dbi/dbi_strategy_Ising_model.ipynb b/examples/dbi/dbi_strategy_Ising_model.ipynb index a6c84b0747..ad3b1b5d73 100644 --- a/examples/dbi/dbi_strategy_Ising_model.ipynb +++ b/examples/dbi/dbi_strategy_Ising_model.ipynb @@ -28,7 +28,8 @@ "from qibo.hamiltonians import Hamiltonian, SymbolicHamiltonian\n", "from qibo.quantum_info import random_hermitian\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", - "from qibo.models.dbi.utils import *" + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_strategies import *" ] }, { @@ -103,7 +104,7 @@ "metadata": {}, "outputs": [], "source": [ - "iters = 30\n", + "iters = 15\n", "off_diagonal_norm_1 = [dbi.off_diagonal_norm]\n", "s_step = [0]\n", "for i in range(iters):\n", @@ -163,7 +164,7 @@ "metadata": {}, "outputs": [], "source": [ - "iters = 30\n", + "iters = 15\n", "off_diagonal_norm_2 = [dbi.off_diagonal_norm]\n", "s_step = [0]\n", "for i in range(iters):\n", diff --git a/examples/dbi/dbi_strategy_Pauli-Z.ipynb b/examples/dbi/dbi_strategy_Pauli-Z.ipynb index d89fdd5e74..2b60e12896 100644 --- a/examples/dbi/dbi_strategy_Pauli-Z.ipynb +++ b/examples/dbi/dbi_strategy_Pauli-Z.ipynb @@ -40,7 +40,9 @@ "\n", "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketIteration\n", - "from qibo.models.dbi.utils import *" + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_scheduling import *\n", + "from qibo.models.dbi.utils_strategies import *" ] }, { diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 124977d51f..c7f1d9eb38 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -2,7 +2,6 @@ from enum import Enum, auto from typing import Optional -import hyperopt import numpy as np from qibo.hamiltonians import Hamiltonian diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 0aeb738cf0..9f3767debb 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -1,19 +1,28 @@ import math -from copy import deepcopy from itertools import combinations, product -from typing import Optional -import hyperopt import numpy as np from qibo import symbols from qibo.backends import _check_backend from qibo.hamiltonians import SymbolicHamiltonian -from qibo.models.dbi.double_bracket import ( - DoubleBracketGeneratorType, - DoubleBracketIteration, - DoubleBracketScheduling, -) + + +def commutator(A, B): + """Compute commutator between two arrays.""" + return A @ B - B @ A + + +def variance(A, state): + """Calculates the variance of a matrix A with respect to a state: Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" + B = A @ A + return B[state, state] - A[state, state] ** 2 + + +def covariance(A, B, state): + """Calculates the covariance of two matrices A and B with respect to a state: Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$""" + C = A @ B + B @ A + return C[state, state] - 2 * A[state, state] * B[state, state] def generate_Z_operators(nqubits: int, backend=None): @@ -73,72 +82,6 @@ def str_to_symbolic(name: str): return tensor_op -def select_best_dbr_generator( - dbi_object: DoubleBracketIteration, - d_list: list, - step: Optional[float] = None, - compare_canonical: bool = True, - scheduling: DoubleBracketScheduling = None, - **kwargs, -): - """Selects the best double bracket rotation generator from a list and execute the rotation. - - Args: - dbi_object (`DoubleBracketIteration`): the target DoubleBracketIteration object. - d_list (list): list of diagonal operators (np.array) to run from. - step (float): fixed iteration duration. - Defaults to ``None``, optimize with `scheduling` method and `choose_step` function. - compare_canonical (boolean): if `True`, the diagonalization effect with operators from `d_list` is compared with the canonical bracket. - scheduling (`DoubleBracketScheduling`): scheduling method for finding the optimal step. - - Returns: - The updated dbi_object, index of the optimal diagonal operator, respective step duration, and evolution direction. - """ - if scheduling is None: - scheduling = dbi_object.scheduling - norms_off_diagonal_restriction = [dbi_object.off_diagonal_norm] * (len(d_list) + 1) - optimal_steps = np.zeros(len(d_list) + 1) - flip_list = np.ones(len(d_list) + 1) - for i, d in enumerate(d_list): - # prescribed step durations - dbi_eval = deepcopy(dbi_object) - flip_list[i] = cs_angle_sgn(dbi_eval, d) - if flip_list[i] != 0: - if step is None: - step_best = dbi_eval.choose_step( - d=flip_list[i] * d, scheduling=scheduling, **kwargs - ) - else: - step_best = step - dbi_eval(step=step_best, d=flip_list[i] * d) - optimal_steps[i] = step_best - norms_off_diagonal_restriction[i] = dbi_eval.off_diagonal_norm - # canonical - if compare_canonical is True: - dbi_eval = deepcopy(dbi_object) - dbi_eval.mode = DoubleBracketGeneratorType.canonical - if step is None: - step_best = dbi_eval.choose_step(scheduling=scheduling, **kwargs) - else: - step_best = step - dbi_eval(step=step_best) - optimal_steps[-1] = step_best - norms_off_diagonal_restriction[-1] = dbi_eval.off_diagonal_norm - # find best d - idx_max_loss = np.argmin(norms_off_diagonal_restriction) - flip = flip_list[idx_max_loss] - step_optimal = optimal_steps[idx_max_loss] - dbi_eval = deepcopy(dbi_object) - if idx_max_loss == len(d_list) and compare_canonical is True: - # canonical - dbi_eval(step=step_optimal, mode=DoubleBracketGeneratorType.canonical) - - else: - d_optimal = flip * d_list[idx_max_loss] - dbi_eval(step=step_optimal, d=d_optimal) - return dbi_eval, idx_max_loss, step_optimal, flip - - def cs_angle_sgn(dbi_object, d): """Calculates the sign of Cauchy-Schwarz Angle :math:`\\langle W(Z), W({\\rm canonical}) \\rangle_{\\rm HS}`.""" norm = np.trace( @@ -152,132 +95,6 @@ def cs_angle_sgn(dbi_object, d): return np.sign(norm) -def dGamma_di_Pauli( - dbi_object: DoubleBracketIteration, n: int, Z_i: np.array, d: np.array -): - """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. - - Args: - dbi_object (DoubleBracketIteration): the target dbi object - n (int): the number of nested commutators in `Gamma` - i (int/tupple): the index of onsite-Z coefficient - d (np.array): the diagonal operator - - Returns: - (list): [dGamma_0_di, dGamma_1_di, ..., dGamma_n_di] - """ - nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) - dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) - Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) - W = dbi_object.commutator(d, dbi_object.h.matrix) - dW_di = dbi_object.commutator(Z_i, dbi_object.h.matrix) - for k in range(n + 1): - if k == 0: - continue - elif k == 1: - dGamma_di[k] = dW_di - else: - dGamma_di[k] = dbi_object.commutator( - dW_di, Gamma_list[k - 1] - ) + dbi_object.commutator(W, dGamma_di[k - 1]) - return dGamma_di - - -def ds_di_Pauli( - dbi_object: DoubleBracketIteration, - d: np.array, - Z_i: np.array, - taylor_coef: Optional[list] = None, -): - r"""Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients\ - Args: - dbi_object (DoubleBracketIteration): the target dbi object - d (np.array): the diagonal operator - i (int): the index of onsite-Z coefficient - taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}`, from the highest order to the lowest. - onsite_Z_ops (list): onsite Z operators of `dbi_object.h` - Returns: - floats da, db, dc, ds - """ - # generate the list of derivatives w.r.t ith Z operator coefficient - dGamma_di = dGamma_di_Pauli(dbi_object, n=4, Z_i=Z_i, d=d) - Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) - - def derivative_product(k1, k2): - r"""Calculate the derivative of a product $\sigma(\Gamma(n1,i))@\sigma(\Gamma(n2,i))""" - return dbi_object.sigma(dGamma_di[k1]) @ dbi_object.sigma( - Gamma_list[k2] - ) + dbi_object.sigma(dbi_object.sigma(Gamma_list[k1])) @ dbi_object.sigma( - dGamma_di[k2] - ) - - # calculate the derivatives of s polynomial coefficients - da = np.trace(3 * derivative_product(1, 2) + 3 * derivative_product(3, 0)) - db = np.trace(2 * derivative_product(1, 1) + 2 * derivative_product(0, 2)) - dc = np.trace(2 * derivative_product(1, 0)) - - ds = 0 - if taylor_coef != None: - a, b, c = taylor_coef[len(taylor_coef) - 3 :] - delta = b**2 - 4 * a * c - ddelta = 2 * (b * db - 2 * (a * dc + da * c)) - - ds = (-db + 0.5 * ddelta / np.sqrt(delta)) * a - (-b + np.sqrt(delta)) * da - ds /= 2 * a**2 - - return da, db, dc, ds - - -def gradient_Pauli( - dbi_object: DoubleBracketIteration, - d: np.array, - pauli_operator_dict: dict, - use_ds=False, - n=3, - **kwargs, -): - r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients - Args: - dbi_object (DoubleBracketIteration): the target dbi object - d (np.array): the diagonal operator - n_taylor (int): the highest order of the taylore expansion of w.r.t `s` - onsite_Z_ops (list): list of Pauli-Z operators - taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` - use_ds (boolean): if False, ds is set to 0 - """ - # n is the highest order for calculating s - - # pauli_index is the list of positions \mu - pauli_operators = list(pauli_operator_dict.values()) - num_paul = len(pauli_operators) - grad = np.zeros(num_paul) - coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) - s = dbi_object.choose_step( - d=d, - **kwargs, - ) - - a, b, c = coef[len(coef) - 3 :] - - for i, operator in enumerate(pauli_operators): - da, db, dc, ds = ds_di_Pauli( - dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] - ) - if use_ds is True: - ds = 0 - grad[i] = ( - s**3 / 3 * da - + s**2 / 2 * db - + 2 * s * dc - + s**2 * ds * a - + s * ds * b - + 2 * ds * c - ) - grad = np.array(grad) - grad = grad / np.linalg.norm(grad) - return grad, s - - def decompose_into_Pauli_basis(h_matrix: np.array, pauli_operators: list): """finds the decomposition of hamiltonian `h_matrix` into Pauli-Z operators""" nqubits = int(np.log2(h_matrix.shape[0])) @@ -301,89 +118,16 @@ def generate_pauli_index(nqubits, order): raise ValueError("Order must be a positive integer") -def generate_pauli_operator_dict(nqubits: int, parameterization_order: int): +def generate_pauli_operator_dict( + nqubits: int, parameterization_order: int = 1, symbols_pauli=symbols.Z +): pauli_index = generate_pauli_index(nqubits, order=parameterization_order) pauli_operators = [ - generate_Pauli_operators(nqubits, symbols.Z, index) for index in pauli_index + generate_Pauli_operators(nqubits, symbols_pauli, index) for index in pauli_index ] return {index: operator for index, operator in zip(pauli_index, pauli_operators)} -def gradient_descent_pauli( - dbi_object: DoubleBracketIteration, - d_coef: list, - d: Optional[np.array] = None, - pauli_operator_dict: dict = None, - parameterization_order: int = 1, - n: int = 3, - onsite_Z_ops: Optional[list] = None, - lr_min: float = 1e-5, - lr_max: float = 1, - max_evals: int = 100, - space: callable = None, - optimizer: callable = None, - verbose: bool = False, - use_ds: bool = True, -): - """calculate the elements of one gradient descent step on `dbi_object`. - - Args: - dbi_object (DoubleBracketIteration): the target dbi object - d_coef (list): the initial decomposition of `d` into Pauli-Z operators - d (np.array, optional): the initial diagonal operator. Defaults to None. - n_taylor (int, optional): the highest order to expand the loss function derivative. Defaults to 2. - onsite_Z_ops (list, optional): list of onsite-Z operators. Defaults to None. - lr_min (float, optional): the minimal gradient step. Defaults to 1e-5. - lr_max (float, optional): the maximal gradient step. Defaults to 1. - max_evals (int, optional): the max number of evaluations for `hyperopt` to find the optimal gradient step `lr`. Defaults to 100. - space (callable, optional): the search space for `hyperopt`. Defaults to None. - optimizer (callable, optional): optimizer for `hyperopt`. Defaults to None. - verbose (bool, optional): option to print out the 'hyperopt' progress. Defaults to False. - use_ds (bool, optional): if False, ds is set to 0. Defaults to True. - - Returns: - the optimal step found, coeffcients of `d` in Pauli-Z basis, matrix of `d` - - """ - nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) - if pauli_operator_dict is None: - pauli_operator_dict = generate_pauli_operator_dict( - nqubits, parameterization_order - ) - - grad, s = gradient_Pauli( - dbi_object, d, n=n, pauli_operator_dict=pauli_operator_dict, use_ds=use_ds - ) - # optimize gradient descent step with hyperopt - if space is None: - space = hyperopt.hp.loguniform("lr", np.log(lr_min), np.log(lr_max)) - if optimizer is None: - optimizer = hyperopt.tpe - - def func_loss_to_lr(lr): - d_coef_eval = [d_coef[j] - grad[j] * lr for j in range(nqubits)] - d_eval = sum( - [ - d_coef_eval[i] * list(pauli_operator_dict.values())[i] - for i in range(nqubits) - ] - ) - return dbi_object.loss(step=s, d=d_eval) - - best = hyperopt.fmin( - fn=func_loss_to_lr, - space=space, - algo=optimizer.suggest, - max_evals=max_evals, - verbose=verbose, - ) - lr = best["lr"] - - d_coef = [d_coef[j] - grad[j] * lr for j in range(nqubits)] - d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) - return s, d_coef, d - - def diagonal_min_max(matrix: np.array): L = int(np.log2(matrix.shape[0])) D = np.linspace(np.min(np.diag(matrix)), np.max(np.diag(matrix)), 2**L) @@ -391,29 +135,6 @@ def diagonal_min_max(matrix: np.array): return D -def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) - Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) - sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients for rotation with [W,H] and H - c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] - c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] - # product coefficient - trace_coefficients = [0] * (2 * n + 1) - for k in range(n + 1): - for j in range(n + 1): - power = k + j - product_matrix = c1[k] @ c2[j] - trace_coefficients[power] += 2 * np.trace(product_matrix) - # coefficients from high to low (n:0) - coef = list(reversed(trace_coefficients[: n + 1])) - return coef - - def generate_Pauli_operators(nqubits, symbols_pauli, positions): # generate matrix of an nqubit-pauli operator with `symbols_pauli` at `positions` if isinstance(positions, int): diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py new file mode 100644 index 0000000000..35295fe6de --- /dev/null +++ b/src/qibo/models/dbi/utils_analytical.py @@ -0,0 +1,210 @@ +from qibo.models.dbi.utils import * + + +def dGamma_di_Pauli(dbi_object, n: int, Z_i: np.array, d: np.array): + """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. + + Args: + dbi_object (DoubleBracketIteration): the target dbi object + n (int): the number of nested commutators in `Gamma` + i (int/tupple): the index of onsite-Z coefficient + d (np.array): the diagonal operator + + Returns: + (list): [dGamma_0_di, dGamma_1_di, ..., dGamma_n_di] + """ + nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) + dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) + Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) + W = dbi_object.commutator(d, dbi_object.h.matrix) + dW_di = dbi_object.commutator(Z_i, dbi_object.h.matrix) + for k in range(n + 1): + if k == 0: + continue + elif k == 1: + dGamma_di[k] = dW_di + else: + dGamma_di[k] = dbi_object.commutator( + dW_di, Gamma_list[k - 1] + ) + dbi_object.commutator(W, dGamma_di[k - 1]) + return dGamma_di + + +def ds_di_Pauli( + dbi_object, + d: np.array, + Z_i: np.array, + taylor_coef: Optional[list] = None, +): + r"""Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients\ + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d (np.array): the diagonal operator + i (int): the index of onsite-Z coefficient + taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}`, from the highest order to the lowest. + onsite_Z_ops (list): onsite Z operators of `dbi_object.h` + Returns: + floats da, db, dc, ds + """ + # generate the list of derivatives w.r.t ith Z operator coefficient + dGamma_di = dGamma_di_Pauli(dbi_object, n=4, Z_i=Z_i, d=d) + Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) + + def derivative_product(k1, k2): + r"""Calculate the derivative of a product $\sigma(\Gamma(n1,i))@\sigma(\Gamma(n2,i))""" + return dbi_object.sigma(dGamma_di[k1]) @ dbi_object.sigma( + Gamma_list[k2] + ) + dbi_object.sigma(dbi_object.sigma(Gamma_list[k1])) @ dbi_object.sigma( + dGamma_di[k2] + ) + + # calculate the derivatives of s polynomial coefficients + da = np.trace(3 * derivative_product(1, 2) + 3 * derivative_product(3, 0)) + db = np.trace(2 * derivative_product(1, 1) + 2 * derivative_product(0, 2)) + dc = np.trace(2 * derivative_product(1, 0)) + + ds = 0 + if taylor_coef != None: + a, b, c = taylor_coef[len(taylor_coef) - 3 :] + delta = b**2 - 4 * a * c + ddelta = 2 * (b * db - 2 * (a * dc + da * c)) + + ds = (-db + 0.5 * ddelta / np.sqrt(delta)) * a - (-b + np.sqrt(delta)) * da + ds /= 2 * a**2 + + return da, db, dc, ds + + +def gradient_Pauli( + dbi_object, + d: np.array, + pauli_operator_dict: dict, + use_ds=False, + n=3, + **kwargs, +): + r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d (np.array): the diagonal operator + n_taylor (int): the highest order of the taylore expansion of w.r.t `s` + onsite_Z_ops (list): list of Pauli-Z operators + taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` + use_ds (boolean): if False, ds is set to 0 + """ + # n is the highest order for calculating s + + # pauli_index is the list of positions \mu + pauli_operators = list(pauli_operator_dict.values()) + num_paul = len(pauli_operators) + grad = np.zeros(num_paul) + coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) + s = dbi_object.choose_step( + d=d, + **kwargs, + ) + + a, b, c = coef[len(coef) - 3 :] + + for i, operator in enumerate(pauli_operators): + da, db, dc, ds = ds_di_Pauli( + dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] + ) + if use_ds is True: + ds = 0 + grad[i] = ( + s**3 / 3 * da + + s**2 / 2 * db + + 2 * s * dc + + s**2 * ds * a + + s * ds * b + + 2 * ds * c + ) + grad = np.array(grad) + grad = grad / np.linalg.norm(grad) + return grad, s + + +def dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list): + # Derivative of gamma with respect to diagonal elements of D (full-diagonal ansatz) + A = np.zeros(d.shape) + A[i, i] = 1 + B = commutator(commutator(A, H), Gamma_list[n - 1]) + W = commutator(d, H) + return B + commutator(W, dGamma[-1]) + + +def dpolynomial_diDiagonal(dbi_object, s, d, H, i): + # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) + # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation + derivative = 0 + A = np.zeros(d.shape) + Gamma_list = dbi_object.generate_Gamma_list(4, d) + A[i, i] = 1 + dGamma = [commutator(A, H)] + derivative += np.real( + np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s + ) + for n in range(2, 4): + dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) + derivative += np.real( + np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) + ) + + return derivative + + +def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) + Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) + sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients for rotation with [W,H] and H + c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] + c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] + # product coefficient + trace_coefficients = [0] * (2 * n + 1) + for k in range(n + 1): + for j in range(n + 1): + power = k + j + product_matrix = c1[k] @ c2[j] + trace_coefficients[power] += 2 * np.trace(product_matrix) + # coefficients from high to low (n:0) + coef = list(reversed(trace_coefficients[: n + 1])) + return coef + + +def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: int = 3): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients + coef = np.empty(n) + for i in range(n): + coef[i] = np.real(exp_list[i] * np.trace(d @ Gamma_list[i + 1])) + coef = list(reversed(coef)) + return coef + + +def energy_fluctuation_polynomial_expansion_coef( + dbi_object, d: np.array = None, n: int = 3, state=0 +): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + # coefficients + coef = np.empty(3) + coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1], state)) + coef[1] = np.real(2 * variance(Gamma_list[1], state)) + coef[2] = np.real( + covariance(Gamma_list[0], Gamma_list[3], state) + + 3 * covariance(Gamma_list[1], Gamma_list[2], state) + ) + coef = list(reversed(coef)) + return coef diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 488d52ca40..4284c6caef 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -1,29 +1,17 @@ import math -from copy import deepcopy from functools import partial from typing import Optional import hyperopt import numpy as np -error = 1e-3 - - -def commutator(A, B): - """Compute commutator between two arrays.""" - return A @ B - B @ A - - -def variance(A, state): - """Calculates the variance of a matrix A with respect to a state: Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" - B = A @ A - return B[state, state] - A[state, state] ** 2 +from qibo.models.dbi.utils_analytical import ( + energy_fluctuation_polynomial_expansion_coef, + least_squares_polynomial_expansion_coef, + off_diagonal_norm_polynomial_expansion_coef, +) - -def covariance(A, B, state): - """Calculates the covariance of two matrices A and B with respect to a state: Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$""" - C = A @ B + B @ A - return C[state, state] - 2 * A[state, state] * B[state, state] +error = 1e-3 def grid_search_step( @@ -156,126 +144,6 @@ def polynomial_step( return None -def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) - Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) - sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients for rotation with [W,H] and H - c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] - c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] - # product coefficient - trace_coefficients = [0] * (2 * n + 1) - for k in range(n + 1): - for j in range(n + 1): - power = k + j - product_matrix = c1[k] @ c2[j] - trace_coefficients[power] += 2 * np.trace(product_matrix) - # coefficients from high to low (n:0) - coef = list(reversed(trace_coefficients[: n + 1])) - return coef - - -def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: int = 3): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients - coef = np.empty(n) - for i in range(n): - coef[i] = np.real(exp_list[i] * np.trace(d @ Gamma_list[i + 1])) - coef = list(reversed(coef)) - return coef - - -# TODO: add a general expansion formula not stopping at 3rd order -def energy_fluctuation_polynomial_expansion_coef( - dbi_object, d: np.array = None, n: int = 3, state=0 -): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - # coefficients - coef = np.empty(3) - coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1], state)) - coef[1] = np.real(2 * variance(Gamma_list[1], state)) - coef[2] = np.real( - covariance(Gamma_list[0], Gamma_list[3], state) - + 3 * covariance(Gamma_list[1], Gamma_list[2], state) - ) - coef = list(reversed(coef)) - return coef - - -def dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list): - # Derivative of gamma with respect to diagonal elements of D (full-diagonal ansatz) - A = np.zeros(d.shape) - A[i, i] = 1 - B = commutator(commutator(A, H), Gamma_list[n - 1]) - W = commutator(d, H) - return B + commutator(W, dGamma[-1]) - - -def dpolynomial_diDiagonal(dbi_object, d, H, i): - # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) - # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation - derivative = 0 - s = polynomial_step(dbi_object, n=3, d=d) - A = np.zeros(d.shape) - Gamma_list = dbi_object.generate_Gamma_list(4, d) - A[i, i] = 1 - dGamma = [commutator(A, H)] - derivative += np.real( - np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s - ) - for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) - derivative += np.real( - np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) - ) - - return derivative - - -def gradientDiagonal(dbi_object, d, H): - # Gradient of potential function with respect to diagonal elements of D (full-diagonal ansatz) - grad = np.zeros(len(d)) - for i in range(len(d)): - derivative = dpolynomial_diDiagonal(dbi_object, d, H, i) - grad[i] = d[i, i] - derivative - return grad - - -def gradient_ascent(dbi_object, d, step, iterations): - H = dbi_object.h.matrix - loss = np.zeros(iterations + 1) - grad = np.zeros((iterations, len(d))) - dbi_eval = deepcopy(dbi_object) - s = polynomial_step(dbi_object, n=3, d=d) - dbi_eval(s, d=d) - loss[0] = dbi_eval(d) - diagonals = np.empty((len(d), iterations + 1)) - diagonals[:, 0] = np.diag(d) - - for i in range(iterations): - dbi_eval = deepcopy(dbi_object) - grad[i, :] = gradientDiagonal(dbi_object, d, H) - for j in range(len(d)): - d[j, j] = d[j, j] - step * grad[i, j] - s = polynomial_step(dbi_object, n=3, d=d) - dbi_eval(s, d=d) - loss[i + 1] = dbi_eval.least_squares(d) - diagonals[:, i + 1] = np.diag(d) - - return d, loss, grad, diagonals - - def simulated_annealing_step( dbi_object, d: Optional[np.array] = None, @@ -289,6 +157,41 @@ def simulated_annealing_step( min_temp=1e-5, max_iter=200, ): + """ + Perform a single step of simulated annealing optimization. + + Parameters: + dbi_object: DBI object + The object representing the problem to be optimized. + d: Optional[np.array], optional + The diagonal matrix 'd' used in optimization. If None, it uses the diagonal + matrix 'diagonal_h_matrix' from dbi_object. + initial_s: float or None, optional + Initial value for 's', the step size. If None, it is initialized using + polynomial_step function with 'n=4'. If 'polynomial_step' returns None, + 'initial_s' is set to 'step_min'. + step_min: float, optional + Minimum value for the step size 's'. + step_max: float, optional + Maximum value for the step size 's'. + s_jump_range: float or None, optional + Range for the random jump in step size. If None, it's calculated based on + 'step_min', 'step_max', and 's_jump_range_divident'. + s_jump_range_divident: int, optional + Dividend to determine the range for random jump in step size. + initial_temp: float, optional + Initial temperature for simulated annealing. + cooling_rate: float, optional + Rate at which temperature decreases in simulated annealing. + min_temp: float, optional + Minimum temperature threshold for termination of simulated annealing. + max_iter: int, optional + Maximum number of iterations for simulated annealing. + + Returns: + float: + The optimized step size 's'. + """ if d is None: d = dbi_object.diagonal_h_matrix diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_strategies.py new file mode 100644 index 0000000000..dfc5a00ae1 --- /dev/null +++ b/src/qibo/models/dbi/utils_strategies.py @@ -0,0 +1,181 @@ +import hyperopt + +from qibo.models.dbi.double_bracket import * +from qibo.models.dbi.utils import cs_angle_sgn +from qibo.models.dbi.utils_analytical import * +from qibo.models.dbi.utils_scheduling import polynomial_step + + +def select_best_dbr_generator( + dbi_object: DoubleBracketIteration, + d_list: list, + step: Optional[float] = None, + compare_canonical: bool = True, + scheduling: DoubleBracketScheduling = None, + **kwargs, +): + """Selects the best double bracket rotation generator from a list and execute the rotation. + + Args: + dbi_object (`DoubleBracketIteration`): the target DoubleBracketIteration object. + d_list (list): list of diagonal operators (np.array) to run from. + step (float): fixed iteration duration. + Defaults to ``None``, optimize with `scheduling` method and `choose_step` function. + compare_canonical (boolean): if `True`, the diagonalization effect with operators from `d_list` is compared with the canonical bracket. + scheduling (`DoubleBracketScheduling`): scheduling method for finding the optimal step. + + Returns: + The updated dbi_object, index of the optimal diagonal operator, respective step duration, and evolution direction. + """ + if scheduling is None: + scheduling = dbi_object.scheduling + norms_off_diagonal_restriction = [dbi_object.off_diagonal_norm] * (len(d_list) + 1) + optimal_steps = np.zeros(len(d_list) + 1) + flip_list = np.ones(len(d_list) + 1) + for i, d in enumerate(d_list): + # prescribed step durations + dbi_eval = deepcopy(dbi_object) + flip_list[i] = cs_angle_sgn(dbi_eval, d) + if flip_list[i] != 0: + if step is None: + step_best = dbi_eval.choose_step( + d=flip_list[i] * d, scheduling=scheduling, **kwargs + ) + else: + step_best = step + dbi_eval(step=step_best, d=flip_list[i] * d) + optimal_steps[i] = step_best + norms_off_diagonal_restriction[i] = dbi_eval.off_diagonal_norm + # canonical + if compare_canonical is True: + dbi_eval = deepcopy(dbi_object) + dbi_eval.mode = DoubleBracketGeneratorType.canonical + if step is None: + step_best = dbi_eval.choose_step(scheduling=scheduling, **kwargs) + else: + step_best = step + dbi_eval(step=step_best) + optimal_steps[-1] = step_best + norms_off_diagonal_restriction[-1] = dbi_eval.off_diagonal_norm + # find best d + idx_max_loss = np.argmin(norms_off_diagonal_restriction) + flip = flip_list[idx_max_loss] + step_optimal = optimal_steps[idx_max_loss] + dbi_eval = deepcopy(dbi_object) + if idx_max_loss == len(d_list) and compare_canonical is True: + # canonical + dbi_eval(step=step_optimal, mode=DoubleBracketGeneratorType.canonical) + + else: + d_optimal = flip * d_list[idx_max_loss] + dbi_eval(step=step_optimal, d=d_optimal) + return dbi_eval, idx_max_loss, step_optimal, flip + + +def gradient_descent_pauli( + dbi_object: DoubleBracketIteration, + d_coef: list, + d: Optional[np.array] = None, + pauli_operator_dict: dict = None, + parameterization_order: int = 1, + n: int = 3, + onsite_Z_ops: Optional[list] = None, + lr_min: float = 1e-5, + lr_max: float = 1, + max_evals: int = 100, + space: callable = None, + optimizer: callable = None, + verbose: bool = False, + use_ds: bool = True, +): + """calculate the elements of one gradient descent step on `dbi_object`. + + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d_coef (list): the initial decomposition of `d` into Pauli-Z operators + d (np.array, optional): the initial diagonal operator. Defaults to None. + n_taylor (int, optional): the highest order to expand the loss function derivative. Defaults to 2. + onsite_Z_ops (list, optional): list of onsite-Z operators. Defaults to None. + lr_min (float, optional): the minimal gradient step. Defaults to 1e-5. + lr_max (float, optional): the maximal gradient step. Defaults to 1. + max_evals (int, optional): the max number of evaluations for `hyperopt` to find the optimal gradient step `lr`. Defaults to 100. + space (callable, optional): the search space for `hyperopt`. Defaults to None. + optimizer (callable, optional): optimizer for `hyperopt`. Defaults to None. + verbose (bool, optional): option to print out the 'hyperopt' progress. Defaults to False. + use_ds (bool, optional): if False, ds is set to 0. Defaults to True. + + Returns: + the optimal step found, coeffcients of `d` in Pauli-Z basis, matrix of `d` + + """ + nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) + if pauli_operator_dict is None: + pauli_operator_dict = generate_pauli_operator_dict( + nqubits, parameterization_order + ) + + grad, s = gradient_Pauli( + dbi_object, d, n=n, pauli_operator_dict=pauli_operator_dict, use_ds=use_ds + ) + # optimize gradient descent step with hyperopt + if space is None: + space = hyperopt.hp.loguniform("lr", np.log(lr_min), np.log(lr_max)) + if optimizer is None: + optimizer = hyperopt.tpe + + def func_loss_to_lr(lr): + d_coef_eval = [d_coef[j] - grad[j] * lr for j in range(nqubits)] + d_eval = sum( + [ + d_coef_eval[i] * list(pauli_operator_dict.values())[i] + for i in range(nqubits) + ] + ) + return dbi_object.loss(step=s, d=d_eval) + + best = hyperopt.fmin( + fn=func_loss_to_lr, + space=space, + algo=optimizer.suggest, + max_evals=max_evals, + verbose=verbose, + ) + lr = best["lr"] + + d_coef = [d_coef[j] - grad[j] * lr for j in range(nqubits)] + d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) + return s, d_coef, d + + +def gradientDiagonal(dbi_object, d, H, n=3): + # Gradient of potential function with respect to diagonal elements of D (full-diagonal ansatz) + grad = np.zeros(len(d)) + for i in range(len(d)): + s = polynomial_step(dbi_object, n=3, d=d) + derivative = dpolynomial_diDiagonal(dbi_object, s, d, H, i) + grad[i] = d[i, i] - derivative + return grad + + +def gradient_ascent(dbi_object, d, step, iterations): + H = dbi_object.h.matrix + loss = np.zeros(iterations + 1) + grad = np.zeros((iterations, len(d))) + dbi_eval = deepcopy(dbi_object) + s = polynomial_step(dbi_object, n=3, d=d) + dbi_eval(s, d=d) + loss[0] = dbi_eval(d) + diagonals = np.empty((len(d), iterations + 1)) + diagonals[:, 0] = np.diag(d) + + for i in range(iterations): + dbi_eval = deepcopy(dbi_object) + grad[i, :] = gradientDiagonal(dbi_object, d, H) + for j in range(len(d)): + d[j, j] = d[j, j] - step * grad[i, j] + s = polynomial_step(dbi_object, n=3, d=d) + dbi_eval(s, d=d) + loss[i + 1] = dbi_eval.least_squares(d) + diagonals[:, i + 1] = np.diag(d) + + return d, loss, grad, diagonals From 8b96110ddee56ff68c8f14121c98d1cca2dab5bc Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 8 Apr 2024 17:30:42 +0800 Subject: [PATCH 062/154] Import dependency (optional) --- src/qibo/models/dbi/utils_analytical.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py index 35295fe6de..b23fd0a33e 100644 --- a/src/qibo/models/dbi/utils_analytical.py +++ b/src/qibo/models/dbi/utils_analytical.py @@ -1,3 +1,5 @@ +from typing import Optional + from qibo.models.dbi.utils import * From ecc15ad04cb7d4c36c9659544f14bd545376ad96 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Mon, 15 Apr 2024 09:43:39 -0400 Subject: [PATCH 063/154] remove circular dependency and added numpy casting on cost functions --- ...t_functions_and_d_gradients_tutorial.ipynb | 1183 +++++++++++++++++ examples/dbi/dbi_costs.ipynb | 687 ---------- src/qibo/models/dbi/double_bracket.py | 47 +- src/qibo/models/dbi/utils.py | 219 +-- src/qibo/models/dbi/utils_analytical.py | 213 +++ src/qibo/models/dbi/utils_gradients.py | 22 +- src/qibo/models/dbi/utils_scheduling.py | 88 +- tests/test_models_dbi_utils_scheduling.py | 4 +- 8 files changed, 1589 insertions(+), 874 deletions(-) create mode 100644 examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb delete mode 100644 examples/dbi/dbi_costs.ipynb create mode 100644 src/qibo/models/dbi/utils_analytical.py diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb new file mode 100644 index 0000000000..5dd4bcebd4 --- /dev/null +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -0,0 +1,1183 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Double-bracket Iteration other cost functions and respective scheduling\n", + "\n", + "This notebook presents two additional cost functions for the double-bracket flow: least-squares and energy fluctuation with their respectice scheduling methods." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCost\n", + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_scheduling import *" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Least-squares\n", + "\n", + "The cost function is defined as: $\\frac{1}{2}||D-H_k||^2 =\\frac{1}{2}(||D||^2+||H||^2) -Tr(D H_k)$ as in (the negative of https://epubs.siam.org/doi/abs/10.1137/S0036141092229732?journalCode=sjmael) We seek to minimize this function at each DBF iteration. For numerical optimizations, we also ignore the norm of H term as for a given hamiltonian it is fixed through out the flow.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-04-15 08:49:42]: Using numpy backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCost.least_squares\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.6060645454545454\n", + "100%|██████████| 100/100 [00:01<00:00, 94.46trial/s, best loss: -30.633828072194902]\n", + "hyperopt_search step: 0.5999730843447625\n", + "polynomial_approximation step: 0.5267891182131145\n" + ] + } + ], + "source": [ + "# generate data for plotting sigma decrease of the first step\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", + "s_space = np.linspace(1e-5, 1.0, 500)\n", + "off_diagonal_norm_diff = []\n", + "potential = []\n", + "for s in s_space:\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s,d=d)\n", + " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", + " potential.append(dbi_eval.least_squares(d=d))\n", + "\n", + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", + "print('hyperopt_search step:', step_hyperopt)\n", + "# polynomial\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + "print('polynomial_approximation step:', step_poly)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.6060645454545454\n" + ] + }, + { + "data": { + "image/png": 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", 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nPknnzp2LGTNm6B/n5OTAw8PD4K+zYVQ7AIBOJ6JIo0NhiRZ384uRnlOEjNxCpGUV4kpGHuIzchGfnoeCEi0ir91G5LXbAABzhQydGzvgKT8X9PBzQQM7C4NnJCIyGvn5wN8f0jUuLw9QlW9oxtzcXGzcuBHfffcdnnrqKQBAeHg43Nzcyqz35JNPlila/nuWZt26dWjcuDFWrFgBAGjWrBn++usv/N///V+5ciQnJ+P5559Hy5YtAQA+Pj765z799FO0adMGixcv1i/76quv4OHhgcuXL6Np06Z4/vnny+zvq6++gpOTE+Li4sq0G5o2bRqee+45/eMPPvgAM2fOxNSpU/XL2rVrV2ZfYWFhGDZsGABg8eLF+Pjjj3Hy5En07t27XO+tooy2yHF0dIRcLkd6enqZ5enp6WjQoMEDt1EqlVAqlTURDwAgkwmwNJfD0lyOeipz+Djd/0uq04m4eisPUYl3cSrxDk4k3EFqVgEOXLqFA5du4Z2d59Heuz6eD2qIPi0bwMbCrMbyExFR+V27dg0lJSUICQnRL7Ozs0OzZs3KrNe2bdtH7ufChQto3759mWUdO3Ysd44pU6bg1Vdfxd69e9GjRw88//zzaNWqFQDg7NmzOHDggP7Mzr9dvXoVTZs2RXx8PObNm4cTJ04gMzMTOl3pVYnk5OQyRc6/30dGRgbS0tL0xd3D3MsBACqVCra2ttXaWchoixxzc3MEBwdj3759GDRoEABAp9Nh3759910DrM1kMgG+LjbwdbHBS+0bQRRFxGfk4fe4dOy7kI7olCycSCgtft796Tz6tnTFqE5eCPSwlzo6kcnQqrU4bH0YANA1ryvkqppvE6MuVnNah4exsio9oyLVaxuYqpxnhipr7Nix6NWrF3bv3o29e/diyZIlWLFiBV5//XXk5eXhmWeeeeBZoXtzRD3zzDPw9PTE+vXr4ebmBp1OhxYtWqC4uPih7+PeNAyPY2ZW9ou6IAj6Iqo6GG2RA5Re+xw1ahTatm2LkJAQrFq1Cmq1GqNHj5Y6WqUJgoCmLjZo6mKDSd2bIC2rADuiU/Hjmeu4ekuNHdGp2BGdiqBG9gjr7I0+LRrATG607ceJiB5PEMp9yUhKPj4+MDMzQ1RUFBo1agQAyM7OxuXLl/HEE0+Uez9+fn73Ncg9fvx4hbJ4eHhg4sSJmDhxIubOnYv169fj9ddfR1BQELZv3w4vL68H9ua6ffs2Ll26hPXr16Nr164AgCNHjjz29WxsbODl5YV9+/ahe/fuFcpanYy6yHnhhRdw69YtzJs3Dzdv3kRgYCB+/fXX+xojGzM3e0tM6t4Er4U2RkxKFr6JTML/zqXhTHIWziRHo1F9K0x+sgmebePOYoeokmRWMnTK6KS/T1QZNjY2GDVqFGbPno369evD2dkZ8+fPh0wmq9AUBRMnTsSKFSswe/ZsjB07FqdPn0ZERES5t582bRr69OmDpk2b4u7duzhw4AD8/PwAlDZKXr9+PYYNG4Y33ngD9evXx5UrV7BlyxZs2LAB9erVg4ODA7744gu4uroiOTkZc+bMKdfrLliwABMnToSzszP69OmD3NxcHD16FK+//nq5sxua0f82T548GUlJSSgqKsKJEyfuu45pKgRBQJtG9bDyhUAcnfMkpj7lCweVOZLv5OONbefw1IpD+OFUCrQ6UeqoREZHEASYO5nD3MmccyZRlaxcuRIdO3ZE//790aNHD3Tu3Bl+fn6wsCh/B5JGjRph+/bt2LlzJ1q3bo3PPvusTEPhx9FqtZg0aRL8/PzQu3dvNG3aFGvXrgUAuLm54ejRo9BqtejZsydatmyJadOmwd7eHjKZDDKZDFu2bMHp06fRokULTJ8+HcuWLSvX644aNQqrVq3C2rVrERAQgP79+yM+Pr7cuauDIIpinf1UzMnJgZ2dHbKzs2Frayt1nArLL9bg2+NJ+OLPa8jMK71W2ryBDd7u54euvk4SpyOiirivTU79v89IV6B3jykoLCxEQkICvL29K1QY1FZqtRru7u5YsWIFXnnlFanjGJVH/SyU9/Pb6M/k1GVW5gqMf6Ix/nyjO97q2xx2lma4eDMXI748idHhJ3ElI1fqiERGQVekw+VJl3F50mXoijg4J1VedHQ0Nm/ejKtXr+LMmTMYPnw4AGDgwIESJ6ubWOSYgHvFzqHZoRjT2RsKmYADl26hz+rDWLH3EgpLtFJHJKrVRI2ItLVpSFubBlFTZ09uk4EsX74crVu3Ro8ePaBWq3H48GE4OjoabP/3Rit+0K0il7XqAqNueExl2VuZY94z/ni5QyN8sPsC9l/MwCf7r+B/Z9Ow6NmW6NzEcL9kRGRYcpkcfX376u+TcWrTpg1Onz5dra+xYcMGFBQUPPC56ho52FixyDFBPk7W+HJUW/x6/ibm74pF4u18DN9wAsNCPPBOP3+olPxvJ6ptLBQW2P3S7tIHnNaBHsHd3V3qCEaDl6tMlCAI6NPSFX/M7IaRHT0hCMDmkynos/owohLvSB2PiIio2rHIMXG2FmZ4b2ALfDe2A9ztLZF8Jx9DP4/Eh79cRAlnPyciIhPGIqeO6NjYAb9M64rBwQ0hisBnh67ihc8jkZr14Ou6RFSz1MVqqBaroFqsgrqYl6uIDIFFTh1ia2GG5UNaY93wINhYKHAmOQv9Pj6M/RfTH78xEVW7/JJ85JfkSx2DyGSwyKmD+rR0xe7Xu6JVQztk5ZdgTMQpLP31InQcLZmIiEwIi5w6qpGDFbZO7IiwTl4AgLUHr2Lc16eQW1gibTAiIiMVGhqKadOmSR2D/oVFTh2mVMixYEAAVr0QCHOFDPsuZuDZtceQmMn2AEREVNaCBQsQGBgodYwKYZFDGNTGHVsndISLrRJXMvIwcM1RHL2SKXUsIiKqIq1WC52u7vakZZFDAIDWHvbYNbkLWnvYI7ugBCO/OokfolKkjkVEZFR0Oh3eeOMN1K9fHw0aNMCCBQsAAGPGjEH//v3LrFtSUgJnZ2d8+eWXAEovd02ePBmTJ0+GnZ0dHB0d8e677+Lf82gXFRVh1qxZcHd3h0qlQvv27XHw4EH98xEREbC3t8euXbvg7+8PpVKJ5ORk3L17FyNHjkS9evVgZWWFPn36lJkh/N52O3fuhK+vLywsLNCrVy+kpKTon1+4cCHOnj0LQRAgCAIiIiKq5yAaEIsc0nOxtcD34ztgUKAbtDoRb2w/h0/2xaMOT1RPdYUMsOtmB7tudpL9VZQJMnTz7IZunt0gE/in+UHUxeqH3go1heVet6CkoFzrVsbGjRuhUqlw4sQJLF26FO+99x5+//13jB07Fr/++itu3LihX/fnn39Gfn4+XnjhhTLbKxQKnDx5EqtXr8bKlSuxYcMG/fOTJ09GZGQktmzZgnPnzmHIkCHo3bt3mYIlPz8f//d//4cNGzYgNjYWzs7OCAsLw6lTp7Br1y5ERkZCFEX07dsXJSUlZbZbtGgRvv76axw9ehRZWVl48cUXAQAvvPACZs6ciYCAANy4cQM3btwok7u2EsQ6/AlW3qna6xpRFLHst0tYe/AqAGB4+0Z4b2ALyGWCxMmI6gi1GrC2Lr2flweoVNLmqUGFhYVISEiAt7c3LCwsyjwnLHz436C+vn3/mRYDgGqx6qHd8bt5dsPBsIP6x07LnJCZf/8lenF+xT4eQ0NDodVqcfjwYf2ykJAQPPnkk/jwww8REBCAUaNG4Y033gAADBgwAA4ODggPD9dvn5GRgdjYWAhC6XudM2cOdu3ahbi4OCQnJ8PHxwfJyclwc3PTv0aPHj0QEhKCxYsXIyIiAqNHj0ZMTAxat24NAIiPj0fTpk1x9OhRdOrUCQBw+/ZteHh4YOPGjRgyZIh+u+PHj6N9+/YAgIsXL8LPzw8nTpxASEgIFixYgJ07dyImJqZCx6WyHvWzUN7Pb35doPsIgoA3ejfHwgEBEARg04lkvLbpNGczJyJ6jFatWpV57OrqioyMDADA2LFj9QVNeno6fvnlF4wZM6bM+h06dNAXOADQsWNHxMfHQ6vV4q+//oJWq0XTpk3LzDx+6NAhXL16Vb+Nubl5mRwXLlyAQqHQFy8A4ODggGbNmuHChQv6ZQqFAu3atdM/bt68Oezt7cusY2w4UyM91KhOXnCyUWLa9zH4LTYdI786ia/C2sGaE3wSkQTy5uY99Ln/ztyeMSvjoev+93Jg4tTEKuX6NzMzszKPBUHQN/wdOXIk5syZg8jISBw7dgze3t7o2rVrufedl5cHuVyO06dPQy4v+36t7535A2BpaVmmUKrL+GlFj9S3pSscVOYYu/EUTibcwYgvTyBidAjsLM0evzGRkdCqtTjudRwA0CGxA+Qq+WO2MDx1sRpeq70AAIljY1F3LlCVn8q8/EelutatCgcHBwwaNAjh4eGIjIzE6NGj71vnxIkTZR4fP34cvr6+kMvlaNOmDbRaLTIyMipUHPn5+UGj0eDEiRNlLlddunQJ/v7++vU0Gg1OnTqFkJAQAMClS5eQlZUFPz8/AKVniLRa4zqjz8tV9FjtfRywaVx72FmaITo5Cy9vOIG76mKpYxEZVElmCUoypR0MMzM/84FtQ8h0jB07Fhs3bsSFCxcwatSo+55PTk7GjBkzcOnSJWzevBmffPIJpk6dCgBo2rQphg8fjpEjR+LHH39EQkICTp48iSVLlmD37t337eseX19fDBw4EOPGjcORI0dw9uxZvPzyy3B3d8fAgQP165mZmeH111/HiRMncPr0aYSFhaFDhw76osfLywsJCQmIiYlBZmYmioqKDHx0DI9FDpVLq4b22DyuAxxU5vgrNRvD1h9HZl7t/wEnKg+ZpQztzrdDu/PtILPkn0WqPj169ICrqyt69epVpvHwPSNHjkRBQQFCQkIwadIkTJ06FePHj9c/Hx4ejpEjR2LmzJlo1qwZBg0ahKioKDRq1OiRrxseHo7g4GD0798fHTt2hCiK2LNnT5nLa1ZWVnjzzTfx0ksvoXPnzrC2tsb333+vf/75559H79690b17dzg5OWHz5s0GOCLVi72r2LuqQuLTczF8wwlk5BahsZMKm8d3gLONxeM3JKJHUherYb2ktF1F3pR0qOq7lD7B3lUmJS8vD+7u7ggPD8dzzz1X5rnQ0FAEBgZi1apVNZ4rIiIC06ZNQ1ZWVo2/9sOwdxXVOF8XG3w/oSNc7Sxw9ZYaL284gTu8dEVE9Eg6nQ4ZGRl4//33YW9vjwEDBkgdqU5gkUMV5u2owpbxHeBiq8Tl9DyM+PIEsgs4sScZL12xDgkLEpCwIAG64ro7BD5Vn+TkZLi4uOC7777DV199BYWC/X5qAi9X8XJVpV3JyMOLX0QiM68YbRrZ45tX2rN7ORklrVqLw9alA7h1zesqWe8qXq4y/ctVVH68XEWSauJsjW9eaQ97q9JeV2MiolBQbFzdC4lqC5kgQ1u3tmjr1pbTOhAZCH+TqEr8XG3xzZj2sFEqcDLhDiZ8exrFGp7uJ6ooSzNLRI2LQtS4KFiaWUodh8gksMihKmvZ0A4RY0JgaSbHn5dv4c3t56DT1dmroEREVEuwyCGDCPash3UvB0EhE7AjOhVLfjHeuU6IiMg0sMghgwlt5oylg0snhVt/OAHr/7wmcSIi45Ffkg+vVV7wWuX10NmziahiWOSQQT0X1BBz+zQHACzacwE7oq9LnIjIOIiiiKTsJCRlJ6EOd3olMigWOWRw45/wwStdvAEAs7eew5F4zsVDRPQ4ERERsLe3lzpGuSxYsACBgYEV2kYQBOzcubNa8jwMixwyOEEQ8HZfPwxo7QaNTsSrm04jPj1X6lhERGQgs2bNwr59+6SO8VgscqhayGQClg1phXZe9ZBbqMGYjVGc0JOIyERYW1vDwcFB6hiPxSKHqo1SIcfnI9rC08EKKXcKMP7rUygs4WCBRGSaQkNDMXnyZEyePBl2dnZwdHTEu+++q29jdffuXYwcORL16tWDlZUV+vTpg/j4+AfuKzExETKZDKdOnSqzfNWqVfD09IROp8PBgwchCAL27duHtm3bwsrKCp06dcKlS5fKbLNu3To0btwY5ubmaNasGb755psyzwuCgM8//xz9+/eHlZUV/Pz8EBkZiStXriA0NBQqlQqdOnXC1atX9dv893JVVFQUnn76aTg6OsLOzg7dunXDmTNnqnI4DYJFDlWr+ipzfBXWDrYWCpxJzsKsrWc5hg4RVYpWra3wTfevwUl1Gl3p8gJtufZbGRs3boRCocDJkyexevVqrFy5Ehs2bAAAhIWF4dSpU9i1axciIyMhiiL69u2LkpL75/7z8vJCjx49EB4eXmZ5eHg4wsLCIJP98/H99ttvY8WKFTh16hQUCgXGjBmjf27Hjh2YOnUqZs6cifPnz2PChAkYPXo0Dhw4UGa/77//PkaOHImYmBg0b94cL730EiZMmIC5c+fi1KlTEEURkydPfuj7zs3NxahRo3DkyBEcP34cvr6+6Nu3L3JzpW2qwImGqNo1drLGZyOCMfLLk/j53A34OKowo2czqWMR/UMArPyt9PcliSAI8Hfy19+n+92bX6wi/H/wh/MQZwBA5o5MxA2Ng103O7Q52Ea/znGv4yjJvL/QCBVDK/x6Hh4e+OijjyAIApo1a4a//voLH330EUJDQ7Fr1y4cPXoUnTp1AgBs2rQJHh4e2LlzJ4YMGXLfvsaOHYuJEydi5cqVUCqVOHPmDP766y/89NNPZdZbtGgRunXrBgCYM2cO+vXrh8LCQlhYWGD58uUICwvDa6+9BgCYMWMGjh8/juXLl6N79+76fYwePRpDhw4FALz55pvo2LEj3n33XfTq1QsAMHXqVIwePfqh7/vJJ58s8/iLL76Avb09Dh06hP79+1f0MBoMz+RQjejU2BGLn2sJAPh4/xX88tcNiRMR/UNuJUdIbAhCYkMgt6r5yTkBwMrMCrGvxSL2tVhYmVlJkoGqrkOHDmWK1I4dOyI+Ph5xcXFQKBRo3769/jkHBwc0a9YMFy48ePDUQYMGQS6XY8eOHQBKe191794dXl5eZdZr1aqV/r6rqysAICMjAwBw4cIFdO7cucz6nTt3vu81/70PF5fSyWFbtmxZZllhYSFycnIemDU9PR3jxo2Dr68v7OzsYGtri7y8PCQnJz9w/ZrCMzlUY4a29cDlm7nYcCQBM7eehY+TNZo1sJE6FhEZia55XSu8jaD8p+BwfNaxdB//+XrfIbFDVaNVC3Nzc4wcORLh4eF47rnn8N1332H16tX3rWdmZqa/f6/A0ukqNofgg/ZRkf2OGjUKt2/fxurVq+Hp6QmlUomOHTuiuLi4QjkMjWdyqEbN6dMcnZs4IL9Yi3Ffn0JWvrS/AERkPOQqeYVvMsU/H3Myhax0uaW8XPutjBMnTpR5fK99ir+/PzQaTZnnb9++jUuXLsHf3/+h+xs7diz++OMPrF27FhqNBs8991yF8vj5+eHo0aNllh09evSRr1kZR48exZQpU9C3b18EBARAqVQiM1P6MdJY5FCNUshl+HRYEBrWs0TynXy8vjkaWjZEJolp87U4GXASJwNOQpsvTQ/A/JJ8BKwNQMDaAE7rYMSSk5MxY8YMXLp0CZs3b8Ynn3yCqVOnwtfXFwMHDsS4ceNw5MgRnD17Fi+//DLc3d0xcODAh+7Pz88PHTp0wJtvvolhw4bB0rJiM9TPnj0bERERWLduHeLj47Fy5Ur8+OOPmDVrVlXfahm+vr745ptvcOHCBZw4cQLDhw+vcNbqwCKHalw9lTm+GNEWlmZyHI7PxNLfLkodieo6EciPy0d+XD4gUc0tiiLibsUh7lYcp3UwYiNHjkRBQQFCQkIwadIkTJ06FePHjwdQ2jMqODgY/fv3R8eOHSGKIvbs2VPmstCDvPLKKyguLi7Ta6q8Bg0ahNWrV2P58uUICAjA559/jvDwcISGhlbm7T3Ul19+ibt37yIoKAgjRozAlClT4OzsbNDXqAxBNNLfpkWLFmH37t2IiYmBubk5srKyKryPnJwc2NnZITs7G7a2toYPSY/087k0TP4uGgDw8bA2GNDaTeJEVFeJWhFZh7MAAPZd7SHIa753k7pYDesl1gCAvCnpUNUvbfyJvDxAparxPFIpLCxEQkICvL29YWFhIXWcCgkNDUVgYCBWrVpl0P2+//772Lp1K86dO2fQ/dZ2j/pZKO/nt9GeySkuLsaQIUPw6quvSh2FKql/Kze8GtoYAPDGtrO4cOPBrfaJqpsgF1AvtB7qhdaTpMAhepC8vDycP38en376KV5//XWp4xgloy1yFi5ciOnTp5fp4kbGZ1bPZniiqRMKS3SYtOkMcgvvH6uCiKgumjx5MoKDgxEaGlqpS1VUx7qQFxUVoajon/mTHtbfn2qOXCZg9QuB6PfxYVzLVGPOj3/h02FtOBga1ShdiQ43vigdu8l1vCtkZkb7/Y8kdPDgQYPuLyIiAhEREQbdZ11Tp36TlyxZAjs7O/3Nw8ND6kiE0obIn7wUBIVMwO5zN/B1ZJLUkaiOEYtFxE+OR/zkeIjFRtlMkYgeoFYVOXPmzIEgCI+8XbxY+Z44c+fORXZ2tv6WkpJiwPRUFcGe9TC3rx8A4IPdcYhJyZI2EFENEwQBnnae8LTz5JlMgD3MyCA/A7XqctXMmTMRFhb2yHV8fHwqvX+lUgmlUlnp7al6jenshaiEO/g19iYmbTqD3VO6wN7KXOpYRDXCyswKidMSSx+o1ZJmkdK97tT5+fm1YpwVkk5+ful4UY/rYv8otarIcXJygpOTk9QxSCKCIGDpkFaIu5GD5Dv5mPnDWawf2RYyGb/VEtUVcrkc9vb2+rmXrKyseGarjhFFEfn5+cjIyIC9vT3k8srPJ1eripyKSE5Oxp07d5CcnAytVouYmBgAQJMmTWBtbS1tOKo0WwszrB0ehOfWHcO+ixn44vA1TOzWWOpYRFSDGjRoAOCfSSapbrK3t9f/LFSW0RY58+bNw8aNG/WP27RpAwA4cOCAwUdypJrVwt0OC54JwFs7/sLy3y6hg48DAj3spY5FVK0KSgrwRMQTAIA/h/yCunyhRhAEuLq6wtnZGSUlHFaiLjIzM6vSGZx7jLbIYdc60zYsxANHrtzCnr9uYuqWaOye0hXWSqP9cSV6LJ2ow6m0U/r7VHrpyhAfdFR31areVUT3CIKAJc+2gru9JZJu52PezvNSRyIiIiPDIodqLTsrM6x6MRAyAfgxOhU7o1OljkREREaERQ7Vau286uP1J30BAO/sPI/k2/kSJyIiImPBIodqvdefbIK2nvWQV6TB61uiUaJlewUiIno8FjlU6ynkMqx6MRA2FgqcTcnCqj8uSx2JiIiMAIscMgoN61nhw+daAQDWHryKE9duS5yITI2ZoxnMHCs/sqohOFo5wtHKUdIMRKZEEOvwBCE5OTmws7NDdnY2bG1tpY5D5TB761lsPX0dDetZ4tdpT7BbOZkmtRq4N6hpXh6gUkmbh6iWKe/nN8/kkFGZ94w/GtazxPW7BXj/f3FSxyEiolqMRQ4ZFRsLMywf0hqCAHx/KgW/x6VLHYmIiGopFjlkdDr4OGBsF28AwNwfz+F2XpHEicjYaQu0iA6NRnRoNLQFWkkyFJQUIDQiFKERoSgoKZAkA5GpYZFDRmlmz2Zo5mKDzLxizP3xL9ThpmVkCDog+1A2sg9lAxKNUKATdTiUdAiHkg5xWgciA2GRQ0bJwkyOlS+0hplcwN64dGw7fV3qSGTEBKUA/x/84f+DPwSlIHUcIjIQFjlktALc7DCtR1MAwML/xSHlDkdDpsqRKWRwHuIM5yHOkCn4Z5HIVPC3mYzaxG6NEfz3aMhzfjzHy1ZERKTHIoeMmlwmYMWQ1rAwk+HoldvYfDJF6khkhHQaHTK2ZiBjawZ0GraHITIVLHLI6Hk5qjCrZzMAwOI9F5CaxZ4pVDFikYi4oXGIGxoHsYhnA4lMBYscMgmjO3sjqJF96WWr7bxsRcbJyswKVmZWUscgMhkscsgkyGUClg1pDXOFDIfjM7H1FHtbkXFRmaugfksN9VtqqMw5jQORIbDIIZPR2MkaM58u7W31/u443MwulDgRERFJiUUOmZSxXX0Q6GGP3EIN3trBQQKJiOoyFjlkUuQyAcsGt4K5XIb9FzPw45lUqSMRlUuhphD9vuuHft/1Q6GGZyGJDIFFDpkcXxcbTO3hCwBY+L9YZOTwA4NqP61Oiz3xe7Anfg+0OmnmzyIyNSxyyCRNeMIHLd3tkFOowds7z/OyFRFRHcQih0ySQi7D8iGlc1v9HpeOX8/flDoSERHVMBY5ZLKaNbDBq90aAwDm74pFdkGJxImIiKgmscghk/Za9ybwcVIhI7cI//frRanjEBFRDWKRQybNwkyOxc+2BAB8dyIZUYl3JE5EREQ1hUUOmbwOPg54sZ0HAGDO9nMo0rDnChFRXcAih+qEuX384GitxNVbaqw9cFXqOFTLyFVyhIqhCBVDIVfJJcmgMldBnC9CnC9yWgciA2GRQ3WCnZUZFg4IAACsPXgFVzJyJU5ERETVjUUO1Rl9WzbAU82dUaIVMWf7X9DpOHYOEZEpY5FDdYYgCHh/UAuozOU4lXQXm6OSpY5EtYS2UIvYIbGIHRILbaE0bbYKNYUYsnUIhmwdwmkdiAyERQ7VKW72lpjVqxkA4MM9FznlA5XSAre23cKtbbcAidqla3VabIvbhm1x2zitA5GBsMihOmdkRy+09rBHbpEG7+++IHUcqgUEcwG+n/rC91NfCOaC1HGIyEBY5FCdI5cJWDSoBWQC8L+zaTgSnyl1JJKYzEwG90nucJ/kDpkZ/ywSmQr+NlOd1MLdDiM7egEA5v10nmPnEBGZIBY5VGfN6NkUTjZKXMtU4/ND16SOQxIStSLuHryLuwfvQtSy1x2RqWCRQ3WWrYUZ3u3vDwD49MAVJN1WS5yIpKIr1OFs97M42/0sdIU6qeMQkYGwyKE67ZlWrujcxAHFGh3m74qFKPJbPBGRqWCRQ3WaIAh4f2ALmMtlOHjpFn49f1PqSFRHWZlZIW9uHvLm5sHKzErqOEQmgUUO1Xk+TtaY2M0HALDwf3HIK9JInIjqIkEQoDJXQWWugiCwGzuRIbDIIQLwWvcmaFTfCjdzCrH6j8tSxyEiIgNgkUMEwMJMjoUDSyfw/OpoIi7ezJE4EdU1RZoihO0MQ9jOMBRpiqSOQ2QSjLLISUxMxCuvvAJvb29YWlqicePGmD9/PoqLi6WORkasezNn9A5oAK1OxDs7znMCT6pRGp0GG89uxMazG6HR8ZIpkSEYZZFz8eJF6HQ6fP7554iNjcVHH32Ezz77DG+99ZbU0cjIzXvGH1Z/T+C57cx1qeMQEVEVGGWR07t3b4SHh6Nnz57w8fHBgAEDMGvWLPz4449SRyMj52ZviWk9fAEAS3+9iOyCEokTERFRZRllkfMg2dnZqF+//iPXKSoqQk5OTpkb0X+FdfKGj5MKmXnFWMVGyERERsskipwrV67gk08+wYQJEx653pIlS2BnZ6e/eXh41FBCMibmChkWPFPaCPnryCRcupkrcSIiIqqMWlXkzJkzB4IgPPJ28eLFMtukpqaid+/eGDJkCMaNG/fI/c+dOxfZ2dn6W0pKSnW+HTJiTzR1Qq8AF2h1IhZwJGQiIqOkkDrAv82cORNhYWGPXMfHx0d/Py0tDd27d0enTp3wxRdfPHb/SqUSSqWyqjGpjninnz8OXrqFyGu3sfuvG+jfyk3qSEREVAGCaKRfUVNTU9G9e3cEBwfj22+/hVwur/A+cnJyYGdnh+zsbNja2lZDSjJ2q/64jFV/xMPVzgL7ZnaDlXmt+l5ABiKKIkoySxuZmzmaSTLisCiKyMzPBAA4ipYQbGxKn8jLA1SqGs9DVJuV9/O7Qn+xvb29K/XLP23aNEyZMqXC2z1MamoqQkND4enpieXLl+PWrVv65xo0aGCw1yGa2K0xtp2+jut3C7DmwBXM7tVc6khUDQRBgLmTueQZnFROpQ/UakmzEJmKChU5ERERlXoRLy+vSm33ML///juuXLmCK1euoGHDhmWeM9ITU1RLWZjJ8U4/f0z89jTW/5mAIcEe8HLkt2oiImNgtJerDIGXq6g8RFHEyK9O4nB8Jp5q7owvw9pJHYkMTFekw5UZVwAATVY2gUxZ830yijRFmPHbDADAyi7vQ2nvUPoEL1cR3ae8n9+1qncVUW0kCALmPxMAhUzAvosZ2H8xXepIZGCiRkTa2jSkrU2DqJHme59Gp8HaU2ux9tRaTutAZCAscojKoYmzNV7p4g0AeO9/cSjSaCVORIYkmAnwnO8Jz/meEMxqvtExEVUPo2x4TCSF15/yxY7oVCTezseGwwmY1L2J1JHIQGTmMngv8JY6BhEZmFE2PCaSgrVSgbl9m2P692fx6f4reLaNO9zsLaWORURED1GhIqdbt27VlYPIKAwKdMd3J5IRlXgXS365iE+GtZE6EhmAqBORfyEfAGDlZwVBxktWRKagSm1ySkpKkJKSgkuXLuHOnTuGykRUa91rhCwIwP/OpuFUIn/uTYGuQIeoFlGIahEFXYFO6jhEZCAVLnJyc3Oxbt06dOvWDba2tvDy8oKfnx+cnJzg6emJcePGISoqqjqyEtUKLdztMDS4dHLXhf+Lg05XZ0dhICKq1SpU5KxcuRJeXl4IDw9Hjx49sHPnTsTExODy5cuIjIzE/PnzodFo0LNnT/Tu3Rvx8fHVlZtIUrN6NYO1UoG/UrOx/cx1qeOQCbA0s0TC1AQkTE2ApRnbehEZQoXa5ERFReHPP/9EQEDAA58PCQnBmDFj8NlnnyE8PByHDx+Gr6+vQYIS1SZONkpMfrIJPvzlIpb+dgl9WrrCWsl5rajyZIIMXvZepQ84rQORQVTor/LmzZvLtZ5SqcTEiRMrFYjIWIzu7IXNJ5ORdDsf6w5yXisiotrGYIMBbtiwwVC7IjIKSoUcb/X1AwCsP5yAlDv5EiciY1asLcbsvbMxe+9sFGuLpY5DZBIMVuT8/PPP2L9/v/5xfn4+XnzxRUPtnqhW6unvgk6NHVCs0WHJLxekjkNGrERbguWRy7E8cjlKtCVSxyEyCQYrcr7++mvMmzcPFy9exOXLl/HEE0+gV69ehto9Ua0kCALmPeMPmQDs+esmjl+7LXUkIiL6W5VbSk6fPh2BgYFo3bo1NmzYgOHDh0On0yE8PByBgYEGiEhUuzVvYIthIY2w6UQy3vtfHP73ehfIOZgcEZHkqnwmp3v37khJScHixYsxZMgQJCQkwM3NDb/99ht2795tiIxEtd6Mp5vCxkKBuBs52HoqReo4REQEA5zJGTBgAAYMGKB/XFhYiPPnz+PcuXP4448/0K9fv6q+BFGt52CtxNSnfPHB7gtYvvcS+rVyhY2FmdSxiIjqNIMP7GFhYYG2bduibdu2ht41Ua02sqMXvjuRjGuZany6/wrm/t3zioiIpFHhy1U2NjZ44oknMH36dHzzzTeIjY2FKHJYeyJzhQzv9C8tbL46moDETA7oRkQkJUGsYIWydu1anD59GqdOnUJcXBx0Oh0sLS3RqlUrBAcHIygoCEFBQWjdunV1ZTaYnJwc2NnZITs7G7a2tlLHIRMgiiJGhUfhz8u38LS/C9aP5BlNY1AbZiHXiTpcuFU6DIGfVSPIbP7+m5SXB6hUNZ6HqDYr7+d3hYucfysoKIBKpcKbb76JO3fu4MyZMzh//jyKi4uh1Woru9sawyKHqkN8ei56rz4MrU7Et6+0RxdfR6kjkbFRqwFr69L7LHKI7lPez+8q9a6ytCydRG7YsGH4/PPPERUVhdzcXERHR1dlt0RGzdfFBiM6eAIAPtgdBy1nKScikoTBBgO8R6FQoFWrVobeLZFRmdbDF7YWCly8mYttp9mlvLbTFeuQsCABCQsSoCvWSZKhWFuMBQcXYMHBBZzWgchADF7kEBFgb2WOKU/5AgCW772MvCKNxInoUcQSEUkLk5C0MAliiTRn3kq0JVh4aCEWHlrIaR2IDKTCRc7YsWOxbt06REVFoaioCEDp0PZEVNbIjl7wcrDCrdwifH7oqtRx6BEEhQC319zg9pobBAX/nhGZigqPkxMfH4+tW7ciNzcXCkXp5gsXLkRoaCiCgoIQGBgIKysrgwclMjbmChnm9GmOid+ewfrD1zAspBHc7C2ljkUPIFPK0HRNU6ljEJGBVfhMzqFDh5CdnY1Lly7h66+/xqxZs5CVlYV58+ahS5cusLOzQ0BAQHVkJTI6vQIaIMSrPgpLdFj+2yWp4xAR1SmVHvHY19cXvr6+ePHFF/XLEhIScOrUKfauIvqbIAh4p78fBnx6FD9GpyKssxdaNbSXOhb9hyiKKMksbQdj5mjGS/BEJqJCZ3KSk5Mf+by3tzeGDBmCxYsXAwBSU1Mrn4zIRLRqaI9n27gDAD7YfYEjhNdCunwdjjkfwzHnY9DlS9O7iogMr0JFTrt27TBhwgRERUU9dJ3s7GysX78eLVq0wPbt26sckMgUzO7VDEqFDCcT7uC32HSp4xAR1QkVulwVFxeHRYsW4emnn4aFhQWCg4Ph5uYGCwsL3L17F3FxcYiNjUVQUBCWLl2Kvn37VlduIqPiZm+JcV198OmBK/jwlwt4srkzzBUcwYH+YaGwwMmxJ/X3iajqKjWtQ0FBAXbv3o0jR44gKSkJBQUFcHR0RJs2bdCrVy+0aNGiOrIaHKd1oJqUV6RB6LKDyMwrwrv9/fFKF2+pI9HftGotDlsfBgB0zesKuUoubSBO60D0SOX9/K5Uw2NLS0v07t0bgwcPrnRAorrGWqnArJ5NMefHv/Dxvng8H+QOeytzqWMREZmsSp8vt7OzY5sbogoa0tYDzRvYILugBKv3xUsdh2qRYm0xlh1dhmVHl3FaByIDqXSRI4oiPv/8c3Tu3BldunTBtGnTHtkgmYgAuUzA2/38AADfRCbh2q08iRNRbVGiLcEbf7yBN/54g9M6EBlIlVo+RkdHIygoCF26dEFsbCy6du2KWbNmGSobkUnq6uuE7s2coNGJ+PCXi1LHISIyWZUeDBAAvvvuOzz99NP6x+fOncPAgQPh7u6O6dOnVzkckal6q68f/ozPxN64dBy/dhsdfBykjkREZHIqfSanfv368PDwKLOsVatW+PTTT7Fu3boqByMyZb4uNhgWUvr788HuOOh0HCCQiMjQKl3kBAYGIjw8/L7lTZo0eezIyEQETOvRFNZKBc6n5mBHNEcHJyIytEoXOR988AE+/vhjjBgxApGRkVCr1cjIyMDixYvh7c3xP4gex9FaiUndmwAAlv12CQXFWokTERGZlkoXOR06dMDx48eRkpKCrl27wtbWFq6urti2bRtWrFhhyIxEJmt0Zy+421viZk4h1h++JnUcIiKTUqkRj/8rIyMDp0+fhk6nQ/v27eHo6GiIbNWOIx5TbbDrbBqmbI6GpZkcB2eHwsWWQ/rXNFErIutwFgDAvqs9BHnNz0Ku1WlxOPnvUZcdgiC3tSt9giMeE92nvJ/fBilyjBWLHKoNRFHEc+uOITo5C0PbNsTSwa2ljkRS47QORI9U3s9vo50hcMCAAWjUqBEsLCzg6uqKESNGIC0tTepYRBUmCALe+XuAwK2nryMuLUfiREREpsFoi5zu3bvjhx9+wKVLl7B9+3ZcvXqVc2mR0Qr2rI9+LV0hisDiPRdQh0+wSkJXokPqmlSkrkmFrkQnSYYSbQnWnFyDNSfXcMRjIgMxyOWqy5cvw8fHBwpFlcYWrJJdu3Zh0KBBKCoqgpmZWbm24eUqqk2Sb+ejx8pDKNbqEB7WDt2bO0sdqc6oDbOQq4vVsF5Seokqb0o6VPVdSp/g5Sqi+9To5So/Pz9cuyZdz5A7d+5g06ZN6NSp0yMLnKKiIuTk5JS5EdUWjRysMKqTJwBg0Z4L0GilOaNQJ8kBp8FOcBrsBNR8fUNE1cQgRY5Up9bffPNNqFQqODg4IDk5GT/99NMj11+yZAns7Oz0t/+O2EwktcndfWFvZYYrGXnYEpUidZw6Q24hR8DWAARsDYDcglUOkamoVW1y5syZA0EQHnm7ePGfCQ1nz56N6Oho7N27F3K5HCNHjnxkwTV37lxkZ2frbykp/BCh2sXOygxTn/IFAHz0+2XkFrJtBhFRZUnXiOYBZs6cibCwsEeu4+Pjo7/v6OgIR0dHNG3aFH5+fvDw8MDx48fRsWPHB26rVCqhVCoNGZnI4Ia398TXkUlIyFRj3cGreKN3c6kjEREZpVpV5Dg5OcHJyalS2+p0pe0XioqKDBmJqMaZK2SY06c5JnxzGl8eScDwDp5wt7eUOpZJqw0Nj4nI8GrV5aryOnHiBD799FPExMQgKSkJ+/fvx7Bhw9C4ceOHnsUhMiY9/V0Q4l0fRRodlv168fEbEBHRfYyyyLGyssKPP/6Ip556Cs2aNcMrr7yCVq1a4dChQ7wcRSbh3wME7oxJw9mULGkDUbVTKpT4edjP+HnYz1Aq+HeMyBBq1eWq8mrZsiX2798vdQyiatWqoT2ebeOOHdGpWLT7Ar6f0AGCUPNzKlHNUMgU6Ne0X+kDtVraMEQmwiBnct588004ODgYYldE9C+zezWDUiHDycQ7+C02Xeo4RERGxSBFzpIlS1jkEFUDN3tLjO3qDQD48JcLKNZwgEBTVaItQURMBCJiIjitA5GBGGWbHKK65NXQJnC0Nkfi7Xx8ezxJ6jhUTYq1xRj902iM/mk0irXFUschMgkscohqOWulAjOebgYA+Hh/PLLz+S2fiKg8WOQQGYGhbRuiqYs1svJL8Mn+eKnjEBEZBRY5REZAIZfhrb6lXco3RiYi6TZ73xARPQ6LHCIjEdrMGV19HVGiFfF/HCCQiOixKjROjre3d6XG6Zg2bRqmTJlS4e2IqKy3+/mh7+rD2PPXTZxKvIO2XvWljkREVGtVqMiJiIio1It4eXlVajsiKqt5A1sMbeuBLVEp+GD3Bex4rRMHCCQieogKFTndunWrrhxEVE4zejbFrrNpiEnJwv/O3cCA1m5SRzJ6glKA/w/++vtSUCqU+GHwD/r7RFR1giiKotQhpJKTkwM7OztkZ2fD1tZW6jhE5fbxvnis/P0y3O0tsW9mN1iYcdZsk6JWA9bWpffz8gCVSto8RLVMeT+/2fCYyAiN6+qDBrYWSM0qQMSxRKnjEBHVSmx4TGSELM3lmNWrGWZtPYs1+69gSHBDOFjzEkdl6TQ6ZO7IBAA4PusImaLmv/9pdBrsuLADAPBso57GOXsyUS3DhsdERuq5Nu4IP5qA2LQcrN4Xj/cGtpA6ktESi0TEDY0DAHTN61rBv4yGUaQpwtBtQwEAeVPSWeQQGQAbHhMZKZlMwNv9/PDS+hPYdCIZIzt6oYmztdSxjJMMsOtmp79PRKahSr/OJSUlSElJwaVLl3Dnzh1DZSKicurU2BE9/Jyh1Yn48JcLUscxWnJLOdocbIM2B9tAbslG3ESmosJFTm5uLtatW4du3brB1tYWXl5e8PPzg5OTEzw9PTFu3DhERUVVR1YieoA5ffwglwn440IGjl3NlDoOEVGtUaEiZ+XKlfDy8kJ4eDh69OiBnTt3IiYmBpcvX0ZkZCTmz58PjUaDnj17onfv3oiP50SCRNWtibM1hrdvBABYtPsCdLo6OyoEEVEZFWqTExUVhT///BMBAQEPfD4kJARjxozBZ599hvDwcBw+fBi+vr4GCUpEDzf1KV/sOJOK2LQc/BidisHBDaWOZFS0ai2Oex0HAHRI7AC5ipesiExBhYqczZs3l2s9pVKJiRMnVioQEVWcg7USk55sgg9/uYjlv11Cv5ausDTnB3VFlGSWSB2BiAyM/QiITERYJy+421viZk4h1h++JnUcqiBzuTnCB4YjfGA4zOXmUschMgkGGYrh7t272Lt3L1JTUwEAbm5u6NWrF+rVq2eI3RNROViYyfFmn+aYsjkanx26ihfbecDZ1kLqWFROZnIzhAWGlT5QqyXNQmQqqnwm58svv0THjh1x4sQJ6HQ66HQ6nDhxAp06dcKXX35piIxEVE7PtHJFm0b2yC/WYuXvl6WOQ0QkqSpP0NmsWTOcOXMGqv9MIJeXl4egoCBcvlx7/9Bygk4yRaeT7uD5dZGQCcCeqV3RvAF/th9Hq9bisPVhAKUjHkvR8Fij0+C3K78BAHq5doHC1r70CU7QSXSfGpugUxAE5Obm3rc8Nze3UvNcEVHVBHvWR7+WrtCJpV3Kq/g9hmpIkaYI/Tf3R//N/VGkKZI6DpFJqHKbnOXLl6Nbt25o0aIF3N3dAQDXr19HbGwsVqxYUeWARFRxb/Zujt/j0nE4PhMHL99C92bOUkciIqpxVS5y+vfvjz59+uDkyZNIS0sDUNrwOCQkBHI5u7ASSaGRgxVGdfLE+sMJWLz7Aro2cYRCzs6URFS3VLjIsbGxQZs2bRAcHIygoCAEBQXB398fHTt2rI58RFRJk7v7Yuvp64jPyMP3p1IwvL2n1JGIiGpUhb/a/d///R98fX2xf/9+jBkzBq1atYKNjQ06deqE119/HeHh4Th79mx1ZCWiCrCzMsPUp0pHHP/o98vILeRgd0RUt1T4TM5rr72mv19QUACVSoXXX38dd+7cwfHjx7FhwwYUFxdDq9UaNCgRVdzw9p74OjIJCZlqfHboKmb3ai51JCKiGlOli/SWlpYAgGHDhuHzzz9HVFQUcnNzER0dbZBwRFQ15goZ5vQpLWw2HE5AWlaBxImIiGqOQUY8LrNDhQKtWrUy9G6JqJJ6+rsgxLs+TibcwbLfLuGjFwKljlTrCOYCfD/11d+XgrncHJ/2+VR/n4iqjt0tiEycIAh4p58fAGBHdCrOXc+SNlAtJDOTwX2SO9wnuUNmJs2fRTO5GSaFTMKkkEkwk5tJkoHI1FT4t3ns2LFYt24doqKiUFRUOmAVB/0jqt1aNbTHs21Kx7H6gAMEElEdUeHLVfHx8di6dStyc3OhUJRuvnDhQoSGhiIoKAiBgYGwsrIyeFAiqprZvZphz183cDLhDvbGpaNXQAOpI9UaolZE1uEsAIB9V3sI8pr/4qbVaXE4+e+pJRyCwFHGiKqu0nNXxcfH4/Tp0zhz5oz+lpWVBblcjqZNmyI2NtbQWQ2Oc1dRXbPst4tYc+AqvB1V+G3aEzBX8Io1UDvmrlIXq2G9xBoAkDclHar6LqVPcO4qovuU9/O70g2PfX194evrixdffFG/LCEhAadOnWLvKqJa6tXQJvg+KgUJmWpsOpGE0Z29pY5UOwiAlb+V/j4RmYYKfY1LTk5+5PPe3t4YMmQIFi9eDABITU2tfDIiMjhrpQLTn24KAFi9Lx7Z+RwgEADkVnKExIYgJDYEciteKCIyFRUqctq1a4cJEyYgKirqoetkZ2dj/fr1aNGiBbZv317lgERkWC+09YCvszWy8kvw6YF4qeMQEVWbCl2uiouLw6JFi/D000/DwsICwcHBcHNzg4WFBe7evYu4uDjExsYiKCgIS5cuRd++fasrNxFVkkIuw1v9/DA6PAobjyXh5Q6e8HRgmw8iMj0VOpPj4OCAlStX4saNG/j000/h6+uLzMxMxMeXfhscPnw4Tp8+jcjISBY4RLVYaFMndPV1RLFWh0W7L0gdR3LafC1OBpzEyYCT0OZzShoiU1GphseWlpbo3bs3Bg8ebOg8RFQDBEHAvP7+6L36MPbGpeNIfCa6+DpKHUs6IpAfl6+/T0SmodL9R+3s7NjmhsiI+brYYEQHTwDAez/HQqPVSZyobjOTm2Fpj6VY2mMpRzwmMpBKFzmiKOLzzz9H586d0aVLF0ybNu2RDZKrS1FREQIDAyEIAmJiYmr89YmM2fQeTVHPygyX0/Pw3clH956k6mUuN8fszrMxu/Nszl1FZCBVGgksOjoaQUFB6NKlC2JjY9G1a1fMmjXLUNnK5Y033oCbm1uNviaRqbCzMsOMns0AACv2XsZddbHEiYiIDKdKs5B/9913ePrpp/WPz507h4EDB8Ld3R3Tp0+vcrjH+eWXX7B3715s374dv/zyS7W/HpEpGtbOA5uOJ+HizVys+uMyFg5sIXWkOkmr0+LMjTMAgCDbZpzWgcgAKn0mp379+vDw8CizrFWrVvj000+xbt26Kgd7nPT0dIwbNw7ffPNNuefKKioqQk5OTpkbUV2nkMswr78/AODbE8m4dDNX4kR1U6GmECEbQhCyIQSFmkKp4xCZhEoXOYGBgQgPD79veZMmTR47MnJViaKIsLAwTJw4EW3bti33dkuWLIGdnZ3+9t8ijaiu6tTEEb0DGkCrE/Hez7GcpZyITEKli5wPPvgAH3/8MUaMGIHIyEio1WpkZGRg8eLF8Pau3Hw4c+bMgSAIj7xdvHgRn3zyCXJzczF37twK7X/u3LnIzs7W31JSUiqVk8gUvdXXD+YKGY5euY3f49KljkNEVGWVbpPToUMHHD9+HFOnTkXXrl313/wsLCywdevWSu1z5syZCAsLe+Q6Pj4+2L9/PyIjI6FUKss817ZtWwwfPhwbN2584LZKpfK+bYioVCMHK4zr6o01B67ig90X8ERTJ1iYsWUIERmvKjU8bt26NQ4ePIiMjAycPn0aOp0O7du3h6Nj5QYVc3JygpOT02PX+/jjj/HBBx/oH6elpaFXr174/vvv0b59+0q9NhEBr4U2wbbT15F8Jx9fHU3Aa6FNpI5ERFRpVSpy7nF2dkafPn0MsatyadSoUZnH1tbWAIDGjRujYcOGNZaDyNSolArM6dMc078/i0/3X8HzQQ3hYmshdSwiokqp0jg5RGR6BrZ2R5tG9sgv1mLpr5ekjkNEVGkVOpPj7e0NQRAq/CLTpk3DlClTKrxdeXl5ebE3CJGByGQC5j8TgEFrjmL7mesY0dETgR72UseqVoKZAM/5nvr7UjCTm2F+t/n6+0RUdYJYgerg0KFDlXoRLy8veHp6Vmrb6pSTkwM7OztkZ2fD1tZW6jhEtcrMH85i+5nrCPSwx4+vdoJMJs2Hf52kVgN/X4ZHXh6gUkmbh6iWKe/nd4XO5HTr1q3KwYjIOLzZuxl+PX8DMSlZ+DE6FYOD2d6NiIwL2+QQ0QM521pg8pO+AIAPf7mA7IISiRNVH1EnQh2rhjpWDVEnzaVvnahDbEYsYjNioRM5IzyRIZhEmxwiqh6vdPHG1tMpuHZLjY9+v4wFAwKkjlQtdAU6RLWIAgB0zesKuarmxwcqKClAi3Wl84blTUkHL1ARVV2FipyIiIhKvYiXl1eltiMiaZkrZFg4IAAjvjyJryMT8UI7D/i5mmb7NTNHNvYlMjVsk0NEj9TV1wl9WjTAL+dvYv5Psfh+QodKndGtzeQqOTrf6ix1DCIyMLbJIaLHeqe/PyzN5DiZeAc/xaRJHYeIqFxY5BDRY7nbW2Lyk6VTPCzacwG5habbCJmITAeLHCIql7FdveHtqMKt3CKs/iNe6jgGpS3QIjo0GtGh0dAWaKWOQ0QGwiKHiMpFqZBj/jP+AIDwY4m4nJ4rcSID0gHZh7KRfSgbYO9tIpPBIoeIyi20mTN6+rtAqxMx76fznE7FgMzkZpjVcRZmdZzFaR2IDMQgs5ATUd3xbn9/HLp8C8ev3cH/zt3AgNZuUkcyCeZycyzruaz0gVotbRgiE8EzOURUIR71rTCp+9+NkHfHIa9II3EiIqIHY5FDRBU2/gkfeDpYIT2nCJ/sM61GyFLRiTokZiUiMSuR0zoQGQiLHCKqMAuzfxohf3kkAfGm1AhZIgUlBfBe7Q3v1d4oKCmQOg6RSWCRQ0SV8mRzF/Twc4ZGJ+KdnWyETES1D4scIqq0+c8EwMJMhhMJd7Dt9HWp4xARlcEih4gqzaO+Fab1aAoAWLznAu6oiyVORET0DxY5RFQlr3TxRvMGNribX4Iley5IHYeISI9FDhFViZlchkXPtgQAbD19Hcev3ZY4ERFRKRY5RFRlwZ718FL7RgCAt3f8hSIN538iIulxxGMiMog3ezXH3tibuHpLjS8OXcPrT/lKHancBIUAt9fc9PeloJAp8Frb1/T3iajqBLEO9/vMycmBnZ0dsrOzYWtrK3UcIqP3U0wqpm6JgblChr3TnoCXo0rqSMZJrQasrUvv5+UBKh5Hon8r7+c3L1cRkcEMaO2GLk0cUazR4V1O4ElEEmORQ0QGIwgCPhjUAuYKGQ7HZ2LX2TSpI5WLKIoovlWM4lvFkhVmoijilvoWbqlvsTgkMhAWOURkUF6OKkz+ewLP93++gOyCEokTPZ4uX4djzsdwzPkYdPnSzBuVX5IP5+XOcF7ujPySfEkyEJkaFjlEZHATuvnAx0mFzLwifPjLRanjEFEdxSKHiAxOqZBj8d9j52w+mVzrx86Rq+QIFUMRKoZCrpJLHYeIDIRFDhFViw4+DhgWUjp2zpzt51BYwrFziKhmscghomozt29zuNgqkXg7Hx/9cVnqOERUx7DIIaJqY2thhg8GlV62Wv/nNZy7niVtoIfQFmoROyQWsUNioS3kGSciU8Eih4iq1dP+LujfyhU6EXhj2zmUaKXpvfRIWuDWtlu4te0WwBqHyGSwyCGiardgQADsrcxw8WYuvvjzmtRxaiWFTIFRrUdhVOtRnNaByEBY5BBRtXO0VmL+M/4AgNV/xONKRp7EiWofpUKJiEERiBgUAaVCKXUcIpPAIoeIasSgQHeENnNCsVaHOdvPQafjqL5EVL1Y5BBRjbg35YPKXI5TSXfx7YkkqSPVKqIoQl2shrpYzWkdiAyERQ4R1ZiG9azwZp/mAIAPf7mI5NucvuCe/JJ8WC+xhvUSa07rQGQgLHKIqEa93N4TIV71kV+sxextZ3nZioiqDYscIqpRMpmAZUNawcpcjhMJdxBxLFHqSERkoljkEFGN83RQYW5fPwDA//16EVdvsbcVERkeixwiksTL7Ruhq68jijQ6zNp6FlpetiIiA2ORQ0SSEAQB//d8K9goFYhOzuIggURkcEZb5Hh5eUEQhDK3Dz/8UOpYRFQBbvaWmPf3IIEf/X4Zl27mSpyIiEyJUY8d/t5772HcuHH6xzY2NhKmIaLKGBzcEL+ev4l9FzMw44cY7JzUGWbyGv7+JQecBjvp70tBLpNjsP9g/X0iqjqjLnJsbGzQoEEDqWMQURUIgoAlz7VEz1V/IjYtB2sOXMG0Hk1rNIPcQo6ArQE1+pr/ZaGwwNYhW0sfqNWSZiEyFUZ7uQoAPvzwQzg4OKBNmzZYtmwZNBrNI9cvKipCTk5OmRsRSc/Z1gLvDWwBAPh0/xWcu54lbSAiMglGW+RMmTIFW7ZswYEDBzBhwgQsXrwYb7zxxiO3WbJkCezs7PQ3Dw+PGkpLRI/zTCtX9GvpCo1OxLQtMcgvfvSXFiKixxHEWjRJypw5c/B///d/j1znwoULaN68+X3Lv/rqK0yYMAF5eXlQKh88g29RURGKior0j3NycuDh4YHs7GzY2tpWLTwRVVlWfjF6rzqMmzmFGBbSCEuea1kjr6tVa3HY+jAAoGteV8hVNd8mRl2shvUSawBA3pR0qOq7lD6RlweoVDWeh6g2y8nJgZ2d3WM/v2tVm5yZM2ciLCzskev4+Pg8cHn79u2h0WiQmJiIZs2aPXAdpVL50AKIiKRnb2WOlS+0xvANJ7D5ZDJCmzmhVwDb3RFR5dSqIsfJyQlOTk6V2jYmJgYymQzOzs4GTkVENalTY0eMf8IHnx+6hjnbzyHQwx4uthbV+poyKxk6ZXTS3yci02CUv82RkZFYtWoVzp49i2vXrmHTpk2YPn06Xn75ZdSrV0/qeERURTOfboYW7ra4m1+CWVurfxJPQRBg7mQOcydzCIJQra9FRDXHKIscpVKJLVu2oFu3bggICMCiRYswffp0fPHFF1JHIyIDMFfIsOqFNrAwk+FwfCa+OpogdSQiMkK16nJVeQUFBeH48eNSxyCiatTE2Rrv9PPHOzvPY+mvl9CpsSP83aqng4CuSIcrM66Uvu7KJpApjfL7HxH9B3+TiajWGt6+EXr4OaNYq8Prm89AXVQ93cpFjYi0tWlIW5sGUVNrOpwSURWxyCGiWuveJJ4utkpcvaXGuzvPoxaNemFQcpkcfX37oq9vX07rQGQgLHKIqFZzsFbi4xfbQCYAP0anYuvp61JHqhYWCgvsfmk3dr+0GxaK6u1NRlRXsMgholqvvY8DZjxdOp/VvJ/O43I6ZysnosdjkUNERuG10Cbo6uuIwhIdXtt0htM+ENFjscghIqMgkwn46IVAONsocSUjD/N+ipU6kkGpi9VQLVZBtVgFdTFnIScyBBY5RGQ0HK2VWP13+5xtp69jm4m1z8kvyUd+Sb7UMYhMBoscIjIqHRs7YFqP0vY57+48j4s3cyRORES1FYscIjI6k7qXts8pKNFiwjenkZ1fInUkIqqFWOQQkdGRywR8/GIbuNtbIul2PqZ9H13t81sRkfFhkUNERqmeyhyfjwiGUiHDgUu3sGpfvNSRiKiWYZFDREarhbsdFj/bEgDw8b54/B6XLnEiIqpNWOQQkVF7PrghRnX0BADM+D4G127lVXwnMsCumx3sutlJ9ldRJsjQzbMbunl2g0zgn2YiQxBEU50IphxycnJgZ2eH7Oxs2NpWz+zGRFT9ijU6DN9wHFGJd+HrbI0dkzrDWqmQOlblqdWAtXXp/bw8QKWSNg9RLVPez29+XSAio2eukGHN8CA42ygRn5GH6d/HsCEyEbHIISLT4Gxjgc9GBMNcLsPvcelYtveS1JGISGIscojIZAQ1qoelg1sBANYdvFruEZG1ai2OOh3FUaej0Kq11RnxodTFajgtc4LTMidO60BkIEZ80ZqI6H6D2rjjSkYePj1wBXN/PAdPByu086r/2O1KMqUfUDAzP1PqCFSHiKKIO+pipGUV4mZOIbILSpBTUILcQg1yCktQpNFCJwKlLXdFiCJgZa6AtVIOawsFrJVmqGdlBld7S7jZWcDRWgmZTJD6bZXBIoeITM6Mp5vi6q08/HL+JiZ8cxo/TeoMj/pWD11fZilDu/Pt9PeJTElOYQni0/NwJSMX8el5iM/IQ/KdfKRlFaBIozPY65jJBTSws4CPozWaNbBBUxcbNHOxga+LNSzM5AZ7nYpgkUNEJkcmE7BiaGuk3M3H+dQcjImIwrZXO8HO0uyB6wsyAaoA9mAi41es0SHuRg5iku8iJiULZ69nIyHz0Zc/nWyUcLWzgL2VOWwtFLCxMIOtpQIWCjlkggBBAAQAIoD8Yi3URRrkFWmQW6jBHXURbmQXIj2nECVaESl3CpBypwCHLt/S7/+TYW3wTGu36n3jD8Eih4hMkpW5AhtGtsPANUcQn5GH8V+fwtevhECpkOYbJVF10Gh1OJ+Wg2NXMxF59TaiEu+gsOT+szMNbC3g62INX+fSMyteDiq421vCxU5pkN8JjVaH9NwipN4twJWMPFxOz8Wlm7mIz8hFUxebKu+/sljkEJHJamBngfCwEAz9PBInEu5gxg9n8cmLbe5rN6Ar1iFpcRIAwPMtT8jMecmKaq+76mIcuJSBfRcy8OflW8gt0pR5vp6VGQI97NHawx6Bf9/srcyrNZNCLoO7vSXc7S0R4l22DZyUw/GxyCEik+bvZovPRwQjLPwkdp+7AVdbC7zT37/MOmKJiKSFpUVOo9mNgOr9PCCqsKTbauyNTcfvF9JxKvEO/j0MlK2FAh18HNCpsQM6NXGEr7M1BKH2NACWMguLHCIyeZ2bOGL5kNaYuiUGG44koIGdBcZ29ZE6VhkyQYa2bm3194luZhfi53Np2HU2DeeuZ5d5rnkDG/Twc8FTfs5o1dAe8lrWq6m2YJFDRHXCwEB33MwuxJJfLuKD3RfgZKPEwEB3qWPpWZpZImpcVOkDNcfJqauyC0qw+9wN/BSTipOJd3DvSo9cJqCjjwOe9nfBk82dH9lbkP7BIoeI6ozxT/jgRnYhIo4lYuYPZ6EyV6CHv4vUsaiOE0URJxPu4PuoFOw5f6NMw+G2nvUwINANfVu6wtFaKWFK48Qih4jqDEEQMK+/P7ILSrAjOhWvfXcGEWHt0N61ntTRqA7KyC3E9tOp+OFUSplu3k1drPF8UEP0b+0Gd3tLCRMaPxY5RFSnyGQClg1uBXWRBnvj0jH261P45qW2UsdCfkk+/NeUNoiOC4sCL0aYpntnbSKOJWJvXDq0f7cgVpnL8UxrN7zQzgOBHva1quGwMWORQ0R1jkIuwycvtcHYjadwOD4TE74+jeWQ9lKAKIpIyk7S3yfTUliixa6YNIQfS8SFGzn65UGN7PFiu0bo18oVKiU/kg2NR5SI6iSlQo7PRwRjxJcn8Vf8XUDiIodMU1pWAb45noQtJ5NxN790fjQLMxmebdMQozp5onkDW4kTmjYWOURUZ1mZK/BVWDuErYkEUNrY89LNHPg3ZhsdqprYtGysO3gVv5y/qb8k5W5viVGdPDG0rUe1D85HpVjkEFGdZmdphi9HheDc7OMAgLDwKHz1anu0cLeTOBkZo9NJd7DmwFXsv5ihX9apsQNGdfJCDz8XjmdTw1jkEFGdZ2f1z8SdWfklGL7hBL59pT1aNmShQ48niiKOXMnEmgNXcPzaHQCATACeae2Gid0aw8+Vl6SkwiKHiOhfAj3scDI9Gy9tOI6vx4SgTSNeuqIH0+lE/H4hHWsPXMHZv0ckNpMLeD6oISZ2awwvR85sLzUWOUREAmDlX9ppe31Ya4z9/jSiEu9i+IYT+OzlYDzR1Kn6IwgC/J389fep9tJodfj53A2sPXgFl9PzAJQ2Jh4W0gjjn/CBqx3HtqktBLEO91XMycmBnZ0dsrOzYWvL04lEVEpdpMHEb0/jcHwmzOQClg9pXbNTQKjVgLV16f28PEDFMwK1QZFGi+2nU/HZoatIvpMPALBRKjCykyfGdPaGA0ckrjHl/fzmmRwiov9QKRX4clQ7zPghBj+fu4GpW2JwR12M0Z29pY5GEsgv1uC7E8lYf/ga0nOKAAD1VeZ4pYs3RnT0hK2F2WP2QFJhkUNE9ADmChk+frENHFTm2BiZhIX/i8PtvGLM7NmUl5PqiOyCEnx9LBFfHU3Qj3HTwNYC45/wwbCQRrA0l0uckB6HRQ4R1XnafC1OtzsNAAiOCobcqvTDSyYTsGBAABytlVjx+2V8euAKku7kY9ngVrAwM+wHXH5JPtqtbwcAiHrpIKd1kNCt3CJ8dTQB30QmIa9IAwDwdLDCq90a49kgdygVLG6MBYscIiIRyI/L19//N0EQ8PpTvnC2VeLtHefxv7NpSLmTjy9GBsPZxsJwEUQRcbfi9Pep5qVmFWD9n9ew+WQyijSlg0M2c7HBa90bo19LVyjkMokTUkWxyCGiOk9mIUPrA6319x/khXaN4FHfCq9+ewYxKVkY9OlRbBjVDv5u7LRg7K7dysNnh67ixzOp0Pw9OnFrD3tM7t4ETzV3howD+BktFjlEVOcJcgH1Qh8/Hk6nxo7YOakzXomIwrVMNQZ/dgwrh7ZG7xauNZCSDC0uLQdrD17Bnr9u4O/aBp0aO2BS9ybo1NiBba9MAIscIqIK8HZUYcdrnTHpuzM4ciUTE789g7FdvPFmn+Yw4+UMo3A66S7WHriCff+aeqGHnzNe694EQRz80aQY9W/k7t270b59e1haWqJevXoYNGiQ1JGIyAjpSnRIXZOK1DWp0JXoHru+nZUZwke3w/gnfAAAG44k4MUvjuNGdkF1R6VKEkURR+IzMeyL43h+3THsu5ihn3rhl6ldsWFUOxY4Jshoz+Rs374d48aNw+LFi/Hkk09Co9Hg/PnzUsciIiMkFouInxwPAGgQ1gAox7AnZnIZ3urrh2DPepi19SxOJ91Fv4+PYPWLgejqW/0jJFP5aHUifou9iXUHr+Kv1H+mXniuTUNMDG0Mb069YNKMssjRaDSYOnUqli1bhldeeUW/3N/fX8JURFQX9QpogOYNbPDapjOITcvBiC9P4pUu3pjdq1mFupkLggBPO0/9faqaIo0WO6NT8fmha7iWqQZQOvXCi+1Kp15ws+fUC3WBURY5Z86cQWpqKmQyGdq0aYObN28iMDAQy5YtQ4sWLR66XVFREYqKivSPc3JyaiIuEZk4TwcVtr/aCe//HIdNJ5Lx5ZEE/Hn5Fj56IRAt3Ms3k7mVmRUSpyWWPlCrqy+sicsr0mDziWRsOPLP6MR2lmYY1ckLozp6cuqFOsYoi5xr164BABYsWICVK1fCy8sLK1asQGhoKC5fvoz69es/cLslS5Zg4cKFNRmViOoICzM5Fj3bEk/5OeONbX8hPiMPg9YcxdSnfPFqaGOOsVLNbucVIeJYIjYeS0ROYekAfg1sLTC2qzeGhTSCSmmUH3dURbXqt27OnDkQBOGRt4sXL0KnK20Y+Pbbb+P5559HcHAwwsPDIQgCtm7d+tD9z507F9nZ2fpbSkpKTb01Iqojnmzugr3Tn0CfFg2g0YlY8ftlDFxzFGdTsqSOZpIu3szBm9vOoeOH+/HJ/ivIKdTAx0mFpYNb4c83umNsVx8WOHVYrfqfnzlzJsLCwh65jo+PD27cuAGgbBscpVIJHx8fJCcnP3RbpVIJpZKnKomoetVXmWPt8CD8FJOGeT+dR2xaDgatPYoRHTwxq1ezB07oWFBSgCcingAA/DnkF7DFyMPpdCL2X8zAV0cTcOzqbf3y1h72eLVbY/T0d+EAfgSglhU5Tk5OcHJ6fK+E4OBgKJVKXLp0CV26dAEAlJSUIDExEZ6entUdk4josQRBwKA27ujcxBGL91zAjuhUfB2ZhF/P38SsXs3wfFBDyP/1QawTdTiVdkp/n+6XV6TBtlMpiDiWiMTbpdNwyGUCerdogDGdvRHUyJ6NtqmMWlXklJetrS0mTpyI+fPnw8PDA56enli2bBkAYMiQIRKnIyL6h5ONEh+9EIjBwQ3xzs7zSMhU441t5xBxNBHv9PNDpyaOUkes9WLTsrH5ZDJ2RqfpJ8y0tVBgWPtGGNnRC+7sKUUPYZRFDgAsW7YMCoUCI0aMQEFBAdq3b4/9+/ejXj0O5kREtU/nJo74dVpXfH0sCR/vj0fcjRy8tOEEujdzwrQeTdHEpRyD89Qh6iIN/nc2DZtPJuPs9Wz9ch8nFUZ39sbzQe6wMjfajzCqIYJYh6e7zcnJgZ2dHbKzs2Fry0n2iOoqrVqLw9aHAQBd87pCrir/+DaVcUddjNV/XMa3J5Kh/XvSpK5NbfBtSjcAQN6UdKjqu5SunJcHqOrGgHWiKOJMcha2n7mOn6JToS7WAigdvK9XQAO8FNIIHXwc2N6Gyv35zTKYiKiG1VeZY+HAFgjr7I1P9sdjZ3QqDl2+hXutjQ9dzkBfaSPWqKu38vBTdCp2xqQh+U6+frm3owrDQjzwfFBDjm9DlcIih4hIIt6OKqwcGojXn/TFyj/O4dNLpcunf39WX+Rk55fAzgRP5CTdVmNvbDr+dy4N5/51OcrKXI5eAQ0wtK0HOvjUZ0NiqhIWOUREAMwcpWsT4+2owofPtcJ3HzmiUKOFtcU/f5qfWHYAT7TxwtC2DdGpsWOZHlnGRBRFxKblYG/sTeyNS8fFm7n65+QyAU/4OmJQG3c87e/CtjZkMGyTwzY5RFTL5N3JhrWDPQDAb/o2FJhbAAAcrZXoGeCCPi0aoIOPA8xq+SjKmXlFOHolE0evZOJIfCbSsgv1z8llAtp710fvFg3Qt6UrHHk5iiqgvJ/fLHJY5BBRbaNWA9bWAIDzl1Lxw4U7+CkmDdkFJfpVbC0U6NzEEZ2bOKJLE0d4OlhJemlHFEVcv1uAM8l3EZ2chePXbpc5WwMAlmZydGvqhJ4BLniyuTPsrcwlSkvGjkVOObDIIaJa6V9Fzr3eVcUaHSKv3cav52/i97ibyMwrLrOJi60SrRraI9DDHq0a2qGpiw2cbZTVUvgUabS4dkuNy+m5iE/Pw8WbOYhJybovEwD4udqiSxMHdGriiI4+DhWamZ3oYVjklAOLHCICAG2BFuf6nAMAtPqlFeSWNf9BXFBSgD6b+gAAfhm0DZb1/h79/QFdyLU6EWevZ+HYlUwcuZKJ00l3UaK9/0+5tVIBHycVPB1UcLFRwsXWAs62SthZmkGlVEBlroCluRwCgHtbF2m0yC3UILewBLmFGuQUlOBGdiFuZBciLasAN7ILkZpVoO/6/m9mcgH+bnZo42GPIM966NTYgZehqFqwCzkRUXnpgOxD2fr7kkQQdTiUdEh//1HkMgFBjeohqFE9TH7SFwXFWpxPy8bZlCycvZ6N86nZSLqtRl6RBueuZ5fpvWQoNhYKNHWx+ftmjVYN7RDgZsczNVSrsMghojpPUArw/8Fff9/YWJrL0c6rPtp51dcvK9JokXw7H1dvqZFyJx8ZuYXIyC1Cek4hcgs1yC/WIq9Ig8K/B9wDAAiAuVwGGwsFbCzMYK1UwNZSARdbC7jZW8LVrvRfj3pWcLGtnkthRIbEIoeI6jyZQgbnIc5SxzAopUIOXxcb+LrYSB2FSDK1u/8hERERUSXxTA4R1Xk6jQ6ZOzIBAI7POkKm4Pc/IlPAIoeI6jyxSETc0DgApRN08i8jkWngrzIRUS1hZWYldQQik8Iih4ioFlCZq6B+S136QK2WNgyRieCFZyIiIjJJLHKIiIjIJLHIISKqBQo1hej3XT/0+64fCjWFj9+AiB6LbXKIiGoBrU6LPfF79PeJqOp4JoeIiIhMEoscIiIiMkkscoiIiMgkscghIiIik8Qih4iIiExSne5dJYoiACAnJ0fiJEQkJa1aCzVKRxnOycmBXCuv8QzqYjXwd8/xnJxc6PtX5eQAWva2Ivq3e5/b9z7HH0YQH7eGCbt+/To8PDykjkFERESVkJKSgoYNGz70+Tpd5Oh0OqSlpcHGxgaCIBhsvzk5OfDw8EBKSgpsbW0Ntl+6H491zeBxrhk8zjWDx7lmVOdxFkURubm5cHNzg0z28JY3dfpylUwme2QFWFW2trb8BaohPNY1g8e5ZvA41wwe55pRXcfZzs7useuw4TERERGZJBY5REREZJJY5FQDpVKJ+fPnQ6lUSh3F5PFY1wwe55rB41wzeJxrRm04znW64TERERGZLp7JISIiIpPEIoeIiIhMEoscIiIiMkkscoiIiMgkscippDVr1sDLywsWFhZo3749Tp48+cj1t27diubNm8PCwgItW7bEnj17aiipcavIcV6/fj26du2KevXqoV69eujRo8dj/1/oHxX9mb5ny5YtEAQBgwYNqt6AJqKixzkrKwuTJk2Cq6srlEolmjZtyr8f5VDR47xq1So0a9YMlpaW8PDwwPTp01FYWFhDaY3Tn3/+iWeeeQZubm4QBAE7d+587DYHDx5EUFAQlEolmjRpgoiIiOoNKVKFbdmyRTQ3Nxe/+uorMTY2Vhw3bpxob28vpqenP3D9o0ePinK5XFy6dKkYFxcnvvPOO6KZmZn4119/1XBy41LR4/zSSy+Ja9asEaOjo8ULFy6IYWFhop2dnXj9+vUaTm58Knqs70lISBDd3d3Frl27igMHDqyZsEasose5qKhIbNu2rdi3b1/xyJEjYkJCgnjw4EExJiamhpMbl4oe502bNolKpVLctGmTmJCQIP7222+iq6urOH369BpOblz27Nkjvv322+KPP/4oAhB37NjxyPWvXbsmWllZiTNmzBDj4uLETz75RJTL5eKvv/5abRlZ5FRCSEiIOGnSJP1jrVYrurm5iUuWLHng+kOHDhX79etXZln79u3FCRMmVGtOY1fR4/xfGo1GtLGxETdu3FhdEU1GZY61RqMRO3XqJG7YsEEcNWoUi5xyqOhxXrdunejj4yMWFxfXVESTUNHjPGnSJPHJJ58ss2zGjBli586dqzWnKSlPkfPGG2+IAQEBZZa98MILYq9evaotFy9XVVBxcTFOnz6NHj166JfJZDL06NEDkZGRD9wmMjKyzPoA0KtXr4euT5U7zv+Vn5+PkpIS1K9fv7pimoTKHuv33nsPzs7OeOWVV2oiptGrzHHetWsXOnbsiEmTJsHFxQUtWrTA4sWLodVqayq20anMce7UqRNOnz6tv6R17do17NmzB3379q2RzHWFFJ+FdXqCzsrIzMyEVquFi4tLmeUuLi64ePHiA7e5efPmA9e/efNmteU0dpU5zv/15ptvws3N7b5fKiqrMsf6yJEj+PLLLxETE1MDCU1DZY7ztWvXsH//fgwfPhx79uzBlStX8Nprr6GkpATz58+vidhGpzLH+aWXXkJmZia6dOkCURSh0WgwceJEvPXWWzURuc542GdhTk4OCgoKYGlpafDX5JkcMkkffvghtmzZgh07dsDCwkLqOCYlNzcXI0aMwPr16+Ho6Ch1HJOm0+ng7OyML774AsHBwXjhhRfw9ttv47PPPpM6mkk5ePAgFi9ejLVr1+LMmTP48ccfsXv3brz//vtSR6Mq4pmcCnJ0dIRcLkd6enqZ5enp6WjQoMEDt2nQoEGF1qfKHed7li9fjg8//BB//PEHWrVqVZ0xTUJFj/XVq1eRmJiIZ555Rr9Mp9MBABQKBS5duoTGjRtXb2gjVJmfaVdXV5iZmUEul+uX+fn54ebNmyguLoa5uXm1ZjZGlTnO7777LkaMGIGxY8cCAFq2bAm1Wo3x48fj7bffhkzG8wGG8LDPQltb22o5iwPwTE6FmZubIzg4GPv27dMv0+l02LdvHzp27PjAbTp27FhmfQD4/fffH7o+Ve44A8DSpUvx/vvv49dff0Xbtm1rIqrRq+ixbt68Of766y/ExMTobwMGDED37t0RExMDDw+PmoxvNCrzM925c2dcuXJFX0QCwOXLl+Hq6soC5yEqc5zz8/PvK2TuFZYip3c0GEk+C6utSbMJ27Jli6hUKsWIiAgxLi5OHD9+vGhvby/evHlTFEVRHDFihDhnzhz9+kePHhUVCoW4fPly8cKFC+L8+fPZhbwcKnqcP/zwQ9Hc3Fzctm2beOPGDf0tNzdXqrdgNCp6rP+LvavKp6LHOTk5WbSxsREnT54sXrp0Sfz5559FZ2dn8YMPPpDqLRiFih7n+fPnizY2NuLmzZvFa9euiXv37hUbN24sDh06VKq3YBRyc3PF6OhoMTo6WgQgrly5UoyOjhaTkpJEURTFOXPmiCNGjNCvf68L+ezZs8ULFy6Ia9asYRfy2uqTTz4RGzVqJJqbm4shISHi8ePH9c9169ZNHDVqVJn1f/jhB7Fp06aiubm5GBAQIO7evbuGExunihxnT09PEcB9t/nz59d8cCNU0Z/pf2ORU34VPc7Hjh0T27dvLyqVStHHx0dctGiRqNFoaji18anIcS4pKREXLFggNm7cWLSwsBA9PDzE1157Tbx7927NBzciBw4ceODf3HvHdtSoUWK3bt3u2yYwMFA0NzcXfXx8xPDw8GrNKIgiz8URERGR6WGbHCIiIjJJLHKIiIjIJLHIISIiIpPEIoeIiIhMEoscIiIiMkkscoiIiMgkscghIiIik8Qih4iIiEwSixwiIiIySSxyiIiIyCSxyCEik7Nt2za0bNkSlpaWcHBwQI8ePaBWq6WORUQ1TCF1ACIiQ7px4waGDRuGpUuX4tlnn0Vubi4OHz4MTtNHVPdwgk4iMilnzpxBcHAwEhMT4enpKXUcIpIQL1cRkUlp3bo1nnrqKbRs2RJDhgzB+vXrcffuXaljEZEEeCaHiEyOKIo4duwY9u7dix07duDmzZs4ceIEvL29pY5GRDWIRQ4RmTStVgtPT0/MmDEDM2bMkDoOEdUgNjwmIpNy4sQJ7Nu3Dz179oSzszNOnDiBW7duwc/PT+poRFTDWOQQkUmxtbXFn3/+iVWrViEnJweenp5YsWIF+vTpI3U0IqphvFxFREREJom9q4iIiMgkscghIiIik8Qih4iIiEwSixwiIiIySSxyiIiIyCSxyCEiIiKTxCKHiIiITBKLHCIiIjJJLHKIiIjIJLHIISIiIpPEIoeIiIhMEoscIiIiMkn/D4EJ5GPKKcvFAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(s_space, potential)\n", + "plt.xlabel('s')\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.title('First DBI step')\n", + "plt.ylabel('Least squares cost function')\n", + "plt.legend()\n", + "plt.figure()\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('First DBI step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparison of the least-squares cost function with the original cost function using the polynomial scheduling method" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", + "off_diagonal_norm_diff_least_squares = [dbi.off_diagonal_norm]\n", + "iters = 100\n", + "dbi_ls = deepcopy(dbi)\n", + "cost = DoubleBracketCost.off_diagonal_norm\n", + "dbi_od = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "for _ in range(iters):\n", + " step_poly = dbi_od.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_od(step_poly,d=d)\n", + " step_poly = dbi_ls.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_ls(step_poly,d=d)\n", + " off_diagonal_norm_diff.append(dbi_od.off_diagonal_norm)\n", + " off_diagonal_norm_diff_least_squares.append(dbi_ls.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff_least_squares, label=r'Least squares')\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Energy fluctuation\n", + "\n", + "This cost function is defined as: $\\Xi_k^2 (\\mu) = \\langle \\mu | H_k^2| \\mu \\rangle - \\langle \\mu | H_k| \\mu \\rangle^2$. We must specify the state $| \\mu \\rangle$ for which we want to minimize the fluctuation. The overall diagonalization isn't guaranteed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-04-15 08:49:51]: Using numpy backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 3\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the energy fluctuation cost function\n", + "cost = DoubleBracketCost.energy_fluctuation\n", + "# define the state\n", + "state = np.zeros(2**nqubits)\n", + "state[3] = 1\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.5353581818181817\n", + "100%|██████████| 100/100 [00:01<00:00, 76.45trial/s, best loss: 3.4331110272451792]\n", + "hyperopt_search step: 0.5334935579135086\n", + "polynomial_approximation step: 0.222657579130477\n" + ] + } + ], + "source": [ + "# generate data for plotting sigma decrease of the first step\n", + "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))/2**nqubits\n", + "s_space = np.linspace(-1, 1, 1000)\n", + "off_diagonal_norm_diff = []\n", + "fluctuation = []\n", + "for s in s_space:\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s,d=d)\n", + " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", + " fluctuation.append(dbi_eval.energy_fluctuation(state=state))\n", + "\n", + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", + "print('hyperopt_search step:', step_hyperopt)\n", + "# polynomial\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + "print('polynomial_approximation step:', step_poly)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.5353581818181817\n" + ] + }, + { + "data": { + "image/png": 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WrLCwsNby58+fZ9u3b2dRUVHs5MmTbOTIkcza2pqlpaU99hwfffQRA1BjKygoqFG2tLSUxcbGstLS0n93FhczVpVqNO9WXNzg57GwsJCJxWL266+/qvfl5+czCwsLtnDhQsYYYx4eHmzMmDEaxyUnJzMALCoqijHG2JIlS5i/v79GmXfeeYcBYHl5efXGERgYyJYuXVrrfcuXL2eDBw/W2JeWlsYAsISEhFqPefDgAQPArl+/rhHvl19+qVHO1dWVvffee4+NCwB7//331beLi4sZAPbPP/889pha/xdMxI17+WzCxnPM452/mMc7fzHPxX+x6VsjWNjZ2yw6LZ8VliqYSqViSqWKZRaUsrOJD9iawwls4JqT6mPaLvmbfbjvOissVfB9OcSYPPp5rMVnJDEOBQUFj/3+/i9e+9wMGzZM/XfHjh3Rs2dPeHh4YPfu3XjxxRdrlA8ODkZwcLD6dkhICPz8/LBp0yYsX7681nMsWbIEixYtUt8uLCyEu7u7Dq+Cf7dv30ZFRQV69Oih3mdjYwMfHx+Nct26davzceLi4tCzZ0+NfY8+3/VZsGABXnnlFRw+fBiDBg3C+PHj0bFjRwDAtWvXcOLECXVNzqNu3boFb29vJCYm4sMPP0RERASys7PVNTapqakanZ4fvY6srCzcv38fAwcOrDO26jgAQCaTwdraWr3EAqlSoVTh6+NJ2HAiCZUqBqlIgJkhbTAjpA1cbc1rlOc4wMnaDE7WZujd3gH/G+SFK6l5+Pp4Ek4mPMD28BQcjMnA5xM6oZ+3Iw9XRAgxVbx3KH6Ura0tvL29kZSU1KDyYrEYQUFBdZaXSqWQSqWND8rCAigubvzxT3JeHZPJmnbC8tmzZ2PIkCH4+++/cfjwYaxcuRJr1qzBa6+9huLiYowaNQqffvppjeOq128aNWoUPDw8sGXLFri6ukKlUqFDhw5QKBQa5R+9juqlEurz307HHMfVaO4yZTnF5Xh1xxVEJFc11Q3r4IIPR/mjpU3Dnl+g6jnt6tEC22b1wLmkbLz3+3XcySnBjLCLeG1Aeywc5A2hgAYA1EcpV+KM5RkAQN/ivhDKqGkKAOQKOSxXWwJLafkFUj/e+9w8qri4GLdu3WrwYoVKpRLXr19v2sUNOQ6QyZp/02IUWNu2bSEWi3Hp0iX1voKCAty8eVOrS/Xz88PFixc19l24cEGrx3B3d8fLL7+MvXv34o033sCWLVsAAF26dEFMTAzatGmD9u3ba2wymQw5OTlISEjA+++/j4EDB8LPzw95eXn1ns/Kygpt2rTBsWPHtIqT/Cv2fiGe+eYcIpJzIZMI8fWUIGyc1lWrxOa/erd3wMHX+2Fqz9ZgDPjqeBIW/ByFsgqlDiMnhJDa8ZrcvPnmmzh16hTu3LmD8+fPY+zYsRAKhZgyZQoAYPr06ViyZIm6/Mcff4zDhw/j9u3buHLlCqZNm4aUlBTMnj2br0vQC1ZWVpgxYwbeeustnDhxAjExMXjxxRchEAi0Gir/8ssvIzExEW+99RYSEhKwc+dObNu2rcHHv/766zh06BCSk5Nx5coVnDhxAn5+fgCqOhvn5uZiypQpuHTpEm7duoVDhw5h1qxZUCqVsLOzg729PTZv3oykpCQcP35cozmxLkuXLsWaNWvw1VdfITExEVeuXMHXX3/d4LhNWVRqHiZvDse9/FJ4Osiwb15vjOrkqpPHNhMLsWJsIL54thPEQg5/X0/HzLCLKKQRVXUSWAgQkhWCkKwQCCz06vcnIQaD13fO3bt3MWXKFPj4+GDSpEmwt7fHhQsX4OhY1T6fmpqK9PR0dfm8vDzMmTMHfn5+GD58OAoLC3H+/Hn4+/vzdQl6Y+3atQgODsbIkSMxaNAg9O7dG35+flpNcti6dWv89ttv2LdvHzp16oRvv/0Wn3zySYOPVyqVmDdvHvz8/DB06FB4e3tjw4YNAABXV1ecO3cOSqUSgwcPRmBgIF5//XXY2tpCIBBAIBBg165duHz5Mjp06ID//e9/+Pzzzxt03hkzZuDLL7/Ehg0bEBAQgJEjRyIxMbHBcZuqi8m5mPZdBArLKtHNww77Xu0NL2crnZ9nbFArbJ/VA5ZSES7czsXM7y9CXk7DXR6H4zhIHCWQOEpoHi9CGoljTMsxxwausLAQNjY2KCgogLW1tcZ9ZWVlSE5Ohqenp8HPfCyXy+Hm5oY1a9bU2jmb1M2Y/hdqc+NeAZ7dFA65QomQdvbYMr0bZNKm7YJ3414Bpn4XgYLSCvRub4+tM7rDTEz9SUjDyBVyWK6sGpBQvAKQ5RVXNeETk1HX9/d/UZ2nkYiKisLPP/+MW7du4cqVK5g6dSoAYPTo0TxHRvRNSo4cM8MuQq5QIritPb6f2b3JExsA6OBmg+0v9IBMIsS5pBws3BUFlcqkfls1iKpchZvzbuLmvJtQlVOnd0Iag5IbI1I9Sd+gQYMgl8tx5syZemd81kb17MK1bdo0XxH+5JcoMOP7i8guVsCvpTU2T+/arLUnnd1tsXVmd0hEAhyKycSaIwn1H2RiWCXD/Q33cX/DfbBKSv4IaQy9GgpOGi8oKAiXL19u0nN89913KC0trfW+Fi1aNOm5yZNTqhgW7LqKOzklcLM1x/ZZ3WFl1vzrcvVqa49Pxwfif79cw/oTt+DtbIXRnd2aPQ5iWIQCIYZ7DgEOHaLlF0i9KLkhDebmRl9AhuyLIzdx+uYDmIkF2DK9G5ys+etLNDaoFRIyqtasentPNHxcrODrUncbOjFtZiIz/D3+N2BGzYlACfkvapYixAQci8vENyeqJrtcNa4j/F35TyTeGuKDUB9HlFeqMH9nFEoUNIKKEKIblNwQYuQeFJXj7T3RAICZIW0wJkg/auCEAg5rJnaCk5UUSVnF+PjPWL5DIoQYCUpuCDFijDG881s0cuQK+LpYYclwX75D0mBvKcWXz3YGxwG7LqXhn+vp9R9ETJJcIYfsSyfI3gXkzd9VjBgYSm4IMWI7IlJxPD4LEqEAX07uDKlI/+aVCWnvgFf6twMAfPDHDeTKFfUcQUxVSWUJSiR8R0EMASU3hBiplBw5/u/vqqaet4f66HWH3YWDvODtbInsYgWW/RnDdziEEANHyY2RCA0Nxeuvv853GERPMMbw/r4bKKtQoVfbFnihtyffIdVJKhLi8wmdIOCAP67ex5HYTL5DIoQYMEpuiEFYunQpOnfuzHcYBmP/tfs4k5gNiUiAleM6QiDQ/zWKOrnbYk6/tgCApftjUKqgFcQJIY1DyQ1pMkqlEioVTR/f3PJLFOqRR68NaA9PB8NZf+f1gd5wszXHvfxSrH84dJ0QQrRFyY0RUalUePvtt9GiRQu4uLhg6dKlAIAXXngBI0eO1ChbUVEBJycnbN26FUBVs9b8+fMxf/582NjYwMHBAR988AEeXVe1vLwcb775Jtzc3CCTydCzZ0+cPHlSff+2bdtga2uL/fv3w9/fH1KpFKmpqcjLy8P06dNhZ2cHCwsLDBs2TGPV7urj9u3bBy8vL5iZmWHIkCFIS0tT379s2TJcu3YNHMeB4zhs27ataZ5EI7Dqn3jkyBVo72SJlx521DUU5hIhPhjpDwDYfPo2krPlPEdECDFElNw0kFwhf+xWVlnW4LKlFaX1lm2s7du3QyaTISIiAp999hk+/vhjHDlyBLNnz8bBgweRnv7vMNu//voLJSUlePbZZzWOF4lEuHjxItatW4e1a9fiu+++U98/f/58hIeHY9euXYiOjsbEiRMxdOhQjUSlpKQEn376Kb777jvExMTAyckJM2fORGRkJPbv34/w8HAwxjB8+HBUVFRoHLdixQr88MMPOHfuHPLz8zF58mQAwLPPPos33ngDAQEBSE9PR3p6ukbc5F837hXgl8iqpPCTsYGQiAzvLT4kwBn9vB2hUKqwdH+MRoJtEgSATX8b2PS3oU/oRwg4Afq36oP+dwCBif1LkEZgJqagoIABYAUFBTXuKy0tZbGxsay0tLTGfViKx27DdwzXKGuxwuKxZfuH9dco6/CZQ40yjdG/f3/Wp08fjX3du3dn77zzDmOMMX9/f/bpp5+q7xs1ahSbOXOmxvF+fn5MpVKp973zzjvMz8+PMcZYSkoKEwqF7N69exrnGDhwIFuyZAljjLGwsDAGgF29elV9/82bNxkAdu7cOfW+7OxsZm5uznbv3q1x3IULF9Rl4uLiGAAWERHBGGPso48+Yp06ddL+iWmkuv4X9JVKpWITvz3PPN75iy34+Qrf4TyRW1lFrP27fzOPd/5iR2Iy+A6H6IviYsaAqq24mO9oSDOr6/v7v+h3gRHp2LGjxu2WLVsiKysLADB79myEhYUBADIzM/HPP//ghRde0Cjfq1cvcNy/HU+Dg4ORmJgIpVKJ69evQ6lUwtvbW2M18FOnTuHWrVvqYyQSiUYccXFxEIlE6Nmzp3qfvb09fHx8EBcXp94nEonQvXt39W1fX1/Y2tpqlCF1O3gjAxeTc2EmFuCdofo1WZ+22jpa4oU+VSO8Pj0Yj0ol9d0ihDQcLZzZQMVLih97n1CgOTFa1ptZjy0r4DTzyTsL7zxRXI8SizWn7eQ4Tt2hd/r06Vi8eDHCw8Nx/vx5eHp6om/fvg1+7OLiYgiFQly+fBlCoeb1Wlr+u5Cdubm5RoJEmkdZhRKf/FOVCM7t1w6utuY8R/TkXg1tj18upSExqxi/XbmLZ7u35jskQoiBoOSmgWSSho84aaqyT8Le3h5jxoxBWFgYwsPDMWvWrBplIiIiNG5fuHABXl5eEAqFCAoKglKpRFZWllZJkZ+fHyorKxEREYGQkBAAQE5ODhISEuDv768uV1lZicjISPTo0QMAkJCQgPz8fPj5+QGoqhFSKmlo8ONsP38HabmlcLaW4uX+bfkORydszMWYP6A9/u/vOKw9chPPdHKDuUT/ZljWNaVciQttLgAAet3pBaHM+K+5IeQKOdqs9wDeAu58CRjOGEDCB2qWMiGzZ8/G9u3bERcXhxkzZtS4PzU1FYsWLUJCQgJ+/vlnfP3111i4cCEAwNvbG1OnTsX06dOxd+9eJCcn4+LFi1i5ciX+/vvvx57Ty8sLo0ePxpw5c3D27Flcu3YN06ZNg5ubG0aPHq0uJxaL8dprryEiIgKXL1/GzJkz0atXL3Wy06ZNGyQnJ+Pq1avIzs5GeXm5jp8dw1VYVoGNp6qaBt8c7AMLifH8Znk+2ANutubILCzH9+eS+Q6n2VRkV6Aiu6L+giYmuzQH2ZTVkAag5MaEDBo0CC1btsSQIUPg6upa4/7p06ejtLQUPXr0wLx587Bw4ULMnTtXfX9YWBimT5+ON954Az4+PhgzZgwuXbqE1q3rbi4ICwtD165dMXLkSAQHB4MxhgMHDmg0o1lYWOCdd97Bc889h969e8PS0hK//PKL+v7x48dj6NChGDBgABwdHfHzzz/r4BkxDt+dSUZ+SQXaO1liXJdWfIejU1KREG8O8QYAfHvyFgpKjP8LX2AuQPcb3dH9RncIzOkjmpDG4BgzrXGWhYWFsLGxQUFBAaytNdfaKSsrQ3JyMjw9PWFmZsZThE2nuLgYbm5uCAsLw7hx4zTuCw0NRefOnfHll182e1zbtm3D66+/jvz8/GY/9+MYyv9CrlyBvp8eh1yhxIapXTA8sCXfIemcSsUw/KsziM8owoKBXlj0tDffIREeyBVyWK6s6t9XvAKQ5RUDMqrGMSV1fX//F/0sMAEqlQpZWVlYvnw5bG1t8cwzz/AdEtGRjSeTIFcoEeBqjaEBLnyH0yQEAg4LB3oBAMLOJptE7Q0h5MlQcmMCUlNT4ezsjJ07d+L777+HSGQ8fTJMWWZhGX4ITwFQ1dfGENaPaqwhAS7wcbZCUXklthp53xuVQoXkpclIXpoMlYKGwBPSGJTcmIA2bdqAMYa0tDQMHDiw1jInT57kpUkKAGbOnKlXTVKG4pvjSSivVKGrhx1CfRz5DqdJCQQcFg56WHtzLhkFpcZbe8MqGFKWpSBlWQpYhUn1GiBEZyi5IcQAZRaW4ZdLVcssvDHY2yTmFhpaXXtTVonvzxp37Q2pScAJ0M25C7rdo+UXSP0ouSHEAH135jYUyqpam+C29nyH0ywEAg4LHva92Xb+DuTllTxHRJqTudgcl54/jUtbAHN66Uk9KLkhxMDkyRXYEZEKAJg/oL1J1NpUG9rBBW3sLVBQWoHdDxcIJYSQ/6LkhhADE3b+DkoUSvi3tDb6vjb/JRRwmNOvagbm784k05pThJBaUXJDiAEpKqvAtoejheaZWK1NtfFdWsHBUoJ7+aX4+3o63+GQZlJSUYI2m/3R5nWgRFxvcWLiKLkhxIDsiEhFYVkl2jrKMLSDcc5rUx8zsRAzgtsAADadug0Tm4fUZDHGkFKYihRbgF5xUh9KbgiAqlmCbW1t+Q6jQZYuXYrOnTtrdQzHcdi3b1+TxNNcyiuV2PpwlNCroe0hNOJ5berzfLAHzMVCxKYX4mxSNt/hEEL0DCU3xOC8+eabOHbsGN9hNLs/r6XjQVE5XKzNMLpzzbXBTImthQSTe7gDqKq9IYSQR1FyQwyOpaUl7O1NY/hzNcYYvjtT9SU+I6QNxEJ6677YxxMCDjiblI2bmUV8h0MI0SP0CWkkQkNDMX/+fMyfPx82NjZwcHDABx98oO6PkJeXh+nTp8POzg4WFhYYNmwYEhMTa32sO3fuQCAQIDIyUmP/l19+CQ8PD6hUKpw8eRIcx+HYsWPo1q0bLCwsEBISgoSEBI1jNm7ciHbt2kEikcDHxwc//vijxv0cx2HTpk0YOXIkLCws4Ofnh/DwcCQlJSE0NBQymQwhISG4deuW+pj/NktdunQJTz/9NBwcHGBjY4P+/fvjypUrT/J06p3wWzmIzyiCuViI53rUvQq7qWhlZ4HB/lX9jrafv8NvMIQQvULJTQMp5UqtN1Xlv8NUVZWqqv2lynoft7G2b98OkUiEixcvYt26dVi7di2+++47AFVLHERGRmL//v0IDw8HYwzDhw9HRUXNaezbtGmDQYMGISwsTGN/WFgYZs6cCYHg33+b9957D2vWrEFkZCREIhFeeOEF9X2///47Fi5ciDfeeAM3btzASy+9hFmzZuHEiRMaj7t8+XJMnz4dV69eha+vL5577jm89NJLWLJkCSIjI8EYw/z58x973UVFRZgxYwbOnj2LCxcuwMvLC8OHD0dRkfH8mv/uYV+bid1awcaChopUmxHSBgCw98o9o16SgRCiHVpBsYHOWJ7R+hj/3f5wmugEAMj+PRuxk2Jh098GQSeD1GUutLmAimzND+VQFtqoGN3d3fHFF1+A4zj4+Pjg+vXr+OKLLxAaGor9+/fj3LlzCAkJAQDs2LED7u7u2LdvHyZOnFjjsWbPno2XX34Za9euhVQqxZUrV3D9+nX88ccfGuVWrFiB/v37AwAWL16MESNGoKysDGZmZli9ejVmzpyJV199FQCwaNEiXLhwAatXr8aAAQPUjzFr1ixMmjQJAPDOO+8gODgYH3zwAYYMGQIAWLhwIWbNmvXY637qqac0bm/evBm2trY4deoURo4cqe3TqHduPSjG8fgscBwwq7cn3+HolV5tW8DH2QoJmUX4NTINs/u25TukJ8cBFv4W6r9JFY7j4G/vC8TF09NC6kU1N0akV69eGvOeBAcHIzExEbGxsRCJROjZs6f6Pnt7e/j4+CAuLq7WxxozZgyEQiF+//13AFWjqQYMGIA2bdpolOvYsaP675YtWwIAsrKyAABxcXHo3bu3RvnevXvXOOejj+Hs7AwACAwM1NhXVlaGwsLCWmPNzMzEnDlz4OXlBRsbG1hbW6O4uBipqam1ljc01esoDfR1hqeDjOdo9AvHceramx8vpEClMvxBwkILIXrE9ECPmB4QWgj5DkdvWIgtEDMrEjEbAAuqpCP1oJqbBupb3FfrYzjpv4mGw1iHqsf4TzrZ606vJw2tSUgkEkyfPh1hYWEYN24cdu7ciXXr1tUoJxb/20RSnVipVNrNGlvbY2jzuDNmzEBOTg7WrVsHDw8PSKVSBAcHQ6FQaBWHPsqTK/DblbsAqjrQkprGBLli1T9xSMkpwcmbWXjK15nvkAghPKOamwYSyoRabwLRv0+vQCSo2m8urPdxGysiIkLjdnX/E39/f1RWVmrcn5OTg4SEBPj7+z/28WbPno2jR49iw4YNqKysxLhx47SKx8/PD+fOndPYd+7cuTrP2Rjnzp3DggULMHz4cAQEBEAqlSI72zjmPtl5MRVlFSoEuFqjV9sWfIejlywkIjzbvWpY+LbzKTxHQwjRB5TcGJHU1FQsWrQICQkJ+Pnnn/H1119j4cKF8PLywujRozFnzhycPXsW165dw7Rp0+Dm5obRo0c/9vH8/PzQq1cvvPPOO5gyZQrMzc21iuett97Ctm3bsHHjRiQmJmLt2rXYu3cv3nzzzSe9VA1eXl748ccfERcXh4iICEydOlXrWPWRUsWw8+ECmTND2pjkUgsNNT24DTgOOH3zAe5ky/kO54koS5S4GHARFwMuQlnS+AEGxqakogQBYd0Q8Cotv0DqR8mNEZk+fTpKS0vRo0cPzJs3DwsXLsTcuXMBVI106tq1K0aOHIng4GAwxnDgwAGN5p/avPjii1AoFBqjoBpqzJgxWLduHVavXo2AgABs2rQJYWFhCA0NbczlPdbWrVuRl5eHLl264Pnnn8eCBQvg5OSk03Pw4UR8Fu7ll8LWQoxRnUx70r76uLewQH/vqkVEd10y8NXCGVASW4KS2BJaZ+ARjDHE5sQj1omeFlI/jpnYwiyFhYWwsbFBQUEBrK2tNe4rKytDcnIyPD09YWZmxlOEjRMaGorOnTvjyy+/1OnjLl++HL/++iuio6N1+rj6Th/+F2Z8fxGnbj7AS/3aYslwP15iMCQHb2Tg5Z8uw8FSgvOLB0IiMszfbkzJkH8mHwBg29cWnJBq7ABArpDDcqUlAKB4BSDLKwZk1MHelNT1/f1fhvnuJ02uuLgYN27cwDfffIPXXnuN73BMTkqOHKduPgDHAc/1pEn7GmKgnxMcraTILlbgWFwm3+E0GifkYBdqB7tQO0psCGkkSm5IrebPn4+uXbsiNDS0UU1S5MnseNjXpr+3Izzs6ddpQ4iFAkzs2goA8LOhN00RQp4IDQU3EidPntTp423btg3btm3T6WOShimrUGJ3ZNWX8/O9PHiOxrBM7t4aG07ewpnEB0jLLYF7Cwu+Q9KaqkKF9M3pAICWc1tCIKbfoIRoi941hOiZP6/dR35JBdxszRHqY/gdo5tTa3sL9GnvAMagThANDVMwJM5PROL8RDCFSXWJJERnKLmphYn1sSa14PN/4KcLVXO1TO3VGkIB9bnQ1pSHC4vujkxDpVK7CSWJ/uI4Dh7WreGRT6tSkPpRcvOI6mHRJSUlPEdC+Fb9P1DfUHldu3GvANfuFkAiFGBSN/dmPbexeNrfGfYyCTILy3Ei4QHf4RAdsRBb4M7cWNz5kpZfIPWjPjePEAqFsLW1Va+NZGFhQROnmRjGGEpKSpCVlQVbW1sIhc27ts8vDzvCDg5whoOltFnPbSwkIgEmdG2FTadvY9fFVDztT8sxEGJqKLn5DxcXFwD/Lv5ITJOtra36f6G5lCqU2Hf1HoCqjrGk8SZ2c8em07dx8uYDPCgqh6MVJYqEmBJKbv6D4zi0bNkSTk5OqKiguk9TJBaLm73GBgD+uZGOorJKtLIzR0g7+2Y/vzFp72SJTu62uJaWjz+u3sPsvm35Dok8odKKUvT7sR8wBzgdBhj+AiukKVFy8xhCoZCXLzhiuqqbpJ7t5g4BdSR+YhO6uOFaWj5+u0LJjTFQMRUiM68AboCK3h6kHtShmBA9cPtBMSKScyHggAndWvEdjlEY1ckVEqEAcemFiL1fyHc4hJBmRMkNIXpgd+RdAECojxNa2lCFuy7YWkgw0K9qnqC9V+7yHA0hpDlRckMIzyqUKuy5XPXlS8O/dWtcl6pasH1X79OcN4SYEEpuCOHZ8fgsZBeXw8Hy35oGohuhPo6wl0mQXVyO04k05w0hpoKSG0J4tvthR+LxXVtBLKS3pC6JhQI809kVAPDb5Xs8R0MIaS70SUoIjzIKynAioWpOpWepSapJjH/YNHUkNhMFJYYxvYPYQQyxQ/POjm0IHMzt4SDnOwpiCGgoOCE82nM5DSoG9PBsgbaOlnyHY5QCXK3h62KF+Iwi/H09Hc/11O8JEoUyIXo/6M13GHpHJpHhwbwUwJLeJ6R+VHNDCE8YY/jtSlVTCXUkbjocx2F0ZzcAwP5r1DRFiCmg5IYQnlxJzUdythwWEiGGdWjepR5MzahOLQEAEcm5yCgo4zkaQkhTo+SGEJ789nDulaEdXCCTUgtxU2plZ4FuHnZgDPgr+j7f4dRJWapEVGgUokKjoCxV8h2O3iitKEXorqEInQmU0tuF1IOSG0J4UFahxF/Xqr5kJ3ShGYmbQ/Woqf3X9Du5gQooOFWAglMFAE3No6ZiKpy6exan2tDyC6R+lNwQwoNjcVkoLKuEq40ZerWlRTKbw/DAlhAKOETfLUBytv4OueGkHPx3+8N/tz84KX2LE9IYlNwQwoPqJqmxXdxokcxm4mApRe/2DgCA/Vf1t/ZGIBLAaaITnCY6QSCij2hCGoPeOYQ0swdF5Th1s2q23HHUJNWsRneqapr649o9MMZ4joYQ0lQouSGkmf1x9R6UKobO7rZoR3PbNKvBAc6QigS4/UCOGD1dKVxVqULWr1nI+jULqkrqdENIY1ByQ0gzq57bZnwXN54jMT1WZmL1+l1/6mnHYlbOEDspFrGTYsHKqXaJkMag5IaQZhR7vxBx6YUQCzmMethEQprXM53+HTWlUlHyYEgsRBawUPAdBTEENFsAIc1o78OOxAN9nWFrIeE5GtMU6uMEK6kI6QVliEzJQw/PFnyHRBpAJpFB/noWLb9AGoTXmpulS5eC4ziNzdfXt85jfv31V/j6+sLMzAyBgYE4cOBAM0VLyJOpVKqw7+EonfFdqSMxX8zEQgwOqJoR+m89n9CPENI4vDdLBQQEID09Xb2dPXv2sWXPnz+PKVOm4MUXX0RUVBTGjBmDMWPG4MaNG80YMSGNcyYxG9nF5WghkyDUx5HvcEzaiI5Vyc0/NzKoaYoQI8R7ciMSieDi4qLeHBwcHlt23bp1GDp0KN566y34+flh+fLl6NKlC7755ptmjJiQxtkbVdWR+JlOrhALeX/rmbTe7R1gJRUhq6gcl1Pz+A6HNEBZZRlG/DYeI54DyqhDBakH75+wiYmJcHV1Rdu2bTF16lSkpqY+tmx4eDgGDRqksW/IkCEIDw9/7DHl5eUoLCzU2AhpbvLyShyJzQAAjA2iUVJ8k4qEeNrfGQDwd3Q6z9GQhlCqlDiQfAgHvAElzXtJ6sFrctOzZ09s27YNBw8exMaNG5GcnIy+ffuiqKio1vIZGRlwdnbW2Ofs7IyMjIzHnmPlypWwsbFRb+7u7jq9BkIa4khsJsoqVGhjb4GOrWz4DoegajkGADhITVOEGB1ek5thw4Zh4sSJ6NixI4YMGYIDBw4gPz8fu3fv1tk5lixZgoKCAvWWlpams8cmpKH+uPqwSaqzGziOfnbqg77eDrCUipBRWIaoNGqaIsSY8N4s9ShbW1t4e3sjKSmp1vtdXFyQmZmpsS8zMxMuLi6PfUypVApra2uNjZDmlFNcjtOJ2QCA0Z1pbht9IRUJMejhhH5/Rz++9pcQYnj0KrkpLi7GrVu30LJly1rvDw4OxrFjxzT2HTlyBMHBwc0RHiGNcuB6OpQqhkA3G1puQc9UN039cyOdmqYIMSK8JjdvvvkmTp06hTt37uD8+fMYO3YshEIhpkyZAgCYPn06lixZoi6/cOFCHDx4EGvWrEF8fDyWLl2KyMhIzJ8/n69LIKRefzyc24ZqbfRPP29HWD6c0O/q3Xy+wyGE6Aivyc3du3cxZcoU+Pj4YNKkSbC3t8eFCxfg6Fg1B0hqairS0/8dyRASEoKdO3di8+bN6NSpE/bs2YN9+/ahQ4cOfF0CIXVKyy1BZEoeOA4Y2ZGSG31jJhaq15o6QKOmCDEaHGPMpOpiCwsLYWNjg4KCAup/Q5rchpNJ+OxgAoLb2uPnub34DofU4uCNDLz802W42Zrj7DsDqMO3PpPL/11+obgYkMn4jYc0K22+v/Wqzw0hxmY/NUnpvVAfR8gkQtzLL8W1uwV8h0MI0QFKbghpIvEZhYjPKIJEKMCwDrV3kif8MxML8ZRf1fxZB65T0xQhxoCSG0KaSHVH4lAfR9hYiHmOhtRleIeq6SQOXE8H3y31yjIlYibGIGZiDJRlSl5j0SdllWWYuH8aJk6k5RdI/Si5IaQJqFTskSYpWm5B34X6OMFMLMDdvFLEpdc+Q3qzUQIP9jzAgz0PAMpt1JQqJfbc3Ic9AbT8AqkfJTeENIErqXm4l18KmeTf0ThEf5lLhOjnVTVK81AMvxP6cRIOXt94wesbL3AS+hYnpDEouSGkCVQ3SQ3p4AIzsZDnaEhDDA6oapo6HJtZT8mmJRAL4DbPDW7z3CAQ00c0IY1B7xxCdKxCqcLfDzumUpOU4Rjo6wShgENceiHSckv4DocQ8gQouSFEx84mZSNXroCDpQS929nzHQ5pIDuZBD3atADAb9MUUzLkncxD3sk8MKVJTUNGiM5QckOIjv0RVbUC+MiOrhAJ6S1mSIYEVA0JPxzDX9OUqkyFawOu4dqAa1CVqXiLgxBDRp+8hOhQiaJS3WfjGZq4z+BU97u5lJKL7OJynqMhhDQWJTeE6NDRuCyUKJRwb2GOIHdbvsMhWnK1NUegmw0YA47F8duxmGiyEFugeEEmilcAFhV8R0P0HSU3hOjQ/qtVTVKjO7nRGkUGqrpp6hCPTVOkJo7jIJPIIKsA6J1F6kPJDSE6kidX4GTCAwDAmCBqkjJU1U1TZ5OyUVxeyXM0hJDGoOSGEB3550YGKlUM/i2t0d7Jiu9wSCN5OVnC00EGRaUKpx4mq4R/5ZXlmPnPS5g5BiinqaNIPSi5IURH/qhukqKOxAaN4zgMVjdN8TtbMflXpaoS22N2YHtnoJK+uUg96F+EEB24n1+KiORcAMCoTpTcGLrB/lVNUyfis6CopOHYhBgaSm4I0YE/r1Utt9DDswVcbc15joY8qSB3WzhaSVFUXonw2zl8h0MI0VKjFo4/duwYjh07hqysLKhUmr9qvv/+e50ERogh+UO9AjjV2hgDgYDD0/7O2BmRisMxGejv7ch3SIQQLWhdc7Ns2TIMHjwYx44dQ3Z2NvLy8jQ2QkxNYmYRYtMLIRZyGN6hJd/hEB0Z8nDU1JHYTKhUtAwCIYZE65qbb7/9Ftu2bcPzzz/fFPEQYnD2P2yS6u/tCDuZhOdoiK70atsCMokQWUXluHG/AB1b2fIdEiGkgbSuuVEoFAgJCWmKWAgxOIwxdZPUM7QCuFGRioTo97A56mhcFs/REEK0oXVyM3v2bOzcubMpYiHE4ESl5SM1twQWEiEG+TnxHQ7RsYF+VUPCj8Y232zFAgsBQrJCEJIVAoEFjfmoZiG2QNarycj6jJZfIPXTulmqrKwMmzdvxtGjR9GxY0eIxWKN+9euXauz4AjRd/sf1to87e8MC0mj+ucTPTbAxxECDohNL8T9/NJmGQnHcRwkjtS8+V8cx8HRwhEo4TsSYgi0/jSOjo5G586dAQA3btzQuI/W0iGmpFKpwl/RVcnNGGqSMkr2llJ0aW2HyJQ8HIvPwvO9PPgOiRDSAFonNydOnGiKOAgxOOdv5SC7WIEWMgn6eDnwHQ5pIgP9nKuSm7jMZkluVOUqJC1KAgC0X9seAik1TQFVyy8sOvo/YDiw9hAg5Tsgotee6F1z9+5d3L17V1exEGJQqjsSjwhsCbGQvoCMVXVfqvNJOZA3w0KarJLh/ob7uL/hPlglDUGvVqmqxIarW7ChBy2/QOqn9b+ISqXCxx9/DBsbG3h4eMDDwwO2trZYvnx5jQn9CDFWZRVK9bpDNHGfcWvvZAkPewsolCqcScxu8vNxYg4eH3nA4yMPcGJq6iekMbRulnrvvfewdetWrFq1Cr179wYAnD17FkuXLkVZWRlWrFih8yAJ0TfH47NQXF4JN1tzdGltx3c4pAlxHIeBvs74/lwyjsVlYmgHlyY9n0AigOdSzyY9ByHGTuvkZvv27fjuu+/wzDPPqPd17NgRbm5uePXVVym5ISahegXwZzq7QiCgX9fGbpCfE74/l4zj8VlQqhiE9JoTote0bpbKzc2Fr69vjf2+vr7Izc3VSVCE6LOC0gqciH8AgJqkTEV3zxawMhMhR67A1bT8Jj0XUzHIY+SQx8jBaNkHQhpF6+SmU6dO+Oabb2rs/+abb9CpUyedBEWIPjt0IwMKpQo+zlbwdbHmOxzSDMRCAUJ9qjoWH4tr2gn9VKUqXOpwCZc6XIKqlPoxEtIYWjdLffbZZxgxYgSOHj2K4OBgAEB4eDjS0tJw4MABnQdIiL7549q/TVLEdAzyc8Kf1+7jWFwW3h5as/aaEKI/tK656d+/P27evImxY8ciPz8f+fn5GDduHBISEtC3b9+miJEQvZFVWIbzt3IAAM90ouTGlIR6O0Eo4JCQWYS0XJomt7mZi82RPCcGyV8C5k0/Ip8YuEbNF+/q6kodh4lJ+jM6HYwBXT3s4N7Cgu9wSDOysRCjexs7XLidi6NxmZjVm0Y0NScBJ0AbGw8gn+9IiCFoUHITHR2NDh06QCAQIDo6us6yHTt21ElghOij/Q9HSVFHYtM0yM8ZF27n4lhcFiU3hOixBiU3nTt3RkZGBpycnNC5c2dwHAfGavbi5zgOSqVS50ESog+Ss+W4drcAQgGH4YEt+Q6H8GCgnzP+7+84XLidg8KyClibies/iOiEQqnAeyffA54GVhwHaGlRUpcGJTfJyclwdHRU/02IKaqe26ZPewc4WNLKNqbI00GGto4y3H4gx+mbDzCyI9XgNZcKZQVWR64DegNLT1JyQ+rWoA7FHh4e6hW/U1JS4Obmpl56oXpzc3NDSkpKkwZLCF8YY9j/cC0papIybU/7OQMAjsVl8RwJIeRxtB4tNWDAgFon6ysoKMCAAQN0EhQh+ubGvULczpZDKhJgcEDTTr9P9NtTvlXz3ZxMqJqtmBCif7RObhhj6lqcR+Xk5EAmk+kkKEL0TXWT1CB/Z1hKGzXIkBiJrh52sDYTIa+koslnKyaENE6DP6XHjRsHoKrT8MyZMyGV/tvnQKlUIjo6GiEhIbqPkBCeKVUMf0Y/bJKiuW1MnkgoQD9vR/wVnY4T8Vno6kELpxKibxpcc2NjYwMbGxswxmBlZaW+bWNjAxcXF8ydOxc//fRTU8ZKCC8iknOQWVgOazMR+vs48h0O0QMDHi7FcDye+t0Qoo8aXHMTFhYGAGjTpg3efPNNaoIiJuOPqKpamxEdW0IqEvIcDdEHoT6O4DggNr0QGQVlcLEx4zskQsgjtO5z89FHH1FiQ0xGWYUSB66nAwCe6eTGczREX9hbStGplS2Aqo7FuiQwF6D7je7ofqM7BOZaf0QbLXOxOW7MvIgb62n5BVK/RvWM3LNnD3bv3o3U1FQoFAqN+65cuaKTwAjRB0fjMlFUXgk3W3P09GzBdzhEjzzl64Srafk4Hp+FyT1a6+xxOQEHWQD9gPwvASdAgIM/8IDvSIgh0PpnwVdffYVZs2bB2dkZUVFR6NGjB+zt7XH79m0MGzasKWIkhDd7r1SNkhob5AaBoOYoQWK6qoeEn03KRnklzcxOiD7ROrnZsGEDNm/ejK+//hoSiQRvv/02jhw5ggULFqCgoKApYiSEFw+KynHqZtXPxLFdqEmKaPJvaQ1HKylKFEpcTK4591djqRQqJC9NRvLSZKgUKp09rqFTKBVYem4FloYCCur6RuqhdXKTmpqqHvJtbm6OoqIiAMDzzz+Pn3/+WbfREcKjP6/dh1LF0MndFu0cLfkOh+gZgYDDgIej53Q5aopVMKQsS0HKshSwCpoksFqFsgLLwldiWShQQV2RSD20/hdxcXFRz1DcunVrXLhwAUDVmlO1LaZJiKHaG3UXADAuiGptSO3+na1Ydx1BOBEH11dd4fqqKzgRNYUS0hhadyh+6qmnsH//fgQFBWHWrFn43//+hz179iAyMlI90R8hhu5mZhFu3CuESMBhFE3cRx6jj5cjxEIOydlyJGfL4enw5B2BBVIBvNd76yA6QkyX1snN5s2boVJVtQPPmzcP9vb2OH/+PJ555hm89NJLOg+QED5UdyQO9XFCCxmtP0xqZykVoYdnC5xLysHx+Cy82MeT75AIIWhEciMQCCAQ/NuaNXnyZEyePFmnQRHCJ6WKqdeSGk8diUk9Bvg44VxSDk7oKLlhjKEiuwIAIHYQ17qWHyGkblonN6dPn67z/n79+jU6GEL0wYXbOUgvKIO1mQhP+TnxHQ7RcwN8nfB/f8chIjkHxeWVT7ywqqpEhfNO5wEAfYv7QiijoUGEaEvrd2FoaGiNfY/+slAqab4HYtiqm6RGdnKl5RZIvdo6yOBhb4GUnBKcS8rGkAAXvkMixORpPVoqLy9PY8vKysLBgwfRvXt3HD58uCliJKTZlCgq8c+NquUWaJQUaQiO49QLaZ6ghTSbjJnIDBennsLFzYAZLb9A6qF1zY2NjU2NfU8//TQkEgkWLVqEy5cv6yQwQvhwOCYTJQolWrewQFcPO77DIQbiKV8nbDt/BycSssAYo34yTUAoEKJ7y67Afb4jIYZAZ1MhOTs7IyEhQVcPRwgv9lyumttmbJAbfUGRBuvZtgXMxUJkFpYj5n4h3+EQYvK0rrmJjo7WuM0YQ3p6OlatWoXOnTvrKi5Cml1abgnOJmUDACZ0bcVzNMSQSEVC9G7vgKNxmTgRn4UObjVruMmTUSgVWHfxSyAEWBgB0AQNpC5aJzedO3cGx3E1ZiPu1asXvv/+e50FRkhzq6616d3eHu4tLHiOhhiap3ydcDQuE8cTsvDaQC++wzE6FcoKvH36fWAw8OolSm5I3bRObpKTkzVuCwQCODo6wszMTGdBEdLcVCqmTm4mdXPnORpiiAb4Vq0zdTUtH7lyBU3+SAiPtO5zc+rUKbi4uMDDwwMeHh5wd3eHmZkZFAoFfvjhh6aI0eAwxnAvvxSRd3JxLikbEbdzkJwth6KSVvjVV+dv5eBefimszEQ0lJc0Sksbc/i1tAZjwKmbNGqKED5pXXMza9YsDB06FE5OmpObFRUVYdasWZg+fbrOgjMkWUVlOBaXhWNxWbhwu2oyr/+SiAQIdLNBqLcjRnZy1ck6NEQ3folMAwCM6ewGMzHNbUMa5ylfR8SlF+J4/AOMDaJ+W4TwRevk5nHDHO/evVvrMHFT8Xd0Opb9Gau+LRJwcLU1h7lYCIVShczCMpQolLickofLKXlYc+Qmere3x9x+7dDPy4FG5vAov0SBQzEZAKhJijyZp3ydsP7ELZxKyEKlUgWRUGcDUgkhWmhwchMUFASO48BxHAYOHAiR6N9DlUolkpOTMXTo0CYJ0hAM9HXGH1fvY6CvEwb4OsHHxQriRz7YGGO4k1OCi8k5OHA9A2eTsnEuKQfnknLQq20LfDgyAP6u1jxegenaf+0+FJUq+LW0Rgc3eg1I43V2t4OthRj5JRW4kpqPHp4t+A6JEJPU4ORmzJgxAICrV69iyJAhsLS0VN8nkUjQpk0bjB8/XucBGorW9hbYN6/3Y+/nOA6eDjJ4OsjwbPfWuJdfiq1nkrEjIgUXbudi5NdnMKu3J94a4kPNIs3sl0tVTVKTurWiGjTyRIQCDv29HfHH1fs4kZBFyQ0hPOHYf8d012P79u2YPHkypFJpU8XUpAoLC2FjY4OCggJYW/P/K/1uXglW/ROPv6Krpvz3drbEl88GUS1OM7lxrwAjvz4LiVCAiHcHwo5GuJAn9MfVe1i46yp8Xaxw8HXtFxJmSob8M/kAANu+tuCElHADgFKlxJmEw8Dw4eibAgiLigEZ9Vs0Jdp8f2vdIOzv74+rV6/W2B8REYHIyEhtH87ktbKzwDfPdcHWGd3gYCnBzcxijNt4Dvuv0RzjzeHXhx2Jnw5wpsSG6EQ/L0cIOCA+owj38ku1Pp4TcrALtYNdqB0lNo8QCoQIbd0PoXcAoVY/yYkp0jq5mTdvHtLS0mrsv3fvHubNm6eToEzRQD9nHHy9H/p5O6KsQoUFP0fhs4PxUKnoXdxUShSV2BtVtQL4s9SRmOiInUyCoNZV65KdTKAh4YTwQevkJjY2Fl26dKmxPygoCLGxsbUcQRrKwVKKsJnd8VL/tgCADSdv4a090ahU0vw4TeHPa/dRVFYJD3sL9GnvwHc4xIg85dv4VcJVFSrcW38P99bfg6qC3vvVKpQVWB+1Ceu7AxU0CI3UQ+t/EalUiszMzBr709PTNUZQkcYRCjgsGeaH1RM7QSjg8NuVu3j5pysoq1DyHZrR2RGRCgB4rkdrCARU/U90Z4BPVXJzLilH6/cuUzAkzk9E4vxEMAXV3FZTKBWYf+wNzB8BKGjMBamH1snN4MGDsWTJEhQUFKj35efn491338XTTz/d6EBWrVoFjuPw+uuvP7bMtm3b1MPRqzdjXfZhQtdW+HZaV0hEAhyNy8QL2y5RgqND0XfzEX23ABKhgBbJJDrn19IKLtZmKK1QIiI5V7uDhYDjBEc4TnAE6EuckEbROrlZvXo10tLS4OHhgQEDBmDAgAHw9PRERkYG1qxZ06ggLl26hE2bNqFjx471lrW2tkZ6erp6S0lJadQ5DcHT/s744YUesJSKcP5WDl768TLKKynB0YWdD2tthge6wN7SMEf+Ef3FcZx6rSltm6aEZkIE/BqAgF8DIDSj7IaQxtA6uXFzc0N0dDQ+++wz+Pv7o2vXrli3bh2uX78Od3ftO2UWFxdj6tSp2LJlC+zs7Ootz3EcXFxc1Juzs7PW5zQkvdra4/uZ3WEuFuLUzQeYvzMKFdQH54kUllXgj6tVo9Gm9vLgORpirEIfNk0dj8+CljNuEEKeUKO6ZclkMsydOxfr16/H6tWrMX36dIjF4kYFMG/ePIwYMQKDBg1qUPni4mL1gp2jR49GTExMneXLy8tRWFiosRmaHp4t8N2MbpCIBDgSm4m390TTh+UT+P3KPZRWKOHtbIluHvUn1IQ0Rp/2DpAIBUjNLcHtbDnf4RBiUrTuAVzfyt/aLJy5a9cuXLlyBZcuXWpQeR8fH3z//ffo2LEjCgoKsHr1aoSEhCAmJgatWtXeb2LlypVYtmxZg2PSV73bO2DTtK6Y/UMkfo+6B1dbM7w1xJfvsAwOYww7IqqaMqf18qAZiUmTkUlF6Nm2Bc4kZuNEfBbaOVrWfxAApVyJM5ZnAAB9i/tCKKOmKUK0pXVys3DhQo3bFRUVKCkpgUQigYWFRYOTm7S0NCxcuBBHjhxpcKfg4OBgBAcHq2+HhITAz88PmzZtwvLly2s9ZsmSJVi0aJH6dmFhYaOaz/TBAF8nrBwXiLf3RGP9iVtwtTXH1J7UrKKNi8m5uJlZDHOxEGOC3PgOhxi5UB+nquQmIQuz+7blOxxCTIbWzVJ5eXkaW3FxMRISEtCnTx/8/PPPDX6cy5cvIysrC126dIFIJIJIJMKpU6fw1VdfQSQSQamsv+OsWCxGUFAQkpKSHltGKpXC2tpaYzNkk7q54/VBXgCAD/bdwLG4msPyyeOFnbsDABgT5AZrs8Y1pRLSUNXz3VxMzkVxeSXP0Rg2qUiKv8buwV87ACmNqyD10MlUSF5eXli1alWNWp26DBw4ENevX8fVq1fVW7du3TB16lRcvXoVQmH9VbFKpRLXr19Hy5YtnyR8g7NwoBcmdWsFFQMW/ByFm5lFfIdkENJyS3A4NgMA8ELvNvwGQ0yCp4MMbewtUKFkOJuYzXc4Bk0kEGFEu6EYkQiIaEwFqYfO5nkUiUS4f7/h6yFZWVmhQ4cOGptMJoO9vT06dOgAoKr/zpIlS9THfPzxxzh8+DBu376NK1euYNq0aUhJScHs2bN1dRkGgeM4rBgbiF5tW0CuUGLOD5HIL1HwHZbe+yH8DlQM6OvlAC9nK77DISZiwBPMVkwIaRyt+9zs379f4zZjDOnp6fjmm2/Qu3dvnQUGAKmpqRAI/s2/8vLyMGfOHGRkZMDOzg5du3bF+fPn4e/vr9PzGgKxUIANU7vimW/OIiWnBPN3RmHbrO4QCWle8trIyyux61LVmmgv9PbkORpiSp7ydULYuTs4kVA1JJw6sTdOhbICO278BHQGpkYD1KhM6sIxLccUP5psAFW1CI6OjnjqqaewZs0avW8i0mbJdEMQl16I8RvPo0ShxIt9PPHBSNNL9Brih/A7+PCPGLR1kOHoov603AJpNuWVSgR9fAQlCiX+eq0POrjZ1FmeRkvVTq6Qw3Jl1Yiz4hWALK8YkMl4joo0J22+v7X+ma9SqTQ2pVKJjIwM7Ny5U+8TG2Pk19IaayZ2AgBsPZuMPZfv8hyR/lGpmLoj8czebSixIc1KKhKi98OFWalpipDmQW0YRmBYYEssGFg1guq9368j5n5BPUeYlpM3s5CcLYeVmQjju9A6UqT5VS+keSKBkhtCmkOD+tw8Ok9MfdauXdvoYEjjvT7QCzfuFeB4fBZe3XEF++f3gY05tUoDwLcnbwMAJnd3h0xKK9eT5le9zlRUWj5y5Qq0kEl4jogQ49agT/qoqKgGPRh1lOOPQMBh7aROGPFVVQfjt369hk3PdzX51yTyTi4u3smFWMjhxT40iRrhR0sbc/i1tEZceiFO3czC2CCqQSSkKTUouVm3bh0CAgIaNPcM4Y+thQQbp3XBhI3hOBybiS1nbmNuv3Z8h8Wrb0/dAgCM79IKLjYNmwmbkKYwwMcRcemFOBH/gJIbQppYg/rcBAUFITc3FwDQtm1b5OTkNGlQpPE6trLFh6OqRkx9ejABEbdN97VKyCjC0bgscBwwtx/V2hB+Vc9WfOrmA1QqaRY6QppSg5IbW1tb3L5d1W/hzp07UKnojanPpvZsjbFBblCqGOb/HIWsojK+Q+JFda3NsA4uaNvARQsJaSqd3W1hYy5GQWkFrqblP7YcJ+Xgv9sf/rv9wUlNu1n5UVKRFLtH/YDdu2n5BVK/BjVLjR8/Hv3790fLli3BcRy6dev22Caq6iSI8KdqBuMOiLlfgJuZxXhtZxR2zO5pUhP8peWWYP+1qhmzX+nfnudoCAFEQgH6ezti/7X7OB6fhW5tWtRaTiASwGmiUzNHp/9EAhEm+owDYhu2ODMxbQ1KbjZv3oxx48YhKSkJCxYswJw5c2BlRdPX6zMLiQgbp3XFM1+fRURyLtYcuYl3hvryHVaz+fbULShVDH29HBDYqu5J0whpLk/5OqmTm7dN6P1ISHNr8LjYoUOHAqhazXvhwoWU3BiAdo6W+HRCR8zfGYWNJ2+hS2s7PO3vzHdYTe5uXgl2R1YttTBvANXaEP3Rz9sRHAfEZxQhvaAULW3Ma5RRVaqQ/XvVIpsOYx0gEJlOjWtdKlWV+D1hL+APjI1vxNpBxKRo/a4JCwujxMaAjOzoipkhbQAAb+y+itScEn4DagZfH0tChZKhd3t79Gprz3c4hKi1kEkQ5G4LADgR/6DWMqycIXZSLGInxYKVa7U6jlErryzHpD+nY9IkoJwG7pJ60E8CE/DucD8EtbZFYVklXt15GWUVxtsb7062HHuuVC1BsehpH56jIaSm6lFTj52tWADY9LeBTX8b+oQmpJHorWMCJCIB1j/XBS1kEty4V4hlf8bwHVKT+epYIpQqhlAfR3T1sOM7HEJqCH24FMO5pGyUV9b8oSE0FyLoZBCCTgZBaE5VFIQ0BiU3JsLV1hxfPtsZHAf8fDHNKBfYTMoqwr6r9wAAi5725jkaQmoX4GoNJyspShRKRNzO5TscQoyS1smNXC5vijhIM+jn7YiFDxfYfH/fdcRnFPIckW6tPBAPFQMG+zujYytbvsMhpFYcx9FCmoQ0Ma2TG2dnZ7zwwgs4e/ZsU8RDmtiCp7zQz9sRZRUqvPLTFRSVVfAdkk6cTczGsfgsiAQcFg+jIbZEvw2o7ncTXzO5UcqVOOd4Duccz0EpN97+cYQ0Ja2Tm59++gm5ubl46qmn4O3tjVWrVuH+/ftNERtpAgIBhy+f7QxXGzMkZ8vx9p5oMGbYIzKUKob/+zsWADCtlwfNRkz0Xh8vB4iFHO7klOD2g+Ia91dkV6Ai2zh+eBDCB62TmzFjxmDfvn24d+8eXn75ZezcuRMeHh4YOXIk9u7di8rKyqaIk+hQC5kE66d2gVjI4Z8bGdh6NpnvkJ7InstpiM8ogrWZSN3sRog+s5SK0MOzaobiEwm1DwknmiRCCcKGfouwfYCEKrRIPRrdodjR0RGLFi1CdHQ01q5di6NHj2LChAlwdXXFhx9+iJIS459PxZAFtbbD+yOqFthc9U88Iu8YZsfGwrIKrD58EwCwYKAX7GQSniMipGHU/W5qaZoiNYmFYszsMA0zrwJiWt6Q1KPRyU1mZiY+++wz+Pv7Y/HixZgwYQKOHTuGNWvWYO/evRgzZowOwyRNYXqwB0Z1ckWliuGVHVdwP7+U75C0tuZQAh4UlcPTQYbpwW34DoeQBqvudxORnAN5OdV4E6JLWs9gvXfvXoSFheHQoUPw9/fHq6++imnTpsHW1lZdJiQkBH5+frqMkzQBjuOwalwgEjOLEJ9RhDk/ROLXl4NhITGMic2vpeXjhwspAIAVYzpAQtPUEwPS1kEGD3sLpOSU4GxSNoYEuPAdkl6rVFXi0K2DgBcw5BYtv0DqpvW3waxZs+Dq6opz587h6tWrmD9/vkZiAwCurq547733dBUjaUIyqQhbpneDvUyCmPuFeGP3NahU+t/BuFKpwru/XwdjwNggN4S0d+A7JEK0ojEknJqm6lVeWY6Rv0/AyKm0/AKpn9bJTXp6OjZt2oTu3bs/toy5uTk++uijJwqMNB/3FhbY9HxXSIQC/HMjA18evcl3SPXafOY2Yu4XwtpMhHeHUy0hMUwDHlmKwdBHLRKiT7RObiorK1FYWFhjKyoqgkKhaIoYSTPo1qYFPhkXCAD46ngS/ng4068+ir1fiC+OVCVgH44KgKOVlOeICGmcnp4tYC4WIrOwHLHpxjWpJiF80jq5sbW1hZ2dXY3N1tYW5ubm8PDwwEcffQSVirqzG5oJXVvhpX5tAQBv/RqNc0nZPEdUU3mlEot2X0WFkmGwvzPGd3HjOyRCGs1MLETv9lUr15+kIeGE6IzWyc22bdvg6uqKd999F/v27cO+ffvw7rvvws3NDRs3bsTcuXPx1VdfYdWqVU0RL2libw/1xfBAFyiUKsz9IRLX7xbwHZKGVf/EIz6jCPYyCT4ZFwiO4/gOiZAnUt00dZz63RCiM1p3ON++fTvWrFmDSZMmqfeNGjUKgYGB2LRpE44dO4bWrVtjxYoVePfdd3UaLGl6QgGHL57tjPySSzh/Kwczwy5izysh8HSQ8R0a/oq+j7BzdwAAn47vCAdLao4ihq+6U3FUah7y5ApYg3rLEvKktK65OX/+PIKCgmrsDwoKQnh4OACgT58+SE1NffLoCC+kIiE2Pd8VHdyskSNXYNp3EUjL5XdSxqSsIryzJxoA8EpoOwzyd+Y1HkJ0xdXWHL4uVlAx4HQiNU0RogtaJzfu7u7YunVrjf1bt26Fu7s7ACAnJwd2dnZPHh3hjZWZGGEze8DTQYZ7+aV4dlM4UnL4WRE+p7gcc364DLlCieC29njjaW9e4iCkqTzaNMVJOHh94wWvb7zASajZtZpEKME3A9fgm79p+QVSP45pOf5w//79mDhxInx9fdXDwSMjIxEfH489e/Zg5MiR2LhxIxITE7F27domCfpJFBYWwsbGBgUFBbC2tuY7HL2XWViGKVsu4PYDOVyszbBzTs9mXZiyVKHElC0XcDUtH2625tg3rzeNjiJG52JyLiZtCoethRiX338aQgElNbWSywHLh58/xcWAjP/mctJ8tPn+1rrm5plnnkFCQgKGDx+O3Nxc5ObmYtiwYYiPj8fIkSMBAK+88opeJjZEe87WZtg1txe8nCyRUViGCd+G43JK86xDpahUYf7OK7ialg9bCzG2v9CDEhtilLq0toWNuRj5JRW4mpbHdziEGDytOhRXVFRg6NCh+Pbbb7Fy5cqmionoGScrM/w8txde2HYJ0XcLMGVLBL6Y1BkjOrZssnOWVSjx6o4rOB6fBalIgK0zuqG9U/PVGBHSnERCAfp5O+LPa/dxPCYTbZOram5s+9qCE1ItDgAoVUqcST0NtAH6poC6XZM6aVVzIxaLER0d3VSxED3mYCnFrrm9MMjPGYpKFebtvILPDsajUqn7+Yzy5ArMDLuI4/FZMBML8N2Mbujq0ULn5yFEnwzwcQQAnL3xANcGXMO1AdegKqP5wqqVVZZhwO7hGDATKKOFpUg9tG6WmjZtWq0dionxs5CIsOn5rpjVuw0AYMPJW5i8+YJOR1LdzCzC2A3ncOF2LmQSIcJm9kBfL0edPT4h+qq/tyM4DojLLILE1xwW/hYAVdoQ0iha57+VlZX4/vvvcfToUXTt2hWy/3Toor42xk0o4PDRqAB09bDDkt+uIzIlD4O/OI0FA70wu68nxMLGrcytUjFsO38Hqw7GQ1GpgputObbO7AZfF+r0TUyDvaUUnVrZ4mpaPlK3t8TkHq35DokQg6V1cnPjxg106dIFAHDzpuYCizRbrOkY2dEVHd1s8eaea7iYnItPD8bjl0upeDW0PcYEuUEialiSwxjDuaQcfHIgTr22Tn9vR6yZ1Ikm6SMm5ylfJ1xNy8ex+CxKbgh5AloPBTd0NBRctxhj2HvlHj45EIccedXCqc7WUozu7IYhAS4IdLOpNdFJzSnB8fhM7LqUhviMIgCAlVSEt4f5YlrP1pQoE5N0414BRn59FuZiIaI+fBpmYuo2W02ukMNyZdWgguIVgCyPhoKbGm2+vxvdLSspKQm3bt1Cv379YG5uDsYYfSGZII7jML5rKwzt4IIdESnYciYZmYXl2Hz6Njafvg0zsQBt7GVwsjYDB0BeXok7OXJkF/+7gryZWIDJ3VtjwUAvtJBJ+LsYQngW4GqN1uZmeOlrDuE/R6BfdE8ILSjBIURbWic3OTk5mDRpEk6cOAGO45CYmIi2bdvixRdfhJ2dHdasWdMUcRI9J5OKMLdfO8wIaYMT8Q/w57X7OH8rG3klFYjPKFLXzlQTCTh09bDD0/7OmNjVHTYWYp4iJ0R/cByHAT5OcMvJAXIUgEnVqxOiO1onN//73/8gFouRmpoKPz8/9f5nn30WixYtouTGxElFQgzt4IKhHVzAGENythxpeaXIKiwDx3EwFwvhYW8BTwcZZFIaz0nIf1UtxZADoKqjPdXbVBELxfis3/8B778PMY2QJ/XQ+tvl8OHDOHToEFq1aqWx38vLCykpKToLjBg+juPQ1tGyWZdrIMTQ9fC0w8WHf8emF6CTlT2v8egLiVCCt3q8Dpx/n+9QiAHQetyuXC6HhYVFjf25ubmQSml0CyGEPAmp6N+6mhPxWTxGQojh0jq56du3L3744Qf1bY7joFKp8Nlnn2HAgAE6DY4QQkzZ8fgHfIegN5QqJS6lX8YlV0BJY1dIPbRulvrss88wcOBAREZGQqFQ4O2330ZMTAxyc3Nx7ty5poiREEJMUnxGEe7mlaCVXc3aclNTVlmGHjv6A3MfDgXnOyCi17SuuenQoQNu3ryJPn36YPTo0ZDL5Rg3bhyioqLQrl27poiREEJM1rE4apoiRFuNGq5iY2OD9957T9exEEII+Y+jcZmYEdKG7zAIMSiNSm7y8/Nx8eJFZGVlQaXSHJM3ffp0nQRGCCEEuHA7B0VlFbAyo7mgCGkorZObP//8E1OnTkVxcTGsra01ZiXmOI6SG0II0RFPBxkSCuU4fTMbIzq25DscQgyG1n1u3njjDbzwwgsoLi5Gfn4+8vLy1Ftubm5TxEgIISbpKV8nAFVNU4SQhtM6ubl37x4WLFhQ61w3hBBCdGfAw+TmeHwWKpU0LS8hDaV1cjNkyBBERkY2RSyEEGLyODEHj4884PGRB4La2sHOQoyC0gpEpuTxHRqvxEIxPgpego9OgpZfIPXSus/NiBEj8NZbbyE2NhaBgYEQizU7uT3zzDM6C44QQkyNQCKA51JP9e2nfJ3x25W7OBqbiV5tTXcpBolQgqW93wNOruQ7FGIAOMaYVuvOCgSPr+zhOA5KpfKJg2pKhYWFsLGxQUFBAaytrfkOhxBC6vTP9XS8suMK2thb4MSboRqDOEyOXA5YPlyrrrgYkNFUfqZEm+9vrZulVCrVYzd9T2wIIUTfMRWDPEYOeYwcTMXQ19sREqEAd3JKcOuBnO/weKNiKsRkxyLGEVCZcH5HGkbr5IYQQkjTUZWqcKnDJVzqcAmqUhUspSIEt6tqjjoSa7qjpkorStFhWw90mAeUNmqGNmJKGpzcDB8+HAUFBerbq1atQn5+vvp2Tk4O/P39dRocIYSYIrGDGGKHf/szDvJ3BgAcjs3gKyRCDEqDk5tDhw6hvLxcffuTTz7RmNemsrISCQkJuo2OEEJMjFAmRO8HvdH7QW8IZUIAwBB/Z3AcEJWaj4yCMp4jJET/NTi5+W+/Yy37IRNCCGkkJ2szBLnbAgCOUO0NIfWiPjeEEGIAhnZwAQAcjKHkhpD6NDi54TiuxhBEkx6SSAghTUBZqkRUaBSiQqOgLP13BOqQgKrk5sLtXOSXKPgKjxCD0OA+54wxzJw5E1KpFABQVlaGl19+GbKH8ww82h+HEEJII6mAglMF6r+redjL4OtihfiMIhyLy8L4rq34iY8QA9Dg5GbGjBkat6dNm1ajDK0ITgghTWdIgAviM4pwMCbD5JIbsVCMN7stBNato+UXSL0anNyEhYU1ZRyEEELqMSTABeuOJeL0zQcoUVTCQmI6E75IhBJ8HroCGLmO71CIAaAOxYQQYiD8WlqhdQsLlFeqcCrhAd/hEKK3KLkhhBADwXEchgRUTeh3yMRGTamYCncKUnDHlpZfIPWj5IYQQgxI9ZDwY/FZUFSaTueT0opSeG4JgOfrtPwCqR8lN4QQYkCC3O3gaCVFUVklwm/n8B0OIXqJkhtCCDEgAgGHpx+uNXXwhmk1TRHSUJTcEEKIgRn6cEK/I7GZUKpoKRxC/ouSG0IIMTC92trDykyE7OJyXEnN4zscQvSO3iQ3q1atAsdxeP311+ss9+uvv8LX1xdmZmYIDAzEgQMHmidAQgjRExKRAIP8qGmKkMfRi+Tm0qVL2LRpEzp27FhnufPnz2PKlCl48cUXERUVhTFjxmDMmDG4ceNGM0VKCCH6YdjDUVP/XE+HipqmCNHAe3JTXFyMqVOnYsuWLbCzs6uz7Lp16zB06FC89dZb8PPzw/Lly9GlSxd88803zRQtIYQ0LU7EwfVVV7i+6gpO9PgJXfp5O8JSKsL9gjJEpeU3X4A8EQlEeLXzHLx6ERCZzgh40ki8Jzfz5s3DiBEjMGjQoHrLhoeH1yg3ZMgQhIeHP/aY8vJyFBYWamyEEKKvBFIBvNd7w3u9NwTSx39Em4mFGOTnBAD4Ozq9ucLjjVQkxfpBX2D9AUCqrL88MW28Jje7du3ClStXsHLlygaVz8jIgLOzs8Y+Z2dnZGQ8vs155cqVsLGxUW/u7u5PFDMhhOiLER1dAQAHqGmKEA28JTdpaWlYuHAhduzYATMzsyY7z5IlS1BQUKDe0tLSmuxchBDypBhjUDxQQPFAAcbqTlj6ejnASipCRmGZ0Y+aYozhQckDPLAAKI0j9eEtubl8+TKysrLQpUsXiEQiiEQinDp1Cl999RVEIhGUypr1ji4uLsjMzNTYl5mZCRcXl8eeRyqVwtraWmMjhBB9pSpR4bzTeZx3Og9VSd2dS8zEQvWEfn9fN+6mqZKKEjht8ITT20CJmO9oiL7jLbkZOHAgrl+/jqtXr6q3bt26YerUqbh69SqEQmGNY4KDg3Hs2DGNfUeOHEFwcHBzhU0IIXpleGBLANQ0RcijeFt+zMrKCh06dNDYJ5PJYG9vr94/ffp0uLm5qfvkLFy4EP3798eaNWswYsQI7Nq1C5GRkdi8eXOzx08IIU1BKBMilIU2uHxf76qmqczCclxOzUP3Ni2aLjhCDATvo6XqkpqaivT0f6taQ0JCsHPnTmzevBmdOnXCnj17sG/fvhpJEiGEmAqpSIinAx42TZnAqClCGoJj9fVYMzKFhYWwsbFBQUEB9b8hhBiF4/GZeGFbJJyspLiwZCAEgsfPj2Oo5Ao5LFdaAgCKVwCyvGJAJuM5KtKctPn+1uuaG0IIMTXKMiViJsYgZmIMlGUNm9ClT3tHWJmJkFVUjsgU4x41RUhDUHJDCCH6RAk82PMAD/Y8ABo4WZ1EJMBg/6pRo39H32/C4AgxDJTcEEKIERjZ8eGoqRsZUBrhqCmRQIQZAVMx4yotv0DqR8kNIYQYgd7tHWBjLsaDonJcuJ3Ddzg6JxVJsW3YJmzbR8svkPpRckMIIUZAIhKo57zZF3WP52gI4RclN4QQYiTGdK5aa+rgjQyUVRhX9QZjDHKFHHIxLb9A6kfJDSGEGInubVrA1cYMReWVOB6fxXc4OlVSUQLLr5xh+R4tv0DqR8kNIYQYCYGAwzOd3QBQ0xQxbZTcEEKIERkTVNU0dTLhAQpKKniOhhB+UHJDCCFGxNfFGr4uVlAoVThwg5ZjIKaJkhtCCDEyo6lpipg4Sm4IIcTIPPNw1FREci7u55fyHA0hzY+SG0IIMTJutubo0aYFAGD/NVqOgZgeSm4IIUSfCAHHCY5wnOAICBv/MKMfdiw2lqYpoUCICd5jMCEGENJEN6QeHGPMpP5NtFkynRBCDFV+iQLdVxxFhZLhn4V94dfSCD7v5HLA0rLq7+JiQCbjNx7SrLT5/qaaG0IIMUK2FhIM9HUGAOy5fJfnaAhpXpTcEEKIkZrQtRWAqqapCiUtpU1MByU3hBCiR5RyJU5yJ3GSOwml/MnWhwr1cYSDpRQ5cgVOGPhyDHKFHNxqS3BLATktv0DqQckNIYQYKZFQgHFdqua8+ZWapogJoeSGEEL0iMBCgJCsEIRkhUBg8eQf0dVNUyfis5BdXP7Ej0eIIaDkhhBC9AjHcZA4SiBxlIDjuCd+PG9nK3RqZYNKFTOaYeGE1IeSG0IIMXITurkDqBo1ZWKzfxATRckNIYToEVW5Cjfn3cTNeTehKtfNCKdnOrpCIhIgPqMIN+4V6uQxCdFnlNwQQogeYZUM9zfcx/0N98EqdVPLYmMhxmD/qjlvfr2cppPHJESfUXJDCCEmYNLDpqnfo+6hVPFkQ8z5IBQIMdxzCIbfpOUX9FF5pRK3HhQj/FYO/rx2H5fu5PIaj4jXsxNCCGkWfdo7wL2FOdJyS/FX9H1MfJjsGAozkRn+Hv8bMMOS71BMmkrFcCdHjui7Bbh2Nx83M4twJ7sE6QWlUD2SdI4NckP3h4u38oGSG0IIMQECAYcpPVrjs4MJ2BGRanDJDeFPSo4cpxOzcTbxAcJv5aCwrLLWchYSIVxszOBgKUU7R37X/aLkhhBCTMTEru5Ye/gmrqblI+Z+AQJcbfgOieghxhjiM4rwd3Q6/r6ejuRsucb9UpEAAa7W6NjKFgGu1vB0kMHDXgYHS91MX6ALlNwQQoiJcLSSYkgHF/wdnY6dEalYMTaQ75AaTK6Qw+lLJ+BdIOtzgNYD172sojL8GnkXv125i9sP/k1oRAIOXTzs0M/LAX28HBHgag2xUL+77FJyQwghJmRqj9b4Ozod+6LuYclwP1hKDedroKSyBJDwHYVxYYzhTGI2dkak4mhcJiofdpyRiAQI9XbEiI4t8ZSvE6zMDGtBL8P5ryaEEPLEgtvZo62DDLez5dh/9T6e69ma75AIDyqUKvx57T42n76N+Iwi9f6uHnaY3N0dQzu4GFxC8yhKbgghxIRwXFXH4hUH4rDzYgolNyamrEKJnRGp+O7MbdwvKAMAyCRCTOzmjik9WsPHxYrnCHWDkhtCCDEx47u2wueHE3DjXiGupeWjk7st3yGRJlahVGF3ZBq+OpaIzMKqBVQdLKWY1bsNpvX0gI2F4dbS1IaSG0IIMTEtZBKMCGyJ36PuYdv5O/ji2c58h0SaiErF8Nf1dKw9nIA7OSUAADdbc7w6oB3Gd2kFM7GQ5wibBiU3hBBigmaGtMHvUffwV/R9LBnmCydrM75DIjp2/W4BPtx/A1Gp+QAAB0sJ5g9ojyk9W0MqMs6kpholN4QQok8EgE1/G/XfTaWTuy26etjhckoefopIxaKnvZvuZDog4ATo36oPcPYsBLT8Qp1yisux+nACdl1KA2NVfWpeCW2HWb09ITOg0XFPgmOMmdS/SWFhIWxsbFBQUABra2u+wyGEEN78FX0f83dGwcFSgnOLn9L/X/NyOWD5cPmF4mJARrPdPEqlYtgRkYLPDyWoZxEeG+SGxcN84WwENXPafH+bRgpHCCGkhiEBLmhpY4b0gjL8eS0dE7q24jsk0ki3HhRj8W/RuHQnDwDg39Iay0YH8Lq+E5/0e4pBQgghTUYsFOD5YA8AQNi5ZJhYRb5RqFSqsPHkLQxbdwaX7uTBQiLE0lH++PO1Piab2ACU3BBCiF5RypU453gO5xzPQSlXNvn5pnRvDTOxADH3C9W/+vWRXCGH43oPOL4FyI1r1HKj3cwswtgN5/HpwXgoKlXo6+WAw//rh5m9PSEU6McaT3yh5IYQQvRMRXYFKrIrmuVcdjIJxgZVNUdtPn27Wc7ZWNmlOcimbjZgjGHbuWSM+vosrt8rgLWZCJ9P6IgfXuiBVnYWfIenF6jPDSGE6BGBuQDdb3RX/90c5vT1xK5LVWsL3cwsgrezccxSa4yyisrw9p5onEx4AADo7+2Izyd0pKH8/0E1N4QQokc4AQdZgAyyABm4ZmpaaOtoiaEBLgCAb0/dapZzEu0di8vEsC/P4GTCA0hEAix7JgDbZnWnxKYWlNwQQgjBy/3bAQD2X72Pe/mlPEdDHqWoVOHjP2Px4vZI5MgV8HWxwl+v9cGMkDbgONPuW/M4lNwQQogeUSlUSF6ajOSlyVApVM123k7utghpZ49KFcN3Z/S7740puZ9fimc3h+P7c8kAgNl9PPHH/N7UdFgPSm4IIUSPsAqGlGUpSFmWAlbRvEOzXwmtqr3ZdTENuXJFs56b1HQyIQsjvjqDqNR8WJuJsGV6N7w/0l//J1vUA5TcEEIIAQD0ae+ADm7WKK1QYtv5O3yHo0HACdDNuQu63YPRL7+gVDGsOZyAWdsuIa+kAoFuNvh7QV887e/Md2gGg5IbQgghAACO4/BqaHsAQNjZZBSUNM9w9IYwF5vj0vOncWkLYF7JdzRNp6CkAjPDLuLr40lgDHi+lwf2vBIM9xY0xFsblNwQQghRGxrgAl8XKxSVV2IL9b1pVrceFGPMhnM4k5gNC4kQ6yZ3xvIxHagZqhEouSGEEKImEHB4fVDVCuFh55Kp700zOX3zAcasP4fkbDncbM2x5+UQjO7sxndYBouSG0IIIRqGBDgjwNUacoUSm07rx7w3JRUlaLPZH21eB0qMaPkFxhjCziVjZthFFJVVopuHHf6Y3xv+rnWvek3qRskNIYQQDRzHYdHTVbU3P5xPwYOicp4jqkoCUgpTkWILGEt/YkWlCu/+fgPL/oyFigETurbCjjk94WAp5Ts0g0fJDSGEkBqe8nVCJ3dblFYoadbiJpAnV+D5rRH4+WIqOA54b7gfPp/QkfrX6AglN4QQQmp4tPbmx/AUpOWW8ByR8biZWYTR688hIjkXllIRvp/RHXP6taXZhnWIkhtCCCG16uflgN7t7aFQqvD5oQS+wzEKx+MzMW7DeaTmlqB1CwvsfTUEA3yd+A7L6FByQwghpFYcx+Hd4X7gOGD/tfu4mpbPd0gGizGGLadv48XtkSgur0RPzxbYN4+WUWgqlNwQQgh5rABXG4wLagUAWPF3LBgzlu68zae8Uom390RjxYE4MAZM6eGOH1/siRYyCd+hGS1KbgghRJ9wgIW/BSz8LQA96YLx5hBvmIkFuHQnDwdvZPASA8dx8Lf3hX+W3jwtDZJdXI6pWyLw6+W7EHDA0lH++GRsICQi+vptShwzsTS8sLAQNjY2KCgogLU1zSNACCENseZwAr4+ngQ3W3McWdQPFhJR8wchlwOWllV/FxcDMlnzx6CFuPRCzN4eiXv5pbAyE2H9c13Qz9uR77AMljbf35Q6EkIIqderoe3hZmuOe/ml+Pp4Et/h6L3DMRkYv/E87uWXwtNBhn3zelNi04wouSGEEFIvc4kQS58JAABsOX0bSVlFPEeknxhjWH8iCS/9dBklCiX6tHfAvld7o52jJd+hmRRKbgghRI8oS5S4GHARFwMuQlmi5DscDU/7O2OQnxMqVQwf7Itp1s7FJRUlCAjrhoBX9Xf5hbIKJf73y1V8figBjAEzgj0QNqs7bCz0NGAjRskNIYToEwaUxJagJLZEL9cZ+GhUAKQiAcJv5+DXyLvNdl7GGGJz4hHrpJdPC7IKyzB58wXsu3ofQgGH/xvTActGd4BYSF+zfKBnnRBC9IjATIBOJzqh04lOEJjp30e0ewsL9czFy/+Kxf38Up4j4t/1uwUYvf4crqblw8ZcjB9f6IFpvTz4Dsuk6d87hxBCTBgn5GAXage7UDtwQv0c9Dy7b1sEtbZFUXkl3vkt2qTnvtl/7T4mfHse6QVlaOcowx/zeiOkvQPfYZk8Sm4IIYRoRSjgsHpiJ0hFApxJzMauS2l8h9TslCqGTw/GY8HPUSivVOEpXyf8Pq832jjo9/B0U0HJDSGE6BFVhQr31t/DvfX3oKpQ8R3OY7VztMRbQ3wAVDVPJWUV8xxR8ykqq8CcHyKx8WTVaukv92+HLdO7wdqMOg7rC0puCCFEjzAFQ+L8RCTOTwRT6Hdzz6zenghua48ShRLzd15BWYV+je5qCsnZcozdcB7H47MgFQmwbnJnLB7mC6FAP5sQTRUlN4QQQhpFKOCwbkpnOFhKEZ9RhGV/xjTZuTiOg4d1a3jk87f8wqmbDzBm/TkkZRXDxdoMv74cjNGd3XiKhtSFkhtCCCGN5mRlhnWTO4PjgJ8vpmHvlaYZHm4htsCdubG48yVgUdEkp3gspYph7ZGbmBl2EQWlFQhqbYv983ujYyvb5g2ENBivyc3GjRvRsWNHWFtbw9raGsHBwfjnn38eW37btm3gOE5jMzMza8aICSGE/Ffv9g547SkvAMDivddxOSWX54h0J1euwMywi/jqWCIYA6b2bI2f5/SCkzV99+gzHlY++1erVq2watUqeHl5gTGG7du3Y/To0YiKikJAQECtx1hbWyMhIUF9m+OonZMQQvi2cKAX4tILcSQ2E3N/uIx983rDvYUF32E9kSupeZi34wrSC8pgJhbgk7GBGNelFd9hkQbgteZm1KhRGD58OLy8vODt7Y0VK1bA0tISFy5ceOwxHMfBxcVFvTk7OzdjxIQQQmojFHBYN7kzAlytkSNX4IVtl1BQqrv2o9KKUnT/sR+6zwFKm/hnuUrFsOX0bTy7KRzpBWVo6yDDH/P6UGJjQPSmz41SqcSuXbsgl8sRHBz82HLFxcXw8PCAu7s7Ro8ejZiYujuwlZeXo7CwUGMjhBCiexYSEbbO6A5naykSs4oxM+wiissrdfLYKqZCZOYVRLoBqiassM8oKMPz30dgxYE4VCgZhge64I/5veHjYtV0JyU6x3tyc/36dVhaWkIqleLll1/G77//Dn9//1rL+vj44Pvvv8cff/yBn376CSqVCiEhIbh79/Ed2FauXAkbGxv15u7u3lSXQgghJs/FxgzbZvWAjbkYUan5eGHbJZQqDGOI+MEb6Ri67jTOJeXAXCzEJ2MDsf65LrCi+WsMDsd4njdboVAgNTUVBQUF2LNnD7777jucOnXqsQnOoyoqKuDn54cpU6Zg+fLltZYpLy9HeXm5+nZhYSHc3d1RUFAAa2trnV0HIYToglKuxBnLMwCAvsV9IZQJeY6ocaLv5mPqlggUlVeiR5sW2DKjG2zMG58kyBVyWK60BAAUrwBkecWATDezAefJFVj+Vyz2Rt0DAAS62eDLyZ3RztFSJ49PdKOwsBA2NjYN+v7mveZGIpGgffv26Nq1K1auXIlOnTph3bp1DTpWLBYjKCgISUlJjy0jlUrVo7GqN0IIIU2rYytbhM3qDiupCBfv5OLZTeHIKizjOywNjDHsv3Yfg9aewt6oe+C4qtmGf3slhBIbA8d7cvNfKpVKo6alLkqlEtevX0fLli2bOCpCCCHa6tamBXa91Es9yd+Y9ecQfTef77AAACk5cszeHokFP0chR66At7Ml9r4SgsXDfCER6d1XI9ESr0PBlyxZgmHDhqF169YoKirCzp07cfLkSRw6dAgAMH36dLi5uWHlypUAgI8//hi9evVC+/btkZ+fj88//xwpKSmYPXs2n5dBCCHkMQJcbbD3lRDMDLuI29lyTPg2HMtHB2BSN3depvIoKqvAN8eTEHbuDhRKFcRCDvMHeOGV0HaU1BgRXpObrKwsTJ8+Henp6bCxsUHHjh1x6NAhPP300wCA1NRUCAT//rPl5eVhzpw5yMjIgJ2dHbp27Yrz5883qH8OIYQYCrGDcXVgbW1vgX3ze2PRL9dwNC4T7/x2HUfjsrBibAc4WTV8MjwHc3sgO6dRMZQqlNgRkYJvT91CdrECANDXywEfjfJHeycaCWVseO9Q3Ny06ZBECCFEd1Qqhm9P38IXR26iQslgayHG/wZ547merSEWNqDWRC4HLB/2hSluWIfi4vJK7LqYim9P3UZ2cVWXh7YOMrw/0g8DfJxoIlgDos33NyU3hBBCmlVceiHe/PUaYu5XzTvm6SDDvAHtMapTS0hFdYwO0yK5Scgowo6IFOy9ck89104rO3O89lR7jOvSqmHJFNErlNzUgZIbQgjhX6VShV2X0vDl0ZvqZiIHSwkmdHXHiMCW6OBmXbNWpY7kRqViiM8owtG4TPwdnY6EzCL1fW0dZXi5XzuM7eJGSY0Bo+SmDpTcEEL0mbJUiehh0QCAjv90hNDcMOe5aaji8kr8GJ6CH8LvIL3g36HiLtZm6Ophh07uNmjdwgJ2MmDBofEwjwjHPz8BN6JScL9SiKSsYsSlF+Jici4Ky/6dDVks5PCUrxOmB7dBSDt7an4yApTc1IGSG0KIPjOWSfy0VaFU4WhsJv6KTsfx+CyUVmjOaqxCGdLMJwComsSv2/w9KJVodkaWSYTo2dYewwNb4ml/5yeaNJDoH22+v3kdLUUIIUQTJ+Xgv9tf/bepEAsFGBbYEsMCW6JUocTVtHxEpeUh5n4h7ueX4m5eHtIeWabK08EClva2aOcoQztHS3Rr0wIdXK0homYnAqq54TscQgghDdCUyy8Qw2BQyy8QQgghhOgSNUsRQogeUVWqkP17NgDAYawDBDRrLiFao+SGEEL0CCtniJ0UC6CqQzF9ShOiPfpJQAghxCBYiCxgoeA7CmII6DcBIYQQvSeTyCB/PevfSfwIqQPV3BBCCCHEqFByQwghhBCjQskNIYQQvVdWWYYRv43HiOeAMupQQepB/yKEEEL0nlKlxIHkQ4A3oDSdiZtJI1HNDSGEEEKMCiU3hBBCCDEqlNwQQgghxKhQckMIIYQQo0LJDSGEEEKMismNlmKMAahaOp0QQvSNUq6EHHIAVZ9TQqWQ54j0g1whB8qq/i5kgLKwEFAq+Q2KNKvq7+3q7/G6mFxyU1RUBABwd3fnORJCCKmHK98B6CdXAHClJ8dUFRUVwcbGps4yHGtICmREVCoV7t+/DysrK3CcbidLKCwshLu7O9LS0mBtba3Tx9YHdH2Gz9iv0divDzD+a6TrM3xNdY2MMRQVFcHV1RUCQd29akyu5kYgEKBVq1ZNeg5ra2uj/acF6PqMgbFfo7FfH2D810jXZ/ia4hrrq7GpRh2KCSGEEGJUKLkhhBBCiFGh5EaHpFIpPvroI0ilUr5DaRJ0fYbP2K/R2K8PMP5rpOszfPpwjSbXoZgQQgghxo1qbgghhBBiVCi5IYQQQohRoeSGEEIIIUaFkhtCCCGEGBVKbrSwYsUKhISEwMLCAra2tg06hjGGDz/8EC1btoS5uTkGDRqExMREjTK5ubmYOnUqrK2tYWtrixdffBHFxcVNcAV10zaOO3fugOO4Wrdff/1VXa62+3ft2tUcl1RDY57r0NDQGvG//PLLGmVSU1MxYsQIWFhYwMnJCW+99RYqKyub8lJqpe315ebm4rXXXoOPjw/Mzc3RunVrLFiwAAUFBRrl+HwN169fjzZt2sDMzAw9e/bExYsX6yz/66+/wtfXF2ZmZggMDMSBAwc07m/Ie7I5aXN9W7ZsQd++fWFnZwc7OzsMGjSoRvmZM2fWeK2GDh3a1JfxWNpc37Zt22rEbmZmplFG314/QLtrrO3zhOM4jBgxQl1Gn17D06dPY9SoUXB1dQXHcdi3b1+9x5w8eRJdunSBVCpF+/btsW3bthpltH1fa42RBvvwww/Z2rVr2aJFi5iNjU2Djlm1ahWzsbFh+/btY9euXWPPPPMM8/T0ZKWlpeoyQ4cOZZ06dWIXLlxgZ86cYe3bt2dTpkxpoqt4PG3jqKysZOnp6RrbsmXLmKWlJSsqKlKXA8DCwsI0yj16/c2pMc91//792Zw5czTiLygoUN9fWVnJOnTowAYNGsSioqLYgQMHmIODA1uyZElTX04N2l7f9evX2bhx49j+/ftZUlISO3bsGPPy8mLjx4/XKMfXa7hr1y4mkUjY999/z2JiYticOXOYra0ty8zMrLX8uXPnmFAoZJ999hmLjY1l77//PhOLxez69evqMg15TzYXba/vueeeY+vXr2dRUVEsLi6OzZw5k9nY2LC7d++qy8yYMYMNHTpU47XKzc1trkvSoO31hYWFMWtra43YMzIyNMro0+vHmPbXmJOTo3F9N27cYEKhkIWFhanL6NNreODAAfbee++xvXv3MgDs999/r7P87du3mYWFBVu0aBGLjY1lX3/9NRMKhezgwYPqMto+Z41ByU0jhIWFNSi5UalUzMXFhX3++efqffn5+UwqlbKff/6ZMcZYbGwsA8AuXbqkLvPPP/8wjuPYvXv3dB774+gqjs6dO7MXXnhBY19D3hDNobHX2L9/f7Zw4cLH3n/gwAEmEAg0PoQ3btzIrK2tWXl5uU5ibwhdvYa7d+9mEomEVVRUqPfx9Rr26NGDzZs3T31bqVQyV1dXtnLlylrLT5o0iY0YMUJjX8+ePdlLL73EGGvYe7I5aXt9/1VZWcmsrKzY9u3b1ftmzJjBRo8eretQG0Xb66vvs1XfXj/Gnvw1/OKLL5iVlRUrLi5W79On1/BRDfkcePvtt1lAQIDGvmeffZYNGTJEfftJn7OGoGapJpScnIyMjAwMGjRIvc/GxgY9e/ZEeHg4ACA8PBy2trbo1q2busygQYMgEAgQERHRbLHqIo7Lly/j6tWrePHFF2vcN2/ePDg4OKBHjx74/vvvG7Rkva49yTXu2LEDDg4O6NChA5YsWYKSkhKNxw0MDISzs7N635AhQ1BYWIiYmBjdX8hj6Op/qaCgANbW1hCJNJeea+7XUKFQ4PLlyxrvH4FAgEGDBqnfP/8VHh6uUR6oei2qyzfkPdlcGnN9/1VSUoKKigq0aNFCY//Jkyfh5OQEHx8fvPLKK8jJydFp7A3R2OsrLi6Gh4cH3N3dMXr0aI33kD69foBuXsOtW7di8uTJkMlkGvv14TVsjPreg7p4zhrC5BbObE4ZGRkAoPGlV327+r6MjAw4OTlp3C8SidCiRQt1meagizi2bt0KPz8/hISEaOz/+OOP8dRTT8HCwgKHDx/Gq6++iuLiYixYsEBn8TdEY6/xueeeg4eHB1xdXREdHY133nkHCQkJ2Lt3r/pxa3uNq+9rLrp4DbOzs7F8+XLMnTtXYz8fr2F2djaUSmWtz218fHytxzzutXj0/Va973Flmktjru+/3nnnHbi6ump8UQwdOhTjxo2Dp6cnbt26hXfffRfDhg1DeHg4hEKhTq+hLo25Ph8fH3z//ffo2LEjCgoKsHr1aoSEhCAmJgatWrXSq9cPePLX8OLFi7hx4wa2bt2qsV9fXsPGeNx7sLCwEKWlpcjLy3vi//uGMPnkZvHixfj000/rLBMXFwdfX99miki3Gnp9T6q0tBQ7d+7EBx98UOO+R/cFBQVBLpfj888/19kXY1Nf46Nf9IGBgWjZsiUGDhyIW7duoV27do1+3IZqrtewsLAQI0aMgL+/P5YuXapxX1O/hkR7q1atwq5du3Dy5EmNTreTJ09W/x0YGIiOHTuiXbt2OHnyJAYOHMhHqA0WHByM4OBg9e2QkBD4+flh06ZNWL58OY+RNY2tW7ciMDAQPXr00NhvyK+hvjD55OaNN97AzJkz6yzTtm3bRj22i4sLACAzMxMtW7ZU78/MzETnzp3VZbKysjSOq6ysRG5urvr4J9HQ63vSOPbs2YOSkhJMnz693rI9e/bE8uXLUV5erpO1R5rrGqv17NkTAJCUlIR27drBxcWlRk//zMxMADCY17CoqAhDhw6FlZUVfv/9d4jF4jrL6/o1rI2DgwOEQqH6uayWmZn52OtxcXGps3xD3pPNpTHXV2316tVYtWoVjh49io4dO9ZZtm3btnBwcEBSUlKzfjE+yfVVE4vFCAoKQlJSEgD9ev2AJ7tGuVyOXbt24eOPP673PHy9ho3xuPegtbU1zM3NIRQKn/j/okF01nvHhGjboXj16tXqfQUFBbV2KI6MjFSXOXToEG8dihsbR//+/WuMsHmc//u//2N2dnaNjrWxdPVcnz17lgFg165dY4z926H40Z7+mzZtYtbW1qysrEx3F1CPxl5fQUEB69WrF+vfvz+Ty+UNOldzvYY9evRg8+fPV99WKpXMzc2tzg7FI0eO1NgXHBxco0NxXe/J5qTt9THG2Keffsqsra1ZeHh4g86RlpbGOI5jf/zxxxPHq63GXN+jKisrmY+PD/vf//7HGNO/14+xxl9jWFgYk0qlLDs7u95z8PkaPgoN7FDcoUMHjX1Tpkyp0aH4Sf4vGhSrzh7JBKSkpLCoqCj1cOeoqCgWFRWlMezZx8eH7d27V3171apVzNbWlv3xxx8sOjqajR49utah4EFBQSwiIoKdPXuWeXl58TYUvK447t69y3x8fFhERITGcYmJiYzjOPbPP//UeMz9+/ezLVu2sOvXr7PExES2YcMGZmFhwT788MMmv57aaHuNSUlJ7OOPP2aRkZEsOTmZ/fHHH6xt27asX79+6mOqh4IPHjyYXb16lR08eJA5OjryNhRcm+srKChgPXv2ZIGBgSwpKUlj6GllZSVjjN/XcNeuXUwqlbJt27ax2NhYNnfuXGZra6semfb888+zxYsXq8ufO3eOiUQitnr1ahYXF8c++uijWoeC1/eebC7aXt+qVauYRCJhe/bs0Xitqj+DioqK2JtvvsnCw8NZcnIyO3r0KOvSpQvz8vJq1kS7sde3bNkydujQIXbr1i12+fJlNnnyZGZmZsZiYmLUZfTp9WNM+2us1qdPH/bss8/W2K9vr2FRUZH6uw4AW7t2LYuKimIpKSmMMcYWL17Mnn/+eXX56qHgb731FouLi2Pr16+vdSh4Xc+ZLlByo4UZM2YwADW2EydOqMvg4Xwg1VQqFfvggw+Ys7Mzk0qlbODAgSwhIUHjcXNyctiUKVOYpaUls7a2ZrNmzdJImJpLfXEkJyfXuF7GGFuyZAlzd3dnSqWyxmP+888/rHPnzszS0pLJZDLWqVMn9u2339Zatjloe42pqamsX79+rEWLFkwqlbL27duzt956S2OeG8YYu3PnDhs2bBgzNzdnDg4O7I033tAYSt1ctL2+EydO1Po/DYAlJyczxvh/Db/++mvWunVrJpFIWI8ePdiFCxfU9/Xv35/NmDFDo/zu3buZt7c3k0gkLCAggP39998a9zfkPdmctLk+Dw+PWl+rjz76iDHGWElJCRs8eDBzdHRkYrGYeXh4sDlz5uj0S0Nb2lzf66+/ri7r7OzMhg8fzq5cuaLxePr2+jGm/f9ofHw8A8AOHz5c47H07TV83GdE9TXNmDGD9e/fv8YxnTt3ZhKJhLVt21bjO7FaXc+ZLnCM8TAmlxBCCCGkidA8N4QQQggxKpTcEEIIIcSoUHJDCCGEEKNCyQ0hhBBCjAolN4QQQggxKpTcEEIIIcSoUHJDCCGEEKNCyQ0hhBBCjAolN4QQQggxKpTcEEIIIcSoUHJDCDEKe/bsQWBgIMzNzWFvb49BgwZBLpfzHRYhhAcivgMghJAnlZ6ejilTpuCzzz7D2LFjUVRUhDNnzoCWziPENNHCmYQQg3flyhV07doVd+7cgYeHB9/hEEJ4Rs1ShBCD16lTJwwcOBCBgYGYOHEitmzZgry8PL7DIoTwhGpuCCFGgTGG8+fP4/Dhw/j999+RkZGBiIgIeHp68h0aIaSZUXJDCDE6SqUSHh4eWLRoERYtWsR3OISQZkYdigkhBi8iIgLHjh3D4MGD4eTkhIiICDx48AB+fn58h0YI4QElN4QQg2dtbY3Tp0/jyy+/RGFhITw8PLBmzRoMGzaM79AIITygZilCCCGEGBUaLUUIIYQQo0LJDSGEEEKMCiU3hBBCCDEqlNwQQgghxKhQckMIIYQQo0LJDSGEEEKMCiU3hBBCCDEqlNwQQgghxKhQckMIIYQQo0LJDSGEEEKMCiU3hBBCCDEqlNwQQgghxKj8P4bM0PgEKzkQAAAAAElFTkSuQmCC", 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A2LNnT53bn3rqKRETE1PvYyorK0VRUZHxkpmZKQCIoqKiq/atqKgQJ0+eFBUVFYYbSkuFMKQbbX8pLW3y36W4uFio1WqxZs0a422FhYXCxcVFPPbYY0IIIUJDQ8XUqVPrPC49PV0AEIcPHxZCCLF48WLRvXv3Ovs888wzAoAoKCi4Zhy9evUSS5Ysqfe+l19+WYwdO7bObbWvRXJycr2Pyc3NFQDE8ePH68T77rvv1tkvKChI/Otf/2owLgDiueeeM14vLS0VAMRvv/1W7/5X/R+QRdiWnCNGv71NhD7ziwh95hcR8ewG8eBXB8XqfedEUlaRKKuqFkIIodXpRXZRhdiZkive/uN0nceEL/pFPLP2qMgvrZL5bMhkf/88NuHzkWxHUVFRg9/ff2e1BRh+fn5QKpXIzs6uc3t2djYCAwPrfYyjoyMcHW17MbezZ8+iuroaMTExxts8PT3RtWvXOvsNGDCg0eOcOnUKgwYNqnPbkCFDmhzH/Pnz8dBDD2Hjxo0YM2YMbrnlFvTu3RsAcPToUWzdutXYsvN3Z86cQWRkJFJTU/HCCy9g3759yMvLM7bgnD9/vk5x9N/PIycnBxcvXsTo0aMbja02DgBwdXWFh4cHi9WtRHFlNZasT8KPiVkAAE9nNe4f2Qm3x3SEt6vDVfsrFRICPJwQ4OGE4RF+ePz6SBzOLMRHW89g06lsfHsgExtPZuPVm3phfM/6PzeIyHpZbZLj4OCA/v37Y/PmzZg6dSoAQK/XY/PmzVfVYJiFiwtQKlPzdivMuuzq6mr2Y/7dfffdh3HjxuHXX3/Fxo0b8dprr+Htt9/Go48+itLSUtxwww144403rnpc7RpRN9xwA0JDQ/HZZ58hKCgIer0ePXv2hEajafA8apdhuBa1Wl3nuiRJV3WDkeVJyS7BA18dQnpeGSQJmDM0DI9fHwkPJ/W1H1xDkiT06+iNz2cPwMGMfPzrpxNIzi7Bg/87hNlDQvGvSd3hoLKcXnxdmQ473XYCAEaUjoDSlV1WAFCmKYPbEsN2qaas1T/PyHpZbZIDGGpPZs+ejQEDBiAmJgbvvvsuysrKcPfdd5v/ySQJsII3UqdOnaBWq3HgwAF07NgRAFBUVISUlBSMHDmyyceJioq6qiB37969JsUSEhKCBx98EA8++CAWL16Mzz77DI8++ij69euHH374AWFhYfWO5rpy5QqSk5Px2WefYcSIEQCAXbt2XfP53N3dERYWhs2bN2PUqFEmxUqWbWPSZSz47gjKNToEeTrhg9v7on+oT4uOOSDMB7/MH46lG5Pxyfaz+DLhHNJyS7Hizv4mJU5EZLks5ydLM9x2221YunQpXnjhBURHR+PIkSP4/fffrypGtifu7u6YPXs2nnrqKWzduhVJSUm49957oVAoTFqi4MEHH0RqaiqeeuopJCcnY/Xq1Vi1alWTH79gwQL88ccfSE9PR2JiIrZu3YqoqCgAhqLk/Px8zJw5EwcOHMCZM2fwxx9/4O6774ZOp4O3tzd8fX3x6aefIi0tDVu2bMHChQub9LxLlizB22+/jffffx+pqalITEzEBx980OS4yfL8cOgCHvo6EeUaHYZ18cX/PTq8xQlOLbVSgcUTovDFnAFwdVBid9oV3PpxAi4XVZrl+C2lcFFgaM5QDM0ZCoWLVX9cE8nC6t818+bNw7lz51BVVYV9+/ZdVUdij5YtW4YhQ4Zg8uTJGDNmDIYNG4aoqCg4OTV9FELHjh3xww8/YN26dejTpw8+/vhjvPrqq01+vE6nwyOPPIKoqCiMHz8ekZGR+OijjwAAQUFB2L17N3Q6HcaOHYtevXphwYIF8PLygkKhgEKhwLfffotDhw6hZ8+eePzxx/HWW2816Xlnz56Nd999Fx999BF69OiByZMnIzU1tclxk2X5au85PLHmKHR6gen9O+DLu2Pg62b+urrrurXDdw8Mgb+7I05fLsHtn+1FTon8iY4kSXDwd4CDvwPXUSNqBkkIE8cn25Di4mJ4enqiqKgIHh4ede6rrKxEeno6wsPDTUoOLFFZWRmCg4Px9ttv495775U7HKtiS/8H1uanwxfw+HdHAQB3DwvD85O6Q6Fo3S/6zPxyzPh0L7IKKxDZzg3f3j8EPvUUNJO8ygpy4Pa+ocW+dH42XL0DZI6I2lpj399/Z/UtOXS1w4cP45tvvsGZM2eQmJiIO+64AwAwZcoUmSMjapqtp3Pw1JpjAIB7hoXjhcmtn+AAQIiPC1bPHYR2Ho5IyS7F7C/2o0Kja/XnbYi+So+UR1KQ8kgK9FUsjicyFZMcG1U72d+YMWNQVlaGnTt3ws/Pz2zHr52tuL6LKd1aRP90IqsID319CFq9wE19g/HcpKg27aoJ9XXF6rmD4ePqgONZRVj4/RHoZVoaQmgFLn50ERc/ugihtdtGd6Jms+rRVVS/vn374tChQ636HJ9//jkqKirqvc/HxzxFoWR/8ss0eOCrQ6is1mNEhB/enNa7TVpw/qmzvxs+uas/7vhsH347cRlv/5mMp8Z1a/M4qH5KhRITU/7aJmoIkxxqluDgYLlDIBuj1ekxb3UisgorEOrrguUz+0GtlK+xeWCYD167uReeWHMUH249gz4dvDC2BycMtAROKif8urrmyqeslaOGsbuKiCzCu5tSsefMFbg4KPHpXQPg6SL/XDW39O+Ae4eHAwCeXHMUmfnlMkdERKZgkkNEstt79go+3JYGAHhzWm90DXSXOaK/PDO+G/qEeKG4UotHvzkMjZYFwETWgkkOEcmqqLwaj393BEIAtw7ogMm9g+QOqQ4HlQLLZ/aFh5MKRzILsXwL512SW5mmDK7PAq7PGraJGsIkh4hk9fzPJ3CpqBLhfq548YYecodTrxAfF7x2s2Fh1w+3ncHxC0UyR0TlDoYLUWOY5BCRbDafysb6oxehkIB3b4uGq6PljoWY1Ls9JvduD51e4Ik1R1CllW/+HCJqGiY5NiYuLg4LFiyQOwyiayqt0uK5dScAAPeN6IQ+IV7yBtQE/57SE35uDkjJLsUHm9PkDoeIroFJDlmVJUuWIDo6Wu4wyAze/P00LhVVoqOPCx4fEyl3OE3i4+qA/0ztBQD4ZMcZnMktlTkiImoMkxxqdTqdDno9R6TQXw6dK8BXe88BAF67uRecHaxnQrdxPdrhum4BqNYJvPDzCdjx8n9EFo9Jjg3S6/V4+umn4ePjg8DAQCxZsgQAcM8992Dy5Ml19q2urkZAQABWrlwJwNDdNW/ePMybNw+enp7w8/PD888/X+eDvKqqCk8++SSCg4Ph6uqKQYMGYdu2bcb7V61aBS8vL6xfvx7du3eHo6Mjzp8/j4KCAsyaNQve3t5wcXHBhAkT6qwQXvu4devWISIiAk5OThg3bhwyMzON97/00ks4evQoJEmCJElYtWpV6/wRqdXo9QJL1idBCOCWfh0wrIv5lhtpC5IkYckNPeCoUmB32hX8cuyS3CERUQOY5JioTFPW4KVSW9nkfSuqK5q0b3N8+eWXcHV1xb59+/Dmm2/i3//+N/7880/cd999+P3333Hp0l8fyr/88gvKy8tx22231Xm8SqXC/v378d5772HZsmX4/PPPjffPmzcPCQkJ+Pbbb3Hs2DFMnz4d48ePr5OwlJeX44033sDnn3+OpKQkBAQEYM6cOTh48CDWr1+PhIQECCEwceJEVFdX13ncK6+8gv/+97/YvXs3CgsLMWPGDADAbbfdhieeeAI9evTApUuXcOnSpTpxk3VYm3gBx7OK4OaowqIJ1rlUQkdfFzwyqgsA4OVfTqKksvoaj2gmBeAZ6wnPWE9+Wv+NQlIgNgOIzTBsEzXEcocyWCi319wavG9ixET8evuvxusBSwNQXl3/DKmxobHYNmeb8XrYe2HIK8+7aj/xoulN4b1798aLL74IAIiIiMDy5cuxefNmvP766+jatSu++uorPP300wCA+Ph4TJ8+HW5uf51XSEgI3nnnHUiShK5du+L48eN45513MHfuXJw/fx7x8fE4f/48goIM85k8+eST+P333xEfH29cnLO6uhofffQR+vTpAwBITU3F+vXrsXv3bgwdOhQA8PXXXyMkJATr1q3D9OnTjY9bvnw5Bg0aBMCQcEVFRWH//v2IiYmBm5sbVCoVAgM5vb41Kqmsxpu/JwMA5o/uAn93R5kjar77R3bCT4ezkJ5XhhXbzuDp8eZP2JTOSvTd1tfsx7V2zmpnbFtVc2W5s5yhkIVjCmyDevfuXed6+/btkZOTAwC47777EB8fDwDIzs7Gb7/9hnvuuafO/oMHD66z6vOQIUOQmpoKnU6H48ePQ6fTITIyss7K49u3b8eZM2eMj3FwcKgTx6lTp6BSqYzJCwD4+vqia9euOHXqlPE2lUqFgQMHGq9369YNXl5edfYh67V8axrySqsQ7ueKOUPD5Q6nRZzUSjw7MQoAsHJXOi4W1r9gLRHJhy05Jipd3PBoin+uhpvzZE6D+/6ziTXjsYwWxfV3anXdNX8kSTIW/s6aNQuLFi1CQkIC9uzZg/DwcIwYMaLJxy4tLYVSqcShQ4egVNY937+3Bjk7O9dJlIgy88vxxa50AMC/JkbBQWX9v7HGRAUgJswH+zPy8fbGFLx9ax+5QyKiv2GSYyJXB1fZ920JX19fTJ06FfHx8UhISMDdd9991T779u2rc33v3r2IiIiAUqlE3759odPpkJOTY1JyFBUVBa1Wi3379hm7q65cuYLk5GR0797duJ9Wq8XBgwcRExMDAEhOTkZhYSGiogy/mB0cHKDTcRI2a/TOphRU6wSGdvbF6KgAucMxC0mS8OykKEz9cDd+PHwB9w4PR/cgD7MdX1emw96wvQCAwRmDoXS1nlForalMU4awpwzbGZoyuLq2zecnWR/r/ylFJrvvvvvw5Zdf4tSpU5g9e/ZV958/fx4LFy5EcnIyvvnmG3zwwQd47LHHAACRkZG44447MGvWLPz4449IT0/H/v378dprr+HXX3+96li1IiIiMGXKFMydOxe7du3C0aNHceeddyI4OBhTpkwx7qdWq/Hoo49i3759OHToEObMmYPBgwcbk56wsDCkp6fjyJEjyMvLQ1VVlZn/OtQaUrJL8NPhLADA0+O72VQrX3SIFyb3bg8hgNd+M3+3anVeNarzWqmw2YrluRouRI1hkmOHxowZg/bt22PcuHHG4uG/mzVrFioqKhATE4NHHnkEjz32GO6//37j/fHx8Zg1axaeeOIJdO3aFVOnTsWBAwfQsWPHRp83Pj4e/fv3x+TJkzFkyBAIIbBhw4Y63WsuLi545plncPvtt2PYsGFwc3PDd999Z7z/lltuwfjx4zFq1Cj4+/vjm2++McNfhFrb2xuTIYRhjploK5jZ2FRPj+sGtVLCztQ87D17xWzHVTgrMPDEQAw8MRAKZ35cE5lKEnY8k1VxcTE8PT1RVFQED4+6TcyVlZVIT09HeHg4nJycZIqwdZSWliI4OBjx8fG4+eab69wXFxeH6OhovPvuu20e16pVq7BgwQIUFha2+XM3xJb/D9rK0cxCTPlwNxQS8MeCkYho5y53SK3iuXXH8b+95zEo3AffPTBE7nBsWllBDtzebwcAKJ2fDVdv2+j+pKZr7Pv77/jTwI7o9Xrk5OTg5ZdfhpeXF2688Ua5QyI7sHSjYcj4TX072GyCAwCPjOoCB6UC+9LzsefM1dNBEFHbY5JjR86fP4927dph9erV+OKLL6BSse6cWteRzELsTM2DSiFhwZgIucNpVe09nTEjJgQA8O6fqWZZ7kGv0SN9STrSl6RDr+HSKESm4recHQkLC7vmB+/fl2doa3PmzMGcOXNke34yv+VbDCt1T+0bjBAfF5mjaX0Px3XBtwcysT8jH3vOXGnxkhWiWuDcS4Y1vjo+1RFwMEeURPaDLTlE1CpOXSrGplPZkCTgobjOcofTJgI9nXB7jKEA/71NqdfYm5pLISkwIAsYkMVlHahx/O8golbx4VZDK87EXu3R2b/h5VBszYOxnaFWStifkY9D5wrkDscmOaudceAz4MBnhm2ihjDJISKzO5Nbil+PGxaCnVezkKW9CPR0wtToYADAJ9vPXGNvImpNTHKIyOxWbDsDIQzLHkS1N98MwNbigdhOAIA/T2UjLafhpWCIqHUxySEis7pcVIl1NbMbP2JnrTi1ugS44/ru7SAE8OkOtuaYW3l1OcIWAGELDNtEDWGSQ0Rm9WVCBrR6gZhwH/Tt6C13OLJ5sKY156fDWbhcVClzNLZFCIFzXsA5L5hlqD7ZLiY5VMeqVavg5eUldxhNsmTJEkRHR5v0GEmSsG7dulaJh4ByjRar950HANw7PFzmaOTVP9QHA8O8Ua0T+GJ3utzhENklJjlktZ588kls3rxZ7jDob35IzEJRRTVCfV0wJqqd3OHI7sFYw9D51fvOo7RKK3M0RPaHSQ5ZLTc3N/j6+sodBtXQ6wXidxlaLO4eGgalwnZWGm+uUV0DEO7nitIqLX5MvCB3OER2h0mOjYmLi8O8efMwb948eHp6ws/PD88//7yx37qgoACzZs2Ct7c3XFxcMGHCBKSm1j9pWUZGBhQKBQ4ePFjn9nfffRehoaHQ6/XYtm0bJEnC5s2bMWDAALi4uGDo0KFITk6u85gVK1agc+fOcHBwQNeuXfHVV1/VuV+SJHzyySeYPHkyXFxcEBUVhYSEBKSlpSEuLg6urq4YOnQozpz5q4jzn91VBw4cwPXXXw8/Pz94enoiNjYWiYmJLflzkgm2JufgbF4Z3J1UmD4gRO5wLIJCIWH2kFAAwJd7MqDXs36EqC0xyTGRrkxn8kWv/WvNGb1Wb7i9Qtek4zbHl19+CZVKhf379+O9997DsmXL8PnnnwMwLJ1w8OBBrF+/HgkJCRBCYOLEiaiurr7qOGFhYRgzZgzi4+Pr3B4fH485c+ZAofjr3+df//oX3n77bRw8eBAqlQr33HOP8b6ffvoJjz32GJ544gmcOHECDzzwAO6++25s3bq1znFffvllzJo1C0eOHEG3bt1w++2344EHHsDixYtx8OBBCCEwb968Bs+7pKQEs2fPxq5du7B3715ERERg4sSJKCkpadbfkUyzsqYV5/aYjnB15IoxtW7p3wGuDkqcyS3DrjQu3EnUlvhJZKKdbjtNfkz377sjYHoAACDvpzycvPUkPGM90XdbX+M+e8P2ojrv6kQjTsSZ/HwhISF45513IEkSunbtiuPHj+Odd95BXFwc1q9fj927d2Po0KEAgK+//hohISFYt24dpk+fftWx7rvvPjz44INYtmwZHB0dkZiYiOPHj+Pnn3+us98rr7yC2NhYAMCiRYswadIkVFZWwsnJCUuXLsWcOXPw8MMPAwAWLlyIvXv3YunSpRg1apTxGHfffTduvfVWAMAzzzyDIUOG4Pnnn8e4ceMAAI899hjuvvvuBs/7uuuuq3P9008/hZeXF7Zv347Jkyeb+mckEyRfLsGeM1egVEiYPTRM7nAsiruTGtMHhGDVngx8uScDIyP9m/5gCXDp7mLcJgNJktA9569tooawJccGDR48uM4bf8iQIUhNTcXJkyehUqkwaNAg432+vr7o2rUrTp06Ve+xpk6dCqVSiZ9++gmAYfTVqFGjEBYWVme/3r17G7fbt28PAMjJMXwKnTp1CsOGDauz/7Bhw656zr8fo107Q9Fqr1696txWWVmJ4uLiemPNzs7G3LlzERERAU9PT3h4eKC0tBTnz5+vd38yn//tNSwiObZ7OwR5cZr9f5pV02W1JTkH566UNflxShclYpJiEJMUA6WLsrXCszouahckfQQkfWTYJmoIW3JMNKJ0hMmPkRz/Sjj8bvIzHOMf6eXgjMEtDa1VODg4YNasWYiPj8fNN9+M1atX47333rtqP7VabdyuTbD0ev1V+zWmvmOYctzZs2fjypUreO+99xAaGgpHR0cMGTIEGo3GpDjINGVVWvxUM/nfnYNDZY7GMnXyd0NspD+2p+Tivwnn8Pzk7nKHRGQX2JJjIqWr0uSLQvXXn1mhUhhud1Y26bjNsW/fvjrXa+tTunfvDq1WW+f+K1euIDk5Gd27N/yhe99992HTpk346KOPoNVqcfPNN5sUT1RUFHbv3l3ntt27dzf6nM2xe/duzJ8/HxMnTkSPHj3g6OiIvDzWQLS2dUeyUFqlRSd/VwztzNFuDZkzLAwA8P3BTJRrOJycqC2wJccGnT9/HgsXLsQDDzyAxMREfPDBB3j77bcRERGBKVOmYO7cufjkk0/g7u6ORYsWITg4GFOmTGnweFFRURg8eDCeeeYZ3HPPPXB2Nq074qmnnsKtt96Kvn37YsyYMfi///s//Pjjj9i0aVNLT7WOiIgIfPXVVxgwYACKi4vx1FNPmRwrmUYIga8SDF1VdwwKZX1EI2Ij/BHq64JzV8rxy7FLuLUJI9B05TocGngIAND/QH92WdUory7HQEOJHw5Ul8MFrvIGRBaLLTk2aNasWaioqEBMTAweeeQRPPbYY7j//vsBGEZG9e/fH5MnT8aQIUMghMCGDRvqdAvV595774VGo6kzaqqppk6divfeew9Lly5Fjx498MknnyA+Ph5xcXHNOb0GrVy5EgUFBejXrx/uuusuzJ8/HwEBAWZ9Dqor8XwBTl8ugZNagWn9OsgdjkVTKCTMGNgRAPDN/ibWiQmg/GQ5yk+WAxx9biSEwMkA4GQAl3WgxknCjv9DiouL4enpiaKiInh41F0pubKyEunp6QgPD4eTk5NMEZouLi4O0dHRePfdd8163Jdffhlr1qzBsWPHzHpcS2et/wdt5fHvjuCnw1mY3r8D3preR+5wLF5uSRWGvLYZWr3Ab4+NuOYK7UInULizEADgNcILkpItZQBQVpADt/cNgxNK52fD1Zs/ZuxNY9/ff8eWHGpUaWkpTpw4geXLl+PRRx+VOxyyIPllGvx6/BIAFhw3lb+7I8b2MHw5N6U1R1JK8I7zhnecNxMcomZgkkONmjdvHvr374+4uLhmdVWR7fox8QI0Wj16BnugT4iX3OFYjZkxhi6rnxKzUKFp3oSfRNQ0LDy2Mdu2bTPr8VatWoVVq1aZ9Zhk/YQQ+P5gJgAY60yoaYZ19kOIjzMy8yvwy7GLjS6Boa/W49Knhtay9ve3h0LN36VEpuA7hohMduxCEVKyS+GoUuCGPkFyh2NVTClAFhqB1HmpSJ2XCqGx2/JJomZjknMNdlyXTeDr35A1hwytOON7BsLTufGReXS16QM6QKWQkHi+EMmXubaaqSRJQmghEFrIZR2ocUxyGlA7pLq8vFzmSEhOta//tYbY25PKah1+PnIRAJo01wtdLcDdCdd1M4wI+iHxgszRWB8XtQsy3gUy3uWyDtQ41uQ0QKlUwsvLy7j+kouLC38x2BEhBMrLy5GTkwMvLy8olZyErdYfSZdRUqlFsJczhnTiDMfNdUv/Dth4Mhs/Jmbh6XFdoVLyNyeRuTHJaURgYCCAvxaaJPvj5eVl/D8gg9qC4+kDOkChYOLfXKO6BsDH1QF5pVXYmZqHUd041wuRuTHJaYQkSWjfvj0CAgJQXV0tdzjUxtRqNVtw/iEzvxy7065AkoBp/TnDcUs4qBS4sU8QVu3JwNrEC0xyTFBRXYGRcw3bO6or4MxlHagBTHKaQKlU8suOCMDaQ4b6kWGd/dDBm7UQLTWtfwes2pOBP5OyUVReDU8X1n41hV7ocTD4r22ihrATmIiaRK8XxiLZ6QPYimMOPYI80C3QHRqdHv937KLc4RDZHCY5RNQkB88V4EJBBdwcVRjXg3VK5iBJEm6pWdi0tpWMiMyHSQ4RNcm6I1kAgAk9A+GkZvetuUzpGwSlQsKRzEKcyS2VOxwim8Ikh4iuqUqrw6/HDMsL3NQ3WOZobEuAuxNiI/0BAD+wNYfIrJjkENE1bUvORVFFNdp5OGIQ58Yxu9ouq58OZ0Gv5yzbRObCJIeIrunnmq6qKdHBUHJuHLMbHRUAd0cVLhVV4uC5gjr3qf3UUPtx1NU/+ZUZLkSNYZJDRI0qqqjGplOGCTGnRrOrqjU4qZUY19NQzF2bUAKA0lWJYbnDMCx3GJSurIOq5ergity3gNy3DNtEDWGSQ0SN+v3EJWi0ekS2c0NUe3e5w7FZN9as5r7h+CVU6zj3C5E5MMkhokatO2yYv2Vq32Cu39aKhnb2hZ+bAwrKq7ErNU/ucIhsApMcImrQpaIK7E2/AsBQj0OtR6VUYHJvQ2tObZeVrkKHw3GHcTjuMHQVOjnDsygV1RWImwPEzTFsEzWESQ4RNWj9kYsQAogJ90Gwl7Pc4di8G2q6rDaezEaFRgfogaLtRSjaXgSwB8tIL/TYHgZsD+OyDtQ4rl1FRA36pWZunCnRQTJHYh/6dfRCB29nXCiowKZT2ZjUIxDdv+8OAJAc2VVIZCq25BBRvc5dKcPxrCIoFRLGcxmHNiFJkjGhXH/0IhQqBQKmByBgegAUKn5cE5mK7xoiqldtK87Qzr7wdXOUORr7cWMfQ+3TtuQcFJVXyxwNkXVjkkNE9apdxmFSr/YyR2Jfuga6o1ugO6p1Ar8fvYicNTnIWZMDvZa1J0Smstok55VXXsHQoUPh4uICLy8vucMhsilnc0tx8lIxVAqJK47LoLYA+Y8jl3Hy1pM4eetJiCou90BkKqtNcjQaDaZPn46HHnpI7lCIbM6G44ZWnGFd/ODt6iBzNPZnQs3sx3vPXJE5EsvlojFciBpjtaOrXnrpJQDAqlWr5A2EyAbV1uNM6s2uKjl08ndDt0B3nD1fIncoFsnVwRVlr9ZceZbLOlDDrLYlpzmqqqpQXFxc50JEdaXllOD05RKolRLGdWdXlVxYC0XUcnaV5Lz22mvw9PQ0XkJCQuQOicji/HrsMgBgeBc/eLpw9Wu5TGCSQ9RiFpXkLFq0CJIkNXo5ffp0s4+/ePFiFBUVGS+ZmZlmjJ7INvxyzLBWVe0SAySPLgFuiAxwkzsMi1SprcSk24FJtxu2iRpiUTU5TzzxBObMmdPoPp06dWr28R0dHeHoyPk+iBqSkl2C1JxSOCgVGNO9ndzh2D3DyLZLcodhcXR6HTZE/rVN1BCLSnL8/f3h7+8vdxhEdqu24HhkpB88ndlVJbexPdrhUk2SU1xZDW9XpcwREVkXi+quMsX58+dx5MgRnD9/HjqdDkeOHMGRI0dQWloqd2hEVuuPE4Z6nAk9WQ9iCbq0czdubz2dI2MkRNbJolpyTPHCCy/gyy+/NF7v27cvAGDr1q2Ii4uTKSoi65WeV4bk7BKoFBLGRLGrytL8kZSNm4d1lDsMIqtitS05q1atghDiqgsTHKLm+SPJ0IozpLMvR1VZoN1peSip5FpWRKaw2iSHiMzr95quqrFcxsEiabR6bD7FLisiUzDJISJcLqrEkcxCSBIwjqOqLFZtaxsRNQ2THCLCxpOGL8++IV4I8HCSORqqpXRVIk7Ewe9CX2gcgO0puais5pBpVwdXiCWAWGLYJmoIkxwiMrYQjO/JripL1CPIA0GeTijX6LA7LU/ucIisBpMcIjtXUKbB3rP5AGonnyNLI0mSsVZqY1K2zNEQWQ8mOUR2bvPpHOj0At0C3RHqy6Z/S6Kr1CFpehKSpifh+s6GiVI3ncqGTi9kjkxeldpKTJ8OTJ/OZR2ocUxyiOxcbVcVW3EskA7IXZuL3LW56B/iDU9nNa6UaXDoXIHckclKp9dhbQ9gbQ8u60CNY5JDZMfKNVrsSMkFwCTHEkkOEiKWRyBieQQcnJUY3S0AALCRo6yImoRJDpEd256ciyqtHh19XBDV3v3aD6A2pVArEPxIMIIfCYZCrcDYHobh/RtPZkMI++6yImoKJjlEdux3Y1dVO0iSJHM0dC0jI/3hqFLgfH45krNL5A6HyOIxySGyUxqtHltqZtDl0HHLJHQCBdsKULCtAEIn4OKgwogIQwEyR1kRXRuTHCI7tS/9CkqqtPBzc0TfEG+5w6F66Cv1ODrqKI6OOgp9pR4A/tZlxbocomthkkNkp2rXQRrdLQAKBbuqrMXobgFQSMCJrGJcKCiXOxwii8Ykh8gOCSHw50lDd8foqACZoyFT+Lo5YkCYDwAYX0N746J2QekrQOkrhm2ihjDJIbJDydklyCqsgKNKgeERfnKHQyYaW7OIqr3W5UiSBNdqwLUaLJinRjHJIbJDtV1Vw7r4wcVBJXM0ZKraOY32Z+SjsFwjczRElotJDpEdYleVdQvxcUHXdu7Q6QW210zmaE+qtFWYMxWYM9WwTdQQJjlEdia3pApHLxQCAEZ3aydvMNRs19UkqLWtcvZEq9fiy2jgy2jDNlFDmOQQ2Zmtp3MgBNAr2BOBnk5yh0PNVLvEw7bkHGh1epmjIbJMTHKI7Myfp9hVZQv6dvSGt4saxZVaHLTzBTuJGsIkh8iOVFbrsCs1DwAwJopdVdZMqZAwqqshUd1y2v66rIiagkkOkR1JOHMFFdU6tPd0Qo8gD7nDoRb6qy7HPoeSE10LkxwiO1LbVXVdtwDOL2IDRkb6Q6WQcCa3DBl5ZXKHQ2RxmOQQ2QkhhHFBzjHd2VVlCzyc1IgJN8x+vJldVkRXYZJDZCeSLhbjcnElXByUGNLJV+5wqAkULgoMzRmKoTlDoXCp/+P6um61dTn202XlonZBzptAzptc1oEaZ9JUp+Hh4c1q4l6wYAHmz59v8uOIyHxqJwAc3sUPTmqlzNFQU0iSBAd/h0b3GRPVDv/59RT2nc1HcWU1PJzUbRSdfCRJgn+58YqssZBlMynJWbVqVbOeJCwsrFmPIyLz2VzzS59dVbYlzM8VnfxdcTa3DDtT8jCpd3u5QyKyGCYlObGxsa0VBxG1ouziSpzIKoYk/dW9QZZPX6VH2sI0AECXZV2gcKy/y2pMVDt8mnsWm09l20WSU6WtwsKJhu1l2io4wlXegMhisSaHyA5sSzYUpfbu4AU/N0eZo6GmElqBix9dxMWPLkJoRYP71SauW5NzoNM3vJ+t0Oq1+CgG+CiGyzpQ47j8MJEd2HrasIjjqK7+MkdCppDUEkJfDDVuN6R/qDc8nFQoKK/GkcwC9A/1aasQiSwaC4+JbJxGq8euNMMsx7Uz5JJ1UDgoEL4k/Jr7qZUKxHUNwPqjF7HpVA6THKIaLDwmsnEHz+WjtEoLX1cH9Ar2lDscaiWjowxJzuZT2XhmfDe5wyGyCCw8JrJx25INXVWxXf2hUHC4rTUReoHyU4ax0i5RLpAaef1iI/2hkICU7FJkFVYg2Mu5rcIkslgtKjyurq5GZmYmkpOTkZ+fb66YiMiMttbMhMuuKuujr9DjQM8DONDzAPQV+kb39XJxQN+O3gD+KjQnsncmJzklJSVYsWIFYmNj4eHhgbCwMERFRcHf3x+hoaGYO3cuDhw40BqxEpGJMvPLkZpTCoUEjIxg0bGti4s0vMa1rXdE9s6kJGfZsmUICwtDfHw8xowZg3Xr1uHIkSNISUlBQkICXnzxRWi1WowdOxbjx49Hampqa8VNRE2wLcXwZdc/1BueLrY/E669G1UzlHxPWh6qtDqZo2k9zmpnpL8LpL9r2CZqiEk1OQcOHMCOHTvQo0ePeu+PiYnBPffcg48//hjx8fHYuXMnIiIizBIoEZlue023RRy7quxC9/Ye8HNzRF5pFQ5mFGBYFz+5Q2oVCkmBsMKaKxKne6OGmZTkfPPNN03az9HREQ8++GCzAiIi86is1mF32hUArMexFwqFhNhIf/yQeAHbknNsNskhaiqzpcCff/65uQ5FRGawPz0fFdU6tPNwRFR7d7nDoTYS19X263I0Og2euh546nrDNlFDzJbk/PLLL9iyZYvxenl5OWbMmGGuwxORibYm/zWqqjmTeJJ1GhlhGEqemlOKCwXl136AFarWVWPpMGDpMMM2UUPMluT897//xQsvvIDTp08jJSUFI0eOxLhx48x1eCIyUe0vedbj2BdPFzX6GYeS225rDlFTtHjtqscffxzR0dHo06cPPv/8c9xxxx3Q6/WIj49HdHS0GUIkIlOl55UhPa8MaqWEYV185Q6H2lhcV38cPFeAbcm5uHNwqNzhEMmmxS05o0aNQmZmJl599VVMnz4d6enpCAoKwh9//IFff/3VHDESkYlqJ4MbGOYDdycOHbc3ta13e87Y9lByomtpcUvOjTfeiBtvvNF4vbKyEidOnMCxY8ewadMmTJo0qaVPQUQm2ppcu+o4u6rsUff2HvB3d0RuSRUOpBdgeARHWZF9anGS809OTk4YMGAABgwYYO5DE1ETlGu02Hu2Zuh4N85ybI9qh5KvPWQYSs4kh+yVyd1V7u7uGDlyJB5//HF89dVXSEpKghCiNWIjomZIOHMFGq0eHbyd0dnfTe5wSCbGoeQpLD4m+2VyS84bb7yBQ4cOYcuWLVi+fDn0ej2cnZ3Ru3dv9O/fH/369UO/fv3Qp0+f1oiXiK6BQ8dth8JZgYEnBhq3TTGii2EoeVpOKTLzyxHi49IaIcrCWe2MEx/WbD/BZR2oYSYnOQ8//LBxu6KiAq6urnj00UeRn5+PvXv34vPPP4dGo4FOx2I3orYmhMDW0zX1OOyqsnqSQoJrD9dmPbZ2KPnBcwXYlpKLu2xolJVCUqBHbQMVl3WgRrSoJsfZ2ZBBz5w5E7179wYAaLVanDx5suWREZHJzuaVIauwAg5KBQZ34tBxezeqWwAOnivA9uQcm0pyiJrK7CmwSqUyJjxE1LZ21NRfDAz3houD2ccVUBvTa/RIX5KO9CXp0Gv0Jj8+NtLQmrfnzBWbGkqu0WmwJA5YEsdlHahxbOcjsiE7U/MAGKb2J+snqgXOvXQO5146B1Ft+gCPHkGGoeTlGh0OpBe0QoTyqNZV46U44KU4LutAjTM5ybnvvvuwYsUKHDhwAFVVVQDA4kYiC1Cl1SHhjGHo+AgmOTZBUkkIejgIQQ8HQVKZ/jkrSZKxNae2IJ3Inpjcnp2amoo1a9agpKQEKpXh4S+99BLi4uLQr18/REdHw8XFdqr4iazFoYwCVFTr4O/OVcdthcJRgcgPI1t0jFFdA4zz5Tw/ubuZIiOyDiYnOdu3bwdgSHYOHTqExMREJCYm4oUXXkBhYSGUSiUiIyORlJRk9mCJqGHbUw31OCMi/Ni6SkbDu/hBIQFncg1F6cFeHHJN9qPZlYkRERGIiIjAjBkzjLelp6fj4MGDOHz4sFmCI6Km25liqMep7Z4g6yeEQHWeoeZE7aduVvLq6aJGnxAvHD5fiB0puZgZ09HcYRJZLJNqcs6fP9/o/eHh4Zg+fTpeffVVAEBWVlbzIyOiJsstqcLJS8UAgGFdOIW/rdCX67EnYA/2BOyBvtz00VW1agvRd6Zy9mOyLyYlOQMHDsQDDzyAAwcONLhPUVERPvvsM/Ts2RM//PBDiwMkomur/fLqGewBPzdHmaMhSzOypnVvV2oetLrmJ0tE1sak7qqTJ0/ilVdewfXXXw8nJyf0798fQUFBcHJyQkFBAU6ePImkpCT069cPb775JiZOnNhacRPR39TOj8Oh41SfPh084eGkQnGlFkcvFKF/qLfcIbWIk8oJ+z+t2V7oJG8wZNFMasnx9fXFsmXLcOnSJSxfvhwRERHIy8tDamoqAOCOO+7AoUOHkJCQwASHqI3o9QK70gz1OBw6TvVRKRXGlch32MCCnUqFEgMvAgMvGraJGtKswmNnZ2eMHz8e06ZNM3c8RGSik5eKkVeqgauD0up/oVPrGRnhjw3HL2NHai4ev75lw9KJrEWzZzz29PRkzQ2RBdhRU48zpLMvHFScxJzqV1uXczSzEEXl1j1LsEanwVtDgbeGclkHalyzPxGFEPjkk08wbNgwDB8+HAsWLGi0IJmIWoexHodDx6kRQV7O6BLgBr2AsXvTWlXrqvH0WODpsVzWgRrXop99hw8fRr9+/TB8+HAkJSVhxIgRePLJJ80VGxFdQ1mVFofOGdYkYj0OXUttYbot1OUQNUWLlilevXo1rr/+euP1Y8eOYcqUKQgODsbjjz/e4uCIqHF7z15BtU4gxMcZYb5cToUaNzLSD1/sTseO1FwIITgzNtm8Zrfk+Pj4ICQkpM5tvXv3xvLly7FixYoWB0ZE1/b3oeP8wqJrGRRuqNu6VFSJtJxSucMhanXNTnKio6MRHx9/1e1dunS55szIRGQeO1INtRWsx6GmcHZQYlC4DwBgO7usyA40O8n5z3/+g/fffx933XUXEhISUFZWhpycHLz66qsIDw83Z4xEVI/M/HKk55VBqZAwpLOv3OGQlTDW5aRad/ExUVM0O8kZPHgw9u7di8zMTIwYMQIeHh5o37491q5di7ffftucMRJRPWqHjvfr6AUPJ7XM0ZC1qG3123f2CiqrdTJHQ9S6WlR43KdPH2zbtg05OTk4dOgQ9Ho9Bg0aBD8/LhBI1Nq4lIPtUzgp0GdrH+O2OUS2c0OghxMuF1dif3q+VXZ1OqmcsHVVzTaXdaBGmOVdExAQgAkTJmDSpEltkuBkZGTg3nvvRXh4OJydndG5c2e8+OKL0Gg4KRTZB61Ojz1pVwCwHseWSUoJ3nHe8I7zhqQ0T2G5JEkYUbPEg7WuSq5UKBGXAcRlcFkHapxVTo96+vRp6PV6fPLJJ0hKSsI777yDjz/+GM8++6zcoRG1iSOZhSip0sLLRY2ewZ5yh0NWpjYx3pHCuhyybS3qrpLL+PHjMX78eOP1Tp06ITk5GStWrMDSpUtljIyobdR2VQ3v4gelgkPHbZW+Wo9Ln14CALS/vz0UavP8Lh3exQ+SBCRnl+ByUSUCPa2ry6daV41PBxq279dVgxVp1BCzvGNSUlKg1WrNcahmKyoqgo+PT6P7VFVVobi4uM6FyBpt59BxuyA0AqnzUpE6LxVCI8x2XG9XB/SuaQHcYYVdVhqdBvMmAfMmce0qapxZkpyoqCicPXvWHIdqlrS0NHzwwQd44IEHGt3vtddeg6enp/Hyz8kMiaxBYbkGxy4UAoCxtoJslBLwn+YP/2n+gJlLT/7qsrK+JIeoqcyS5Ahhnl8YixYtgiRJjV5Onz5d5zFZWVkYP348pk+fjrlz5zZ6/MWLF6OoqMh4yczMNEvcRG1pV1oehDCMkmnv6Sx3ONSKlE5K9FjTAz3W9IDSybxZTm2SsystDzq9+VqJiCyJRdXkPPHEE5gzZ06j+3Tq1Mm4ffHiRYwaNQpDhw7Fp59+es3jOzo6wtHRsaVhEsmKQ8fJHKJDvODuqEJheTWOZxUhOsRL7pCIzM6ikhx/f3/4+zftgzsrKwujRo1C//79ER8fD4XCKgeKEZlECGEcEcN6HGoJtVKBoV188UdSNnak5DLJIZtklZlBVlYW4uLi0LFjRyxduhS5ubm4fPkyLl++LHdoRK0qLacUl4sr4ahSICa88UJ7sn66Mh22SduwTdoGXZn5ZydmXQ7ZOotqyWmqP//8E2lpaUhLS0OHDh3q3Geu+iAiS1S7qGJMuA+c1JwEjVqmtsvzcGYhiiuruTwI2RyrbMmZM2cOhBD1XohsWe2iirHsqiIzCPFxQSc/V+j0AnvSrGdiQEeVI375Gvjla8M2UUOsMskhskeV1TrsO8ulHMi8av+XtlvR7McqhQqTUoFJqYZtooaYJcl55pln4Ovra45DEVEDDmTko0qrR6CHEyIC3OQOh2zEyMi/1rFiazjZGrOkwK+99po5DkNEjagtDh0R4QdJ4lIOZB6DO/lCrZRwoaAC6Xll6ORv+Ql0ta4aX0cbtu/gsg7UCHZXEVkJDh2n1uDioMKAUMNIPWsZZaXRaXD3VODuqVzWgRrHJIfIClwuqkRydgkkybC4IpE51SbOO1Otpy6HqCmY5BBZgZ01iyj2DvaEt6uDzNGQramty0k4ewUarV7maIjMh0kOkRXYwVXHqRVFBXrAz80R5RodDp7LlzscIrNhkkNk4XR6gV01LTlMcqg1KBSScUX7HVY0lJzoWkwaXRUeHt6sUR0LFizA/PnzTX4cEQEnsopQUF4NN0cV1xeiVjMy0g8/Hc7CztRcLJrQTe5wiMzCpCRn1apVzXqSsLCwZj2OiP6qxxna2RdqJRtfqXWMqFniIeliMXJLquDvzpmEyfqZlOTExsa2VhxE1AAOHbdfkqOE7t93N263Jj83R/QI8kDSxWLsSsvFTX07XPtBMnFUOeL772u2FzIZo4ZxPmwiC1ZSWY3E8wUAuF6VPVKoFAiYHtBmzzciwh9JF4uxIyXPopMclUKF6SdrrnBZB2oE276JLFjCmSvQ6gXCfF0Q4uMidzhk4/5a4iEPej2XeCDrx8JjIgu2g6Oq7Jpeq0feT4buSr+b/KBQte7v0gGhPnBxUCKvtAqnLhejR5Bnqz5fc2n1Wvxk6MXDTXotuySoQSw8JrJgxnqcCCY59khUCZy81dAvM6J0RKsXGDioFBjSyRebT+dgR0qexSY5Vdoq3HqrYbtUW8UkhxrEwmMiC5WRV4bz+eVQKyUM6ewrdzgkBwXgGetp3G4LIyL8apKcXDwU17ltnpSolbQoAa6ursbly5dRXl4Of39/+Pj4mCsuIrtXO3S8X0dvuDryt6o9Ujor0Xdb3zZ9ztqu0YPn8lGu0cLFgf97ZL1M/m1QUlKCFStWIDY2Fh4eHggLC0NUVBT8/f0RGhqKuXPn4sCBA60RK5Fd2c6h4ySDcD9XdPB2RrVOYO/ZK3KHQ9QiJiU5y5YtQ1hYGOLj4zFmzBisW7cOR44cQUpKChISEvDiiy9Cq9Vi7NixGD9+PFJTU1srbiKbptHqkXDGkORw6Di1JUmSjBMDcokHsnYmtUMeOHAAO3bsQI8ePeq9PyYmBvfccw8+/vhjxMfHY+fOnYiIiDBLoET2JPF8Aco0Ovi6OqB7ew+5wyGZ6Mp02Bu2FwAwOGMwlK7KNnne2Eg/fLP/PHak5LbJ8xG1FpOSnG+++aZJ+zk6OuLBBx9sVkBE9Fc9zvAIPygUrTvTLVm26rzqNn/OoV38oFRIOJtXhsz8cs7RRFaLkwESWSAOHSc5eTip0bdmMdidqZbXZeWgdED8OiB+nWGbqCFmKZsvKCjAxo0bkZWVBQAICgrCuHHj4O3tbY7DE9mVK6VVOHGxCIBhOC+RHEZE+OPguQLsSMnF7YM6yh1OHWqlGnOO1FxRquUMhSxci1tyVq5ciSFDhmDfvn3Q6/XQ6/XYt28fhg4dipUrV5ojRiK7sistD0IAUe09EODhJHc4ZKdql3jYfSYPWp1e5miImqfFLTlvvvkmEhMT4erqWuf2l19+Gf369cO9997b0qcgsivbU2qXcmArDsmndwcveLmoUVhejaMXCtE/1HLmQdPqtfijZkzLOC7rQI1ocUuOJEkoKSm56vaSkpJmrXNFZM+EEMYaiFjW45CMlAoJw7oYEu3tFjaUvEpbhcl3AJPvMGwTNaTFCfDSpUsRGxuLnj17Ijg4GABw4cIFJCUl4e23325xgET25NSlEuSWVMFZrUT/MNa0kbxGRvjh12OXsCMlFwuvj5Q7HCKTtTjJmTx5MiZMmID9+/fj4sWLAAyFxzExMVAq22ZOByJbUbvq+JDOvnBU8f1D8qqdbfvYhUIUlmvg5cKRTGRdTE5y3N3d0bdvX/Tv3x/9+vVDv3790L17dwwZMqQ14iOyK7WTr43kqCqyAO09nRER4IbUnFLsTruCSb3byx0SkUlMTnLeeOMNHDp0CFu2bMHy5cuh1+vh7OyM3r1710l8+vTp0xrxEtmsco0WBzMKAHC9KrIcIyP9kZpTih0puUxyyOqYnOQ8/PDDxu2Kigq4urri0UcfRX5+Pvbu3YvPP/8cGo0GOp3OrIES2bq9Z69Ao9Ojg7czwv1cr/0AojYwIsIPK3elY0dqLoQQHFBCVqVFNTnOzs4AgJkzZ6J3794AAK1Wi5MnT7Y8MiI7s+Nvq47zi4QsxaBwXzioFLhUVIm0nFJEtHOXOySiJjP79AIqlcqY8BBR0/1Vj8OuKjKQHCRELI8wbsvB2UGJQeE+2Jmahx2peRaR5DgoHbD815rtx1kMTQ3jHEpEFiAzvxxn88qgVEgY2sVX7nDIQijUCgQ/Eix3GBgZ4W9IclJyce/wcLnDgVqpxiMHaq5wWQdqhMmTAd53331YsWIFDhw4gKoqwyRMbFonapnaoeP9OnrBw4kf2mRZRtTMvr0v/Qoqq1lvSdbD5Jac1NRUrFmzBiUlJVCpDA9/6aWXEBcXh379+iE6OhouLi5mD5TIlrGriuojdAKFOwsBAF4jvCAp5flB2bWdO9p5OCK7uAoHMvIxQub/U51eh51hhu0Reh04oxQ1xOQkZ/v27QAMyc6hQ4eQmJiIxMREvPDCCygsLIRSqURkZCSSkpLMHiyRLarW6bEn7QoADh2nuvSVehwddRQAMKJ0BJSu8nydS5KEERH+WHvoAnam5sme5FRqKzFqjmG7VFsJV3jIGg9ZrmbX5ERERCAiIgIzZsww3paeno6DBw/i8OHDZgmOyB4cySxESZUW3i5q9Az2lDscsiQS4NLdxbgtpxERflh76AJ2pOTi2YlR8gZD1EQmJTnnz59Hx44dG7w/PDwc4eHhmD59OgAgKyvLuJ4VEdWvtqtqeIQ/lArWt9FflC5KxCTFyB0GAGBEhD8kCTh9uQTZxZVo5+Ekd0hE12RS4fHAgQPxwAMP4MCBAw3uU1RUhM8++ww9e/bEDz/80OIAiWwdl3Iga+Dj6oBeNS2Ntf+zRJbOpJackydP4pVXXsH1118PJycn9O/fH0FBQXByckJBQQFOnjyJpKQk9OvXD2+++SYmTpzYWnET2YT8Mg2OZRUBYD0OWb6REf44dqEIO1PzMH1AiNzhEF2TSS05vr6+WLZsGS5duoTly5cjIiICeXl5SE1NBQDccccdOHToEBISEpjgEDXBrrQ8CAF0C3Rn8z9dRVeuw/4e+7G/x37oyuUfuj2iprVxV1oe9HohczRE19aswmNnZ2eMHz8e06ZNM3c8RHbF2FXFVhyqjwDKT5Ybt+XWL9Qbbo4q5JdpcOJiEXp38JI7JKJGmTwZYC1PT0/W3BC1gBACO1M5Pw5ZD7VSgSGdDTNyy1mXo1aq8eZG4M2Nhm2ihjQ7yRFC4JNPPsGwYcMwfPhwLFiwoNGCZCKqKzm7BNnFVXBSKzAgzFvucIiapLbVcUdqnmwxOCgd8NQe4Kk9hm2ihjQ7yQGAw4cPo1+/fhg+fDiSkpIwYsQIPPnkk+aKjcim1f4SHtzJF05qztlK1qF2FGDiuQKUVFbLHA1R41q0QOfq1atx/fXXG68fO3YMU6ZMQXBwMB5//PEWB0dky3akGH4Js6uKrEmorytCfV1w7ko5Es5cwdgegW0eg06vQ2KQYbsfl3WgRjS7JcfHxwchIXWHEPbu3RvLly/HihUrWhwYkS2r0OiwPyMfAIuOyfrUJua1C8u2tUptJWLuB2LuN2wTNaTZSU50dDTi4+Ovur1Lly44f/58i4IisnV7069Ao9Uj2MsZnf1d5Q6HyCS1iflOGetyiJqi2d1V//nPfzBq1ChcvHgRDz/8MHr37o2ysjK8+uqrCA8PN2eMRDZne3Lt0HE/SBKXciDrMriTD1QKCeeulOPclTKE+jJRJ8vU7JacwYMHY+/evcjMzMSIESPg4eGB9u3bY+3atXj77bfNGSORzdleU3QcGxkgcyREpnN3UqNfqGFEIJd4IEvWosLjPn36YNu2bcjJycGhQ4eg1+sxaNAg+PlxDR6ihmTklSE9rwxqpYRhXXzlDoeoWWIj/bE/PR/bU/Jw15AwucMhqleLkpxaAQEBmDBhgjkORWTztiXnAAAGhPrA3YkTmZF1Ghnhj7f+SEbCmTxotHo4qFo0IwlRq+B/JVEb21pTjzOqG0dVkfXqEeQBPzcHlGl0OFgzUpDI0pjUkhMeHt6sIskFCxZg/vz5Jj+OyNZUaHTYe/YKACCuK+txqHGSWkLoi6HGbUuiUEiIjQzAD4kXsDU5B0O7tF2ZglqpxovbarYfZ2soNcykJGfVqlXNepKwsLBmPY7I1uw9ewVVWj2CPJ0QEeAmdzhk4RQOCoQvsdzRqqO6+dckObn416S2e14HpQOWbKu5wmUdqBEmJTmxsbGtFQeRXaitx4nrFsCh42T1RnTxh1IhIS2nFJn55QjxcZE7JKI6WJND1EaEEMZ6nDjOckxNIPQCZUllKEsqg9ALucO5iqeLGv07GoaS1ybwbUEv9EjyB5L8DdtEDWFNDlEbSc8rw/n88pqh45xmga5NX6HHgZ4HAAAjSkdA6Wp5qzTFdfPH/ox8bE3ObbOh5BXVFej5iGG7tLoCrnBvk+cl68OaHKI2UtuKExPuA1dHs8zeQHZA7WfZhbWjugbgzd+TsedMHiqrdXBSW14iRvaLNTlEbaS2OX8UR1VREyldlRiWO0zuMBrVLdAdgR5OuFxcib1nr3DUIFkU1uQQtYFyjRb7zhrmEonrynocsh2SJBnnfNqWzCUeyLIwySFqAwlnrkCj06ODtzM6+3PoONmW2tabLadzIITlFUiT/WKSQ9QGan/hxnX159BxajJdhQ6H4w7jcNxh6Cp0cofToGFd/KBWSjifX470vDK5wyEyYpJD1MoMQ8dr5sfhquNkCj1QtL0IRduLAAseKe3mqEJMuA+AvwrsiSwBkxyiVnYmtwwXCirgoFRgKFcdJxtVW1DfFvPlqJVqPLkbeHK3YZuoIUxyiFpZ7Yf+oE4+cHHg0HGyTaO6GZKcfWfzUValbdXnclA64K0/gbf+NGwTNYRJDlEr+6seh11VZLs6+bmio48LNDo9dqflyR0OEQArTnJuvPFGdOzYEU5OTmjfvj3uuusuXLx4Ue6wiOoordJiX3rtquMcOk62S5IkjKr5H2/tuhy90CPDC8jw4rIO1DirTXJGjRqF77//HsnJyfjhhx9w5swZTJs2Te6wiOrYmZKLap1AmK8LOvm5yh0OUauK6/ZXXU5rDiWvqK5A+AIgfIFhm6ghVlsg8Pjjjxu3Q0NDsWjRIkydOhXV1dVQq1mIRpZh0ylDPc7oqHYcOk42b0gnXziqFLhUVInk7BJ0C/SQOySyc1bbkvN3+fn5+PrrrzF06NBGE5yqqioUFxfXuRC1Fp1eGIuOR0exHodsn5NaiaGdDSMIt57mUHKSn1UnOc888wxcXV3h6+uL8+fP4+eff250/9deew2enp7GS0hISBtFSvboSGYhrpRp4O6kwsAwH7nDIWoTtaOstp5u/aHkRNdiUUnOokWLIElSo5fTp08b93/qqadw+PBhbNy4EUqlErNmzWq0H3jx4sUoKioyXjIzM9vitMhObT6VDQCIjfSHWmlRbzWiVlM7X86h8wUoKNPIHA3ZO4uqyXniiScwZ86cRvfp1KmTcdvPzw9+fn6IjIxEVFQUQkJCsHfvXgwZMqTexzo6OsLR0dGcIRM1aEvNL9kxUe1kjoSo7YT4uKBboDtOXy7BtpQc3NS3g9whkR2zqCTH398f/v7NG2ar1xuGEVZVVZkzJKJmuVBQjtOXS6CQDC05RPZkTFQ7nL5cgk0nmeSQvCwqyWmqffv24cCBAxg+fDi8vb1x5swZPP/88+jcuXODrThEbWlzzaiqAaE+8HbljKzUPJJKQtDDQcZtazGmezss35qG7Sm50Gj1cFCZt7tWpVDh4f1/bRM1xCr/O1xcXPDjjz/ixRdfRFlZGdq3b4/x48fjueeeY3cUWYTNpzmqilpO4ahA5IeRcodhst7BnvB3d0RuSRX2pV/BiAjztmY6qhzx4YaaKyp+5lPDrDLJ6dWrF7Zs2SJ3GET1Kq3SYu8ZwyzHo1mPQ3ZIoZAwulsAvj2QiU0ns82e5BA1FYd8EJnZrtRcaHR6hPq6oLM/Zzmm5hNCQJOrgSZX06ozCLeG2oL7TafMP/uxEAK5LkCuC6zu70JtyypbcogsWW09zuhunOWYWkZfrseegD0AgBGlI6B0VcocUdMN6+IHJ7UCWYUVOH25BFHtzTf7cXl1OQKeNmyXVpfDFW5mOzbZFrbkEJmRXi+wNbl26Djrcch+OTsoMbyLHwBg08lsmaMhe8Ukh8iMjlwoRF6pBu6OKgzgLMfUQkpXJeJEHOJEnFW14tT6q8uKSQ7Jg0kOkRnV/mId2dXf7MNmiazNdTWtmUcvFCG7uFLmaMge8VOYyIw21iQ5Y7tzVBVRgLsTokO8APw1AzhRW2KSQ2QmZ3JLkZZTCrVSMi5SSNQSukodkqYnIWl6EnSVOrnDaZbraxJ+1uWQHJjkEJnJxiTDh/iQzn7wcFLLHA3ZBB2QuzYXuWtzAevMcYwTYu5Ky0O5RitzNGRvmOQQmckfSZcBsKuK6O+6tnNHB29nVGn12JmaZ5ZjqhQqzD4CzD7CZR2ocUxyiMwgu7gSRzILIUlMcoj+TpIkjO0eCOCvHwIt5ahyxKp1wKp1hm2ihjDJITKD2oLjviFeCPBwkjkaIssyvqchydl0MhvVOr3M0ZA9YZJDZAYba7uqegTKHAmR5ekf6g0/NwcUV2qx9+yVFh9PCIEyNVCm5rIO1DgmOUQtVFRRjYSaBTnHMckhuopSIeH6mi6r30+0vMuqvLocbv8C3P5l2CZqCJMcohbaejoHWr1ARIAbwv24ICdRfWq7rP5IyoZOz9YXahtMcohaaONJwy9TtuIQNWxIJ1+4O6mQV1qFw+cL5A6H7ASTHKIWqKzWYVtyLgBgbA+OqiJqiINKYVzL6jczdFkRNQWTHKIW2J2Wh3KNDu09ndAr2FPucIgsWm1r5+8nLrNgmNoEkxyiFqj9RTq2eztIkiRzNESWLTbSH05qBbIKK5B0sVjucMgOcKrIVrBqdzpSckohBOCoUqCdhxPC/VwRHeKFQE/OoWIrNFq9cej4xF7tZY6GyPI5OygRFxmA35Mu4/cTl9GTrZ82Q6vT48TFYqRcLkHGlTKUVGqh0erhoFLgriGhiGznLktcTHJawZbkXOxIya33vs7+rpjYqz1u7BOECJledDKP3Wl5KK7Uwt/dEQPCfOQOh2yREvCf5m/ctgXjewYakpyky3hyXNdmHUOpUGJa0l/bJI9qnR6bTmbj5yMXsftMHkoq61+b7Pru7Zjk2JKb+wajf0dvSBJQrtEhu7gSpy+XIPlyMc7kluGDLWn4YEsaRkT44YGRnTGsiy+7OqzQr8cvAQAm9gyEUsHXj8xP6aREjzU95A7DrEZ1C4BaKSEtpxRpOSXoEmD6l5+Tyglr1tRciWfreFsrrdIiflc6/rv3HHJLqoy3ezqr0buDJ8L9XOHlrIajWgmNVi/r1BpMclrB1L7B9d5eXFmNLady8OvxS9h8Khs7U/OwMzUPIyL88Nyk7ugayJYda8GuKqLm8XRWY2hnP2xPycWG45cxfzQ/96yFVqfHV3vPYfmWNFwp0wAA/NwcMX1AB4zrEYhewZ4W94OPSU4b8nBSY2rfYEztG4zM/HKs3JWO1fvOY2dqHia8twNzR3TC49dHwknN5ldLt/sMu6qImmtS7/bYnpKLX45dxPzREXKHQ01wIqsIi388juNZRQCAcD9XLBgTgYm92kOttNwxTJYbmY0L8XHBkht7YNPCWIzvEQi9AD7ZcRY3Lt+FpItFcodH1/DrMUNX1QR2VVEr0pXpsE3ahm3SNujKdHKHYzbjugdCrZSQkl2KlOwSkx9fpimDtASQlhi2qfXo9QIrtp3BlA9343hWETycVHjlpp748/GRmBIdbNEJDsAkR3YdfV3w8V398eld/eHn5oCU7FLc/NEerD10Qe7QqAHsqiJqGU8XNUZGGAqqfzl6UeZoqCEFZRrc99+DeOP309DpBSb2CsSmJ2Jxx6BQqCw8uallHVHagbE9AvHHgpG4rlsAqrR6PLnmKJ5fdwJanV7u0Ogf/t5VNZBdVdSKFC4KDM0ZiqE5Q6Fwsa2P68l9DD8Qfjl2iRMDWqC0nBLcsHwXtpzOgaNKgddv7oUPb++HAHfrKvS2rXeNlfN1c8Tnswbg8TGRkCTgq73n8OD/ElGhsZ1maluwgV1V1EYkSYKDvwMc/B1sbgTmmKh2cFQpcDavDCcvcWJAS7Lv7BXc/NEeXCioQKivC356eBhmxHS0yv9BJjkWRqGQ8NiYCHx8Z384qhTYdCobd67ch8JyjdyhEQxdVX+wq4qoxdyd1BjVNQCAoTWHLMOvxy7hrpX7UVypRf9Qb/z08DB0D/KQO6xmY5Jjocb1CMT/7hsEDycVDp0rwIxP9+JKadW1H0itakdKLruqqM3oq/RIeSQFKY+kQF9le13XtV1W/3f0IrusLMDPR7Lw6DeJ0Oj0mNAzEF/fNwg+rg5yh9UiTHIs2MAwH6x9aCgC3B1x+nIJ7vh8H/LL2KIjp3VHsgAAN/QOYlcVtTqhFbj40UVc/OgihNb2koDrugXAWa3EhYIKHL3AUaVy+unwBTz+3RHoBXDbgBAsv72fTUxnwiTHwkW2c8c39w+G/98SnQImOrIordJi06lsAMDUvkEyR0Nk/VwcVBgdVdNlZcIoK6VCiYkpwMQULutgDuuPXsTC749CL4CZMSF47eZeNvMjjkmOFejs74Zv5hoSnVOXinHPlwdYjCyDP05cRmW1Hp38XNGLCwsSmcXk3oYfDL8evwS9vmmtVU4qJ/y6Gvh1tWGbmm97Si4WfncEQgC3D+qIV6b2gsJGEhyASY7V6BLghtX3DYKnsxqHzxdi3upEDi9vY7VdVVOig61ylAGRJYrr6g93RxUuFVViX3q+3OHYlSOZhXjof4eg1Qvc2CcI/5nS06YSHIBJjlWJaOeOL+YMgKNKgc2nc/DsT8dZrNdGckoqsTstDwAwJZpdVUTm4qRWYlJvQwHyT4c5CWpbScspxd3x+1Gu0WFEhB+WTu9jcwkOwCTH6vQP9cHy2/tBIQHfH7yAdzalyh2SXfjl6CXoBRAd4oUwGVfUJbJFN9Usarzh+OUmdcWXacrg+izg+iyXdWiOgjIN7v3yAArKq9EnxAsf39kfDirbTAds86xs3PXd2+GVm3oBAN7fnGpcR4laz881XVVT2YpDZHYDw3zQwdsZpVVa/FlT3H8t5Q6GC5lGo9Xjoa8P4dyVcoT4OOOL2QPg6mi7a3UzybFSM2M64r7h4QCAJ9YcwYksDr9sLel5ZTh6oQhKhYRJvZnkEJmbQiEZW3N+TGSXVWsRQuDF9UnYezYfbo4qrJw9EL5ujnKH1aqY5FixxROjEBvpj8pqPe7/70HklnCywNZQ24ozrIsf/N1t+wOBSC61Sc7O1DzklFTKHI1tWrUnA9/sPw9JAt6fGY3Idu5yh9TqmORYMaVCwvsz+6KTvysuFlXiga8OokrLoeXmpNcL/JjIriqi1tbJ3w3RIV7Q6QXWH+HK5Oa250weXv7lJADg2QlRuK5bO5kjahtMcqycp7Man88aAA8nFRLPF+LVX0/JHZJN2Z+Rj/P55XBzVGF8z0C5wyGyabf0M7Tm/HQ4S+ZIbEt2cSXmf3MYegHc0q8D7hsRLndIbYZJjg3o5O+G92b0BQB8mXAO602YOZQa9/3BTADA5N7t4eJgu8V5RJZgcu8gqJUSki4WI/lyidzh2IRqnR7zVicir1SDboHu+M/UnnY1zxeTHBsxqlsAHhnVGQCw6IdjSMvhB0RLlVRW47fjhhXHpw8IkTkasksKwDPWE56xnnbxae3t6mBcmbyxAmSFpEBsBhCbYdimhr31RzIOZBTA3VGFFXf2h7ODfS2Dwf8OG/L4mEgM6eSLco0OD/0vEeUardwhWbVfj11CRbUOnfxd0a+jl9zhkB1SOivRd1tf9N3WF0pn+/hyuqV/BwDAD4kXoNHWP6u7s9oZ21YB21YZtql+v5+4jE93nAUAvDW9N8LtcI4vJjk2RKVU4L2Z0Qhwd0RqTime/ZEzIrfEmkOGX5K3Dgixq+ZdIjld1y0Afm6OyCvVYMvpps2ZQ1dLzyvDU2uOAgDmjgjH+J7tZY5IHkxybEyAuxM+mNkXSoWEdUcuGmtKyDRpOaU4dK4ASoWEm2uGthJR61MrFZg+wNCa881+fn41R5VWh3mrE1FSpcXAMG88Pb6b3CHJhkmODRrUyRdPjI0EACxZfxJpOaUyR2R91hwyfLjGRfojwIOrHJM8dGU67Pbfjd3+u6Ers5/pIW6rqYHbkZqLCwXlV91fpimD/1OA/1Nc1qE+S/9IRtLFYni7qPHBzH5QK+33q95+z9zGPTiyM4Z29kVFtQ7zvznM+XNMoNHq8cMhwxDW2l+URHKpzqtGdV613GG0qTA/Vwzp5AshDGv01SfP1XChunam5uKznekAgDen9UGgp33/SGOSY6MUCgnv3BYNbxc1Tl4qxpu/J8sdktXYePIy8kqr4O/uiNFR9jFhFlkmhbMCA08MxMATA6Fwtq+P6xkxhtacNQczodOztrAp8ss0eOJ7Qx3OHYM64vru/Pyyr3eNnWnn4YS3pvUBAKzclY5tyTkyR2Qd/rf3HABg5sAQu27mJflJCgmuPVzh2sMVksK+it/H9QiEl4sal4oqsSMlV+5wLJ4QAk+vPYackip0CXDDc5O6yx2SReAnuI0b070dZg8JBQA8ueYo17e6htTsEuw9mw+FBMyI6Sh3OER2y0mtNK5ntXr/eZmjsXz/23cem05lw0GpwPsz+trdfDgNYZJjBxZPjEK3QHfklWrwxJqj0LPpt0Ff7zN8mI6JaocgL86/QfLSa/RIX5KO9CXp0GvqnzPGlt1e80Nj86nseguQySA1uwT/qVmX6unxXdE9yEPmiCwHkxw74KRW4oOZfeGoUmBHSi5W7kqXOySLVFalxQ81c+PcVdP6RSQnUS1w7qVzOPfSOYhq+/txEtHOHcO6+EIvgK9qupGpriqtDvO/PYIqrR4jI/1xzzD7WZeqKZjk2ImIdu54frKhj/bNP07jRFaRzBFZnvVHL6KkSoswXxcM6+wndzhEBGD2kDAAwHcHMlGhMYwSVUgKDMgCBmRxWYc3f0/GqUvF8HV1wNLpvaGws9qta7Hv/w47U1ttX60TeOzbw1z24W+EEPgqwfBL8c7BofygILIQo6PaoYO3MwrLq/HzEcPUDs5qZxz4DDjwmX0v67AtOcfYMv/mtN4IcLfv4eL1YZJjRyRJwhu39EY7D0ecyS3Dy7+ckjski7E/PR8nLxXDUaXAtP6cG4fIUigVEmbVdB+v2pPBpWpq5JVW4ck1xwAAs4aEcrqLBjDJsTM+rg5Ydms0JAn4Zv95/H7istwhWYTaybNu6d8BXi4OMkdDRH9324COcFYrcfpyCfan58sdjuxqh4vnlVYhsp0bnp0YJXdIFotJjh0a1sUP94/sBABY9OMxXC6qlDkieZ3NLcXmmoUA7x3Ooj0iS+PposbUmuHkq/ZkoLy6HGELgLAFQHm1/Y26+m/COWw5nQMHlQLvz+wLJzWHizeESY6deuL6rugV7InC8mo8/t0Ru55R9Ivd6RACGBMVgM7+bnKHQ0T1mDM0DADwR9JlnL9SinNewDkv2F33VfLlEryywVBqsHhCN3QL5HDxxjDJsVMOKgXemxENZ7USCWev4NMdZ+UOSRYFZRqsrRk2fu/wTjJHQ0QN6RrojthIf+gF8F87HU5eWbMWoUarR1xXf2PiRw1jkmPHOvm7YcmNhmHlb29MxrELhfIGJIP/7T2Hymo9egZ7YHAnH7nDIaJGPBTXGQDwf0cvyRyJPF7/7TSSs0vg5+aAt6b1gSRxFOi1MMmxc7cOCMHEXoHQ6gUe+/YIyqrsZ1h5uUaL+D0ZAIC5IzrxA4PIwg0K90Hfjl7QaO1v9uetp3Owqubz6q3pfeDv7ihvQFaCSY6dkyQJr93UG+09nZCeV4aX/i9J7pDazOp955FfpkGorwsm9WovdzhEdA2SJOGh2M5yh9Hmckuq8NRaw+ridw8Lw6iuATJHZD2Y5BA8XdR45zbDsPLvD17Ar8dsvym4slqHj7cb6pAejusMFVcbJ7IKY6LaIdzPVe4w2oxeL/DkmqPIK9WgW6A7nhnfTe6QrAo/2QkAMLiTLx6u6e9e/OMxZBVWyBxR6/p2/3nklVYh2MsZN/Xl5H9koSTApbsLXLq7AOxNBQAoFBLmDAtH9xygew5QVrPUg61atScD21Ny4cjh4s3CJIeMFoyJRJ8QLxRXam16WHmV9q9WnAfjOsNBxbcBWSalixIxSTGISYqB0oVfbrWm9e2EpI+ApI+Anw7lyh1Oqzl1qRiv/3YaAPDcpChEtnOXOSLrw093MlIrFXh/RjRcHZTYn56PFdvS5A6pVXx/8AIuF1einYcjpnMJByKr8/fu5S92paOoolrGaFpHhaZmuLhOjzFRAbhzcKjcIVklJjlUR6ivK/49pScA4J1NqTh8vkDmiMyrrEqL9zalAgAejuvCpl8iK1dcqTUuUmlLXvj5BFJzSuHv7og3bunN0Z/NxCSHrnJzv2Dc0CcIupph5SWVtvMraeWudOSVViHU1wUzYzrKHQ5Ro3TlOuzvsR/7e+yHrty2a09MUV5djh4PAz0eBvSowhe70lFQppE7LLNZe+gC1hy6AIUEvD+jL3zdOFy8uaw+yamqqkJ0dDQkScKRI0fkDscmSJKE/0ztiWAvZ5zPL8eiH4/bxNTpeaVV+GT7GQDAk2O7shaHLJ8Ayk+Wo/xkOWD9b0GzEULgZABwMgCIbOeG0iot3t+SKndYZpGSXYLn1h0HADw+JhJDOvvKHJF1s/pP+aeffhpBQUFyh2FzPJ3VeH9mX6gUEn49dglf7M6QO6QWW74lDWUaHXoFe3JeHLIKCicF+mztgz5b+0DhZPUf163isTFdAABfJZxDWk6pzNG0TFmVFg9/nYjKaj1GRPjhkVFd5A7J6ln1u+a3337Dxo0bsXTpUrlDsUn9Q73x3KQoAMBrG07hQEa+zBE139ncUny9z7DezeIJ3aBQsH+bLJ+klOAd5w3vOG9ISv7P1mdIJz+MiQqAVi/wyq8n5Q6n2YQQeH7dCaTllKKdhyPevS2an1NmYLVJTnZ2NubOnYuvvvoKLi4ucodjs2YPDcONfYKg1Qs88nUickoq5Q7JZEIIvLg+CdU6gbiu/hjaxU/ukIjIjP41qTvUSglbk3OxLTlH7nCa5au95/Dj4SwoFRI+mNmPdThmYpVJjhACc+bMwYMPPogBAwY0+XFVVVUoLi6uc6HGSZKE127uhYgAN+SUVGHe6sOo1lnXujF/JF3GztQ8OCgVWHJDD7nDIWoyfbUeWR9mIevDLOirret915bC/Vwxe0gYAOA/v56yurWtEs5cwUv/Z2iFemZ8V8SEc7Fgc7GoJGfRokWQJKnRy+nTp/HBBx+gpKQEixcvNun4r732Gjw9PY2XkJCQVjoT2+LqqMLHd/WHm6MK+9Pz8cqvp+QOqcnKNVr8u+bD44HYTgizo+ngyfoJjUDqvFSkzkuF0LDyuDGPjo6Ar6sD0nJK8emOM3KH02QXCsrxyOpE6PQCU6ODMHdEJ7lDsikWleQ88cQTOHXqVKOXTp06YcuWLUhISICjoyNUKhW6dDEUZw0YMACzZ89u8PiLFy9GUVGR8ZKZmdlWp2b1Ovu7Yen0PgAM04x/lZAhb0BNtGxjCi4WVSLYyxkPx7GIj8gWSJKE0EIgtBDG+WM8ndV44YbuAID3t6ThTK7lFyFXaHS4/7+HkF+mQc9gD7zO+XDMThJWODb4/PnzdbqaLl68iHHjxmHt2rUYNGgQOnRo2iy2xcXF8PT0RFFRETw8PForXJvy4dY0vPVHMpQKCV/MGYjYSH+5Q2rQwYx8TP8kAUIA8XMGYlQ3rtxL1kVXpsNOt50AgBGlI6B05eSVAICyMsDNzbBdWgq4GlpohRCYE38A21NyERPug2/nDrbY4l29XmDeN4nYcPwy/Nwc8PO84Qj2cpY7LKvR1O9vi2rJaaqOHTuiZ8+exktkZCQAoHPnzk1OcKh5Ho7rjFv6dYBOLzDv60SkZJfIHVK9KjQ6PLnmKIQApvXvwASHyA5IkoRXbuoJl5qlaVbtyZA7pAa9suEUNhy/DLVSwkd39GeC00qsMskh+UiShFdv7omYMB+UVGlxz6oDyCm2vBFXr/92ChlXyhHo4YTnJ3eXOxwiaiMdvF2waEI3AMDrv53GqUuWN8Dk851njUtRLJ3eh4XGrcgmkpywsDAIIRAdHS13KHbBUaXEx3f1R5ivCy4UVODOlfssakr1Dccv4csEw5w4r9/SC57OapkjIiJzqqiuwMC5wMC5hu1/umtwKEZ3C4BGp8f8bw6jQmM5S2L8euwSXtlgGLyxaEI3TIkOljki22YTSQ61PR9XB3x17yC083BESnYp5sTvR2mVVu6wkJFXhmfWHgNgGE0V15XdVES2Ri/0OBgMHAw2bP+TJEl4c1pv+Ls7IjWnFM//fMIilqbZfCobC747DCGA2UNC8cBIjqRqbUxyqNlCfFzwv3sHwdtFjaMXinDflwdk/cVUWqXFQ18noqRKi4Fh3nhybFfZYiEiefm61cwaLBkWvJR7aZptyTl46H+JqNYJTOrdHi/c0IMjqdoAkxxqkYh27vjvPYPg7qjC3rP5mP3FfhTLsGq5VqfHI18n4tSlYvi5OeCDmf2gVvLfm8ieDevih39NMtTkvfLrSWxPyZUlju0pubj/q0PQ6PSY0DMQ794WDaWFjvqyNfwWoBbr1cETq+4ZCHcnFfZn5OP2z/Yivw1rdIQQeP7nE9iekgsntQIrZw9EoKdTmz0/EVmue4aFYXr/DtAL4OH/HULi+YI2ff51h7Nw76oD0Gj1uL57O7w/sy9/gLUh/qXJLPqH+uDb+wfD19UBJ7KKMe3jPUjPK2v15xVC4OVfTuGb/ZmQJOD9GX3RJ8Sr1Z+XiKyDJEn4z009MayLL8o0Osz+Yj9OZBW1yXN/vvMsFnx3BFq9wI19gvDh7Wxhbmv8a5PZ9AjyxPcPDkGQpxPO5pZh6oe7sTstr9WeT6cXWLI+CV/sNgzFfPWmXhjbI7DVno+IrJOjSonPZg3AwDBvlFRqcefKfTh0rvVadCqrdXh67VH8p2YJnHuHh+Pd26LhoOJXblvjX5zMqrO/G9bNG4a+Hb1QVFGNu1buwzt/pkBr5kU9yzVaPPi/Q/gy4RwkCXj95l6YGdPRrM9BZAnUfmqo/TgNwj/5lRkuTeXioMIXcwYiOsQLheXVuP2zvfj9xGWzx3XuShmmfbwH3x+8AIUE/GtiFJ6f3N1iZ162dVa5rIO5cFmH1lNZrcNz605g7aELAIB+Hb3w5rTe6BLg3uJjn7pUjMe+PYyU7FI4qBR4e3of3NAnqMXHJSIr0cCyDk1RrtFi3urD2HI6BwBw3/BwPD2+W4tbWfR6gVV7MvDWH8moqNbBx9UB78/oi+ERfi06LtWvqd/fTHKY5LSqn49k4bl1J1BSqYVKIWH20DDMvy4Cni6m/zKt0Ojw6Y6z+HBrGjQ6PfzcHPHJXf3RP9S7FSInIovVgiQHMIzGfHXDaWNXd9d27vjPTT0xMKx5Mw/vScvD67+fxrELhlqfwZ188Pat0VyqoRUxyWkCJjlt40JBOV76v5P482Q2AMDVQYnbBnbEnKFh6Ojrcs3Hl1RW4/uDF/D5zrO4VGRYQmJMVADeuKU3fN0cWzV2IrJALUxyam1MuoxnfjiGgnLDtBejuwXggdjOGBjmfc05bLQ6PTadysGXezKQcPYKAMNn2+KJUbg9piO7p1oZk5wmYJLTtran5OLVX08h+W+LevYM9sDwLv7o08ETQV7O8HRWQ6PTI7ekCsmXS7DnTB52peWhstpQ0xPs5YxFE7phcu/2nEiLbJ6uQodjEwwzePf+rTeUzlyFHAAqCvMw4XF/AMBv7+TC2av5XUIFZRq8+cdpfHsgE7Xfhh28nXF993aIDvFCqK8rPJ3VqNbpUVCmQUpOKQ5l5GNbSi4Ka5IjtVLCHYNC8cioLvB35w+vtsAkpwmY5LQ9IQR2pOZh5a507ErNhb6J/31dAtxw97Aw3NKvA5zU/KAn+6Ar02Gn204AwIjSEVC68n8fAMoKcuD2fjsAQOn8bLh6t3z5lrO5pfhk+1msP3oRFdVNm7nd19UB0weE4I5BHRHic+1WaTKfpn5/q9owJiJIkoTYSH/ERvrjSmkVNp/OweHzBUi6WIzckioUV1TDUa2El7MaXQLc0CfEC9d1C0C3QHe23JDdkRwldP++u3GbWk8nfze8Ma03ltzYA1uTc7A/PR/HLhQiu7gKRRXVcFAp4O6kQic/V3QP8kBsZAD6dfSCivPeWDS25LAlh4jIqrRGSw5Zl6Z+fzMFJSIiIpvE7ioiIgul1+qR95Nh1nC/m/yg4Iy5RCZhkkNEZKFElcDJW08CMBQe8xObyDR8yxARkdVx0cgdAVkDJjlERGRVXB1cUfZqzZVnmzcRINkHdvASERGRTWKSQ0RERDaJ3VVERGRVKrWVuOV2w/YP2ko4gV1WVD8mOUREZFV0eh02RP61TdQQdlcRERGRTWKSQ0RERDaJSQ4RERHZJCY5REREZJOY5BAREZFNsuvRVUIIAIYl24mILI2uTIcylAEwfE4pdUqZI7IMZcUlQKVhu7i4BDqlk7wBUZur/d6u/R5viCSutYcNu3DhAkJCQuQOg4iIiJohMzMTHTp0aPB+u05y9Ho9Ll68CHd3d0iSZLbjFhcXIyQkBJmZmfDw8DDbcS2JrZ8jz8/62fo52vr5AbZ/jjy/5hNCoKSkBEFBQVAoGq68sevuKoVC0WgG2FIeHh42+Y/7d7Z+jjw/62fr52jr5wfY/jny/JrH09Pzmvuw8JiIiIhsEpMcIiIisklMclqBo6MjXnzxRTg6OsodSqux9XPk+Vk/Wz9HWz8/wPbPkefX+uy68JiIiIhsF1tyiIiIyCYxySEiIiKbxCSHiIiIbBKTHCIiIrJJTHKa6ZVXXsHQoUPh4uICLy+vJj1GCIEXXngB7du3h7OzM8aMGYPU1NQ6++Tn5+OOO+6Ah4cHvLy8cO+996K0tLQVzqBxpsaRkZEBSZLqvaxZs8a4X333f/vtt21xSnU05+8cFxd3VewPPvhgnX3Onz+PSZMmwcXFBQEBAXjqqaeg1Wpb81QaZOo55ufn49FHH0XXrl3h7OyMjh07Yv78+SgqKqqzn1yv4YcffoiwsDA4OTlh0KBB2L9/f6P7r1mzBt26dYOTkxN69eqFDRs21Lm/Ke/HtmbKOX722WcYMWIEvL294e3tjTFjxly1/5w5c656rcaPH9/ap9EgU85v1apVV8Xu5FR3jSpLew1NOb/6Pk8kScKkSZOM+1jS67djxw7ccMMNCAoKgiRJWLdu3TUfs23bNvTr1w+Ojo7o0qULVq1addU+pr6vTSaoWV544QWxbNkysXDhQuHp6dmkx7z++uvC09NTrFu3Thw9elTceOONIjw8XFRUVBj3GT9+vOjTp4/Yu3ev2Llzp+jSpYuYOXNmK51Fw0yNQ6vVikuXLtW5vPTSS8LNzU2UlJQY9wMg4uPj6+z39/NvK835O8fGxoq5c+fWib2oqMh4v1arFT179hRjxowRhw8fFhs2bBB+fn5i8eLFrX069TL1HI8fPy5uvvlmsX79epGWliY2b94sIiIixC233FJnPzlew2+//VY4ODiIL774QiQlJYm5c+cKLy8vkZ2dXe/+u3fvFkqlUrz55pvi5MmT4rnnnhNqtVocP37cuE9T3o9tydRzvP3228WHH34oDh8+LE6dOiXmzJkjPD09xYULF4z7zJ49W4wfP77Oa5Wfn99Wp1SHqecXHx8vPDw86sR++fLlOvtY0mto6vlduXKlzrmdOHFCKJVKER8fb9zHkl6/DRs2iH/961/ixx9/FADETz/91Oj+Z8+eFS4uLmLhwoXi5MmT4oMPPhBKpVL8/vvvxn1M/Zs1B5OcFoqPj29SkqPX60VgYKB46623jLcVFhYKR0dH8c033wghhDh58qQAIA4cOGDc57fffhOSJImsrCyzx94Qc8URHR0t7rnnnjq3NeXN0dqae36xsbHisccea/D+DRs2CIVCUeeDeMWKFcLDw0NUVVWZJfamMtdr+P333wsHBwdRXV1tvE2O1zAmJkY88sgjxus6nU4EBQWJ1157rd79b731VjFp0qQ6tw0aNEg88MADQoimvR/bmqnn+E9arVa4u7uLL7/80njb7NmzxZQpU8wdarOYen7X+my1tNewpa/fO++8I9zd3UVpaanxNkt6/f6uKZ8BTz/9tOjRo0ed22677TYxbtw44/WW/s2agt1VbSQ9PR2XL1/GmDFjjLd5enpi0KBBSEhIAAAkJCTAy8sLAwYMMO4zZswYKBQK7Nu3r81iNUcchw4dwpEjR3Dvvfdedd8jjzwCPz8/xMTE4IsvvoBo46maWnJ+X3/9Nfz8/NCzZ08sXrwY5eXldY7bq1cvtGvXznjbuHHjUFxcjKSkJPOfSCPM9b9UVFQEDw8PqFR1l7lry9dQo9Hg0KFDdd47CoUCY8aMMb53/ikhIaHO/oDhtajdvynvx7bUnHP8p/LyclRXV8PHx6fO7du2bUNAQAC6du2Khx56CFeuXDFr7E3R3PMrLS1FaGgoQkJCMGXKlDrvI0t6Dc3x+q1cuRIzZsyAq6trndst4fVrjmu9B83xN2sKu16gsy1dvnwZAOp8AdZer73v8uXLCAgIqHO/SqWCj4+PcZ+2YI44Vq5ciaioKAwdOrTO7f/+979x3XXXwcXFBRs3bsTDDz+M0tJSzJ8/32zxX0tzz+/2229HaGgogoKCcOzYMTzzzDNITk7Gjz/+aDxufa9v7X1tyRyvYV5eHl5++WXcf//9dW5v69cwLy8POp2u3r/t6dOn631MQ6/F399rtbc1tE9bas45/tMzzzyDoKCgOl8a48ePx80334zw8HCcOXMGzz77LCZMmICEhAQolUqznkNjmnN+Xbt2xRdffIHevXujqKgIS5cuxdChQ5GUlIQOHTpY1GvY0tdv//79OHHiBFauXFnndkt5/ZqjofdgcXExKioqUFBQ0OL/+aZgkvM3ixYtwhtvvNHoPqdOnUK3bt3aKCLzaur5tVRFRQVWr16N559//qr7/n5b3759UVZWhrfeesssX5CtfX5//7Lv1asX2rdvj9GjR+PMmTPo3Llzs49rirZ6DYuLizFp0iR0794dS5YsqXNfa76G1Dyvv/46vv32W2zbtq1Oce6MGTOM27169ULv3r3RuXNnbNu2DaNHj5Yj1CYbMmQIhgwZYrw+dOhQREVF4ZNPPsHLL78sY2Tmt3LlSvTq1QsxMTF1brfm189SMMn5myeeeAJz5sxpdJ9OnTo169iBgYEAgOzsbLRv3954e3Z2NqKjo4375OTk1HmcVqtFfn6+8fEt0dTza2kca9euRXl5OWbNmnXNfQcNGoSXX34ZVVVVLV7fpK3Or9agQYMAAGlpaejcuTMCAwOvGhmQnZ0NAGZ5/YC2OceSkhKMHz8e7u7u+Omnn6BWqxvd35yvYX38/PygVCqNf8ta2dnZDZ5LYGBgo/s35f3YlppzjrWWLl2K119/HZs2bULv3r0b3bdTp07w8/NDWlpam35JtuT8aqnVavTt2xdpaWkALOs1bMn5lZWV4dtvv8W///3vaz6PXK9fczT0HvTw8ICzszOUSmWL/yeaxGzVPXbK1MLjpUuXGm8rKiqqt/D44MGDxn3++OMP2QqPmxtHbGzsVSNyGvKf//xHeHt7NzvW5jDX33nXrl0CgDh69KgQ4q/C47+PDPjkk0+Eh4eHqKysNN8JNEFzz7GoqEgMHjxYxMbGirKysiY9V1u8hjExMWLevHnG6zqdTgQHBzdaeDx58uQ6tw0ZMuSqwuPG3o9tzdRzFEKIN954Q3h4eIiEhIQmPUdmZqaQJEn8/PPPLY7XVM05v7/TarWia9eu4vHHHxdCWN5r2Nzzi4+PF46OjiIvL++azyHn6/d3aGLhcc+ePevcNnPmzKsKj1vyP9GkWM12JDtz7tw5cfjwYeMw6cOHD4vDhw/XGS7dtWtX8eOPPxqvv/7668LLy0v8/PPP4tixY2LKlCn1DiHv27ev2Ldvn9i1a5eIiIiQbQh5Y3FcuHBBdO3aVezbt6/O41JTU4UkSeK333676pjr168Xn332mTh+/LhITU0VH330kXBxcREvvPBCq5/PP5l6fmlpaeLf//63OHjwoEhPTxc///yz6NSpkxg5cqTxMbVDyMeOHSuOHDkifv/9d+Hv7y/rEHJTzrGoqEgMGjRI9OrVS6SlpdUZtqrVaoUQ8r2G3377rXB0dBSrVq0SJ0+eFPfff7/w8vIyjmS76667xKJFi4z77969W6hUKrF06VJx6tQp8eKLL9Y7hPxa78e2ZOo5vv7668LBwUGsXbu2zmtV+xlUUlIinnzySZGQkCDS09PFpk2bRL9+/URERESbJ93NOb+XXnpJ/PHHH+LMmTPi0KFDYsaMGcLJyUkkJSUZ97Gk19DU86s1fPhwcdttt111u6W9fiUlJcbvOQBi2bJl4vDhw+LcuXNCCCEWLVok7rrrLuP+tUPIn3rqKXHq1Cnx4Ycf1juEvLG/mTkwyWmm2bNnCwBXXbZu3WrcBzXzidTS6/Xi+eefF+3atROOjo5i9OjRIjk5uc5xr1y5ImbOnCnc3NyEh4eHuPvuu+skTm3lWnGkp6dfdb5CCLF48WIREhIidDrdVcf87bffRHR0tHBzcxOurq6iT58+4uOPP65339Zm6vmdP39ejBw5Uvj4+AhHR0fRpUsX8dRTT9WZJ0cIITIyMsSECROEs7Oz8PPzE0888USd4ddtydRz3Lp1a73/0wBEenq6EELe1/CDDz4QHTt2FA4ODiImJkbs3bvXeF9sbKyYPXt2nf2///57ERkZKRwcHESPHj3Er7/+Wuf+prwf25op5xgaGlrva/Xiiy8KIYQoLy8XY8eOFf7+/kKtVovQ0FAxd+5cs36BmMqU81uwYIFx33bt2omJEyeKxMTEOseztNfQ1P/R06dPCwBi48aNVx3L0l6/hj4fas9p9uzZIjY29qrHREdHCwcHB9GpU6c634e1GvubmYMkRBuP3yUiIiJqA5wnh4iIiGwSkxwiIiKySUxyiIiIyCYxySEiIiKbxCSHiIiIbBKTHCIiIrJJTHKIiIjIJjHJISIiIpvEJIeIiIhsEpMcIiIisklMcojI5qxduxa9evWCs7MzfH19MWbMGJSVlckdFhG1MZXcARARmdOlS5cwc+ZMvPnmm7jppptQUlKCnTt3gsv0EdkfLtBJRDYlMTER/fv3R0ZGBkJDQ+UOh4hkxO4qIrIpffr0wejRo9GrVy9Mnz4dn332GQoKCuQOi4hkwJYcIrI5Qgjs2bMHGzduxE8//YTLly9j3759CA8Plzs0ImpDTHKIyKbpdDqEhoZi4cKFWLhwodzhEFEbYuExEdmUffv2YfPmzRg7diwCAgKwb98+5ObmIioqSu7QiKiNMckhIpvi4eGBHTt24N1330VxcTFCQ0Px9ttvY8KECXKHRkRtjN1VREREZJM4uoqIiIhsEpMcIiIisklMcoiIiMgmMckhIiIim8Qkh4iIiGwSkxwiIiKySUxyiIiIyCYxySEiIiKbxCSHiIiIbBKTHCIiIrJJTHKIiIjIJjHJISIiIpv0/8Gw6S2QAubaAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(s_space, fluctuation)\n", + "plt.xlabel('s')\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label ='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.title('First DBI step')\n", + "plt.ylabel('Energy fluctuation')\n", + "plt.legend()\n", + "plt.figure()\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('First DBI step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", + "energy_fluc = [dbi.energy_fluctuation(state=state)]\n", + "iters = 10\n", + "dbi_ = deepcopy(dbi)\n", + "for _ in range(iters):\n", + " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_(step_poly,d=d)\n", + " off_diagonal_norm_diff.append(dbi_.off_diagonal_norm)\n", + " energy_fluc.append(dbi_.energy_fluctuation(state=state))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Energy fluctuation')" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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jqKoe0z7ehZLqetGxyEmdLKvFf7fmAgCeG9/Vac4zc46UdEWjuofipuRwmCTgyeXp0BvYniKytb0ny/F+034fc29Jcqihen8vDT7/6wB07OCBE2V1uPvjVFTW8Uw6ar25qzPRYDRPlL8+0XkObWZxIxMv3tQdgd4aHC2uwcINOaLjEMlaXYMBj3+TDpME3JoSgTE9HG+/j1CdO7746wAEemuRVVSN+z5JRV0DdzWnltuWU4rfDhdDpVRgzo2OvfT7j1jcyIS/lwb/nmhuT/3fxmM4WMCVEkS28tovWThRVocwnTteuKm76DiXFR3ohc//2h++7mrsy6vAPz7fy5FdahGD0YR/Ny39njYwCgkh1j9GxJZY3MjIuKQwjEsKhdEk4cnlB9BgMImORCQ7W7NL8dkO81EHr0/qCZ2Hm+BEV9Y1zBdL7+sPDzcVtmSX4rGv02A08aBNurKvUvNwpLgafp5umDnSOqfa2xOLG5l56aYe6ODphszCKsv5H0RkHVX1jXhyeToA82+z1yQECU7UMn2iOuCDu/tAo1Li54wiPPt9Bk8Sp8uqqGvAW2vNq28fv6Ez/Dw1ghO1HosbmQny0eLFpmHy9zZkI6uoSnAiIvl46cfDKKysR1SAJ2aPSxQdp1WuSQjCu3f2glIBfL0nH6/+nMkChy7p7XXZqKhrRJcQH9zZv5PoOG3C4kaGbkoOx8iuIWg0Snjy2wMwGNmeImqv3w4V4bt9p6BQAG9NToanRi06UquN6RGG15oO3v1wSy4W/c7FB3Sx7OJqywnzcyZ0g1rlnGWCc6amK1IoFHj1lh7wdVcjo6ASH2w5LjoSkVMrq9Hj2RUZAIC/XxuLvtH+ghO13ZS+kXhufFcAwJu/HcVnO06IDUQOQ5Ik/HvVYRhNEm7oFoIh8YGiI7UZixuZCvZ1x5wJ5vbU22uzuQ07URtJkoR/rTiI0poGdAnxwawbOouO1G73XxOLR66PBwDM+eEQVu4vEJyIHMGGrBJsyS6FRqXEv8Z1FR2nXVjcyNhtvSMwvEsQGowmPLn8AFdIELXByrQC/HqoCGqlAm9NSYZWrRIdySoeu6Ez7m3aSv/xb9Ox7nCx2EAkVIPBhFdWZwIA7hsajehAL8GJ2ofFjYyZ21NJ8NaqsT+vAku35YqORORUCivPYc4PhwAAj45IQI8IneBE1qNQmDdmuzUlAkaThAeX7cOOY2WiY5Egn24/gdzSWgR6a/HQdfGi47QbixuZC/fzwL+a+utvrDmC3NJawYmInIMkSXj6uwxU1xuQ3FGHfw6PEx3J6pRKBeZP6omRXUPQYDDh/k9348CpCtGxyM5Ka/R4t+lk+6fGdIGPu2Pv3dQSLG5cwB39IjE0PhB6gwlPLz8AE9tTRFf15a48bD56Blq1Em9N6eW0q0auxk2lxMK7UjAoNgC1DUbc899UZBdzjp4reeu3I6jWG5AUocOk3h1Fx7EKef5rpYsoFArMuzUJnhoVUk+Uc3UE0VWcLKvFqz+b5x88NSYR8cHeghPZlrubCh/e0xfJHXU4W9eIaR+nIr+8TnQssoODBZX43+58AMALE7pBqXSe86OuhMWNi4j098QzY82bjs3/9Qg/uIguw2iS8Pg36ahrMGJgrD/ua5p0K3feWjU+ua8/EoK9UVRVj798vAslVfWiY5ENNS/9liRgQnK4U29x8EcsblzIXwZEYUCMP841GvH0dwe4OynRJXy05Tj2nDwLb60ab0xKls1vsi3RwUuDz/86AJH+HjhZVoe7/5uKiroG0bHIRn7OKEJqbjnc3ZSWX37lgsWNC1EqFZh/W0+4uymx/VgZlqXmiY5E5FCOFlfjrd/MZ+o8f2NXRPp7Ck5kf6E6d3zx1wEI8tEiq6ga932yG7V6g+hYZGX1jUZL6/Uf18Yhws9DcCLrYnHjYqIDvfDkaHOFPu/nLBRUnBOciMgxNBpNmPVNGhqMJlyfGIwpfSNFRxImKsALX/x1AHQebtifV4EHvtgLvcEoOhZZ0Yebj6Og4hzCdO54YJgMVwKKDkD2d+/gaPSJ6oAavQHPsD1FBAB4b0MODhZUwc/TDa/dmgSFwnXaUZfSJdQHn9zXD54aFbZkl+LRr9J4Tp1MFFXW4/82HgMAzB7XFR4aeWxMeSGHKm42b96MCRMmIDw8HAqFAitXrrzo+y+++CISExPh5eWFDh06YOTIkdi1a5eYsE5M1dSe0qiV2JJdim/3nBIdiUio9PwKyyGSL0/sgWBfd8GJHENKpw748O6+0KiU+PVQEWZ/n8GtJGRg/q9ZONdoRN+oDpjQM0x0HJtwqOKmtrYWycnJWLRo0SW/37lzZyxcuBAZGRnYunUroqOjMWrUKJw5c8bOSZ1ffLC35Yycf686zNVT5LLqG42Y9U0ajCYJN/YMw4TkcNGRHMqQ+EC8e2cKlArg272nMPfnTI72OrF9eWexYn8BFArghQndZTtCqZAc9G+pQqHAihUrcPPNN1/2PlVVVdDpdFi3bh1GjBjRoudtfkxlZSV8fX2tlNY5GU0Spry/A3tPnkX/GH989beBULnQyhAiAHh51WF8vDUXQT5a/DbzWnTw0oiO5JCW7z2FJ75NBwDMuqEzHhmRIDgRtZbJJOGWxduRnl+ByX064o3JyaIjtUprfn471MhNazQ0NOCDDz6ATqdDcvLl/wfp9XpUVVVddJGZSqnAf6b0gpdGhdTccny89bjoSER2tfN4Gf7bdOba67f1ZGFzBZP6dMScG7sBABasPYpPeFad01mxvwDp+RXw0qjw5JguouPYlNMVN6tWrYK3tzfc3d3xn//8B2vXrkVgYOBl7z9v3jzodDrLFRnpuisgLqVTgCeeb/rAenPNUWQWsvgj11CjN+CJb9MhSeYjSq5LDBYdyeFNHxqDR5tGbF786TCyivh54Sxq9QbM/zULAPDQ9QkI9pH3vDKnK26uu+46pKWlYfv27RgzZgymTJmCkpKSy95/9uzZqKystFz5+fl2TOscbu8XiZFdg9FgNOGxr9O45JNcwtzVh3Hq7Dl07OCB55oKfLq6mSMTcG3nIADA+szLf/aSY/m/jTkoqdYjKsAT04dGi45jc05X3Hh5eSE+Ph4DBw7Exx9/DLVajY8//viy99dqtfD19b3ooouZz57qiQAvDbKKqrGgaRMzIrn6PasEX6Waf9F5Y1IyvLVqwYmch0KhwMiu5lGubTmlgtNQS+SX1+HDLeY24r/GdYVWLb+l33/kdMXNH5lMJuj1etExnF6Qjxbzbk0CAHyw5Th2Hi8TnIjINirqGvD0dwcAANOHxGBQXIDgRM5nSLx5KsCeE2dxroEjvY7u1Z8z0WAwYUh8AG7oFiI6jl04VHFTU1ODtLQ0pKWlAQByc3ORlpaGvLw81NbW4tlnn8XOnTtx8uRJ7N27F9OnT0dBQQEmT54sNrhMjOoeiil9O0KSgMe/SUd1faPoSERW9/wPh1BSrUdckBeekvmkSluJDfRCuM4dDUYTdp8oFx2HrmDHsTL8crAISgUw50b5Lv3+I4cqbvbs2YOUlBSkpKQAAGbNmoWUlBTMmTMHKpUKWVlZuO2229C5c2dMmDABZWVl2LJlC7p37y44uXzMmdAdkf4eKKg4h5d+Oiw6DpFVrTpwGj+ln4ZKqcCCKb3g7ib/4XlbUCgUltEbtqYcl9Ek4aWfDgEApg6IQpdQH8GJ7MehGs3Dhw+/4uZQ33//vR3TuCZvrRoLpvTClPd3YPneUxjZNRhjeshzB0tyLSXV9Xh+5UEAwIzhcUiO9BMbyMkNTQjEt3tPYUt2KWaLDkOX9L/decgqqobOw82yaaurcKiRG3IM/aL9LQepzf4+AyXV9YITEbWPJEmY/V0GztY1onu4Lx66nhvQtdfgOPPIzeHCKpTVcN6jo6k812g54X7myASX28OJxQ1d0mMjO6NrmC/O1jXime8yuN06ObVv95zC+qwSaFRKLJjSCxo1P/raK8hHi8SmNsf2Y1yA4GjeXZ+N8toGxAd74y8Do0THsTv+C6dL0qiVePv2XtColNhwwbJZImeTX16Hf68yzx+bNaqzS807sLWhTfNutmZz3o0jySmpwafbTwAAnr+xG9xUrvej3vX+xNRiXUJ9LKtJXl51GCdKawUnImodk0nCk8vTUaM3oG9UB/ztmljRkWRlaEJTcZNTytFdBzJ39WEYTBJGJAZjWNOGi66GxQ1d0fQhMRgUG4BzjUY89k0aDEaT6EhELfbpjhPYebwcHm4qvDk5mQfDWln/GH9oVEoUVJzDybI60XEI5g0qfz9yBm4qBf41vqvoOMKwuKErUioVeHNKMny0auzPq8DijcdERyJqkWNnavDaL+azdJ4d3xXRgV6CE8mPp0aN3lF+AIAtXBIuXKPRhJdXm1uw9w6ORmyQt+BE4rC4oauK8PPAv2827yX0zvpsZJyqFJyI6MoMRhNmfZMOvcGEaxIC8ZcBnURHkq3meTfbOO9GuM92nMTxM7UI8NLg4RGuvSKQxQ21yM29IjAuKRQGk4SZX+9HfSO3XCfHtWTTMaTnV8DHXY3XJ/V0mV1ZRWjezG/7sVIYTZx3I0pZjR5vrzMv/X5idBf4ursJTiQWixtqEYVCgbk3JyHYR4tjZ2otw/1EjubQ6Uq8sz4bAPDSTd0RpvMQnEjeenb0g4+7GlX1BmQUcFRXlAVrj6K63oBuYb6Y0jdSdBzhWNxQi3Xw0uD1ST0BAJ9sP4Et2WcEJyK6mN5gxKyv09FolDC6ewhuSYkQHUn2VEoFBjcdPsqjGMTILKzCV6l5AIAXJnTjxHmwuKFWGt4lGNOaNoR68tsDqKzj4ZrkON5el40jxdUI8NLg1VuS2I6yk+Z5N/yFx/4kScK/fzoMkwSMTwrDgFiecg+wuKE2mD0uEbGBXiiqqsfzPxwUHYcIAHCuwYiPthwHAMy9JQkB3lrBiVzH0ATzXir7TlagrsEgOI1rWXOoCDuOl0GrVuKZsYmi4zgMFjfUap4aNRbc3gsqpQI/pp/GD2kFoiMR4UhxNRqNEgK9NRjTI1R0HJcSHeCJCD8PNBhN2H3irOg4LqO+0YhXVmcCAP5+bSwi/T0FJ3IcLG6oTXpF+uGh6+IBAM+vPIjCynOCE5GryyysAgB0DfMVnMT1KBQKDIk3t0O2sjVlNx9vzcWps+cQ6uuOfw6PEx3HobC4oTZ76Pp4JHfUoaregCe+TYeJy0BJIBY3YjW3prbm8BBNeyiuqsei33MAAE+P7QJPjVpwIsfC4obazE2lxILbe8HdTYltOWX4dMcJ0ZHIhWUVVgMAuobxYEwRmldMZRZWobRGLziN/L3+6xHUNRiR0skPE5O5KvCPWNxQu8QFeeNf48znl7z2SxZySqoFJyJXJEkSMos4ciNSoLfW8t5zSbhtpeVX4Lt9pwAAL0zoDiWXfv8Jixtqt78MjMK1nYOgN5gw8+s0NBh4uCbZ16mz51Bdb4CbSoHYQNc9T0e0a5pOCWdxYzuSJOGlnw4BAG7tHYFekX5iAzkoFjfUbgqFAm9M6gmdhxsOFlThvQ3ZoiORi2mebxMf7AONmh9rojQfxbA1uxSSxDl4tvBD2mnsz6uAp0aFp8dw6ffl8FOArCLE1x2v3pIEAFj0ew72nuRyULKfTM63cQj9o/2hUSlxurIeuaW1ouPITl2DwXL0zYzr4hHi6y44keNicUNWM75nGG5JiYBJAh7/Jg21em7mRfbRPHLTjfNthPLQqNAnqgMAtqZsYcnGYyiqqkfHDh7469AY0XEcGosbsqoXb+qOMJ07TpTVYe7PmaLjkIvgZGLHMTSh+SgGFjfWdOpsHd7fbN6B+1/jusLdTSU4kWNjcUNWpfNww1uTkwEAy3blYUNWseBEJHe1egNOltUBYHHjCJrPmdpxvAwGIxcXWMu8X7KgN5gwMNafO3C3AIsbsrrB8YGWIdOnlmegjHtekA1lFZnn24T4auHvpRGchnpE6ODrrkZ1vQEZBZWi48jCruNlWH2gEEoFMOfG7jwQtgVY3JBNPDm6CxKCvVFao8ezKzK4coJspnm+TWIoR20cgUqpwOC486umqH2MJgn/XnUYAHBH/07oFs6/5y3B4oZswt1Nhf/c3gtuKgXWHCrGd/t4uCbZBo9dcDzN8262clJxu327Jx+HTlfBx12Nx2/oLDqO02BxQzbTI0KHmSPN/xhf/PEQ8svrBCciOTpf3HAZuKNonnezL+8s6hq4arKtquob8caaIwCAR0ckIMBbKziR82BxQzb1wLA49InqgBq9AY9/kw4jD9ckKzKZJMucGy4DdxxRAZ6I8PNAo1HCrtxy0XGc1vubjqGstgGxQV64e1C06DhOhcUN2ZRKqcB/pvSCl0aF1BPl+GjLcdGRSEbyyutQ12CERq1ETKCX6DjURKFQnD+KgfNu2sRkkvB9Uzv/8Ru6cOftVuK7RTbXKcATcyZ0AwC89dtRSxuBqL2ymva36RLiA7WKH2eOxHIUA+fdtMn+/AoUVtbDS6PCiK7BouM4HYf6NNi8eTMmTJiA8PBwKBQKrFy50vK9xsZGPP3000hKSoKXlxfCw8Nx99134/Tp0+ICU4tN6RuJkV1D0GA04bGv06A3GEVHIhk4zGMXHNbguAAA5qX6Z6q5HURrrT5QCAAY2S2EG/a1gUMVN7W1tUhOTsaiRYv+9L26ujrs27cPzz//PPbt24fvv/8eR44cwU033SQgKbWWQqHAa7clIcBLg6yiaiz47ajoSCQDXAbuuAK8tejetGx5+zGO3rSGySTh5wxzcTM+KUxwGuekFh3gQmPHjsXYsWMv+T2dToe1a9dedNvChQvRv39/5OXloVOnTvaISO0Q6K3FvFuT8PfP9+KDLcdxXWIwBsYGiI5FTozLwB3b0PhAHDpdha3ZpZjYK0J0HKexP/8siqrq4a1V49rOQaLjOCWHGrlprcrKSigUCvj5+YmOQi00qnsobu8bCUkCHv8mHVX1jaIjkZOqqm/EqbPnAHCllKO6cN4NN/JsuVVNLakb2JJqM6ctburr6/H000/jzjvvhK/v5T/Y9Ho9qqqqLrpIrOcndEOkvwcKKs7hpR8Pi45DTiqrab5NuM4dOk83wWnoUvrH+EOjVqKwsh7HS2tFx3EKF7akxrEl1WZOWdw0NjZiypQpkCQJixcvvuJ9582bB51OZ7kiIyPtlJIux1urxn+m9IJSAXy37xR+PVgoOhI5IbakHJ+7mwp9ozoA4FEMLbUv7yyKq/Tw0aoty+mp9ZyuuGkubE6ePIm1a9decdQGAGbPno3KykrLlZ+fb6ekdCV9o/3xj2FxAIDZ32egpLpecCJyNs3LwFncODYuCW8dtqSsw6mKm+bCJjs7G+vWrUNAwNUno2q1Wvj6+l50kWN4bGRndA3zxdm6Rjy9/AB78tQqzcvAE7kM3KE1jz7sPFYGg9EkOI1jM5kk/HKQLSlrcKjipqamBmlpaUhLSwMA5ObmIi0tDXl5eWhsbMSkSZOwZ88efPnllzAajSgqKkJRUREaGhrEBqc20aiVePv2XtColfj9yBksS80THYmchNEk4QhHbpxC93AddB5uqNYbkH6qUnQch7b3wpZUZ7ak2sOhips9e/YgJSUFKSkpAIBZs2YhJSUFc+bMQUFBAX788UecOnUKvXr1QlhYmOXavn274OTUVl1CffDU6C4AgFdWZeIEJx1SC5woq0V9ownubkpEB/DYBUemUiowJN48yr6Nrakrat6474buIdCq2ZJqD4cqboYPHw5Jkv50ffLJJ4iOjr7k9yRJwvDhw0VHp3aYPiQGg2IDcK7RiMe+SePQNV1V82TiLqG+UCkVgtPQ1XDezdUZuXGfVTlUcUOuSalU4M0pyfDRqrE/rwKLNx4THYkcXHNx043zbZzC0KbiZn/eWdTqDYLTOKY9J8pRUq2Hj7saQ7lKqt1Y3JBDiPDzwL9v7g4AeGd9Ng6cqhAbiBxapuVMKc63cQZRAV6I9PdAo1FCam656DgOqXnUZlS3ULakrIDFDTmMm3tFYHxSGAwmCY99nYb6Rh6uSZeWxT1unM5QtqYuy2iS8PPBIgDA+J6hgtPIA4sbchgKhQKv3NwDwT5aHDtTi9d+yRIdiRxQRV0DTlea90XqEsq2lLOwzLvhZn5/svtEOc40t6TieZaUNbC4IYfSwUuDNyYnAwA+2X4CW7LPCE5Ejqa5JdWxgwd83XnsgrMYHBcIhQI4UlzNTTv/oLklNbp7KDRq/li2Br6L5HCGdQ7CtIFRAIAnvk1HRR33MaLzeOyCc/L30qB7uPn/2facMsFpHId5lVRzS4qrpKyFxQ05pNnjEhEb6IXiKj2e/+GQ6DjkQFjcOK/m1tQWtqYsUnPLUVqjh6+7GkPiuErKWljckEPy1Kix4PZeUCkV+Cn9NH5IKxAdiRxEZhGXgTura5rmk2zLKeVxK03YkrINvpPksHpF+uHh6+MBAM+vPIjCynOCE5FoBqMJR4trAHDkxhn1je4AjVqJoqp6HDtTIzqOcMYLzpJiS8q6WNyQQ5txXTySO+pQVW/AE9+mw2Tib3uuLLe0Fg0GE7w0KkR28BQdh1rJ3U2FftEdAHDVFADsyi1DaU0DdB5ulpYdWQeLG3JobiolFtzeC+5uSmzLKcOnO06IjkQCHbYcu+ADJY9dcErNS523clLxBS2pELip+OPYmvhuksOLC/LGv8Z1BQC89ksWsourBSciUbgzsfNr3sxv5/EyNLrwOXIGowm/WjbuCxecRn5Y3JBT+MvAKFzbOQh6gwmPfZOGBoPrfii6Mq6Ucn7dw33h5+mGGr3BpY9ZMa+SaoCfpxsGxwWIjiM7LG7IKSgUCrwxqSf8PN1wsKAKC3/PER2JBGBx4/yUSoVlyfPWbNdtTa1ubkl1C2VLygb4jpLTCPF1x8sTewAA/rs1l6cLu5iyGj1KqvVQKIBEHrvg1CxHMeS45g7kF7ekuErKFljckFMZnxSG6ABP1OgN+Cn9tOg4ZEdZReb5NlH+nvDSqgWnofa4JsFc3OzPq0CNC/6Ssiu3HGW1Dejg6YZBbEnZBIsbcipKpQJ3DegEAFiWmic4DdkTW1LyEenviU7+njCYJKTmul5ratWB8xv3sSVlG3xXyelM6hMJjUqJA6cqkXGqUnQcspPmZeCJoSxu5MBVj2IwGE1Yc4gtKVtjcUNOx99Lg7FJoQCAZaknBachezm/DJzzbeSguTW1Lce1ipudx8tR3tySimVLylZY3JBTuqu/uTX1Q9ppVNU3Ck5DttZgMCGnhHvcyMmg2AAoFMDR4hqUVNWLjmM3qzPMcwXH9AiFmi0pm+E7S06pf4w/4oO9UddgxA/7eaim3B07U4NGowQfdzU6dvAQHYesoIOXBj3CdQCArS4yenPRKqkkbtxnSyxuyCkpFApMbZpY/OWuPJ4wLHOWycShvlAoeOyCXAxNaF4S7hrFzY7jZThb1wh/Lw0GxvqLjiNrLG7Iad2a0hFatRJZRdXYl1chOg7ZUPMycM63kZfmoxi2Zpe6xC8oqy9YJcWWlG3x3SWnpfN0w4Rk89Dusl1cFi5nzSM3iZxvIyt9ojpAq1aipFqPnJIa0XFsqvGCVVI3cpWUzbVqJ6yYmJg2DQnPnDkTjzzySKsfR3Q1dw3ohOV7T2HVgdN4/sau8PPUiI5ENsA9buTJ3U2F/jH+2JJdiq05pUgIke/I3I5j5pZUgJcGA2LYkrK1VhU3n3zySZteJDo6uk2PI7qalEg/dA3zRWZhFb7bV4C/Do0RHYmsrKS6HqU1DVAqgC4y/uHnqobEB5qLm+xS3DdEvv9+LS0prpKyi1YVN8OGDbNVDqI2aZ5Y/NzKg1i26ySmD4nmhFOZad7fJjrQCx4aleA0ZG3N8252Hi9Do9Ekyx17G40mrDnc1JJKYkvKHuT3t4hczsRe4fDUqHDsTC125ZaLjkNWxpaUvHUL80UHTzfUNhiRnl8hOo5NbD9Whoq6RgR6a9CfLSm7YHFDTs/H3Q0Te0UA4MRiOWoubrqxuJElpVKBwTI/imH1AW7cZ2+cUEyyMHVAJ3yVmodfDhaitKYbAr21oiORlWTx2AXZuyY+EKsPFGJbTikeu6Gz6DhWZV4lVQwAGMeWlN1wQjHJQo8IHZI76pB+qhLL957CA8PiREciK9AbjDh2xrxEmAdmylfzIZr78ytQXd8IH3c3wYmsZ1tOKSrPmVtSA2J4lpS9ONSE4s2bN+ONN97A3r17UVhYiBUrVuDmm2+2fP/777/HkiVLsHfvXpSXl2P//v3o1auXTTOR85g6IArppw7gq9Q8/P2aWCiVnFjs7LKLa2AwSdB5uCFM5y46DtlIpL8nogI8cbKsDruOl2NktxDRkaymeZXU2B5hUPEzyW7a1fxrbGxEfn4+jhw5gvLy9k/krK2tRXJyMhYtWnTZ7w8dOhTz589v92uR/NyYHAYfrRony+qw7Zg8e/eu5vxkYh+ugpM5y27FMjqKocFgwm+H2ZISoVUjNwBQXV2NL774Av/73/+QmpqKhoYGSJIEhUKBjh07YtSoUfj73/+Ofv36tTrM2LFjMXbs2Mt+f9q0aQCAEydOtPq5Sf48NWrc2jsCn+44iWW78nBNQpDoSNROmYU8CdxVDI0PxJe78rBNRsXNtmPNLSktV0nZWatGbhYsWIDo6GgsXboUI0eOxMqVK5GWloajR49ix44deOGFF2AwGDBq1CiMGTMG2dnZtsrdYnq9HlVVVRddJF93DYgCAPx2uBjFVfWC01B7cRm46xgUFwCFAsguqUFRpTz+7Ta3pMYlhbIlZWetGrnZvXs3Nm/ejO7du1/y+/3798f06dOxZMkSLF26FFu2bEFCQoJVgrbVvHnz8NJLLwnNQPbTJdQHfaM6YM/Js/hmdz4eHiH27x+1nSRJyCziMnBX4eepQc8I86KAbTmluK1PR9GR2qXBYMJvTWdJsSVlf60aufnqq68uW9hcSKvV4oEHHsD06dPbHMxaZs+ejcrKSsuVn58vOhLZ2NSBnQAA/9udD6NJ/icNy1VxlR4VdY1QKRWID/YWHYfsYIiM5t1syylFVb0BQT5a9ItmS8reZL+bkFarha+v70UXydvYHmHw83RDQcU5bDpaIjoOtVFzSyo20Avubjx2wRVcOKlYkpz7F5NVzS2pHmxJiWC14uajjz6y1lMRtYu7mwqTepuHtLljsfM6zPk2Lqd3VAe4uylxplqP7JIa0XHaTG8w4rfDbEmJZLXiZtWqVdiwYYPl67q6Otxxxx2teo6amhqkpaUhLS0NAJCbm4u0tDTk5Zl/QJWXlyMtLQ2HDx8GABw5cgRpaWkoKiqyzh+CZOPOAebW1IasEhRUnBOchtqCk4ldj7ubytLCceajGLbllKK63oBgHy36siUlhNWKm88++wxz5sxBVlYWjh49imuvvRajR49u1XPs2bMHKSkpSElJAQDMmjULKSkpmDNnDgDgxx9/REpKCsaPHw8AuOOOO5CSkoIlS5ZY649BMhEX5I1BsQEwScDXqRy9cUYX7nFDrqO5NeXMS8ItLakkbtwnSqv3ufmjxx57DL169UJycjI++ugjTJ06FSaTCUuXLm317sHDhw+/Yp/13nvvxb333tu+wOQypg7shB3Hy/C/plVTbjywzmnUNxqRW1oLgCulXM3QhEDgF2Dn8TI0Gk1O9+9WbzBibdNZUuN7siUlSrv/1lx33XXIz8/Hq6++ismTJyM3Nxfh4eFYs2YNVq9ebY2MRG0yqlsoAr01KKnWY30mJxY7k6PF1TBJQICXBkE+PATVlXQN9UWAlwZ1DUbsz6sQHafVtmaXolpvQIivFn06dRAdx2W1u7i56aab8Nxzz+Gbb75BRkYGTp8+jZdeeglBQUFYt26dNTIStYlGrcTkvpEAgGVsTTmV5pZUIo9dcDlKpQKDnXhJ+IVnSfF8O3Fa3Zby8fFBSkoK+vTpg969e6N3797o1q2b5QPI3d0dffv2Rd++fa0elqi17uzXCYs3HsPmo2eQV1aHTgGeoiNRC1iOXeBJ4C5paHwAfko/jW05pZh1Q2fRcVpMbzBibdNZUjeyJSVUq0du5s+fj4SEBGzYsAHTp09Hz5494ePjg8GDB+Phhx/G0qVLkZ6ebousRK3WKcAT13Y2nzHF0RvnwWXgrq15M7+0/ApU1TcKTtNyW46aW1Khvu7ozZaUUK0euXnwwQct/33u3Dl4eXnh4YcfRnl5OXbu3ImPPvoIDQ0NMBqNVg1K1FZ39e+EzUfP4Ns9+Zh1Q2do1M41QdHVSJLEZeAurmMHT8QEeiG3tBa7jpfjhm4hoiO1yOqMppZUUihbUoK1a7WUh4cHAODOO+9Ez549AQAGg8GyDw2RIxjRNRghvloUV+mx5lARJiSHi45EV1BQcQ7V9Qa4qXjsgisbEh+A3NJabMspdYripr6RLSlHYvVfYdVqtaXQIXIEbiolbu9n3tSPOxY7vub5NnFB3hxlc2HN+91syT4jOEnLbMkuRU1TSyolki0p0fjJQS7hjn6RUCqAHcfLkOPE27q7gqxCngROwKDYQCgVwLEztSisdPxdxlcfOA3AvHEfW1Litbq4uf/++7F48WLs3r0ber0eALhUkxxeuJ8Hrk8MBgB8xYnFDi2z6PwycHJdOk83JHX0A2DeO8aRXdiS4sZ9jqHVxU12djaeeeYZDBgwAD4+5g+fl156CQsXLsT27dtRV1dn9ZBE1nBX03lT3+07hfpGTnh3VJZl4By5cXlD4wMAOP5RDJuOnkFtgxHhOnekRPqJjkNoQ3GzadMmVFZW4siRI/jss8/wxBNPoKKiAnPmzMHQoUOh0+nQvXt3W2QlapdhnYMR4eeBirpG/Ny0qoEcS12DASfKzMcusLihofHmbRy25pRd8Wge0X62rJJiS8pRtHm1VEJCAhISEi46+Ts3Nxd79uzB/v37rRKOyJpUSgXu7B+JN387imW78nBr746iI9EfZBVVQ5KAIB8tAr157IKr6x3lB3c3JUpr9DhSXI1EB9zUsb7RiHVsSTmcVo3c5OVdea5CTEwMJk+ejFdffRUAUFBQ0PZkRDYwpW8k1EoF9pw8i6ymuR3kOLi/DV1Iq1ahf4y5NeWo8242HjG3pCL8PNiSciCtKm769euHf/zjH9i9e/dl71NZWYkPP/wQPXr0wHfffdfugETWFOzrbtkzg8vCHc/54oaTicnsmqYl4Y4678bSkuoRysU1DqRVbanDhw9j7ty5uOGGG+Du7o4+ffogPDwc7u7uOHv2LA4fPoxDhw6hd+/eeP311zFu3Dhb5SZqs7sGdMIvB4uwYl8BnhmbCE9Nu/ayJCvKappMzGXg1Kz5KIZdueVoMJgcau+j+kYj1mWyJeWIWvW3JCAgAAsWLEBhYSEWLlyIhIQElJaWIjs7GwAwdepU7N27Fzt27GBhQw5rSFwgogI8Ua034Kf006LjUBOTSUJWkbm4ccS5FSRGYqgPArw0qGswYn/eWdFxLrLxSAnqmlpSvdiSciht+pXVw8MDY8aMwaRJk6ydh8jmlEoF7urfCfN+ycKyXXmW3YtJrFNnz6FGb4BGpURskJfoOOQglEoFhsQH4semU8IHxAaIjmSxOqMIADAuiS0pR9Pm8T2dTsc5NeS0JvXpCI1KifRTlThYUCk6DuH8SeAJId5wUzlO64HEsxzF4EDzbs41GLHe0pLieXWOps2fIJIk4f3338eQIUMwdOhQzJw584oTjYkcSYC3FmN6hAIAvuTEYofAlVJ0OUMSzMVNen4FquobBacxu7AlldxRJzoO/UG7fj3av38/evfujaFDh+LQoUO45ppr8MQTT1grG5FNNe9Y/ENaAaod5APTlbG4ocuJ8PNAbKAXTBKw81iZ6DgAgNVNq6TG9wxjS8oBtWuZyLJly3DDDTdYvj5w4AAmTpyIiIgIPPbYY+0OR2RLA2L8ERfkhWNnarEy7TSmDYwSHcmlNZ8pxWXgdClD4gNxvLQWW3NKMap7qNAs5pZUCQBgfBJXSTmiNo/c+Pv7IzIy8qLbevbsiYULF2Lx4sXtDkZkawqFAlMHmAuaZbvyHHp7d7mrrm9Efrn55OeuXClFlzC0qTW11QHm3fx+pATnGo3o2MEDPdmSckhtLm569eqFpUuX/un2+Pj4q+5kTOQobuvdEVq1EpmFVdifXyE6jss60rQEPNTXHR28NILTkCMaGBsApQI4fqYWpyvOCc3ClpTja3Nx88orr+Ddd9/FtGnTsGPHDtTW1qKkpASvvvoqYmJirJmRyGZ0nm64sWmlA3csFoc7E9PV6Dzc0LOjHwCxozd1DQZsYEvK4bW5uBk4cCB27tyJ/Px8XHPNNfD19UVYWBiWL1+Ot956y5oZiWyqeWLxT+mnUVnHicUiHG7amZiTielKrkkQfxTD71lncK7RiEh/DyRFsCXlqNq1Wio5ORkbN27E6dOnsWrVKvz44484efIkdycmp9K7kx8SQ32gN5jw3b5TouO4JK6UopYYcsE5UyaTmDlyzWdJjU8KZ0vKgVllp6zg4GCMHTsW48ePR2BgoDWekshuFAoFpjatlFqWyonF9mY0SZY5Nyxu6EpSOvnBw02F0poGHCmutvvr1zUYsD6raeM+tqQcGrcBJQJwc69weGpUyCmpQWpuueg4LuVkWS3ONRqhVSsRE8hjF+jytGoVBsT6AxDTmtqQVYL6RhM6+XuiRwQLcUfWqn1uYmJi2jQMN3PmTDzyyCOtfhyRvfi4u2Fir3B8lZqPZal5DnV+jdw1H5bZJdQHKiWH+enKhsYHYuORM9iSXYr7r4m162uvPsBVUs6iVcXNJ5980qYXiY6ObtPjiOzprv5R+Co1H79kFGHOjXoEeGtFR3IJlvk23N+GWqB53k1qbjn0BiO0apVdXrdWb8DvR7hKylm0qrgZNmyYrXIQCZfUUYeeHXU4cKoSy/eewj+GxYmO5BK4DJxaIzHUB4HeGpTWNGB/XgUG2mmUtbklFRXgie7hLMQdnUPNudm8eTMmTJiA8HDzLPSVK1de9H1JkjBnzhyEhYXBw8MDI0eORHZ2tpiwJEtTm5aFf5WaJ2w1hqvJ5DJwagWFQmEZvdmabb95N5aWVBJbUs6gVcVNTEwMYmNjW329++67LXr+2tpaJCcnY9GiRZf8/uuvv453330XS5Yswa5du+Dl5YXRo0ejvr6+NX8MosuakBwOH60aJ8rqsN1BDuiTs8q6RhQ07TabyOKGWshS3NhpUvFFLamebEk5A4eaczN27FiMHTv2kt+TJAlvv/02nnvuOUycOBEA8NlnnyEkJAQrV67EHXfc0aZsRBfy1KhxS+8IfLbjJJalnrScZ0O20XxYZoSfB3QeboLTkLMY2lTcHDhVgcpzjTb/u7M+qwR6gwnRAZ7oxiLcKTjNnJvc3FwUFRVh5MiRltt0Oh0GDBiAHTt2XLa40ev10Ov1lq+rqqpsnpWc210DOuGzHSfx26FilFTVI9jXXXQk2cri5n3UBuF+HogN8sLxM7XYcawMY3rY9pTw1QdOA+AqKWfiUHNurqSoqAgAEBISctHtISEhlu9dyrx586DT6SzXH08yJ/qjxFBf9InqAINJwjd78kXHkbXz8204mZha55p4+xzFUKM3YOORMwDMuxKTc3Ca4qatZs+ejcrKSsuVn88fVnR15ycW58PIicU209yW4sgNtZa95t2szyyG3mBCTKAXi3An4jTFTWioedixuLj4otuLi4st37sUrVYLX1/fiy6iqxmXFAadhxsKKs5h89EzouPIksFo4rEL1GYD4wKgUiqQW1qLU2frbPY6XCXlnJymuImJiUFoaCjWr19vua2qqgq7du3CoEGDBCYjOXJ3U2FSn44AgC935QlOI08nymqhN5jgqVEhyt9TdBxyMr7ubkjuaD6Ve3uObVY21ugN2Nj0yw1XSTkXhypuampqkJaWhrS0NADmScRpaWnIy8uDQqHAzJkz8corr+DHH39ERkYG7r77boSHh+Pmm28Wmpvk6c7+5tbUhqxinG5arkzWc7jw/LELSh67QG3QvGpqi41aU+szi9FgMCE20AuJoWxJOROHKm727NmDlJQUpKSkAABmzZqFlJQUzJkzBwDw1FNP4eGHH8bf//539OvXDzU1Nfj111/h7s7VLGR98cHeGBjrD5ME/G8352pZWyZXSlE7Nc+72Z5TapNNN1fxLCmn5VDFzfDhwyFJ0p+u5v11FAoF/v3vf6OoqAj19fVYt24dOnfuLDY0ydrUAVEAgK9358FgNAlOIy+WZeD8jZjaKKVTB3hqVCirbbAcwGot1fWN2MSWlNNyqOKGyNGM7h6KAC8Niqv0WJ9VIjqOrPDYBWovjVqJATH+AICtOdad+L8+s8TckgryQpcQFuDOhsUN0RVo1EpM7mveG2kZJxZbzdnaBhRVmY9N4bEL1B7nl4Rbd1Jxc0vqRq6Sckosboiu4s7+5uJmc/YZ5JXZbsmpK2meb9PJ3xPe2lZtlE50kWsSggAAqbll0BuMVnnOqvpGyxYQ43ty4z5nxOKG6CqiArxwTUIgJAn4ajdHb6zhsGUyMYf7qX06h3gj0FuL+kYT9p48a5XnXJ9ZjAajCfHB3ugc4m2V5yT7YnFD1ALNE4u/3ZOPBgMnFrcX59uQtSgUCgyNDwBgvaMYmjfuG8eWlNNicUPUAiO6BiPYR4vSmgb8dvjyZ5lRy3AZOFnT0KbWlDXm3ZhbUuYi6UauknJaLG6IWsBNpcQd/Tix2BoajSbklNQAALqGsrih9mvezC/jVAUq6xrb9VzrDptbUgnB3ujMVVJOi8UNUQvd3r8TlApg+7EyHDtTIzqO0zp+phYNRhO8tWp07OAhOg7JQKjOHfHB3jBJwI7j7WtNXdiSIufF4oaohSL8PHBdl2AAwFccvWmz5pZUIo9dICuyHMWQ3fbipvJcIzZnc+M+OWBxQ9QKUweaz5tavu8U6huts+zU1XC+DdlC83437ZlUvO5wMRqNEjqHsCXl7FjcELXCsM7BiPDzQEVdI345WCg6jlM6zOKGbGBgrD9USgVOlNUhv7xt+1GtzmBLSi5Y3BC1gkqp4MTidjq/DJy/GZP1+Li7oVekH4C2jd5UnmvEluaWFIsbp8fihqiVpvSLhEqpwO4TZ3HEyof1yd2Zaj1Ka/RQKIAuPDCTrOz8UQytL27WNrWkuoT4IIEtKafH4oaolUJ83XFD1xAAwLJdJwWncS5ZReaWVHSAFzw1PHaBrOuaBHNxs/1YGUwmqVWPXX3gNAC2pOSCxQ1RGzRPLP5+fwHqGgyC0ziPTB67QDbUK9IPXhoVymsbLHO7WqKyrtEy2jO+Z6it4pEdsbghaoMhcYHo5O+J6noDVqVzYnFLWebbcPM+sgE3lRIDYlt/FMNvh4vQaJSQGOqD+GAW3nLA4oaoDZRKBe4aYB69+TKVE4tbisvAydaGtmHeTfMqKU4klg8WN0RtNKlPR7ipFEjPr8DBgkrRcRye3mA8f+xCOIsbso2hTfNuUnPLW7QXVUVdA7Y2bfw3jhv3yQaLG6I2CvTWYkwP84fhl1wWflU5JTUwmCT4uqsRrnMXHYdkKiHYG8E+WugNJuw7efaq9//tUDEMJnNLKi7I2w4JyR5Y3BC1w139za2pH9MKUKPnxOIraZ5vkxjmC4WCxy6QbSgUila1pppbUjwBXF5Y3BC1w8BYf8QGeaG2wYiV+wtEx3FoWU3zbbpxvg3ZWEv3uzlb22CZeMwl4PLC4oaoHRQKBaYOiAJgbk1JUuv21nAlmUVcBk720VzcZBRUoqKu4bL3++1wEQwmCV3DfBHLlpSssLghaqfbekdAo1Yis7AKafkVouM4JEmSLjh2gSM3ZFuhOnckBHtDkswb+l3O6owiAGxJyRGLG6J28vPUWD4ced7UpZVU61Fe2wClAjxtmeziaq0ptqTkjcUNkRVMbdrz5qcDp1FZ1yg4jeNp3i02Nsgb7m4qwWnIFTQfxXC5zfzWHCqC0SShW5gvYgK97BmN7IDFDZEV9O7UAYmhPqhvNOH7/adEx3E43LyP7G1AbABUSgVOltUhv7zuT9+3bNzHlpQssbghsgLzxGLz6M0yTiz+k6zmZeA8CZzsxFurRkqkH4A/t6bKaxssc3G4K7E8sbghspKJKRHwcFMhu6QGu09cffMwV5LJZeAkQPNuxX8sbppbUt3DfRHNlpQssbghshJfdzdM7BUOAFi266TgNI6jvtGI46W1ANiWIvtq3sxve04pTKbzo6k/syUleyxuiKyo+TDNnzOKUF57+f01XEl2cQ2MJgkdPN0Q4qsVHYdcSHKkH7y1apyta7RMai+r0bMl5QJY3BBZUc+OfkiK0KHBaMLyvfmi4ziECycT89gFsic3lRIDY/0BnG9NrTlUDKNJQo8IX0QFsCUlV05X3FRXV2PmzJmIioqCh4cHBg8ejN27d4uORWTRPLH4q9T8i4bCXdVhrpQigSz73TSd/G1pSSWFC8tEtud0xc3999+PtWvX4vPPP0dGRgZGjRqFkSNHoqCA5/qQY5iQHA5vrRq5pbXYcfzyu6O6Ci4DJ5Ga592knihHQcU5bD9mLnLYkpI3pypuzp07h++++w6vv/46rr32WsTHx+PFF19EfHw8Fi9eLDoeEQDAS6vGLSkRALhjsSRJyCriMnASJz7YGyG+WjQYTHh1dSZMEpAUoUOnAE/R0ciGnKq4MRgMMBqNcHd3v+h2Dw8PbN269ZKP0ev1qKqquugisrXmicVrDhWhpLpecBpxCivrUXmuEWqlAgkhPJiQ7E+hUFhaU9y4z3U4VXHj4+ODQYMG4eWXX8bp06dhNBrxxRdfYMeOHSgsLLzkY+bNmwedTme5IiMj7ZyaXFHXMF/07uQHg0nCt3tcd8fi5pZUXJA3tGoeu0BiNLemmrElJX9OVdwAwOeffw5JkhAREQGtVot3330Xd955J5TKS/9RZs+ejcrKSsuVn88VLGQfUwdEAQC+Ss2D0UUnFp+fb8OWFIlzYXHTs6MOkf5sScmd0xU3cXFx2LRpE2pqapCfn4/U1FQ0NjYiNjb2kvfXarXw9fW96CKyh/E9w6DzcMOps+ewOfuM6DhCZDYdu8DJxCRSsK87Oje1RTlq4xqcrrhp5uXlhbCwMJw9exZr1qzBxIkTRUciuoi7mwq39e4IwHUnFnOlFDmKF2/qjmkDo/CXgVGio5AdqEUHaK01a9ZAkiR06dIFOTk5ePLJJ5GYmIj77rtPdDSiP7lrQCT+uy0X6zOLUVh5DmE6D9GR7KauwYDcMvOxC4lsS5Fgg+MCMTgu8Op3JFlwupGbyspKzJgxA4mJibj77rsxdOhQrFmzBm5ubqKjEf1JfLAPBsT4wyQB/0t1rfleR4trIElAoLcGwT7uV38AEZGVON3IzZQpUzBlyhTRMYhabOrAKOzKLcey1Dz8c3gc3N1cY9UQW1JEJIrTjdwQOZsx3UMRrnPHmWo9vt7tOqM3LG6ISBQWN0Q2plEr8c/r4gEAizceg95gFJzIPrgMnIhEYXFDZAdT+nZEqK87iqrq8Y0LbOonSRKyuAyciARhcUNkB1q1Cv8cHgcAWPx7DhoMJsGJbOvU2XOo1hvgplIgLojHLhCRfbG4IbKT2/tFIthHi9OV9Vi+V96jN4ebWlLxwT5wU/Fjhojsi586RHbi7qbCA8PMozeLfs9Bo1G+ozfnW1Kcb0NE9sfihsiO7hrQCYHeWhRUnMP3++Q7etM8mbgb59sQkQAsbojsyDx6Yz4HbaGMR28yi7gMnIjEYXFDZGdTB0Qh0FuD/PJzWLm/QHQcq6vRG3CyrA4AixsiEoPFDZGdeWhU+Ns15tGbRb/nwCCz0ZsjTaM2Ib5a+HtpBKchIlfE4oZIgL8MjIK/lwYnyurwY/pp0XGs6jD3tyEiwVjcEAngpVXj/mtiAAALN+TAaJIEJ7Ke5snEiaEsbohIDBY3RILcPSgafp5uOF5ai1UH5DN6k8VjF4hIMBY3RIJ4a9W4f6h59OY9mYzemEwSsorMbSkuAyciUVjcEAl09+Bo+LqrkVNSg58zCkXHabe88jrUNRihUSsRE+glOg4RuSgWN0QC+bq74a9DzSun3tuQDZOTj940z7fpEuIDNY9dICJB+OlDJNi9Q6Lh467G0eIa/HqoSHScdsnkfBsicgAsbogE03m44b4h5rk376537tGb5mXgXClFRCKxuCFyANOHRMNbq0ZWUTV+O1wsOk6bZfHYBSJyACxuiByAn6cG9w6OBmAevZEk5xu9qapvxKmz5wBwpRQRicXihshB/HVoDLw0KhwurMK6zBLRcVotq6klFa5zh87TTXAaInJlLG6IHEQHLw3uduLRm/OTiTlqQ0RisbghciD3D42Bh5sKGQWV2HjkjOg4rcLihogcBYsbIgcS4K3F3YOiAABvO9noDYsbInIULG6IHMz918TC3U2J9PwKbM4uFR2nRYwmCUeKm5aBc48bIhKMxQ2Rgwny0eIvA8yjN++sO+oUozcnympR32iCu5sS0QE8doGIxGJxQ+SA/j4sFlq1EvvyKrAtp0x0nKuyHLsQ6guVUiE4DRG5OhY3RA4o2Mcddw3oBAB4Z73jj940Fzfd2JIiIgfA4obIQT0wLA4atRK7T5zFjuOOPXqT2bTHDScTE5EjYHFD5KBCfN1xZ79IAMA767IFp7kyrpQiIkfiVMWN0WjE888/j5iYGHh4eCAuLg4vv/yyww/ZE7XVA8PjoFEpsSu3HDsddPSmoq4BhZX1AIAuoWxLEZF4TlXczJ8/H4sXL8bChQuRmZmJ+fPn4/XXX8d7770nOhqRTYTpPDClX0cA5l2LHdHhplGbjh084OvOYxeISDynKm62b9+OiRMnYvz48YiOjsakSZMwatQopKamio5GZDP/HB4PN5UC24+VYfeJctFx/iSL822IyME4VXEzePBgrF+/HkePHgUApKenY+vWrRg7dqzgZES2E+HngUl9zHNvHHH0hvNtiMjRqEUHaI1nnnkGVVVVSExMhEqlgtFoxNy5czF16tTLPkav10Ov11u+rqqqskdUIqt6cHgcvt2Tjy3Zpdh78iz6RHUQHckis4jLwInIsTjVyM0333yDL7/8EsuWLcO+ffvw6aef4s0338Snn3562cfMmzcPOp3OckVGRtoxMZF1RPp74rbejjf3xmA04WhxDQCO3BCR41BITrTUKDIyEs888wxmzJhhue2VV17BF198gaysrEs+5lIjN5GRkaisrISvLz+MyXnkldXhurc2wmiSsHLGEPSK9BMdCUeLqzHqP5vhpVEh48XRUHJ3YiKykaqqKuh0uhb9/HaqkZu6ujoolRdHVqlUMJlMl32MVquFr6/vRReRM+oU4IlbUiIAOM7ozfljF3xY2BCRw3Cq4mbChAmYO3cuVq9ejRMnTmDFihVYsGABbrnlFtHRiOxixnXxUCqADVklyDhVKTqOZRk4W1JE5Eicqrh57733MGnSJDz44IPo2rUrnnjiCfzjH//Ayy+/LDoakV3EBHrh5l7m0Zt3HGD0hsvAicgROdVqKR8fH7z99tt4++23RUchEmbG9fFYmVaAdZnFOFhQiR4ROmFZuAyciByRU43cEBEQF+SNCcnhAID3NogbvSmr0aOkWg+FAkjksQtE5EBY3BA5oYevj4dCAaw5VGwZPbG35pPAo/w94aV1qkFgIpI5FjdETig+2Afjk8IAiBu9YUuKiBwVixsiJ/Xw9QkAgJ8zinCkqNrur99c3CSGsrghIsfC4obISXUJ9cG4pFAAYkZvzi8D53wbInIsLG6InFjz6M3qjELklNhv9KbBYMKxMzx2gYgcE4sbIifWNcwXo7uHQJKA9zbk2O11j52pQaNRgo+7Gh07eNjtdYmIWoLFDZGTax69+Sn9tGU0xdYsk4lDfaFQ8NgFInIsLG6InFyPCB1Gdg2BSQIW2Wn0JpPzbYjIgbG4IZKBR0eYR29WphXgRGmtzV8vk8cuEJEDY3FDJANJHXW4PjEYJglY+LttR28kSTq/DJzFDRE5IBY3RDLxSNPozYr9Bcgrq7PZ65yp0aOstgFKBdAlhG0pInI8LG6IZKJXpB+GdQ6C0STh/zbabvSmuSUVHegFD43KZq9DRNRWLG6IZKR59Gb53lPIL7fN6A2PXSAiR8fihkhG+kR1wND4QBhMEhZvOmaT12gubrqxuCEiB8XihkhmHh1pHr35dk8+CirOWf35uQyciBwdixsimekX7Y9BsQFoNEpYstG6ozf1jUYcO2Neas4DM4nIUbG4IZKh5tGbr3fno7DSeqM3OSU1MJok6DzcEKZzt9rzEhFZE4sbIhkaGBuA/jH+aDCa8P6m41Z73gtbUjx2gYgcFYsbIpma2bRyallqHoqr6q3ynNyZmIicAYsbIpkaFBeAvlEd0GCw3ugNl4ETkTNgcUMkUwqFwjL35stdJ1FS3b7RG0mSkFnEZeBE5PhY3BDJ2ND4QKR08oPeYMKHm9s3elNUVY+KukaolArEB3tbKSERkfWxuCGSMYVCYTkx/POdJ1Fao2/zczW3pGIDveDuxmMXiMhxsbghkrlhnYOQ3FGH+kYTPtzS9tEbTiYmImfB4oZI5i6ce/P5jpMor21o0/NwMjEROQsWN0Qu4LouwUiK0KGuwYiP2jh6w2MXiMhZsLghcgEKhcJyYvin20/gbCtHb+objcgtNR+7wJVSROToWNwQuYiRXYPRLcwXtQ1G/Hdbbqsee6SoGiYJCPDSIMhHa6OERETWweKGyEVcOHrzybYTqKxrbPFjL5xvw2MXiMjRsbghciGjuoUgMdQH1XpDq0ZvmoubxFDOtyEix+d0xU10dDQUCsWfrhkzZoiORuTwlMrzozf/3ZaLynMtG73hMnAiciZOV9zs3r0bhYWFlmvt2rUAgMmTJwtORuQcxnQPRecQb1TXG/Dp9hNXvf+Fxy6wuCEiZ+B0xU1QUBBCQ0Mt16pVqxAXF4dhw4aJjkbkFJRKBR6+3jx68/HWXFTXX3n0pqDiHKrrDXBT8dgFInIOTlfcXKihoQFffPEFpk+fftlJjnq9HlVVVRddRK5uXFIY4oK8UHmuEZ/tOHnF+za3pOKCvKFRO/VHBhG5CKf+pFq5ciUqKipw7733XvY+8+bNg06ns1yRkZH2C0jkoFQXzL35cMtx1OgNl71v82Ri7m9DRM7CqYubjz/+GGPHjkV4ePhl7zN79mxUVlZarvz8fDsmJHJcN/YMR2ygFyrqGvH5FUZvLCuluDMxETkJpy1uTp48iXXr1uH++++/4v20Wi18fX0vuojIPHrz0PXxAMyjN7WXGb3hmVJE5GyctrhZunQpgoODMX78eNFRiJzWTcnhiA7wRHltA77c9efRm1q9ASfL6wCwuCEi5+GUxY3JZMLSpUtxzz33QK1Wi45D5LTUKiVmXGcevflg83GcazBe9P0jxdWQJCDIR4tAbx67QETOwSmLm3Xr1iEvLw/Tp08XHYXI6d2cEoFIfw+U1vx59IYtKSJyRk5Z3IwaNQqSJKFz586ioxA5PTeVEg81jd68v/k46hvPj96cL244mZiInIdTFjdEZF23pHREhJ8HzlTr8VVqnuX25j1uuAyciJwJixsigkZ9fu7Nkk3HUN9ohMkkIctyYCaLGyJyHixuiAgAMKlPR4Tr3FFcpcc3e/KRf7YOtQ1GaFRKxAZ5iY5HRNRiLG6ICIB59OafTaM3izceQ/qpSgBAQog33FT8qCAi58FPLCKymNK3I0J93VFYWY831xwBwJVSROR8WNwQkYVWrcI/h8cBAPK4eR8ROSkWN0R0kdv7RSLY5/yGfVwGTkTOhsUNEV3E3U2FB4bFWb7uypVSRORkeHYBEf3JXQM6Ye3hYoTp3NHBSyM6DhFRq7C4IaI/cXdT4au/DxQdg4ioTdiWIiIiIllhcUNERESywuKGiIiIZIXFDREREckKixsiIiKSFRY3REREJCssboiIiEhWWNwQERGRrLC4ISIiIllhcUNERESywuKGiIiIZIXFDREREckKixsiIiKSFRY3REREJCtq0QHsTZIkAEBVVZXgJERERNRSzT+3m3+OX4nLFTfV1dUAgMjISMFJiIiIqLWqq6uh0+mueB+F1JISSEZMJhNOnz4NHx8fKBQKqz53VVUVIiMjkZ+fD19fX6s+N53H99k++D7bB99n++D7bB+2fJ8lSUJ1dTXCw8OhVF55Vo3LjdwolUp07NjRpq/h6+vLfzx2wPfZPvg+2wffZ/vg+2wftnqfrzZi04wTiomIiEhWWNwQERGRrLC4sSKtVosXXngBWq1WdBRZ4/tsH3yf7YPvs33wfbYPR3mfXW5CMREREckbR26IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLixkoWLVqE6OhouLu7Y8CAAUhNTRUdSVbmzZuHfv36wcfHB8HBwbj55ptx5MgR0bFk77XXXoNCocDMmTNFR5GlgoIC/OUvf0FAQAA8PDyQlJSEPXv2iI4lK0ajEc8//zxiYmLg4eGBuLg4vPzyyy06n4gub/PmzZgwYQLCw8OhUCiwcuXKi74vSRLmzJmDsLAweHh4YOTIkcjOzrZbPhY3VvD1119j1qxZeOGFF7Bv3z4kJydj9OjRKCkpER1NNjZt2oQZM2Zg586dWLt2LRobGzFq1CjU1taKjiZbu3fvxvvvv4+ePXuKjiJLZ8+exZAhQ+Dm5oZffvkFhw8fxltvvYUOHTqIjiYr8+fPx+LFi7Fw4UJkZmZi/vz5eP311/Hee++JjubUamtrkZycjEWLFl3y+6+//jreffddLFmyBLt27YKXlxdGjx6N+vp6+wSUqN369+8vzZgxw/K10WiUwsPDpXnz5glMJW8lJSUSAGnTpk2io8hSdXW1lJCQIK1du1YaNmyY9Oijj4qOJDtPP/20NHToUNExZG/8+PHS9OnTL7rt1ltvlaZOnSookfwAkFasWGH52mQySaGhodIbb7xhua2iokLSarXSV199ZZdMHLlpp4aGBuzduxcjR4603KZUKjFy5Ejs2LFDYDJ5q6ysBAD4+/sLTiJPM2bMwPjx4y/6e03W9eOPP6Jv376YPHkygoODkZKSgg8//FB0LNkZPHgw1q9fj6NHjwIA0tPTsXXrVowdO1ZwMvnKzc1FUVHRRZ8fOp0OAwYMsNvPRZc7ONPaSktLYTQaERISctHtISEhyMrKEpRK3kwmE2bOnIkhQ4agR48eouPIzv/+9z/s27cPu3fvFh1F1o4fP47Fixdj1qxZePbZZ7F792488sgj0Gg0uOeee0THk41nnnkGVVVVSExMhEqlgtFoxNy5czF16lTR0WSrqKgIAC75c7H5e7bG4oaczowZM3Dw4EFs3bpVdBTZyc/Px6OPPoq1a9fC3d1ddBxZM5lM6Nu3L1599VUAQEpKCg4ePIglS5awuLGib775Bl9++SWWLVuG7t27Iy0tDTNnzkR4eDjfZxljW6qdAgMDoVKpUFxcfNHtxcXFCA0NFZRKvh566CGsWrUKv//+Ozp27Cg6juzs3bsXJSUl6N27N9RqNdRqNTZt2oR3330XarUaRqNRdETZCAsLQ7du3S66rWvXrsjLyxOUSJ6efPJJPPPMM7jjjjuQlJSEadOm4bHHHsO8efNER5Ot5p99In8usrhpJ41Ggz59+mD9+vWW20wmE9avX49BgwYJTCYvkiThoYcewooVK7BhwwbExMSIjiRLI0aMQEZGBtLS0ixX3759MXXqVKSlpUGlUomOKBtDhgz503YGR48eRVRUlKBE8lRXVwel8uIfdSqVCiaTSVAi+YuJiUFoaOhFPxerqqqwa9cuu/1cZFvKCmbNmoV77rkHffv2Rf/+/fH222+jtrYW9913n+hosjFjxgwsW7YMP/zwA3x8fCx9W51OBw8PD8Hp5MPHx+dP85i8vLwQEBDA+U1W9thjj2Hw4MF49dVXMWXKFKSmpuKDDz7ABx98IDqarEyYMAFz585Fp06d0L17d+zfvx8LFizA9OnTRUdzajU1NcjJybF8nZubi7S0NPj7+6NTp06YOXMmXnnlFSQkJCAmJgbPP/88wsPDcfPNN9snoF3WZLmA9957T+rUqZOk0Wik/v37Szt37hQdSVYAXPJaunSp6Giyx6XgtvPTTz9JPXr0kLRarZSYmCh98MEHoiPJTlVVlfToo49KnTp1ktzd3aXY2FjpX//6l6TX60VHc2q///77JT+T77nnHkmSzMvBn3/+eSkkJETSarXSiBEjpCNHjtgtn0KSuE0jERERyQfn3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiEj2oqOj8fbbb4uOQUR2wuKGiKzq3nvvtWyxPnz4cMycOdNur/3JJ5/Az8/vT7fv3r0bf//73+2Wg4jE4tlSROTwGhoaoNFo2vz4oKAgK6YhIkfHkRsisol7770XmzZtwjvvvAOFQgGFQoETJ04AAA4ePIixY8fC29sbISEhmDZtGkpLSy2PHT58OB566CHMnDkTgYGBGD16NABgwYIFSEpKgpeXFyIjI/Hggw+ipqYGALBx40bcd999qKystLzeiy++CODPbam8vDxMnDgR3t7e8PX1xZQpU1BcXGz5/osvvohevXrh888/R3R0NHQ6He644w5UV1db7rN8+XIkJSXBw8MDAQEBGDlyJGpra230bhJRa7C4ISKbeOeddzBo0CD87W9/Q2FhIQoLCxEZGYmKigpcf/31SElJwZ49e/Drr7+iuLgYU6ZMuejxn376KTQaDbZt24YlS5YAAJRKJd59910cOnQIn376KTZs2ICnnnoKADB48GC8/fbb8PX1tbzeE0888adcJpMJEydORHl5OTZt2oS1a9fi+PHjuP322y+637Fjx7By5UqsWrUKq1atwqZNm/Daa68BAAoLC3HnnXdi+vTpyMzMxMaNG3HrrbeCR/UROQa2pYjIJnQ6HTQaDTw9PREaGmq5feHChUhJScGrr75que2///0vIiMjcfToUXTu3BkAkJCQgNdff/2i57xw/k50dDReeeUVPPDAA/i///s/aDQa6HQ6KBSKi17vj9avX4+MjAzk5uYiMjISAPDZZ5+he/fu2L17N/r16wfAXAR98skn8PHxAQBMmzYN69evx9y5c1FYWAiDwYBbb70VUVFRAICkpKR2vFtEZE0cuSEiu0pPT8fvv/8Ob29vy5WYmAjAPFrSrE+fPn967Lp16zBixAhERETAx8cH06ZNQ1lZGerq6lr8+pmZmYiMjLQUNgDQrVs3+Pn5ITMz03JbdHS0pbABgLCwMJSUlAAAkpOTMWLECCQlJWHy5Mn48MMPcfbs2Za/CURkUyxuiMiuampqMGHCBKSlpV10ZWdn49prr7Xcz8vL66LHnThxAjfeeCN69uyJ7777Dnv37sWiRYsAmCccW5ubm9tFXysUCphMJgCASqXC2rVr8csvv6Bbt25477330KVLF+Tm5lo9BxG1HosbIrIZjUYDo9F40W29e/fGoUOHEB0djfj4+IuuPxY0F9q7dy9MJhPeeustDBw4EJ07d8bp06ev+np/1LVrV+Tn5yM/P99y2+HDh1FRUYFu3bq1+M+mUCgwZMgQvPTSS9i/fz80Gg1WrFjR4scTke2wuCEim4mOjsauXbtw4sQJlJaWwmQyYcaMGSgvL8edd96J3bt349ixY1izZg3uu+++KxYm8fHxaGxsxHvvvYfjx4/j888/t0w0vvD1ampqsH79epSWll6yXTVy5EgkJSVh6tSp2LdvH1JTU3H33Xdj2LBh6Nu3b4v+XLt27cKrr76KPXv2IC8vD99//z3OnDmDrl27tu4NIiKbYHFDRDbzxBNPQKVSoVu3bggKCkJeXh7Cw8Oxbds2GI1GjBo1CklJSZg5cyb8/PygVF7+Iyk5ORkLFizA/Pnz0aNHD3z55ZeYN2/eRfcZPHgwHnjgAdx+++0ICgr604RkwDzi8sMPP6BDhw649tprMXLkSMTGxuLrr79u8Z/L19cXmzdvxrhx49C5c2c899xzeOuttzB27NiWvzlEZDMKiWsXiYiISEY4ckNERESywuKGiIiIZIXFDREREckKixsiIiKSFRY3REREJCssboiIiEhWWNwQERGRrLC4ISIiIllhcUNERESywuKGiIiIZIXFDREREckKixsiIiKSlf8H+CvksDMn8hcAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n", + "plt.figure()\n", + "plt.plot(range(iters+1), energy_fluc)\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'Energy fluctuation')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "iters = 30\n", + "states = [0,1,2,3,4,5,6,7]\n", + "energy = np.empty((len(states),iters))\n", + "\n", + "\n", + "d = (np.diag(np.linspace(1,2**nqubits,2**nqubits)))\n", + "for i in range(len(states)):\n", + " dbi_ = deepcopy(dbi)\n", + " dbi_.state = states[i]\n", + " for j in range(iters):\n", + " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " if step_poly is not None:\n", + " dbi_(step_poly, d=d)\n", + " energy[i,j] = np.real(dbi_.h.matrix[states[i],states[i]])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eigenvalues: [-9.29150262 -5.21110255 -2. -2. 1.29150262 4.\n", + " 4. 9.21110255]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Iterations')" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", + "print('Eigenvalues:', eigvals )\n", + "plt.figure()\n", + "for i in range(len(states)):\n", + " plt.plot(range(iters), energy[i,:],'.', label='State ' + str(states[i]))\n", + "for eigvals in eigvals:\n", + " plt.axhline(y=eigvals, color='r', linestyle='--')\n", + "plt.xlabel('Iterations')\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradients for finding optimal $D$\n", + "\n", + "An advantage of the least-squares cost function is that one can use gradient descent and the learning is more stable than with the off-diagonal cost function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from qibo.models.dbi.utils_gradients import *" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-04-15 08:50:11]: Using numpy backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 3\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the energy fluctuation cost function\n", + "cost = DoubleBracketCost.energy_fluctuation\n", + "# define the state\n", + "state = np.zeros(2**nqubits)\n", + "state[3] = 1\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Diagonal elements')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cost = DoubleBracketCost.least_squares\n", + "nqubits = 5\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "\n", + "step = 1e-2\n", + "iterations = 200\n", + "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", + "\n", + "plt.figure()\n", + "plt.plot(range(iterations+1), loss)\n", + "plt.xlabel('Learning iterations')\n", + "plt.ylabel('Loss: Least squares')\n", + "\n", + "plt.figure()\n", + "for i in range(2**nqubits):\n", + " plt.plot(diags[i,:], label='State ' + str(i))\n", + "plt.xlabel('Learning iterations')\n", + "plt.ylabel('Diagonal elements')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training for $D$ can greatly improve the decrease of the off-diagonal norm at each iteration. Nonetheless, during training the ascending values condition may be no longer satisfied creating a exponential decrease after few iterations" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cost = DoubleBracketCost.least_squares\n", + "nqubits = 5\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "d_fixed = np.diag(params)\n", + "dbi_trained = deepcopy(dbi)\n", + "flows = 50\n", + "iterations = 200\n", + "off_diagonal_norm = np.empty((2,flows+1))\n", + "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", + "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", + "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", + "for i in range(flows):\n", + "\n", + " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", + " dbi_trained(s,d=d_trained)\n", + " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", + " s = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_fixed, n=3)\n", + " dbi(s,d=d_fixed)\n", + " off_diagonal_norm[1,i+1] = dbi.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(off_diagonal_norm[0,:], label='Trained')\n", + "plt.plot(off_diagonal_norm[1,:], label='Untrained')\n", + "plt.legend()\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A solution can be to redo the training at each step, with a $D$ having ascending values" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cost = DoubleBracketCost.least_squares\n", + "nqubits = 5\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "d_fixed = np.diag(params)\n", + "dbi_trained = deepcopy(dbi)\n", + "flows = 50\n", + "iterations = 20\n", + "off_diagonal_norm = np.empty((2,flows+1))\n", + "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", + "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", + "\n", + "for i in range(flows):\n", + " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", + " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", + " dbi_trained(s,d=d_trained)\n", + " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", + " s = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_fixed, n=3)\n", + " dbi(s,d=d_fixed)\n", + " off_diagonal_norm[1,i+1] = dbi.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(off_diagonal_norm[0,:], label='Trained')\n", + "plt.plot(off_diagonal_norm[1,:], label='Untrained')\n", + "plt.legend()\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The numerical gradients may be preferred as they decrease more the loss at each iteration and are computationally faster. They may be more precise as the previous analytic since the analytic computations use the polynomial approximation as a starting point" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "nqubits = [3,4,5,6]\n", + "iterations = 30\n", + "step = 1e-2\n", + "differences = np.empty((len(nqubits),iterations+1))\n", + "loss_max = np.empty(len(nqubits))\n", + "for q in range(len(nqubits)):\n", + " # define the hamiltonian\n", + " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", + "\n", + " # define the least-squares cost function\n", + " cost = DoubleBracketCost.least_squares\n", + " # initialize class\n", + " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + " loss_max [q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", + " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", + " d_analytic, loss_analytic, grad_analytic, diags_analytic = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", + " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", + " d_numerical, loss_numerical, grad_numerical, diags_numerical = gradient_descent_dbr_d_ansatz(dbi, params,iterations,step, analytic=False)\n", + " differences[q,:] = loss_analytic - loss_numerical\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "for q in range(len(nqubits)):\n", + " plt.plot(differences[q,:],label= 'nqubits = {}'.format(nqubits[q]))\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel('Difference in analytic and numerical loss function')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.title('Normalized difference')\n", + "for q in range(len(nqubits)):\n", + " plt.plot(differences[q,:]/loss_max[q],label= 'nqubits = {}'.format(nqubits[q]))\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel('Difference in analytic and numerical loss function')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1-local ansatz\n", + "\n", + "We can consider, as an alternative to the a fully parametrized diagonal, a diagonal matrix of the form: $D = \\sum \\alpha_i Z_i$. This has the advantage of having a linear number of parameters to optimize instead of an exponential as well as being easier to implement in a quantum computer " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-04-15 09:19:52]: Using numpy backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCost.least_squares\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], + "source": [ + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,1e-2, analytic=False, d_type = d_ansatz_type.element_wise)\n", + "flows = 50\n", + "off_diagonal_norm = np.empty((flows+1,2))\n", + "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", + "\n", + "\n", + "\n", + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,nqubits,nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, 1e-3, analytic=False, d_type = d_ansatz_type.local_1)\n", + "best = np.argmin(loss_opt)\n", + "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", + "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "c4f92193806e2908606a5f23edd55a5282f2f433b73b1c504507f9256ed9f0b4" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dbi/dbi_costs.ipynb b/examples/dbi/dbi_costs.ipynb deleted file mode 100644 index 2170ce8c9b..0000000000 --- a/examples/dbi/dbi_costs.ipynb +++ /dev/null @@ -1,687 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Double-bracket Iteration other cost functions and respective scheduling\n", - "\n", - "This notebook presents two additional cost functions for the double-bracket flow: least-squares and energy fluctuation with their respectice scheduling methods." - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Least-squares\n", - "\n", - "The cost function is defined as: $\\frac{1}{2}||D-H_k||^2 =\\frac{1}{2}(||D||^2+||H||^2) -Tr(D H_k)$ as in https://epubs.siam.org/doi/abs/10.1137/S0036141092229732?journalCode=sjmael. We seek to maximize this function at each iteration." - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-20 10:46:15]: Using qibojit (numba) backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 9\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.39394545454545454\n", - "hyperopt_search step: 0.017463998220887386\n", - "polynomial_approximation step: 0.0010293852957746303\n" - ] - } - ], - "source": [ - "# generate data for plotting sigma decrease of the first step\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", - "s_space = np.linspace(1e-5, 0.3, 100)\n", - "off_diagonal_norm_diff = []\n", - "potential = []\n", - "for s in s_space:\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval(s,d=d)\n", - " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", - " potential.append(dbi_eval.least_squares(D=d))\n", - "\n", - "# grid_search\n", - "#step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", - "print('grid_search step:', step_grid)\n", - "# hyperopt\n", - "#step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", - "print('hyperopt_search step:', step_hyperopt)\n", - "# polynomial\n", - "#step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", - "print('polynomial_approximation step:', step_poly)" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", - "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.39394545454545454\n" - ] - }, - { - "data": { - "image/png": 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qC8xNjLB8YifMG9IaALAhNhUvbjgGZSEHiBMREf1dnQhNM2bMQHh4OA4cOAB3d/cq662srODl5YVevXph3bp1MDY2xrp167TevqurK27fvl1leVZWlrp3ydXVtUqPUk5ODsrKyqr0QN339ttvQ6lUql/Xr1/Xuqa6RCaTYcagVljzfBdYmBjh8KU7GLv6CK5m3ZO6NCIiojpD0tAkhEBoaCi2b9+O/fv3o3nz5lp/rqSkROvv8fPzg1KpxNGjR9XLEhISoFQq0bt3b3Wb06dPIz09Xd1mz549MDMzQ9euXavdrpmZGWxtbTVehmyYb2Nsfc0PbgpzXL1TgDGrjiDm0h2pyyIiIqoTJA1N06dPx08//YRNmzbBxsYGGRkZyMjIQFFREQCgoKAA77zzDuLj43Ht2jUkJibi5Zdfxo0bN/Dss8+qt5ORkYHk5GRcvnwZAHDq1CkkJycjO7vykSFt27bFsGHDMGXKFMTHxyM+Ph5TpkzByJEj0aZNGwCAv78/fHx8EBwcjKSkJOzbtw+vv/46pkyZYvBhSBft3BT4LbQPOje1Q15xOV5YfxQb41KlLouIiEh6QkIAqn2tX79eCCFEUVGRGDt2rHBzcxOmpqaicePGIiAgQBw9elRjOx9++OFDtyOEEHfv3hXPPfecsLGxETY2NuK5554TOTk5Gtu5du2aGDFihLCwsBAODg4iNDRUFBcXa70/SqVSABBKpfJxD0mdUVRaLmaHJQnPtyKE51sR4p3tJ0VpeYXUZREREemdtr/fdWqeJkNnSPM0aUMIgTXRV/HZ7vMQAvBr0Qirn+sCeytTqUsjIiLSG4Ocp4nqFplMhteeaolvgrvBytQIcVfvYszqI7icmS91aURERLWOoYkeaYiPC7a+1htN7Cxw7W4hxq6KxcELuk35QEREZOgYmkgrbRvbIjy0D7o3s0d+STn+veEY1sWkgFd3iYiooWBoIq01sjbDTy/3xLNd3aESwH8izuLt7adQWs4ZxImIqP5jaCKdmBkb4bPxHfDeiLaQy4CwY9fx/HcJuHtP+3mziIiIDBFDE+lMJpPh5X4tsO6F7rAxM8bR1GyMXnUEFzI4QJyIiOovhiZ6bAO9nbF9Wm80dbDEjZwijFt9BHvPVn1cDRERUX3A0ERPpJWLDXZO74NeLRxQUFqBKT/+hTXRVzhAnIiI6h2GJnpi9lam+PGlngjq2RRCAJ/uOo95v55AcVmF1KURERHpDUMT6YWJkRz/HeOLhQHtYCSXYXviTQR9G4/M/GKpSyMiItILhibSG5lMhhd6N8OGF7vD1twYiWm5GLPyCE7fVEpdGhER0RNjaCK969fKCb9N74MWjla4pSzGs2visOtUutRlERERPRGGJqoRLZyssWNaH/Rr5Yiisgq89nMilu+7xAHiRERksBiaqMYoLE2wPqQ7XuzTDADwZdRFzNichKJSDhAnIiLDw9BENcrYSI4PR7XDonHtYSyXIeJkOgLXxiFDyQHiRERkWBiaqFZM6tEUP7/cE/aWJjh1U4mAlTFIvp4rdVlERERaY2iiWtOzRSOEh/ZFaxdrZOaXIHBtHHYm35S6LCIiIq0wNFGt8nCwxLbXemNwW2eUlqswKywZn0Weh0rFAeJERFS3MTRRrbMxN8Ha4G547amWAIDVB6/glZ+O415JucSVERERPRhDE0nCSC7DW8O8sXRCR5gayxF19jbGfx2L69mFUpdGRERULYYmktTYzu4Im9oLjtZmOJ+Rj9GrjuBYarbUZREREVXB0ESS69LUHuGhfdDOzRbZBaUI+jYevxy7LnVZREREGhiaqE5ws7PAr6/6YUT7xiirEHhz20n8J+IsyitUUpdGREQEgKGJ6hBLU2OsmNQZswe3AgCsi0nBSz/8hbziMokrIyIiYmiiOkYul2H24NZY/VwXmJvIEX0xC2NXHUHKnQKpSyMiogaOoYnqpKfbN8bWV3ujscIcV7IKMHplDGIu3ZG6LCIiasAYmqjO8m2iwM7QPujc1A55xeV4Yf1R/BCbCiE4ESYREdU+hiaq05xtzLF5Si+M69IEFSqBD8PP4J0dp1FazgHiRERUuxiaqM4zNzHCkmc74p2nvSGTAZuPpuH5dQnILiiVujQiImpAGJrIIMhkMkzt3xLrXugGazNjHE3JRsDKGJzPyJO6NCIiaiAYmsig/MvbBTum9YZnI0vcyCnCM6tjsedMhtRlERFRA8DQRAanlYsNfpvWB34tGqGgtAKv/HQcqw5c5gBxIiKqUQxNZJDsrUyx8aUeCO7lCSGAz3dfwOwtySguq5C6NCIiqqcYmshgmRjJ8Z8xvvh4jC+M5TLsTL6FCWvjkKEslro0IiKqhxiayOA938sTP77UE3aWJjhxQ4mAlTFIvp4rdVlERFTPMDRRveDXshHCp/dFaxdrZOaXIHBtHHYm35S6LCIiqkcYmqjeaNrIEtte643BbZ1RWq7CrLBkLI48D5WKA8SJiOjJMTRRvWJjboK1wd3w2lMtAQBfH7yCqT/+hfziMokrIyIiQ8fQRPWOkVyGt4Z5Y9mETjA1lmPvuUw883Us0u4WSl0aEREZMIYmqrfGdG6CX17xg7ONGS7evoeAVTGIvXJH6rKIiMhAMTRRvdbJww7hoX3RwV2B3MIyBK87ih/jUqUui4iIDBBDE9V7rgpz/PKKH0Z3ckOFSuD9nWfw7o5TKKtQSV0aEREZEIYmahDMTYywbEInvDXMGzIZ8HNCGp7/LgHZBaVSl0ZERAaCoYkaDJlMhteeaonvJneDtZkxElKyEbAyBucz8qQujYiIDABDEzU4g9q6YPu03mjqYIkbOUUYtzoWu89kSF0WERHVcQxN1CC1drHBzul90LtlIxSWVuCVH49jxb5LEIITYRIRUfUYmqjBsrcyxQ//7oEX/DwBAEuiLmLG5iQUlVZIXBkREdVFxo/zIZVKhcuXLyMzMxMqleYdSP3799dLYUS1wcRIjoWjfdHG1RYf7DyNiJPpSL1bgG+Cu8HNzkLq8oiIqA6RCR2vR8THxyMoKAjXrl2rcilDJpOhoqLh/pWel5cHhUIBpVIJW1tbqcshHSVcvYvXfk5EdkEpHK3NsDa4C7p6OkhdFhER1TBtf791vjz36quvolu3bjh9+jSys7ORk5OjfmVnZz9R0URS6tmiEXZO7wNvVxvcuVeCSd8k4Je/rktdFhER1RE69zRZWVnhxIkT8PLyqqmaDBZ7muqHgpJyzPvlBCL/d0fdS32b4+3h3jA24hBAIqL6qMZ6mnr27InLly8/UXFEdZmVmTFWP9cFswa1AgCsi0nBixuOQVlYJnFlREQkJZ0Hgs+YMQPz5s1DRkYG2rdvDxMTE431HTp00FtxRFKRy2WYM6Q1vF1tMPeXEzh86Q5Gr4rBdy90g5ezjdTlERGRBHS+PCeXV+2ckslkEEJwIDgvz9VLZ24pMXXjcdzMLYKNmTGWT+qMgd7OUpdFRER6ou3vt86h6dq1aw9d7+npqcvm6hWGpvrrzr0STPspEUdTsyGTAW8O9carA1pAJpNJXRoRET2hGgtN9GAMTfVbabkKC34/g00JaQCA0Z3csPiZDjA3MZK4MiIiehLa/n4/1uSWV65cwbJly3Du3DnIZDK0bdsWs2bNQsuWLR+7YKK6ztRYjk/GtkdbVxss/P0sdibfQsqdAqwN7orGCk6ESURU3+l899zu3bvh4+ODo0ePokOHDvD19UVCQgLatWuHqKiomqiRqE4J9muGH1/qCXtLE5y8oUTAyiNITMuRuiwiIqphOl+e69y5M4YOHYpPP/1UY/n8+fOxZ88eJCYm6rVAQ8LLcw3L9exCTNn4F85n5MPUSI5PxrXH+K7uUpdFREQ6qrExTebm5jh16hRatWqlsfzixYvo0KEDiouLH6/ieoChqeEpKCnH3F+SsfvMbQCcCJOIyBDV2OSWTk5OSE5OrrI8OTkZzs68DZsaFiszY3z9XFfM5ESYRET1ns4DwadMmYKpU6fi6tWr6N27N2QyGWJiYrB48WLMmzevJmokqtPkchnmDmmNtpwIk4ioXtP58pwQAsuWLcOSJUtw69YtAICbmxveeOMNzJw5s0HPW8PLc3T2Vh6mbPwLN3OLYG1mjK8mdsKgti5Sl0VERA9RK/M05efnAwBsbPjXNMDQRJXu3ivBtJ8TkZBSORHm6/5tMO2plg36DwoiorqsxsY0/Z2NjQ0DE9E/NLI2w08v90RwL08IAXy++wJmbE5CUWnDfcQQEVF9oNWYpi5dumDfvn2wt7dH586dH/oXc0OecoDoPhMjOf4zxhdtG9vig52nEXEyHSl3CvDN5G5oYseJMImIDJFWoWn06NEwMzNT/zcvMxBpJ6hnU3g5W+O1n47jzK08BKyIwdfPd0WP5g5Sl0ZERDris+f0iGOa6EFu5hZh6sa/cOZWHozlMiwc3Q7P9Wy4D7cmIqpLamxMU4sWLXD37t0qy3Nzc9GiRQtdN0fUIDSxs8DWV3tjZIfGKFcJvLvjNN7dcQql5SqpSyMiIi3pHJpSU1NRUVF1QGtJSQlu3Lihl6KI6iMLUyOsmNQZbwxtA5kM+DkhDc+vS8CdeyVSl0ZERFrQenLL8PBw9X/v3r0bCoVC/b6iogL79u1D8+bN9VsdUT0jk8kwfaAXvF1tMCssGUdTsjF65RGsDe4K3yaKR2+AiIgko3VP05gxYzBmzBjIZDK88MIL6vdjxozBxIkTERUVhSVLluj05YsWLUL37t1hY2MDZ2dnjBkzBhcuXNBos2DBAnh7e8PKygr29vYYPHgwEhISNNqUlJRgxowZcHR0hJWVFQICAqr0euXk5CA4OBgKhQIKhQLBwcHIzc3VaJOWloZRo0bBysoKjo6OmDlzJkpLS3XaJyJtDGrrgt+m90ZzRyvczC3C+DWx+P3ELanLIiKih9A6NKlUKqhUKjRt2hSZmZnq9yqVCiUlJbhw4QJGjhyp05dHR0dj+vTpiI+PR1RUFMrLy+Hv74+CggJ1m9atW2PlypU4deoUYmJi0KxZM/j7+yMrK0vdZvbs2dixYwfCwsIQExODe/fuYeTIkRqXEYOCgpCcnIzIyEhERkYiOTkZwcHB6vUVFRUYMWIECgoKEBMTg7CwMGzbto2PhqEa4+Vsg9+m98GA1k4oLlNhxuYkfBZ5HhUq3ptBRFQniTokMzNTABDR0dEPbKNUKgUAsXfvXiGEELm5ucLExESEhYWp29y8eVPI5XIRGRkphBDi7NmzAoCIj49Xt4mLixMAxPnz54UQQvz5559CLpeLmzdvqtts3rxZmJmZCaVSqVX992vTtj2REEKUV6jEJ3+cFZ5vRQjPtyLEi+uPCmVRqdRlERE1GNr+fus8EHzmzJlYvnx5leUrV67E7NmznyjAKZVKAICDQ/Vz2JSWluKbb76BQqFAx44dAQDHjx9HWVkZ/P391e3c3Nzg6+uL2NhYAEBcXBwUCgV69uypbtOrVy8oFAqNNr6+vnBzc1O3GTp0KEpKSnD8+PFq6ykpKUFeXp7Gi0hXRnIZ3n66LZZN6AQzYzn2n8/EmFVHcCXrntSlERHR3+gcmrZt24Y+ffpUWd67d29s3br1sQsRQmDu3Lno27cvfH19NdZFRETA2toa5ubmWLp0KaKiouDo6AgAyMjIgKmpKezt7TU+4+LigoyMDHUbZ2fnKt/p7Oys0cbFRfPBqvb29jA1NVW3+adFixapx0gpFAp4eHg83s4TARjTuQm2vtobjRXmuJpVgDErj+DA+UypyyIiov/ROTTdvXtX4865+2xtbXHnzp3HLiQ0NBQnT57E5s2bq6wbOHAgkpOTERsbi2HDhiEwMBCZmQ//MRFCaMxcXt0s5o/T5u/efvttKJVK9ev69esPrYnoUdq7KxAe2hfdPO2RX1KOf/9wDKsPXobgHLRERJLTOTR5eXkhMjKyyvJdu3Y99uSWM2bMQHh4OA4cOAB3d/cq662srODl5YVevXph3bp1MDY2xrp16wAArq6uKC0tRU5OjsZnMjMz1T1Hrq6uuH37dpXtZmVlabT5Z49STk4OysrKqvRA3WdmZgZbW1uNF9GTcrIxw6YpvRDUsymEAD6L5AN/iYjqAp1D09y5c/Hmm2/iww8/RHR0NKKjo/HBBx9g/vz5mDNnjk7bEkIgNDQU27dvx/79+7We50kIgZKSygkBu3btChMTE0RFRanXp6en4/Tp0+jduzcAwM/PD0qlEkePHlW3SUhIgFKp1Ghz+vRppKenq9vs2bMHZmZm6Nq1q077RfSkTI3l+GRse/x3rC+M5TJEnEzHM1/H4kZOodSlERE1XI8zynz16tWiSZMmQiaTCZlMJpo3by5++OEHnbfz2muvCYVCIQ4ePCjS09PVr8LCQiGEEPfu3RNvv/22iIuLE6mpqeL48ePipZdeEmZmZuL06dPq7bz66qvC3d1d7N27VyQmJop//etfomPHjqK8vFzdZtiwYaJDhw4iLi5OxMXFifbt24uRI0eq15eXlwtfX18xaNAgkZiYKPbu3Svc3d1FaGio1vvDu+eoJiRcvSu6/meP8HwrQnT+aI+Iu3JH6pKIiOoVbX+/n2jKgczMTJGfn//YnwdQ7Wv9+vVCCCGKiorE2LFjhZubmzA1NRWNGzcWAQEB4ujRoxrbKSoqEqGhocLBwUFYWFiIkSNHirS0NI02d+/eFc8995ywsbERNjY24rnnnhM5OTkaba5duyZGjBghLCwshIODgwgNDRXFxcVa7w9DE9WUmzmFYsTyQ8LzrQjR8u0/xA+xKUKlUkldFhFRvaDt77dMCI4w1Rdtn5JM9DiKyyowf9tJ/JZcOXP4hG4e+GhMO5gZG0lcGRGRYdP291vnMU23b99GcHAw3NzcYGxsDCMjI40XEdUMcxMjLJ3QCe887Q25DNjy13VM/CYet/OKpS6NiKhB0PqBvfeFhIQgLS0N77//Pho3bvzA2/GJSP9kMhmm9m8Jb1dbhG5KRFJaLkatiMGa4K7o0tT+0RsgIqLHpvPlORsbGxw+fBidOnWqoZIMFy/PUW1KvVOAqT/+hYu378HUSI6Px/gisDsnWCUi0lWNXZ7z8PDgRHtEdUAzRytsn9YHQ9u5oLRChTe3ncSHO0+jrEIldWlERPWSzqFp2bJlmD9/PlJTU2ugHCLShbWZMb5+rivmDG4NAPgh7hqe/y4Bd++VSFwZEVH9o/PlOXt7exQWFqK8vByWlpYwMTHRWJ+dna3XAg0JL8+RlKLO3sacLcm4V1KOJnYWWBvcFb5Nqj7yiIiINGn7+63zQPBly5Y9SV1EVEOG+Ljgt+m9MWXjcaTcKcAzX8fis/EdMLpTE6lLIyKqFzhPkx6xp4nqAmVRGWaHJeHAhSwAwJR+zfHWMG8YG+l8NZ6IqEHQ9vdb59CUlpb20PVNmzbVZXP1CkMT1RUVKoEvoy5g1YErAIC+Xo5YMakz7K1MJa6MiKjuqbHQJJfLHzo3U0VFw30SO0MT1TV/nEzH67+eQFFZBTwcLPBNcDe0bcxzk4jo72psTFNSUpLG+7KyMiQlJeHLL7/Ef//7X90rJaIaM6JDY7RwssLUH//C9ewijFsdiy+e7YgRHRpLXRoRkcHR25imP/74A59//jkOHjyoj80ZJPY0UV2VU1CKGZuTEHP5DgDgtada4nX/NjCSc0Z/IqIam9zyQVq3bo1jx47pa3NEpEf2VqbY8GJ3TO3fAgDw9cEr+PeGY1AWlklcGRGR4dA5NOXl5Wm8lEolzp8/j/fffx+tWrWqiRqJSA+MjeR45+m2+GpiJ5ibyBF9MQujV8Xg4u18qUsjIjIIOo9psrOzqzIQXAgBDw8PhIWF6a0wIqoZozs1gZezNaZuPI7Uu4UYs+oIljzbEcPbc5wTEdHD6DymKTo6WuO9XC6Hk5MTvLy8YGyscwarVzimiQxJdkEpQjclIvbKXQDA9IEtMXcIxzkRUcOj1zFNXbp0QU5ODoDK0NS9e3cMGDAAAwYMQL9+/eDt7d3gAxORoXGwMsXGf/fAS32bAwBWHbiCl344BmURxzkREVVHq54mCwsLXLp0Ce7u7jAyMkJGRgacnJxqoz6Dwp4mMlS/Jd3EW9tOoqRchWaNLPHN5G5o7WIjdVlERLVCr/M0derUCS+++CL69u0LIQQ+//xzWFtbV9v2gw8+eLyKiUgyYzpXjnN65UeOcyIiehCtepouXLiADz/8EFeuXEFiYiJ8fHyqvRwnk8mQmJhYI4UaAvY0kaG7e68EMzYncZwTETUoNfoYlYyMDDg7Oz9xkfUNQxPVB+UVKizadR7rYlIAAE+1ccJXEzpDYWkicWVERDWjxia3VKlUDExE9ZixkRzvj/TBsgmdYGYsx8ELWQhYFYMLGZzPiYgaNr3NCE5E9cuYzk2w7bXeaGJngWt3CzF29RH8cTJd6rKIiCTD0ERED+TbRIHfZ/RFH69GKCytwPRNiVgceR4VKr08spKIyKAwNBHRQzlYmeKHF3toPLcuZP1R5BaWSlwZEVHtYmgioke6/9y65ZM6w9xEjsOX7mDUyhicvZUndWlERLVG59DUokUL3L17t8ry3NxctGjRQi9FEVHdFNDRDTum9YGHgwWuZxdh3NdHsDP5ptRlERHVCp1DU2pqKioqKqosLykpwc2b/MeTqL5r29gWv4f2Rb9WjiguU2FWWDI+jjiL8gqV1KUREdUorR8YFx4erv7v3bt3Q6FQqN9XVFRg3759aNasmV6LI6K6yc7SFBte7IEley5g9cEr+C4mBWdu5WFlUGc0sjaTujwiohqh9eSWcnllp5RMJsM/P2JiYoJmzZphyZIlGDlypP6rNBCc3JIaol2n0jHv1xMoLK2Am8Ica4K7ooO7ndRlERFpTe+TW6pUKqhUKjRt2hSZmZnq9yqVCiUlJbhw4UKDDkxEDdXw9o2xc3ofNHe0wi1lMcavicOvf12XuiwiIr3TeUxTSkoKHB0dNZbl5ubqqx4iMkCtXGywM7QPBrd1Rmm5Cm9sPYn3fzuN0nKOcyKi+kPn0LR48WJs2bJF/f7ZZ5+Fg4MDmjRpghMnTui1OCIyHLbmJvgmuBvmDG4NmQz4Mf4aJn0bj9t5xVKXRkSkFzqHprVr18LDwwMAEBUVhb179yIyMhLDhw/HG2+8ofcCichwyOUyzBrcCute6AYbc2Mcv5aDkSticCw1W+rSiIiemM6hKT09XR2aIiIiEBgYCH9/f7z55ps4duyY3gskIsPzL28XhIf2RWsXa2Tll2DSN/HYGJda5SYSIiJDonNosre3x/XrlYM8IyMjMXjwYACAEKLa+ZuIqGFq7miFHdP6YESHxihXCXyw8wzm/XoCxWX8d4KIDJPOoWncuHEICgrCkCFDcPfuXQwfPhwAkJycDC8vL70XSESGy8rMGCsndca7T7eFXAZsT7yJZ76OxfXsQqlLIyLSmc6haenSpQgNDYWPjw+ioqJgbW0NoPKy3bRp0/ReIBEZNplMhin9W+Cnl3rCwcoUZ27lYdTKGBy+lCV1aUREOtF6ckt6NE5uSfRwN3OL8NpPx3HyhhJyGfD60DZ4bUBLyGQyqUsjogZM75Nb/t2VK1cwY8YMDB48GEOGDMHMmTNx9erVxy6WiBqGJnYW+OUVPwR2c4dKAJ9FXsBrPyUiv7hM6tKIiB5J59C0e/du+Pj44OjRo+jQoQN8fX2RkJCgvlxHRPQw5iZGWPxMB3wytj1MjGSIPJOBMauO4HLmPalLIyJ6KJ0vz3Xu3BlDhw7Fp59+qrF8/vz52LNnDxITE/VaoCHh5Tki3SSl5eC1nxKRkVcMK1MjLAnsiGG+jaUui4gaGG1/v3UOTebm5jh16hRatWqlsfzixYvo0KEDiosb7uy/DE1EusvKL0HopkQkpFROgPnqgJZ43b81jI0ea/QAEZHOamxMk5OTE5KTk6ssT05OhrOzs66bI6IGzsnGDD+/3BMv920OAFgTfQUvrD+Ku/dKJK6MiEiTsa4fmDJlCqZOnYqrV6+id+/ekMlkiImJweLFizFv3ryaqJGI6jljIzneG+mDjh52eGvbSRy5fBejVsTg6+e7oqOHndTlEREBeIzLc0IILFu2DEuWLMGtW7cAAG5ubnjjjTcwc+bMBn3rMC/PET25i7fz8cqPx5FypwCmRnIsHN0Ok3o0lbosIqrHamxM09/l5+cDAGxsbB53E/UKQxORfuQVl+H1X05gz9nbAIDAbu74aLQvzE2MJK6MiOqjGhvTlJKSgkuXLgGoDEv3A9OlS5eQmpr6eNUSEf2NrbkJ1jzfFW8MbQO5DPjlrxt4dk0cbuTw8StEJB2dQ1NISAhiY2OrLE9ISEBISIg+aiIiglwuw/SBXtj4756wtzTBqZtKjFrBx68QkXR0Dk1JSUno06dPleW9evWq9q46IqIn0beVI36f0Rcd3BXIKSzD5O+PYtWBy1Cp+AQoIqpdOocmmUymHsv0d0qlEhUVFXopiojo79ztLfHLK36Y2N0DQgCf776AqT8eh7KIj18hotqjc2jq168fFi1apBGQKioqsGjRIvTt21evxRER3WduYoRPn+mAxc+0h6mxHHvP3UbAyhicS8+TujQiaiB0vnvu7Nmz6N+/P+zs7NCvXz8AwOHDh5GXl4f9+/fD19e3Rgo1BLx7jqh2nLqhxKs/HcfN3CKYm8ixaFx7jO3sLnVZRGSgauzuOR8fH5w8eRKBgYHIzMxEfn4+Jk+ejPPnzzfowEREtae9uwIRM/qif2snFJepMGfLCXyw8zRKy1VSl0ZE9dgTzdNEmtjTRFS7KlQCX+29iOX7LwMAOje1w+rnuqCxwkLiyojIkNRYTxMRUV1hJJdhrn8bfB/SDbbmxkhKy8XI5TGIvXxH6tKIqB5iaCIig/cvbxdEzOgHn8a2uFtQiufXJWD1wctgRzoR6RNDExHVC00bWWL7tN4Y39UdKgF8Flk5LUFeMaclICL9YGgionrD3MQIn4/vgE/GtoepkRxRZ28jYAWnJSAi/dA5NBUVFaGw8P+f/3Tt2jUsW7YMe/bs0WthRESPQyaTIahnU2x9zQ9N7CyQercQY1cfwfbEG1KXRkQGTufQNHr0aGzcuBEAkJubi549e2LJkiUYPXo0vv76a70XSET0ODq42yFiRl8M+N+0BHN/OYF3d5xCSTmfXEBEj0fn0JSYmKie1HLr1q1wcXHBtWvXsHHjRixfvlzvBRIRPS57K1OsD+mO2YNbQSYDfk5IQ+CaONzIKXz0h4mI/kHn0FRYWAgbGxsAwJ49ezBu3DjI5XL06tUL165d03uBRERPQi6XYfbg1lgf0h12liY4cUOJkStiEH0xS+rSiMjA6ByavLy88Ntvv+H69evYvXs3/P39AQCZmZmc0JGI6qyn2jgjYkZfdHBXILewDCHrj2LZ3otQqTgtARFpR+fQ9MEHH+D1119Hs2bN0KNHD/j5+QGo7HXq3Lmz3gskItIXd3tL/PqqH57r2RRCAMv2XkLIhmPILiiVujQiMgCP9RiVjIwMpKeno2PHjpDLK3PX0aNHYWtrC29vb70XaSj4GBUiw7Ht+A28+9spFJep0MTOAque64JOHnZSl0VEEqjRx6i4urrCxsYGUVFRKCoqAgB07969QQcmIjIsz3R1x2/T+6C5oxVu5hbh2TWx+DEulbOIE9ED6Rya7t69i0GDBqF169Z4+umnkZ6eDgB4+eWXMW/ePL0XSERUU7xdbbEztA+GtXNFWYXA+zvPYPaWZBSUlEtdGhHVQTqHpjlz5sDExARpaWmwtLRUL58wYQIiIyP1WhwRUU2zNTfB1893wXsj2sJILsPO5FsYs+oILmfmS10aEdUxOoemPXv2YPHixXB3d9dY3qpVK045QEQGSSaT4eV+LRA2tRdcbM1wKfMeAlYewe8nbkldGhHVITqHpoKCAo0epvvu3LkDMzMznba1aNEidO/eHTY2NnB2dsaYMWNw4cIF9fqysjK89dZbaN++PaysrODm5obJkyfj1i3Nf8iuXLmCsWPHwsnJCba2tggMDMTt27c12uTk5CA4OBgKhQIKhQLBwcHIzc3VaJOWloZRo0bBysoKjo6OmDlzJkpLeVcNUUPRvZkDImb0Q++WjVBYWoEZm5OwIPwMSstVUpdGRHWAzqGpf//+6seoAJV/oalUKnz++ecYOHCgTtuKjo7G9OnTER8fj6ioKJSXl8Pf3x8FBQUAKifSTExMxPvvv4/ExERs374dFy9eREBAgHobBQUF8Pf3h0wmw/79+3HkyBGUlpZi1KhRUKn+/x+6oKAgJCcnIzIyEpGRkUhOTkZwcLB6fUVFBUaMGIGCggLExMQgLCwM27Zt4zgtogbGycYMP77UE9MHtgQAbIhNReDaONzMLZK4MiKSnNDRmTNnhJOTkxg2bJgwNTUV48ePF23bthUuLi7i8uXLum5OQ2ZmpgAgoqOjH9jm6NGjAoC4du2aEEKI3bt3C7lcLpRKpbpNdna2ACCioqKEEEKcPXtWABDx8fHqNnFxcQKAOH/+vBBCiD///FPI5XJx8+ZNdZvNmzcLMzMzjW0/jFKpFAC0bk9Eddu+cxmiw4LdwvOtCNFx4W5x4PxtqUsiohqg7e+3zj1NPj4+OHnyJHr06IEhQ4agoKAA48aNQ1JSElq2bPlEAU6pVAIAHBwcHtpGJpPBzs4OAFBSUgKZTKZxadDc3BxyuRwxMTEAgLi4OCgUCvTs2VPdplevXlAoFIiNjVW38fX1hZubm7rN0KFDUVJSguPHj1dbS0lJCfLy8jReRFR//MvbRWMW8Rc3HMOSPRdQwVnEiRoknUJTWVkZBg4ciLy8PCxcuBARERH4888/8fHHH6Nx48ZPVIgQAnPnzkXfvn3h6+tbbZvi4mLMnz8fQUFB6smnevXqBSsrK7z11lsoLCxEQUEB3njjDahUKvV0CBkZGXB2dq6yPWdnZ2RkZKjbuLi4aKy3t7eHqampus0/LVq0SD1GSqFQwMPD47H3n4jqJg+HylnEn+9VOYv4iv2XMfn7BNy5VyJ1aURUy3QKTSYmJjh9+jRkMpneCwkNDcXJkyexefPmateXlZVh4sSJUKlUWL16tXq5k5MTfv31V/z++++wtrZWz+jZpUsXGBkZqdtVV7MQQmO5Nm3+7u2334ZSqVS/rl+/rvX+EpHhMDM2wsdj2uOriZ1gYWKEI5fv4umvDuNoSrbUpRFRLdL58tzkyZOxbt06vRYxY8YMhIeH48CBA1WmMgAqA1NgYCBSUlIQFRVVZYpzf39/XLlyBZmZmbhz5w5+/PFH3Lx5E82bNwdQOYP5P++mA4CsrCx175Krq2uVHqWcnByUlZVV6YG6z8zMDLa2thovIqq/RndqgvDQPvBytkZmfgkmfRuPtdFXOIs4UQNhrOsHSktL8d133yEqKgrdunWDlZWVxvovv/xS620JITBjxgzs2LEDBw8eVIecv7sfmC5duoQDBw6gUaNGD9yeo6MjAGD//v3IzMxU32Xn5+cHpVKJo0ePokePHgCAhIQEKJVK9O7dW93mv//9L9LT09WXGvfs2QMzMzN07dpV630iovqtlYsNdk7vg3d3nMJvybewaNd5HEvNwZJnO0JhaSJ1eURUg3R+YO/DphW4f9u/tqZNm4ZNmzZh586daNOmjXq5QqGAhYUFysvL8cwzzyAxMREREREaPT4ODg4wNTUFAKxfvx5t27aFk5MT4uLiMGvWLISEhGDJkiXq9sOHD8etW7ewdu1aAMDUqVPh6emJ33//HUDllAOdOnWCi4sLPv/8c2RnZyMkJARjxozBihUrtNofPrCXqOEQQmDT0TQsDD+L0goVPBwssDqoK9q7K6QujYh0pO3vt86hSZ8eNFZo/fr1CAkJQWpqarW9TwBw4MABPPXUUwCA+fPnY8OGDcjOzkazZs3w6quvYs6cORrbz87OxsyZMxEeHg4ACAgIwMqVK9V34QGVk1tOmzYN+/fvh4WFBYKCgvDFF19oPWknQxNRw3P6phKv/Xwc17OLYGokx/sj2+L5Xp41MvaTiGqGQYSm+oahiahhUhaV4Y1fT2DP2cqxk6M6umHRuPawNtN5BAQRSaBGQ9OxY8fw66+/Ii0trcpjRrZv3657tfUEQxNRwyWEwLqYFHy66zzKVQItnKyw+rku8HblvwVEdZ22v9863z0XFhaGPn364OzZs9ixYwfKyspw9uxZ7N+/HwoFr+UTUcP094f+utqa42pWAcasOoJf/uJUJET1hc6h6ZNPPsHSpUsREREBU1NTfPXVVzh37hwCAwPRtGnTmqiRiMhgdGvmgD9m9kX/1k4oLlPhza0n8fqvJ1BUWiF1aUT0hHQOTVeuXMGIESMAVM5TVFBQAJlMhjlz5uCbb77Re4FERIamkbUZNoR0x+v+rSGXAVuP38CYVUdwOfOe1KUR0RPQOTQ5ODggPz8fANCkSROcPn0aAJCbm4vCwkL9VkdEZKDkchlC/9UKP73cE47WZrhwOx8BK2PwW9JNqUsjosekc2jq168foqKiAACBgYGYNWsWpkyZgkmTJmHQoEF6L5CIyJD1bumIP2f1hV+LRigsrcDsLcl4e/spFJfxch2RodH57rns7GwUFxfDzc0NKpUKX3zxBWJiYuDl5YX3338f9vb2NVVrnce754joQSpUAl/tu4QV+y9BCKBtY1usfq4LmjtaPfrDRFSjOE+TBBiaiOhRDl/KwuywZNwtKIW1mTEWjWuPUR3dpC6LqEGrsdCUlpb20PUN+Q46hiYi0sbtvGLM2JyEoynZAIDnezXFeyN8YG5iJHFlRA1TjYUmuVz+0McDVFQ03Ov0DE1EpK3yChWW7b2ElQcuAwB8/ne5rhkv1xHVOm1/v3We4z8pKUnjfVlZGZKSkvDll1/iv//9r+6VEhE1QMZGcrw+tA26N3fAnC3JOJueh5ErYvDpM+0xsgMv1xHVRXob0/THH3/g888/x8GDB/WxOYPEniYiehwZymLM3JyEo6m8XEckhRp7jMqDtG7dGseOHdPX5oiIGgxXhTk2TemJaU+1BAD8FJ+GcatjkXKnQOLKiOjvdA5NeXl5Gi+lUonz58/j/fffR6tWrWqiRiKies/YSI43h3ljw4vd4WBlirPpeRi1Iga/n7gldWlE9D96GQguhICHhwfCwsLg5+en1wINCS/PEZE+/PNyXVDPpvhgJC/XEdWUGrt7Ljo6WuO9XC6Hk5MTvLy8YGys87jyeoWhiYj0pbxChaV7L2L1wSsQAvB2tcGq57qgpZO11KUR1Tuc3FICDE1EpG+HLmZhzpbKyTAtTY3wydj2GNO5idRlEdUrNRaawsPDtW4bEBCgy6YNHkMTEdWE23nFmBWWhPirlZfrJnTzwIKAdrAw5eU6In2o8ckt//mxfy6TyWQNbqJLhiYiqikVKoHl+y5h+f+eXdfaxRqrgrqglYuN1KURGbwam3Jgz5496NSpE3bt2oXc3FwolUrs2rULXbp0we7du6FSqaBSqRpcYCIiqklGchnmDGmNn1/qCScbM1y8fQ+jVsbgl7+uV/kjlohqhs49Tb6+vlizZg369u2rsfzw4cOYOnUqzp07p9cCDQl7moioNmTll2DuL8k4fOkOAGBs5yb4zxhfWJs17JtxiB5XjfU0XblyBQqFospyhUKB1NRUXTdHREQ6crIxww8v9sAbQ9vASC7DjqSbCFgRgzO3lFKXRlSv6RyaunfvjtmzZyM9PV29LCMjA/PmzUOPHj30WhwREVVPLpdh+kAvhE3thcYKc1y9U4Cxq2OxMS6Vl+uIaojOoen7779HZmYmPD094eXlBS8vLzRt2hTp6elYt25dTdRIREQP0L2ZA/6c2Q+D2zqjtFyFD3aewWs/JUJZVCZ1aUT1zmPN0ySEQFRUFM6fPw8hBHx8fDB48OAqM4U3NBzTRERSEULg+yOp+HTXOZRVCDSxs8DySZ3R1dNe6tKI6rxandwyNzcXdnZ2T7oZg8fQRERSO3kjFzM2J+Ha3UIYyWV43b8NXunfAnJ5w/6jluhhamwg+OLFi7Flyxb1+8DAQDRq1AhNmjTBiRMnHq9aIiLSiw7udoiY0RejOrqhQiWwOPI8Xlh/FFn5JVKXRmTwdA5Na9euhYeHBwAgKioKUVFR2LVrF4YPH4433nhD7wUSEZFubMxNsHxiJyx+pj3MTeQ4fOkOhn91GDH/m6KAiB6PzqEpPT1dHZoiIiIQGBgIf39/vPnmmzh27JjeCyQiIt3JZDJM6N4U4aF90drFGnfulSD4+wR8FnkeZRUqqcsjMkg6hyZ7e3tcv34dABAZGYnBgwcDqByEyFnAiYjqltYuNtg5vS+CejaFEMDqg1cwYW0crmcXSl0akcHROTSNGzcOQUFBGDJkCO7evYvhw4cDAJKTk+Hl5aX3AomI6MlYmBrhk7HtsSqoC2zMjZGYlounlx/GrlPpj/4wEanpHJqWLl2K0NBQ+Pj4ICoqCtbW1gAqL9tNmzZN7wUSEZF+jOjQGH/O7IdOHnbILy7Haz8n4p0dp1BcxqsERNrQy5QDVIlTDhCRISirUOHLqItYE30FQgCtXayxMqgLWrvYSF0akSRqbMoBIiIybCZGcrw1zBsb/90DTjZmuHj7HkatiMHPCdf4CBaih2BoIiJqoPq1csKuWf0woLUTSspVeHfHaUz7ORHKQj6Chag6DE1ERA2Yo7UZ1od0x7tPt4WJkQy7Tmdg+FeHcCw1W+rSiOochiYiogZOLpdhSv8W2PZabzRrZIlbymJMWBuHr/ZeQoWKl+uI7tM5NLVo0QJ3796tsjw3NxctWrTQS1FERFT7OrjbIWJmP4zr3AQqASzdexGTvo3HrdwiqUsjqhN0Dk2pqanVTmJZUlKCmzdv6qUoIiKShrWZMb6c0AlfBnaElakRjqZkY/hXhxF5mnM6ERlr2zA8PFz937t374ZCoVC/r6iowL59+9CsWTO9FkdERNIY18UdXZraY1ZYEk7cUOLVnxIxqUdTfDDSBxamRlKXRyQJredpkssrO6VkMlmVW1JNTEzQrFkzLFmyBCNHjtR/lQaC8zQRUX1TWq7CkqgLWBt9FQDg5WyNFZM6o21j/htH9Ye2v986T27ZvHlzHDt2DI6Ojk9cZH3D0ERE9VXMpTuY80sysvJLYGosx9vDvRHSuxlkMpnUpRE9sRqb3DIlJaVKYMrNzdW5QCIiMhx9WzkiclY/DPJ2Rmm5Cgt/P4t/bziGO/dKpC6NqNboHJoWL16MLVu2qN8/++yzcHBwQJMmTXDixAm9FkdERHVHI2szfPdCNywMaAdTYzkOXMjCsGWHEX0xS+rSiGqFzqFp7dq18PDwAABERUVh7969iIyMxPDhw/HGG2/ovUAiIqo7ZDIZXujdDOGhfdDaxRp37pXghe+P4uOIsygp54N/qX7TOTSlp6erQ1NERAQCAwPh7++PN998E8eOHdN7gUREVPd4u9oiPLQvgnt5AgC+i0nB2FWxuJx5T+LKiGqOzqHJ3t4e169fBwBERkZi8ODBAAAhRLXzNxERUf1kbmKE/4zxxbeTu8He0gRn0/MwcsVhPviX6i2dQ9O4ceMQFBSEIUOG4O7duxg+fDgAIDk5GV5eXnovkIiI6rYhPi6InN0ffb0cUVxW+eDfV348jpyCUqlLI9IrnUPT0qVLERoaCh8fH0RFRcHa2hpA5WW7adOm6b1AIiKq+1xszbHx3z3UD/7dc/Y2hn11CDGX7khdGpHe6DxPEz0Y52kiIgJO31RiVlgSrmQVAACm9m+Bef6tYWbMmcSpbqqxyS3vO3v2LNLS0lBaqtn9GhAQ8DibqxcYmoiIKhWVVuA/f5zFpoQ0AEA7N1t8NbEzvJytJa6MqKoaC01Xr17F2LFjcerUKY1HqtyfFbYhDwZnaCIi0rT7TAbmbzuJnMIymJvI8d4IHzzXsylnEqc6pcZmBJ81axaaN2+O27dvw9LSEmfOnMGhQ4fQrVs3HDx48ElqJiKiemZoO1dEzu6Pfq0qB4m/99tpTNn4F+5yJnEyQDqHpri4OHz00UdwcnKCXC6HXC5H3759sWjRIsycObMmaiQiIgPmYmuOH17sgfdGtIWpkRx7z2Vi6LLDOHghU+rSiHSic2iqqKhQ3zHn6OiIW7duAQA8PT1x4cIF/VZHRET1glwuw8v9WuC36f8/k3jI+mNYEH4GxWUNd1gHGRadQ5Ovry9OnjwJAOjZsyc+++wzHDlyBB999BFatGih9wKJiKj+8HGrnEk8pHczAMCG2FQErIzB2Vt50hZGpAWdQ9N7770HlUoFAPj4449x7do19OvXD3/++SeWL1+u9wKJiKh+MTcxwoKAdlj/Ync4Wpvh4u17GLPqCL49dBUqFWfBobpLL/M0ZWdnw97evsHfDcG754iIdHP3Xgne2nYKe8/dBgD0btkISwI7orHCQuLKqCGpsbvn7rt8+TJ2796NoqIiODg4PO5miIioAWtkbYZvJ3fFJ2Pbw8LECLFX7mLo0kP4/cQtqUsjqkLn0HT37l0MGjQIrVu3xtNPP4309HQAwMsvv4x58+bpvUAiIqrfZDIZgno2xR8z+6KjuwJ5xeWYsTkJc7YkI6+4TOryiNR0Dk1z5syBiYkJ0tLSYGlpqV4+YcIEREZG6rU4IiJqOFo4WWPra70x819ekMuAHUk3MXzZYcRfvSt1aUQAHiM07dmzB4sXL4a7u7vG8latWuHatWt6K4yIiBoeEyM55vq3wa+v+qGpgyVu5hZh0rfxWLTrHErKOTUBSUvn0FRQUKDRw3TfnTt3YGZmppeiiIioYevq6YA/Z/VDYDd3CAGsjb6KMaticfF2vtSlUQOmc2jq378/Nm7cqH4vk8mgUqnw+eefY+DAgXotjoiIGi5rM2N8Nr4j1gZ3hYOVKc6l52Hkihisi0nh1AQkCZ2nHDh79iyeeuopdO3aFfv370dAQADOnDmD7OxsHDlyBC1btqypWus8TjlARFQzMvOL8ebWkzh4IQtA5dQEXzzbEW52nJqAnlyNTTng4+ODkydPokePHhgyZAgKCgowbtw4JCUlNejARERENcfZxhzrQ7rj4zG+/z81wbJD2Jl8U+rSqAHRy+SWAHD9+nV8+OGH+P777/WxOYPEniYiopp3Nese5vxyAieu5wIARnZojI/H+MLO0lTawshg1fjklv+UnZ2NH374QV+bIyIiqlYLJ2tse9UPcwa3hpFchoiT6Ri67BCiL2ZJXRrVc3oLTURERLXF2EiOWYNbYftrvdHCyQq380rwwvdH8cHO0ygq5dQEVDMYmoiIyGB19LDDHzP64QU/TwDAxrhrGLH8MJL/d+mOSJ8YmoiIyKBZmBph4Whf/PhSD7jamuPqnQI883UsvtxzAWUVKqnLo3pE69A0bty4h77mzJmj85cvWrQI3bt3h42NDZydnTFmzBhcuHBBvb6srAxvvfUW2rdvDysrK7i5uWHy5Mm4dUvzQY4ZGRkIDg6Gq6srrKys0KVLF2zdulWjTU5ODoKDg6FQKKBQKBAcHIzc3FyNNmlpaRg1ahSsrKzg6OiImTNnorS0VOf9IiKi2tevlRN2z+6PgI5uqFAJLN9/GWNXH8ElTohJeqJ1aLofNh708vT0xOTJk3X68ujoaEyfPh3x8fGIiopCeXk5/P39UVBQAAAoLCxEYmIi3n//fSQmJmL79u24ePEiAgICNLYTHByMCxcuIDw8HKdOncK4ceMwYcIEJCUlqdsEBQUhOTkZkZGRiIyMRHJyMoKDg9XrKyoqMGLECBQUFCAmJgZhYWHYtm0bH0JMRGRAFJYmWD6pM1YGdYadpQlO38zDiBUx+O7wVU6ISU9O1CGZmZkCgIiOjn5gm6NHjwoA4tq1a+plVlZWYuPGjRrtHBwcxHfffSeEEOLs2bMCgIiPj1evj4uLEwDE+fPnhRBC/Pnnn0Iul4ubN2+q22zevFmYmZkJpVKpVf1KpVIA0Lo9ERHVnAxlkXjh+wTh+VaE8HwrQgSuiRVpdwukLovqIG1/v+vUmCalUgkAcHBweGgbmUwGOzs79bK+fftiy5YtyM7OhkqlQlhYGEpKSvDUU08BAOLi4qBQKNCzZ0/1Z3r16gWFQoHY2Fh1G19fX7i5uanbDB06FCUlJTh+/Hi1tZSUlCAvL0/jRUREdYOLbeWEmP8d6wtLUyMkpGRj2LJD2HIsDUI/UxRSA1NnQpMQAnPnzkXfvn3h6+tbbZvi4mLMnz8fQUFBGpNPbdmyBeXl5WjUqBHMzMzwyiuvYMeOHeoZyjMyMuDs7Fxle87OzsjIyFC3cXFx0Vhvb28PU1NTdZt/WrRokcYlSg8Pj8fadyIiqhkymQzP9fTErln90L2ZPQpKK/DWtlN46Ye/kJlXLHV5ZGDqTGgKDQ3FyZMnsXnz5mrXl5WVYeLEiVCpVFi9erXGuvfeew85OTnYu3cv/vrrL8ydOxfPPvssTp06pW4jk8mqbFMIobFcmzZ/9/bbb0OpVKpf169f12pfiYiodnk2skLYVD+8PdwbpkZy7D+fCf9lh/D7iVuP/jDR/xhLXQAAzJgxA+Hh4Th06BDc3d2rrC8rK0NgYCBSUlKwf/9+jV6mK1euYOXKlTh9+jTatWsHAOjYsSMOHz6MVatWYc2aNXB1dcXt27erbDcrK0vdu+Tq6oqEhASN9Tk5OSgrK6vSA3WfmZkZzMzMHnu/iYio9hjJZXhlQEs81cYZc39JxplbeZixOQmRZzLwn9G+cLDiY1jo4STtaRJCIDQ0FNu3b8f+/fvRvHnzKm3uB6ZLly5h7969aNSokcb6wsJCAIBcrrkrRkZGUKkq5+fw8/ODUqnE0aNH1esTEhKgVCrRu3dvdZvTp08jPT1d3WbPnj0wMzND165d9bPDREQkuTauNtgxrQ9mDmoFI7kMf5xMh//SQ9h7tuof10R/p7cH9j6OadOmYdOmTdi5cyfatGmjXq5QKGBhYYHy8nI888wzSExMREREhEaPj4ODA0xNTVFWVgYfHx80btwYX3zxBRo1aoTffvsNb7zxBiIiIvD0008DAIYPH45bt25h7dq1AICpU6fC09MTv//+O4DKKQc6deoEFxcXfP7558jOzkZISAjGjBmDFStWaLU/fGAvEZFhOXkjF3N/OYHLmfcAAOO7uuODUT6wNTeRuDKqTdr+fksamh40Vmj9+vUICQlBampqtb1PAHDgwAH13XGXLl3C/PnzERMTg3v37sHLywuvv/66xjxM2dnZmDlzJsLDwwEAAQEBWLlypcZdeGlpaZg2bRr2798PCwsLBAUF4YsvvtD6EhxDExGR4Skuq8CSPRfwXUwKhADcFOZYPL4D+rVykro0qiUGEZrqG4YmIiLDdSw1G6//egLX7lYO+3i+V1O8PbwtrMzqxPBfqkHa/n7XmbvniIiIpNS9mQN2zfr/h//+FJ+GYV8dQvzVuxJXRnUFQxMREdH/WJoaY+FoX/z8ck80sbPA9ewiTPwmHgvCz6CotELq8khiDE1ERET/0MfLEZGz+2FSj6YAgA2xqRj+1SH8lZotcWUkJYYmIiKiatiYm2DRuPb44d894GprjtS7hXh2bRw+jjiL4jL2OjVEDE1EREQPMaC1E3bP6Y/xXd0hBPBdTAqe/uowjl/Lkbo0qmUMTURERI+gsDDBF892xPch3eBsY4ardwrw7JpYfPLnOfY6NSAMTURERFr6l7cLouYMwLguTaASwDeHrmLE8sNITGOvU0PA0ERERKQDhaUJvgzshO8md4OTjRmuZBVg/NfsdWoIGJqIiIgew2AfF0TN6Y9xnf+/1+np5RzrVJ8xNBERET0mO0tTfDmhstfJ2cYMV7MKMH5NLP77B++wq48YmoiIiJ5QZa9T5VgnIYBvD6dg+FeHcYzzOtUrDE1ERER6cH+s0/ch3eBqa46UOwUIXBuHBeFnUFhaLnV5pAcMTURERHr0L28X7J7THxO6eUCIytnEhy07jNgrd6QujZ4QQxMREZGeKSxMsHh8B2z8dw80sbNAWnYhgr5NwLs7TiG/uEzq8ugxMTQRERHVkP6tnRA5ux+e71X5DLufE9IwdOkhHLyQKXFl9DgYmoiIiGqQjbkJPh7THpum9ERTB0vcUhYjZP0xzPvlBHILS6Uuj3TA0ERERFQLerd0ROTsfnipb3PIZMC2xBsYsvQQIk9nSF0aaYmhiYiIqJZYmhrj/ZE+2PqqH1o6WSErvwSv/nQc039ORFZ+idTl0SMwNBEREdWyrp4O+GNmP0wf2BJGchn+OJWOIUujsSPpBoQQUpdHD8DQREREJAFzEyO8MdQbO6f3gU9jW+QWlmHOlhN4ccMx3Mwtkro8qgZDExERkYR8myiwM7QP3hjaBqbGchy8kAX/L6PxY1wqVCr2OtUlDE1EREQSMzGSY/pAL/w5sx+6edqjoLQC7+88gwnfxOFy5j2py6P/YWgiIiKqI7ycrfHLK374aHQ7WJka4VhqDp7+6jBW7r+EsgqV1OU1eAxNREREdYhcLsNkv2bYM3cAnmrjhNIKFb7YcxGjVsTgxPVcqctr0BiaiIiI6qAmdhZYH9IdyyZ0gr2lCc5n5GPs6iP4OOIsHwAsEYYmIiKiOkomk2FM5ybYO3cARndyg0oA38WkwH/pIRy6mCV1eQ0OQxMREVEd18jaDF9N7Iz1Id3hpjDHjZwiTP7+KOb+kozsAj6KpbYwNBERERmIgd7O2DN3AEJ6N4NMBmxPvInBX0bjt6SbnBSzFjA0ERERGRBrM2MsCGiHba/1RmsXa2QXlGL2lmSErD+G69mFUpdXrzE0ERERGaAuTe0RMaMf5g1pDVMjOaIvZsF/6SF8d/gqyjk9QY1gaCIiIjJQpsZyzBjUCrtm90PP5g4oKqvAx3+cw5jVR3D6plLq8uodhiYiIiID19LJGpun9MKn49rD1twYp2/mIWBlDD6OOIuCEk5PoC8MTURERPWAXC7DxB5NsXfeAIzs0FhjeoID5zOlLq9eYGgiIiKqR5xtzLEyqAvWv9gdTewscDO3CC9uOIbpmxKRmVcsdXkGjaGJiIioHhrYxhlRc/tjSr/mMJLL8MfJdAz6Mho/xV+DSsXpCR4HQxMREVE9ZWlqjHdH+GDn9D7o4K5AfnE53vvtNMavicWFjHypyzM4DE1ERET1nG8TBXZM64MPR/nAytQIiWm5GLH8MD7ddR5FpRVSl2cwGJqIiIgaACO5DC/2aY698wbA38cF5SqBNdFXMGRpNA5c4EBxbTA0ERERNSCNFRb4ZnI3fDu5m/o5di+uP4bpPyfiNgeKPxRDExERUQM0xMcFUXMH/P9A8VPpGLQkGhuOpKCCA8WrJRN8wp/e5OXlQaFQQKlUwtbWVupyiIiItHLmlhLv7jiN5Ou5AID2TRT4ZGx7tHdXSFtYLdH295s9TURERA1cOzcFtr3WGx+P8YWNuTFO3VRi9KoYLAg/g7ziMqnLqzMYmoiIiAhGchme7+WJffMGYHQnN6gEsCE2FYOXRCP8xC3wwhRDExEREf2Ns405vprYGT+91BPNHa2QmV+CmZuTELzuKFLuFEhdnqQYmoiIiKiKvq0csWtWP8wZ3BqmxnLEXL6DoUsP4cuoiygua5hzOzE0ERERUbXMTYwwa3Ar7JndH/1bO6G0QoXl+y5VPgS4Ac7txNBERERED9XM0Qo/vNgdq5/rAldbc6RlF+LF9cfw6o/HcSu3SOryag1DExERET2STCbD0+0bY++8/5/bKfJMBgYticbXB6+gtFwldYk1jvM06RHnaSIioobifEYe3v/tNI6l5gAAWjpZ4T+jfdHby1HiynTHeZqIiIioxni72uKXV/yw5NmOcLQ2xZWsAgR9l4AZm5OQoayfj2NhaCIiIqLHIpPJ8ExXd+yb9xQm+3lCLgN+P3ELg5YcxLeHrqKson5dsuPlOT3i5TkiImrITt9U4v2dp5GUlgsAaOVsjY9G+8KvZSNpC3sEbX+/GZr0iKGJiIgaOpVKYGviDXy66zyyC0oBAAEd3fDuiLZwsTWXuLrqcUwTERER1Tq5XIbAbh44MO8pBPeqvGQXfuIW/vXFQayNNuy77NjTpEfsaSIiItJ0+qYSH+w8jcT/XbJr6WSFhQG+6Nuq7txlx8tzEmBoIiIiqkqlEtiedBOf7jqHO/cqL9kNa+eK90a2hbu9pcTV8fIcERER1RFyuQzj/3eXXUjvZhoTY36195LBPMuOPU16xJ4mIiKiRzufkYcPd55BQko2AMDd3gLvj/SBv48LZDJZrdfDy3MSYGgiIiLSjhACESfT8d8/ziEjr3IyzH6tHPHhKB94OdvUai0MTRJgaCIiItJNQUk5Vh24jO8Op6C0QgVjuQwhvZth5uBWsDU3qZUaGJokwNBERET0eFLvFODjP85h77nbAABHa1O8OdQb47u6Qy6v2Ut2DE0SYGgiIiJ6MgcvZOKjiLO4mlUAAOjgrsCHo9qhq6d9jX0nQ5MEGJqIiIieXGm5Cj/EpmL5vkvILykHAIzt3ATzh3vXyKzinHKAiIiIDJKpsRxT+rfA/tefwoRuHpDJgB1JNzHwi4OIuXRHsroYmoiIiKhOcrIxw+LxHbBzeh90aWoHCxMjtHdXSFaPsWTfTERERKSFDu522PZab6RlF0JhUTt31FWHPU1ERERU58lkMng2spK0BoYmIiIiIi0wNBERERFpgaGJiIiISAuShqZFixahe/fusLGxgbOzM8aMGYMLFy6o15eVleGtt95C+/btYWVlBTc3N0yePBm3bt1St0lNTYVMJqv29euvv6rb5eTkIDg4GAqFAgqFAsHBwcjNzdWoJy0tDaNGjYKVlRUcHR0xc+ZMlJaW1vhxICIiorpP0tAUHR2N6dOnIz4+HlFRUSgvL4e/vz8KCipnAS0sLERiYiLef/99JCYmYvv27bh48SICAgLU2/Dw8EB6errGa+HChbCyssLw4cPV7YKCgpCcnIzIyEhERkYiOTkZwcHB6vUVFRUYMWIECgoKEBMTg7CwMGzbtg3z5s2rvQNCREREdVadmhE8KysLzs7OiI6ORv/+/attc+zYMfTo0QPXrl1D06ZNq23TuXNndOnSBevWrQMAnDt3Dj4+PoiPj0fPnj0BAPHx8fDz88P58+fRpk0b7Nq1CyNHjsT169fh5uYGAAgLC0NISAgyMzO1muGbM4ITEREZHoOcEVypVAIAHBwcHtpGJpPBzs6u2vXHjx9HcnIyXnrpJfWyuLg4KBQKdWACgF69ekGhUCA2NlbdxtfXVx2YAGDo0KEoKSnB8ePHq/2ukpIS5OXlabyIiIiofqozoUkIgblz56Jv377w9fWttk1xcTHmz5+PoKCgBybBdevWoW3btujdu7d6WUZGBpydnau0dXZ2RkZGhrqNi4uLxnp7e3uYmpqq2/zTokWL1GOkFAoFPDw8tNpXIiIiMjx1JjSFhobi5MmT2Lx5c7Xry8rKMHHiRKhUKqxevbraNkVFRdi0aZNGL9N9MpmsyjIhhMZybdr83dtvvw2lUql+Xb9+vdp2REREZPjqxGNUZsyYgfDwcBw6dAju7u5V1peVlSEwMBApKSnYv3//A3uZtm7disLCQkyePFljuaurK27fvl2lfVZWlrp3ydXVFQkJCRrrc3JyUFZWVqUH6j4zMzOYmZlptY9ERERk2CTtaRJCIDQ0FNu3b8f+/fvRvHnzKm3uB6ZLly5h7969aNSo0QO3t27dOgQEBMDJyUljuZ+fH5RKJY4ePapelpCQAKVSqb6M5+fnh9OnTyM9PV3dZs+ePTAzM0PXrl2fdFeJiIjIwEl699y0adOwadMm7Ny5E23atFEvVygUsLCwQHl5OZ555hkkJiYiIiJCo8fHwcEBpqam6veXL19G69at8eeff2LYsGFVvmv48OG4desW1q5dCwCYOnUqPD098fvvvwOonHKgU6dOcHFxweeff47s7GyEhIRgzJgxWLFihVb7w7vniIiIDI+2v9+ShqYHjRVav349QkJCkJqaWm3vEwAcOHAATz31lPr9O++8gx9//BHXrl2DXF61Ay07OxszZ85EeHg4ACAgIAArV67UuAsvLS0N06ZNw/79+2FhYYGgoCB88cUXWl+CY2giIiIyPAYRmuobpVIJOzs7XL9+naGJiIjIQOTl5cHDwwO5ublQKBQPbFcnBoLXF/n5+QDAqQeIiIgMUH5+/kNDE3ua9EilUuHWrVuwsbF54KXHx3E/AbMH69F4rLTHY6UbHi/t8Vhpj8dKezV5rIQQyM/Ph5ubW7VDfO5jT5MeyeXyaqdM0BdbW1v+T6UlHivt8VjphsdLezxW2uOx0l5NHauH9TDdV2cmtyQiIiKqyxiaiIiIiLTA0GQAzMzM8OGHH3L2cS3wWGmPx0o3PF7a47HSHo+V9urCseJAcCIiIiItsKeJiIiISAsMTURERERaYGgiIiIi0gJDExEREZEWGJoksnr1ajRv3hzm5ubo2rUrDh8+/ND20dHR6Nq1K8zNzdGiRQusWbOmSptt27bBx8cHZmZm8PHxwY4dO2qq/Fql72O1YcMGyGSyKq/i4uKa3I1aocuxSk9PR1BQENq0aQO5XI7Zs2dX247nlXbHiudVpe3bt2PIkCFwcnKCra0t/Pz8sHv37irteF5pd6x4XlWKiYlBnz590KhRI1hYWMDb2xtLly6t0q7GzytBtS4sLEyYmJiIb7/9Vpw9e1bMmjVLWFlZiWvXrlXb/urVq8LS0lLMmjVLnD17Vnz77bfCxMREbN26Vd0mNjZWGBkZiU8++UScO3dOfPLJJ8LY2FjEx8fX1m7ViJo4VuvXrxe2trYiPT1d42XodD1WKSkpYubMmeKHH34QnTp1ErNmzarShudVJW2OFc+rSrNmzRKLFy8WR48eFRcvXhRvv/22MDExEYmJieo2PK8qaXOseF5VSkxMFJs2bRKnT58WKSkp4scffxSWlpZi7dq16ja1cV4xNEmgR48e4tVXX9VY5u3tLebPn19t+zfffFN4e3trLHvllVdEr1691O8DAwPFsGHDNNoMHTpUTJw4UU9VS6MmjtX69euFQqHQe61S0/VY/d2AAQOqDQI8r6p60LHiefVgPj4+YuHCher3PK8e7J/HiufVg40dO1Y8//zz6ve1cV7x8lwtKy0txfHjx+Hv76+x3N/fH7GxsdV+Ji4urkr7oUOH4q+//kJZWdlD2zxom4agpo4VANy7dw+enp5wd3fHyJEjkZSUpP8dqEWPc6y0wfNKNzyvqlKpVMjPz4eDg4N6Gc+r6lV3rACeV9VJSkpCbGwsBgwYoF5WG+cVQ1Mtu3PnDioqKuDi4qKx3MXFBRkZGdV+JiMjo9r25eXluHPnzkPbPGibhqCmjpW3tzc2bNiA8PBwbN68Gebm5ujTpw8uXbpUMztSCx7nWGmD55X2eF5Vb8mSJSgoKEBgYKB6Gc+r6lV3rHheaXJ3d4eZmRm6deuG6dOn4+WXX1avq43zylhvWyKdyGQyjfdCiCrLHtX+n8t13aah0Pex6tWrF3r16qVe36dPH3Tp0gUrVqzA8uXL9VW2JGriHOB5pR2eV1Vt3rwZCxYswM6dO+Hs7KyXbdZ1+j5WPK80HT58GPfu3UN8fDzmz58PLy8vTJo06Ym2qQuGplrm6OgIIyOjKsk3MzOzSkK+z9XVtdr2xsbGaNSo0UPbPGibhqCmjtU/yeVydO/e3aD/cnucY6UNnlePr6GfV1u2bMFLL72EX3/9FYMHD9ZYx/NK08OO1T819POqefPmAID27dvj9u3bWLBggTo01cZ5xctztczU1BRdu3ZFVFSUxvKoqCj07t272s/4+flVab9nzx5069YNJiYmD23zoG0agpo6Vv8khEBycjIaN26sn8Il8DjHShs8rx5fQz6vNm/ejJCQEGzatAkjRoyosp7n1f971LH6p4Z8Xv2TEAIlJSXq97VyXultSDlp7f6tluvWrRNnz54Vs2fPFlZWViI1NVUIIcT8+fNFcHCwuv392+jnzJkjzp49K9atW1flNvojR44IIyMj8emnn4pz586JTz/9tF7dwqvPY7VgwQIRGRkprly5IpKSksSLL74ojI2NRUJCQq3vnz7peqyEECIpKUkkJSWJrl27iqCgIJGUlCTOnDmjXs/z6v896ljxvKq0adMmYWxsLFatWqVxi3xubq66Dc+rStocK55XlVauXCnCw8PFxYsXxcWLF8X3338vbG1txbvvvqtuUxvnFUOTRFatWiU8PT2Fqamp6NKli4iOjlave+GFF8SAAQM02h88eFB07txZmJqaimbNmomvv/66yjZ//fVX0aZNG2FiYiK8vb3Ftm3bano3aoW+j9Xs2bNF06ZNhampqXBychL+/v4iNja2Nnalxul6rABUeXl6emq04XlV6VHHiudVpQEDBlR7rF544QWNbfK80u5Y8byqtHz5ctGuXTthaWkpbG1tRefOncXq1atFRUWFxjZr+rySCfG/UbJERERE9EAc00RERESkBYYmIiIiIi0wNBERERFpgaGJiIiISAsMTURERERaYGgiIiIi0gJDExEREZEWGJqIiIiItMDQRERERKQFhiYiIiIiLTA0ERE9wtatW9G+fXtYWFigUaNGGDx4MAoKCqQui4hqmbHUBRAR1WXp6emYNGkSPvvsM4wdOxb5+fk4fPgw+NhOooaHD+wlInqIxMREdO3aFampqfD09JS6HCKSEC/PERE9RMeOHTFo0CC0b98ezz77LL799lvk5ORIXRYRSYA9TUREjyCEQGxsLPbs2YMdO3YgIyMDCQkJaN68udSlEVEtYmgiItJBRUUFPD09MXfuXMydO1fqcoioFnEgOBHRQyQkJGDfvn3w9/eHs7MzEhISkJWVhbZt20pdGhHVMoYmIqKHsLW1xaFDh7Bs2TLk5eXB09MTS5YswfDhw6UujYhqGS/PEREREWmBd88RERERaYGhiYiIiEgLDE1EREREWmBoIiIiItICQxMRERGRFhiaiIiIiLTA0ERERESkBYYmIiIiIi0wNBERERFpgaGJiIiISAsMTURERERaYGgiIiIi0sL/AWSYqWHiPTI8AAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "plt.figure()\n", - "plt.plot(s_space, potential)\n", - "plt.xlabel('s')\n", - "#plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "#plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", - "#plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.title('First DBI step')\n", - "plt.ylabel('Least squares cost function')\n", - "plt.legend()\n", - "plt.figure()\n", - "plt.plot(s_space, off_diagonal_norm_diff)\n", - "#plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "#plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", - "#plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", - "plt.xlabel('s')\n", - "plt.title('First DBI step')\n", - "plt.legend()\n", - "print('The minimum for cost function in the tested range is:', step_grid)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Comparison of the least-squares cost function with the original cost function using the polynomial scheduling method" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", - "off_diagonal_norm_diff_least_squares = [dbi.off_diagonal_norm]\n", - "iters = 100\n", - "dbi_ls = deepcopy(dbi)\n", - "cost = DoubleBracketCostFunction.off_diagonal_norm\n", - "dbi_od = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "for _ in range(iters):\n", - " step_poly = dbi_od.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " dbi_od(step_poly,d=d)\n", - " step_poly = dbi_ls.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " dbi_ls(step_poly,d=d)\n", - " off_diagonal_norm_diff.append(dbi_od.off_diagonal_norm)\n", - " off_diagonal_norm_diff_least_squares.append(dbi_ls.off_diagonal_norm)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", - "plt.plot(range(iters+1), off_diagonal_norm_diff_least_squares, label=r'Least squares')\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Energy fluctuation\n", - "\n", - "This cost function is defined as: $\\Xi_k^2 (\\mu) = \\langle \\mu | H_k^2| \\mu \\rangle - \\langle \\mu | H_k| \\mu \\rangle^2$" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-20 10:23:17]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-3. -1. -1. -0. -1. -0. -0. -0.]\n", - " [-1. 1. -0. -1. -0. -1. -0. -0.]\n", - " [-1. -0. 1. -1. -0. -0. -1. -0.]\n", - " [-0. -1. -1. 1. -0. -0. -0. -1.]\n", - " [-1. -0. -0. -0. 1. -1. -1. -0.]\n", - " [-0. -1. -0. -0. -1. 1. -0. -1.]\n", - " [-0. -0. -1. -0. -1. -0. 1. -1.]\n", - " [-0. -0. -0. -1. -0. -1. -1. -3.]]\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 3\n", - "h = 1.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the energy fluctuation cost function\n", - "cost = DoubleBracketCostFunction.energy_fluctuation\n", - "# define the state\n", - "state = 0\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, state=state)\n", - "print(np.real(dbi.h.matrix))" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.33334\n", - "hyperopt_search step: 0.33819673200950817\n", - "polynomial_approximation step: 0.10712604100508318\n" - ] - } - ], - "source": [ - "# generate data for plotting sigma decrease of the first step\n", - "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))\n", - "s_space = np.linspace(1e-5, 0.9, 1000)\n", - "off_diagonal_norm_diff = []\n", - "fluctuation = []\n", - "for s in s_space:\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval(s,d=d)\n", - " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", - " fluctuation.append(dbi_eval.energy_fluctuation(state=state))\n", - "\n", - "# grid_search\n", - "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", - "print('grid_search step:', step_grid)\n", - "# hyperopt\n", - "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", - "print('hyperopt_search step:', step_hyperopt)\n", - "# polynomial\n", - "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", - "print('polynomial_approximation step:', step_poly)" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.33334\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "plt.figure()\n", - "plt.plot(s_space, fluctuation)\n", - "plt.xlabel('s')\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label ='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.title('First DBI step')\n", - "plt.ylabel('Energy fluctuation')\n", - "plt.legend()\n", - "plt.figure()\n", - "plt.plot(s_space, off_diagonal_norm_diff)\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", - "plt.xlabel('s')\n", - "plt.title('First DBI step')\n", - "plt.legend()\n", - "print('The minimum for cost function in the tested range is:', step_grid)" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [], - "source": [ - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", - "energy_fluc = [dbi.energy_fluctuation(state=state)]\n", - "iters = 10\n", - "dbi_ = deepcopy(dbi)\n", - "for _ in range(iters):\n", - " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " dbi_(step_poly,d=d)\n", - " off_diagonal_norm_diff.append(dbi_.off_diagonal_norm)\n", - " energy_fluc.append(dbi_.energy_fluctuation(state=state))" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Energy fluctuation')" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "\n", - "plt.figure()\n", - "plt.plot(range(iters+1), energy_fluc)\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'Energy fluctuation')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\andre\\AppData\\Local\\Temp\\ipykernel_23312\\3703842558.py:14: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " energy[i,j] = dbi_.h.matrix[states[i],states[i]]\n" - ] - } - ], - "source": [ - "iters = 30\n", - "states = [0,1,2,3,4,5,6,7]\n", - "energy = np.empty((len(states),iters))\n", - "\n", - "\n", - "d = (np.diag(np.linspace(1,2**nqubits,2**nqubits)))\n", - "for i in range(len(states)):\n", - " dbi_ = deepcopy(dbi)\n", - " dbi_.state = states[i]\n", - " for j in range(iters):\n", - " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " if step_poly is not None:\n", - " dbi_(step_poly,d=d)\n", - " energy[i,j] = dbi_.h.matrix[states[i],states[i]]\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eigenvalues: [-4.00000000e+00 -3.46410162e+00 -8.06606266e-16 3.61709753e-17\n", - " 2.32474753e-15 2.00000000e+00 2.00000000e+00 3.46410162e+00]\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Iterations')" - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", - "print('Eigenvalues:', eigvals )\n", - "plt.figure()\n", - "for i in range(len(states)):\n", - " plt.plot(range(iters), energy[i,:],'.', label='State ' + str(states[i]))\n", - "for eigvals in eigvals:\n", - " plt.axhline(y=eigvals, color='r', linestyle='--')\n", - "plt.xlabel('Iterations')\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gradients for finding optimal $D$\n", - "\n", - "An advantage of the least-squares cost function is that one can use gradient descent and the learning is more stable than with the off-diagonal cost function." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Diagonal elements')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cost = DoubleBracketCostFunction.least_squares\n", - "nqubits = 5\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "\n", - "step = 1e-2\n", - "iterations = 100\n", - "d, loss, grad, diags = gradient_ascent(dbi, d,step, iterations)\n", - "\n", - "plt.figure()\n", - "plt.plot(range(iterations+1), loss)\n", - "plt.xlabel('Learning iterations')\n", - "plt.ylabel('Loss: Least squares')\n", - "\n", - "plt.figure()\n", - "for i in range(2**nqubits):\n", - " plt.plot(diags[i,:], label='State ' + str(i))\n", - "plt.xlabel('Learning iterations')\n", - "plt.ylabel('Diagonal elements')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cost = DoubleBracketCostFunction.least_squares\n", - "nqubits = 5\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "d_fixed = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "d_trained = deepcopy(d_fixed)\n", - "dbi_trained = deepcopy(dbi)\n", - "flows = 10\n", - "iterations = 30\n", - "off_diagonal_norm = np.empty((2,flows+1))\n", - "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", - "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", - "\n", - "for i in range(flows):\n", - " d_trained, loss, grad, diags = gradient_ascent(dbi_trained, d_trained,step, iterations)\n", - " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", - " dbi_trained(s,d=d_trained)\n", - " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", - " s = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_fixed, n=3)\n", - " dbi(s,d=d_fixed)\n", - " off_diagonal_norm[1,i+1] = dbi.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(off_diagonal_norm[0,:], label='Trained')\n", - "plt.plot(off_diagonal_norm[1,:], label='Untrained')\n", - "plt.legend()\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18 (main, Sep 11 2023, 14:09:26) [MSC v.1916 64 bit (AMD64)]" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "c4f92193806e2908606a5f23edd55a5282f2f433b73b1c504507f9256ed9f0b4" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 0017355c11..022c681923 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -6,6 +6,17 @@ from qibo.hamiltonians import Hamiltonian +class DoubleBracketGeneratorType(Enum): + """Define DBF evolution.""" + + canonical = auto() + """Use canonical commutator.""" + single_commutator = auto() + """Use single commutator.""" + group_commutator = auto() + """Use group commutator approximation""" + # TODO: add double commutator (does it converge?) + class DoubleBracketCost(Enum): """Define the DBI cost function.""" @@ -17,27 +28,13 @@ class DoubleBracketCost(Enum): energy_fluctuation = auto() """Use energy fluctuation as cost function.""" - -# This is needed due to mutual dependencies between double_bracket.py and utils_scheduling.py from qibo.models.dbi.utils_scheduling import ( grid_search_step, hyperopt_step, polynomial_step, + simulated_annealing_step, ) - -class DoubleBracketGeneratorType(Enum): - """Define DBF evolution.""" - - canonical = auto() - """Use canonical commutator.""" - single_commutator = auto() - """Use single commutator.""" - group_commutator = auto() - """Use group commutator approximation""" - # TODO: add double commutator (does it converge?) - - class DoubleBracketScheduling(Enum): """Define the DBI scheduling strategies.""" @@ -47,10 +44,9 @@ class DoubleBracketScheduling(Enum): """Use greedy grid search.""" polynomial_approximation = polynomial_step """Use polynomial expansion (analytical) of the loss function.""" - # simulated_annealing = simulated_annealing_step + simulated_annealing = simulated_annealing_step """Use simulated annealing algorithm""" - - + class DoubleBracketIteration: """ Class implementing the Double Bracket iteration algorithm. @@ -154,8 +150,9 @@ def backend(self): def least_squares(self, d: np.array): """Least squares cost function. (without the constant term norm(H))""" - h = self.h.matrix - return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h @ d)) + h_np = np.diag(np.diag(self.backend.to_numpy(self.h.matrix))) + + return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h_np @ d)) def choose_step( self, @@ -168,7 +165,7 @@ def choose_step( step = scheduling(self, d=d, **kwargs) if ( step is None - and scheduling == DoubleBracketScheduling.polynomial_approximation + and scheduling is DoubleBracketScheduling.polynomial_approximation ): kwargs["n"] = kwargs.get("n", 3) kwargs["n"] += 1 @@ -216,8 +213,12 @@ def energy_fluctuation(self, state): Args: state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ - - return np.real(self.h.energy_fluctuation(state)) + h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix)))) + h2 = h_np @ h_np + a = state.conj()@ h2 @ state + b = state.conj()@ h_np @ state + return (np.sqrt(np.real((a - b ** 2)))).item() + r#eturn np.real(self.h.energy_fluctuation(state)) def sigma(self, h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index f8b15029d6..5bb444bce8 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -1,19 +1,36 @@ import math -from copy import deepcopy -from itertools import product -from typing import Optional +from itertools import combinations, product -import hyperopt import numpy as np from qibo import symbols from qibo.backends import _check_backend from qibo.hamiltonians import SymbolicHamiltonian -from qibo.models.dbi.double_bracket import ( - DoubleBracketGeneratorType, - DoubleBracketIteration, - DoubleBracketScheduling, -) + + +def commutator(a, b): + """Compute commutator between two arrays.""" + return a @ b - b @ a + + +def variance(a, state): + """Calculates the variance of a matrix A with respect to a state: + Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" + b = a @ a + return state.conj().T @ b @ state - (state.conj().T @ a @ state) ** 2 + + +def covariance(a, b, state): + """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, + applied to two operators A and B with respect to a state: + Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ + """ + + c = a @ b + b @ a + return ( + state.conj().T @ c @ state + - 2 * state.conj().T @ a @ state * state.conj().T @ b @ state + ) def generate_Z_operators(nqubits: int, backend=None): @@ -73,74 +90,6 @@ def str_to_symbolic(name: str): return tensor_op -def select_best_dbr_generator( - dbi_object: DoubleBracketIteration, - d_list: list, - step: Optional[float] = None, - compare_canonical: bool = True, - scheduling: DoubleBracketScheduling = None, - **kwargs, -): - """Selects the best double bracket rotation generator from a list and execute the rotation. - - Args: - dbi_object (`DoubleBracketIteration`): the target DoubleBracketIteration object. - d_list (list): list of diagonal operators (np.array) to run from. - step (float): fixed iteration duration. - Defaults to ``None``, optimize with `scheduling` method and `choose_step` function. - compare_canonical (boolean): if `True`, the diagonalization effect with operators from `d_list` is compared with the canonical bracket. - scheduling (`DoubleBracketScheduling`): scheduling method for finding the optimal step. - - Returns: - The updated dbi_object, index of the optimal diagonal operator, respective step duration, and evolution direction. - """ - if scheduling is None: - scheduling = dbi_object.scheduling - norms_off_diagonal_restriction = [ - dbi_object.off_diagonal_norm for _ in range(len(d_list)) - ] - optimal_steps, flip_list = [], [] - for i, d in enumerate(d_list): - # prescribed step durations - dbi_eval = deepcopy(dbi_object) - flip_list.append(cs_angle_sgn(dbi_eval, d)) - if flip_list[i] != 0: - if step is None: - step_best = dbi_eval.choose_step( - d=flip_list[i] * d, scheduling=scheduling, **kwargs - ) - else: - step_best = step - dbi_eval(step=step_best, d=flip_list[i] * d) - optimal_steps.append(step_best) - norms_off_diagonal_restriction[i] = dbi_eval.off_diagonal_norm - # canonical - if compare_canonical is True: - flip_list.append(1) - dbi_eval = deepcopy(dbi_object) - dbi_eval.mode = DoubleBracketGeneratorType.canonical - if step is None: - step_best = dbi_eval.choose_step(scheduling=scheduling, **kwargs) - else: - step_best = step - dbi_eval(step=step_best) - optimal_steps.append(step_best) - norms_off_diagonal_restriction.append(dbi_eval.off_diagonal_norm) - # find best d - idx_max_loss = np.argmin(norms_off_diagonal_restriction) - flip = flip_list[idx_max_loss] - step_optimal = optimal_steps[idx_max_loss] - dbi_eval = deepcopy(dbi_object) - if idx_max_loss == len(d_list) and compare_canonical is True: - # canonical - dbi_eval(step=step_optimal, mode=DoubleBracketGeneratorType.canonical) - - else: - d_optimal = flip * d_list[idx_max_loss] - dbi_eval(step=step_optimal, d=d_optimal) - return dbi_eval, idx_max_loss, step_optimal, flip - - def cs_angle_sgn(dbi_object, d): """Calculates the sign of Cauchy-Schwarz Angle :math:`\\langle W(Z), W({\\rm canonical}) \\rangle_{\\rm HS}`.""" norm = np.trace( @@ -154,82 +103,52 @@ def cs_angle_sgn(dbi_object, d): return np.sign(norm) -def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = dbi_object.commutator(d, dbi_object.h.matrix) - Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) - sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients for rotation with [W,H] and H - c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] - c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] - # product coefficient - trace_coefficients = [0] * (2 * n + 1) - for k in range(n + 1): - for j in range(n + 1): - power = k + j - product_matrix = c1[k] @ c2[j] - trace_coefficients[power] += 2 * np.trace(product_matrix) - # coefficients from high to low (n:0) - coef = list(reversed(trace_coefficients[: n + 1])) - return coef - - -def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: int = 3): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients - coef = np.empty(n) - for i in range(n): - coef[i] = np.real(exp_list[i] * np.trace(d @ gamma_list[i + 1])) - coef = list(reversed(coef)) - return coef - - -# TODO: add a general expansion formula not stopping at 3rd order -def energy_fluctuation_polynomial_expansion_coef( - dbi_object, d: np.array = None, n: int = 3, state=0 -): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - # coefficients - coef = np.empty(3) - coef[0] = np.real(2 * covariance(gamma_list[0], gamma_list[1], state)) - coef[1] = np.real(2 * variance(gamma_list[1], state)) - coef[2] = np.real( - covariance(gamma_list[0], gamma_list[3], state) - + 3 * covariance(gamma_list[1], gamma_list[2], state) - ) - coef = list(reversed(coef)) - return coef +def decompose_into_Pauli_basis(h_matrix: np.array, pauli_operators: list): + """finds the decomposition of hamiltonian `h_matrix` into Pauli-Z operators""" + nqubits = int(np.log2(h_matrix.shape[0])) + decomposition = [] + for Z_i in pauli_operators: + expect = np.trace(h_matrix @ Z_i) / 2**nqubits + decomposition.append(expect) + return decomposition -def commutator(a, b): - """Compute commutator between two arrays.""" - return a @ b - b @ a +def generate_pauli_index(nqubits, order): + if order == 1: + return list(range(nqubits)) + elif order > 1: + indices = list(range(nqubits)) + return indices + [ + comb for i in range(2, order + 1) for comb in combinations(indices, i) + ] + else: + raise ValueError("Order must be a positive integer") -def variance(a, state): - """Calculates the variance of a matrix A with respect to a state: - Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" - b = a @ a - return state.conj().T @ b @ state - (state.conj().T @ a @ state) ** 2 +def generate_pauli_operator_dict( + nqubits: int, parameterization_order: int = 1, symbols_pauli=symbols.Z +): + pauli_index = generate_pauli_index(nqubits, order=parameterization_order) + pauli_operators = [ + generate_Pauli_operators(nqubits, symbols_pauli, index) for index in pauli_index + ] + return {index: operator for index, operator in zip(pauli_index, pauli_operators)} -def covariance(a, b, state): - """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, - applied to two operators A and B with respect to a state: - Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ - """ - c = a @ b + b @ a - return ( - state.conj().T @ c @ state - - 2 * state.conj().T @ a @ state * state.conj().T @ b @ state - ) + +def diagonal_min_max(matrix: np.array): + L = int(np.log2(matrix.shape[0])) + D = np.linspace(np.min(np.diag(matrix)), np.max(np.diag(matrix)), 2**L) + D = np.diag(D) + return D + + +def generate_Pauli_operators(nqubits, symbols_pauli, positions): + # generate matrix of an nqubit-pauli operator with `symbols_pauli` at `positions` + if isinstance(positions, int): + return SymbolicHamiltonian( + symbols_pauli(positions), nqubits=nqubits + ).dense.matrix + else: + terms = [symbols_pauli(pos) for pos in positions] + return SymbolicHamiltonian(math.prod(terms), nqubits=nqubits).dense.matrix diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py new file mode 100644 index 0000000000..6719660587 --- /dev/null +++ b/src/qibo/models/dbi/utils_analytical.py @@ -0,0 +1,213 @@ +from typing import Optional +import math +import numpy as np +from qibo.models.dbi.utils import commutator, variance, covariance + + +def dGamma_di_Pauli(dbi_object, n: int, Z_i: np.array, d: np.array): + """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. + + Args: + dbi_object (DoubleBracketIteration): the target dbi object + n (int): the number of nested commutators in `Gamma` + i (int/tupple): the index of onsite-Z coefficient + d (np.array): the diagonal operator + + Returns: + (list): [dGamma_0_di, dGamma_1_di, ..., dGamma_n_di] + """ + nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) + dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) + Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) + W = dbi_object.commutator(d, dbi_object.h.matrix) + dW_di = dbi_object.commutator(Z_i, dbi_object.h.matrix) + for k in range(n + 1): + if k == 0: + continue + elif k == 1: + dGamma_di[k] = dW_di + else: + dGamma_di[k] = dbi_object.commutator( + dW_di, Gamma_list[k - 1] + ) + dbi_object.commutator(W, dGamma_di[k - 1]) + return dGamma_di + + +def ds_di_Pauli( + dbi_object, + d: np.array, + Z_i: np.array, + taylor_coef: Optional[list] = None, +): + r"""Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients\ + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d (np.array): the diagonal operator + i (int): the index of onsite-Z coefficient + taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}`, from the highest order to the lowest. + onsite_Z_ops (list): onsite Z operators of `dbi_object.h` + Returns: + floats da, db, dc, ds + """ + # generate the list of derivatives w.r.t ith Z operator coefficient + dGamma_di = dGamma_di_Pauli(dbi_object, n=4, Z_i=Z_i, d=d) + Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) + + def derivative_product(k1, k2): + r"""Calculate the derivative of a product $\sigma(\Gamma(n1,i))@\sigma(\Gamma(n2,i))""" + return dbi_object.sigma(dGamma_di[k1]) @ dbi_object.sigma( + Gamma_list[k2] + ) + dbi_object.sigma(dbi_object.sigma(Gamma_list[k1])) @ dbi_object.sigma( + dGamma_di[k2] + ) + + # calculate the derivatives of s polynomial coefficients + da = np.trace(3 * derivative_product(1, 2) + 3 * derivative_product(3, 0)) + db = np.trace(2 * derivative_product(1, 1) + 2 * derivative_product(0, 2)) + dc = np.trace(2 * derivative_product(1, 0)) + + ds = 0 + if taylor_coef != None: + a, b, c = taylor_coef[len(taylor_coef) - 3 :] + delta = b**2 - 4 * a * c + ddelta = 2 * (b * db - 2 * (a * dc + da * c)) + + ds = (-db + 0.5 * ddelta / np.sqrt(delta)) * a - (-b + np.sqrt(delta)) * da + ds /= 2 * a**2 + + return da, db, dc, ds + + +def gradient_Pauli( + dbi_object, + d: np.array, + pauli_operator_dict: dict, + use_ds=False, + n=3, + **kwargs, +): + r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d (np.array): the diagonal operator + n_taylor (int): the highest order of the taylore expansion of w.r.t `s` + onsite_Z_ops (list): list of Pauli-Z operators + taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` + use_ds (boolean): if False, ds is set to 0 + """ + # n is the highest order for calculating s + + # pauli_index is the list of positions \mu + pauli_operators = list(pauli_operator_dict.values()) + num_paul = len(pauli_operators) + grad = np.zeros(num_paul) + coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) + s = dbi_object.choose_step( + d=d, + **kwargs, + ) + + a, b, c = coef[len(coef) - 3 :] + + for i, operator in enumerate(pauli_operators): + da, db, dc, ds = ds_di_Pauli( + dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] + ) + if use_ds is True: + ds = 0 + grad[i] = ( + s**3 / 3 * da + + s**2 / 2 * db + + 2 * s * dc + + s**2 * ds * a + + s * ds * b + + 2 * ds * c + ) + grad = np.array(grad) + grad = grad / np.linalg.norm(grad) + return grad, s + + +def dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list): + # Derivative of gamma with respect to diagonal elements of D (full-diagonal ansatz) + A = np.zeros(d.shape) + A[i, i] = 1 + B = commutator(commutator(A, H), Gamma_list[n - 1]) + W = commutator(d, H) + return B + commutator(W, dGamma[-1]) + + +def dpolynomial_diDiagonal(dbi_object, s, d, H, i): + # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) + # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation + derivative = 0 + A = np.zeros(d.shape) + Gamma_list = dbi_object.generate_Gamma_list(4, d) + A[i, i] = 1 + dGamma = [commutator(A, H)] + derivative += np.real( + np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s + ) + for n in range(2, 4): + dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) + derivative += np.real( + np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) + ) + + return derivative + + +def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) + Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) + sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients for rotation with [W,H] and H + c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] + c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] + # product coefficient + trace_coefficients = [0] * (2 * n + 1) + for k in range(n + 1): + for j in range(n + 1): + power = k + j + product_matrix = c1[k] @ c2[j] + trace_coefficients[power] += 2 * np.trace(product_matrix) + # coefficients from high to low (n:0) + coef = list(reversed(trace_coefficients[: n + 1])) + return coef + + +def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: int = 3): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients + coef = np.empty(n) + for i in range(n): + coef[i] = np.real(exp_list[i] * np.trace(d @ Gamma_list[i + 1])) + coef = list(reversed(coef)) + return coef + + +def energy_fluctuation_polynomial_expansion_coef( + dbi_object, d: np.array = None, n: int = 3, state=0 +): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + # coefficients + coef = np.empty(3) + coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1], state)) + coef[1] = np.real(2 * variance(Gamma_list[1], state)) + coef[2] = np.real( + covariance(Gamma_list[0], Gamma_list[3], state) + + 3 * covariance(Gamma_list[1], Gamma_list[2], state) + ) + coef = list(reversed(coef)) + return coef diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index adb78919f5..f31bc38b4a 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -18,7 +18,7 @@ class d_ansatz_type(Enum): # ising = auto() # for future implementation -def d_ansatz(params: np.array, d_type: d_ansatz_type == d_ansatz_type.element_wise): +def d_ansatz(params: np.array, d_type: d_ansatz_type): r""" Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. If $\alpha_i$ are our parameters and d the number of qubits then: @@ -37,11 +37,13 @@ def d_ansatz(params: np.array, d_type: d_ansatz_type == d_ansatz_type.element_wi elif d_type is d_ansatz_type.local_1: - op_list = [params[i] * symbols.Z(i) for i in len(params)] - from functools import reduce + op_list = [params[i] * symbols.Z(i) for i in range(len(params))] + symbolHam = op_list[0] + for i in range(len(params)-1): + symbolHam += op_list[i+1] - d = SymbolicHamiltonian(reduce(symbols.Z.add, op_list), nqubits=len(params)) - d.dense.matrix + d = SymbolicHamiltonian(symbolHam, nqubits=len(params)) + d = d.dense.matrix else: raise ValueError(f"Parameterization type {type} not recognized.") @@ -137,7 +139,7 @@ def gradientDiagonalEntries( params_new = deepcopy(params) params_new[i] += delta d_new = d_ansatz(params_new, d_type) - grad[i] = (dbi_object.cost(d_new) - dbi_object.cost(d)) / delta + grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta return grad @@ -176,21 +178,21 @@ def gradient_descent_dbr_d_ansatz( dbi_new = deepcopy(dbi_object) s = polynomial_step(dbi_object, n=3, d=d) dbi_new(s, d=d) - loss[0] = dbi_new.least_squares(d) + loss[0] = dbi_new.loss(0.0, d) params_hist = np.empty((len(params), nmb_iterations + 1)) params_hist[:, 0] = params for i in range(nmb_iterations): dbi_new = deepcopy(dbi_object) grad[i, :] = gradientDiagonalEntries( - dbi_object, params, h, analytic=analytic, ansatz=d_type + dbi_object, params, h, analytic=analytic, d_type=d_type ) for j in range(len(params)): params[j] = params[j] - lr * grad[i, j] d = d_ansatz(params, d_type) - s = polynomial_step(dbi_new, n=5, d=d) + s = polynomial_step(dbi_new, n=3, d=d) dbi_new(s, d=d) - loss[i + 1] = dbi_new.cost(d) + loss[i + 1] = dbi_new.loss(0.0,d=d) params_hist[:, i + 1] = params return d, loss, grad, params_hist diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index d8ba5c9d80..d007965f75 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -5,7 +5,7 @@ import numpy as np from qibo.models.dbi.double_bracket import DoubleBracketCost -from qibo.models.dbi.utils import ( +from qibo.models.dbi.utils_analytical import ( energy_fluctuation_polynomial_expansion_coef, least_squares_polynomial_expansion_coef, off_diagonal_norm_polynomial_expansion_coef, @@ -123,7 +123,7 @@ def polynomial_step( coef = least_squares_polynomial_expansion_coef(dbi_object, d, n) elif cost is DoubleBracketCost.energy_fluctuation: coef = energy_fluctuation_polynomial_expansion_coef( - dbi_object, d, n, dbi_object.state + dbi_object, d, n, dbi_object.ref_state ) else: raise ValueError(f"Cost function {cost} not recognized.") @@ -142,3 +142,87 @@ def polynomial_step( # solution does not exist, return None else: return None + +def simulated_annealing_step( + dbi_object, + d: Optional[np.array] = None, + initial_s=None, + step_min=1e-5, + step_max=1, + s_jump_range=None, + s_jump_range_divident=5, + initial_temp=1, + cooling_rate=0.85, + min_temp=1e-5, + max_iter=200, +): + """ + Perform a single step of simulated annealing optimization. + + Parameters: + dbi_object: DBI object + The object representing the problem to be optimized. + d: Optional[np.array], optional + The diagonal matrix 'd' used in optimization. If None, it uses the diagonal + matrix 'diagonal_h_matrix' from dbi_object. + initial_s: float or None, optional + Initial value for 's', the step size. If None, it is initialized using + polynomial_step function with 'n=4'. If 'polynomial_step' returns None, + 'initial_s' is set to 'step_min'. + step_min: float, optional + Minimum value for the step size 's'. + step_max: float, optional + Maximum value for the step size 's'. + s_jump_range: float or None, optional + Range for the random jump in step size. If None, it's calculated based on + 'step_min', 'step_max', and 's_jump_range_divident'. + s_jump_range_divident: int, optional + Dividend to determine the range for random jump in step size. + initial_temp: float, optional + Initial temperature for simulated annealing. + cooling_rate: float, optional + Rate at which temperature decreases in simulated annealing. + min_temp: float, optional + Minimum temperature threshold for termination of simulated annealing. + max_iter: int, optional + Maximum number of iterations for simulated annealing. + + Returns: + float: + The optimized step size 's'. + """ + + if d is None: + d = dbi_object.diagonal_h_matrix + if initial_s is None: + initial_s = polynomial_step(dbi_object=dbi_object, d=d, n=4) + if initial_s is None: + initial_s = step_min + if s_jump_range is None: + s_jump_range = (step_max - step_min) / s_jump_range_divident + current_s = initial_s + current_loss = dbi_object.loss(d=d, step=current_s) + temp = initial_temp + + for _ in range(max_iter): + candidate_s = max( + step_min, + min( + current_s + np.random.uniform(-1 * s_jump_range, s_jump_range), step_max + ), + ) + candidate_loss = dbi_object.loss(d=d, step=candidate_s) + + # Calculate change in loss + delta_loss = candidate_loss - current_loss + + # Determine if the candidate solution is an improvement + if delta_loss < 0 or np.random.rand() < math.exp(-delta_loss / temp): + current_s = candidate_s + current_loss = candidate_loss + # Cool down + temp *= cooling_rate + if temp < min_temp: + break + + return current_s \ No newline at end of file diff --git a/tests/test_models_dbi_utils_scheduling.py b/tests/test_models_dbi_utils_scheduling.py index 392727f144..e928ca9771 100644 --- a/tests/test_models_dbi_utils_scheduling.py +++ b/tests/test_models_dbi_utils_scheduling.py @@ -13,13 +13,13 @@ from qibo.quantum_info import random_hermitian NSTEPS = 1 -seed = 10 +SEED = 10 """Number of steps for evolution.""" @pytest.mark.parametrize("nqubits", [5, 6]) def test_polynomial_fail_cases(backend, nqubits): - h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + h0 = random_hermitian(2**nqubits, backend=backend, seed=SEED) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.single_commutator, From 43ee5961ccb2175c48aa1c624ec8f0f20990d81f Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 15 Apr 2024 13:45:08 +0000 Subject: [PATCH 064/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/double_bracket.py | 14 +++++++++----- src/qibo/models/dbi/utils.py | 2 +- src/qibo/models/dbi/utils_analytical.py | 6 ++++-- src/qibo/models/dbi/utils_gradients.py | 6 +++--- src/qibo/models/dbi/utils_scheduling.py | 3 ++- 5 files changed, 19 insertions(+), 12 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 022c681923..a3bcedbcad 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -6,6 +6,7 @@ from qibo.hamiltonians import Hamiltonian + class DoubleBracketGeneratorType(Enum): """Define DBF evolution.""" @@ -28,6 +29,7 @@ class DoubleBracketCost(Enum): energy_fluctuation = auto() """Use energy fluctuation as cost function.""" + from qibo.models.dbi.utils_scheduling import ( grid_search_step, hyperopt_step, @@ -35,6 +37,7 @@ class DoubleBracketCost(Enum): simulated_annealing_step, ) + class DoubleBracketScheduling(Enum): """Define the DBI scheduling strategies.""" @@ -46,7 +49,8 @@ class DoubleBracketScheduling(Enum): """Use polynomial expansion (analytical) of the loss function.""" simulated_annealing = simulated_annealing_step """Use simulated annealing algorithm""" - + + class DoubleBracketIteration: """ Class implementing the Double Bracket iteration algorithm. @@ -215,10 +219,10 @@ def energy_fluctuation(self, state): """ h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix)))) h2 = h_np @ h_np - a = state.conj()@ h2 @ state - b = state.conj()@ h_np @ state - return (np.sqrt(np.real((a - b ** 2)))).item() - r#eturn np.real(self.h.energy_fluctuation(state)) + a = state.conj() @ h2 @ state + b = state.conj() @ h_np @ state + return (np.sqrt(np.real(a - b**2))).item() + r # eturn np.real(self.h.energy_fluctuation(state)) def sigma(self, h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 5bb444bce8..85dbd9518c 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -25,7 +25,7 @@ def covariance(a, b, state): applied to two operators A and B with respect to a state: Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ """ - + c = a @ b + b @ a return ( state.conj().T @ c @ state diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py index 6719660587..6d09a76ffd 100644 --- a/src/qibo/models/dbi/utils_analytical.py +++ b/src/qibo/models/dbi/utils_analytical.py @@ -1,7 +1,9 @@ -from typing import Optional import math +from typing import Optional + import numpy as np -from qibo.models.dbi.utils import commutator, variance, covariance + +from qibo.models.dbi.utils import commutator, covariance, variance def dGamma_di_Pauli(dbi_object, n: int, Z_i: np.array, d: np.array): diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index f31bc38b4a..7790f3d2c5 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -39,8 +39,8 @@ def d_ansatz(params: np.array, d_type: d_ansatz_type): op_list = [params[i] * symbols.Z(i) for i in range(len(params))] symbolHam = op_list[0] - for i in range(len(params)-1): - symbolHam += op_list[i+1] + for i in range(len(params) - 1): + symbolHam += op_list[i + 1] d = SymbolicHamiltonian(symbolHam, nqubits=len(params)) d = d.dense.matrix @@ -192,7 +192,7 @@ def gradient_descent_dbr_d_ansatz( d = d_ansatz(params, d_type) s = polynomial_step(dbi_new, n=3, d=d) dbi_new(s, d=d) - loss[i + 1] = dbi_new.loss(0.0,d=d) + loss[i + 1] = dbi_new.loss(0.0, d=d) params_hist[:, i + 1] = params return d, loss, grad, params_hist diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index d007965f75..3015d7efa3 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -143,6 +143,7 @@ def polynomial_step( else: return None + def simulated_annealing_step( dbi_object, d: Optional[np.array] = None, @@ -225,4 +226,4 @@ def simulated_annealing_step( if temp < min_temp: break - return current_s \ No newline at end of file + return current_s From 6a41ccfc345504650a20b711cb32b661d5f90b2a Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Thu, 25 Apr 2024 13:56:59 +0200 Subject: [PATCH 065/154] added group commutator tests ``` --- ...t_functions_and_d_gradients_tutorial.ipynb | 798 ++++++++++++------ examples/dbi/dbi_group_commutator_tests.ipynb | 0 examples/dbi/dbi_misc.ipynb | 48 +- src/qibo/models/dbi/double_bracket.py | 2 +- 4 files changed, 545 insertions(+), 303 deletions(-) create mode 100644 examples/dbi/dbi_group_commutator_tests.ipynb diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index 5dd4bcebd4..2761e99464 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -39,14 +39,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-15 08:49:42]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-24 09:00:13]: Using numpy backend on /CPU:0\n" ] } ], @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -78,8 +78,8 @@ "output_type": "stream", "text": [ "grid_search step: 0.6060645454545454\n", - "100%|██████████| 100/100 [00:01<00:00, 94.46trial/s, best loss: -30.633828072194902]\n", - "hyperopt_search step: 0.5999730843447625\n", + "100%|██████████| 100/100 [00:00<00:00, 435.30trial/s, best loss: -30.63376733025578]\n", + "hyperopt_search step: 0.5997631214283555\n", "polynomial_approximation step: 0.5267891182131145\n" ] } @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -121,7 +121,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", 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" ] @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -195,22 +195,22 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -240,14 +240,14 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-15 08:49:51]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-24 09:00:16]: Using numpy backend on /CPU:0\n" ] } ], @@ -274,16 +274,16 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "grid_search step: 0.5353581818181817\n", - "100%|██████████| 100/100 [00:01<00:00, 76.45trial/s, best loss: 3.4331110272451792]\n", - "hyperopt_search step: 0.5334935579135086\n", + "grid_search step: 1e-05\n", + "100%|██████████| 100/100 [00:00<00:00, 523.08trial/s, best loss: 0.0]\n", + "hyperopt_search step: 0.45340309474280477\n", "polynomial_approximation step: 0.222657579130477\n" ] } @@ -313,19 +313,19 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The minimum for cost function in the tested range is: 0.5353581818181817\n" + "The minimum for cost function in the tested range is: 1e-05\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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" ] @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -396,13 +396,13 @@ "Text(0, 0.5, 'Energy fluctuation')" ] }, - "execution_count": 38, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -435,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -458,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -475,13 +475,13 @@ "Text(0.5, 0, 'Iterations')" ] }, - "execution_count": 40, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -513,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -522,14 +522,14 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-15 08:50:11]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-24 09:00:18]: Using numpy backend on /CPU:0\n" ] } ], @@ -555,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -564,13 +564,13 @@ "Text(0, 0.5, 'Diagonal elements')" ] }, - "execution_count": 43, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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AypUrce+995o9IN1dj8eEAgC2nsnClbxiidMQERHdHTJhpoNFSkpKYGdnBwcHB3MszqLodDqo1WpotVqoVCqp4zS4R7/cj30XcvBCn2Z4bXCk1HGIiIjqxZTP73pdhygvLw/Lli3DrFmzkJtbdSG/06dPIzs726TlLF68GO3atYNKpYJKpUJMTAx+++03w/ySkhJMnjwZXl5ecHV1xahRo5CVlWW0jLS0NMTGxsLZ2Rm+vr6YMWMGKioqjMbs3LkTHTt2hFKpRPPmzREXF1ef1bYZ429sJfr5UDpKKyolTkNERNTwTC5Ex48fR0REBN5//318+OGHyMvLAwCsXr0as2bNMmlZQUFBeO+993DkyBEcPnwY/fr1w/Dhw3Hq1CkAwCuvvIL169djxYoV2LVrFzIyMjBy5EjD6ysrKxEbG4uysjLs27cP3377LeLi4jB79mzDmJSUFMTGxqJv375ITEzE1KlT8dRTT2HLli2mrrrNGNDKDwFqR+QUlmHTiUyp4xARETU8YaL+/fuLGTNmCCGEcHV1FRcuXBBCCLF3714RGhpq6uJq8PDwEMuWLRN5eXnCwcFBrFixwjDvzJkzAoBISEgQQgixadMmIZfLhUajMYxZvHixUKlUorS0VAghxGuvvSbatGlj9D3GjBkjBg0a9I8ZSkpKhFarNTzS09MFAKHVau94/azFgq1nRejMDWLEoj1SRyEiIqoXrVZb589vk7cQHTp0CM8++2yN6U2aNIFGo6l3MausrMTy5ctRWFiImJgYHDlyBOXl5RgwYIBhTGRkJEJCQpCQkAAASEhIQFRUlNHZbYMGDYJOpzNsZUpISDBaRvWY6mXUZu7cuVCr1YZHcHBwvdfLWo3pGgwHOxmOpeXh5BWt1HGIiIgalMmFSKlUQqfT1Zh+9uxZ+Pj4mBzgxIkTcHV1hVKpxHPPPYc1a9agdevW0Gg0UCgUcHd3Nxrv5+dnKF4ajabGqf7Vz283RqfTobi49rOoZs2aBa1Wa3ikp6ebvF7WztfNEYPbBgDgKfhERNT4mVyIhg0bhjlz5qC8vBwAIJPJkJaWhpkzZ2LUqFEmB2jZsiUSExNx4MABPP/885gwYQJOnz5t8nLMSalUGg70rn7YouqDq39NzEBeEe9TR0REjZfJheijjz5CQUEBfH19UVxcjN69e6N58+Zwc3PDf//7X5MDKBQKNG/eHJ06dcLcuXMRHR2NTz/9FP7+/igrKzMctF0tKysL/v7+AAB/f/8aZ51VP7/dGJVKBScnJ5Pz2pLOoR6I9HdDaYUevxy2va1kRERkO0wuRGq1GvHx8diwYQMWLFiAKVOmYNOmTdi1axdcXFzuOJBer0dpaSk6deoEBwcHbNu2zTAvOTkZaWlpiImJAQDExMTgxIkTRqf7x8fHQ6VSoXXr1oYxNy+jekz1MuifyWQyTLg3DADw7b5LqKjUSxuIiIiogdibMri8vBxOTk5ITExE9+7d0b179zv65rNmzcKQIUMQEhKC/Px8/Pjjj9i5cye2bNkCtVqNSZMmYdq0afD09IRKpcKLL76ImJgY3HPPPQCAgQMHonXr1nj88cfxwQcfQKPR4D//+Q8mT54MpVIJAHjuueewcOFCvPbaa3jyySexfft2/PLLL9i4ceMdZbcVD3Zogg82J+FKXjG2nskyHFdERETUmJi0hcjBwQEhISGorDTPxfqys7Mxfvx4tGzZEv3798ehQ4ewZcsW3HfffQCAjz/+GA888ABGjRqFXr16wd/fH6tXrza83s7ODhs2bICdnR1iYmLw2GOPYfz48ZgzZ45hTHh4ODZu3Ij4+HhER0fjo48+wrJlyzBo0CCzrENj5+hgh0e7hQAAvt6TKm0YIiKiBmLyrTu++uorrF69Gv/73//g6enZULksiq3duuPvNNoS9Hh/Oyr0Ahte7IG2TdRSRyIiIrotUz6/TdplBgALFy7E+fPnERgYiNDQ0BrHDR09etTURZKF81c74v6oAKz7MwNf703B/NHtpY5ERERkViYXohEjRjRADLJ0T/YIx7o/M7D+zwz8a0gkfN0cpY5ERERkNiYXojfffLMhcpCFax/sjo4h7jialofv96dh2n0tpI5ERERkNvW62z3Zpie6hwMAfth/CSXl5jmwnoiIyBKYXIgqKyvx4YcfomvXrvD394enp6fRgxqvwW39EaB2RE5hGdb/mSF1HCIiIrMxuRC9/fbbmD9/PsaMGQOtVotp06Zh5MiRkMvleOuttxogIlkKBzs5xseEAQC+3psKE09QJCIislgmF6IffvgBX375JaZPnw57e3uMHTsWy5Ytw+zZs7F///6GyEgWZGzXYDg6yHEmU4d9F3KkjkNERGQWJhcijUaDqKgoAICrqyu0Wi0A4IEHHuDVn22Au7MCozsHAwC+2H1R4jRERETmYXIhCgoKQmZmJgCgWbNm+P333wEAhw4dMtwugxq3ST3CIZcBu89exZlMndRxiIiI7pjJhejBBx803Cz1xRdfxBtvvIGIiAiMHz8eTz75pNkDkuUJ9XLB4Lb+AIAv/+BWIiIisn4m37rj7xISEpCQkICIiAgMHTrUXLksiq3fuqM2iel5GLFoL+zlMvwxsy8C1E5SRyIiIjLSoLfu+LuYmBjExMTc6WLIyrQPdkfXcE8cTMlF3N5UzLq/ldSRiIiI6s3kQvTdd9/dcv748ePrHYasy7O9muJgSi5+PJCGKf2aw83RQepIRERE9WLyLjMPDw+j5+Xl5SgqKoJCoYCzszNyc3PNGtAScJdZ7fR6gYGf7Mb57AK8fn8rPN2rqdSRiIiIDEz5/Db5oOrr168bPQoKCpCcnIwePXrgp59+qndosj5yuQxP96y6ncfXe1NQXqmXOBEREVH9mOVeZhEREXjvvffw8ssvm2NxZEVGdGgCHzclMrUlvJ0HERFZLbPd3NXe3h4ZGfxAtDVKeztMvDcMALBk1wXo9bydBxERWR+TD6pet26d0XMhBDIzM7Fw4UJ0797dbMHIejx2TyiW7LyAs1kF2HomCwPb+EsdiYiIyCQmF6IRI0YYPZfJZPDx8UG/fv3w0UcfmSsXWRG1kwMejwnF5zsvYNHOC7ivtR9kMpnUsYiIiOrM5EKk1/PAWarpyR7h+GpPCv5Mz0PChRzc29xb6khERER1ZrZjiMi2ebsqMbZrCABg0c7zEqchIiIyjclbiKZNm1bnsfPnzzd18WTFnu7VFN/vv4S953NwLO06OoR43P5FREREFsDkQnTs2DEcO3YM5eXlaNmyJQDg7NmzsLOzQ8eOHQ3jeAyJ7Wni7oQRHZpg5ZHL+HznBXw5vrPUkYiIiOrE5EI0dOhQuLm54dtvvzVctfr69et44okn0LNnT0yfPt3sIcl6PNe7GVYdvYz401lI1uSjpb+b1JGIiIhuy+RjiD766CPMnTvX6BYeHh4eePfdd3mWGaG5ryuGtK067X4xjyUiIiIrYXIh0ul0uHr1ao3pV69eRX5+vllCkXV7oU9zAMC6PzOQeq1Q4jRERES3Z3IhevDBB/HEE09g9erVuHz5Mi5fvoxVq1Zh0qRJGDlyZENkJCvTtokafVr6QC+ARTu4lYiIiCyfyYVoyZIlGDJkCB599FGEhoYiNDQUjz76KAYPHozPP/+8ITKSFXq5fwQAYPWxK0jLKZI4DRER0a3JhBD1uvlUYWEhLly4AABo1qwZXFxczBrMkuh0OqjVami1WqhUKqnjWI3xXx/E7rNXMaZzMN5/qJ3UcYiIyMaY8vld7wszuri4oF27dggLC0N8fDySkpLquyhqpKq3Eq06ehnpudxKRERElsvkQjR69GgsXLgQAFBcXIzOnTtj9OjRiIqKwqpVq8wekKxXp1AP9IzwRoVe8FgiIiKyaCYXot27d6Nnz54AgDVr1kAIgby8PCxYsADvvvuu2QOSdaveSrTyCLcSERGR5TK5EGm1Wnh6egIANm/ejFGjRsHZ2RmxsbE4d+6c2QOSdesc5onuzb1QoRf4fOcFqeMQERHVyuRCFBwcjISEBBQWFmLz5s0YOHAggKqrVTs6Opo9IFm/l/u3AACsPJKOK3nFEqchIiKqyeRCNHXqVIwbNw5BQUEIDAxEnz59AFTtSouKijJ3PmoEuoZ7IqapF8orBRZu57FERERkeUwuRC+88AL279+Pr7/+Gnv27IFcXrWIpk2b8hgi+kev3Fe1lWjF4XRel4iIiCxOva9DZEt4HSLzqL4u0ciOTTB/dHup4xARUSN3V65DRGSq6Te2Eq09dgXns3nfOyIishwsRHTXRAe7Y2BrP+gF8HE8z0gkIiLLwUJEd9W0gS0gkwEbT2Ti5BWt1HGIiIgA1KMQpaWlobbDjoQQSEtLM0soarwi/VUY2i4QADA//qzEaYiIiKqYXIjCw8Nx9erVGtNzc3MRHh5ullDUuL1yXwvYyWXYnpSNI5euSx2HiIjI9EIkhIBMJqsxvaCggBdmpDoJ93bBQx2DAAAf/Z4scRoiIiLAvq4Dp02bBgCQyWR444034OzsbJhXWVmJAwcOoH379mYPSI3Ti/2bY82xK9h3IQd/nLuKnhE+UkciIiIbVudCdOzYMQBVW4hOnDgBhUJhmKdQKBAdHY1XX33V/AmpUQrycMa4e0Lwzd5UvPdbEro384ZcXnPLIxER0d1Q50K0Y8cOAMATTzyBTz/9lBcopDv2Yr8IrDh8GacydFh/PAPD2zeROhIREdkok48h+uabb4zKkE6nw9q1a5GUlGTWYNT4eboo8FzvpgCAD39PRmlFpcSJiIjIVplciEaPHo2FCxcCAIqLi9G5c2eMHj0aUVFRWLVqldkDUuP2ZI9w+LopkZ5bjB8P8LINREQkDZML0e7du9GzZ08AwJo1ayCEQF5eHhYsWMCbu5LJnBX2mDqg6pYen20/j/yScokTERGRLTK5EGm1Wnh6egIANm/ejFGjRsHZ2RmxsbE4d463YyDTje4chKY+LsgtLMPS3ReljkNERDbI5EIUHByMhIQEFBYWYvPmzRg4cCAA4Pr167wOEdWLvZ0crw2KBAAs+yMF2boSiRMREZGtMbkQTZ06FePGjUNQUBACAwPRp08fAFW70qKiosydj2zEoDZ+6BjijuLySny8lbf0ICKiu8vkQvTCCy9g//79+Prrr7Fnzx7I5VWLaNq0KY8honqTyWT49/2tAAA/H0pHkkYncSIiIrIlMlHbnVrJiE6ng1qthlar5fWXGtgLPxzBphMa9IzwxndPdq31NjFERER1Ycrnd50vzHizy5cvY926dUhLS0NZWZnRvPnz59dnkUQAgH8NboWtp7Pxx7lr2Jl8FX0jfaWORERENsDkQrRt2zYMGzYMTZs2RVJSEtq2bYvU1FQIIdCxY8eGyEg2JMTLGU90D8MXuy/i3Y2n0SPCGw52Ju/ZJSIiMonJnzSzZs3Cq6++ihMnTsDR0RGrVq1Ceno6evfujYcffrghMpKNmdyvOTxdFLhwtRA/HeTFGomIqOGZXIjOnDmD8ePHAwDs7e1RXFwMV1dXzJkzB++//77ZA5LtUTk64JX7qi7W+HH8WWiLeLFGIiJqWCYXIhcXF8NxQwEBAbhw4YJh3rVr18yXjGza2C7BiPB1xfWicizcwQt+EhFRwzK5EN1zzz3Ys2cPAOD+++/H9OnT8d///hdPPvkk7rnnHrMHJNtkbyfH67FVp+HH7UtFyrVCiRMREVFjZnIhmj9/Prp16wYAePvtt9G/f3/8/PPPCAsLw1dffWXSsubOnYsuXbrAzc0Nvr6+GDFiBJKTk43GlJSUYPLkyfDy8oKrqytGjRqFrKwsozFpaWmIjY2Fs7MzfH19MWPGDFRUVBiN2blzJzp27AilUonmzZsjLi7O1FWnu6xPS1/0buGD8kqBOetPSR2HiIgaMZMLUdOmTdGuXTsAVbvPlixZguPHj2PVqlUIDQ01aVm7du3C5MmTsX//fsTHx6O8vBwDBw5EYeFfWwNeeeUVrF+/HitWrMCuXbuQkZGBkSNHGuZXVlYiNjYWZWVl2LdvH7799lvExcVh9uzZhjEpKSmIjY1F3759kZiYiKlTp+Kpp57Cli1bTF19ustmD20NBzsZdiRfxfakrNu/gIiIqB7qdWHGvLw8rFy5EhcuXMCMGTPg6emJo0ePws/PD02aNKl3mKtXr8LX1xe7du1Cr169oNVq4ePjgx9//BEPPfQQACApKQmtWrVCQkIC7rnnHvz222944IEHkJGRAT8/PwDAkiVLMHPmTFy9ehUKhQIzZ87Exo0bcfLkScP3euSRR5CXl4fNmzfXyFFaWorS0lLDc51Oh+DgYF6YUSJzN53BF7svIszLGVte6QWlvZ3UkYiIyAqYcmFGk7cQHT9+HC1atMD777+PDz/8EHl5eQCA1atXY9asWfUKXE2r1QIAPD09AQBHjhxBeXk5BgwYYBgTGRmJkJAQJCQkAAASEhIQFRVlKEMAMGjQIOh0Opw6dcow5uZlVI+pXsbfzZ07F2q12vAIDg6+o/WiO/Ni/wj4uimRmlOEr/akSB2HiIgaIZML0bRp0zBx4kScO3fO6O72999/P3bv3l3vIHq9HlOnTkX37t3Rtm1bAIBGo4FCoYC7u7vRWD8/P2g0GsOYm8tQ9fzqebcao9PpUFxcXCPLrFmzoNVqDY/09PR6rxfdOVelPWbdHwkAWLj9PDTaEokTERFRY2NyITp06BCeffbZGtObNGliKCD1MXnyZJw8eRLLly+v9zLMRalUQqVSGT1IWiPaN0GnUA8UlVXi/zadkToOERE1MiYXIqVSCZ2u5p3Iz549Cx8fn3qFmDJlCjZs2IAdO3YgKCjIMN3f3x9lZWWG3XLVsrKy4O/vbxjz97POqp/fboxKpYKTk1O9MtPdJZPJ8PawNpDJgHV/ZuDAxRypIxERUSNiciEaNmwY5syZg/LyqqsHy2QypKWlYebMmRg1apRJyxJCYMqUKVizZg22b9+O8PBwo/mdOnWCg4MDtm3bZpiWnJyMtLQ0xMTEAABiYmJw4sQJZGdnG8bEx8dDpVKhdevWhjE3L6N6TPUyyDq0baLG2K4hAIDZv55CeaVe4kRERNRoCBPl5eWJAQMGCHd3d2FnZyeCg4OFg4OD6NmzpygoKDBpWc8//7xQq9Vi586dIjMz0/AoKioyjHnuuedESEiI2L59uzh8+LCIiYkRMTExhvkVFRWibdu2YuDAgSIxMVFs3rxZ+Pj4iFmzZhnGXLx4UTg7O4sZM2aIM2fOiEWLFgk7OzuxefPmOuXUarUCgNBqtSatH5lfbkGp6DDndxE6c4P4Ytd5qeMQEZEFM+Xzu16n3QPA3r178eeff6KgoAAdO3ascRZXXchkslqnf/PNN5g4cSKAqgszTp8+HT/99BNKS0sxaNAgfP7554bdYQBw6dIlPP/889i5cydcXFwwYcIEvPfee7C3tzeM2blzJ1555RWcPn0aQUFBeOONNwzf43ZMOW2PGt4vh9Px2srjcFbYIX5abzRx525PIiKqyZTP73oXor9LSkrCsGHDcPbsWXMszqKwEFkWvV5gzNIEHEq9joGt/bB0fGepIxERkQVq0OsQ/ZPS0lKjG70SNRS5XIZ3R0TBXi7D76ezsO0Mr2BNRER3xmyFiOhuaunvhkk9qg7Cf3PdKRSXVUqciIiIrBkLEVmtl/pHIFDtiMvXi/HZ9nNSxyEiIivGQkRWy0Vpj7eGtQEALN19EcmafIkTERGRtbK//ZAqHh4e/3hWGABUVFSYJRCRKQa28ceAVn7YeiYLM1cdx6rn74Wd/J//nhIREdWmzoXok08+acAYRPX3zog22H8xB4npefhfQiomdg+//YuIiIhuYrbT7hsznnZv+f63/xLeWHuS1yYiIiIDSU67J5LSuK4h6Hzj5q//WXMC7PlERGQKFiJqFORyGd4bFQWFnRw7kq9i/fFMqSMREZEVYSGiRqO5rxum9GsOAHh73SlcLyyTOBEREVkLFiJqVJ7r3Qwt/FyRU1iGORtOSx2HiIisBAsRNSoKezneH9UOchmw5tgVbD3N23oQEdHt1asQ7d69G4cPHzaadvjwYezevdssoYjuRIcQDzzdsykAYNaaE8gr4q4zIiK6tXoVoj59+mD8+PFG0x5//HH07dvXLKGI7tQr97VAMx8XXM0vxdvrueuMiIhurV6FKCUlBVu3bjWatm3bNly8eNEsoYjulKODHT58ONqw6yyeu86IiOgW6lWIQkNDERgYaDQtMDAQoaGhZglFZA4dQjzwdK+qXWf/5q4zIiK6BZMLUXp6Oi5fvmx4fvDgQUydOhVLly41azAic3hlQAs093XF1fxSvLXulNRxiIjIQplciB599FHs2LEDAKDRaHDffffh4MGDeP311zFnzhyzByS6EzfvOlubmIFNJ3jBRiIiqsnkQnTy5El07doVAPDLL7+gbdu22LdvH3744QfExcWZOx/RHWsf7I7n+zQDULXrLFtXInEiIiKyNCYXovLyciiVSgDA1q1bMWzYMABAZGQkMjP5v2+yTC/3b4E2gSrkFZVjxsrjvNcZEREZMbkQtWnTBkuWLMEff/yB+Ph4DB48GACQkZEBLy8vswckMgeFvRyfjGkPpb0cu85exff7L0kdiYiILIjJhej999/HF198gT59+mDs2LGIjo4GAKxbt86wK43IEkX4ueFfQyIBAP/ddAYXrhZInIiIiCyFTNRj30FlZSV0Oh08PDwM01JTU+Hs7AxfX1+zBrQEOp0OarUaWq0WKpVK6jh0B/R6gfFfH8Se89fQLkiNVc/fCwc73sGGiKgxMuXz2+RPguLiYpSWlhrK0KVLl/DJJ58gOTm5UZYhalzkchnmPdwOKkd7HL+sxYJt56SOREREFsDkQjR8+HB89913AIC8vDx069YNH330EUaMGIHFixebPSCRuQWonfDfB6MAAAt3nEfChRyJExERkdRMLkRHjx5Fz549AQArV66En58fLl26hO+++w4LFiwwe0CihjA0OhAPdQqCEMArPyfieiGvYk1EZMtMLkRFRUVwc3MDAPz+++8YOXIk5HI57rnnHly6xDN3yHq8PawNmnq7QKMrwWureCo+EZEtM7kQNW/eHGvXrkV6ejq2bNmCgQMHAgCys7N5wDFZFRelPRaM7QCFnRzxp7N4Kj4RkQ0zuRDNnj0br776KsLCwtC1a1fExMQAqNpa1KFDB7MHJGpIbZuoMfPGqfjvbDyDJI1O4kRERCSFep12r9FokJmZiejoaMjlVZ3q4MGDUKlUiIyMNHtIqfG0+8ZNCIFJ3x7G9qRsRPi64tcp3eGssJc6FhER3SFTPr/rVYiqVd/1PigoqL6LsAosRI1fTkEphnz6B7LzSzGqYxA+fLgdZDKZ1LGIiOgONOh1iPR6PebMmQO1Wo3Q0FCEhobC3d0d77zzDvR6fb1DE0nJy1WJTx/pALkMWHX0MlYcvix1JCIiuotMLkSvv/46Fi5ciPfeew/Hjh3DsWPH8H//93/47LPP8MYbbzRERqK7IqaZF6YPbAkAeOPXkzidweOJiIhshcm7zAIDA7FkyRLDXe6r/frrr3jhhRdw5coVswa0BNxlZjv0eoFJ3x7CjuSrCPd2wbop3eHm6CB1LCIiqocG3WWWm5tb64HTkZGRyM3NNXVxRBZFLpdh/uj2CFQ7IuVaIf616gSvT0REZANMLkTR0dFYuHBhjekLFy5EdHS0WUIRScnDRYGF4zrCwU6GjScyEbcvVepIRETUwEw+t/iDDz5AbGwstm7dargGUUJCAtLT07Fp0yazBySSQscQD8wa0gpzNpzGfzeeQZtANbqGe0odi4iIGojJW4h69+6Ns2fP4sEHH0ReXh7y8vIwcuRIJCcnG+5xRtQYPNE9DEOjA1GhF3jhh6PQaEukjkRERA3kjq5DdLPLly9jzpw5WLp0qTkWZ1F4ULXtKiqrwMjP9yFJk48OIe5Y/sw9UNrbSR2LiIjqoEEPqv4nOTk5+Oqrr8y1OCKL4KywxxePd4LK0R7H0vLw9vrTUkciIqIGYLZCRNRYhXq54NOxHSCTAT8eSMPPh9KkjkRERGbGQkRUB31b+mLagBYAgDfWnsKRS9clTkRERObEQkRUR5P7NsfA1n4oq9Tj2f8dQUZesdSRiIjITOp82v3IkSNvOT8vL+9OsxBZNLlcho/HtMeoxVUHWT/93WGseC4GzgqTr15BREQWps5biNRq9S0foaGhGD9+fENmJZKci9IeyyZ0hpeLAqcydJj+y5/Q63klayIia2e20+4bM552T393KDUXj365H+WVAi/3j8Ar97WQOhIREf2NJKfdE9mSLmGe+O+IKADAp9vOYcPxDIkTERHRnWAhIqqn0V2C8VSPcADAtF/+xJFLvLkxEZG1YiEiugOz7m+FAa38UFahx9PfHUHqtUKpIxERUT2wEBHdATu5DAvGtkdUEzVyC8vwRNwhXC8skzoWERGZiIWI6A45K+zx1cTOaOLuhJRrhXjmf4dRUl4pdSwiIjIBCxGRGfi6OeKbJ7rAzdEeh1KvY8bK4zwdn4jIirAQEZlJCz83fPFYJ9jLZVj/Zwbe25wkdSQiIqojFiIiM7q3uTc+eKgdAGDp7otYuvuCxImIiKguWIiIzGxkxyD8+/5IAMD/bUrC6qOXJU5ERES3w0JE1ACe6dUMT/esukbRayuPY2dytsSJiIjoVliIiBrIrCGtMKJ9ICr0As9/fxTH0q5LHYmIiP4BCxFRA5HLZfjgoWj0auGD4vJKTPzmEJI0OqljERFRLViIiBqQwl6OxeM6okOIO7TF5Xhs2UFcvFogdSwiIvobFiKiBuaitEfcxK5oHaDCtYJSPLbsAK7kFUsdi4iIbiJpIdq9ezeGDh2KwMBAyGQyrF271mi+EAKzZ89GQEAAnJycMGDAAJw7d85oTG5uLsaNGweVSgV3d3dMmjQJBQXG/wM/fvw4evbsCUdHRwQHB+ODDz5o6FUjMqJ2dsB3k7qiqY8LMrQlGPflfmTnl0gdi4iIbpC0EBUWFiI6OhqLFi2qdf4HH3yABQsWYMmSJThw4ABcXFwwaNAglJT89UEybtw4nDp1CvHx8diwYQN2796NZ555xjBfp9Nh4MCBCA0NxZEjRzBv3jy89dZbWLp0aYOvH9HNvF2V+OGpbgjycEJqThEeX3YQubzvGRGRRZAJISzi/gIymQxr1qzBiBEjAFRtHQoMDMT06dPx6quvAgC0Wi38/PwQFxeHRx55BGfOnEHr1q1x6NAhdO7cGQCwefNm3H///bh8+TICAwOxePFivP7669BoNFAoFACAf/3rX1i7di2Skmq/knBpaSlKS0sNz3U6HYKDg6HVaqFSqRrwp0C24FJOIR5ekoDs/FJE+rvhx6fvgaeLQupYRESNjk6ng1qtrtPnt8UeQ5SSkgKNRoMBAwYYpqnVanTr1g0JCQkAgISEBLi7uxvKEAAMGDAAcrkcBw4cMIzp1auXoQwBwKBBg5CcnIzr12s/DXru3LlQq9WGR3BwcEOsItmoUC8X/Pj0PfBxUyJJk49Hv9yPnILS27+QiIgajMUWIo1GAwDw8/Mzmu7n52eYp9Fo4OvrazTf3t4enp6eRmNqW8bN3+PvZs2aBa1Wa3ikp6ff+QoR3aS5ryuWP3MPfA2l6ACusRQREUnGYguRlJRKJVQqldGDyNya+bjipxulKDmraksRSxERkTQsthD5+/sDALKysoymZ2VlGeb5+/sjO9v4lggVFRXIzc01GlPbMm7+HkRSaeZTtaXIT6XE2awCliIiIolYbCEKDw+Hv78/tm3bZpim0+lw4MABxMTEAABiYmKQl5eHI0eOGMZs374der0e3bp1M4zZvXs3ysvLDWPi4+PRsmVLeHh43KW1IfpnTX1csfyZGEMpGrt0P67msxQREd1NkhaigoICJCYmIjExEUDVgdSJiYlIS0uDTCbD1KlT8e6772LdunU4ceIExo8fj8DAQMOZaK1atcLgwYPx9NNP4+DBg9i7dy+mTJmCRx55BIGBgQCARx99FAqFApMmTcKpU6fw888/49NPP8W0adMkWmuimsK9XbD8mRj4qxxxLrsAjyxNQKaWF28kIrpbJD3tfufOnejbt2+N6RMmTEBcXByEEHjzzTexdOlS5OXloUePHvj888/RokULw9jc3FxMmTIF69evh1wux6hRo7BgwQK4uroaxhw/fhyTJ0/GoUOH4O3tjRdffBEzZ86sc05TTtsjuhOp1wox9sv9yNSWoIm7E75/qhvCvV2kjkVEZJVM+fy2mOsQWTIWIrqbLl8vwuNfHUTKtUJ4uyoQ90RXtG2iljoWEZHVaRTXISKyVUEezljxXMyNe5+VYezS/TiYkit1LCKiRo2FiMgCebsqsfzZe9A1zBP5pRV4/KsD2J6UdfsXEhFRvbAQEVkolWPVDWH7R/qitEKPZ747grXHrkgdi4ioUWIhIrJgjg52WPJ4J4xoH4gKvcDUnxPx9Z4UqWMRETU6LEREFs7BTo75o9tj4r1hAIA5G07jrXWnUKnn+RBERObCQkRkBeRyGd4c2hqvDW4JAIjbl4pn/3cERWUVEicjImocWIiIrIRMJsMLfZpj4aMdoLCXY+uZLIz+IgHZuhKpoxERWT0WIiIr80C7QPz0dDd4uihw8ooOIxbtRZJGJ3UsIiKrxkJEZIU6hXpizQv3oqm3CzK0JXhocQJ2n70qdSwiIqvFQkRkpUK9XLD6hXvRNdwTBaUVeCLuEP6XkApefJ6IyHQsRERWzN1Zgf9N6ooHOzRBpV7gjV9PYeaq4ygpr5Q6GhGRVWEhIrJySns7zB8djX8NiYRcBvxy+DLGLN2PTG2x1NGIiKwGCxFRIyCTyfBc72aIe6Ir1E4O+DM9D0M/28N7oBER1RELEVEj0quFD9ZP6YFIfzdcKyjDo1/ux3c8roiI6LZYiIgamRAvZ6x+4V480C4AFXqB2b+ewmsreVwREdGtsBARNULOCnt8NrYDZt04rmjFkcsYsWgvzmcXSB2NiMgisRARNVIymQzP9m6G757sBm9XBZI0+Rj62R6sPHJZ6mhERBaHhYiokesR4Y1NL/dE9+ZeKC6vxKsr/sS0nxNRWMr7oBERVWMhIrIBvm6O+O7Jbnh1YAvIZcDqY1cw9LM9OJ3BW34QEQEsREQ2w04uw5R+EVj+TAz8VY64eK0QIz7fy6tbExGBhYjI5nQN98Sml3uif6Qvyir0eOPXU5j4zSFotCVSRyMikgwLEZEN8nRRYNmEzpj9QGso7eXYdfYqBn68C78mXuHWIiKySSxERDZKJpPhyR7h2PhST0QHqaErqcDLyxMx+cejyC0skzoeEdFdxUJEZOOa+7pi1fP3Ytp9LWAvl2HTCQ0GfrwbW09nSR2NiOiuYSEiItjbyfFS/wisndwdEb6uuFZQiqe+O4xpvyRyaxER2QQWIiIyaNtEjfUv9sAzvZpCJgNWH72C/h/txOqjl3lsERE1aixERGTE0cEO/76/FVY9fy9a+rnhelE5pv3yJx776gBSrxVKHY+IqEGwEBFRrTqGeGDDSz0wY1BLKO3l2Hs+B4M+2Y1FO86jvFIvdTwiIrNiISKif+RgJ8fkvs3x+yu90KO5N0or9Ji3JRkPLNiDQ6m5UscjIjIbFiIiuq1QLxf8b1JXfDwmGp4uCiRn5ePhJQl48adjyMgrljoeEdEdYyEiojqRyWR4sEMQtk7rjbFdgyGTAev/zEC/j3ZiwbZzKCmvlDoiEVG9yQRPHbktnU4HtVoNrVYLlUoldRwii3DyihZvrz+FQ6nXAQBN3J3wemwrDGnrD5lMJnE6IiLTPr9ZiOqAhYiodkIIbDieibmbziDjxr3Q7mnqiX/f3wrtgtylDUdENo+FyMxYiIhurbisEot3XcAXuy6gtKLqDLTYdgF4dWBLhHu7SJyOiGwVC5GZsRAR1c3l60WY//tZrEm8AiEAe7kMY7oE4+X+EfBVOUodj4hsDAuRmbEQEZnmTKYO87YkY3tSNgDAycEOT/YIw7O9m0Hl6CBxOiKyFSxEZsZCRFQ/By7m4L3NSTiWlgcAUDs5YFKPcEy4NwxqJxYjImpYLERmxkJEVH9CCPx+OgvztiTjfHYBAMDN0R5PdA/HpO7hUDuzGBFRw2AhMjMWIqI7V6kX2HgiE59tO4dzN4qRq9IeE+8Nw6Qe4fBwUUickIgaGxYiM2MhIjIfvV5g8ykNFmw7hyRNPgDAWWGHx+4JxRPdwxCgdpI4IRE1FixEZsZCRGR+er1A/JksLNh2DqcydACqzkobFh2Ip3o2RetA/lsjojvDQmRmLEREDUcIgR3J2Vi6+yL2X/zrhrE9I7zxdM+m6BnhzStfE1G9sBCZGQsR0d1x/HIelu6+iE0nMqG/8Zsp0t8NT3YPx9DoQDgp7KQNSERWhYXIzFiIiO6u9NwifL03BT8fSkdRWdVNY9VODhjdOQiP3ROKUC9e/ZqIbo+FyMxYiIikoS0qx0+H0vD9/ku4fL0YACCTAb1b+GB8TCh6t/CFnZy704iodixEZsZCRCStSr3AzuRsfJdwCbvOXjVMD/Z0wpjOwXioUzD81bw1CBEZYyEyMxYiIsuReq0Q3++/hF8Op0NXUgEAkN/YajSmSzD6RfpBYS+XOCURWQIWIjNjISKyPMVlldh4IhO/HErHwdS/zk7zclHgwQ5NMLpLMFr4uUmYkIikxkJkZixERJbt4tUCrDhyGSuPXMbV/FLD9DaBKoxo3wTD2gfCT8VdakS2hoXIzFiIiKxDRaUeO5Ov4ufD6diRlI2KG+fuy2TAvc28MLx9Ewxu6w+VI++fRmQLWIjMjIWIyPpcLyzDxhOZWHvsCg5fum6YrrCXo3+kL4ZEBaBfpC9clfYSpiSihsRCZGYsRETWLT23CL8mXsGaY1dw4WqhYbrCXo5eET64P8of/Vv5Qe3ELUdEjQkLkZmxEBE1DkIInMrQYdOJTPx2UoOUa3+VIwc7GXo098aQtgG4r7UfPFwUEiYlInNgITIzFiKixkcIgeSsfGw6ocFvJzJxLrvAMM9OLkO3cE/0i/RF/1Z+CPfmlbGJrBELkZmxEBE1fuez8/HbCQ02ndTgTKbOaF64t0tVOYr0RecwT17niMhKsBCZGQsRkW1JvVaIbUnZ2JGUjQMpOSiv/OvXpKvSHr1aeKNfpB96t/CBj5tSwqREdCssRGbGQkRku/JLyrHn3DVsT8rGjuRsXCsoM5of6e+GHs290T3CG93CPeGs4FlrRJaChcjMWIiICAD0eoETV7TYlpSN7UlZOHnFeNeag50MHUM80DPCG92beyOqiRr2dty9RiQVFiIzYyEiotrkFJRi34Uc7D1/DX+cu4YrecVG890c7dEt3Avdwj3RJdwTbQJVcGBBIrprWIjMjIWIiG5HCIFLOUXYc/4a9py7hn0XrhluPlvNWWGHjiEe6BLmia7hnugQ4g5HBzuJEhM1fixE/2DRokWYN28eNBoNoqOj8dlnn6Fr1663fR0LERGZqvLG7rWDKTk4mJKLQ6nXoS0uNxrjYCdDuyB3dA71QHSwO9oHuyNA7QiZTCZRaqLGhYWoFj///DPGjx+PJUuWoFu3bvjkk0+wYsUKJCcnw9fX95avZSEiojul1wuczc7HoZRcHEy9joMpOcjSldYY5+umNJSj9sHuaBekhhvvvUZULyxEtejWrRu6dOmChQsXAgD0ej2Cg4Px4osv4l//+pfR2NLSUpSW/vWLSqfTITg4mIWIiMxGCIH03GIcTM3F0bTr+DM9D0mafFTqjX8ly2RAcx9XRAWp0TpAhdaBKrQOUMHdmVfSJrodFqK/KSsrg7OzM1auXIkRI0YYpk+YMAF5eXn49ddfjca/9dZbePvtt2ssh4WIiBpScVklTmZo8Wd6Ho6l5+HP9Dxcvl5c69gm7k5odVNBahOoQpCHE3e3Ed3ElEJkExfMuHbtGiorK+Hn52c03c/PD0lJSTXGz5o1C9OmTTM8r95CRETUkJwUdugS5okuYZ6GaVfzS/Fneh5OZehwOlOL05k6pOcW40pe1WPrmSzDWBeFHZr7uqK5rxsi/FwR4euKCF83BHk4QS5nUSK6FZsoRKZSKpVQKnn1WSKSno+bEgNa+2FA67/+Q6ctLkdSpg6nM3U4nVH159msfBSWVeLPy1r8eVlrtAxHBzmaersiws8VzXxcEerljDAvF4R5uUDtzOOTiAAbKUTe3t6ws7NDVlaW0fSsrCz4+/tLlIqIqH7UTg7o1tQL3Zp6GaaVVehxKacQ57ILcD67AOeyC3AuKx8XrxWipFxfVZ7+do82AHB3dkColwvCvJwNf4Z4OqOJhxN83Rxhxy1LZCNsohApFAp06tQJ27ZtMxxDpNfrsW3bNkyZMkXacEREZqCwlyPCzw0Rfm5G0yv1Aum5RVUFKTsfqdcKkXqtCKk5hcjOL0VeUTnyiqqOV/o7e7kM/mpHNHF3qnp4OCHwxtfVfzopeB0lahxsohABwLRp0zBhwgR07twZXbt2xSeffILCwkI88cQTUkcjImowdnIZwrxdEObtgvtaGx9HWVRWgUs5RbiUU4jUG3+mXCvE5evF0GhLUKEXuHy9+B8P7AYATxcFfN2U8FU5Vv3ppoRf9dcqJXzdHOHjpuQFKMni2UwhGjNmDK5evYrZs2dDo9Ggffv22Lx5c40DrYmIbIWzwh6tAlRoFVDz7JtKvUB2fgmuXP/rAO6MvGJcuV6MjLwSXMkrRkFpBXILy5BbWIYkTf4tv5fayQG+bkp4uSrg6XLj4Vz1p4fLTdNcFPBwVrBA0V1nE6fd3ylemJGIyJgQArqSCmTkFSM7vxTZuhLjP/NLkXXj67IKvcnLd1HYGYqSu7MCKkd7qJwcoHJ0gMrJHipHB7jdNE19Y5rKyQFKezkvP0AAeNo9ERE1MJlMBrWTA9RODmgV8M/jhBDQFVcgK78E2bpS5BaVIbegFLlF5bh+Y+tSbmEZrheVIaewDNcLy1ChFygsq0Rh2a131/0ThZ0cbo72cFHaw1lhB2eFneFrF4U9nJV2cFYYP3dR2MPJaL4dHO3toHSQQ2lvB6W9HEp7Oex5c95Gi4WIiIgajEwmg9rZAWpnB7T42wHftane8nS9sOxGeaoqS/klFdCVlENXXIH8knLD17qbvs4vKYdeAGWVeuQUVhUsc7OTy+BoL4fS4a+SpDQUp6qvHR3kcLCrKk8OctmNr2/8KZdVTbeTwV5ePb3qawe7qnn2N73GXi6HXAbIZTLYyWWQ1fJ11fOqn3XV87+my+U3fX1jutHPu5af/63mV42pMaXGfL0ABAT0ekAvxI1pAgLVzwX0ouqWNtXTHOzkRtfguttYiIiIyGLcvOUpDC4mvVavFygsqzCUp8LSShSVVfz1Z1klims8r0RhaQWKyipRWFZR9bysAkWllSit0KO0ohLllX994Fcatl5VmnvVbZ6fSokD/x4g2fdnISIiokZBLpfBzdEBbo4OCIST2ZZbqRcou1GOSsqr/iyt0KP0pq9Lyv8qUFXT9Siv1KNCL1BRqUd5pUCFXo+KSvHX1zfmVVQKlNc6rmqM/sbWlKqtKgKV+r++rt7KYvhaiBvPb3xdy/Raj66qZWJt42o7Nqu2w7Wqt0jJbtoyJbvx+pu3WsluvF4uk8HLVdr787EQERER3YKdXAYnhR2vudTI8egwIiIisnksRERERGTzWIiIiIjI5rEQERERkc1jISIiIiKbx0JERERENo+FiIiIiGweCxERERHZPBYiIiIisnksRERERGTzWIiIiIjI5rEQERERkc1jISIiIiKbx0JERERENs9e6gDWQAgBANDpdBInISIiorqq/tyu/hy/FRaiOsjPzwcABAcHS5yEiIiITJWfnw+1Wn3LMTJRl9pk4/R6PTIyMuDm5gaZTGbWZet0OgQHByM9PR0qlcqsy7YEjX39gMa/jlw/69fY15HrZ/0aah2FEMjPz0dgYCDk8lsfJcQtRHUgl8sRFBTUoN9DpVI12r/oQONfP6DxryPXz/o19nXk+lm/hljH220ZqsaDqomIiMjmsRARERGRzWMhkphSqcSbb74JpVIpdZQG0djXD2j868j1s36NfR25ftbPEtaRB1UTERGRzeMWIiIiIrJ5LERERERk81iIiIiIyOaxEBEREZHNYyGS0KJFixAWFgZHR0d069YNBw8elDpSvc2dOxddunSBm5sbfH19MWLECCQnJxuN6dOnD2QymdHjueeekyixad56660a2SMjIw3zS0pKMHnyZHh5ecHV1RWjRo1CVlaWhIlNExYWVmP9ZDIZJk+eDMA637vdu3dj6NChCAwMhEwmw9q1a43mCyEwe/ZsBAQEwMnJCQMGDMC5c+eMxuTm5mLcuHFQqVRwd3fHpEmTUFBQcBfX4p/dav3Ky8sxc+ZMREVFwcXFBYGBgRg/fjwyMjKMllHb+/7ee+/d5TX5Z7d7DydOnFgj/+DBg43GWOt7CKDWf5MymQzz5s0zjLHk97Aunwt1+d2ZlpaG2NhYODs7w9fXFzNmzEBFRYXZ87IQSeTnn3/GtGnT8Oabb+Lo0aOIjo7GoEGDkJ2dLXW0etm1axcmT56M/fv3Iz4+HuXl5Rg4cCAKCwuNxj399NPIzMw0PD744AOJEpuuTZs2Rtn37NljmPfKK69g/fr1WLFiBXbt2oWMjAyMHDlSwrSmOXTokNG6xcfHAwAefvhhwxhre+8KCwsRHR2NRYsW1Tr/gw8+wIIFC7BkyRIcOHAALi4uGDRoEEpKSgxjxo0bh1OnTiE+Ph4bNmzA7t278cwzz9ytVbilW61fUVERjh49ijfeeANHjx7F6tWrkZycjGHDhtUYO2fOHKP39cUXX7wb8evkdu8hAAwePNgo/08//WQ031rfQwBG65WZmYmvv/4aMpkMo0aNMhpnqe9hXT4Xbve7s7KyErGxsSgrK8O+ffvw7bffIi4uDrNnzzZ/YEGS6Nq1q5g8ebLheWVlpQgMDBRz586VMJX5ZGdnCwBi165dhmm9e/cWL7/8snSh7sCbb74poqOja52Xl5cnHBwcxIoVKwzTzpw5IwCIhISEu5TQvF5++WXRrFkzodfrhRDW/d4JIQQAsWbNGsNzvV4v/P39xbx58wzT8vLyhFKpFD/99JMQQojTp08LAOLQoUOGMb/99puQyWTiypUrdy17Xfx9/Wpz8OBBAUBcunTJMC00NFR8/PHHDRvOTGpbxwkTJojhw4f/42sa23s4fPhw0a9fP6Np1vQe/v1zoS6/Ozdt2iTkcrnQaDSGMYsXLxYqlUqUlpaaNR+3EEmgrKwMR44cwYABAwzT5HI5BgwYgISEBAmTmY9WqwUAeHp6Gk3/4Ycf4O3tjbZt22LWrFkoKiqSIl69nDt3DoGBgWjatCnGjRuHtLQ0AMCRI0dQXl5u9H5GRkYiJCTEKt/PsrIyfP/993jyySeNbmZsze/d36WkpECj0Ri9Z2q1Gt26dTO8ZwkJCXB3d0fnzp0NYwYMGAC5XI4DBw7c9cx3SqvVQiaTwd3d3Wj6e++9By8vL3To0AHz5s1rkF0RDWnnzp3w9fVFy5Yt8fzzzyMnJ8cwrzG9h1lZWdi4cSMmTZpUY561vId//1yoy+/OhIQEREVFwc/PzzBm0KBB0Ol0OHXqlFnz8eauErh27RoqKyuN3mAA8PPzQ1JSkkSpzEev12Pq1Kno3r072rZta5j+6KOPIjQ0FIGBgTh+/DhmzpyJ5ORkrF69WsK0ddOtWzfExcWhZcuWyMzMxNtvv42ePXvi5MmT0Gg0UCgUNT5o/Pz8oNFopAl8B9auXYu8vDxMnDjRMM2a37vaVL8vtf0brJ6n0Wjg6+trNN/e3h6enp5W976WlJRg5syZGDt2rNGNM1966SV07NgRnp6e2LdvH2bNmoXMzEzMnz9fwrR1N3jwYIwcORLh4eG4cOEC/v3vf2PIkCFISEiAnZ1do3oPv/32W7i5udXYFW8t72Ftnwt1+d2p0Whq/XdaPc+cWIjI7CZPnoyTJ08aHWMDwGi/fVRUFAICAtC/f39cuHABzZo1u9sxTTJkyBDD1+3atUO3bt0QGhqKX375BU5OThImM7+vvvoKQ4YMQWBgoGGaNb93tq68vByjR4+GEAKLFy82mjdt2jTD1+3atYNCocCzzz6LuXPnWsVtIh555BHD11FRUWjXrh2aNWuGnTt3on///hImM7+vv/4a48aNg6Ojo9F0a3kP/+lzwZJwl5kEvL29YWdnV+NI+qysLPj7+0uUyjymTJmCDRs2YMeOHQgKCrrl2G7dugEAzp8/fzeimZW7uztatGiB8+fPw9/fH2VlZcjLyzMaY43v56VLl7B161Y89dRTtxxnze8dAMP7cqt/g/7+/jVOcqioqEBubq7VvK/VZejSpUuIj4832jpUm27duqGiogKpqal3J6CZNW3aFN7e3oa/l43hPQSAP/74A8nJybf9dwlY5nv4T58Ldfnd6e/vX+u/0+p55sRCJAGFQoFOnTph27Zthml6vR7btm1DTEyMhMnqTwiBKVOmYM2aNdi+fTvCw8Nv+5rExEQAQEBAQAOnM7+CggJcuHABAQEB6NSpExwcHIzez+TkZKSlpVnd+/nNN9/A19cXsbGxtxxnze8dAISHh8Pf39/oPdPpdDhw4IDhPYuJiUFeXh6OHDliGLN9+3bo9XpDIbRk1WXo3Llz2Lp1K7y8vG77msTERMjl8hq7mazF5cuXkZOTY/h7ae3vYbWvvvoKnTp1QnR09G3HWtJ7eLvPhbr87oyJicGJEyeMim11uW/durXZA5MEli9fLpRKpYiLixOnT58WzzzzjHB3dzc6kt6aPP/880KtVoudO3eKzMxMw6OoqEgIIcT58+fFnDlzxOHDh0VKSor49ddfRdOmTUWvXr0kTl4306dPFzt37hQpKSli7969YsCAAcLb21tkZ2cLIYR47rnnREhIiNi+fbs4fPiwiImJETExMRKnNk1lZaUICQkRM2fONJpure9dfn6+OHbsmDh27JgAIObPny+OHTtmOMvqvffeE+7u7uLXX38Vx48fF8OHDxfh4eGiuLjYsIzBgweLDh06iAMHDog9e/aIiIgIMXbsWKlWycit1q+srEwMGzZMBAUFicTERKN/k9Vn5uzbt098/PHHIjExUVy4cEF8//33wsfHR4wfP17iNfvLrdYxPz9fvPrqqyIhIUGkpKSIrVu3io4dO4qIiAhRUlJiWIa1vofVtFqtcHZ2FosXL67xekt/D2/3uSDE7X93VlRUiLZt24qBAweKxMREsXnzZuHj4yNmzZpl9rwsRBL67LPPREhIiFAoFKJr165i//79UkeqNwC1Pr755hshhBBpaWmiV69ewtPTUyiVStG8eXMxY8YModVqpQ1eR2PGjBEBAQFCoVCIJk2aiDFjxojz588b5hcXF4sXXnhBeHh4CGdnZ/Hggw+KzMxMCRObbsuWLQKASE5ONppure/djh07av07OWHCBCFE1an3b7zxhvDz8xNKpVL079+/xrrn5OSIsWPHCldXV6FSqcQTTzwh8vPzJVibmm61fikpKf/4b3LHjh1CCCGOHDkiunXrJtRqtXB0dBStWrUS//d//2dUJqR2q3UsKioSAwcOFD4+PsLBwUGEhoaKp59+usZ/Kq31Paz2xRdfCCcnJ5GXl1fj9Zb+Ht7uc0GIuv3uTE1NFUOGDBFOTk7C29tbTJ8+XZSXl5s9r+xGaCIiIiKbxWOIiIiIyOaxEBEREZHNYyEiIiIim8dCRERERDaPhYiIiIhsHgsRERER2TwWIiIiIrJ5LERERERk81iIiMiihYWF4ZNPPmnw7yOTybB27doG/z6m2LlzJ2QyWY2bXxKR+fFK1USEiRMnIi8vz+IKAQBcvXoVLi4ucHZ2btDvo9Fo4OHhAaVSidTUVISHh+PYsWNo3759g37fan369EH79u2Nyl9ZWRlyc3Ph5+cHmUx2V3IQ2Sp7qQMQkW0qLy+Hg4PDbcf5+PjchTSAv79/gyy3rutZG4VC0WC5iMgYd5kR0W2dPHkSQ4YMgaurK/z8/PD444/j2rVrhvmbN29Gjx494O7uDi8vLzzwwAO4cOGCYX5qaipkMhl+/vln9O7dG46Ojvjhhx8wceJEjBgxAh9++CECAgLg5eWFyZMno7y83PDav+8yk8lkWLZsGR588EE4OzsjIiIC69atM8q7bt06REREwNHREX379sW33357211PN+8yCw8PBwB06NABMpkMffr0MYxbtmwZWrVqBUdHR0RGRuLzzz+/7Xrm5ORg7NixaNKkCZydnREVFYWffvrJ8LqJEydi165d+PTTTyGTySCTyZCamlrrLrNVq1ahTZs2UCqVCAsLw0cffWS0HmFhYfi///s/PPnkk3Bzc0NISAiWLl1qmF9WVoYpU6YgICAAjo6OCA0Nxdy5c//x50JkM8x+u1gisjoTJkwQw4cPr3Xe9evXhY+Pj5g1a5Y4c+aMOHr0qLjvvvtE3759DWNWrlwpVq1aJc6dOyeOHTsmhg4dKqKiokRlZaUQQhjuvh4WFiZWrVolLl68KDIyMsSECROESqUSzz33nDhz5oxYv369cHZ2FkuXLjUsOzQ0VHz88ceG5wBEUFCQ+PHHH8W5c+fESy+9JFxdXUVOTo4QQoiLFy8KBwcH8eqrr4qkpCTx008/iSZNmggA4vr16//4MwAg1qxZI4QQ4uDBgwKA2Lp1q8jMzDQs+/vvvxcBAQGGdVi1apXw9PQUcXFxt1zPy5cvi3nz5oljx46JCxcuiAULFgg7Oztx4MABIYQQeXl5IiYmRjz99NMiMzNTZGZmioqKCsPd0KtzHz58WMjlcjFnzhyRnJwsvvnmG+Hk5GR09/DQ0FDh6ekpFi1aJM6dOyfmzp0r5HK5SEpKEkIIMW/ePBEcHCx2794tUlNTxR9//CF+/PHHW/8FIbIBLEREdMtC9M4774iBAwcaTUtPTxcARHJycq2vuXr1qgAgTpw4IYT4qyh88sknNb5vaGioqKioMEx7+OGHxZgxYwzPaytE//nPfwzPCwoKBADx22+/CSGEmDlzpmjbtq3R93n99ddNKkTVeY8dO2Y0plmzZjXKwzvvvCNiYmJuuZ61iY2NFdOnTzc87927t3j55ZeNxvy9ED366KPivvvuMxozY8YM0bp1a8Pz0NBQ8dhjjxme6/V64evrKxYvXiyEEOLFF18U/fr1E3q9/rYZiWwJd5kR0S39+eef2LFjB1xdXQ2PyMhIADDsFjt37hzGjh2Lpk2bQqVSISwsDACQlpZmtKzOnTvXWH6bNm1gZ2dneB4QEIDs7OxbZmrXrp3haxcXF6hUKsNrkpOT0aVLF6PxXbt2rePa/rPCwkJcuHABkyZNMvpZvPvuu0a7B4Ga61lZWYl33nkHUVFR8PT0hKurK7Zs2VLj53M7Z86cQffu3Y2mde/eHefOnUNlZaVh2s0/H5lMBn9/f8PPZ+LEiUhMTETLli3x0ksv4ffffzcpA1FjxYOqieiWCgoKMHToULz//vs15gUEBAAAhg4ditDQUHz55ZcIDAyEXq9H27ZtUVZWZjTexcWlxjL+fsCxTCaDXq+/Zab6vOZOFRQUAAC+/PJLdOvWzWjezYUOqLme8+bNw6effopPPvkEUVFRcHFxwdSpU2v8fMzlVj+fjh07IiUlBb/99hu2bt2K0aNHY8CAAVi5cmWDZCGyFixERHRLHTt2xKpVqxAWFgZ7+5q/MnJycpCcnIwvv/wSPXv2BADs2bPnbsc0aNmyJTZt2mQ07dChQyYtQ6FQAIDRVhc/Pz8EBgbi4sWLGDdunEnL27t3L4YPH47HHnsMAKDX63H27Fm0bt3a6Hve/P1q06pVK+zdu7fGslu0aFGjlN2KSqXCmDFjMGbMGDz00EMYPHgwcnNz4enpacJaETUuLEREBADQarVITEw0mlZ91teXX36JsWPH4rXXXoOnpyfOnz+P5cuXY9myZfDw8ICXlxeWLl2KgIAApKWl4V//+pc0KwHg2Wefxfz58zFz5kxMmjQJiYmJiIuLA4A6X8vH19cXTk5O2Lx5M4KCguDo6Ai1Wo23334bL730EtRqNQYPHozS0lIcPnwY169fx7Rp0/5xeREREVi5ciX27dsHDw8PzJ8/H1lZWUaFKCwsDAcOHEBqaipcXV1rLSfTp09Hly5d8M4772DMmDFISEjAwoULjc50u5358+cjICAAHTp0gFwux4oVK+Dv7w93d/c6L4OoMeIxREQEoOqqyB06dDB6vP322wgMDMTevXtRWVmJgQMHIioqClOnToW7uzvkcjnkcjmWL1+OI0eOoG3btnjllVcwb948ydYjPDwcK1euxOrVq9GuXTssXrwYr7/+OgBAqVTWaRn29vZYsGABvvjiCwQGBmL48OEAgKeeegrLli3DN998g6ioKPTu3RtxcXGG0/T/yX/+8x907NgRgwYNQp8+feDv748RI0YYjXn11VdhZ2eH1q1bw8fHp9bjizp27IhffvkFy5cvR9u2bTF79mzMmTMHEydOrNN6AYCbmxs++OADdO7cGV26dEFqaio2bdoEuZwfB2TbeKVqImr0/vvf/2LJkiVIT0+XOgoRWSjuMiOiRufzzz9Hly5d4OXlhb1792LevHmYMmWK1LGIyIKxEBFRo3Pu3Dm8++67yM3NRUhICKZPn45Zs2ZJHYuILBh3mREREZHN41F0REREZPNYiIiIiMjmsRARERGRzWMhIiIiIpvHQkREREQ2j4WIiIiIbB4LEREREdk8FiIiIiKyef8P+oZe4uH0rbwAAAAASUVORK5CYII=", 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", 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", 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", 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" ] @@ -621,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -630,13 +630,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 44, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -686,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -695,13 +695,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 45, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -750,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -777,22 +777,22 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 47, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -839,14 +839,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-15 09:19:52]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-24 09:01:53]: Using numpy backend on /CPU:0\n" ] } ], @@ -870,225 +870,225 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-15 09:19:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" ] } ], @@ -1119,22 +1119,22 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1151,11 +1151,261 @@ "plt.ylabel(r'$||\\sigma(H_k)||$')\n", "plt.legend()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCost.least_squares\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 7.8269358 +0.j 2.60894672+0.j 3.6525541 +0.j -1.56543498+0.j\n", + " 4.69616148+0.j -0.5218276 +0.j 0.52177978+0.j -4.6962093 +0.j\n", + " 5.73976886+0.j 0.52177978+0.j 1.56538716+0.j -3.65260192+0.j\n", + " 2.60899454+0.j -2.60899454+0.j -1.56538716+0.j -6.78337624+0.j\n", + " 6.78337624+0.j 1.56538716+0.j 2.60899454+0.j -2.60899454+0.j\n", + " 3.65260192+0.j -1.56538716+0.j -0.52177978+0.j -5.73976886+0.j\n", + " 4.6962093 +0.j -0.52177978+0.j 0.5218276 +0.j -4.69616148+0.j\n", + " 1.56543498+0.j -3.6525541 +0.j -2.60894672+0.j -7.8269358 +0.j]\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,nqubits,nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 20, 1e-3, analytic=False, d_type = d_ansatz_type.local_1)\n", + "best = np.argmin(loss_opt)\n", + "\n", + "\n", + "plt.figure()\n", + "plt.plot(loss_opt)\n", + "\n", + "s = np.linspace(-0.1,0.1,100)\n", + "least_squares = np.empty(100)\n", + "off_diagonal_norm = np.empty(100)\n", + "for i in range(100):\n", + " dbi_eval(s[i],d=d_opt)\n", + " least_squares[i] = dbi_eval.least_squares(d=d_opt)\n", + " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", + "plt.figure()\n", + "plt.plot(s,loss)\n", + "plt.xlabel('s')\n", + "plt.ylabel('Least squares cost function')\n", + "plt.figure()\n", + "plt.plot(s,off_diagonal_norm)\n", + "plt.xlabel('s')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n", + "\n", + "print(np.diag(d_opt))" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "dbi", "language": "python", "name": "python3" }, @@ -1169,12 +1419,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.19" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "c4f92193806e2908606a5f23edd55a5282f2f433b73b1c504507f9256ed9f0b4" + "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" } } }, diff --git a/examples/dbi/dbi_group_commutator_tests.ipynb b/examples/dbi/dbi_group_commutator_tests.ipynb new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/dbi/dbi_misc.ipynb b/examples/dbi/dbi_misc.ipynb index 05f8f81117..cabe5f12b0 100644 --- a/examples/dbi/dbi_misc.ipynb +++ b/examples/dbi/dbi_misc.ipynb @@ -1024,14 +1024,14 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-02 09:51:24]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-03 10:06:29]: Using numpy backend on /CPU:0\n" ] } ], @@ -1070,17 +1070,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "17920.0\n" - ] - } - ], + "outputs": [], "source": [ "nqubits = [7]\n", "iterations = 100\n", @@ -1099,19 +1091,18 @@ " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", " loss_max[q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", " params = np.linspace(2**nqubits[q],1,2**nqubits[q])\n", - " d_full, loss_full[q,:], grad_full, diags_full = gradient_descent(dbi, params, iterations, step,analytic=False, ansatz = 'Full')\n", + " d_full, loss_full[q,:], grad_full, diags_full = gradient_descent(dbi, params, iterations, step, analytic=False, ansatz = 'Full')\n", " params = np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])/2**nqubits[q])\n", " params = Z_decomposition(params)\n", " params = np.linspace(nqubits[q],1,nqubits[q])\n", " d = d_ansatz(params, type='Pauli')\n", - " print(np.linalg.norm(d)**2)\n", " d_pauli, loss_pauli[q,:], grad_pauli, diags_pauli = gradient_descent(dbi, params, iterations, step, analytic=False, ansatz = 'Pauli')\n", " differences[q,:] = loss_full[q,:] - loss_pauli[q,:]\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1120,7 +1111,7 @@ "Text(0, 0.5, 'Parameters')" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -1333,14 +1324,14 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-02 09:27:21]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-04-03 10:15:40]: Using numpy backend on /CPU:0\n" ] } ], @@ -1524,22 +1515,22 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 96, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1549,13 +1540,14 @@ } ], "source": [ - "par1 = np.linspace(-10,10,100)\n", - "par2 = np.linspace(-10,10,100)\n", + "size = 100\n", + "par1 = np.linspace(-7,7,size)\n", + "par2 = np.linspace(-7,7,size)\n", "\n", - "loss = np.empty((100,100))\n", + "loss = np.empty((size,size))\n", "\n", - "for i in range(100):\n", - " for j in range(100):\n", + "for i in range(size):\n", + " for j in range(size):\n", " dbi_eval = deepcopy(dbi)\n", " d = d_ansatz([par1[i],par2[j]],type='Pauli')\n", " poly_step = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", @@ -1563,7 +1555,7 @@ " loss[i,j] = dbi_eval.least_squares(d)\n", "\n", "plt.figure()\n", - "plt.contourf(par1,par2,loss)\n", + "plt.contourf(par1,par2,loss,levels=100)\n", "plt.xlabel('Parameter 1')\n", "plt.ylabel('Parameter 2')\n", "plt.colorbar()" diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index a3bcedbcad..6561b4b2de 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -154,7 +154,7 @@ def backend(self): def least_squares(self, d: np.array): """Least squares cost function. (without the constant term norm(H))""" - h_np = np.diag(np.diag(self.backend.to_numpy(self.h.matrix))) + h_np = self.backend.to_numpy(self.h.matrix) return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h_np @ d)) From 24bb12d47c3064b4ac9101ac9dcb7ecfdb82962a Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 2 May 2024 12:21:22 +0800 Subject: [PATCH 066/154] Resolve cost function notebook issues based on commit 6a41ccf --- ...t_functions_and_d_gradients_tutorial.ipynb | 706 ++++++++++++++++++ examples/dbi/dbi_costs.ipynb | 367 --------- src/qibo/models/dbi/double_bracket.py | 46 +- src/qibo/models/dbi/utils.py | 28 +- src/qibo/models/dbi/utils_analytical.py | 5 +- src/qibo/models/dbi/utils_gradients.py | 198 +++++ src/qibo/models/dbi/utils_scheduling.py | 16 +- 7 files changed, 961 insertions(+), 405 deletions(-) create mode 100644 examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb delete mode 100644 examples/dbi/dbi_costs.ipynb create mode 100644 src/qibo/models/dbi/utils_gradients.py diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb new file mode 100644 index 0000000000..6607eaf6dc --- /dev/null +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -0,0 +1,706 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Double-bracket Iteration other cost functions and respective scheduling\n", + "\n", + "This notebook presents two additional cost functions for the double-bracket flow: least-squares and energy fluctuation with their respectice scheduling methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_scheduling import *" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Least-squares\n", + "\n", + "The cost function is defined as: $\\frac{1}{2}||D-H_k||^2 =\\frac{1}{2}(||D||^2+||H||^2) -Tr(D H_k)$ as in (the negative of https://epubs.siam.org/doi/abs/10.1137/S0036141092229732?journalCode=sjmael) We seek to minimize this function at each DBF iteration. For numerical optimizations, we also ignore the norm of H term as for a given hamiltonian it is fixed through out the flow.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCostFunction.least_squares\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate data for plotting sigma decrease of the first step\n", + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", + "s_space = np.linspace(1e-5, 1.0, 500)\n", + "off_diagonal_norm_diff = []\n", + "potential = []\n", + "for s in s_space:\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s,d=d)\n", + " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", + " potential.append(dbi_eval.least_squares(d=d))\n", + "\n", + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", + "print('hyperopt_search step:', step_hyperopt)\n", + "# polynomial\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + "print('polynomial_approximation step:', step_poly)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(s_space, potential)\n", + "plt.xlabel('s')\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.title('First DBI step')\n", + "plt.ylabel('Least squares cost function')\n", + "plt.legend()\n", + "plt.figure()\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('First DBI step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparison of the least-squares cost function with the original cost function using the polynomial scheduling method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", + "off_diagonal_norm_diff_least_squares = [dbi.off_diagonal_norm]\n", + "iters = 100\n", + "dbi_ls = deepcopy(dbi)\n", + "cost = DoubleBracketCostFunction.off_diagonal_norm\n", + "dbi_od = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "for _ in range(iters):\n", + " step_poly = dbi_od.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_od(step_poly,d=d)\n", + " step_poly = dbi_ls.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_ls(step_poly,d=d)\n", + " off_diagonal_norm_diff.append(dbi_od.off_diagonal_norm)\n", + " off_diagonal_norm_diff_least_squares.append(dbi_ls.off_diagonal_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff_least_squares, label=r'Least squares')\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Energy fluctuation\n", + "\n", + "This cost function is defined as: $\\Xi_k^2 (\\mu) = \\langle \\mu | H_k^2| \\mu \\rangle - \\langle \\mu | H_k| \\mu \\rangle^2$. We must specify the state $| \\mu \\rangle$ for which we want to minimize the fluctuation. The overall diagonalization isn't guaranteed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 3\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the energy fluctuation cost function\n", + "cost = DoubleBracketCostFunction.energy_fluctuation\n", + "# define the state\n", + "state = np.zeros(2**nqubits)\n", + "state[3] = 1\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate data for plotting sigma decrease of the first step\n", + "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))/2**nqubits\n", + "s_space = np.linspace(-1, 1, 1000)\n", + "off_diagonal_norm_diff = []\n", + "fluctuation = []\n", + "for s in s_space:\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s,d=d)\n", + " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", + " fluctuation.append(dbi_eval.energy_fluctuation(state=state))\n", + "\n", + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", + "print('hyperopt_search step:', step_hyperopt)\n", + "# polynomial\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + "print('polynomial_approximation step:', step_poly)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(s_space, fluctuation)\n", + "plt.xlabel('s')\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label ='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.title('First DBI step')\n", + "plt.ylabel('Energy fluctuation')\n", + "plt.legend()\n", + "plt.figure()\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('First DBI step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", + "energy_fluc = [dbi.energy_fluctuation(state=state)]\n", + "iters = 10\n", + "dbi_ = deepcopy(dbi)\n", + "for _ in range(iters):\n", + " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_(step_poly,d=d)\n", + " off_diagonal_norm_diff.append(dbi_.off_diagonal_norm)\n", + " energy_fluc.append(dbi_.energy_fluctuation(state=state))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n", + "plt.figure()\n", + "plt.plot(range(iters+1), energy_fluc)\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'Energy fluctuation')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iters = 30\n", + "states = [0,1,2,3,4,5,6,7]\n", + "energy = np.empty((len(states),iters))\n", + "\n", + "\n", + "d = (np.diag(np.linspace(1,2**nqubits,2**nqubits)))\n", + "for i in range(len(states)):\n", + " dbi_ = deepcopy(dbi)\n", + " dbi_.state = states[i]\n", + " for j in range(iters):\n", + " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " if step_poly is not None:\n", + " dbi_(step_poly, d=d)\n", + " energy[i,j] = np.real(dbi_.h.matrix[states[i],states[i]])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", + "print('Eigenvalues:', eigvals )\n", + "plt.figure()\n", + "for i in range(len(states)):\n", + " plt.plot(range(iters), energy[i,:],'.', label='State ' + str(states[i]))\n", + "for eigvals in eigvals:\n", + " plt.axhline(y=eigvals, color='r', linestyle='--')\n", + "plt.xlabel('Iterations')\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradients for finding optimal $D$\n", + "\n", + "An advantage of the least-squares cost function is that one can use gradient descent and the learning is more stable than with the off-diagonal cost function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from qibo.models.dbi.utils_gradients import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 3\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the energy fluctuation cost function\n", + "cost = DoubleBracketCostFunction.energy_fluctuation\n", + "# define the state\n", + "state = np.zeros(2**nqubits)\n", + "state[3] = 1\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost = DoubleBracketCostFunction.least_squares\n", + "nqubits = 5\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "\n", + "step = 1e-2\n", + "iterations = 200\n", + "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", + "\n", + "plt.figure()\n", + "plt.plot(range(iterations+1), loss)\n", + "plt.xlabel('Learning iterations')\n", + "plt.ylabel('Loss: Least squares')\n", + "\n", + "plt.figure()\n", + "for i in range(2**nqubits):\n", + " plt.plot(diags[i,:], label='State ' + str(i))\n", + "plt.xlabel('Learning iterations')\n", + "plt.ylabel('Diagonal elements')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training for $D$ can greatly improve the decrease of the off-diagonal norm at each iteration. Nonetheless, during training the ascending values condition may be no longer satisfied creating a exponential decrease after few iterations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost = DoubleBracketCost.least_squares\n", + "nqubits = 5\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "d_fixed = np.diag(params)\n", + "dbi_trained = deepcopy(dbi)\n", + "flows = 50\n", + "iterations = 200\n", + "off_diagonal_norm = np.empty((2,flows+1))\n", + "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", + "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", + "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", + "for i in range(flows):\n", + "\n", + " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", + " dbi_trained(s,d=d_trained)\n", + " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", + " s = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_fixed, n=3)\n", + " dbi(s,d=d_fixed)\n", + " off_diagonal_norm[1,i+1] = dbi.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(off_diagonal_norm[0,:], label='Trained')\n", + "plt.plot(off_diagonal_norm[1,:], label='Untrained')\n", + "plt.legend()\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A solution can be to redo the training at each step, with a $D$ having ascending values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost = DoubleBracketCost.least_squares\n", + "nqubits = 5\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "d_fixed = np.diag(params)\n", + "dbi_trained = deepcopy(dbi)\n", + "flows = 50\n", + "iterations = 20\n", + "off_diagonal_norm = np.empty((2,flows+1))\n", + "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", + "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", + "\n", + "for i in range(flows):\n", + " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", + " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", + " dbi_trained(s,d=d_trained)\n", + " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", + " s = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_fixed, n=3)\n", + " dbi(s,d=d_fixed)\n", + " off_diagonal_norm[1,i+1] = dbi.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(off_diagonal_norm[0,:], label='Trained')\n", + "plt.plot(off_diagonal_norm[1,:], label='Untrained')\n", + "plt.legend()\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The numerical gradients may be preferred as they decrease more the loss at each iteration and are computationally faster. They may be more precise as the previous analytic since the analytic computations use the polynomial approximation as a starting point" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nqubits = [3,4,5,6]\n", + "iterations = 30\n", + "step = 1e-2\n", + "differences = np.empty((len(nqubits),iterations+1))\n", + "loss_max = np.empty(len(nqubits))\n", + "for q in range(len(nqubits)):\n", + " # define the hamiltonian\n", + " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", + "\n", + " # define the least-squares cost function\n", + " cost = DoubleBracketCost.least_squares\n", + " # initialize class\n", + " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + " loss_max [q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", + " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", + " d_analytic, loss_analytic, grad_analytic, diags_analytic = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", + " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", + " d_numerical, loss_numerical, grad_numerical, diags_numerical = gradient_descent_dbr_d_ansatz(dbi, params,iterations,step, analytic=False)\n", + " differences[q,:] = loss_analytic - loss_numerical\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "for q in range(len(nqubits)):\n", + " plt.plot(differences[q,:],label= 'nqubits = {}'.format(nqubits[q]))\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel('Difference in analytic and numerical loss function')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.title('Normalized difference')\n", + "for q in range(len(nqubits)):\n", + " plt.plot(differences[q,:]/loss_max[q],label= 'nqubits = {}'.format(nqubits[q]))\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel('Difference in analytic and numerical loss function')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1-local ansatz\n", + "\n", + "We can consider, as an alternative to the a fully parametrized diagonal, a diagonal matrix of the form: $D = \\sum \\alpha_i Z_i$. This has the advantage of having a linear number of parameters to optimize instead of an exponential as well as being easier to implement in a quantum computer " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCost.least_squares\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,1e-2, analytic=False, d_type = d_ansatz_type.element_wise)\n", + "flows = 50\n", + "off_diagonal_norm = np.empty((flows+1,2))\n", + "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", + "\n", + "\n", + "\n", + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,nqubits,nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, 1e-3, analytic=False, d_type = d_ansatz_type.local_1)\n", + "best = np.argmin(loss_opt)\n", + "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", + "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCost.least_squares\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,nqubits,nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 20, 1e-3, analytic=False, d_type = d_ansatz_type.local_1)\n", + "best = np.argmin(loss_opt)\n", + "\n", + "\n", + "plt.figure()\n", + "plt.plot(loss_opt)\n", + "\n", + "s = np.linspace(-0.1,0.1,100)\n", + "least_squares = np.empty(100)\n", + "off_diagonal_norm = np.empty(100)\n", + "for i in range(100):\n", + " dbi_eval(s[i],d=d_opt)\n", + " least_squares[i] = dbi_eval.least_squares(d=d_opt)\n", + " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", + "plt.figure()\n", + "plt.plot(s,loss)\n", + "plt.xlabel('s')\n", + "plt.ylabel('Least squares cost function')\n", + "plt.figure()\n", + "plt.plot(s,off_diagonal_norm)\n", + "plt.xlabel('s')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n", + "\n", + "print(np.diag(d_opt))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dbi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dbi/dbi_costs.ipynb b/examples/dbi/dbi_costs.ipynb deleted file mode 100644 index e772b94bce..0000000000 --- a/examples/dbi/dbi_costs.ipynb +++ /dev/null @@ -1,367 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Double-bracket Iteration other cost functions and respective scheduling\n", - "\n", - "This notebook presents two additional cost functions for the double-bracket flow: least-squares and energy fluctuation with their respectice scheduling methods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Least-squares\n", - "\n", - "The cost function is defined as: $\\frac{1}{2}||D-H_k||^2 =\\frac{1}{2}(||D||^2+||H||^2) -Tr(D H_k)$ as in https://epubs.siam.org/doi/abs/10.1137/S0036141092229732?journalCode=sjmael. We seek to maximize this function at each iteration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Hamiltonian\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 5\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate data for plotting sigma decrease of the first step\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "s_space = np.linspace(1e-5, 0.6, 1000)\n", - "off_diagonal_norm_diff = []\n", - "potential = []\n", - "for s in s_space:\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval(s,d=d)\n", - " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", - " potential.append(dbi_eval.least_squares(D=d))\n", - "\n", - "# grid_search\n", - "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", - "print('grid_search step:', step_grid)\n", - "# hyperopt\n", - "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", - "print('hyperopt_search step:', step_hyperopt)\n", - "# polynomial\n", - "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", - "print('polynomial_approximation step:', step_poly)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "plt.figure()\n", - "plt.plot(s_space, potential)\n", - "plt.xlabel('s')\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.title('First DBI step')\n", - "plt.ylabel('Least squares cost function')\n", - "plt.legend()\n", - "plt.figure()\n", - "plt.plot(s_space, off_diagonal_norm_diff)\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", - "plt.xlabel('s')\n", - "plt.title('First DBI step')\n", - "plt.legend()\n", - "print('The minimum for cost function in the tested range is:', step_grid)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Comparison of the least-squares cost function with the original cost function using the polynomial scheduling method" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", - "off_diagonal_norm_diff_least_squares = [dbi.off_diagonal_norm]\n", - "iters = 100\n", - "dbi_ls = deepcopy(dbi)\n", - "cost = DoubleBracketCostFunction.off_diagonal_norm\n", - "dbi_od = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "for _ in range(iters):\n", - " step_poly = dbi_od.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " dbi_od(step_poly,d=d)\n", - " step_poly = dbi_ls.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " dbi_ls(step_poly,d=d)\n", - " off_diagonal_norm_diff.append(dbi_od.off_diagonal_norm)\n", - " off_diagonal_norm_diff_least_squares.append(dbi_ls.off_diagonal_norm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", - "plt.plot(range(iters+1), off_diagonal_norm_diff_least_squares, label=r'Least squares')\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Energy fluctuation\n", - "\n", - "This cost function is defined as: $\\Xi_k^2 (\\mu) = \\langle \\mu | H_k^2| \\mu \\rangle - \\langle \\mu | H_k| \\mu \\rangle^2$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Hamiltonian\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 3\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the energy fluctuation cost function\n", - "cost = DoubleBracketCostFunction.energy_fluctuation\n", - "# define the state\n", - "state = 0\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, state=state)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate data for plotting sigma decrease of the first step\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "s_space = np.linspace(1e-5, 0.9, 1000)\n", - "off_diagonal_norm_diff = []\n", - "fluctuation = []\n", - "for s in s_space:\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval(s,d=d)\n", - " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", - " fluctuation.append(dbi_eval.energy_fluctuation(state=state))\n", - "\n", - "# grid_search\n", - "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", - "print('grid_search step:', step_grid)\n", - "# hyperopt\n", - "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", - "print('hyperopt_search step:', step_hyperopt)\n", - "# polynomial\n", - "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", - "print('polynomial_approximation step:', step_poly)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "plt.figure()\n", - "plt.plot(s_space, fluctuation)\n", - "plt.xlabel('s')\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label ='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.title('First DBI step')\n", - "plt.ylabel('Energy fluctuation')\n", - "plt.legend()\n", - "plt.figure()\n", - "plt.plot(s_space, off_diagonal_norm_diff)\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", - "plt.xlabel('s')\n", - "plt.title('First DBI step')\n", - "plt.legend()\n", - "print('The minimum for cost function in the tested range is:', step_grid)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", - "energy_fluc = [dbi.energy_fluctuation(state=state)]\n", - "iters = 50\n", - "dbi_ = deepcopy(dbi)\n", - "for _ in range(iters):\n", - " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " dbi_(step_poly,d=d)\n", - " off_diagonal_norm_diff.append(dbi_.off_diagonal_norm)\n", - " energy_fluc.append(dbi_.energy_fluctuation(state=state))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "\n", - "plt.figure()\n", - "plt.plot(range(iters+1), energy_fluc)\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'Energy fluctuation')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "iters = 10\n", - "columnNorm = np.empty((2**nqubits,iters))\n", - "d = (np.diag(np.linspace(1,2**nqubits,2**nqubits)))\n", - "for i in range(2**nqubits):\n", - " dbi_ = deepcopy(dbi)\n", - " dbi_.state = i\n", - " for j in range(iters):\n", - " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " dbi_(step_poly,d=d)\n", - " columnNorm[i,j] = np.linalg.norm(dbi_.h.matrix[:,i])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", - "plt.figure()\n", - "for i in range(2**nqubits):\n", - " plt.plot(range(iters), columnNorm[i], label='State ' + str(i))\n", - " plt.axhline(y=eigvals[i], color='r', linestyle='--')\n", - "plt.xlabel('Iterations')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cost = DoubleBracketCostFunction.least_squares\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "\n", - "step = 1e-2\n", - "iterations = 100\n", - "\n", - "d, loss, grad, diags = gradient_ascent(dbi, d,step, iterations)\n", - "\n", - "n = 3" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "c4f92193806e2908606a5f23edd55a5282f2f433b73b1c504507f9256ed9f0b4" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index c7f1d9eb38..27b94d2cfb 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -5,6 +5,11 @@ import numpy as np from qibo.hamiltonians import Hamiltonian +from qibo.models.dbi.utils_analytical import ( + energy_fluctuation_polynomial_expansion_coef, + least_squares_polynomial_expansion_coef, + off_diagonal_norm_polynomial_expansion_coef, +) from qibo.models.dbi.utils_scheduling import ( grid_search_step, hyperopt_step, @@ -79,14 +84,14 @@ def __init__( mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.canonical, scheduling: DoubleBracketScheduling = DoubleBracketScheduling.grid_search, cost: DoubleBracketCostFunction = DoubleBracketCostFunction.off_diagonal_norm, - state: int = 0, + ref_state: int = 0, ): self.h = hamiltonian self.h0 = deepcopy(self.h) self.mode = mode self.scheduling = scheduling self.cost = cost - self.state = state + self.ref_state = ref_state def __call__( self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None @@ -150,12 +155,11 @@ def backend(self): """Get Hamiltonian's backend.""" return self.h0.backend - def least_squares(self, D: np.array): + def least_squares(self, d: np.array): """Least squares cost function.""" - H = self.h.matrix - return -np.real( - np.trace(H @ D) - 0.5 * (np.linalg.norm(H) ** 2 + np.linalg.norm(D) ** 2) - ) + h_np = self.backend.to_numpy(self.h.matrix) + + return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h_np @ d)) def choose_step( self, @@ -200,14 +204,14 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): elif self.cost == DoubleBracketCostFunction.least_squares: loss = self.least_squares(d) elif self.cost == DoubleBracketCostFunction.energy_fluctuation: - loss = self.energy_fluctuation(self.state) + loss = self.energy_fluctuation(self.ref_state) # set back the initial configuration self.h = h_copy return loss - def energy_fluctuation(self, state=None): + def energy_fluctuation(self, state): """ Evaluate energy fluctuation @@ -219,11 +223,12 @@ def energy_fluctuation(self, state=None): Args: state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ - if state is None: - state = self.state - state_vector = np.zeros(len(self.h.matrix)) - state_vector[state] = 1.0 - return np.real(self.h.energy_fluctuation(state_vector)) + h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix)))) + h2 = h_np @ h_np + a = state.conj() @ h2 @ state + b = state.conj() @ h_np @ state + return (np.sqrt(np.real(a - b**2))).item() + r # return np.real(self.h.energy_fluctuation(state)) def sigma(self, h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) @@ -235,3 +240,16 @@ def generate_Gamma_list(self, n: int, d: np.array): for _ in range(n - 1): Gamma_list.append(self.commutator(W, Gamma_list[-1])) return Gamma_list + + def cost_expansion(self, d, n): + if self.cost is DoubleBracketCostFunction.off_diagonal_norm: + coef = off_diagonal_norm_polynomial_expansion_coef(self, d, n) + elif self.cost is DoubleBracketCostFunction.least_squares: + coef = least_squares_polynomial_expansion_coef(self, d, n) + elif self.cost is DoubleBracketCostFunction.energy_fluctuation: + coef = energy_fluctuation_polynomial_expansion_coef( + self, d, n, self.ref_state + ) + else: + raise ValueError(f"Cost function {self.cost} not recognized.") + return coef diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 9f3767debb..85dbd9518c 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -8,21 +8,29 @@ from qibo.hamiltonians import SymbolicHamiltonian -def commutator(A, B): +def commutator(a, b): """Compute commutator between two arrays.""" - return A @ B - B @ A + return a @ b - b @ a -def variance(A, state): - """Calculates the variance of a matrix A with respect to a state: Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" - B = A @ A - return B[state, state] - A[state, state] ** 2 +def variance(a, state): + """Calculates the variance of a matrix A with respect to a state: + Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" + b = a @ a + return state.conj().T @ b @ state - (state.conj().T @ a @ state) ** 2 -def covariance(A, B, state): - """Calculates the covariance of two matrices A and B with respect to a state: Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$""" - C = A @ B + B @ A - return C[state, state] - 2 * A[state, state] * B[state, state] +def covariance(a, b, state): + """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, + applied to two operators A and B with respect to a state: + Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ + """ + + c = a @ b + b @ a + return ( + state.conj().T @ c @ state + - 2 * state.conj().T @ a @ state * state.conj().T @ b @ state + ) def generate_Z_operators(nqubits: int, backend=None): diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py index b23fd0a33e..6d09a76ffd 100644 --- a/src/qibo/models/dbi/utils_analytical.py +++ b/src/qibo/models/dbi/utils_analytical.py @@ -1,6 +1,9 @@ +import math from typing import Optional -from qibo.models.dbi.utils import * +import numpy as np + +from qibo.models.dbi.utils import commutator, covariance, variance def dGamma_di_Pauli(dbi_object, n: int, Z_i: np.array, d: np.array): diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py new file mode 100644 index 0000000000..7790f3d2c5 --- /dev/null +++ b/src/qibo/models/dbi/utils_gradients.py @@ -0,0 +1,198 @@ +import math +from copy import deepcopy +from enum import Enum, auto + +import numpy as np + +from qibo import symbols +from qibo.hamiltonians import SymbolicHamiltonian +from qibo.models.dbi.utils import commutator +from qibo.models.dbi.utils_scheduling import polynomial_step + + +class d_ansatz_type(Enum): + + element_wise = auto() + local_1 = auto() + # local_2 = auto() # for future implementation + # ising = auto() # for future implementation + + +def d_ansatz(params: np.array, d_type: d_ansatz_type): + r""" + Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. + If $\alpha_i$ are our parameters and d the number of qubits then: + + element_wise: $D = \sum_{i=0}^{2^d} \alpha_i |i\rangle \langle i|$ + local_1: $D = \sum_{i=1}^{d} \alpha_i Z_i$ + Args: + params(np.array): parameters for the ansatz. + d_type(d_ansatz type): type of parameterization for the ansatz. + """ + + if d_type is d_ansatz_type.element_wise: + d = np.zeros((len(params), len(params))) + for i in range(len(params)): + d[i, i] = params[i] + + elif d_type is d_ansatz_type.local_1: + + op_list = [params[i] * symbols.Z(i) for i in range(len(params))] + symbolHam = op_list[0] + for i in range(len(params) - 1): + symbolHam += op_list[i + 1] + + d = SymbolicHamiltonian(symbolHam, nqubits=len(params)) + d = d.dense.matrix + else: + raise ValueError(f"Parameterization type {type} not recognized.") + + return d + + +def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): + r""" + Gradient of the nth gamma operator with respect to the ith diagonal elements of D. + $Gamma_{n} = [W,[W,...,[W,H]]...]]$, + $\frac{\partial Gamma_{n}}{\partial D_{ii}} = \partial_{D_{ii}} W\Gamma_{n-1}-\partial_{D_{ii}}\Gamma_{n-1} W$. + and thus is can be computed recursively. + Args: + d(np.array): D operator. + h(np.array): Hamiltonian. + n(int): nth Gamma operator. + i(int): Index of the diagonal element of D. + dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). + gamma_list(list): List of the n gamma operators. + Returns: + (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. + """ + dD_di = np.zeros(d.shape) + dD_di[i, i] = 1 + dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) + w = commutator(d, h) + return dW_di + commutator(w, dGamma[-1]) + + +# def dpolynomial_diDiagonal(dbi_object, d, h, i): #element_wise_ansatz +def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): + r""" + TODO: add formula and explain terms + Gradient wrt the ith diagonal elements of D. + We make Double_bracket rotation with duration given by the minimzer of the ´polynomial_step´ function. + Gradient of the Taylor expansion of the least squares loss function as a function of $s$ the duration of Double-Bracket rotation element-wise ansatz: + $\partial_{D_{ii}} \text{Tr}(H_k@D) \approx \sum_{k=0}^{n} \frac{1}{k!!} \partial_{D_ii}\text{Tr}(\Gamma_{k}D)$. + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + d(np.array): D operator. + h(np.array): Hamiltonian. + i(int): Index of the diagonal element of D. + Returns: + derivative(float): Derivative of the polynomial expansion with respect to the ith diagonal elements of D. + """ + derivative = 0 + s = polynomial_step(dbi_object, n=3, d=d) + dD_di = np.zeros(d.shape) + gamma_list = dbi_object.generate_Gamma_list(4, d) + dD_di[i, i] = 1 + dGamma = [commutator(dD_di, h)] + derivative += np.real( + np.trace(gamma_list[0] @ dD_di) + + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s + ) + for n in range(2, 4): + dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) + derivative += np.real( + np.trace(dGamma[-1] @ d + gamma_list[n] @ dD_di) * s**n / math.factorial(n) + ) + + return derivative + + +def gradientDiagonalEntries( + dbi_object, params, h, analytic=True, d_type=d_ansatz_type.element_wise, delta=1e-4 +): + r""" + Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. + As the analytical gradient is applied on the polynomial expansion of the cost function, the numerical gradients may be more accurate. + + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + h(np.array): Hamiltonian. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + d_type(d_ansatz_type): Ansatz used for the D operator. Options are 'Full' and '1-local'. + delta(float): Step size for numerical gradient. + Returns: + grad(np.array): Gradient of the D operator. + """ + + grad = np.zeros(len(params)) + d = d_ansatz(params, d_type) + if analytic == True: + for i in range(len(params)): + derivative = derivative_scalar_product_dbr_approx_element_wise_ansatz( + dbi_object, d, h, i + ) + grad[i] = d[i, i] - derivative + else: + for i in range(len(params)): + params_new = deepcopy(params) + params_new[i] += delta + d_new = d_ansatz(params_new, d_type) + grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta + return grad + + +def gradient_descent_dbr_d_ansatz( + dbi_object, + params, + nmb_iterations, + lr=1e-2, + analytic=True, + d_type=d_ansatz_type.element_wise, +): + r""" + Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. + - Declare variables + - Calculate initial loss + - Iterate, learning at each the optimal D and measure loss + - Return values + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + nmb_iterations(int): Number of gradient descent iterations. + lr(float): Learning rate. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + d_type(d_ansatz_type): Ansatz used for the D operator. + Returns: + d(np.array): Optimized D operator. + loss(np.array): Loss function evaluated at each iteration. + grad(np.array): Gradient evaluated at each iteration. + params_hist(np.array): Parameters evaluated at each iteration. + """ + + h = dbi_object.h.matrix + d = d_ansatz(params, d_type) + loss = np.zeros(nmb_iterations + 1) + grad = np.zeros((nmb_iterations, len(params))) + dbi_new = deepcopy(dbi_object) + s = polynomial_step(dbi_object, n=3, d=d) + dbi_new(s, d=d) + loss[0] = dbi_new.loss(0.0, d) + params_hist = np.empty((len(params), nmb_iterations + 1)) + params_hist[:, 0] = params + + for i in range(nmb_iterations): + dbi_new = deepcopy(dbi_object) + grad[i, :] = gradientDiagonalEntries( + dbi_object, params, h, analytic=analytic, d_type=d_type + ) + for j in range(len(params)): + params[j] = params[j] - lr * grad[i, j] + d = d_ansatz(params, d_type) + s = polynomial_step(dbi_new, n=3, d=d) + dbi_new(s, d=d) + loss[i + 1] = dbi_new.loss(0.0, d=d) + params_hist[:, i + 1] = params + + return d, loss, grad, params_hist diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 4284c6caef..54c728e5f5 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -95,7 +95,7 @@ def polynomial_step( n_max: int = 5, d: np.array = None, coef: Optional[list] = None, - cost: str = None, + cost=None, ): r""" Optimizes iteration step by solving the n_th order polynomial expansion of the loss function. @@ -107,7 +107,7 @@ def polynomial_step( backup_scheduling (`DoubleBracketScheduling`): the scheduling method to use in case no real positive roots are found. """ if cost is None: - cost = dbi_object.cost.name + cost = dbi_object.cost if d is None: d = dbi_object.diagonal_h_matrix @@ -117,17 +117,7 @@ def polynomial_step( "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." ) if coef is None: - if cost == "off_diagonal_norm": - coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n) - elif cost == "least_squares": - coef = least_squares_polynomial_expansion_coef(dbi_object, d, n) - elif cost == "energy_fluctuation": - coef = energy_fluctuation_polynomial_expansion_coef( - dbi_object, d, n, dbi_object.state - ) - else: - raise ValueError(f"Cost function {cost} not recognized.") - + coef = dbi_object.cost_expansion(d=d, n=n) roots = np.roots(coef) real_positive_roots = [ np.real(root) for root in roots if np.imag(root) < error and np.real(root) > 0 From 2977f1c5732fc57a80c2f8a5ff524e740719f4db Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Thu, 2 May 2024 14:19:48 +0200 Subject: [PATCH 067/154] added normalization option for D and projected GD --- ...t_functions_and_d_gradients_tutorial.ipynb | 940 +++++++++--------- examples/dbi/dbi_group_commutator_tests.ipynb | 198 ++++ src/qibo/models/dbi/double_bracket.py | 2 +- src/qibo/models/dbi/utils_gradients.py | 12 +- 4 files changed, 690 insertions(+), 462 deletions(-) diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index 2761e99464..ef47f8d896 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,8 @@ "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCost\n", "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" + "from qibo.models.dbi.utils_scheduling import *\n", + "from qibo.models.dbi.utils_gradients import *" ] }, { @@ -39,14 +40,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-24 09:00:13]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-05-01 21:47:22]: Using numpy backend on /CPU:0\n" ] } ], @@ -70,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -78,8 +79,8 @@ "output_type": "stream", "text": [ "grid_search step: 0.6060645454545454\n", - "100%|██████████| 100/100 [00:00<00:00, 435.30trial/s, best loss: -30.63376733025578]\n", - "hyperopt_search step: 0.5997631214283555\n", + "100%|██████████| 100/100 [00:00<00:00, 357.58trial/s, best loss: -30.63373896549929]\n", + "hyperopt_search step: 0.5996755685204258\n", "polynomial_approximation step: 0.5267891182131145\n" ] } @@ -109,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -121,7 +122,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -173,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -195,16 +196,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -240,14 +241,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-24 09:00:16]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-05-01 21:47:24]: Using numpy backend on /CPU:0\n" ] } ], @@ -274,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -282,8 +283,8 @@ "output_type": "stream", "text": [ "grid_search step: 1e-05\n", - "100%|██████████| 100/100 [00:00<00:00, 523.08trial/s, best loss: 0.0]\n", - "hyperopt_search step: 0.45340309474280477\n", + "100%|██████████| 100/100 [00:00<00:00, 480.33trial/s, best loss: 0.0]\n", + "hyperopt_search step: 0.5411125187601521\n", "polynomial_approximation step: 0.222657579130477\n" ] } @@ -313,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -325,7 +326,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -369,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -387,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -396,7 +397,7 @@ "Text(0, 0.5, 'Energy fluctuation')" ] }, - "execution_count": 17, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, @@ -435,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -458,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -475,7 +476,7 @@ "Text(0.5, 0, 'Iterations')" ] }, - "execution_count": 19, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -513,23 +514,14 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from qibo.models.dbi.utils_gradients import *" - ] - }, - { - "cell_type": "code", - "execution_count": 21, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-24 09:00:18]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-05-01 21:47:27]: Using numpy backend on /CPU:0\n" ] } ], @@ -555,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -564,13 +556,13 @@ "Text(0, 0.5, 'Diagonal elements')" ] }, - "execution_count": 22, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -596,9 +588,9 @@ "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", "params = np.linspace(1,2**nqubits,2**nqubits)\n", "\n", - "step = 1e-2\n", + "step = 1e-1\n", "iterations = 200\n", - "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", + "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step, normalize=True)\n", "\n", "plt.figure()\n", "plt.plot(range(iterations+1), loss)\n", @@ -621,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -630,13 +622,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 23, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -658,7 +650,7 @@ "off_diagonal_norm = np.empty((2,flows+1))\n", "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", - "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", + "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step,normalize=True) \n", "for i in range(flows):\n", "\n", " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", @@ -686,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -695,13 +687,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 24, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -725,7 +717,7 @@ "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", "\n", "for i in range(flows):\n", - " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", + " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step,normalize=True)\n", " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", " dbi_trained(s,d=d_trained)\n", " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", @@ -750,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -777,16 +769,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -839,14 +831,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-04-24 09:01:53]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-05-02 14:03:24]: Using numpy backend on /CPU:0\n" ] } ], @@ -870,271 +862,63 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 45, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:01:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - } - ], + "outputs": [], "source": [ "dbi_eval = deepcopy(dbi)\n", "params = np.linspace(1,2**nqubits,2**nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,1e-2, analytic=False, d_type = d_ansatz_type.element_wise)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,1e-3, analytic=False, d_type = d_ansatz_type.element_wise, normalize=False)\n", "flows = 50\n", - "off_diagonal_norm = np.empty((flows+1,2))\n", + "off_diagonal_norm = np.empty((flows+1,3))\n", "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", "for i in range(flows):\n", " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", " dbi_eval(step_poly,d=d_opt)\n", " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", "\n", + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,nqubits,nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=True)\n", + "best = np.argmin(loss_opt)\n", + "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", "\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", "\n", "dbi_eval = deepcopy(dbi)\n", "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, 1e-3, analytic=False, d_type = d_ansatz_type.local_1)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=False)\n", "best = np.argmin(loss_opt)\n", "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", + "\n", "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n" + " off_diagonal_norm[i+1,2] = dbi_eval.off_diagonal_norm\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 29, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1147,6 +931,7 @@ "plt.figure()\n", "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", + "plt.plot(off_diagonal_norm[:,2],label='1-local ansatz unnormalized')\n", "plt.xlabel('Flows Iterations')\n", "plt.ylabel(r'$||\\sigma(H_k)||$')\n", "plt.legend()" @@ -1154,15 +939,200 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], + "source": [ + "dbi_eval = deepcopy(dbi)\n", + "\n", + "flows = 5\n", + "off_diagonal_norm = np.empty(flows+1)\n", + "off_diagonal_norm[0] = dbi_eval.off_diagonal_norm\n", + "\n", + "for i in range(flows):\n", + " params = np.logspace(1,-1,nqubits)\n", + " d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=True)\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1] = dbi_eval.off_diagonal_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7548398294391669\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s = np.linspace(0,5,100)\n", + "off_diagonal_norm = np.empty(len(s))\n", + "params = np.logspace(1,-1,nqubits)\n", + "d = d_ansatz(params, d_ansatz_type.local_1, normalization=True)\n", + "for step in range(len(s)):\n", + " dbi_eval2 = deepcopy(dbi_eval)\n", + " s_poly = dbi_eval2.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=5)\n", + " dbi_eval2(s[step], d=d)\n", + " off_diagonal_norm[step] = dbi_eval2.off_diagonal_norm\n", + "plt.figure()\n", + "plt.plot(s,off_diagonal_norm)\n", + "plt.axvline(x=s_poly, color='r', linestyle='--')\n", + "\n", + "dbi_eval2 = deepcopy(dbi_eval)\n", + "dbi_eval2(s_poly, d=d)\n", + "\n", + "plt.figure()\n", + "plt.matshow(np.real(dbi_eval2.h.matrix))\n", + "\n", + "s = np.linspace(-5,5,100)\n", + "\n", + "off_diagonal_norm = np.empty(len(s))\n", + "params = -np.linspace(1,nqubits,nqubits)\n", + "d = d_ansatz(params, d_ansatz_type.local_1, normalization=True)\n", + "print(np.linalg.norm(commutator(d, dbi_eval2.h.matrix)))\n", + "for step in range(len(s)):\n", + " dbi_eval3 = deepcopy(dbi_eval2)\n", + " s_poly = dbi_eval3.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=5)\n", + " dbi_eval3(s[step], d=d)\n", + " off_diagonal_norm[step] = dbi_eval3.off_diagonal_norm\n", + "plt.figure()\n", + "plt.plot(s,off_diagonal_norm)\n", + "plt.axvline(x=s_poly, color='r', linestyle='--')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.24449019+0.j 0.24113607+0.j 0.23388354+0.j 0.23052942+0.j\n", + " 0.210949 +0.j 0.20759488+0.j 0.20034235+0.j 0.19698823+0.j\n", + " 0.13842364+0.j 0.13506952+0.j 0.12781699+0.j 0.12446287+0.j\n", + " 0.10488246+0.j 0.10152834+0.j 0.0942758 +0.j 0.09092168+0.j\n", + " -0.09092168+0.j -0.0942758 +0.j -0.10152834+0.j -0.10488246+0.j\n", + " -0.12446287+0.j -0.12781699+0.j -0.13506952+0.j -0.13842364+0.j\n", + " -0.19698823+0.j -0.20034235+0.j -0.20759488+0.j -0.210949 +0.j\n", + " -0.23052942+0.j -0.23388354+0.j -0.24113607+0.j -0.24449019+0.j]\n" + ] + } + ], + "source": [ + "print(np.diag(d))" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[76], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m plt\u001b[39m.\u001b[39mfigure()\n\u001b[1;32m----> 2\u001b[0m plt\u001b[39m.\u001b[39mplot(off_diagonal_norm[:,\u001b[39m0\u001b[39;49m],label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39melement-wise ansatz\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 3\u001b[0m plt\u001b[39m.\u001b[39mplot(off_diagonal_norm[:,\u001b[39m1\u001b[39m],label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m1-local ansatz\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 4\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mFlows Iterations\u001b[39m\u001b[39m'\u001b[39m)\n", + "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", + "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-05-01 21:50:45]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", - "nqubits = 5\n", + "nqubits = 7\n", "h = 3.0\n", "\n", "# define the hamiltonian\n", @@ -1177,173 +1147,19 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 26, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-04-24 09:10:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "[ 7.8269358 +0.j 2.60894672+0.j 3.6525541 +0.j -1.56543498+0.j\n", - " 4.69616148+0.j -0.5218276 +0.j 0.52177978+0.j -4.6962093 +0.j\n", - " 5.73976886+0.j 0.52177978+0.j 1.56538716+0.j -3.65260192+0.j\n", - " 2.60899454+0.j -2.60899454+0.j -1.56538716+0.j -6.78337624+0.j\n", - " 6.78337624+0.j 1.56538716+0.j 2.60899454+0.j -2.60899454+0.j\n", - " 3.65260192+0.j -1.56538716+0.j -0.52177978+0.j -5.73976886+0.j\n", - " 4.6962093 +0.j -0.52177978+0.j 0.5218276 +0.j -4.69616148+0.j\n", - " 1.56543498+0.j -3.6525541 +0.j -2.60894672+0.j -7.8269358 +0.j]\n" + "3.527668414752788\n" ] }, { "data": { - "image/png": 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", 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fNGhQ/OulS5fq/fff12uvvaaioiJddNFFevDBB3X33XfrvvvuUzAYTEbTnMGICgDAC5Iy9fPuu+9q9+7d8vv9Gj58uM455xxdc801CSMqlZWVGjp0qIqKiuLvjRkzRuFwWFu2bDnpZzc2NiocDidcXhRieTIAwAOSElR27NghSbrvvvt0zz33aMmSJcrPz9fXv/517du3T5JUW1ubEFIkxV/X1tae9LPnzJmj3Nzc+FVSUpKMP0LaY0QFAOAFbQoqFRUV8vl8p7y2bt2qaPToL8+ZM2fqhhtu0IgRI7Rw4UL5fD4988wzZ9XgGTNmqL6+Pn5VV1ef1efZihoVAIAXtKlGZdq0aZo0adIpn+nfv79qamokJdakhEIh9e/fX7t27ZIkFRcXa+3atQnfW1dXF793MqFQSKFQqC3NdhI70wIAvKBNQaWgoEAFBQVf+NyIESMUCoVUVVWlyy67TJLU3NysDz74QH379pUklZWVafbs2dq7d68KCwslScuWLVNOTk5CwMGJxUZU2EcFAOCypKz6ycnJ0dSpU/XjH/9YJSUl6tu3r+bNmydJ+u53vytJGj16tAYNGqQJEybo4YcfVm1tre655x6Vl5czYnIamPoBAHhB0vZRmTdvngKBgCZMmKDDhw9r5MiRWrFihfLz8yVJGRkZWrJkiW699VaVlZWpS5cumjhxoh544IFkNckpFNMCALwgaUElMzNTP/3pT/XTn/70pM/07dtXL730UrKa4DRqVAAAXsBZP5YKMaICAPAAgoqlqFEBAHgBQcVS1KgAALyAoGKpYEaGJIIKAMBtBBVLxfdRYeoHAOAwgoqlmPoBAHgBQcVSrcuTW1LcEgAAkoegYilW/QAAvICgYin2UQEAeAFBxVLUqAAAvICgYim20AcAeAFBxVLUqAAAvICgYqlYUGluMYpGTYpbAwBAchBULBULKhKjKgAAdxFULBWrUZEIKgAAdxFULJUQVCioBQA4iqBiKb/fp8wMnySCCgDAXQQVi7FEGQDgOoKKxbIyMyRJjQQVAICjCCoWi22jf6SZgwkBAG4iqFiMERUAgOsIKhYLfRZUGFEBALiKoGIxpn4AAK4jqFgsK/Po/z6mfgAAriKoWCyLqR8AgOMIKhbLCnwWVBhRAQA4iqBisVBs6ocRFQCAowgqFouNqFCjAgBwFUHFYrFiWmpUAACuIqhYjH1UAACuI6hYLCvA8mQAgNsIKhZjRAUA4DqCisVad6ZlRAUA4CaCisVaDyVkRAUA4CaCisVad6ZlRAUA4CaCisU4lBAA4DqCisVap34YUQEAuImgYjE2fAMAuI6gYrEQW+gDABxHULFYFocSAgAcR1CxWHzVDyMqAABHEVQsFjs9mRoVAICrCCoWCx1TTGuMSXFrAABofwQVi8VGVKJGikQJKgAA9xBULBYbUZGY/gEAuImgYrHYzrQS2+gDANxEULGYz+eLhxUOJgQAuIigYjkOJgQAuIygYjkOJgQAuIygYjkOJgQAuIygYjm20QcAuIygYrnYwYRHKKYFADiIoGK51hEVpn4AAO4hqFiu9WBCRlQAAO4hqFiuddUPIyoAAPcQVCwXyuQEZQCAuwgqlosdTMjyZACAiwgqlosV0zKiAgBwUdKCysqVK+Xz+U54vf322/HnNm7cqMsvv1xZWVkqKSnRww8/nKwmOSm+PJkaFQCAgwLJ+uBLLrlENTU1Ce/de++9Wr58uUpLSyVJ4XBYo0eP1qhRo/T4449r06ZNuvnmm5WXl6cpU6Ykq2lOiS9PZtUPAMBBSQsqwWBQxcXF8dfNzc164YUX9C//8i/y+XySpMWLF6upqUkLFixQMBjU4MGDtX79es2fP5+gcpo4lBAA4LIOq1F58cUX9cknn+j73/9+/L3KykpdccUVCgaD8ffGjBmjqqoq7d+/v6OaZrXY8mS20AcAuChpIyqf95vf/EZjxoxR79694+/V1taqX79+Cc8VFRXF7+Xn5x/3OY2NjWpsbIy/DofDSWqxHTiUEADgsjaPqFRUVJy0SDZ2bd26NeF7PvzwQ7366quaPHnyWTd4zpw5ys3NjV8lJSVn/Zk2Y9UPAMBlbR5RmTZtmiZNmnTKZ/r375/weuHCherevbuuu+66hPeLi4tVV1eX8F7s9bH1LceaMWOG7rzzzvjrcDjs6bDCoYQAAJe1OagUFBSooKDgtJ83xmjhwoW66aablJmZmXCvrKxMM2fOVHNzc/zesmXLNGDAgBNO+0hSKBRSKBRqa7OdxaGEAACXJb2YdsWKFdq5c6f+6Z/+6bh748aNUzAY1OTJk7VlyxY9/fTTeuSRRxJGTHBqIQ4lBAA4LOnFtL/5zW90ySWXaODAgcfdy83N1dKlS1VeXq4RI0aoR48emjVrFkuT24BDCQEALkt6UPn9739/yvvDhg3TqlWrkt0MZ2VxKCEAwGGc9WM5DiUEALiMoGI5licDAFxGULFcrJiWVT8AABcRVCyX9VkxbVNLVNGoSXFrAABoXwQVy8WKaSXqVAAA7iGoWC62PFmiTgUA4B6CiuUCGX4F/D5JjKgAANxDUHEAe6kAAFxFUHFAfHdattEHADiGoOKALJYoAwAcRVBxQIhN3wAAjiKoOCAUiJ2gzIgKAMAtBBUHsI0+AMBVBBUHcDAhAMBVBBUHUKMCAHAVQcUB8REVggoAwDEEFQfEalSY+gEAuIag4gB2pgUAuIqg4oD4zrRs+AYAcAxBxQHxnWnZQh8A4BiCigNC8akfRlQAAG4hqDigdeqHERUAgFsIKg5onfphRAUA4BaCigPYQh8A4CqCigM4lBAA4CqCigMYUQEAuIqg4gAOJQQAuIqg4oDYoYSc9QMAcA1BxQFsoQ8AcBVBxQFM/QAAXEVQcUCIYloAgKMIKg6IjaiwhT4AwDUEFQfElic3RlpkjElxawAAaD8EFQfEDiWMGqm5haACAHAHQcUBsUMJJelIhDoVAIA7CCoOCAX88vmOft1InQoAwCEEFQf4fL74qAorfwAALiGoOCIU30uFoAIAcAdBxRGtBxMy9QMAcAdBxRGxbfQZUQEAuISg4ojWGhVGVAAA7iCoOIKDCQEALiKoOIKDCQEALiKoOIKDCQEALiKoOCLEwYQAAAcRVBxx7MGEAAC4gqDiCEZUAAAuIqg4IosaFQCAgwgqjmjd8I0RFQCAOwgqjmBEBQDgIoKKIziUEADgIoKKIziUEADgIoKKIziUEADgIoKKIziUEADgIoKKIziUEADgIoKKI0IcSggAcBBBxREcSggAcBFBxRFZAaZ+AADuIag4ovVQQqZ+AADuSFpQ2bZtm7797W+rR48eysnJ0WWXXabXX3894Zldu3Zp7Nix6ty5swoLCzV9+nRFIpFkNclpHEoIAHBR0oLKtddeq0gkohUrVmjdunW68MILde2116q2tlaS1NLSorFjx6qpqUlvvvmmFi1apCeffFKzZs1KVpOcFh9RYeoHAOCQpASVjz/+WNu3b1dFRYWGDRumCy64QHPnztWhQ4e0efNmSdLSpUv1/vvv63e/+50uuugiXXPNNXrwwQf12GOPqampKRnNchqHEgIAXJSUoNK9e3cNGDBAv/3tb/Xpp58qEono17/+tQoLCzVixAhJUmVlpYYOHaqioqL4940ZM0bhcFhbtmw56Wc3NjYqHA4nXGgNKk0tUbVETYpbAwBA+wgk40N9Pp9ee+01XX/99crOzpbf71dhYaFeeeUV5efnS5Jqa2sTQoqk+OvY9NCJzJkzR/fff38ymm212M600tFt9DsHk/K/FgCADtWmEZWKigr5fL5TXlu3bpUxRuXl5SosLNSqVau0du1aXX/99frWt76lmpqas2rwjBkzVF9fH7+qq6vP6vNcERtRkSioBQC4o03/7J42bZomTZp0ymf69++vFStWaMmSJdq/f79ycnIkSb/85S+1bNkyLVq0SBUVFSouLtbatWsTvreurk6SVFxcfNLPD4VCCoVCbWm2J2T4fcrM8Km5xXAwIQDAGW0KKgUFBSooKPjC5w4dOiRJ8vsTB2z8fr+i0aP/2i8rK9Ps2bO1d+9eFRYWSpKWLVumnJwcDRo0qC3NwmdCgQw1t0QYUQEAOCMpxbRlZWXKz8/XxIkTtWHDBm3btk3Tp0/Xzp07NXbsWEnS6NGjNWjQIE2YMEEbNmzQq6++qnvuuUfl5eWMmJyhLLbRBwA4JilBpUePHnrllVd08OBBXXnllSotLdXq1av1wgsv6MILL5QkZWRkaMmSJcrIyFBZWZluvPFG3XTTTXrggQeS0SRP4GBCAIBrkrY0pLS0VK+++uopn+nbt69eeumlZDXBcziYEADgGs76cQgHEwIAXENQcQgHEwIAXENQcUiIERUAgGMIKg5pPZiQERUAgBsIKg5pPZiQERUAgBsIKg6JnffDhm8AAFcQVBwSG1GhRgUA4AqCikPiQYWpHwCAIwgqDglRTAsAcAxBxSHx5cmMqAAAHEFQcUjroYSMqAAA3EBQcUiX4NGjmw4eiaS4JQAAtA+CikN65nWSJH144FCKWwIAQPsgqDikpNvRoFK973CKWwIAQPsgqDikJL+zJKn+cLPCR5pT3BoAAM4eQcUhXUIBde8SlCRV72P6BwBgP4KKY3p3OzqqQlABALiAoOKYknzqVAAA7iCoOKYkNqKynxEVAID9CCqO6cPUDwDAIQQVx8RW/uwiqAAAHEBQcUxsL5UP9x+WMSbFrQEA4OwQVBzTM6+T/D6pMRLVRw2NqW4OAABnhaDimMwMv87J/WzlDwW1AADLEVQcFJv+oU4FAGA7goqDYgW17KUCALAdQcVBJSxRBgA4gqDioD5s+gYAcARBxUGxGhWmfgAAtiOoOChWo1JTf1jNLdEUtwYAgDNHUHFQQXZIoYBfUSPtOcCoCgDAXgQVB/l8vmMKagkqAAB7EVQcVZLPpm8AAPsRVBwVG1Fh0zcAgM0IKo5q3fSNoAIAsBdBxVHxGpX91KgAAOxFUHFUbC+VDxlRAQBYjKDiqNiIyiefNunTxkiKWwMAwJkhqDgqJytTuZ0yJbHyBwBgL4KKw/qwlwoAwHIEFYe1nvnDiAoAwE4EFYfFlygz9QMAsBRBxWG9u7GXCgDAbgQVh1GjAgCwHUHFYcee92OMSXFrAABoO4KKw3rld5LPJx1qatF71QdS3RwAANqMoOKwUCBDI/rkS5L+4fFK/fy1bWpuiaa4VQAAnD6CiuOeuKlU3xxarEjU6Oevbdd3fvn/VFXbkOpmAQBwWnzG8uKFcDis3Nxc1dfXKycnJ9XNSUvGGP1pY41mvbBZBw41K5jh18BzstU1FFDXUEDZWZnKyvTL7/PJ55N8knw+3wk/6yRvAwAcdtXAIl12QY92/czT/f0daNefirTk8/l03YU99dV+3TTjuU1avnWvNn5Yn+pmAQAsUZAdavegcroIKh5SmJOl/zuxVJt3h7W34YgONkYUPhLRwSMRHWlukdHR0RdjJCOrB9oAAO3o4s/qHVOBoOIxPp9PQ3vnSspNdVMAAPhCFNMCAIC0RVABAABpi6ACAADSFkEFAACkLYIKAABIWwQVAACQtpIWVN5991194xvfUF5enrp3764pU6bo4MGDCc/s2rVLY8eOVefOnVVYWKjp06crEokkq0kAAMAySQkqe/bs0ahRo3T++edrzZo1euWVV7RlyxZNmjQp/kxLS4vGjh2rpqYmvfnmm1q0aJGefPJJzZo1KxlNAgAAFkrKWT9PPPGE7r33XtXU1MjvP5qFNm3apGHDhmn79u06//zz9fLLL+vaa6/Vnj17VFRUJEl6/PHHdffdd+ujjz5SMBg8rZ/FWT8AANjndH9/J2VEpbGxUcFgMB5SJKlTp06SpNWrV0uSKisrNXTo0HhIkaQxY8YoHA5ry5Ytp/zscDiccAEAADclJahceeWVqq2t1bx589TU1KT9+/eroqJCklRTUyNJqq2tTQgpkuKva2trT/rZc+bMUW5ubvwqKSlJxh8BAACkgTYFlYqKCvl8vlNeW7du1eDBg7Vo0SL97Gc/U+fOnVVcXKx+/fqpqKgoYZTlTMyYMUP19fXxq7q6+qw+DwAApK82HUo4bdq0hILYE+nfv78kady4cRo3bpzq6urUpUsX+Xw+zZ8/P36/uLhYa9euTfjeurq6+L2TCYVCCoVCbWk2AACwVJuCSkFBgQoKCtr0A2LTOQsWLFBWVpa+8Y1vSJLKyso0e/Zs7d27V4WFhZKkZcuWKScnR4MGDTrtz4/VAlOrAgCAPWK/t79wTY9JkkcffdSsW7fOVFVVmX//9383nTp1Mo888kj8fiQSMUOGDDGjR48269evN6+88oopKCgwM2bMaNPPqa6uNpK4uLi4uLi4LLyqq6tP+Xs+KcuTJemmm27Sn//8Zx08eFADBw7Uv/7rv2rChAkJz/ztb3/TrbfeqpUrV6pLly6aOHGi5s6dq0Dg9Ad6otGo9uzZo+zsbPl8vnb9M4TDYZWUlKi6upqlz0lEP3cM+rlj0M8dg37uGMnsZ2OMGhoa1LNnz1PWryYtqLiAPVo6Bv3cMejnjkE/dwz6uWOkQz9z1g8AAEhbBBUAAJC2CCqnEAqF9OMf/5jl0ElGP3cM+rlj0M8dg37uGOnQz9SoAACAtMWICgAASFsEFQAAkLYIKgAAIG0RVAAAQNoiqJzEY489pnPPPVdZWVkaOXLkcQcoom3mzJmjL3/5y8rOzlZhYaGuv/56VVVVJTxz5MgRlZeXq3v37uratatuuOGG+EGVODNz586Vz+fT7bffHn+Pfm4fu3fv1o033qju3burU6dOGjp0qN555534fWOMZs2apXPOOUedOnXSqFGjtH379hS22D4tLS2699571a9fP3Xq1EnnnXeeHnzwwYSzYejnM/OXv/xF3/rWt9SzZ0/5fD49//zzCfdPp1/37dun8ePHKycnR3l5eZo8ebIOHjzY/o1t08E6HvHUU0+ZYDBoFixYYLZs2WJuueUWk5eXZ+rq6lLdNGuNGTPGLFy40GzevNmsX7/efPOb3zR9+vQxBw8ejD8zdepUU1JSYpYvX27eeecd89WvftVccsklKWy13dauXWvOPfdcM2zYMHPbbbfF36efz96+fftM3759zaRJk8yaNWvMjh07zKuvvmr++te/xp+ZO3euyc3NNc8//7zZsGGDue6660y/fv3M4cOHU9hyu8yePdt0797dLFmyxOzcudM888wzpmvXrgnnxtHPZ+all14yM2fONM8995yRZP74xz8m3D+dfr366qvNhRdeaN566y2zatUqc/7555vvfe977d5WgsoJfOUrXzHl5eXx1y0tLaZnz55mzpw5KWyVW/bu3WskmTfeeMMYY8yBAwdMZmameeaZZ+LP/M///I+RZCorK1PVTGs1NDSYCy64wCxbtsx87WtfiwcV+rl93H333eayyy476f1oNGqKi4vNvHnz4u8dOHDAhEIh84c//KEjmuiEsWPHmptvvjnhvb//+78348ePN8bQz+3l80HldPr1/fffN5LM22+/HX/m5ZdfNj6fz+zevbtd28fUz+c0NTVp3bp1GjVqVPw9v9+vUaNGqbKyMoUtc0t9fb0kqVu3bpKkdevWqbm5OaHfBw4cqD59+tDvZ6C8vFxjx45N6E+Jfm4vL774okpLS/Xd735XhYWFGj58uP7jP/4jfn/nzp2qra1N6Ofc3FyNHDmSfm6DSy65RMuXL9e2bdskSRs2bNDq1at1zTXXSKKfk+V0+rWyslJ5eXkqLS2NPzNq1Cj5/X6tWbOmXdtz+scUe8THH3+slpYWFRUVJbxfVFSkrVu3pqhVbolGo7r99tt16aWXasiQIZKk2tpaBYNB5eXlJTxbVFSk2traFLTSXk899ZTeffddvf3228fdo5/bx44dO/SrX/1Kd955p370ox/p7bff1g9/+EMFg0FNnDgx3pcn+nuEfj59FRUVCofDGjhwoDIyMtTS0qLZs2dr/PjxkkQ/J8np9Gttba0KCwsT7gcCAXXr1q3d+56ggg5XXl6uzZs3a/Xq1aluinOqq6t12223admyZcrKykp1c5wVjUZVWlqqhx56SJI0fPhwbd68WY8//rgmTpyY4ta547/+67+0ePFi/f73v9fgwYO1fv163X777erZsyf97CFM/XxOjx49lJGRcdwqiLq6OhUXF6eoVe74wQ9+oCVLluj1119X79694+8XFxerqalJBw4cSHiefm+bdevWae/evbr44osVCAQUCAT0xhtv6Be/+IUCgYCKioro53ZwzjnnaNCgQQnvfelLX9KuXbskKd6X/D1ydqZPn66Kigr94z/+o4YOHaoJEybojjvu0Jw5cyTRz8lyOv1aXFysvXv3JtyPRCLat29fu/c9QeVzgsGgRowYoeXLl8ffi0ajWr58ucrKylLYMrsZY/SDH/xAf/zjH7VixQr169cv4f6IESOUmZmZ0O9VVVXatWsX/d4GV111lTZt2qT169fHr9LSUo0fPz7+Nf189i699NLjltdv27ZNffv2lST169dPxcXFCf0cDoe1Zs0a+rkNDh06JL8/8ddURkaGotGoJPo5WU6nX8vKynTgwAGtW7cu/syKFSsUjUY1cuTI9m1Qu5bmOuKpp54yoVDIPPnkk+b99983U6ZMMXl5eaa2tjbVTbPWrbfeanJzc83KlStNTU1N/Dp06FD8malTp5o+ffqYFStWmHfeeceUlZWZsrKyFLbaDceu+jGGfm4Pa9euNYFAwMyePdts377dLF682HTu3Nn87ne/iz8zd+5ck5eXZ1544QWzceNG8+1vf5tls200ceJE06tXr/jy5Oeee8706NHD3HXXXfFn6Ocz09DQYN577z3z3nvvGUlm/vz55r333jN/+9vfjDGn169XX321GT58uFmzZo1ZvXq1ueCCC1ie3JEeffRR06dPHxMMBs1XvvIV89Zbb6W6SVaTdMJr4cKF8WcOHz5s/vmf/9nk5+ebzp07m+985zumpqYmdY12xOeDCv3cPv70pz+ZIUOGmFAoZAYOHGieeOKJhPvRaNTce++9pqioyIRCIXPVVVeZqqqqFLXWTuFw2Nx2222mT58+Jisry/Tv39/MnDnTNDY2xp+hn8/M66+/fsK/kydOnGiMOb1+/eSTT8z3vvc907VrV5OTk2O+//3vm4aGhnZvq8+YY7b4AwAASCPUqAAAgLRFUAEAAGmLoAIAANIWQQUAAKQtggoAAEhbBBUAAJC2CCoAACBtEVQAAEDaIqgAAIC0RVABAABpi6ACAADSFkEFAACkrf8PlvfX3lSZY/oAAAAASUVORK5CYII=", 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", 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", 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", 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", 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" ] @@ -1374,32 +1190,242 @@ ], "source": [ "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 20, 1e-3, analytic=False, d_type = d_ansatz_type.local_1)\n", + "params = np.logspace(1,-1,nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=True)\n", "best = np.argmin(loss_opt)\n", "\n", "\n", "plt.figure()\n", "plt.plot(loss_opt)\n", "\n", - "s = np.linspace(-0.1,0.1,100)\n", - "least_squares = np.empty(100)\n", - "off_diagonal_norm = np.empty(100)\n", - "for i in range(100):\n", + "trials = 1000\n", + "s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + "s = np.linspace(-3*s_poly,3*s_poly,trials)\n", + "least_squares = np.empty(trials)\n", + "off_diagonal_norm = np.empty(trials)\n", + "for i in range(trials):\n", + " dbi_eval = deepcopy(dbi)\n", " dbi_eval(s[i],d=d_opt)\n", " least_squares[i] = dbi_eval.least_squares(d=d_opt)\n", " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", + "\n", "plt.figure()\n", - "plt.plot(s,loss)\n", + "plt.plot(s,least_squares)\n", + "plt.axvline(x=s_poly, color='r', linestyle='--')\n", "plt.xlabel('s')\n", "plt.ylabel('Least squares cost function')\n", "plt.figure()\n", "plt.plot(s,off_diagonal_norm)\n", + "plt.axvline(x=s_poly, color='r', linestyle='--')\n", "plt.xlabel('s')\n", "plt.ylabel(r'$||\\sigma(H_k)||$')\n", "\n", "\n", - "print(np.diag(d_opt))" + "print(s_poly)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-05-01 21:51:19]: Using numpy backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 2\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCost.least_squares\n", + "\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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r164teC72Wb3lllvw3XffYdKkSejcuTNq164Nt9uN/Px8w6YA4PkavvHGG9izZw9efvnloPnESkpKcPDgQTRo0AApKSlqhkpEGSR+YoyMjAzUqVMH1dXVyM3NlWz74Ycfonnz5li+fLng5jV9+vSgtg6HAwMGDMCAAQPgdrtx33334eWXX8bUqVPRsmVLVWGIFi1aYOLEiZg4cSL27t2Lzp0741//+hf++9//okmTJgA8v4KbN2/uO+bkyZNBvxq9vybPnj0rKMEO/BW6adMmnD59GsuXL8dVV13l237gwAFFY/7555/Rp0+fsNdst9vRp08f9OnTB/Pnz8dTTz2Ff/7zn9i4cWPI96ZFixZwu9347bffQt74vK/Nnj17BK9NVVUVDhw4ENR3hw4d0KFDBzz++OP47rvv8Le//Q2LFi3CE088AUBdCCkQue+BElq0aIFff/1Vso2SsTdp0gRffvklSkpKBO6PN9zpfV2NItTYvaG21NTUsH/Dofjrr7+wfv16zJw5E9OmTfNt97pKcsYhRpMmTbBnz56g7Ua8hocPH4bT6cTf/va3oH1vvPEG3njjDaxYsUL2fFVEdENhrxgjLi4ON954Iz766CPRG8fJkycFbQHhr7AtW7YETRx2+vRpwXO73e5zjyorKwEAtWrVAgBZs8SWl5ejoqJCsK1FixaoU6eOr7/c3FwkJCTghRdeEIxPLNbvvVl89dVXvm1lZWVBrpTY9VZVVeE///lP2DF7ueWWW3D06FG8+uqrQfvOnTvnm2PlzJkzQfu9YsZ7jWIMHjwYdrsds2bNCvp17h13bm4uHA4Hnn/+ecG1LF68GEVFRejfvz8ATzWfy+US9NGhQwfY7XbBGGrVqqV5dl+574ESbrzxRvz8889YsWJF0D7vdSv53F177bWorq7Giy++KNj+3HPPwWazoV+/fqrHqoZQY+/atStatGiBefPmobS0NOg4/7/hUIh91gHxvx+lr+HWrVsF3xFlZWV45ZVX0LRpU8lwbSi8+XanTp2SbDdkyBCsWLEi6OEd14oVK5CTk6P4/ER0Qs5PlLJkyRLRvJsHH3wQTz/9NDZu3IicnByMHj0a7dq1w5kzZ7B9+3Z8+eWXvhvzddddh+XLl+P6669H//79ceDAASxatAjt2rUTfOnefffdOHPmDK655hpcdNFFOHToEF544QV07tzZF+vv3Lkz4uLi8Mwzz6CoqAiJiYm45ppr0KBBg6Ax/v777+jTpw9uueUWtGvXDvHx8VixYgUKCwsxZMgQAB4H6+GHH8acOXNw3XXX4dprr8VPP/2EL774wleO66Vv375o3LgxRo0ahUmTJiEuLg5LlixBRkYGDh8+7GvXs2dP1K1bF8OHD8e4ceNgs9nw5ptvKrLghw4divfffx/33HMPNm7ciL/97W+orq7G7t278f7772PNmjXo1q0bZs2aha+++gr9+/dHkyZNcOLECfznP//BRRddJEgWDaRly5b45z//idmzZ+PKK6/EDTfcgMTERPzwww/Izs7GnDlzkJGRgSlTpmDmzJnIz8/HwIEDsWfPHvznP//BZZdd5sth2rBhA8aOHYubb74ZF198MVwuF958802fQPbStWtXfPnll5g/fz6ys7PRrFkzxTcRue+BEiZNmoQPP/wQN998M+666y507doVZ86cwapVq7Bo0SJ06tQJLVq0QHp6OhYtWoQ6deqgVq1ayMnJEc2BGTBgAK6++mr885//xMGDB9GpUyesXbsWH3/8McaPHx+yxD8cNpsNvXr1wqZNmxQdJzX21157Df369cMll1yCkSNH4sILL8TRo0exceNGpKam4pNPPpHsOzU11ZdL53Q6ceGFF2Lt2rWiLmfXrl0BAP/85z8xZMgQJCQkYMCAAT5R5M/kyZPxzjvvoF+/fhg3bhzq1auHZcuW4cCBA/joo49UzQYtt9S9TZs2aNOmjei+Zs2akeNDCIlAhRmhI6EmmvM+jhw5whhjrLCwkN1///2sUaNGLCEhgWVlZbE+ffqwV155xdeX2+1mTz31FGvSpAlLTExkXbp0YZ9++mlQifCHH37I+vbt65swsXHjxuwf//gHO378uGBsr776KmvevDmLi4uTLHs/deoUu//++1mbNm1YrVq1WFpaGsvJyWHvv/++oF11dTWbOXMma9iwoeQkh4wxtm3bNpaTk+Mb3/z580XLrL/99lt2+eWXs+TkZJadnc0eeeQRtmbNmqDxeic5FKOqqoo988wz7JJLLmGJiYmsbt26rGvXrmzmzJmsqKiIMcbY+vXr2aBBg1h2djZzOBwsOzub3Xbbbez3338X7TOQJUuWsC5duvj679WrF1u3bp2gzYsvvsjatGnDEhISWGZmJrv33nsFkxz+8ccf7K677mItWrRgSUlJrF69euzqq69mX375paCf3bt3s6uuuoolJydLTnLoBSFKi+W+B95JDgMJLAVnjLHTp0+zsWPH+ibDvOiii9jw4cMF0zh8/PHHrF27diw+Pj7sJIclJSXsoYceYtnZ2SwhIYG1atVKcpLDQAI/eyUlJQwAGzJkSIhXS/r6Qo2dMc9UBTfccAO74IILWGJiImvSpAm75ZZb2Pr1631tvKXuYlMq/Pnnn+z6669n6enpLC0tjd18883s2LFjomXl3slE7Xa77EkO09PTWVJSEuvevXvISQ4Dp1k4cOBA0HUqLXUPJNR7RcQ2NsZMlEFIEBxo2rQpevfujddffz3SQyFinM8//xzXXXcdfv75Z3To0CHSwyEI4jyU80MQBKETGzduxJAhQ0j4EITJoJwfgiAInQhcEoUgCHNAzg9BEARBEDGFpcTPV199hQEDBiA7Oxs2m823SKQUmzZtwqWXXorExES0bNlSNA9k4cKFaNq0KZKSkpCTk4OtW7fyHzxhGAcPHqR8H4IgCCIklhI/ZWVl6NSpExYuXCir/YEDB9C/f39cffXV2LFjB8aPH4+7774ba9as8bV57733MGHCBEyfPh3bt29Hp06dkJeXJ1jigSAIgiCI6MGy1V42my3sbJ2PPvooPvvsM8FkfkOGDMHZs2d9c+Dk5OTgsssu801s5na70ahRIzzwwAOYPHmyrtdAEARBEITxRHXC8+bNm4Omf8/Ly/Ot/F1VVYVt27ZhypQpvv12ux25ublBsxj7U1lZKZgB17vi+QUXXMBlKQCCIAgiOmGMoaSkBNnZ2aomfZRLRUUFqqqquPTlcDiQlJTEpS+zENXip6CgAJmZmYJtmZmZKC4uxrlz5/DXX3+hurpatI13LRox5syZg5kzZ+oyZoIgCCL6OXLkCNfFg/2pqKhAsya1UXBCfOFgpWRlZeHAgQNRJYCiWvzoxZQpUzBhwgTf86KiIjRu3Bi90m9DvM3B5Ry2xEQu/QAAHBrf5gR118QS5Z+XKRijOyH8iuduR/hfVG6HdD/u+NAunlT/1QlSx0nsC/ESuEP0505Q0IfIdrHjlfTLRPsMjqIz0f4CVg0XOQ4JwSuL2wKPA2AP2BaXEPyF7xA5zpHgCtqWGBe8LSneGbQtOV7YLkWkDQAkxwWv01Y7LrhtSrxYu4qgbZ7t4mu/1baHaB9iex37OdHtNftDrzHn6Veeq1DbxucGHEgde/jvASOpJfO7v6TUjRZdjwgWz+VNVVUVCk5UY9+PjZBaR5u7VFziRstuR1BVVUXixypkZWWhsLBQsK2wsBCpqalITk5GXFwc4uLiRNtkZWWF7DcxMRGJIuIk3uZAvJ2P+IGT8RNALgCOEHc6OVQDcCi/Lhan4ONVLV8AueNltHOHF0Du+DDiR0LEgIXuPx6hBZBUn9Uh3iJbiGNsIu3jIC5ebCIvmdjxobaLbRMVWjLFT5Cw0SJ+EsKLn7h4kW0iIjo+4DPhET7Cv0OP0BH+PSSICGWPoBG2qx1XBSD4BUkUua6kuOAxewRR8Atfxx5ie9w5eD4VwdSSEA+e/kL//dSxV0nu97WzVctqpxSzCZ/aKn70GpEikVrHrln8RCtR/ar06NED69evF2xbt24devToAcATx+zataugjdvtxvr1631tIgmTWN1bMVXiv0zlH688dmyrDP4VLdm+Sl57u8x24fuR/kVqd/KvBbBXmae+wK7xI2EGAoWPFpLi+XyulFJLxPWpE8L14UWqhOtTJ4RTVLOfTx6JWqJB+EQzSqekWb58Of7+978jIyMDqamp6NGjh6AiWy8sJX5KS0uxY8cO7NixA4CnlH3Hjh2+VaGnTJmCYcOG+drfc889+OOPP/DII49g9+7d+M9//oP3338fDz30kK/NhAkT8Oqrr2LZsmXYtWsX7r33XpSVlWHkyJGGXlsoTCWAVKBUAPHEXsXvxqi0/ziOwkkPERbriIW81CIW8hILY+lFKLHicX0iRx2dwl1mgoRPMEqnpPnqq6/w97//HZ9//jm2bduGq6++GgMGDMBPP/2k6zgtFfb68ccfcfXVV/uee/Nuhg8fjtdffx3Hjx/3CSEAaNasGT777DM89NBD+Pe//42LLroIr732GvLy8nxtbr31Vpw8eRLTpk1DQUEBOnfujNWrVwclQUcSVlnJLwRW5VQfAquqUhX+UoKtyiUr/GWvcsGtNZcJHvdHKvfH7mTS4S+OxDlDh77EsDtD5+0EtXWFzgcyGjdHt0YODpGQl1pC5fbIwRPyEiLm+oQ+no8bZITro5fwMZPrQ8JHnH79+qFfv36y2y9YsEDw/KmnnsLHH3+MTz75BF26dOE8uhpM8nUoj969e0NqWiKxWX179+4dVkGOHTsWY8eO1To8XbGyALJVuhQlP8tFjgCyV7llJT8bib2KSSY+E+KI5fvoiViisxEoCXkZ6fqQ8KkhFoVPcXGx4Hmo3FetuN1ulJSUoF69etz79sdcdwVCEq4hMC3onP8jN/eHF+Fyf6SPNSb0pYRoyOVRS7xIsrMceOb7iFduWd/1iSQkfNRRxqpQqvFRxjyf3UaNGiEtLc33mDNnji5jnjdvHkpLS3HLLbfo0r8XSzk/ZodVVgLJ+v5hcHOAtLg/gO4hMJ7hL63ujxlCX0aOIdpRm+8jntvDV2nq6fpYNdxFwsccHDlyBKmpqb7nerg+b7/9NmbOnImPP/4YDRo04N6/PyR+OMMqKmFL4v+hEJzDLAJIIXqFv3gQLvdH+ljl4opCX+YmUiEvMXi5PmqJdLjLLMSy8AGA1NRUgfjhzbvvvou7774bH3zwQdDKDHpAYS8dYBUmCU/JQUsFmEnCX3JK37VWfqmtuNI79KU1xCV2vCnCZjKSouWUufNMdpaDlpCXWV2fSGIW1yfWhY/evPPOOxg5ciTeeecd9O/f35BzkvjRCb0FkGlK4E0igHigJffHCtg1vJSmEEQ6oTbfh3fISwy9XR8Kd4WHhI8ylE5J8/bbb2PYsGH417/+hZycHBQUFKCgoABFRUW6jpPEj46QADKOSLs/avoONeGhyOoHYc9P6AtPoaMk0VkMnq4PD0j4EP78+OOP6NKli69MfcKECejSpQumTZsGAEFT0rzyyitwuVy4//770bBhQ9/jwQcf1HWc5kzAiCL0zgEyTQm8QpTk/xg594+W3J9QxDmZ5HpfeqBkDqBoQU6ll5xkZ7X5PnJDXmKIhbyUuD5qStvNOpMzCR9ro3RKmk2bNuk7oBCQ82MAlnKA1GKR8JfZ3B8eRHNIyqwYEfISQ2l+jlrXJ1LhLhI+hFGQ82MQXgGklwtkigowA2aADgeP0nc93J/Q5xKv+lI623MkMHLWaL0mOJST76NW6GhJdObh+uid5Byt1V3RJHxK3NWAW1u4vMQdmR91ekPOj8Ho6QJxc4AMzP8xa/Izb3hVfVHej3yMrPQyYi0vXlVZPMJd0ZrnE03Ch5CGxE8EIAEkhPfipzySn6Uqv8wY+pKLloov3Ugwj6CTk++j1gkyi+sj2V8M5/mQ8IktSPxEiKgXQDrBc+4fownl/oSq+tKK3FwgK+YMyZnjJxCeK7mHQ26is1yMcn1k9RGFeT4kfGIPEj8RJKoFkAXCX1pcGiPcn1Al76LntKCAiTS88n3UhryUTGoofrzxrg8JHyJaIPETYUgA1RCJ8Jf08eZM6DQi78dqYkrtgqZKkSOGtCxiKhbyMsL1iUS4i4QPEUlI/JgAEkA1yBVAZnd/QmF06MuSqAhpBaIm2dlM63mFQ6nrY8ZwV6Qh4RPbUKm7SdBzMkSuEyGaCDmTH2qd+FBt2bvWleS9WKHk3QqoyffhFfLSkuisRLRQuEs+kRI+pawKZcy4oohSFgcwbd9DpSw6F2Am58dEmN4BMpn7wwuzV2jJxbSLlJoQNet5qQ158UbpbM5aw10kfPhQyiK/DBBRA4kfk0ECyAPP8JeeuT9qEp95hL605P2YstxdAr0mOPRHTciLZ6JzpFwfoyHhQ5gFEj8mhFVU6iaCrCSA5MJDAJnZ/VFS9aUnZnGR1JS5K4XX8hVaFzENxMquDwkfwkyQ+DExUSmAFGB0+EsKte6PVTCLsFFDYKWXHjM78wp5Gen6kPCpgYQPEQiJH5MTdQLIxOEvPdwfPUNfhDwCk53V5PsEojbRWQtqVm4nIiN8SlkVCR+TQ+LHAsS6ADILZnN/xM5JSc/K4VHirjbRmVwffYmU8JGixB1dUwZYFSp1twh6lcJHrAxewQrwtkoXWGL4jyqP0ndeJepy+oxzMlQnyCsjpZJ34wgMcfHK/9EyozMv14eEj75ICZ9IiJ5StwPMre37rIxWdScijWkdIAMSoM1S/WWE+2NE6EtOxRc5RuLwCnnJdX0iMaEhDyIlfGrbHCR8iLCQ+LEYUSeAFGBUArSZK78I86E20VkLZg93RVL4RAISPtaDxI8FiSoBpEP+j97Jz2rW/FKa+CzeVqRfmXk/ViVwjp/AMvdwlV7hkp0D8314hLyMcH30wOrLV5gxsVlM+JSyyK9pRpD4sSyxKoCMXvtLKVYKfQWd0wyiyYA5fLSgZmJDK7g+Vs/zMWOYK1D4lLA4lJDwMQ0kfiyMXpMhRosACofR7k8kMYWwESPB2qX9vJaz0NP1MXLFdhI+4m4PiR7zQeInCogaAcQZs7k/PEJfZsfulK5ec5vY2eFR1aU25CUXPZKcrZznY0XhU+qm1eTNAJW6Rwl6lMIbXgYfgfJ3LaXvald814JYybvdyeCWWTIvOM4FuKP4G0Bpvk8gwfk/QmHDK9FZq+tjhnBXLAgfpfP3BIqeEp/oMe4HQIk7EW63tvemLEoTtsn54QirjOzkfaZzgKIk/KWqT4Xuj3hbDYuXGmS8mWmRVD2WtVBCpFyfUBgV7op14RMqv0fYhtwes0HihzOsspLPzMlqzx9jAkgOWsNfRuT+WCX0ZabcocBKL7PB0/Uxa7iLhE/4MJe/8ClxJ6HEHYFJZYkgSPzoBAkgP3QUQNHu/oihdpV3NcJFq9hhHGel5rmau9IS93AhL71dHzHMEO4yGisJnxK3I0j4EOaBxI+OkADyQ8cEaDkCyAruj3jf8hyhaFhZXk94LGaqBCNcn9D9Rmeej5mFT2AZe2CYy1/4lLiTOY2Q0AKJH52JNgGkCaUCiHP+TzgBpIf7owTeoa9IOD08CZzgUAmByc6RxkjXxwiiWfhITVyoNL/HE+aqET7FJHxMg+XEz8KFC9G0aVMkJSUhJycHW7duDdm2d+/esNlsQY/+/fv72owYMSJof35+PtcxRzIPiLcAMrwE3uAV4KUEkBr3R6/QFy/MlLCsBS3JzrxDXtHu+kS78AmFmvwef0j4mAtLFbq+9957mDBhAhYtWoScnBwsWLAAeXl52LNnDxo0aBDUfvny5ajyu3mePn0anTp1ws033yxol5+fj6VLl/qeJ+pU3h2pFdS9AohXKbxZS+DllL/LWfk9koit9G6vYnA7bAHtaJV3tYQrceeNUa4PCR9tKA1zCfeHDnMFip5Syv0xBea9C4gwf/58jB49GiNHjgQALFq0CJ999hmWLFmCyZMnB7WvV6+e4Pm7776LlJSUIPGTmJiIrKws/QbuR6QEEMB3LiBN11HlBBz63Ll5CCCpuX/UzPujdh4epcg5j90JuKNANCmp9NKS78Mj0TkQLa5PpMNdRmJF4SPl9pRUe/9vnN1a4k5GtcZ5fsppnp/IUlVVhW3btiE3N9e3zW63Izc3F5s3b5bVx+LFizFkyBDUqlVLsH3Tpk1o0KABWrdujXvvvRenT5+W7KeyshLFxcWChxKiJQxmaAK0weEvIzBL6Ms0cKrm0pLvo3WW58CQlxzXh8JdwZhB+KjJ7/FS7E4OIXzI+TELlhE/p06dQnV1NTIzMwXbMzMzUVBQEPb4rVu34tdff8Xdd98t2J6fn4833ngD69evxzPPPIP/+7//Q79+/VBdHfrLYs6cOUhLS/M9GjVqpOqaSADpI4D0Tn7mmfsjhljis1jVl5ySdzMlMJsRHktaKEHTvDwxEu4ySviES2wO2iazjD1Q9PgLHyp3Nw+WET9aWbx4MTp06IDu3bsLtg8ZMgQDBw5Ehw4dMHjwYHz66af44YcfsGnTppB9TZkyBUVFRb7HkSNHVI+LBJB5BZARkPsjD6k5fpQkOyvJ99Ga6GyU66M30Sp8QqGkjF2qmstf9HjbxgpKCpMA4IMPPkCbNm2QlJSEDh064PPPP9d9jJYRP/Xr10dcXBwKCwsF2wsLC8Pm65SVleHdd9/FqFGjwp6nefPmqF+/Pvbt2xeyTWJiIlJTUwUPLUQqDBbtAkgrat2fSKJmvp/Aiq9ocouU5Pvo7QJZ0fUh4aM9zBUokEqro3uGZ29h0vTp07F9+3Z06tQJeXl5OHHihGj77777DrfddhtGjRqFn376CYMHD8bgwYPx66+/6jpOy4gfh8OBrl27Yv369b5tbrcb69evR48ePSSP/eCDD1BZWYk777wz7Hn+/PNPnD59Gg0bNtQ8ZqWQAOJLpMJfRoa+1KBU3ESTGPJHi9hRU94eiNYkZxI+ylGb38MrzFVaHf3uj39hUrt27bBo0SKkpKRgyZIlou3//e9/Iz8/H5MmTULbtm0xe/ZsXHrppXjxxRd1HadlxA8ATJgwAa+++iqWLVuGXbt24d5770VZWZmv+mvYsGGYMmVK0HGLFy/G4MGDccEFFwi2l5aWYtKkSfj+++9x8OBBrF+/HoMGDULLli2Rl5dnyDUFEtMCSAlRFP7S4iJFa96P1ASHUpVeUsnOPENe4QgMeRldpWWl5SvMIHwEzzmEuYLdniTLC5/AIp9Kke96NYVJmzdvFrQHgLy8PNmFTGqxVKn7rbfeipMnT2LatGkoKChA586dsXr1al8S9OHDh2G3C/Xcnj178M0332Dt2rVB/cXFxeF///sfli1bhrNnzyI7Oxt9+/bF7NmzdZvrRw5eAWFkSbwpyuCVlsBznP9HCjWl75Eue9cTbuXyCeZZksPwxGedXR+tGOX6GCF89ApzCdopcHuMFEGl7iRUV2u7zZ9ze35QBBb2TJ8+HTNmzBBskypM2r17t2j/BQUFqguZtGAp8QMAY8eOxdixY0X3iSUpt27dGoyJf8kmJydjzZo1PIfHFaPnBIpmARSOSE9+KCai5E54GNRXFAgsHkjl+ygRO1oTneW4PkqSnKMl3BUNwkcqqVlM5JRUJwGwoBUL4MiRI4L81kgaBDywnPiJNSIhgAA+s0EbJoBkEM3uTyzP9qxlWYtQBIa8eCPm+oi2UxEqI+FTg5Yy9pr/y5m0MLzbU2LxkBcAWcU9agqTsrKyVBUyacVSOT+xSiSqwXjlAaket5IEaE75P5HO/RGD92KnXqyUByRV5i4XqXwfJS6QUa5PJMJdsSh8wuX3ePGv5pJKahbL7YkG4SMXNYVJPXr0ELQHgHXr1oUtZNIKiR8LQQJIqi2fm4KUANK78ktu4rOaqq9wYicaFjjVYyV3pYnO4ZDr+qjqW4PrE6vCR7hfWZhLLKlZ0F91kqjwKXdZO1wUDqWFSQ8++CBWr16Nf/3rX9i9ezdmzJiBH3/8MWR6Cy8o7GUxrJoHZEgITEb+j57hLzOhNbRm+BpgMt0duWt6qc33URLyMpPrY4XqLr2Fj9r8HqPCXGU+0WMh21UFSguTevbsibfffhuPP/44HnvsMbRq1QorV65E+/btdR2n+b/FiSCMrgaLNQGkNvlZae6P3D7EEp8DieW8Hy1oCXkpRYvro6fwMcL1iZTwMTqpOVSIqyzK3Z5AlBYm3XzzzUELjusNiR8LY6QLFHEBxBktAoiX+6PFnZFT9WUUdhfgjsA3idxkZyXz+/gjFfIy2vXRCxI+3v/LC3MJzqvI7YkMpe4kuDT+cVa4oyAmLgKJH4sTMwKIs/ujFzzcH73QEsoyMgwmNcFhKOTm+6gNefF2fcwQ7opV4RNpt6eULFpTQAnPUYCR1WARTYLmnACtpfpLKvlZCXITn+VUfQXO9hxuOQ0rVXzpgZQQUpLoHM71MWO4i4QPv6TmQMpciRLCJzI/yohgSPxEESSAAttGRgAprfxSC6+1vnz9mdDdVlPmrmQxUzWoWcfLHzOEu/RGT+FTyqpEhU/g+lxKyth92zWWsEuJHhI+5oLCXlGGUWEwXpMh6h4CswBiuT+hwmexjNxKL3/k5vuoDXmR6xOM3sJHDN5uj+CcGkUPYU7o2zUKsVoYTFcHKIrcHzUTHipZSd5KqJnZWW5ll5JEZ6XIdX1I+ARjtPDh4fYQ5oXETxRDAsjbTrsAMiuBoa9wq7wryfOxSk6QmskN/YUQr0RnNa6P0nAXCR8heoa5BOcRmbAwVG4PhbisAYW9ohwjw2ARCYHJRWMFmJrSdyWVX5EKfamt4lJznJvDMhXh8M/3UVPirqW8PRxyFy/VYwmLaBM+Vg1zlbsS4aw2boqK0upEuDRWl1VUW+RXkEJI/MQAUS2AOOb/6DH5IW8BI2fCQyIYJZMZqoGX66NHuMuqwicSYS5Bv5xFD2EuKOwVIxiVBxSREJgJwl9KS9/Fcn/UrvcVLvSlJO/HsIqvhMjlIvEIeenl+oTCrMtXWEn4+C9IqraEXXSsJHwsCTk/MYYRLpCpHSCNy1/wDH+pRav7Y2SoSxZ+4TA1ExzyQm2iczjXh0eSs1r0dH0iKXxCzdZMbg8hF3J+YhBygLTdZKSqv5Qg1/2JVfzn+PEvc/ev9PJPdg6V76Mm5MXT9QnE6uGu2jaHocJHbP6emn0O0wmfclciCR8LQM5PjGLE4qhWdoDUrv6uh/ujd+Kz4Su4RxC5IS9/eLs+SjCj8NEDvcJcYvsBofAhtyc2IfET4+gdBuM1GaIiOCVBqw1/KUFu5VcggaGvwIVOA1d5V7uIaqwII/+Ql5lcH3XniB3hY5TbQ6In+iDxQ5g+D0i38elU/m5F94cQR2/Xh3e4KxqFj9pqLnJ7CClI/BAAzB8Gi5bwl3hb41d8l3J07C7AbYFvBqX5PqFCXnIXMdXD9QnZ1kTVXUbn9wiehxE+PNyeaBY9pdVJcGqc56ey2tjvJqOgn5KEAL2TobUkQkcqAVqq/F1p8rOc0nVAXuJz4HIXSmd7tiJqlrVQgtwZnZW6PkrCXWbK8zGb8PEvUVcqfELN0iw6Pkpojnos8PuOMBoj8oBM5wDpAG/3R2voSyrvx8y5PWoWNOWBf8hLieujNclZDVYRPrzDXOT2EGoh54cQRe9JEU3nAFnU/YlW7ByWwlAa8uLl+oRr79nGz/WJJeFT7E6OiPAhtyf6IOeHkERPF8h0DpCG/B+lyc9qCXR/wlV9SfZlMrdH7QSHWtfzEkOu6xPpcJeVhQ+Pai4jRA8RnZDzQ4SFHKAaeK3+Lub+6DHpIe+8H/+V3iO56rualdx5oHQVdqsRCeHjvxp7uEkLA5eo8J1DhfAJt/o6CZ/ohsQPIQs9w2CGCiA5qJwBOlT4S+m6X0oJTHyWQo2YkiNyjFgTTG2ys9KQl1R5uz/R5vpESvjUbNce5gpMai5zJSqet4dCXLEBhb0IRegVBjMsBMYhAZpX+EssgVnOpIfhEp+VhL5C9mFUuTuH3B4vWpe0EMM/5KXV9Ykl4cMzzMUztyfWEprLXYlwubR931W5otMjic6rInRFTwdIrQukaEwmD3/xRm7oK5JhrFCEq/Tyz/dRQiRcH16Q8CG3h9AOOT+EKvScFFGtC8TdAVI5A7TW5Gc1Ze9qV3o3W9Izb9Ss5eVFrutjZLjL6sJHzaSFero9JHhiF3J+CE2YLQ/ISAeIV/m7HPdHaa5O4ISHWvoymnBl7uGSnXmFvEIRrrQ9qD0JH8//ZU5aKCepmZfbYzQp8ZVIjtN3IllCHuT8EJoxWx6QkTlASpe/4On+mBW70wZ3goTAktoXgnDJzkpK3OWEvHi6PmJEu/BREuYKN1NzNLg9Sl1HQn9I/BBc0CsMZgoBZED4S87szeESn6VCX4GrvEcD4fJ9tIS8QsHL9YkkPIWPkvwerWEurfP2kOgh/CHxQ3BFDxfIEAEUDgkBpHbx03BodX/kVn355/0I/u9X8aV3bpDaCQ4DURLy0tv1MWO4y0jhoyWp2epuD4ke80Pih+COJQWQxgToUALIaPdHLlLrfJkFsUovNZMbqlnBXQwp18cK4S6zCB/ebo/VRE9tA1ccLq1OgCNMeX84ROZejQosl/C8cOFCNG3aFElJScjJycHWrVtDtn399ddhs9kEj6Qk4R8TYwzTpk1Dw4YNkZycjNzcXOzdu1fvy4h69JgUUfckaJ0SoJWu/eWP2KzPUkhNeMjrO9dsJfFi+T5KHCAerk9QnxzCXVYRPiXualHhIzZbs9KkZn/MLHxS4itlCh/zhUFjFUuJn/feew8TJkzA9OnTsX37dnTq1Al5eXk4ceJEyGNSU1Nx/Phx3+PQoUOC/c8++yyef/55LFq0CFu2bEGtWrWQl5eHioronsbeKKJSAHFCbOZn3vP+SFV9xRpqFjH14u/6GBHuspLw8f0/YJkKz7/i1VyB+0qrk3zCR04ll9TSFEbO2SNH9NSOq/I9CPNgKfEzf/58jB49GiNHjkS7du2waNEipKSkYMmSJSGPsdlsyMrK8j0yMzN9+xhjWLBgAR5//HEMGjQIHTt2xBtvvIFjx45h5cqVBlxRbKCHAFIjgrgJoAi7P4Gl6kZMlGgUYmXuYpVeYsnOYm6PktwLnrM5B6JmFmceGCl8arYHuz1iwsfXLye3x6yihwjNk08+iZ49eyIlJQXp6elh2zudTjz66KPo0KEDatWqhezsbAwbNgzHjh1TfG7LiJ+qqips27YNubm5vm12ux25ubnYvHlzyONKS0vRpEkTNGrUCIMGDcLOnTt9+w4cOICCggJBn2lpacjJyZHsk1COWcJgZhNAerg/ckNf/iJK7YKlZguBAeFDXkpndNbq+kQqz4eX8CllVbKEj9Iwl7/b42mjze0xAhI9fKmqqsLNN9+Me++9V1b78vJybN++HVOnTsX27duxfPly7NmzBwMHDlR8bsskPJ86dQrV1dUC5wYAMjMzsXv3btFjWrdujSVLlqBjx44oKirCvHnz0LNnT+zcuRMXXXQRCgoKfH0E9undJ0ZlZSUq/W6ixcXFai8r5uCdDK0mEZpbErTKEvhA5Mz9E1j5pSTxWctaX7qu8aVwXS+xZGe58/uEuxmpcX30WMLCjMInkFDCx/Nv6GouvdweI+CVz1MrvhLxOk/CaSVmzpwJwJOfK4e0tDSsW7dOsO3FF19E9+7dcfjwYTRu3Fj2uS0jftTQo0cP9OjRw/e8Z8+eaNu2LV5++WXMnj1bdb9z5szxvWmEciwlgFSitPorELVVXF7ULndhFsKt6RWImpCXVtcnqK1G18fswkdpNVe4EvZYEz1WJ/BHfmJiIhJ1/A6VS1FREWw2m6ywmT+WCXvVr18fcXFxKCwsFGwvLCxEVlaWrD4SEhLQpUsX7Nu3DwB8xyntc8qUKSgqKvI9jhw5ouRSCJgjEVrWGCIY/gpuI537ozdmCXGpXcwUCH8T8ndzlJS2x6LwCRfm8m5XI3wiHeLiFd6qFV8ZUeFzrjrRlwul9nGu2vN6N2rUCGlpab7HnDlzInZdXioqKvDoo4/itttuQ2pqqqJjLSN+HA4HunbtivXr1/u2ud1urF+/XuDuSFFdXY1ffvkFDRs2BAA0a9YMWVlZgj6Li4uxZcsWyT4TExORmpoqeBDK4Z0HZEYBpAWluT9S7f2rvuTk/fDGLRHikprgUGpZC7kl7mI3qFDl7aEI5/powUzCR0l+j+df6WouX78Kc3vEMCKhOVpEjx4cOXJE8KN/ypQpou0mT54cNMVM4CNUqooSnE4nbrnlFjDG8NJLLyk+3lJhrwkTJmD48OHo1q0bunfvjgULFqCsrAwjR44EAAwbNgwXXnihT5HOmjULl19+OVq2bImzZ89i7ty5OHToEO6++24Ankqw8ePH44knnkCrVq3QrFkzTJ06FdnZ2Rg8eHCkLjPm4BmC0i0EpjL/R0n4i0fujz9aQl9WX+1d7Aam1vUxIslZK7yETyBe4WNEmCuSc/bwCG/JETt14iqQYOAkhzyR+0N/4sSJGDFihGSb5s2baxqLV/gcOnQIGzZsUGVAWEr83HrrrTh58iSmTZuGgoICdO7cGatXr/YlLB8+fBh2e42Z9ddff2H06NEoKChA3bp10bVrV3z33Xdo166dr80jjzyCsrIyjBkzBmfPnsUVV1yB1atXB02GSOhLVAigEGhZ/kJp7o/WXCHZ5zFYHEnN7CxV3cXD9REca8Jwlx7CR21+j1y3J+j8EQpxGSl6YoWMjAxkZGTo1r9X+OzduxcbN27EBRdcoKofG2OMZkHTSHFxMdLS0nB13A2It1n457JJ4CWC1CyHIevcUgJIovpLTACJJT+LuT+Bgsbf/Ql0fvzb+js/gRVf3oVO/Y/3FzSC/8eH3u/7N95/n+drhfna+IW2vKu6n9/mDXv5z/HjTXj2hr284sc/38cb9koJ+Nfzf88NSWxiQzHxw8v1iVbho2SJCh5uTzSLnopSJ564fC2Kiop0S5nw3pOuXzcSCbW0fUacZVVY8feluoz38OHDOHPmDFatWoW5c+fi66+/BgC0bNkStWvXBgC0adMGc+bMwfXXXw+n04mbbroJ27dvx6effiqo1K5Xrx4cCqpvLeX8ELEBLxfImwOkRARpdoAUlr+rDX8J2ssMfYUqeTfbOl9yKr2kStzlhrz0cH3k9xV54cMrzGUlt8cI0RNLLo9Wpk2bhmXLlvmed+nSBQCwceNG9O7dGwCwZ88eFBUVAQCOHj2KVatWAQA6d+4s6Mv/GDmQ+CFMSSTDYJrPrTD/Rw6B4SypFd+1hL60hLN4zwkklezsRUvIy7dNpusjPEZ9uMuMwkdrmCsa3B4jRE/tuArEx6mvVow2Xn/99bBz/PgHp5o2bQpewSoSP4Rp8VZi8XKB1ITBQsIxAVpv94cnmnN9EpR/cclZyV1KBOnh+gTtjzLhwyOpWa7bEyuihzAXJH4I08MzDCZXAOmZAC2GnMkP1bo/oUJfcc6avB/RPkxW8SVnfh+pG51a14dnuEsrZhA+YmEuM7o9JHqAclcCElza/oidruhMCybxQ1gCSwogjeEvI90fw/N+FC5t4U9gorMY3hsbD9fHn0iGu7QIH975PXLdHquJnmgQPIQ8SPwQloFXGMysAkhO+EuJ+6MWMcdHKp9Hi0MkNcGhXMItZArwc32C+rCg8Imk20OiB6itw5pwhHJI/BCWg4cLZAYBJIbctb/kwCP0ZRTeMnela3r5I1beHki4G5TSJGc5RIvwMavbYxXR4xXS+s0VTiiBxA9hSSwngERQG/6Scn/MVraullDz+8hdxR2QvrHJWcNL0EZluCvahI+Z3J5Iih6lgocwHyR+CMvCIwxmmADSGP5SSyj3R7StiHCSCmlFMiE6cGJDpYnOYugR7tKCHsInGtweEj0ED0j8EJZHqwvEXQCFQkMCdDj3R9BWhvsTasJDqyMn0Vnuyu2+Nipncdbi+vASPpEKc1lJ9PAKbZHgsRYkfoiogIcAAuTNBh32XBzyf5S6P1oSn8Xyfszq+ChF6sYmJ8nZ6DyfSIS5SPSE6pe/y1Mn7hzi7MYtenvOFQ+nxlJ3l0t7UYIZIfFDRA1GhsF4CyA54S+17o+S0JcZCcz3URLy8iLm+ojBI9xlJuFjZbdH6n3VMkcPD9Gj1OWR4zASxkLih4g6jAqDaRJAIqgJfwn3KXN/xEJfUiEz3stXBBJqMVO5BIa81Lg+gjYqwl3RIHzMLHqA0MLHiHwepS4PYV5I/BBRiekFEKfwlxr3Rwypkne9w1x2DRMeykHK9eEZ7oq08AkX5tLi9kRa9EQytEUuT3RC4oeIWrSGwSIhgLSGv+S4P3JDX3qLHi0THAaGvKQSnQNRc3PSq7pLb+FjBbcnGkQPCR7rQeKHiHq0uEBmEUB6oTT0ZTSh8n3C4b358XB99Ap3qRU+asJcZnR7jBY9JHgIf0j8EDGBKQSQAgIFkFr3R0noyzBkhLmU5vvIReyGpbVE2azCx4puTzSLHq/YNrLaiwgNiR8iZtASBuMigHTO/1GCWOjLDEtdeJOdpQgX8lLq+vij1PUxm/CRCnNF2u3h7fRoDW0ZKXgiRYUrAfFU6i4KiR8i5lDr0BgtgMKFv3i5P3ImPPTm/3grvnzPDZzzR27IKxThXB+rCB8tYS4lbo8VRQ9Pl8eqgoeQB4kfIibRIoCA8JMh6iWAwoW/eOEVTHLFjax2CSzkLqkFTeWs58XD9fHHisJHbZgrEm6PmnJ1LaJHb5eHBI/1IPFDxCx6h8H0CoFJhb/kuD9ezBD6kipz9yY7iyFnBXd/vDc0HqXtZhI+YmEuEj1+x+ro8pDgsTYkfoiYR88wmCoBFIDS8Fc41Ia+jEIs2TlwVmcx5Lg+vrYaq7uUoEb4SJWx8whzGR3iUip6zBzaItETHZD4IQiYTACpCH/5o2TeH97LXWiZBVos2Vkq5CV3bh8x1ydkW87hLl7CR26YSyyp2Qi3xwyixyqCp449/FxUhP6Q+CGI80RMAIkhswLMixz3R2nZe2DeD8/kZm0THMqYyFCl6yPdZ+SFj9ncHquInkgKnsDxlXHpldAKiR+C8ENtHpAmASQz/0dJ8rPSWZ897Tyhr3B5P3qv8QWI5/uIhbxCJTp7kXJ9lIS7Iil8woW5IpHbo7foiZTLo5fgiRSV1fFwubT9sVZXR+e8RCR+CEIENS6QLgIoACXhLzHkJj7L64uPE+Rf6SWV7+OPVKKzXNdHsN0kwkdumCtSbo8VRE+sCx5CHiR+CCIEphBACuf/UeL+mGbG5xCI5fuI3Xzluj6hwl08E5yVCh+eYS6ziB7eoS0SPIQekPghCAnUhMGMEED+8HB/fPsCQl9y1vkK5wC5Oaza7u/+yEl0lroZ6pXnw0v4mMnt0VP0WNXlIcETHZD4IQgZKHWB9BZAUuEvNe5PuNAXt2RnPyHkP8ePf6WXf75PuFmdw5W3i7k+guM5hbsiJXyiTfSQ4CGMgsQPQchEjQACpGeD1pIDpGX1d7Urtxu5nEUg/jdkOa5PuCTnSAgftWEuKbeHRE8wJHiIcJD4IQgF6BEGky2qFIS/tFR+yVrrS4eKL/9kZ/98H7GQlz/hJjX0bpcb7oqk8ImU26NV9JDgIawGiR+CUIEeYbAgOIa/pAgV+lKS96MWqTW9/JFKdBZuC53kLGjHIcmZh/AJDHNJuT2xInqMFDyREDu1FeScEfpB4ocgVMJTAPHM/xFDzP1RKmrChbzkhsSkJjiUk+/jfxOWs5SFZz/fcJcW4SMV5jLK7TFa9JjJ5YmE4PH//JUaeN4qZzzinBrn+XHSPD8EQQSgNAymlwDyR074KxRK5/zhkQMUuKyFnJCX/83XF9YK4frwDnfJFT5awlx65faIiR6t+TxWcHmMFjxKKgqJyEDihyA4oMQF4iaA/FCa/Bzo/gQKI6nZno1Oeg6V6CxngctAQrk+RgkfHmEuPZ0eI1yeaBQ8JHash3lnOAvBwoUL0bRpUyQlJSEnJwdbt24N2fbVV1/FlVdeibp166Ju3brIzc0Naj9ixAjYbDbBIz8/X+/LIKIQrwskq21F6LYh+6kKCANVCb9wbZU1ISNbVc3/7YL/q5tzx+5kCtuHSZgOmPsncGbnwJBXKNenZlt41ydSwqeExfmET4k7SSB8vM+9bk+g8CmtdmgSPinxlSHdHv/XtFZ8ZZDwqRNXEdLpERM+dewV0rNrx52TJXxS7ed8D6V4x2CE8Kljr/I9COUcPHgQo0aNQrNmzZCcnIwWLVpg+vTpqKqS93oyxtCvXz/YbDasXLlS8fkt5fy89957mDBhAhYtWoScnBwsWLAAeXl52LNnDxo0aBDUftOmTbjtttvQs2dPJCUl4ZlnnkHfvn2xc+dOXHjhhb52+fn5WLp0qe95oorFLQHAluiAze8LUsnNkIgOlITBuDhAEvk/cpOfA90fuaEvRRVfCeHFU+B6XlJz/Ijl+kglOUdS+Hj+7zDU7dES3uIZ2pLr8ljB4SGRw5fdu3fD7Xbj5ZdfRsuWLfHrr79i9OjRKCsrw7x588Iev2DBAths6gsxLCV+5s+fj9GjR2PkyJEAgEWLFuGzzz7DkiVLMHny5KD2b731luD5a6+9ho8++gjr16/HsGHDfNsTExORlZXFfbyBNy8SQ7GD3DCYHgIoHF6RE67sXSz0FSrkpSYUJlbpFbikhZy5fULdYMPdrHgKHyVhLiW5PWYVPTxCW2YXPCR29CU/P18QZWnevDn27NmDl156Kaz42bFjB/71r3/hxx9/RMOGDVWd3zLip6qqCtu2bcOUKVN82+x2O3Jzc7F582ZZfZSXl8PpdKJevXqC7Zs2bUKDBg1Qt25dXHPNNXjiiSdwwQUXcB0/QGIo1lAigADxyRDVrC+mxf0xmsBkZyB8yEuO68Nr7S6twkesmiuU26M1oTlQ+BgtevR0ecjdiTzFxcWC54mJiaqjJKEoKioKuj8HUl5ejttvvx0LFy7UZFpYRvycOnUK1dXVyMzMFGzPzMzE7t27ZfXx6KOPIjs7G7m5ub5t+fn5uOGGG9CsWTPs378fjz32GPr164fNmzcjLk78l2FlZSUq/YRL4IdCLiSGoh8eYTBRAaQy/OUllPsjFfryF0iqkp5DrPElZyV3pa6Pb7+GcJca4RMqzCVWwq5XiEuO6CHB4z1HdIudKpcdcS75k3aKUe3ypAY3atRIsH369OmYMWOGpr792bdvH1544YWwrs9DDz2Enj17YtCgQZrOZxnxo5Wnn34a7777LjZt2oSkpBqreciQIb7/d+jQAR07dkSLFi2wadMm9OnTR7SvOXPmYObMmdzHSGIoetEaBlMjgMSQO/FhTXsZsz1zqP4KzPcBgm/qYuXtgPLSdj2Fj9wwl1a3R214y0jRY0bBE+1iR0+OHDmC1NRU3/NQrs/kyZPxzDPPSPa1a9cutGnTxvf86NGjyM/Px80334zRo0eHPG7VqlXYsGEDfvrpJ4WjD8Yy4qd+/fqIi4tDYWGhYHthYWFY62vevHl4+umn8eWXX6Jjx46SbZs3b4769etj3759IcXPlClTMGHCBN/z4uLiIFXMAxJD0YXRAihc+CvQ/QlV9g5AVt6PHKQnOAxOcK4dVxVU3h44rw8gP9zFW/iIhbkC5+7h7faEc3q0hLZI8BChSE1NFYifUEycOBEjRoyQbNO8eXPf/48dO4arr74aPXv2xCuvvCJ53IYNG7B//36kp6cLtt9444248sorsWnTprDj82IZ8eNwONC1a1esX78egwcPBgC43W6sX78eY8eODXncs88+iyeffBJr1qxBt27dwp7nzz//xOnTpyWTqPSIdcrB/8ZHQsiayA2D6S2A5KB0wkO5BJa5+5MS7xSt8gq8eUstXgqoz/PhIXyUuD1Gix6lLo8VBQ+JnciSkZGBjIwMWW2PHj2Kq6++Gl27dsXSpUtht0vPvjN58mTcfffdgm0dOnTAc889hwEDBigap2XEDwBMmDABw4cPR7du3dC9e3csWLAAZWVlvuqvYcOG4cILL8ScOXMAAM888wymTZuGt99+G02bNkVBQQEAoHbt2qhduzZKS0sxc+ZM3HjjjcjKysL+/fvxyCOPoGXLlsjLy4vYdcpB7OZJgsg6yHGBNAkgP8QmQAzn/gjb1oS+AveLlbvLKYEPt6ZXSnxl0M098MYdKtylNs8nnPBREuYK5/boKXqMcHnMJHhI7FiTo0ePonfv3mjSpAnmzZuHkydP+vZ5ozlHjx5Fnz598MYbb6B79+7IysoSjfQ0btwYzZo1U3R+S4mfW2+9FSdPnsS0adNQUFCAzp07Y/Xq1b4k6MOHDwuU40svvYSqqircdNNNgn68iVpxcXH43//+h2XLluHs2bPIzs5G3759MXv27Ig4O1qhMJm14C6A/AmR/yPX/ZGzFIbWXB9HfDUcCS7RfB8vteIrg3J9xG7MWvN85AofuWEuHm6P0aJHD5eHBA8RinXr1mHfvn3Yt28fLrroIsE+xjzzgjmdTuzZswfl5eXcz29j3rMQqikuLkZaWhquSRmCeAULHhoJCSHzIicPKNRcQEHHBro/fgLI3/3xCiC371/7+X89IsGdYPNtq06w+Zyf6oTz+86fxp3gcXncCTUPwLuNgSUA7gS3Z5LDBDds8W7YE9yIT6gWiJ+k8+Eub8jL6/x4xY9/eXuduHOKXB+9hY/cEnajRE+kXR49BE80iZ3SEjd6XnIcRUVFsnJo1OC9J7V4cwriUpLCHyBBdXkF9g+do+t4I4GlnB9CPeQKmRc5eUCyHSCZ+T9yEK4EL6z64jnZYSD+wsefQNdHTnWXkcJHq9tjpOjhKXhI7JiXamccmFNbqbtb4/FmhcRPjEJiyHyEC2WpFkAieMNfoXJ//AlMfA41IaIeC576uz5e/G/MWiYzVCJ85CQ1GyF65IS2jBA8UudRCwkewkhI/BAASAyZBTkCCAgOg0kKoDDuj9jcP6EEjtgq75726oVPUogKL39CuT6+5wrCXXIruvR2e6REj1EuTyQdHhI7RCQh8UOIQmIocqgNgykVQIHJz2Luj1Toy3eckgVOgaB8Hy+enB/xXB8vqfZzXPJ8QhEqzHXUVZeb28NT9FhJ8JDYIcwEiR9CFiSGjEdNGEyuAPISKvwFCN0f8eUuPP/KdXy8yc5KCVXhFbq9OsdHTphLrIRdbkKz3qKHV1iLBA8RC5D4IVRBYsgY9BJA4ZKfQ7k/gCf0BShcCFWG6OHh+mgRPnLDXErdHrmiR21oi4fLw0vwkNghrAKJH4ILJIb0I1wYTJYAEiEw/BXO/QHkrfWlhMAS90ACS9s92/QRPv6LkvqHudS6PbxEDwkeguAPiR+O2BITYbMHf9F6k1RjCRJD/JESNGEFkMzwlz+BlV+ql7tICJ5KLNzkhmL436z1ED5ykprluD1qRI9Wl8dIwUNixzq4XXbAKT1Zqaw+ohASPwYQaoK6WBJFJIb4wFsABYa/Qrk/QPgZn+XiiA9OUBYLefm7PlJz+mhJbg4lfI470wHUuD1GiR4zCh4SO0Q0QuIngsSyKCIxpB6pMJgWARQY/gKC3R/PNgbABsD7rwc5ic+Ba3pJhbwCCbd2lxilrCrI/QmV33PUWU+V2+MveuQmMYcTPVrDWiR4CEIaEj8mJBZFEYkh5YRygRQJoPOIz/8jdHvsVW4A9pChL7G5ftwSic5JAaInnOsDyAt3ecWO/0Kk/s/lhLnk5PbIET1S+Tw8XR4SOwShDBI/FiKWRBGJIXlICSBA+JkRFUAB+T+h3B8gTlZ1l9Rkh/5l7sL5fYJXcA9EbrjL3+URE0FiwueYs64i0eMds+8858euNrQVCcFDYoeIdUj8RAGxIIpIDIVGSRhMVCyFCH8B4qu7xzk9Ccyqkp/hSXb24h/yCuX6iIW71OT5+AufwGquwNweHk6PGpeHBA9BGAOJnygmmkURiaFg5IbBfO3CJECLuT/+ic/e+X4Am8DtsTuDZ3y2xXtcH/9kZ6/wCVzENLC0HZAvfPxzfPwdn2PVQIk7OSjMdbwqPazbw1P0GCl4SOwQRGhI/MQg0SiKSAx5UJIHBCDkIqjh3J9AxASPFN58H6GwqBDm95x3fcIlOAeKncBcH4/w0eb2iIkeswkeEjsEIR8SP4SPaBJFsSyGQoXB/AWQVP6Pf/jLDggWPfWWvQN2xZMdeiu9vPk+XufHm+gMQDTJ2Z9A1ycwx0csv+eYKzUoqbmgMg1AeLfHSNFDgofgDXPZwTTO06P1eLNC4ocISzSIolgUQ2IukFIBBHjCX96vCv+yd/+Sd3eCzbPAqcx1vvxDXp5/K4LFQ4DrIxbuChXm8hc+gSXsgeXrNWOSDm/JET16Cx4SOwTBB8Xi5/PPP8fy5ctRr1493HXXXWjTpo1v319//YUbb7wRGzZs4DpIwpxYWRTFihjSIoCAEDM/OxncISZOFq328qvy8szsLAx5ibk+gTf5cHk+/pS4q3GsOjlsmMufQKdHLJ9HictDgocgzI0iP+vtt9/GwIEDUVBQgM2bN6NLly546623fPurqqrwf//3f9wHSVgLW1Ki6MPM2BITBY9oglVWBok7f4Eq2Fd1vvqqqgq2Sk94ylblgr3KBXuV+3zisycPyFv1BdSs8B6KwNXc/UNewbk+wuousVCX2LIVHtEDn/A55qyL4850HK9KR0FlGgor64gKH8AjeryJ194wnPdR278KzV4z1jpx53wPwCN4vA9//I+TI3zq2KsED4Ig+KPI+Zk7dy7mz5+PcePGAQDef/993HXXXaioqMCoUaN0GaCV8K7tFa0uglas5BRFozMU6AKFdYAQavLDargTvCGx4GovAKIhMEd8NZLiXYKQV6hcn1BJzqHW6hILcwVWcvnn9YRyesRCW1JhrVAOjxJ3hwQOQRiPIvGzd+9eDBgwwPf8lltuQUZGBgYOHAin04nrr7+e+wCtiFznIBpuqDywgiiKFjEkJoAAz3sQFCLzy/8BvDZxPNwOuy/xuTrBLwfofMWX1ESHXlLOOyyARyj4h7uk8nzEQlyAx+3xLlFx3JkuGuKSmpgwXGhLjughwUMQ1kGR+ElNTUVhYSGaNWvm23b11Vfj008/xXXXXYc///yT+wCjGRJJ0phZFFlZDIlVg3ldoKA5gAJmgPYmP3sTn72hL3sCws4A7c33SYl3ChOdQ8xwLJXnE7g2ly/MVZUOIHhNLi/hRI9egofEDkGYC0Xip3v37vjiiy9w+eWXC7b36tULn3zyCa677jqugyM8yBFJVrr5asWMokggJCzyXoQKg4kJIK+sYYFLXgToC7Fwly3eHbSgKeARHqFcH3/hE1jN5Q1xAfCFuUK5Pb4+/ESPlMvDS/CQ2CFMgdMOxGssVXdSqTseeughfPfdd6L7evfujU8++QRvvPEGl4ERyiAXyTyiSHSSQZO+7rIFECAIf7nPu0HexU7dThvcopVeNUnR3nwfb8jL6/oEVneFc3zEhI933p5ApERPKJeHBA9BRD+KxE+vXr3Qq1evkPuvvvpqXH311ZoHRehHLIokM4giM4fJAsNgogIICAh/eau3gt0fMbzLWnhDXkLXp6a6K9RcPjWiJzjMVRYwQaHc0FY4lyec4CGxQxDWRdMkhydOnMCJEyfgdgvLWDt27KhpUETkiQWRFElRZEYx5O8ChRJA3vBXTfJznKcEPsEGewJgd9pgD/hW8Za5JwWWt8cJq7sCQ12BYS7vSuyB1Vyl1Y6QkxIqdXlI8BBEbKBK/Gzbtg3Dhw/Hrl27wJjH1rbZbGCMwWazobpaei0eInqIxnykSIgis+QMSQogQJD/A0e834Kn0jgSakJe/q4PIC58vAQuUXHMWde3PEVgbo+U6PGfi8cfKbFDQocgohdV4ueuu+7CxRdfjMWLFyMzMxM2m7I1fojYQsmkgWYWSkaJokjnDIUUQEBNBVhivKDyK87JfHk/HgcIQHJw357JBIW5Pl7hU+L2iCB/x8e/hN3r9vj3BYTO51Hj8JDgIYjYQJX4+eOPP/DRRx+hZcuWvMdDxDhWDLcZIYqMDpP55wGJCSD/5Gd7lf182MsGpAT8EErwVHp5k51rxVciO+EvXBj/l191V43r41/CfsxV1+f2ABAIH7F8HrUuDwkegog9VImfPn364OeffybxQ0QMK4Tb9BRFRokhrwsUJIDgl/9TFQ+74/x8P04Gu1MogDwzO3tCXg0dZ31Jzv7hrto2B0rgESslLA67qzJRUp2M4850z7bqJF9/cgUPiR0i5nHagHiNkRlndEZ2VImf1157DcOHD8evv/6K9u3bIyFBWN86cOBALoOzHI54j+cvh6owiyERmjGri6SHKNIzZ0hKAHnDX/YqO+KccXA7bXCJ9JES70RmYono+lb+OT7edbl+r2jom7fHize8FSh6wgkeEjsEQQSiSvxs3rwZ3377Lb744ougfZTwLBOHTJGkBBJUqjCLSJJa/FWJMNIjZ8hfAPnOA7+5f6ria6q+nDbfQqe2eDdSkytQP6n0/EKhNa7Psepk1GFVqGPziJdj1cnYXZmNErfH5fEuLOqFRA9BELxQNXXjAw88gDvvvBPHjx+H2+0WPEj4RBBHAv8H4SNw5fdQD13OnZQo+lA7djX4rw7PKs7/v7QMtpJyj/vjZIhzsvPrfgEswVPmnnR+bp/acefn9fELd5W4HThWnYw9zrr44Vxzn/DxrqReO64CDRPOomHCWWQn/IU6cedwYcIZv1mh/QQRrYZOEJZi4MCBaNy4MZKSktCwYUMMHToUx44dC3vc5s2bcc0116BWrVpITU3FVVddhXPnxJfJCYUq5+f06dN46KGHkJmZqeZwwkqQQ6UYI/ORxASQHJdIS5gs0AWyAbBVuRBf6oQ7wYY4p93n/MQnVKN+UrknZOUTK8LS+BJ3EnZXZgu2iTk8grl6SNwQhOW5+uqr8dhjj6Fhw4Y4evQoHn74Ydx0000hV5IAPMInPz8fU6ZMwQsvvID4+Hj8/PPPsNuVeTmqxM8NN9yAjRs3okWLFmoOJ2IdElS6lv8rDZ+pCZMFCaBKF+wOF+zOBNirGLzp0I74ajRILMHFSQVok3gMdexVvhyfOvYqHHOlYndlNo5XpaPOeZcHqBE9geEsEj0EET089NBDvv83adIEkydPxuDBg+F0OoNyif2PGTduHCZPnuzb1rp1a8XnViV+Lr74YkyZMgXffPMNOnToEDTIcePGqemWINQTxYKKZ06SXGEU6pyhVoTH6TOwox48siYZZdme/zkSXGiZUog2iR4ru8TtP3OzA3XsFedFUUVQxRY5PQRhHoqLiwXPExMTkcgxzH/mzBm89dZb6NmzZ0jhc+LECWzZsgV33HEHevbsif3796NNmzZ48sknccUVVyg6n6qcn9deew21a9fG//3f/+HFF1/Ec88953ssWLBATZeyWbhwIZo2bYqkpCTk5ORg69atku0/+OADtGnTBklJSejQoQM+//xzwX7GGKZNm4aGDRsiOTkZubm52Lt3r56XQFgFi+VQac1JkpNXFKoPVlEJdvoM7CUVcJw55wt7JcW7fMLHS4m72jefD+AROd48Hs/8P97wGOXvEPrhWS7F+EepnMXwOGF32WF3any4PDKhUaNGSEtL8z3mzJnDZYyPPvooatWqhQsuuACHDx/Gxx9/HLLtH3/8AQCYMWMGRo8ejdWrV+PSSy9Fnz59FN+3VTk/Bw4cUHOYZt577z1MmDABixYtQk5ODhYsWIC8vDzs2bMHDRo0CGr/3Xff4bbbbsOcOXNw3XXX4e2338bgwYOxfft2tG/fHgDw7LPP4vnnn8eyZcvQrFkzTJ06FXl5efjtt9+QlJQU1CdBaIK3AFLhTinNSQoUQKFyinwOUFVtxJenAgDqJZWjdcJfaBiXHLBAqQffoqYkcCxPiYE3dcJ4jhw5gtTUVN/zUK7P5MmT8cwzz0j2tWvXLrRp0wYAMGnSJIwaNQqHDh3CzJkzMWzYMHz66aeiK0d41xH9xz/+gZEjRwIAunTpgvXr12PJkiWKBJmNeRfnsgA5OTm47LLL8OKLLwLwvBCNGjXCAw88IIj/ebn11ltRVlaGTz/91Lft8ssvR+fOnbFo0SIwxpCdnY2JEyfi4YcfBgAUFRUhMzMTr7/+OoYMGSJrXMXFxUhLS0Nu4/sQb9en2ocgIoYcgeVIABwOsMR4VNdOwrFetVDRuRzdGh/Bf5p86luo1H+RUoBEjxJIXEQHZSVu9O1wCEVFRQIxwRPvPanJ00/CrvFHvLuiAocm/1P2eE+ePInTp09LtmnevDkcjuDP859//olGjRrhu+++Q48ePYL2HzhwAM2bN8ebb76JO++807f91ltvRXx8PN566y0ZV+RB9aruf/75J1atWoXDhw+jqkr4BTZ//ny13YakqqoK27Ztw5QpU3zb7HY7cnNzsXnzZtFjNm/ejAkTJgi25eXlYeXKlQA8L2RBQQFyc3N9+9PS0pCTk4PNmzeHFD+VlZWo9Ptl7IuFJjiAOPqCIqIMkS8plljz1cEc8XA74uF22FFV14HyjDiUNXchMaEav53MxIYGmefzeOR9CfMQRCQUCCIyZGRkICMjQ9WxXmenMkT+YtOmTZGdnY09e/YItv/+++/o16+fonOpEj/r16/HwIED0bx5c+zevRvt27fHwYMHwRjDpZdeqqbLsJw6dQrV1dVB5fWZmZnYvXu36DEFBQWi7QsKCnz7vdtCtRFjzpw5mDlzpuJrIAgz4C9cZLV3CNu7/Z67HXa4HXFw1YpDRbodFRfYUHEBEJfqETBVrjh8VdQGLVMKAQSvsh5IHXsFCReCiAG2bNmCH374AVdccQXq1q2L/fv3Y+rUqWjRooXP9Tl69Cj69OmDN954A927d4fNZsOkSZMwffp0dOrUCZ07d8ayZcuwe/dufPjhh4rOr0r8TJkyBQ8//DBmzpyJOnXq4KOPPkKDBg1wxx13ID8/X02XlmLKlCkCR6m4uBiNGjWK4IiIaESpSFHcvyN0/+4gwWP3+38c3Ak2uB12VCfY4HbYcK6+R/Q4UxlsWUJhs/30RThRWQfNap1C6fmJC0vcSR6hU50cUgjJJdQaXgRBmJeUlBQsX74c06dPR1lZGRo2bIj8/Hw8/vjjvnwip9OJPXv2oLy83Hfc+PHjUVFRgYceeghnzpxBp06dsG7dOsVT76j6dt21axfeeecdTwfx8Th37hxq166NWbNmYdCgQbj33nvVdCtJ/fr1ERcXh8LCQsH2wsJCZGVliR6TlZUl2d77b2FhIRo2bCho07lz55Bj4V3iR1gTvcWJEqSEjBiB4qZmu1Dk+P6fYPPt9wqe6gTPdlcKfMIHANxOO+wJbriccYhPqMapklqocMXjVEUKGtf+y7PGV1wFSquTUDtOvnAJXKXdi3dWaC2QgCIIY+nQoQM2bNgg2aZp06YQS0uePHmyaJ6vElR9e9eqVcuX59OwYUPs378fl1xyCQBPeEoPHA4HunbtivXr12Pw4MEAPPHB9evXY+zYsaLH9OjRA+vXr8f48eN929atW+ez1Jo1a4asrCysX7/eJ3aKi4uxZcsWXQQcYTxmEihKUCpmAgklbjz7gme4EBM6/m0DBY87AXAlA+4EoCoVqE6p+YJiLjvc5/9f5fQcX+p3rnJXIhoklgAAasUnodRvlXbful4qxUgogRQOHgIqEBJUBGFeVH3DXn755fjmm2/Qtm1bXHvttZg4cSJ++eUXLF++HJdffjnvMfqYMGEChg8fjm7duqF79+5YsGABysrKfCVvw4YNw4UXXugrd3vwwQfRq1cv/Otf/0L//v3x7rvv4scff8Qrr7wCwLMI6/jx4/HEE0+gVatWvlL37Oxsn8Ai9MeqAkUpWgWNP1LipqaN+DRe/kIHCC12PM89gsfbzpUCuOOFosedUg0kuIHyONiddsBpB3MKz12FQAGUgJR4J1KqHSiLS0St+EqUVCf5FjINdIW8ITIx/MNmxW7xNv6oFUhK4S2oSEwRSrE5AVtc+Hbh+ohGVH0bz58/H6Wlnq+ymTNnorS0FO+99x5atWqlS6WXl1tvvRUnT57EtGnTUFBQgM6dO2P16tW+hOXDhw8L1vfo2bMn3n77bTz++ON47LHH0KpVK6xcudI3xw8APPLIIygrK8OYMWNw9uxZXHHFFVi9ejXN8RNArAgUJfAUM4HIETc1bUPPVRoodABxsQMIBY/neY3L406oET2uFMCVzFCd4oYt1Ym4BI/P4453w+3yiB84hXN0MMSjCsBZZxyqnPFITa5AebwTKfEJnn+rHagdV4Uyl0cIycErBsREkVQekZkEkhJ4iikSUkSso3ien+rqanz77bfo2LEj0tPTdRqWtfDN89NiPOLjIpcLRAJFPXoKmUCUCBvhcdITsocTOmJ9iAke73GBoqcm1FUjfOwJ7pq+nXYwl73GARKMww0kMNiSXXCkOOGIr4YjwYWkeBfqJXmSGVPinUg5L3xqx3nC6l4hVCcgN0gsV0jODV1rcrU/ZhRIkYLElHyMnOen6Ww+8/wcnCp/nh+roPhbOC4uDn379sWuXbtI/KiABIpxGClo/FErbjzHyltxRkzoAMFiJ7DPaoHrEyx4PP8Gix7vc2+Yy5bs8gmf+ATPUhVVAYLH3y63ww433GAJdricwrGfqUhBUrx8b92bLO2Pf76QoG3ATVlO6EwuVnWQ9IBCfITVUPUt3b59e/zxxx9o1qwZ7/FYGpYYDxZH4kYvIiVmAtEibmr6kL+sXiihA4QXO4B8weP9V0z0eN0epFTDFu8OEj6BhMwTcNrhjrfDFWK3NxdICYFiyN8VkiOIAL6iyB8SSOogMUXojapv8SeeeAIPP/wwZs+eja5du6JWrVqC/dFkjRH6YRYxEwgPcVPTFx+RA4gLHbFzVAeFumx++4R9iYke3/MET24PSzgftkqpRlyyyyd4Ah0cAJ58HxFsTj/3B/FwwyUQQFXOeFQkxCMp3rNVrgjyJkp7CeUK+RN4Y5W6MeolivwhgaQ/JKaIQFR9y1977bUAgIEDBwoWH2OMwWazobpa/NcgEd2YVcz4w1PY1PQpX+DUHBO+BEOu2AGUCR7P//2ETrxwm7/bE0r4ePEXQOz86s+B+T52pw3uBGFqobcc3iuAHPHVqHLWvDdJ8U6Uu4IXgU2Jr0RptWcGaG+CNFCTGxQohAAoFkNA+JubEaLIHxJI5kKLmCpz0/3RDKi6E2zcuJH3OAgTYgUxE4ge4qamb+Uix3OceqEjdV45gse/b39XBwgteoDzuT3nk5SR4BYIH0d8zZe3V/y4ncFVXnZn8DXZnR73B7CDAQIBpIRQIgiQFkIAQobI/FHiDgmOM1gU+UMCiQjE7rTBHhf6u0UWIn/H0YCqO0WvXr14j4MwACuKmUD0FDc151AncjzHyptUg4fY8bSVFjye/wf8GyB6fP8PdHvOV2fZE9wC4eNI8MiVKleN8PF3fWxOceFjcwLM38xxCgVQfEK1r09/vC5Qiogb5C+CAG1CyHO8PDEEKAt9RFIU+UMCiSA8aLqTlJeXi67q3rFjR02DIuQRDWImECPEjec86gVOTR/yZw+TEjuevrQLHv/zuAVC6Py/YUQPAEGYy5vYHCqp2QsLMbdPKHzuj18ozO20BwkgR4ILFa7gz0MoEQRA4AYBCAqLAaGFkOd4eWIIUO8OCfowiSjyhwQSEQuoutOcPHkSI0eOxBdffCG6n3J+1BGNYsYfo4SN8JzGihwgvNDx9KlO7NS0Ez9fONHj/dd/X6DbgwS3QPh4w1xVrrggZ8YdIsFZNufdH1u8W1IAJcW7UOFKEOQCpQgcocTz26RFECBfCHn6CR8i8/Wl0R0S9GVCUeQPCSTC6qi6G40fPx5nz57Fli1b0Lt3b6xYsQKFhYV44okn8K9//Yv3GC1LtIuZQCIhbmrOrV3kePpRPhe8FrEDqBM8gecNDG0B4qLH+6+o2yMhfIDzScl+AsiX7Hze9QlMdBZDEPpy2nzuD3PZwwogf8RCYVIiCOArhDz9hRc2PNwhQX8mF0X+kEAizIyqu9WGDRvw8ccfo1u3brDb7WjSpAn+/ve/IzU1FXPmzEH//v15j9MSMEd0z/MTSXHjOT8fgVPTnz5Cx9O39Fh5Cp6g/4cRPZ7/+7k9gKTw8eX4+FVjuZxxglwfL6Hm9wms+LI77Z5zO20A7ECC2yeAvP0H5gDJcYFq/l8jcEK5QYB4WAwIL4Q8/WkXQwCfsmkriSJ/SCARkULV3aysrAwNGjQAANStWxcnT57ExRdfjA4dOmD79u1cB0gYR6TFjRfeIsfTp7rV/fQUO57jwguewHGoET3e/aJuDyDp+EiiwPUJ2895AeQGYE9wCwSQtxQ+UAABkBRBAMKGxIDQbhAgTwh5+pUfIhP0z9kdEvRtUVHkDwkkQg9U3e1at26NPXv2oGnTpujUqRNefvllNG3aFIsWLULDhg15j5HggFmEjT96iBxPv+qXMeYldgBlgsfTXnosckWP///F3B7P/2vCXAAUCR+lrk8g3tCXwP1JYD4BBHhyicIJIAACFwgQTo6oRgQB/IWQ5xzqxRDAf1K9aBBF/pBAEsfmAuxq5pLwg2k83qyouiM++OCDOH78OABg+vTpyM/Px3//+184HA4sW7aM6wAJeZhR3AD6CZya/vUXOjXnUufueI7VLngCn4cTPd42QW4PEBTmAhBS+HjDXVWuOKHwCVHhJVbmrgT//B8pAQQgrAsEBM8WLScvyEuosBggXwh5zqFeDAH6ukOC80SZKPKHBBLhj6o75p133un7/6WXXopDhw5h9+7daNy4MerXr89tcEQNZhU3Xswscnx9cBY7AB/BA+gjejzPw4e5AITN8ZFCS8grlPsjRwB5x+oVQABku0CebUIRBKhzgwChEAKUiSGlQggwxh0SnC+KRZE/JJBiB9V31MWLF+O5557D3r17AQCtWrXC+PHjcffdd3MbXCxhdnHjRW+R4zmH8ULHc15tYsfTh3rB49km/n+gRtD47wtV5eUvejz7lQmfQERdHz/khLz8k56DJjwUIZwAAhAyDOb5v7gL5P/cs02+CAKk3SAvRrpCvnMaLIiA2BFF/pBAig5U3XGnTZuG+fPn44EHHkCPHj0AAJs3b8ZDDz2Ew4cPY9asWVwHaXWsImy8GCFwas6lXegA+okdgK/gAZS5PIC06BFsC+X2AKL5PYA84RMSTtPeS+X+BCIlgABIukCAehEEKHeDvCgRQp5z8hFDgHHhsqDzxqAo8kdKIJXT2l6mQNVd+aWXXsKrr76K2267zbdt4MCB6NixIx544IGYFT/uhDi4460jdIwUOZ7z8RE6gL5iB1AveDzHirQP4/KIPhcRPf7/lxI9nuehhU/gOl1SwkfK9dFc5RWKEOEvQL0AAsTyf+SJIECeGwTwFUKe82oLkQnOHwF3SHD+GBdFhHlQdad2Op3o1q1b0PauXbvC5YrS1HALY7TI8ZwzskLHMwZ+YsfTnzLBA+grevzbyglzAVAtfIII4fooSXb2D30FuT9+qBVAQHAYDAglePiIIMA4IeQZA4c5giLkDgnGQKKIMBhV4mfo0KF46aWXMH/+fMH2V155BXfccQeXgRHKiITA8ZyXn8jx9alS7ADWETxi28KJHv//y3J7AEXCJzDB2X+CQSnXR26Ju+cY4WSHooiEv8IJIO81+QsgILwLBASLIP/nnm3SIggIL4SkRBCgTgh5xqC/GAIiI4gAEkVasbsAu4K/TzGo1D2AxYsXY+3atbj88ssBAFu2bMHhw4cxbNgwTJgwwdcuUCAR2oiUyPGc21xCB+Avdjx98hE8nu1hnseH3h9+Xh95YS5AXPjIwRvuEsAp1weQkfgskv8jJoAAiFaCAcECCIDiUJhnu7gIAvi5QYB6IeQZB78QmWBMJnCH/CFRRGhFlfj59ddfcemllwIA9u/fDwCoX78+6tevj19//dXXzmbj9yUZa0RS5HjOz1/oAMaKHUBfwQPwcXkC9ysVPZ5twjAXEF74hAt3+SNW4cUbX+gLkAx/AeEFEBA6D8jzXJ4LFLjNs129CALku0EAPyHkGZN+YgiIvCACSBQR8lElfjZu3Mh7HDFJpAWOF72EDmC82AH4CB5PPyGOk+nyiG2TEj2BzwNDXJ5t4cNcQHBFF6Bc+Ii6PucRC3lpndwwJCLhL0CdAAIQ1gUC+IkggI8bBGgTQp4x6SeGAPO5Q/6QKCICsU5pkoUxi8gBzC10gMiKHU9fEscbLHr8j/PPk9FT+HjxT3IWuD4cBE5g3o9o4rP3XN52IQRQ4JjDCSAgvAsEhMr9US6CAGVuEGCMEPKMy1gxBJhLEAEkimIZEj8ccTvscMebQ+joKXJ854iQ2AHMKXjEtmsVPZ7tIcJcQMjEZkCZ8PFfwsJ3DpH1uwAdy9tV4O/+ANoEEBDeBfJuD9zm2a5dBAHKwmIAHyEE6Jcv5I+Z3SF/SBRFPyR+ogCrCB1fXzq6O57+tQkeQJvLAygTPYHP1bo9AB/hE1TaHsL1UVLlpQSl7k+gAPInUAABCBkG82zT5gJ59skTQQBfNwjQRwgBxokhwLyCCCBRFE2Q+LEYRggdIPJiBzCP4PHsk7lN5C9KcskKjW4PwEf4ePEKn1CuTyi05vtIVn0pFED+7g8gFEAAfxdIbLtnn7QIApS7QYA6IQRYQwwB1hNEAIkiK0Lix8QYJXQAc4gdgL/g8fQZph+NoS0gvOgJfB5O9AAibg/AVfj4ExjuCuK8uDEq5CVwfyTQSwB5tom7QEDkRBCgPCzmhZcrBBgTIvPHKuGyQMRE0Tm3cRPn2J2AXeOfLNPJ4Y00JH5MglWFDmCc2PGcS1/B49knf7tS0RP4XLbw8S/75iR8pMJdPtcnhNjRGvKSNdmhPyHcn0C0CiAgOAzm2SY374ePCAL0cYO86CWEgMiIIcA6gogwB+bJXIwh3I64oIeu50uwCR6a+3PYBQ+lVCfYfA9557P5HtL91jxE+wnzGrgTQoe3xBKZxfJ6At2dUM/9j3cnMEGYSzK/h7PjE4iU66M3gYJK0mXy2xcYnvO/hsD8pcD5iwLDfkCNCBJuC/5glLsSfGJH2b5EnxCSorTaIRBDUpS5En0PpZRUJ/kePCitThI8jKLEnSR4ENahsrISnTt3hs1mw44dOyTbFhQUYOjQocjKykKtWrVw6aWX4qOPPlJ8TnJ+dMRIN8d3Ts6uDqC9VF+pu+M5Jx+HB1Dn8oTap9XpCexDbZgL0CZ81Lg+Ea/yEpn40Etg+btSBwiArDAYIO4CAaHcHnGHyLMvvBMEKAuJAerDYgBfR8iL0SEyL+QOWYdHHnkE2dnZ+Pnnn8O2HTZsGM6ePYtVq1ahfv36ePvtt3HLLbfgxx9/RJcuXWSfk5wfjhjp5vjOydnVAbQ7O4Byd8dzXnkOj6f/8CXqWlwe3k5PYB9h3R7AcOETakLDSCLX/QGCHSB/wjlAQLALVOGKl+0CAVDlAnn283eCAG1uEMDfEQIi5wp5IXfIfHzxxRdYu3Yt5s2bJ6v9d999hwceeADdu3dH8+bN8fjjjyM9PR3btm1TdF5yfiyEHq6Or28OEzHq6fB4+pfRH0eXB5Dn9IhtC5XX49kX7PZ4tsvP7wHEhU84xMI8QI3wCXJ9RBKdg8NTRq71FeD+yMz/AcI7QEBwHhDA1wUKtc+zv0ak8MoL8qIlPwjQxxECIpMv5A+5Q5GlsLAQo0ePxsqVK5GSkiLrmJ49e+K9995D//79kZ6ejvfffx8VFRXo3bu3onOT+DEx0Sh2POc2TvB49ivfx0P0BPYTVvQAQW4PoEz4yElwBiTm9DEQxUnPMpAKfwHiAgiAZCI0IC6APNuDK8IAbSLI00afkBigLSwG6CeEgMiFyPyxamWZ3hQXFwueJyYmIjFRnasIAIwxjBgxAvfccw+6deuGgwcPyjru/fffx6233ooLLrgA8fHxSElJwYoVK9CyZUtF5zefzx3D6BHC8vXNIZQFqAtnec6vPKQlp2IrnNMjVbkVKqcnXHhLbJtUiMuzP7LCx59Q4a5Qrk+kCQp9BY4rTPgrMJwnJvjkJkIrCYMBoUNh3n1S+z1t9AmJAdrDYoA+oTEvkQ6ReQkMlVkpXGZ38nkAQKNGjZCWluZ7zJkzR/SckydPhs1mk3zs3r0bL7zwAkpKSjBlyhRF1zR16lScPXsWX375JX788UdMmDABt9xyC3755RdF/ZDzEyH0dHUAPs4OoN7d8YyBr8MDaHN5pPardXrEtoUSPYDyMBfAR/iECnfJwehE57ChLyBs+EupAwTIS4QGlIXBADnhrtBJ0TVtlDlBgLFuEKCvIwREPkTmTyy6Q0eOHEFqaqrveSjXZ+LEiRgxYoRkX82bN8eGDRuwefPmoH66deuGO+64A8uWLQs6bv/+/XjxxRfx66+/4pJLLgEAdOrUCV9//TUWLlyIRYsWyb4ey4ifM2fO4IEHHsAnn3wCu92OG2+8Ef/+979Ru3btkO2nT5+OtWvX4vDhw8jIyMDgwYMxe/ZspKWl+drZbME303feeQdDhgzhOn6riB3AOMHjOZfMfnUIbQHyRY/Y9vDreClwewBDhI9W10evJS3CIXfSQynUCCCATxgMCFf5FT4U5mknTwQB2kJigLmFEGCOEJmXWMgdSk1NFYifUGRkZCAjIyNsu+effx5PPPGE7/mxY8eQl5eH9957Dzk5OaLHlJeXAwDsATM3xsXFwe1W9v1gGfFzxx134Pjx41i3bh2cTidGjhyJMWPG4O233xZtf+zYMRw7dgzz5s1Du3btcOjQIdxzzz04duwYPvzwQ0HbpUuXIj8/3/c8PT1d01j1FjqAecQOEDnB42mjcl+IT76avJ7A/mS5PUDEhE/UoND9EUOrAALA3QWS2l/TTl8RBFhXCAGRF0NAbAgiLTRu3Fjw3GtktGjRAhdddBEA4OjRo+jTpw/eeOMNdO/eHW3atEHLli3xj3/8A/PmzcMFF1yAlStXYt26dfj0008Vnd8S4mfXrl1YvXo1fvjhB3Tr1g0A8MILL+Daa6/FvHnzkJ2dHXRM+/btBRMftWjRAk8++STuvPNOuFwuxMfXXHp6ejqysrI0j9Mdzz9XR9C/SQSPUrHjOZ/MvjUKnnD7tYgesW1y3R7PPnn5PUBwKTugPscnkLCujx/hQl5aK71CJT2Lhb5kuT8Kw1+ANgEEqHeBgMiIIEC9ENIiggC+641JYUYxBHgEUYWBy1tYHafTiT179vgcn4SEBHz++eeYPHkyBgwYgNLSUrRs2RLLli3Dtddeq6hvS4ifzZs3Iz093Sd8ACA3Nxd2ux1btmzB9ddfL6ufoqIipKamCoQPANx///24++670bx5c9xzzz0YOXKkaDjMS2VlJSora74EArPgecFT7ADmFTxAdIkeQJnbA+gjfBSFu/wxWchLEomJD71EWgAB4i4QED7fRw8RBETeDfJihCsEmCtERojTtGlTMMbCbmvVqpWqGZ0DsYT4KSgoQIMGDQTb4uPjUa9ePRQUFMjq49SpU5g9ezbGjBkj2D5r1ixcc801SElJwdq1a3HfffehtLQU48aNC9nXnDlzMHPmTOUXEgYziR3AHILH0079ft6iR6xPxW4PILuiC9Du+MgKd5lwgkMx1Lg/YmgVQIB4IjQQHAbz7FPvAslt42lnjAgCokMIASSGYpWIip/JkyfjmWeekWyza9cuzecpLi5G//790a5dO8yYMUOwb+rUqb7/d+nSBWVlZZg7d66k+JkyZQomTJgg6L9Ro0aKx8Vb7ACRETye8yo4h84uD6BM9ITazs3tAVTl9wAaytlFqruUuj6RXs5CVtUXIO7+yMj/0SKAAH1cICC8CAongDzt1IkgQJsQ4iGCAOOEEBDdYsjuAuwaU/1YlEbpIip+5JbEZWVl4cSJE4LtLpcLZ86cCZurU1JSgvz8fNSpUwcrVqxAQoL0t2lOTg5mz56NysrKkKV8aid3MqPYAcwjeDztNO7XQfSI9atHmAvgL3x4uT56hryUTnaotvLLDALIsy+0CwTwC4V52ioTQYC53CDAWCEEUIgsVoio+JFbEtejRw+cPXsW27ZtQ9euXQEAGzZsgNvtDlkSB3gcmby8PCQmJmLVqlVISgo/OdWOHTtQt25dTTNXetFD7ADWETxAbIkez35zCh89XB+ey1qEgqf7AxgvgIDQYTDPPv1DYZ62xoogIPqEEEBiKJqwRM5P27ZtkZ+fj9GjR2PRokVwOp0YO3YshgwZ4qv0CiyJKy4uRt++fVFeXo7//ve/KC4u9iUmZ2RkIC4uDp988gkKCwtx+eWXIykpCevWrcNTTz2Fhx9+WPVYSfDwEzxy2vAQPWLbxef/0RbmAtQJH61ILlxqkVwfWcgUQHLhIYAAfV0gbxtAfxEEaBdCvEQQYLwQAkgMRROWED8A8NZbb2Hs2LHo06ePb5LD559/3rc/sCRu+/bt2LJlCwAErflx4MABNG3aFAkJCVi4cCEeeughMMbQsmVLzJ8/H6NHj1Y1RrfDzm29EB5iB1AveDxjUHgug0RPKMEjdZwiB4iX2wNwET68w11KKrzMipZJD+W6P4ByAQSIv1/hBBCgzQVS0s7TVrkIAszpBgGREUIAhcisjGXET7169UJOaAgEl8T17t07qEQukPz8fMHkhpEmmgWPp622NmpET6h9ckSPp532MBcQOeGjxvUJDHlFusRddugL4B7+ApQJIEBdGMyzP7wLBESHCAKiWwgBJIbMjmXETzTCS+wA0St4AP1Fj9g5FIkeQFaYC9Bf+PgjtoCnqOujAt75PmpWeNe65AUPAQSIv3dawmBAaBcIUFLxJa+dp23kRBCgT1gMiJwQAkgMmR0SPwZjVcEDmEv0SB2vJcTlaWes8BFDrfARC3eFdX0sFvIKi8bcHyUCCOCfB+TZb7wL5GmvTQQB5nSDgMgKIaBGDFVUG2el2l2AXePvHSp1J1RDgkdZO7OIHoBPfg8gLXx4LVshFu7yR47rY7aQl5dQoS9F7o/M8BdgHgEEhHeBAPOIIIC/GwREnxAiIg+JHx3gKXYAbYIHINEj5zya3B5AVmIzwFf4yAl3xZzr40XGshdelAqgUKgVQEDoPCBPG2kXCFAWCgOsJYIA/cJiAAmhWIXED0eqE2ywmSBp2TMWFedUOPZIix6pfbq5PYCqxGZAP8cHCB/uUuP6WIWQ7o+C8JecFeC9hHJ/AHWVYID2MBigNNHZeBEEmNsNAoxbeJWIPCR+TEQkBA8QGZcHME70hDpXOLfH00ZemAswVviEq+7yRyB8FLg+Roe8wiU9K6r6kkLH/B9AXSUYwCcMBihNdDZOBAHWcYO8kCsUvZD4iSBaxQ5gjODxtOfXzmyiB9AW5gIiJ3xCIRru4oQRMzsrRZH7EwJe+T+ANgEE8AmDAUpEjXzB5GlvPhEEkBAi5EPix2AiKXiA6BM9Uvu4uD2A6sRmILzwUYpUdZci1ycAq4a8VKMw/GWkAAL4ukCAPqEwzzE1wiPSITGAhBAhHxI/BsBD8ADmc3nkttUieqT26+72ALLzewB1wodXnk/YJOdATBbykotU6Eux+2NxAeRpE94FAvQNhdUcZx43CDAmLAaQELIqJH50wmqCx3MM37ZmED2e9vqGuQBjhI+ccJc/cl0fIhgjBRAgnQgNhA+DedrwD4UpaV9znDlFEBB7QsjuBOwab0XMpD+EtELihyNuh02V6AjEyLCW5xjO7XQSPVL71Lo9nnbWEz48XR8zhbzUzPQsC4XujxS8BRBgvAsEWEcEASSECP6Q+DEJZhU8StqaRfR42nMIcwGaEpuByAmfaHZ9VIW+pOBQ/u7FDAIICO8CAWqSnJW1rzlOmwgC+LtBgHFhMUAohBIQpVaKxSDxE0G0CB7AOqInXF+q95nY7QH45/hwQYbrI5XvY8ZKL9koqPzyomYCRL0FECAdBvO0M5cL5DnW3CIIMEYIBa75RUQGEj8RwMwuj5L2WkVPuP16uD2edtYRPuT6KEfS/VER/jKbAAL0cYEA64kgwNpCiIgcsffNGCGqE2oeanAnKM8ncicod3rkJjLLCXGFEzZSCc2hZmgO5faIlbCLhbmiRfhEM3LcJV2q0VQIRakcK7GpB7xUueIk388qZ3zYBHevCxQOrwiSQ7mCtt72So+pOTZRUCqvltJqh0AM8aLMlSgQQ0R0Qc6PjkQirOU5Tp/2kXJ6pM7Nze0BTCV8wqHK9dEY8rISqtwfCaTyf9Q6QICxYTBPO73m+4msEwToExIDyA2KVkj8cEar4AHMFdoC9Bc9UvuViB4geoQP1+ouQj4qwl9AZAUQIC8M5mknLxcIUJfgbCYRBJAQsjsZ7DZt1ZPMqUP1pQmgb06OyBEJIY89H9YyS2gL0D+8FW6/UrdHdZjLQsJHCiWujxrMluwczqWSLOGXuhaJ46QWiFUbAgPCv8dy5nhSEgaTGwpTG9ZSGwrzHMsnHAboFxIDKCxmdcj5iSBa5gQyu9MTrg2vEBcg0+0BFOf3AJEXPoHwcn3MNLdPrGCUAwSED4N52ipzgQDjQmGe4/k4QYB+ITHAem4Q4YG+ASOAGofHc5z5nZ5wbcI5PVrdHk9bEbfHAsJHjHBrd3mRciTkuj5myveR6zJZyf0B+DhAvF0gJah1gWLBCQJq3CByhMwPOT8GYaTLo/QYI5yecPsNc3sAXYWPFpSEu0ImOQNhq5bI9ZGJDvk/gHYHCOCfBwTIS4YGtMz4bD4nCNDHDQKMnUSRUA6JH50xa2gLMLfo8Rwnr5LL01Z5mAsIn98DKBM+RoS7IoHZ8n24oaLyy4tVBBDAPwwGRIcIAvQNiQEUFjMr9BNQB9QmL3uO1Te0BcgLb3n71XLesPsl3B4lJexqwlyAuYUPN9fHgiEvpWgKfQGqw1/h0DsEBshf7FaPZGgv6uf50VYayzMcBugfEgM8QojnmAn1kPPDEXeCDTaD5uZReoySSjSzuT0A3zAXYB7hI0bgTY9naTuFvFSgMvwFmMcBAuSHwTxtreECefrQxwkC9HODjMLuAuK0ruoe4RV59IK+CSOMUtdG6TFyXR65/WotXVfj9vDO7zGT8NES7uLl+pgVnqE2Te5PGCSTzWE+B0hvFygSSdGePvi7Kka4QURkIPETAdSEtvyPk9XWRKLHO57Qxypze/RMbAYiK3zkhrt4YuWQlxfdryHMax9OAIWDlwDiHQbztDVunh8SQYRRkPgxEC2Cx6yix9tGzXi4uT0qJi4EzBXqAqTDXYGocX1iPeSl2f3RMf8H4COAAP55QJ62xrlAWo+t6UM/EURCiB+fffYZcnJykJycjLp162Lw4MGS7RljmDZtGho2bIjk5GTk5uZi7969is8b29+GBmCEywNETvQY4fZ42vPL7wEiL3zk3qC8RHIZi6it9OKM1vAXEBkBZFYXyHusGUUQQG4QDz766CMMHToUI0eOxM8//4xvv/0Wt99+u+Qxzz77LJ5//nksWrQIW7ZsQa1atZCXl4eKigpF56aEZ51Qk8Cs5jjeicxy2oXdr7foAaJO+JDrox2bM/TnyIvkgqdA+NJ3ieRnQHsCtBy8nxVeidCA8mRoQP68QACP8nbl64wF98E3MdqL3qXy0YrL5cKDDz6IuXPnYtSoUb7t7dq1C3kMYwwLFizA448/jkGDBgEA3njjDWRmZmLlypUYMmSI7PNH/zeiwahxedQcx9vpkdsu4sJHLMwFGCZ8tKJG+Ojp+pg938eUrpPG/B+tCdBeeCZCA8rCYJ72xiY283CBPP0kUkhMBcXFxYJHZaU2Ebl9+3YcPXoUdrsdXbp0QcOGDdGvXz/8+uuvIY85cOAACgoKkJub69uWlpaGnJwcbN68WdH5SfxwJNpFD+/cHoBTmEtmRRcP4cM7zyccgTdKpbM5E8HoWfklF6MFkJ5hMCuGwmr6iu6QmL2KcXkAQKNGjZCWluZ7zJkzR9PY/vjjDwDAjBkz8Pjjj+PTTz9F3bp10bt3b5w5c0b0mIKCAgBAZmamYHtmZqZvn1zomzNCxILo8RwfWvTwruYClLk9RgsfreEuWcRoyMsLNydLY/KznOovIwUQEF0uEI/jhX1FtwjiwZEjR1BUVOR7TJkyRbTd5MmTYbPZJB+7d++G2+35Xv7nP/+JG2+8EV27dsXSpUths9nwwQcf6H49lPNjIKqcIR1yeuS21SPEBZgzvwcwn/DR2/WRIxRMGXbiQNjcHzlozP+RQ7hJEL3ImQwR0C8PyNNeeS4QwCcfSMvxwr70zQsCAAdMHmsOQWpqKlJTU8O2mzhxIkaMGCHZpnnz5jh+/DgAYY5PYmIimjdvjsOHD4sel5WVBQAoLCxEw4YNfdsLCwvRuXPnsGPzh8SPAegtepScQ2/R4zleWYjLc4xMtweICuGjC1EqVCKGhnW/vPBIgLaSAPIco2x2aC9ak5qtIIIAoLSaj1tlVjIyMpCRkRG2XdeuXZGYmIg9e/bgiiuuAAA4nU4cPHgQTZo0ET2mWbNmyMrKwvr1631ip7i4GFu2bMG9996raJyx44dHAFUl7grCW0rOwa28PYzboya3R0t+D2Bu4RMKo1yfaAh5KXWfDE3i5vD68iiB96JXCExNGCwSoTBvH7zQKxxGeJyke+65B9OnT8fatWuxZ88en4C5+eabfe3atGmDFStWAABsNhvGjx+PJ554AqtWrcIvv/yCYcOGITs7O+z8QIFY5pvxzJkzuOOOO5Camor09HSMGjUKpaWlksf07t07KNZ4zz33CNocPnwY/fv3R0pKCho0aIBJkybB5dJ2w7Oa6PG2k9zPObfHc4y2MBdgfuGjdBZnQIbwEcPMAsGkyBKGHNw0Hvk/gD4CSM88IM8x6iu7tMAzH8jTH4kgPZg7dy6GDBmCoUOH4rLLLsOhQ4ewYcMG1K1b19dmz549KCoq8j1/5JFH8MADD2DMmDG47LLLUFpaitWrVyMpKUnRuS0T9rrjjjtw/PhxrFu3Dk6nEyNHjsSYMWPw9ttvSx43evRozJo1y/c8JSXF9//q6mr0798fWVlZ+O6773D8+HEMGzYMCQkJeOqppxSP0R0P2HScpwfgG96S005tiAvgJHoA2W4PYH7hI4biJOcocHOiijC5P4C8/J9IhMAAc4fBAK2LnvILhXn60y8cFoskJCRg3rx5mDdvXsg2jAnvCzabDbNmzRLc19VgiW/RXbt2YfXq1XjttdeQk5ODK664Ai+88ALeffddHDt2TPLYlJQUZGVl+R7+CVtr167Fb7/9hv/+97/o3Lkz+vXrh9mzZ2PhwoWoqtJ3wiq9nB5vWx79kfDhj+ZwlwK0hrysnOws19ni5v7I6MesDhBg3jAYwC8URk4Q4Y8lxM/mzZuRnp6Obt26+bbl5ubCbrdjy5Ytkse+9dZbqF+/Ptq3b48pU6agvLxc0G+HDh0Ecwbk5eWhuLgYO3fuDNlnZWVl0IRPctFb9PDK69GS20PCR124SxZiN0cLixQiGCsIIMDYMBjAJ5cn1kRQnJNxeUQjlgh7FRQUoEGDBoJt8fHxqFevnuTERrfffjuaNGmC7Oxs/O9//8Ojjz6KPXv2YPny5b5+xSZL8u4LxZw5czBz5kxF16BXeEtJW1mOkJFuD6BLfg9gPuEjRqRcHyvm+9idNsnPnra+ZZS9y6n84hT+kkukQ2CA+jAYoLwkHuAXxuKxXIawPwqHWY2Iip/JkyfjmWeekWyza9cu1f2PGTPG9/8OHTqgYcOG6NOnD/bv348WLVqo7nfKlCmYMGGC73lxcTEaNWok2jaWRY/nOO1uD2B94cNlMkNyfRQhZ60vRRgogOSuAaaXAALk/014HSCjcoEAc+YDefokEWQVIip+5E6GlJWVhRMnTgi2u1wunDlzxjfpkRxycnIAAPv27UOLFi2QlZWFrVu3CtoUFhYCgGS/iYmJSEyUtjqjRfR4+olu4aM3ckIPPF0fQjlcJj1UgNkFEGB+FwjgteApiaBYJKLiR+5kSD169MDZs2exbds2dO3aFQCwYcMGuN1un6CRw44dOwDANzNkjx498OSTT+LEiRO+sNq6deuQmpoqubKsFHqKHiXtTen2ALrl9wDqhY/Zwl2iKHB9eIS8rJzsrCuc3B+56CGAAPk/BowQQJ7jIusC8exH2CeJILNiiZ+Xbdu2RX5+PkaPHo2tW7fi22+/xdixYzFkyBBkZ2cDAI4ePYo2bdr4nJz9+/dj9uzZ2LZtGw4ePIhVq1Zh2LBhuOqqq9CxY0cAQN++fdGuXTsMHToUP//8M9asWYPHH38c999/f1hnRwy9EpmVtOeR0OzpRwe3R+PEhYD1hI+aJGdyffihJL+J64SQnKq/AJniGMpCqXonQqtNhtaaEG3GpGhPn+ZOjI5FLPMt+9Zbb6FNmzbo06cPrr32WlxxxRV45ZVXfPudTif27Nnjq+ZyOBz48ssv0bdvX7Rp0wYTJ07EjTfeiE8++cR3TFxcHD799FPExcWhR48euPPOOzFs2DDN8weEI1KiB5AneswY5gKiQ/hwc31iENO4URzHEa0CCFBXDeY5zhwzPOslgs5VkwgyA5ao9gKAevXqSU5o2LRpU8FkSI0aNcL//d//he23SZMm+Pzzz7mMMRx65PXIbRuR3B4g6oWPEuTcmGS7PipCXrEK98RnucgMf8mtAOMdAgP0TYQGtIXBAG25QACvdb74VoYZib3KDTvTFoK1O43LhTMS+qY0AN5z9ShpKzfEZViYC4gq4aMl3GW062PFEnejkS0e5bo/ERKjejlAgHFhMM+x0esCEZGFxI+OqMnr4dkvD7eHe5grRoUPuT5RiInDX4C5BBCgLQxmhlwg3n0RkYW+LXVAr2Rmb9uwbQxwe3jm94QqZbei8BFD7CZk5lwf0+TWiKB2bBFLfAZkv48kgMIda54lLkgEWR8SPxzRW/QY4fYA5s3vAcwhfKRQG+7S6vqEg0JeOsE5/BUrAihSLhDALxTm7YtEkDUh8RMB9BI9PNweqSUqIpnfA5hH+BgS7lLoPlDIiy9WeD2tLICA6HGB9OiP0B/z/4VHGbrkAJnR7QFMIXx4o/aL3ouSGxbBF93crwi5P4C5BJDVwmCAPiKIsAaWKXW3OlYUPZ7j+YW5AH6JzYA84RPJcBd314dCXoaiy5IXnMvfAfkl8IB+ZfC+YxTOCA2oXxvMc6y2kngvPMvZ9ZgpWi12F4Md2hYEtruic1V3+hmqM2YNcQEak5pjUPhoDXfp5frwDNGYOdnZiynHqMOY9JrpW08HCLBmGAygUFisQeJHJ3Qrc+fk9hgd5opW4SMXTUnOAK3ezgmlLpguuT869KlnONVKAoiXCOIJiSBzQuKHM7qt2WWA2+Ppw5j8HiA6hI/acFdIKNHZuigRqBbK/wGMF0DR5gLp1SehHvrm5IhuZe4GuT2q83uiWPhIoSXcZZTrQ/k+BhPh8JfeAsioRGjAPC6QHiKIiDwkfgxGqegxhdvDOb/HSsJHa7hLEeTiGIIpQl+AovfbLAIIsE4YzHM8v0kNieiCvm0NhKfo8fQXPgvf6DAXwLeUHTCn8DHM9ZGA903ZlInEITD1WHUIfwGxLYCi1QUiIgeVuhuAonCYGUQPQMJHofDRDOfydoBCXjzRpexdZ/QqgfdiVCm8F7UrxNccn6C5JB4wVyl7OOxV1bC7tc2FZncZO5eaUZDzoyO8Q1yePg1we1Tm90SL8FGD0a4PoQ3dhaEJ3B+lmN0BAviEwSgURgAkfnRDD7dHa1Kzpx/jwlyANYWPXuGukKhwfajKy3h0fc1NEP4CjBVAkQqDefqgBU5jHfoG5YyZ3Z5oEj56oWe4i1yfGEPHvKRoEEBAdLlAJIKsBX0bc0SOkPG24+X2AObL7wH0Fz5WC3eFRIdcH0BNRZOJE4hDoHXMakJfZnF/lEICSKoPWtsrFiHxYzCyBZJM0aN57h4SPj70nMyQp+tDIS8LoVSg6Zj/QwJIqg9ygWIN+hY1CMu5PQYLH7mYRfiEQlGSM4kYS6JYfJok/AVEtwAiF4hQApW6GwBvtyd8P+YKcwHhhY9ZE5xDoevCpRoTnanEXT42p7y/KUORufK7FyUrwKvBqDJ4oOZvMVLl8J4++JTEA5Evi7dXuWF3a/ts2F3WmuJBLvTTU0ci4faYLcwFmFv4hEO3JGdyfbhgmVwlE41TjUg30gECoisMBlAozIzQN7AOyBU9AJ/cHk8/GtweIGaFj5pwV6RcH0IfTJf4DCgWx3qHvwDrCSCzhcEACoWZCRI/nFEiekwT5iLhIxsuSc4qb5x6hbws455YHR2TnwESQGKY0QU6x2FM0cCmTZtgs9lEHz/88IPoMWfOnMEDDzyA1q1bIzk5GY0bN8a4ceNQVFSk+Pz0LnCExQNyvt7kiB6Ag9sDqA5zAbEtfLQmOauCRIilMOOSF0rzf5QsgeFFbQ4QoG4OLy1LYgB88oA8/fDLBSKAnj174vjx44JtU6dOxfr169GtWzfRY44dO4Zjx45h3rx5aNeuHQ4dOoR77rkHx44dw4cffqjo/CR+DISX6PH0pV+YC4hu4RMOJeGuUFCuj7UwLPHZaQv/tyloryz5WQ1GCSBAWyK0VgEEgEsytKcfEkFacTgcyMrK8j13Op34+OOP8cADD8BmE/8h2L59e3z00Ue+5y1atMCTTz6JO++8Ey6XC/Hx8iUNfRMbhFWEj9QaXUD4UnYewkdveOX5AMa5PjS3T2giGbZT9b6YLPwFGBcCA7SVwpshDObph3J3eLNq1SqcPn0aI0eOVHRcUVERUlNTFQkfgJwf3TFU9AC6JTYD2ufwifQkhoD2HAJ/Qt0wIuX6UIk74UVN+bsVHCDAXGEwIHZcoOLiYsHzxMREJCYmcut/8eLFyMvLw0UXXST7mFOnTmH27NkYM2aM4vPRz0kdMdzt0SmxGYgN4cMj3KUHero+lOxcg1rxaEb3BzBuLTmjHSCATyJ0LLhAdmc17FUubQ+n57u7UaNGSEtL8z3mzJkjes7JkyeHTGT2Pnbv3i045s8//8SaNWswatQo2ddWXFyM/v37o127dpgxY4bi14acHx3gKXo8/UUuvweIDuHDG66uDwkQQg4mzf8BrOkAAeQCKeHIkSNITU31PQ/l+kycOBEjRoyQ7Kt58+aC50uXLsUFF1yAgQMHyhpLSUkJ8vPzUadOHaxYsQIJCcoFKIkfzlgpzAXom9gMmEf4WNX1kQuFvCKLqsovpcnPKjAq/AWQAPL0Fb0VYampqQLxE4qMjAxkZGTI7pcxhqVLl2LYsGGyRExxcTHy8vKQmJiIVatWISkpSfa5/KGwF0dMFeYCSPich7fwMdL1oURnefAK35leRBoU/lKbyB+pEJjZwmBmDoWZjQ0bNuDAgQO4++67g/YdPXoUbdq0wdatWwF4hE/fvn1RVlaGxYsXo7i4GAUFBSgoKEB1tTIBTc6PgVjB7QGiS/iEQ+vyFV6Myq8gzIth7o+K8Fe0O0AAuUBWZfHixejZsyfatGkTtM/pdGLPnj0oLy8HAGzfvh1btmwBALRs2VLQ9sCBA2jatKns85L4MQgSPiLtDBA+an8RcittN3GuDyU7E+FQK4DUEo0CCIjuXCCtvP322yH3NW3aFIzV3PN69+4teK4F+rmqM3LW5QJI+OhBxMNdGpEb8jJ9qMZiGP56qhGhJq7+ArTlyml1Y3lMZ8EzDObpj8JgZsMy4ufMmTO44447kJqaivT0dIwaNQqlpaUh2x88eDBkmd0HH3zgaye2/9133+UyZrmiJ9L5PQAJH12gfJ2YQ3WOlokFkNH5P4A5BBDAb1JET1/G5wLZqlxcHtGIZcJed9xxB44fP45169bB6XRi5MiRGDNmTEjLrFGjRkHrhrzyyiuYO3cu+vXrJ9i+dOlS5Ofn+56np6drGiu3EnZAs+gBYk/4aMEw14cSnbljd9pkFx3EMlbI/wH4hMAA7d85PMNgnv7IBTIDlhA/u3btwurVq/HDDz/4Fjx74YUXcO2112LevHnIzs4OOiYuLk6wbggArFixArfccgtq164t2J6enh7UVi1mEj5yvuCMFD5GEUuuD4W89EHLWl+qFzw1KPlZLVYUQAC/PCBA+9pghHmwxM/LzZs3Iz09XbDSa25uLux2uy/zOxzbtm3Djh07RGeQvP/++1G/fn10794dS5YsUZ1QZaUwF2C88DF7uMssro8RULJzFGFg/k+kQmDRGAYjIosl3smCggI0aNBAsC0+Ph716tVDQUGBrD4WL16Mtm3bomfPnoLts2bNwjXXXIOUlBSsXbsW9913H0pLSzFu3LiQfVVWVqKystL3PHDNk1BwcXsA3fN7gOgUPlJwXbhUo+tDIS/rY6j7oxI14S8gMg4QYI5KMIB/GIyIDBH9llWzBogazp07h7ffflvU9Zk6dSr+9re/oUuXLnj00UfxyCOPYO7cuZL9zZkzR7DGSaNGjcKOgYRP5OEZ7jKD60MhL32x1OurUjBbyQECzJUITS6QtYnouyd3DZCsrCycOHFCsN3lcuHMmTOycnU+/PBDlJeXY9iwYWHb5uTkYPbs2aisrAy5dsmUKVMwYcIE3/Pi4uKQAkj2rz8TJDYD1hU+Roa7JCHXJqKYKenZcPfHwPwfLZjBAQL4fDeRC2RdIip+5K4B0qNHD5w9exbbtm1D165dAXimxHa73cjJyQl7/OLFizFw4EBZ59qxYwfq1q0bUvgAngXdpPZ7McrtAUj4qEXNL1E950uhkBdhNEaHv4DICyAgNsJgtkoXbHHa3DJbtTmvTSuW8O3atm2L/Px8jB49GosWLYLT6cTYsWMxZMgQX6XX0aNH0adPH7zxxhvo3r2779h9+/bhq6++wueffx7U7yeffILCwkJcfvnlSEpKwrp16/DUU0/h4Ycf1jxms4S5gOgWPnJQY5WrtfUloSRjy6Gl6guwjvtDAkgbVA1mPSwhfgDgrbfewtixY9GnTx/Y7XbceOONeP755337A9cA8bJkyRJcdNFF6Nu3b1CfCQkJWLhwIR566CEwxtCyZUvMnz8fo0ePVj1ObmEugISPDPQId0kh6foY6NpoyUehSi+LQAIoLGYSQIC5XSBCiI3xWigjhikuLvYkPs+bDXtyknRjg8JcAAkfgH+uj2rxI0NwKAl5kfiRhmfejxbnB1Dwg0gMtdeh8pxqxI8XLWuAaRFAAJ+5xnh+d0kJIFdZJbZe/28UFRUhNTWV2zn98d6TcluMR3xc+BQNKVzVlfhy/wJdxxsJKMHASEwkfOITqkn4iKCL8CEsjdaqL015XGqFqsHVX4C2UHGkq8AAfpVgAFWDWQH6xjYKkwkfOZhV+MjB0HBXOEzk+hAxBAkg5X0447mLIMKckPgxAgMruiIhfIyG55eTP6q/uMn1IcIQEfdHA0auAO+PGQQQwN8FIswHvSt6Ijdeb6LEZkC58ImGcJcUkboR6EEs5Pvogdaqr4gRgbl/tCRAA+ZIggaiJBHaWQVUa/ybd1fxGYvJiJ5vdbMh1+0h4SMbPYWPbq4PhbwighlFXsTcH4uFv4DodYDIBTIPJH70wET5PUB0CJ9IEU2uD6GNWBSeJID4h9krq0kAmQH6ZueJnNXYARI+KohW14eILazm/gDWF0BmTIQmIg+JH6Mh4aMYrcJHCtWl7Zyg5SwIRURIAGkh0gIIMK8LREQO+uY1Chn5PQAJn0B4fNnw+PIMIgKuj/b5ZsiJ0gqP0JcVBa/WHwIkgAizYb2/QisiU/SQ8FGHHuEucn2sT9SKPQuGv3hAAojgCX376g0ntweIPeGjZ7hLEyRaCI1YVfhGMv8HIAFE8IPePT0xMMwFxJ7wCUfEXB8ThrwIfphizh+1i54CEZn7x4vWOYAA7fMAAXznAgLM64qjygXYNX6fuU16bRqx5s8Ps2Nwfg8QHTM3K8Xw6i6Am+tj1V/+RJQQwfAXDweIBzxdY3KBrIc5PoXRhEyxYhXhY1bXR69wlxldHx5Ebf6LhdEsgCP4nkZaAPEqYiABFLuQ+OEJCR/NRDLcFRaT/GIl5KOX6IuKMKTGzzMJIJG+SABZBvo2NxCeFV1A7Aofq7s+Sn/xR8WNlhAl4u5PhAU9CSAiUpD4MQieFV1A9AkfuYT7ktItyZlcH8KsWDj8xQOzCiASQeYm8p/cGICET3hMO5mhXHRyfQjzwsuRi/hnwuLhL8CcAgggF8jM0DujMyR8wmNEuMuqrg+FvAhZaCl9BzSXvzOXXfZ3nRhmKYEH+JXB+/pzxkfsu5VVVoLZNXwuADB3FafRmAv6GaojJHzCw0v46JbkHAXEeqWXFa4/4u4PB8gBkuiPHCDTYf2/OJMiN7E5loUPL7R84XFxfSjkFbOYypmzePIzYD4BRInQ0UvkP+1Rhh4VXdEqfCId7jI7prqxErpjCnEc4fwfwFwCCKBEaD35/fffMWjQINSvXx+pqam44oorsHHjRtnH33PPPbDZbFiwYIHic5vgry16iFSYC4hd4WMV14cgDMEEn0cSQIRcrrvuOrhcLmzYsAHbtm1Dp06dcN1116GgoCDssStWrMD333+P7OxsVecm8WMwJHyMw0yujyl+1ccweub9mM6hM0H4ywwl8AAJIDNz6tQp7N27F5MnT0bHjh3RqlUrPP300ygvL8evv/4qeezRo0fxwAMP4K233kJCgrqF9szxCY0RzCJ8Ig25PsZhhWRfogbTiGQTjIPXjxcSQHwoLi4WPCorKzX1d8EFF6B169Z44403UFZWBpfLhZdffhkNGjRA165dQx7ndrsxdOhQTJo0CZdcconq81Pw0SDMJHxiIdxlJtdHDaZzEwhrobX0nQNay98BPiXwAL8yeIB/KbyesMpKMJvGUnfmKXVv1KiRYPv06dMxY8YM1f3abDZ8+eWXGDx4MOrUqQO73Y4GDRpg9erVqFu3bsjjnnnmGcTHx2PcuHGqzw2Q82MIJHw8GJXoF074GD2vj2l+zRO6wVOsmubzYpLwFzlA5uDIkSMoKiryPaZMmSLabvLkybDZbJKP3bt3gzGG+++/Hw0aNMDXX3+NrVu3YvDgwRgwYACOHz8u2ve2bdvw73//G6+//jpsNm2uNjk/OkPCRzl6hru4QeEkwuzwcH80Tn4IkAMULaSmpiI1NTVsu4kTJ2LEiBGSbZo3b44NGzbg008/xV9//eXr9z//+Q/WrVuHZcuWYfLkyUHHff311zhx4gQaN27s21ZdXY2JEydiwYIFOHjwoOzrIfGjIyR8ajBLuMsKrg+FvPTB7rTBHeFQkFzsTjvcHG720QQJIGuQkZGBjIyMsO3Ky8sBAHa78DvSbrfD7RZ/n4cOHYrc3FzBtry8PAwdOhQjR45UNE6T+KvRBwmfGqJuXguLuD6U7GwsphStPD4DJgl/ARQCiyZ69OiBunXrYvjw4fj555/x+++/Y9KkSThw4AD69+/va9emTRusWLECgCdJun379oJHQkICsrKy0Lp1a0XnJ/HDGT1mbQZiQ/hEm+tDEFowTe4PYCoBxAsSQJGlfv36WL16NUpLS3HNNdegW7du+Oabb/Dxxx+jU6dOvnZ79uxBUVER9/NH2U/yyGJXENeOBeGjBK1fHpqFj1wU/JKmkFfsYXMCTN20I/phgsovL2bK/wEoBBZpunXrhjVr1ki2YUz6s6skz8cfEj8RIFaED89wl+5Jzmb6lU0Q5+GW+2OS5GdekACSB6us4lDqHp2/yOgb32BI+Ii0jXS4i4gZKA9KIyYKf/Gcy4tCYLEH3RUMJNpnbvbCU/iYCp1DXjyhm3zkMG3o0kSfiVgQQJb6botBLCN+nnzySfTs2RMpKSlIT0+XdQxjDNOmTUPDhg2RnJyM3Nxc7N27V9DmzJkzuOOOO5Camor09HSMGjUKpaWl3MdvlPAxg+vDE0NcH5OEvEx70yQiSqSFdBCcxhPtAgiw2I+7GMNkf1Whqaqqws0334x7771X9jHPPvssnn/+eSxatAhbtmxBrVq1kJeXh4qKCl+bO+64Azt37sS6devw6aef4quvvsKYMWO4jj2WhI+R4S5DsZDrQxAh4eX+mOwzTgKIUIplEp5nzpwJAHj99ddltWeMYcGCBXj88ccxaNAgAMAbb7yBzMxMrFy5EkOGDMGuXbuwevVq/PDDD+jWrRsA4IUXXsC1116LefPmITs7W/O4SfiEaMvhCyGaXB8iuuBd9RWtkx7yqP6yAmZLhCYs5Pwo5cCBAygoKBDMBpmWloacnBxs3rwZALB582akp6f7hA8A5Obmwm63Y8uWLZrHwKuCIBxmED68MdXCpQbkSlDIyzhiPh/KZO5PLIS/AHKAzIZlnB+lFBQUAAAyMzMF2zMzM337CgoK0KBBA8H++Ph41KtXz9dGjMrKSlRWVvqeeydgcp+r2RaXUI1qBTc0R7wb1VXy2/uOS3ApOo9eeFwfeWKvymUHID1odzjxE+YLk6vroyTk5bJDze9Y7uIn1m/wYXBX6z/vDeP926cCcPNySVycrv8cuJW/83CA3OeUzbcmRRU83+M8OQcgrspznwg3fw0PXHACGk/jCvNdbVUiKn4mT56MZ555RrLNrl270KZNG4NGJI85c+b4wnD+HL5/bgRGQxAEQViN06dPIy0tTZe+HQ4HsrKy8HXBJ1z6y8rKgsPh4NKXWYio+JG7+qsasrKyAACFhYVo2LChb3thYSE6d+7sa3PixAnBcS6XC2fOnPEdL8aUKVMwYcIE3/OzZ8+iSZMmOHz4sG4fZr0pLi5Go0aNcOTIEVkr95qVaLiOaLgGgK7DTETDNQDRcR1FRUVo3Lgx6tWrp9s5kpKScODAAVRVqQgniOBwOJCUlMSlL7MQUfEjd/VXNTRr1gxZWVlYv369T+wUFxdjy5YtvoqxHj164OzZs9i2bRu6du0KANiwYQPcbjdycnJC9p2YmIjExMSg7WlpaZb9g/SSmppq+WsAouM6ouEaALoOMxEN1wBEx3UErmbOm6SkpKgTLDyxTMLz4cOHsWPHDhw+fBjV1dXYsWMHduzYIZiTx3/1V5vNhvHjx+OJJ57AqlWr8Msvv2DYsGHIzs7G4MGDAQBt27ZFfn4+Ro8eja1bt+Lbb7/F2LFjMWTIEC6VXgRBEARBmA/LJDxPmzYNy5Yt8z3v0qULAGDjxo3o3bs3gODVXx955BGUlZVhzJgxOHv2LK644gqsXr1aoIbfeustjB07Fn369IHdbseNN96I559/3piLIgiCIAjCcCwjfl5//fWwc/wEZs/bbDbMmjULs2bNCnlMvXr18Pbbb2saW2JiIqZPny4aCrMK0XANQHRcRzRcA0DXYSai4RqA6LiOaLiGaMDGjKi3IwiCIAiCMAmWyfkhCIIgCILgAYkfgiAIgiBiChI/BEEQBEHEFCR+CIIgCIKIKUj8yODJJ59Ez549kZKSgvT0dFnHMMYwbdo0NGzYEMnJycjNzcXevXsFbc6cOYM77rgDqampSE9Px6hRowTzFvFG6fkOHjwIm80m+vjggw987cT2v/vuu6a4BgDo3bt30PjuueceQZvDhw+jf//+SElJQYMGDTBp0iS4XPotGKv0Os6cOYMHHngArVu3RnJyMho3boxx48YJpnYA9H8vFi5ciKZNmyIpKQk5OTnYunWrZPsPPvgAbdq0QVJSEjp06IDPP/9csF/O3wlvlFzDq6++iiuvvBJ169ZF3bp1kZubG9R+xIgRQa95fn6+rtcAKLuO119/PWiMgRPgmf29EPs7ttls6N+/v69NJN6Lr776CgMGDEB2djZsNhtWrlwZ9phNmzbh0ksvRWJiIlq2bClayaz0b41QCCPCMm3aNDZ//nw2YcIElpaWJuuYp59+mqWlpbGVK1eyn3/+mQ0cOJA1a9aMnTt3ztcmPz+fderUiX3//ffs66+/Zi1btmS33XabTleh/Hwul4sdP35c8Jg5cyarXbs2Kykp8bUDwJYuXSpo53+dkbwGxhjr1asXGz16tGB8RUVFguts3749y83NZT/99BP7/PPPWf369dmUKVN0uQY11/HLL7+wG264ga1atYrt27ePrV+/nrVq1YrdeOONgnZ6vhfvvvsuczgcbMmSJWznzp1s9OjRLD09nRUWFoq2//bbb1lcXBx79tln2W+//cYef/xxlpCQwH755RdfGzl/JzxReg233347W7hwIfvpp5/Yrl272IgRI1haWhr7888/fW2GDx/O8vPzBa/5mTNndBm/2utYunQpS01NFYyxoKBA0Mbs78Xp06cF4//1119ZXFwcW7p0qa9NJN6Lzz//nP3zn/9ky5cvZwDYihUrJNv/8ccfLCUlhU2YMIH99ttv7IUXXmBxcXFs9erVvjZKXxtCOSR+FLB06VJZ4sftdrOsrCw2d+5c37azZ8+yxMRE9s477zDGGPvtt98YAPbDDz/42nzxxRfMZrOxo0ePch87r/N17tyZ3XXXXYJtcv7geaD2Gnr16sUefPDBkPs///xzZrfbBTeDl156iaWmprLKykouY/eH13vx/vvvM4fDwZxOp2+bnu9F9+7d2f333+97Xl1dzbKzs9mcOXNE299yyy2sf//+gm05OTnsH//4B2NM3t8Jb5ReQyAul4vVqVOHLVu2zLdt+PDhbNCgQbyHKonS6wj33WXF9+K5555jderUYaWlpb5tkXgv/JHz9/fII4+wSy65RLDt1ltvZXl5eb7nWl8bIjwU9tKBAwcOoKCgALm5ub5taWlpyMnJwebNmwEAmzdvRnp6Orp16+Zrk5ubC7vdji1btnAfE4/zbdu2DTt27MCoUaOC9t1///2oX78+unfvjiVLlgRNOMkDLdfw1ltvoX79+mjfvj2mTJmC8vJyQb8dOnRAZmamb1teXh6Ki4uxc+dOU12HP0VFRUhNTUV8vHCuUj3ei6qqKmzbtk3wmbbb7cjNzfV9pgPZvHmzoD3geV297eX8nfBEzTUEUl5eDqfTGbQo5aZNm9CgQQO0bt0a9957L06fPs117P6ovY7S0lI0adIEjRo1wqBBgwSfbSu+F4sXL8aQIUNQq1YtwXYj3ws1hPu74PHaEOGxzAzPVqKgoAAABDdT73PvvoKCAjRo0ECwPz4+HvXq1fO14T0mredbvHgx2rZti549ewq2z5o1C9dccw1SUlKwdu1a3HfffSgtLcW4ceO4jR9Qfw233347mjRpguzsbPzvf//Do48+ij179mD58uW+fsXeK+8+3vB4L06dOoXZs2djzJgxgu16vRenTp1CdXW16Ou0e/du0WNCva7+fwPebaHa8ETNNQTy6KOPIjs7W3Bjys/Pxw033IBmzZph//79eOyxx9CvXz9s3rwZcXFxXK8BUHcdrVu3xpIlS9CxY0cUFRVh3rx56NmzJ3bu3ImLLrrIcu/F1q1b8euvv2Lx4sWC7Ua/F2oI9XdRXFyMc+fO4a+//tL8OSXCE7PiZ/LkyXjmmWck2+zatQtt2rQxaETqkHsdWjl37hzefvttTJ06NWif/7YuXbqgrKwMc+fOlX3D1fsa/AVChw4d0LBhQ/Tp0wf79+9HixYtVPcbiFHvRXFxMfr374927dphxowZgn1a3wsiNE8//TTeffddbNq0SZAsPGTIEN//O3TogI4dO6JFixbYtGkT+vTpE4mhBtGjRw/06NHD97xnz55o27YtXn75ZcyePTuCI1PH4sWL0aFDB3Tv3l2w3QrvBWEOYlb8TJw4ESNGjJBs07x5c1V9Z2VlAQAKCwvRsGFD3/bCwkJ07tzZ1+bEiROC41wuF86cOeM7Xg5yr0Pr+T788EOUl5dj2LBhYdvm5ORg9uzZqKyslLV+jVHX4D8+ANi3bx9atGiBrKysoEqKwsJCADDde1FSUoL8/HzUqVMHK1asQEJCgmR7pe9FKOrXr4+4uDjf6+KlsLAw5JizsrIk28v5O+GJmmvwMm/ePDz99NP48ssv0bFjR8m2zZs3R/369bFv3z5dbrharsNLQkICunTpgn379gGw1ntRVlaGd999V3LNRi96vxdqCPV3kZqaiuTkZMTFxWl+fwkZRDrpyEooTXieN2+eb1tRUZFowvOPP/7oa7NmzRrdE57Vnq9Xr15BlUWheOKJJ1jdunVVjzUUvF6zb775hgFgP//8M2OsJuHZv5Li5ZdfZqmpqayiooLfBZxH7XUUFRWxyy+/nPXq1YuVlZXJOhfP96J79+5s7NixvufV1dXswgsvlEx4vu666wTbevToEZTwLPV3whul18AYY8888wxLTU1lmzdvlnWOI0eOMJvNxj7++GPN4w2Fmuvwx+VysdatW7OHHnqIMWad94Ixz/dwYmIiO3XqVNhzGPFe+AOZCc/t27cXbLvtttuCEp61vL9EeEj8yODQoUPsp59+8pV5//TTT+ynn34SlHu3bt2aLV++3Pf86aefZunp6ezjjz9m//vf/9igQYNES927dOnCtmzZwr755hvWqlUr3Uvdpc73559/statW7MtW7YIjtu7dy+z2Wzsiy++COpz1apV7NVXX2W//PIL27t3L/vPf/7DUlJS2LRp00xxDfv27WOzZs1iP/74Iztw4AD7+OOPWfPmzdlVV13lO8Zb6t63b1+2Y8cOtnr1apaRkaF7qbuS6ygqKmI5OTmsQ4cObN++fYJSXpfLxRjT/7149913WWJiInv99dfZb7/9xsaMGcPS09N9VXJDhw5lkydP9rX/9ttvWXx8PJs3bx7btWsXmz59umipe7i/E54ovYann36aORwO9uGHHwpec+/ffklJCXv44YfZ5s2b2YEDB9iXX37JLr30UtaqVStdhLPa65g5cyZbs2YN279/P9u2bRsbMmQIS0pKYjt37hRcq5nfCy9XXHEFu/XWW4O2R+q9KCkp8d0TALD58+ezn376iR06dIgxxtjkyZPZ0KFDfe29pe6TJk1iu3btYgsXLhQtdZd6bQjtkPiRwfDhwxmAoMfGjRt9bXB+fhUvbrebTZ06lWVmZrLExETWp08ftmfPHkG/p0+fZrfddhurXbs2S01NZSNHjhQIKt6EO9+BAweCrosxxqZMmcIaNWrEqqurg/r84osvWOfOnVnt2rVZrVq1WKdOndiiRYtE20biGg4fPsyuuuoqVq9ePZaYmMhatmzJJk2aJJjnhzHGDh48yPr168eSk5NZ/fr12cSJEwUl5JG+jo0bN4p+BgGwAwcOMMaMeS9eeOEF1rhxY+ZwOFj37t3Z999/79vXq1cvNnz4cEH7999/n1188cXM4XCwSy65hH322WeC/XL+Tnij5BqaNGki+ppPnz6dMcZYeXk569u3L8vIyGAJCQmsSZMmbPTo0YbcpJRcx/jx431tMzMz2bXXXsu2b98u6M/s7wVjjO3evZsBYGvXrg3qK1LvRai/Te/Yhw8fznr16hV0TOfOnZnD4WDNmzcX3Du8SL02hHZsjOlQk0wQBEEQBGFSaJ4fgiAIgiBiChI/BEEQBEHEFCR+CIIgCIKIKUj8EARBEAQRU5D4IQiCIAgipiDxQxAEQRBETEHihyAIgiCImILED0EQBEEQMQWJH4IgTE9FRQVGjBiBDh06ID4+HoMHD470kAiCsDAkfgiC0AxjDC6XS7f+q6urkZycjHHjxiE3N1e38xAEERuQ+CGIGKR3794YO3Ysxo4di7S0NNSvXx9Tp06Fd7WbN998E926dUOdOnWQlZWF22+/HSdOnPAdv2nTJthsNnzxxRfo2rUrEhMT8c0332D//v0YNGgQMjMzUbt2bVx22WX48ssvBedu2rQpnnjiCQwbNgy1a9dGkyZNsGrVKpw8eRKDBg1C7dq10bFjR/z444++Y2rVqoWXXnoJo0ePRlZWljEvEkEQUQuJH4KIUZYtW4b4+Hhs3boV//73vzF//ny89tprAACn04nZs2fj559/xsqVK3Hw4EGMGDEiqI/Jkyfj6aefxq5du9CxY0eUlpbi2muvxfr16/HTTz8hPz8fAwYMwOHDhwXHPffcc/jb3/6Gn376Cf3798fQoUMxbNgw3Hnnndi+fTtatGiBYcOGgZYeJAhCD2hhU4KIQXr37o0TJ05g586dsNlsADxCZtWqVfjtt9+C2v/444+47LLLUFJSgtq1a2PTpk24+uqrsXLlSgwaNEjyXO3bt8c999yDsWPHAvA4P1deeSXefPNNAEBBQQEaNmyIqVOnYtasWQCA77//Hj169MDx48eDnJ4RI0bg7NmzWLlypdaXgSCIGIWcH4KIUS6//HKf8AGAHj16YO/evaiursa2bdswYMAANG7cGHXq1EGvXr0AIMjB6datm+B5aWkpHn74YbRt2xbp6emoXbs2du3aFXRcx44dff/PzMwEAHTo0CFom3+ojSAIghckfgiCEFBRUYG8vDykpqbirbfewg8//IAVK1YAAKqqqgRta9WqJXj+8MMPY8WKFXjqqafw9ddfY8eOHejQoUPQcQkJCb7/ewWY2Da3283vwgiCIM4TH+kBEAQRGbZs2SJ4/v3336NVq1bYvXs3Tp8+jaeffhqNGjUCAEHysRTffvstRowYgeuvvx6Axwk6ePAg13ETBEFohZwfgohRDh8+jAkTJmDPnj1455138MILL+DBBx9E48aN4XA48MILL+CPP/7AqlWrMHv2bFl9tmrVCsuXL8eOHTvw888/4/bbb+fm3vz222/YsWMHzpw5g6KiIuzYsQM7duzg0jdBELEFOT8EEaMMGzYM586dQ/fu3REXF4cHH3wQY8aMgc1mw+uvv47HHnsMzz//PC699FLMmzcPAwcODNvn/Pnzcdddd6Fnz56oX78+Hn30URQXF3MZ77XXXotDhw75nnfp0gUAqCKMIAjFULUXQcQgvXv3RufOnbFgwYJID4UgCMJwKOxFEARBEERMQeKHIAiCIIiYgsJeBEEQBEHEFOT8EARBEAQRU5D4IQiCIAgipiDxQxAEQRBETEHihyAIgiCImILED0EQBEEQMQWJH4IgCIIgYgoSPwRBEARBxBQkfgiCIAiCiClI/BAEQRAEEVP8P6c5PXMcLevPAAAAAElFTkSuQmCC", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "size = 100\n", + "param1 = np.linspace(-1,1,size)\n", + "param2 = np.linspace(-1,1,size)\n", + "iters = 5\n", + "for k in range(iters):\n", + " off_diagonal_norm = np.empty((size,size))\n", + " least_squares = np.empty((size,size))\n", + " for i in range(size):\n", + " for j in range(size):\n", + " dbi_eval = deepcopy(dbi)\n", + " d = d_ansatz([param1[i],param2[j]],d_ansatz_type.local_1,normalization=True)\n", + " s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", + " dbi_eval(s_poly,d=d)\n", + " least_squares[i,j] = dbi_eval.least_squares(d=d)\n", + " off_diagonal_norm[i,j] = dbi_eval.off_diagonal_norm\n", + "\n", + " param1_optim, param2_optim = np.unravel_index(np.argmin(off_diagonal_norm, axis=None), off_diagonal_norm.shape)\n", + " d = d_ansatz([param1[param1_optim],param2[param2_optim]],d_ansatz_type.local_1,normalization=True)\n", + " s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", + " dbi(s_poly,d=d)\n", + "\n", + "\n", + "\n", + " plt.figure()\n", + " plt.title('Least squares cost function, iteration: ' + str(k+1))\n", + " plt.contourf(param1,param2,least_squares,levels=50)\n", + " plt.xlabel('param1')\n", + " plt.ylabel('param2')\n", + " plt.colorbar()\n", + "\n", + " plt.figure()\n", + " plt.title('Off-diagonal norm, iteration: ' + str(k+1))\n", + " plt.contourf(param1,param2,off_diagonal_norm,levels=50)\n", + " plt.xlabel('param1')\n", + " plt.ylabel('param2')\n", + " plt.colorbar()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], + "source": [ + "def ordered(D):\n", + " #verify if a diagonal matrix is ordered\n", + " for i in range(D.shape[0]-1):\n", + " if D[i,i] < D[i+1,i+1]:\n", + " return False\n", + " return True\n", + "d = np.logspace(1,-1,10)\n", + "d = d_ansatz(params,d_ansatz_type.local_1,normalization=True)\n", + "\n", + "print(ordered(d))\n" ] } ], diff --git a/examples/dbi/dbi_group_commutator_tests.ipynb b/examples/dbi/dbi_group_commutator_tests.ipynb index e69de29bb2..47d1e9bc5a 100644 --- a/examples/dbi/dbi_group_commutator_tests.ipynb +++ b/examples/dbi/dbi_group_commutator_tests.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from qibo import hamiltonians, set_backend\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCost\n", + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_scheduling import *" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-04-24 16:23:01]: Using numpy backend on /CPU:0\n" + ] + } + ], + "source": [ + "# Hamiltonian\n", + "set_backend(\"numpy\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3.0\n", + "\n", + "# define the hamiltonian\n", + "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "\n", + "# define the least-squares cost function\n", + "cost = DoubleBracketCost.least_squares\n", + "# initialize class\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.group_commutator,cost=cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "s_space = np.linspace(1e-5, 0.3, 500)\n", + "r = np.array([1,2,4,8])\n", + "off_diagonal_norm_diff = np.empty((500,len(r)+1))\n", + "\n", + "for s in range(len(s_space)):\n", + " for i in range(len(r)):\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval.mode = DoubleBracketGeneratorType.group_commutator\n", + " for j in range(r[i]):\n", + " dbi_eval(np.sqrt(s_space[s]/r[i]),d=d)\n", + " off_diagonal_norm_diff[s,i+1] = dbi_eval.off_diagonal_norm\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval.mode = DoubleBracketGeneratorType.single_commutator\n", + " dbi_eval(s_space[s],d=d)\n", + " off_diagonal_norm_diff[s,0] = dbi_eval.off_diagonal_norm\n", + " \n", + " \n", + "\n", + "plt.figure()\n", + "plt.plot(s_space, off_diagonal_norm_diff[:,0],label=r'$e^{sW}$')\n", + "for i in range(len(r)):\n", + " plt.plot(s_space, off_diagonal_norm_diff[:,i+1],label=r'$V_{GC}, r = $' + str(r[i]))\n", + "plt.xlabel('s')\n", + "plt.ylabel('off-diagonal norm')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "for i in range(len(r)):\n", + " plt.plot(s_space, off_diagonal_norm_diff[:,i+1]-off_diagonal_norm_diff[:,0],label=r'$V_{GC}, r = $' + str(r[i]))\n", + "plt.xlabel('s')\n", + "plt.ylabel('Difference of the off diagonal norm between $V_{GC}$ and $e^{sW}$')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "flows = 30\n", + "r = np.array([1,2,4,8])\n", + "off_diagonal_norm_diff = np.empty((1+flows,len(r)+1))\n", + "s = np.empty(flows)\n", + "dbi_eval = deepcopy(dbi)\n", + "off_diagonal_norm_diff[0,:] = dbi_eval.off_diagonal_norm\n", + "for i in range(flows):\n", + " dbi_eval.mode = DoubleBracketGeneratorType.single_commutator\n", + " s[i] = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + " dbi_eval(s[i],d=d)\n", + " off_diagonal_norm_diff[i+1,0] = dbi_eval.off_diagonal_norm\n", + "\n", + "for j in range(len(r)):\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval.mode = DoubleBracketGeneratorType.group_commutator\n", + " for i in range(flows):\n", + " for k in range(r[j]):\n", + " dbi_eval(np.sqrt(s[i]/r[j]),d=d)\n", + " off_diagonal_norm_diff[i+1,j+1] = dbi_eval.off_diagonal_norm\n", + "\n", + "plt.figure()\n", + "plt.plot(off_diagonal_norm_diff[:,0],label=r'$e^{sW}$')\n", + "for i in range(len(r)):\n", + " plt.plot(off_diagonal_norm_diff[:,i+1],label=r'$V_{GC}, r = $' + str(r[i]))\n", + "plt.xlabel('flow iterarion')\n", + "plt.ylabel('off-diagonal norm')\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dbi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19 (main, Mar 21 2024, 17:21:27) [MSC v.1916 64 bit (AMD64)]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 6561b4b2de..dab02fc431 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -155,7 +155,7 @@ def backend(self): def least_squares(self, d: np.array): """Least squares cost function. (without the constant term norm(H))""" h_np = self.backend.to_numpy(self.h.matrix) - + return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h_np @ d)) def choose_step( diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 7790f3d2c5..d3f4aadfa9 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -18,7 +18,7 @@ class d_ansatz_type(Enum): # ising = auto() # for future implementation -def d_ansatz(params: np.array, d_type: d_ansatz_type): +def d_ansatz(params: np.array, d_type: d_ansatz_type, normalization: bool = False): r""" Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. If $\alpha_i$ are our parameters and d the number of qubits then: @@ -28,6 +28,7 @@ def d_ansatz(params: np.array, d_type: d_ansatz_type): Args: params(np.array): parameters for the ansatz. d_type(d_ansatz type): type of parameterization for the ansatz. + normalization(bool): If True, the diagonal is normalized to 1. """ if d_type is d_ansatz_type.element_wise: @@ -46,7 +47,8 @@ def d_ansatz(params: np.array, d_type: d_ansatz_type): d = d.dense.matrix else: raise ValueError(f"Parameterization type {type} not recognized.") - + if normalization: + d = d / np.linalg.norm(d) return d @@ -150,6 +152,7 @@ def gradient_descent_dbr_d_ansatz( lr=1e-2, analytic=True, d_type=d_ansatz_type.element_wise, + normalize=False, ): r""" Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. @@ -164,6 +167,7 @@ def gradient_descent_dbr_d_ansatz( lr(float): Learning rate. analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. d_type(d_ansatz_type): Ansatz used for the D operator. + normalize(bool): If True, the D operator is normalized at each iteration. Returns: d(np.array): Optimized D operator. loss(np.array): Loss function evaluated at each iteration. @@ -172,7 +176,7 @@ def gradient_descent_dbr_d_ansatz( """ h = dbi_object.h.matrix - d = d_ansatz(params, d_type) + d = d_ansatz(params, d_type, normalization=normalize) loss = np.zeros(nmb_iterations + 1) grad = np.zeros((nmb_iterations, len(params))) dbi_new = deepcopy(dbi_object) @@ -189,7 +193,7 @@ def gradient_descent_dbr_d_ansatz( ) for j in range(len(params)): params[j] = params[j] - lr * grad[i, j] - d = d_ansatz(params, d_type) + d = d_ansatz(params, d_type, normalization=normalize) s = polynomial_step(dbi_new, n=3, d=d) dbi_new(s, d=d) loss[i + 1] = dbi_new.loss(0.0, d=d) From dc349b4ce621ed5e0a505393f08734d53c40bf82 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Thu, 2 May 2024 14:21:40 +0200 Subject: [PATCH 068/154] addded D normalization and projected GD --- src/qibo/hamiltonians/hamiltonians.py | 12 ++++++------ src/qibo/models/dbi/double_bracket.py | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/qibo/hamiltonians/hamiltonians.py b/src/qibo/hamiltonians/hamiltonians.py index 8e17e88fbb..07e0d335c3 100644 --- a/src/qibo/hamiltonians/hamiltonians.py +++ b/src/qibo/hamiltonians/hamiltonians.py @@ -368,12 +368,12 @@ def __init__(self, form=None, nqubits=None, symbol_map={}, backend=None): @property def dense(self): """Creates the equivalent :class:`qibo.hamiltonians.MatrixHamiltonian`.""" - if self._dense is None: - log.warning( - "Calculating the dense form of a symbolic Hamiltonian. " - "This operation is memory inefficient." - ) - self.dense = self.calculate_dense() + #if self._dense is None: + # log.warning( + # "Calculating the dense form of a symbolic Hamiltonian. " + # "This operation is memory inefficient." + # ) + self.dense = self.calculate_dense() return self._dense @dense.setter diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index dab02fc431..b1a5d39760 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -222,7 +222,7 @@ def energy_fluctuation(self, state): a = state.conj() @ h2 @ state b = state.conj() @ h_np @ state return (np.sqrt(np.real(a - b**2))).item() - r # eturn np.real(self.h.energy_fluctuation(state)) + def sigma(self, h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) From 2175f115771998e9ef04f8fc6620b48aeb2914d3 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Mon, 6 May 2024 14:05:21 +0200 Subject: [PATCH 069/154] fixed some issues of the PR --- src/qibo/models/dbi/double_bracket.py | 21 ++++++++++++++++----- src/qibo/models/dbi/utils_gradients.py | 2 +- 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index b1a5d39760..88c1aeea07 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -89,6 +89,14 @@ def __init__( self.scheduling = scheduling self.cost = cost self.ref_state = ref_state + """ + Args: + hamiltonian (Hamiltonian): Starting Hamiltonian; + mode (DoubleBracketGeneratorType): type of generator of the evolution. + scheduling (DoubleBracketScheduling): type of scheduling strategy. + cost (DoubleBracketCost): type of cost function. + ref_state (np.array): reference state for computing the energy fluctuation. + """ def __call__( self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None @@ -164,6 +172,7 @@ def choose_step( scheduling: Optional[DoubleBracketScheduling] = None, **kwargs, ): + if scheduling is None: scheduling = self.scheduling step = scheduling(self, d=d, **kwargs) @@ -175,6 +184,7 @@ def choose_step( kwargs["n"] += 1 # if n==n_max, return None step = scheduling(self, d=d, **kwargs) + # if for a given polynomial order n, no solution is found, we the order by 1 return step def loss(self, step: float, d: np.array = None, look_ahead: int = 1): @@ -217,11 +227,12 @@ def energy_fluctuation(self, state): Args: state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ - h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix)))) - h2 = h_np @ h_np - a = state.conj() @ h2 @ state - b = state.conj() @ h_np @ state - return (np.sqrt(np.real(a - b**2))).item() + #h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix)))) + #h2 = h_np @ h_np + #a = state.conj() @ h2 @ state + #b = state.conj() @ h_np @ state + return np.real(self.h.energy_fluctuation(state)) + #return (np.sqrt(np.real(a - b**2))).item() def sigma(self, h: np.array): diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index d3f4aadfa9..93c6dcd841 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -42,7 +42,7 @@ def d_ansatz(params: np.array, d_type: d_ansatz_type, normalization: bool = Fals symbolHam = op_list[0] for i in range(len(params) - 1): symbolHam += op_list[i + 1] - + d = SymbolicHamiltonian(symbolHam, nqubits=len(params)) d = d.dense.matrix else: From a47e7e1a2844c3bc4d0f4c557f8a1c5993d9bc99 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Mon, 6 May 2024 14:05:25 +0200 Subject: [PATCH 070/154] fixed some issues as suggested on the PR --- src/qibo/hamiltonians/hamiltonians.py | 10 +++++----- src/qibo/models/dbi/utils.py | 4 ++++ src/qibo/models/dbi/utils_analytical.py | 8 ++++---- 3 files changed, 13 insertions(+), 9 deletions(-) diff --git a/src/qibo/hamiltonians/hamiltonians.py b/src/qibo/hamiltonians/hamiltonians.py index 07e0d335c3..e6c9d5897b 100644 --- a/src/qibo/hamiltonians/hamiltonians.py +++ b/src/qibo/hamiltonians/hamiltonians.py @@ -368,11 +368,11 @@ def __init__(self, form=None, nqubits=None, symbol_map={}, backend=None): @property def dense(self): """Creates the equivalent :class:`qibo.hamiltonians.MatrixHamiltonian`.""" - #if self._dense is None: - # log.warning( - # "Calculating the dense form of a symbolic Hamiltonian. " - # "This operation is memory inefficient." - # ) + if self._dense is None: + log.warning( + "Calculating the dense form of a symbolic Hamiltonian. " + "This operation is memory inefficient." + ) self.dense = self.calculate_dense() return self._dense diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 85dbd9518c..49c6b25a94 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -137,6 +137,10 @@ def generate_pauli_operator_dict( def diagonal_min_max(matrix: np.array): + """ + Generate a diagonal matrix D with the same diagonal elements as `matrix` but with minimum and maximum values. + (may be deprecated as a useful ansatz for D) + """ L = int(np.log2(matrix.shape[0])) D = np.linspace(np.min(np.diag(matrix)), np.max(np.diag(matrix)), 2**L) D = np.diag(D) diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py index 6d09a76ffd..8c94ec93c0 100644 --- a/src/qibo/models/dbi/utils_analytical.py +++ b/src/qibo/models/dbi/utils_analytical.py @@ -6,7 +6,7 @@ from qibo.models.dbi.utils import commutator, covariance, variance -def dGamma_di_Pauli(dbi_object, n: int, Z_i: np.array, d: np.array): +def dgamma_di_pauli(dbi_object, n: int, Z_i: np.array, d: np.array): """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. Args: @@ -35,7 +35,7 @@ def dGamma_di_Pauli(dbi_object, n: int, Z_i: np.array, d: np.array): return dGamma_di -def ds_di_Pauli( +def ds_di_pauli( dbi_object, d: np.array, Z_i: np.array, @@ -52,7 +52,7 @@ def ds_di_Pauli( floats da, db, dc, ds """ # generate the list of derivatives w.r.t ith Z operator coefficient - dGamma_di = dGamma_di_Pauli(dbi_object, n=4, Z_i=Z_i, d=d) + dGamma_di = dgamma_di_pauli(dbi_object, n=4, Z_i=Z_i, d=d) Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) def derivative_product(k1, k2): @@ -112,7 +112,7 @@ def gradient_Pauli( a, b, c = coef[len(coef) - 3 :] for i, operator in enumerate(pauli_operators): - da, db, dc, ds = ds_di_Pauli( + da, db, dc, ds = ds_di_pauli( dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] ) if use_ds is True: From fd1737b54468f97179cfa339fad0f52b0611ae0f Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Sun, 19 May 2024 10:34:44 +0800 Subject: [PATCH 071/154] Fix minor errors in notebook, add new test files --- ...t_functions_and_d_gradients_tutorial.ipynb | 17 ++- src/qibo/models/dbi/utils_strategies.py | 2 +- tests/test_models_dbi.py | 102 ++++++------------ tests/test_models_dbi_strategies.py | 63 +++++++++++ 4 files changed, 102 insertions(+), 82 deletions(-) create mode 100644 tests/test_models_dbi_strategies.py diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index 6607eaf6dc..569fef850f 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -187,13 +187,12 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the energy fluctuation cost function\n", - "cost = DoubleBracketCostFunction.energy_fluctuation\n", + "cost = DoubleBracketCostFunction.off_diagonal_norm\n", "# define the state\n", "state = np.zeros(2**nqubits)\n", "state[3] = 1\n", "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)\n", - "\n" + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)" ] }, { @@ -411,7 +410,7 @@ "metadata": {}, "outputs": [], "source": [ - "cost = DoubleBracketCost.least_squares\n", + "cost = DoubleBracketCostFunction.least_squares\n", "nqubits = 5\n", "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", @@ -455,7 +454,7 @@ "metadata": {}, "outputs": [], "source": [ - "cost = DoubleBracketCost.least_squares\n", + "cost = DoubleBracketCostFunction.least_squares\n", "nqubits = 5\n", "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", @@ -508,7 +507,7 @@ " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", "\n", " # define the least-squares cost function\n", - " cost = DoubleBracketCost.least_squares\n", + " cost = DoubleBracketCostFunction.least_squares\n", " # initialize class\n", " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", " loss_max [q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", @@ -567,7 +566,7 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", + "cost = DoubleBracketCostFunction.least_squares\n", "\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" @@ -634,7 +633,7 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", + "cost = DoubleBracketCostFunction.least_squares\n", "\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" @@ -692,7 +691,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.11.7" }, "orig_nbformat": 4, "vscode": { diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_strategies.py index dfc5a00ae1..34a5531f1b 100644 --- a/src/qibo/models/dbi/utils_strategies.py +++ b/src/qibo/models/dbi/utils_strategies.py @@ -1,7 +1,7 @@ import hyperopt from qibo.models.dbi.double_bracket import * -from qibo.models.dbi.utils import cs_angle_sgn +from qibo.models.dbi.utils import cs_angle_sgn, generate_pauli_operator_dict from qibo.models.dbi.utils_analytical import * from qibo.models.dbi.utils_scheduling import polynomial_step diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 01f2cd7f0c..1fd60480d8 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -19,6 +19,7 @@ @pytest.mark.parametrize("nqubits", [1, 2]) def test_double_bracket_iteration_canonical(backend, nqubits): + """Check default (canonical) mode.""" h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), @@ -33,6 +34,7 @@ def test_double_bracket_iteration_canonical(backend, nqubits): @pytest.mark.parametrize("nqubits", [1, 2]) def test_double_bracket_iteration_group_commutator(backend, nqubits): + """Check group commutator mode.""" h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( @@ -51,6 +53,7 @@ def test_double_bracket_iteration_group_commutator(backend, nqubits): @pytest.mark.parametrize("nqubits", [1, 2]) def test_double_bracket_iteration_single_commutator(backend, nqubits): + """Check single commutator mode.""" h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( @@ -69,95 +72,50 @@ def test_double_bracket_iteration_single_commutator(backend, nqubits): @pytest.mark.parametrize("nqubits", [3, 4]) -def test_hyperopt_step(backend, nqubits): - h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) - dbi = DoubleBracketIteration(Hamiltonian(nqubits, h0, backend=backend)) - dbi.scheduling = DoubleBracketScheduling.hyperopt - # find initial best step with look_ahead = 1 - initial_step = 0.01 - delta = 0.02 - step = dbi.choose_step( - step_min=initial_step - delta, step_max=initial_step + delta, max_evals=100 - ) - - assert step != initial_step - - # evolve following with optimized first step - for generator in DoubleBracketGeneratorType: - dbi(mode=generator, step=step, d=d) - - # find the following step size with look_ahead - look_ahead = 3 - - step = dbi.choose_step( - step_min=initial_step - delta, - step_max=initial_step + delta, - max_evals=10, - look_ahead=look_ahead, - ) - - # evolve following the optimized first step - for gentype in range(look_ahead): - dbi(mode=DoubleBracketGeneratorType(gentype + 1), step=step, d=d) - - -def test_energy_fluctuations(backend): - h0 = np.array([[1, 0], [0, -1]]) - h0 = backend.cast(h0, dtype=backend.dtype) - - dbi = DoubleBracketIteration(Hamiltonian(1, matrix=h0, backend=backend)) - energy_fluctuation = dbi.energy_fluctuation() - assert energy_fluctuation == 0.0 - - @pytest.mark.parametrize( "scheduling", [ DoubleBracketScheduling.grid_search, DoubleBracketScheduling.hyperopt, + DoubleBracketScheduling.polynomial_approximation, DoubleBracketScheduling.simulated_annealing, ], ) -@pytest.mark.parametrize("nqubits", [3, 4, 5]) -def test_double_bracket_iteration_scheduling_grid_hyperopt_annealing( - backend, nqubits, scheduling -): +def test_variational_scheduling(backend, nqubits, scheduling): + """Check schduling options.""" h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.single_commutator, + Hamiltonian(nqubits, h0, backend=backend), scheduling=scheduling ) + # find initial best step with look_ahead = 1 initial_off_diagonal_norm = dbi.off_diagonal_norm for _ in range(NSTEPS): - step1 = dbi.choose_step(d=d, scheduling=scheduling) - dbi(d=d, step=step1) - step2 = dbi.choose_step() - dbi(step=step2) + step = dbi.choose_step() + dbi(step=step) assert initial_off_diagonal_norm > dbi.off_diagonal_norm -@pytest.mark.parametrize("nqubits", [3, 4, 6]) -@pytest.mark.parametrize("n", [2, 4]) -@pytest.mark.parametrize( - "cost", - [ - DoubleBracketCostFunction.least_squares, - DoubleBracketCostFunction.off_diagonal_norm, - ], -) -def test_double_bracket_iteration_scheduling_polynomial(backend, nqubits, n, cost): +def test_energy_fluctuations(backend): + """Check energy fluctuation cost function.""" + nqubits = 3 + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration(Hamiltonian(nqubits, h0, backend=backend)) + # define the state + state = np.zeros(2**nqubits) + state[3] = 1 + assert dbi.energy_fluctuation(state=state) < 1e-5 + + +def test_least_squares(backend): + """Check least squares cost function.""" + nqubits = 3 h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.single_commutator, - scheduling=DoubleBracketScheduling.polynomial_approximation, - cost=cost, + cost=DoubleBracketCostFunction.least_squares, ) - initial_off_diagonal_norm = dbi.off_diagonal_norm - for _ in range(NSTEPS): - step1 = dbi.choose_step(d=d, n=n) - dbi(d=d, step=step1) - assert initial_off_diagonal_norm > dbi.off_diagonal_norm + d = np.diag(np.linspace(1, 2**nqubits, 2**nqubits)) / 2**nqubits + initial_potential = dbi.least_squares(d=d) + step = dbi.choose_step(d=d) + dbi(d=d, step=step) + assert dbi.least_squares(d=d) < initial_potential diff --git a/tests/test_models_dbi_strategies.py b/tests/test_models_dbi_strategies.py new file mode 100644 index 0000000000..89aad6ea9f --- /dev/null +++ b/tests/test_models_dbi_strategies.py @@ -0,0 +1,63 @@ +"""Testing DoubleBracketIteration strategies""" + +import numpy as np +import pytest + +from qibo.hamiltonians import Hamiltonian +from qibo.models.dbi.double_bracket import ( + DoubleBracketCostFunction, + DoubleBracketGeneratorType, + DoubleBracketIteration, + DoubleBracketScheduling, +) +from qibo.models.dbi.utils import * +from qibo.models.dbi.utils_strategies import ( + gradient_descent_pauli, + select_best_dbr_generator, +) +from qibo.quantum_info import random_hermitian + +NSTEPS = 1 +seed = 5 +"""Number of steps for evolution.""" + + +@pytest.mark.parametrize("nqubits", [2, 3]) +def test_select_best_dbr_generator(backend, nqubits): + scheduling = DoubleBracketScheduling.grid_search + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + scheduling=scheduling, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + generate_local_Z = generate_Z_operators(nqubits) + Z_ops = list(generate_local_Z.values()) + for _ in range(NSTEPS): + dbi, idx, step, flip_sign = select_best_dbr_generator( + dbi, Z_ops, scheduling=scheduling, compare_canonical=True + ) + assert dbi.off_diagonal_norm < initial_off_diagonal_norm + + +@pytest.mark.parametrize("nqubits", [2, 3]) +def test_gradient_descent_pauli(backend, nqubits): + scheduling = DoubleBracketScheduling.grid_search + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + scheduling=scheduling, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + pauli_operator_dict = generate_pauli_operator_dict( + nqubits=nqubits, parameterization_order=2 + ) + d_coef = decompose_into_Pauli_basis( + dbi.h.matrix, list(pauli_operator_dict.values()) + ) + d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) + step, d_coef, d = gradient_descent_pauli(dbi, d_coef, d) + dbi(d=d, step=step) + assert dbi.off_diagonal_norm < initial_off_diagonal_norm From b6f220a592bc2147a3f6e2cec8cf7f5b2e0a5288 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 20 May 2024 13:18:33 +0800 Subject: [PATCH 072/154] Fix test_dbi.py with pytorch (backend.cast) --- examples/dbi/dbi_strategies_compare.ipynb | 3 ++- src/qibo/models/dbi/double_bracket.py | 6 ++++-- src/qibo/models/dbi/utils_analytical.py | 4 +++- 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 7dfe2c5c60..54d7fe4dff 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -67,7 +67,7 @@ "h0 = random_hermitian(2**nqubits, seed=2)\n", "dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0))\n", "cost = DoubleBracketCostFunction.off_diagonal_norm\n", - "print(\"Initial loss\", dbi.least_squares(D=dbi.diagonal_h_matrix))\n", + "print(\"Initial loss\", dbi.least_squares(d=dbi.diagonal_h_matrix))\n", "visualize_matrix(dbi.h.matrix, title=f'Random hamiltonian with L={nqubits}')" ] }, @@ -133,6 +133,7 @@ "outputs": [], "source": [ "# initialize DBI class for the Pauli-Z strategy\n", + "set_backend(\"pytorch\", platform=\"numba\")\n", "dbi_pauli = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling, cost=cost)" ] }, diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 27b94d2cfb..a997dc1f61 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -225,8 +225,10 @@ def energy_fluctuation(self, state): """ h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix)))) h2 = h_np @ h_np - a = state.conj() @ h2 @ state - b = state.conj() @ h_np @ state + state_cast = self.backend.cast(state) + state_conj = self.backend.cast(state.conj()) + a = state_conj @ h2 @ state_cast + b = state_conj @ h_np @ state_cast return (np.sqrt(np.real(a - b**2))).item() r # return np.real(self.h.energy_fluctuation(state)) diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py index 6d09a76ffd..3910b7c9d8 100644 --- a/src/qibo/models/dbi/utils_analytical.py +++ b/src/qibo/models/dbi/utils_analytical.py @@ -163,7 +163,9 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = dbi_object.commutator(d, dbi_object.sigma(dbi_object.h.matrix)) + W = dbi_object.commutator( + dbi_object.backend.cast(d), dbi_object.sigma(dbi_object.h.matrix) + ) Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) From eda60e957712d85e312026b475ae4e8ab9755251 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Mon, 20 May 2024 17:09:33 +0200 Subject: [PATCH 073/154] corrected suggestions made from the PR --- ...t_functions_and_d_gradients_tutorial.ipynb | 71 +++++++++++-------- src/qibo/models/dbi/double_bracket.py | 36 +++++----- src/qibo/models/dbi/utils.py | 13 +++- src/qibo/models/dbi/utils_gradients.py | 5 +- src/qibo/models/dbi/utils_scheduling.py | 13 ++-- 5 files changed, 78 insertions(+), 60 deletions(-) diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index ef47f8d896..66f2dd5fd1 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -241,15 +241,25 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-05-01 21:47:24]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-05-06 13:37:12]: Using numpy backend on /CPU:0\n" ] + }, + { + "data": { + "text/plain": [ + "(8, 1)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -266,26 +276,31 @@ "# define the energy fluctuation cost function\n", "cost = DoubleBracketCost.energy_fluctuation\n", "# define the state\n", - "state = np.zeros(2**nqubits)\n", - "state[3] = 1\n", + "state = np.zeros((2**nqubits,1))\n", + "state[0,0] = 1\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)\n", - "\n" + "state.shape\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 1e-05\n", - "100%|██████████| 100/100 [00:00<00:00, 480.33trial/s, best loss: 0.0]\n", - "hyperopt_search step: 0.5411125187601521\n", - "polynomial_approximation step: 0.222657579130477\n" + "ename": "ValueError", + "evalue": "shapes (8,1) and (8,1) not aligned: 1 (dim 1) != 8 (dim 0)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[9], line 10\u001b[0m\n\u001b[0;32m 8\u001b[0m dbi_eval(s,d\u001b[39m=\u001b[39md)\n\u001b[0;32m 9\u001b[0m off_diagonal_norm_diff\u001b[39m.\u001b[39mappend(dbi_eval\u001b[39m.\u001b[39moff_diagonal_norm \u001b[39m-\u001b[39m dbi\u001b[39m.\u001b[39moff_diagonal_norm)\n\u001b[1;32m---> 10\u001b[0m fluctuation\u001b[39m.\u001b[39mappend(dbi_eval\u001b[39m.\u001b[39;49menergy_fluctuation(state\u001b[39m=\u001b[39;49mstate))\n\u001b[0;32m 12\u001b[0m \u001b[39m# grid_search\u001b[39;00m\n\u001b[0;32m 13\u001b[0m step_grid \u001b[39m=\u001b[39m dbi\u001b[39m.\u001b[39mchoose_step(scheduling\u001b[39m=\u001b[39mDoubleBracketScheduling\u001b[39m.\u001b[39mgrid_search,d\u001b[39m=\u001b[39md)\n", + "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\double_bracket.py:224\u001b[0m, in \u001b[0;36mDoubleBracketIteration.energy_fluctuation\u001b[1;34m(self, state)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 210\u001b[0m \u001b[39mEvaluate energy fluctuation\u001b[39;00m\n\u001b[0;32m 211\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 218\u001b[0m \u001b[39m state (np.ndarray): quantum state to be used to compute the energy fluctuation with H.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 220\u001b[0m \u001b[39m#h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix))))\u001b[39;00m\n\u001b[0;32m 221\u001b[0m \u001b[39m#h2 = h_np @ h_np\u001b[39;00m\n\u001b[0;32m 222\u001b[0m \u001b[39m#a = state.conj() @ h2 @ state\u001b[39;00m\n\u001b[0;32m 223\u001b[0m \u001b[39m#b = state.conj() @ h_np @ state\u001b[39;00m\n\u001b[1;32m--> 224\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39mreal(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mh\u001b[39m.\u001b[39;49menergy_fluctuation(state))\n", + "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\hamiltonians\\hamiltonians.py:183\u001b[0m, in \u001b[0;36mHamiltonian.energy_fluctuation\u001b[1;34m(self, state)\u001b[0m\n\u001b[0;32m 181\u001b[0m h \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmatrix\n\u001b[0;32m 182\u001b[0m h2 \u001b[39m=\u001b[39m Hamiltonian(nqubits\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnqubits, matrix\u001b[39m=\u001b[39mh \u001b[39m@\u001b[39m h, backend\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbackend)\n\u001b[1;32m--> 183\u001b[0m average_h2 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbackend\u001b[39m.\u001b[39;49mcalculate_expectation_state(h2, state, normalize\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[0;32m 184\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39msqrt(np\u001b[39m.\u001b[39mabs(average_h2 \u001b[39m-\u001b[39m energy\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m))\n", + "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\backends\\numpy.py:758\u001b[0m, in \u001b[0;36mNumpyBackend.calculate_expectation_state\u001b[1;34m(self, hamiltonian, state, normalize)\u001b[0m\n\u001b[0;32m 756\u001b[0m statec \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39mconj(state)\n\u001b[0;32m 757\u001b[0m hstate \u001b[39m=\u001b[39m hamiltonian \u001b[39m@\u001b[39m state\n\u001b[1;32m--> 758\u001b[0m ev \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39mreal(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39msum(statec \u001b[39m*\u001b[39;49m hstate))\n\u001b[0;32m 759\u001b[0m \u001b[39mif\u001b[39;00m normalize:\n\u001b[0;32m 760\u001b[0m ev \u001b[39m/\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39msum(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39msquare(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39mabs(state)))\n", + "File \u001b[1;32mc:\\Users\\andre\\anaconda3\\envs\\dbi\\lib\\site-packages\\numpy\\matrixlib\\defmatrix.py:225\u001b[0m, in \u001b[0;36mmatrix.__rmul__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__rmul__\u001b[39m(\u001b[39mself\u001b[39m, other):\n\u001b[1;32m--> 225\u001b[0m \u001b[39mreturn\u001b[39;00m N\u001b[39m.\u001b[39;49mdot(other, \u001b[39mself\u001b[39;49m)\n", + "\u001b[1;31mValueError\u001b[0m: shapes (8,1) and (8,1) not aligned: 1 (dim 1) != 8 (dim 0)" ] } ], @@ -1226,14 +1241,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-05-01 21:51:19]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-05-03 08:28:38]: Using numpy backend on /CPU:0\n" ] } ], @@ -1257,12 +1272,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", "text/plain": [ "
" ] @@ -1373,14 +1388,14 @@ " for i in range(size):\n", " for j in range(size):\n", " dbi_eval = deepcopy(dbi)\n", - " d = d_ansatz([param1[i],param2[j]],d_ansatz_type.local_1,normalization=True)\n", + " d = d_ansatz([param1[i],param2[j]],d_ansatz_type.local_1,normalization=False)\n", " s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", " dbi_eval(s_poly,d=d)\n", " least_squares[i,j] = dbi_eval.least_squares(d=d)\n", " off_diagonal_norm[i,j] = dbi_eval.off_diagonal_norm\n", "\n", " param1_optim, param2_optim = np.unravel_index(np.argmin(off_diagonal_norm, axis=None), off_diagonal_norm.shape)\n", - " d = d_ansatz([param1[param1_optim],param2[param2_optim]],d_ansatz_type.local_1,normalization=True)\n", + " d = d_ansatz([param1[param1_optim],param2[param2_optim]],d_ansatz_type.local_1,normalization=False)\n", " s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", " dbi(s_poly,d=d)\n", "\n", @@ -1445,7 +1460,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.19 (main, Mar 21 2024, 17:21:27) [MSC v.1916 64 bit (AMD64)]" }, "orig_nbformat": 4, "vscode": { diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 88c1aeea07..af16d7aaaf 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -7,6 +7,15 @@ from qibo.hamiltonians import Hamiltonian +from qibo.models.dbi.utils_scheduling import ( + grid_search_step, + hyperopt_step, + polynomial_step, + simulated_annealing_step, +) + + + class DoubleBracketGeneratorType(Enum): """Define DBF evolution.""" @@ -19,24 +28,17 @@ class DoubleBracketGeneratorType(Enum): # TODO: add double commutator (does it converge?) -class DoubleBracketCost(Enum): +class DoubleBracketCost(str, Enum): """Define the DBI cost function.""" - off_diagonal_norm = auto() + off_diagonal_norm = "off_diagonal_norm" """Use off-diagonal norm as cost function.""" - least_squares = auto() + least_squares = "least_squares" """Use least squares as cost function.""" - energy_fluctuation = auto() + energy_fluctuation = "energy_fluctuation" """Use energy fluctuation as cost function.""" -from qibo.models.dbi.utils_scheduling import ( - grid_search_step, - hyperopt_step, - polynomial_step, - simulated_annealing_step, -) - class DoubleBracketScheduling(Enum): """Define the DBI scheduling strategies.""" @@ -130,6 +132,7 @@ def __call__( ) self.h.matrix = operator @ self.h.matrix @ operator_dagger + self.h = Hamiltonian(nqubits=int(np.log2(len(self.h.matrix))), matrix=self.h.matrix) @staticmethod def commutator(a, b): @@ -184,7 +187,7 @@ def choose_step( kwargs["n"] += 1 # if n==n_max, return None step = scheduling(self, d=d, **kwargs) - # if for a given polynomial order n, no solution is found, we the order by 1 + # if for a given polynomial order n, no solution is found, we increase the order of the polynomial by 1 return step def loss(self, step: float, d: np.array = None, look_ahead: int = 1): @@ -227,14 +230,9 @@ def energy_fluctuation(self, state): Args: state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ - #h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix)))) - #h2 = h_np @ h_np - #a = state.conj() @ h2 @ state - #b = state.conj() @ h_np @ state - return np.real(self.h.energy_fluctuation(state)) - #return (np.sqrt(np.real(a - b**2))).item() - + return np.real(self.h.energy_fluctuation(state)) + def sigma(self, h: np.array): return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 49c6b25a94..21086730f9 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -103,7 +103,7 @@ def cs_angle_sgn(dbi_object, d): return np.sign(norm) -def decompose_into_Pauli_basis(h_matrix: np.array, pauli_operators: list): +def decompose_into_pauli_basis(h_matrix: np.array, pauli_operators: list): """finds the decomposition of hamiltonian `h_matrix` into Pauli-Z operators""" nqubits = int(np.log2(h_matrix.shape[0])) @@ -115,6 +115,9 @@ def decompose_into_Pauli_basis(h_matrix: np.array, pauli_operators: list): def generate_pauli_index(nqubits, order): + """ + Generate all possible combinations of qubits for a given order of Pauli operators. + """ if order == 1: return list(range(nqubits)) elif order > 1: @@ -129,9 +132,13 @@ def generate_pauli_index(nqubits, order): def generate_pauli_operator_dict( nqubits: int, parameterization_order: int = 1, symbols_pauli=symbols.Z ): + """ + Generate a dictionary containing all possible products of a given Pauli operators (X,Y or Z) of a given order (e.g. 1 corresponds to a magnetic field) + for L = n_qubits and their respective names. + """ pauli_index = generate_pauli_index(nqubits, order=parameterization_order) pauli_operators = [ - generate_Pauli_operators(nqubits, symbols_pauli, index) for index in pauli_index + generate_pauli_operators(nqubits, symbols_pauli, index) for index in pauli_index ] return {index: operator for index, operator in zip(pauli_index, pauli_operators)} @@ -147,7 +154,7 @@ def diagonal_min_max(matrix: np.array): return D -def generate_Pauli_operators(nqubits, symbols_pauli, positions): +def generate_pauli_operators(nqubits, symbols_pauli, positions): # generate matrix of an nqubit-pauli operator with `symbols_pauli` at `positions` if isinstance(positions, int): return SymbolicHamiltonian( diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 93c6dcd841..c1a5766e90 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -14,8 +14,7 @@ class d_ansatz_type(Enum): element_wise = auto() local_1 = auto() - # local_2 = auto() # for future implementation - # ising = auto() # for future implementation + def d_ansatz(params: np.array, d_type: d_ansatz_type, normalization: bool = False): @@ -75,7 +74,7 @@ def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): return dW_di + commutator(w, dGamma[-1]) -# def dpolynomial_diDiagonal(dbi_object, d, h, i): #element_wise_ansatz + def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): r""" TODO: add formula and explain terms diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 3015d7efa3..813e545376 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -4,14 +4,13 @@ import hyperopt import numpy as np -from qibo.models.dbi.double_bracket import DoubleBracketCost from qibo.models.dbi.utils_analytical import ( energy_fluctuation_polynomial_expansion_coef, least_squares_polynomial_expansion_coef, off_diagonal_norm_polynomial_expansion_coef, ) -error = 1e-3 + def grid_search_step( @@ -95,7 +94,7 @@ def polynomial_step( n_max: int = 5, d: np.array = None, coef: Optional[list] = None, - cost: DoubleBracketCost = None, + cost: Optional[str] = None, ): r""" Optimizes iteration step by solving the n_th order polynomial expansion of the loss function. @@ -117,11 +116,11 @@ def polynomial_step( "No solution can be found with polynomial approximation. Increase `n_max` or use other scheduling methods." ) if coef is None: - if cost is DoubleBracketCost.off_diagonal_norm: + if cost == 'off_diagonal_norm': coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n) - elif cost is DoubleBracketCost.least_squares: + elif cost == 'least_squares': coef = least_squares_polynomial_expansion_coef(dbi_object, d, n) - elif cost is DoubleBracketCost.energy_fluctuation: + elif cost == 'energy_fluctuation': coef = energy_fluctuation_polynomial_expansion_coef( dbi_object, d, n, dbi_object.ref_state ) @@ -130,7 +129,7 @@ def polynomial_step( roots = np.roots(coef) real_positive_roots = [ - np.real(root) for root in roots if np.imag(root) < error and np.real(root) > 0 + np.real(root) for root in roots if np.imag(root) < 1e-3 and np.real(root) > 0 ] # solution exists, return minimum s if len(real_positive_roots) > 0: From b3b19e4879cf0794e5762ffecabe55c024b3783e Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 21 May 2024 10:33:18 +0800 Subject: [PATCH 074/154] Fix errors with pytorch: backend.cast --- ...bracket_flow_as_a_diagonalization_quantum_algorithm | 1 + src/qibo/models/dbi/double_bracket.py | 10 ++++++---- src/qibo/models/dbi/utils_analytical.py | 4 ++-- src/qibo/models/dbi/utils_strategies.py | 1 + 4 files changed, 10 insertions(+), 6 deletions(-) create mode 160000 double_bracket_flow_as_a_diagonalization_quantum_algorithm diff --git a/double_bracket_flow_as_a_diagonalization_quantum_algorithm b/double_bracket_flow_as_a_diagonalization_quantum_algorithm new file mode 160000 index 0000000000..808dd6325b --- /dev/null +++ b/double_bracket_flow_as_a_diagonalization_quantum_algorithm @@ -0,0 +1 @@ +Subproject commit 808dd6325b327a756a4b6ae5a4d560e01d1790cb diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index a997dc1f61..b9e02fef91 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -109,7 +109,7 @@ def __call__( d = self.diagonal_h_matrix operator = self.backend.calculate_matrix_exp( 1.0j * step, - self.commutator(d, self.h.matrix), + self.commutator(self.backend.cast(d), self.h.matrix), ) elif mode is DoubleBracketGeneratorType.group_commutator: if d is None: @@ -157,7 +157,7 @@ def backend(self): def least_squares(self, d: np.array): """Least squares cost function.""" - h_np = self.backend.to_numpy(self.h.matrix) + h_np = self.backend.cast(self.h.matrix) return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h_np @ d)) @@ -233,11 +233,13 @@ def energy_fluctuation(self, state): r # return np.real(self.h.energy_fluctuation(state)) def sigma(self, h: np.array): - return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h)))) + return self.backend.cast(h) - self.backend.cast( + np.diag(np.diag(self.backend.to_numpy(h))) + ) def generate_Gamma_list(self, n: int, d: np.array): r"""Computes the n-nested Gamma functions, where $\Gamma_k=[W,...,[W,[W,H]]...]$, where we take k nested commutators with $W = [D, H]$""" - W = self.commutator(d, self.sigma(self.h.matrix)) + W = self.commutator(self.backend.cast(d), self.sigma(self.h.matrix)) Gamma_list = [self.h.matrix] for _ in range(n - 1): Gamma_list.append(self.commutator(W, Gamma_list[-1])) diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py index 3910b7c9d8..240003ee7e 100644 --- a/src/qibo/models/dbi/utils_analytical.py +++ b/src/qibo/models/dbi/utils_analytical.py @@ -21,8 +21,8 @@ def dGamma_di_Pauli(dbi_object, n: int, Z_i: np.array, d: np.array): nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) - W = dbi_object.commutator(d, dbi_object.h.matrix) - dW_di = dbi_object.commutator(Z_i, dbi_object.h.matrix) + W = dbi_object.commutator(dbi_object.backend.cast(d), dbi_object.h.matrix) + dW_di = dbi_object.commutator(dbi_object.backend.cast(Z_i), dbi_object.h.matrix) for k in range(n + 1): if k == 0: continue diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_strategies.py index 34a5531f1b..86be56f55f 100644 --- a/src/qibo/models/dbi/utils_strategies.py +++ b/src/qibo/models/dbi/utils_strategies.py @@ -35,6 +35,7 @@ def select_best_dbr_generator( for i, d in enumerate(d_list): # prescribed step durations dbi_eval = deepcopy(dbi_object) + d = dbi_eval.backend.cast(d) flip_list[i] = cs_angle_sgn(dbi_eval, d) if flip_list[i] != 0: if step is None: From 48fe7582c39a36f2aacceb6e7e89f9c3187f24b3 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 21 May 2024 16:42:01 +0800 Subject: [PATCH 075/154] Fix names "CostFunction" for notebooks --- ...t_functions_and_d_gradients_tutorial.ipynb | 997 +----------------- examples/dbi/dbi_group_commutator_tests.ipynb | 70 +- examples/dbi/dbi_misc.ipynb | 2 +- 3 files changed, 20 insertions(+), 1049 deletions(-) diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index e4fc7596c4..ce0217ff69 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -12,11 +12,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, -======= - "execution_count": 1, ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "metadata": {}, "outputs": [], "source": [ @@ -26,16 +22,9 @@ "import matplotlib.pyplot as plt\n", "\n", "from qibo import hamiltonians, set_backend\n", -<<<<<<< HEAD "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", "from qibo.models.dbi.utils import *\n", "from qibo.models.dbi.utils_scheduling import *" -======= - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCost\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_gradients import *" ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 ] }, { @@ -50,23 +39,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-05-01 21:47:22]: Using numpy backend on /CPU:0\n" - ] - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -79,11 +54,7 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", -<<<<<<< HEAD "cost = DoubleBracketCostFunction.least_squares\n", -======= - "cost = DoubleBracketCost.least_squares\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", "\n" @@ -91,26 +62,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.6060645454545454\n", - "100%|██████████| 100/100 [00:00<00:00, 357.58trial/s, best loss: -30.63373896549929]\n", - "hyperopt_search step: 0.5996755685204258\n", - "polynomial_approximation step: 0.5267891182131145\n" - ] - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", @@ -136,43 +90,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.6060645454545454\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "# Plot the results\n", "plt.figure()\n", @@ -206,11 +126,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, -======= - "execution_count": 5, ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "metadata": {}, "outputs": [], "source": [ @@ -219,11 +135,7 @@ "off_diagonal_norm_diff_least_squares = [dbi.off_diagonal_norm]\n", "iters = 100\n", "dbi_ls = deepcopy(dbi)\n", -<<<<<<< HEAD "cost = DoubleBracketCostFunction.off_diagonal_norm\n", -======= - "cost = DoubleBracketCost.off_diagonal_norm\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "dbi_od = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", "for _ in range(iters):\n", " step_poly = dbi_od.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", @@ -236,36 +148,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", @@ -287,33 +172,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-05-06 13:37:12]: Using numpy backend on /CPU:0\n" - ] - }, - { - "data": { - "text/plain": [ - "(8, 1)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -326,51 +187,19 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the energy fluctuation cost function\n", -<<<<<<< HEAD "cost = DoubleBracketCostFunction.off_diagonal_norm\n", "# define the state\n", "state = np.zeros(2**nqubits)\n", "state[3] = 1\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)" -======= - "cost = DoubleBracketCost.energy_fluctuation\n", - "# define the state\n", - "state = np.zeros((2**nqubits,1))\n", - "state[0,0] = 1\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)\n", - "state.shape\n" ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 ] }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "shapes (8,1) and (8,1) not aligned: 1 (dim 1) != 8 (dim 0)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[9], line 10\u001b[0m\n\u001b[0;32m 8\u001b[0m dbi_eval(s,d\u001b[39m=\u001b[39md)\n\u001b[0;32m 9\u001b[0m off_diagonal_norm_diff\u001b[39m.\u001b[39mappend(dbi_eval\u001b[39m.\u001b[39moff_diagonal_norm \u001b[39m-\u001b[39m dbi\u001b[39m.\u001b[39moff_diagonal_norm)\n\u001b[1;32m---> 10\u001b[0m fluctuation\u001b[39m.\u001b[39mappend(dbi_eval\u001b[39m.\u001b[39;49menergy_fluctuation(state\u001b[39m=\u001b[39;49mstate))\n\u001b[0;32m 12\u001b[0m \u001b[39m# grid_search\u001b[39;00m\n\u001b[0;32m 13\u001b[0m step_grid \u001b[39m=\u001b[39m dbi\u001b[39m.\u001b[39mchoose_step(scheduling\u001b[39m=\u001b[39mDoubleBracketScheduling\u001b[39m.\u001b[39mgrid_search,d\u001b[39m=\u001b[39md)\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\models\\dbi\\double_bracket.py:224\u001b[0m, in \u001b[0;36mDoubleBracketIteration.energy_fluctuation\u001b[1;34m(self, state)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 210\u001b[0m \u001b[39mEvaluate energy fluctuation\u001b[39;00m\n\u001b[0;32m 211\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 218\u001b[0m \u001b[39m state (np.ndarray): quantum state to be used to compute the energy fluctuation with H.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 220\u001b[0m \u001b[39m#h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix))))\u001b[39;00m\n\u001b[0;32m 221\u001b[0m \u001b[39m#h2 = h_np @ h_np\u001b[39;00m\n\u001b[0;32m 222\u001b[0m \u001b[39m#a = state.conj() @ h2 @ state\u001b[39;00m\n\u001b[0;32m 223\u001b[0m \u001b[39m#b = state.conj() @ h_np @ state\u001b[39;00m\n\u001b[1;32m--> 224\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39mreal(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mh\u001b[39m.\u001b[39;49menergy_fluctuation(state))\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\hamiltonians\\hamiltonians.py:183\u001b[0m, in \u001b[0;36mHamiltonian.energy_fluctuation\u001b[1;34m(self, state)\u001b[0m\n\u001b[0;32m 181\u001b[0m h \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmatrix\n\u001b[0;32m 182\u001b[0m h2 \u001b[39m=\u001b[39m Hamiltonian(nqubits\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnqubits, matrix\u001b[39m=\u001b[39mh \u001b[39m@\u001b[39m h, backend\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbackend)\n\u001b[1;32m--> 183\u001b[0m average_h2 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbackend\u001b[39m.\u001b[39;49mcalculate_expectation_state(h2, state, normalize\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[0;32m 184\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39msqrt(np\u001b[39m.\u001b[39mabs(average_h2 \u001b[39m-\u001b[39m energy\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m))\n", - "File \u001b[1;32m~\\Documents\\GitHub\\qibo\\src\\qibo\\backends\\numpy.py:758\u001b[0m, in \u001b[0;36mNumpyBackend.calculate_expectation_state\u001b[1;34m(self, hamiltonian, state, normalize)\u001b[0m\n\u001b[0;32m 756\u001b[0m statec \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39mconj(state)\n\u001b[0;32m 757\u001b[0m hstate \u001b[39m=\u001b[39m hamiltonian \u001b[39m@\u001b[39m state\n\u001b[1;32m--> 758\u001b[0m ev \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39mreal(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39msum(statec \u001b[39m*\u001b[39;49m hstate))\n\u001b[0;32m 759\u001b[0m \u001b[39mif\u001b[39;00m normalize:\n\u001b[0;32m 760\u001b[0m ev \u001b[39m/\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39msum(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39msquare(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnp\u001b[39m.\u001b[39mabs(state)))\n", - "File \u001b[1;32mc:\\Users\\andre\\anaconda3\\envs\\dbi\\lib\\site-packages\\numpy\\matrixlib\\defmatrix.py:225\u001b[0m, in \u001b[0;36mmatrix.__rmul__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__rmul__\u001b[39m(\u001b[39mself\u001b[39m, other):\n\u001b[1;32m--> 225\u001b[0m \u001b[39mreturn\u001b[39;00m N\u001b[39m.\u001b[39;49mdot(other, \u001b[39mself\u001b[39;49m)\n", - "\u001b[1;31mValueError\u001b[0m: shapes (8,1) and (8,1) not aligned: 1 (dim 1) != 8 (dim 0)" - ] - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))/2**nqubits\n", @@ -396,43 +225,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 1e-05\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "# Plot the results\n", "plt.figure()\n", @@ -458,11 +253,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, -======= - "execution_count": 10, ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "metadata": {}, "outputs": [], "source": [ @@ -480,46 +271,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Energy fluctuation')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", @@ -534,11 +288,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, -======= - "execution_count": 12, ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "metadata": {}, "outputs": [], "source": [ @@ -561,44 +311,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eigenvalues: [-9.29150262 -5.21110255 -2. -2. 1.29150262 4.\n", - " 4. 9.21110255]\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Iterations')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", "print('Eigenvalues:', eigvals )\n", @@ -622,7 +337,6 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], @@ -635,19 +349,6 @@ "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-05-01 21:47:27]: Using numpy backend on /CPU:0\n" - ] - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -660,11 +361,7 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the energy fluctuation cost function\n", -<<<<<<< HEAD "cost = DoubleBracketCostFunction.energy_fluctuation\n", -======= - "cost = DoubleBracketCost.energy_fluctuation\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "# define the state\n", "state = np.zeros(2**nqubits)\n", "state[3] = 1\n", @@ -674,64 +371,19 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cost = DoubleBracketCostFunction.least_squares\n", -======= - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Diagonal elements')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cost = DoubleBracketCost.least_squares\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "nqubits = 5\n", "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", "params = np.linspace(1,2**nqubits,2**nqubits)\n", "\n", -<<<<<<< HEAD "step = 1e-2\n", "iterations = 200\n", "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", -======= - "step = 1e-1\n", - "iterations = 200\n", - "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step, normalize=True)\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "\n", "plt.figure()\n", "plt.plot(range(iterations+1), loss)\n", @@ -754,14 +406,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cost = DoubleBracketCostFunction.least_squares\n", -======= - "execution_count": 16, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -770,13 +415,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 16, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -786,8 +431,7 @@ } ], "source": [ - "cost = DoubleBracketCost.least_squares\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 + "cost = DoubleBracketCostFunction.least_squares\n", "nqubits = 5\n", "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", @@ -799,11 +443,7 @@ "off_diagonal_norm = np.empty((2,flows+1))\n", "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", -<<<<<<< HEAD "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", -======= - "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step,normalize=True) \n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "for i in range(flows):\n", "\n", " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", @@ -831,14 +471,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cost = DoubleBracketCostFunction.least_squares\n", -======= - "execution_count": 17, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -847,13 +480,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 17, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -863,8 +496,7 @@ } ], "source": [ - "cost = DoubleBracketCost.least_squares\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 + "cost = DoubleBracketCostFunction.least_squares\n", "nqubits = 5\n", "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", @@ -878,11 +510,7 @@ "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", "\n", "for i in range(flows):\n", -<<<<<<< HEAD " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", -======= - " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step,normalize=True)\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", " dbi_trained(s,d=d_trained)\n", " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", @@ -907,11 +535,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": null, -======= - "execution_count": 18, ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -925,11 +549,7 @@ " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", "\n", " # define the least-squares cost function\n", -<<<<<<< HEAD " cost = DoubleBracketCostFunction.least_squares\n", -======= - " cost = DoubleBracketCost.least_squares\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 " # initialize class\n", " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", " loss_max [q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", @@ -942,46 +562,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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uqZBVonWqeMsoaqHKKrmvxDl32w0IxjWrXO2vByjjczWuDipT2CoOUiWPudqrcbFXydpVQlQyFg1H3377LQMHDsTOzq7Ufo1Gww8//MDgwYPNr7gKqQrhCKDgwgViR4ygMDEJG29v41pIdzGT8GLGRb6P+p4NFzaQV5gHgLudO/3r9Wdg/YH4O/lXVOkV41ry9aCU/I8xOKVf5KZLC6gcwDfyemDyu9/YTefgfq+rFhWkuBuwZKC6ccvKKywVrkz78+++xQqMg9eLg5OLfXHAuh60XB1UuNhff158nkvRo7RcCVGxLBqObGxsSEpKwte39MrHV65cwdfXV7rVKhFtSipxL75IQVQUSkdHghYtwvnBDnd1zcyCTNadX8eKqBUkZCcAxtW3u9TqwjMNnqGFX4vKOyup4BqknLoemFJOGF8Xhb0y3EKKWpeKwpJfI/AMl1amak6vN5CtuR6csvIKycrXmlqqsvKNx7KKglTxOcXhKk9bMf8G2tooTaHKGJpUuNhdD1DF4crFXoVr0T5nOxXOJc61Vysr799HIe4xi4YjpVJJSkoKPj4+pfYfO3aMzp07k56ebn7FVUhVCkcAuuxs4keNInf/AVCpjGsh9e1z99fV69gdt5vvTn/HoZRDpv31POrxdIOneaz2YzioqsDSDnodpEdDyj/G8UvJJ4yhKTPu5uerHY2tTL4NjYGp+FHGMokiWp2e7HxjoCoOVsVB6lpxsDI9Xn9+reg92QWFFdJyBcZZgs72KpztioJUifBk2mevwsnWBmd7Nc52NjjbqXGyszHutzOe52Qrq7GLqs8i4ahZs2YoFAqOHTvGfffdh6rENHGdTkd0dDQ9e/Zk1apVd1d9JVfVwhGAQaMhceqbZP38MwA+Y8fi9eJ/K+w3yrNXz7IiagUbL2wkX5cPgKutK/0i+jGw/sB7O8utouRlGMNSSlFYSj5hHAx+q1YmJ9+i1qXiwNQQfBqAugoERFGpFLdcXcsv5FqJQFX8Oiv/hmNFj9n5hWQXVHzAKuZka2MMS/YqXOyMwckUnuxscLI1vna0tcHZToWjnQpnOxscbY3nlNzvqLaRsCXuOYuEoxkzZpgeJ0yYgLOzs+mYra0tYWFhPPnkk9ja2t5F6ZVfVQxHAAa9nssLF15fC2nQQPzfeguFTcV1Ed2sy02BgoeCH+LpyKdpG9C2ajfx63Vw5QKknjR2x6WeMganqzE3P1+hNA72Lm5dKg5OHmHSNScsqvh2NtfyC8kuKA5WxvCUXaKFyhSqNMbHnIKicwqun3unN2H+N462xuBkfLQp+7ooRDnaqW563EFtfO1gq8TBVlX02gY7lXQlipuzaLfasmXLGDRoUJkB2TVFVQ1HxUqthfTwwwS9P9+stZDKQ6fX8UfCH3wf9X2pWW5hrmE83eBpetftjZPaqUI/06oKso033k05WRSYih5zr9z8fJU9+NQ3BiWfBsZH30hwC5bVv0WlYjAYKCjUlw5N+YXkaK6HrdwCnfFRU0h2gY5cjTFk5RToyNFcPyenwPg+C2UtE4UCHNTG8GRfFJgcisKUQ1GwslfZYGc6R4l9ied2Re+76TGV8ZidWomdSomtjQSxqsSi4eivv/5Cr9fTunXrUvsPHjyIjY0NLVu2NL/iKqSqhyOomLWQyis6M5ofon5g/YX15GhzAHBSO9G7Tm8GNRhUsfdyq0wMBshOLdvKdPkMFObf/D22LuDbwBiUfCKLxjZFgrOfhCZRLRSve5VTFKByNcYwlavRkVOgI09btK9AV/qYppA8jY4cjY48jTF45Wt15GmN5+VpdWgK726x0TuhUICdyhia7FTKotB0PUgZjxU9VxvDlG3Ra1tV8fPr+22Lnpc89/o5NqhVCtQ2xmNqGyVqGwXq4vfbKKWr8l9YNBy1atWKiRMn0r9//1L716xZw9y5czl48KD5FVch1SEcQcWthVRe2ZpsNlzYwIqoFcRkxZj2tw1oy6AGg3go+CFsakJXk15n7IZLPW3cLhc9pp0F/S3W5nHwuN7K5NPAGKB8GoCTj4QmIYoU6vTkF+rJ0+iMm9YYrvK0118XP+ZrdeRp9OQXGp8bN70pcJV8bXpeaHx/gRVCWHnZKBXGwFQyQKkUqJVKVDYKVEpjoFLZKFEpjefduF+tVBj3mZ4bz7VRKooeje+5/rrE/uLXRcdtFAqURceVRa9tlAqURY82SkzPr+8zPne0tSHQvWJ7NSwajpydnTl+/Di1a9cutT86OprGjRtz7do18yuuQqpLOIKbrIX06ac4NLrfop+pN+g5kHSAFadX8Fv89Xu5BTgFMKD+APrW7YuXg5dFa6iUdNqi8UynioLTKWNXXfrFm99jDoyhqTgw+TQo6qqTliYhLMlgMKDR6Sko1FOg1VNQaAxPBYW6UvsKCo3hqqCw+Fzjc02hHo2u6LHQeG7JfcXna244V6szbsbnBrQ6vcXGg1UGzWq5s/aV9hV6TYuGIy8vLzZu3Ejbtm1L7d+3bx+PPfYYV69eNb/iKqQ6hSMovRaSwsGB4A8X4vzQQ/fks+OuxbH67GrWnltLRkEGAGqlmu5h3RlUfxBNfJpIf742z9iqlHraGJYunzE+vxrDTRe0BLB3ux6WfCKLHhuAa6CEJiGqEb3egFZvDEvFAep6kDIGKI1OT6HOQKFOj1Zf9KgzUKg37i8OWSX3a3UG43v0xmM6vfG1ruTrUo/G95R8XagzoDcYX+v1BnQGAzo9pufX991wvGhf42A3/m9Y63//j2AGi4ajp59+mqSkJNavX4+bmxsAGRkZ9OnTB19fX5nKXwXpsrNJGD2GnH37wMYG/7ffxmPggHv2+fmF+WyN2crKMyv5J+0f0/4Gng0YVH8Qj4Q/gqNabiJbijYP0s4VBaaSoSn61i1Nti7gHWEMS971ih7rG2fP2ahu/h4hhKgmLBqOEhIS6NixI1euXKFZs2YAHD16FD8/P7Zt20ZISMidV14FVMdwBGDQakmaNp3MNWsA8HrxRXzGjrnnLTcn007yw5kf2BK9hQJdAQAuahd61+3NwPoDCXMLu6f1VDnafLhyvkRoioLU4u65W6zcbGMLnnXAp54xLBWHJ+8IWadJCFFtWPzGszk5OSxfvpxjx47h4OBA48aNefrpp1Gr1XdcdFVRXcMRGPvS0z7+hLQlSwBw7fUEge++i8IKa1dl5Gew7vw6Vp5ZSXx2vGl/jRvAXVEKNcaAlHYGLp81hqa0M5B2/tYLW6IA91olwlKJzakGjgsTQlRpFg9HNVl1DkfFMn5aQ9K0aVBYiGObNgR/tBgbFxer1KI36NmbsJeVZ1bye/zvpQZwP1XvKfpG9MXbQW7dccf0esiMNQamtDPG7rm0s8bH/Ixbv8/B43rrUsnQ5B4qXXRCiErJ4uHo3Llz7Nq1i9TUVPT60uMb3n77bXMvV6XUhHAEkP3HXhJGj0afm4tdRAQhX3yOOiDAqjXFX4tn9dnVrDm3xjSAW6VQ8XDowwysP5CWfi1lAHdFMRgg5/L1sJR27vpjZuyt36dUg1ed66HJK8L43KsuOLjfs/KFEOJGFg1HX375JS+//DLe3t74+/uX+jJSKBQcPnz4zqquImpKOALIP32auP++SOHly6h8fQn54nPsGzSwdlkU6ArYGrOVVWdWcezyMdP+cLdwBtQbwBN1nsDNzs2KFVZzmlxIv3BDaDr7L110GNdl8ooA77olQlMEeISCTfXvkhdCWJdFw1FoaCivvPIKkyZNuqsiq6qaFI4AtImJxqn+586jdHIiaPEinNtX7NoTd+NM+hlWnlnJxosbySv6Yra3sadneE8G1BvA/d73S2vSvaLXQ1Z82ZamK+fhWtKt36dUGWfM3Sw4OXnL8gNCiAph0XDk6urK0aNHyywCWVPUtHAEoMvKIn7kKHL//BNUKgLeeQf3fn2tXVYp2ZpsNl3cxMqzKzl39Zxpf6RnJAPqD+DR8EdlOQBrKrhmDElp5+HKuaLQdM648KU299bvs3MzdtN51S3aSjy3c771+4QQ4gYWDUfDhg3jgQce4KWXXrqrIquqmhiOAPQaDUlvTCVr40YAvEeNxPuVVypdq4zBYODY5WOsOrOKrTFb0eg1ADirnXm89uMMqD+ACI8IK1cpTPR6uJZYFJzOlXg8Bxlx3HKhSwCXAOMSBKXCU11jK5Tq3s+wFEJUbhYNR3PmzOGDDz7gscceo1GjRmWm748ePdr8iquQmhqOAAx6PZc/XMSVL74AwK3/kwRMm4aiki7hcDX/KhsubGDVmVXEXrs+iLi5b3Oeqv8U3UK7YWdjZ8UKxW1p842LWl45X2K7YHzMuXzr9ylsjEsQeNW5Hp6KH91CZDadEDWURcNRePit76KuUCi4ePGiOZercmpyOCp29YcfSH5nJuj1OHXoQNCHH2Lj7GTtsm5Jb9BzMOkgq86sYlfcLnRFiyG62bnRq04v+tfrT223mtlNXGXlZRgHhReHpZLhSZN96/cp1UXjm4oDU+3rwck1GJTKe/UTCCHuMVnnyIIkHBld27WLhPETMOTlYRcZSchnn6L287N2Wf8qNTeVn879xJpza0jOSTbtb+HXgv71+ktrUlVnMEB2yvWgVByg0i8at8L8W79XZQ8e4UXBKdwYmjxrGzfXIAlOQlRxEo4sSMLRdXn//EPcSy+ju3IFlb8/IZ9/hn39+tYuq1x0eh17E/ey+uxqfo//HX3R/cjc7dzpVacXT9Z7UlqTqhu9HrISSgem4gCVHg167a3fa2NXFJhql9686hQFJ1mtXYjKzqLh6IUXXrjt8a+//tqcy1U5Eo5K08THE/ffF9FcvGic6r9oEc4dKs9U//JIzklm7fm1N21NeqreU3QN7SqtSdWdrhAy44qC08XrLU3pF+FqzL8EJ1tjV52ppSn8epByqyVjnISoJCwajvr2LT2FW6vVcuLECTIyMujSpQtrim5cWl1JOCpLl5lpnOr/119gY0PAjOm49+9v7bLMJq1J4qZ0hcb1m0zdc9FFrU1FwUmnufV7iweHm0JTbWPXnWdtY6BS29+rn0KIGu+ed6vp9Xpefvll6tSpw8SJE+/2cpWahKOb02s0JL35JlkbfgbA66UX8RkzptJN9S+v27UmydgkYaLXQWZ8UWgq6p5Ljy4KTtG3H+OEAlwDrwel4gDlUdTyZC+rvAtRkawy5ujMmTN06tSJpKTbrIRrYWFhYVy6dKnUvjlz5jB58mTT6+PHj/Pqq6/y119/4ePjw6hRo8wKdBKObs1gMJD20RLSPvkEANfHHydg9iyUtlV3zZlbtSa52rryRJ0n6BfRj3oe9axcpaiU9HrITi7R2lQUmIpfF2Td/v0OnkWhqURgKn509pcB4kKYySrhaPPmzTz//PNcvnyb9UcsLCwsjGHDhjFixAjTPhcXF5ycjNPMs7KyqFevHl27dmXKlCn8888/vPDCC3z44Yf897//LddnSDj6dxlr1pL09ttQWIhDyxaELFmCjbu7tcu6a8k5yaw9t5a159eSlHP9l4BG3o14MuJJeob3xEldeZc0EJWIwQC56TcEpqLQdDUGclJv/36VvTE4lQxNxUHKvRaopFVTiBtZNByNHz++1GuDwUBSUhKbNm3i+eefZ8mSJeZXXEHCwsIYO3YsY8eOvenxTz/9lKlTp5KcnIxtUWvG5MmTWbduHVFRUeX6DAlH5ZOzfz/xo0ajz87GNjyckC8+xzYkxNplVQidXseBpAP8dO4ndsXuotBQCICDyoFHwh/hyYgnaeTdqMp2KYpKoCDbGJKuFnXTlXzMiIOitbpurqi7rjg8eYSVaIEKA0cvuV+dqJEsGo46depU6h99pVKJj48PXbp04YUXXkClst7MjLCwMPLz89FqtdSqVYtnnnmGcePGmWoaPHgwWVlZrFu3zvSeXbt20aVLF9LT0/Hw8ChzzYKCAgoKCkyvs7KyCAkJkXBUDvlnzxL30ksUJiZh4+lJyCcf49C0qbXLqlBX8q6w4cIG1pxbQ0xWjGl/Xfe69K/Xn8drP46bnYwdERVIpy2aWVcUlq7GFD2/ZHx9u0UwAWydrwcm0xYOHqHS6iSqtQoPRxs2bOCRRx4pc6uQyuaDDz6gefPmeHp6sm/fPqZMmcLQoUP54IMPAOjevTvh4eF8/vnnpvecOnWK++67j1OnThEZGVnmmtOnT2fGjBll9ks4Kh9tairxL71M/qlTKOzsCJw3D9ce3a1dVoUzGAwcTj3MT2d/4tdLv1KgMwZqW6UtD4c+TP+I/rT0b4lSIeNEhAUZDJB7pajVKeZ6N13xlpXAbe9XV9zq5B5aFJpCrwco91Bw9pOxTqLKqvBwZGNjQ3JyMj4+PtjY2JCUlISvr2+FFXw7kydPZu7cubc95/Tp0zRo0KDM/q+//poXX3yR7Oxs7Ozs7igcScvR3dPn5JAwfgLZv/0GCgW+EyfiOeT5atvtlKXJYvPFzfx07iei0q9314a4hNAvoh9P1H4CP6fKv5q4qIa0+cZWpxvDU8Yl4+O/tTqp7I2tSyUDk0fo9UeZYScqsQoPR/7+/nz55Zc88cQTKJVKUlJS8PHxqbCCb+fy5ctcuXLltufUrl3bNIaopJMnT3L//fcTFRVF/fr176hb7UYy5ujOGAoLSZk9m6vfrwDA45ln8HtjCgordsNamsFg4FT6KdacXcOm6E3kaHMAUCqUdAjqQN+6fXko+CHUNpW7RVbUEKZWp0vXu+uKQ9PVGMhM+JexToC9e+mw5F7yeS1QO1j+5xDiFsz5/i7XN9NLL71E7969USgUKBQK/P39b3muTvcvf3nM5OPjc8dB7OjRoyiVSlMrV9u2bZk6dSpardbURbht2zbq169frmAk7pxCpcLvrbdQh9Qidd48rn7/PdrERIIWvI/SqXrO8FIoFNzndR/3tb2PCS0n8OulX1l7bi2HUw/ze/zv/B7/O572njxe+3H61u1LXY+61i5Z1GQKBTh5G7fgFmWP67TGNZ1uDE1XLxlf516B/AxIyoCkYzf/DGe/sq1N7rWMm2swqKrush+iein3gOyoqCjOnz9Pr169WLp0Ke63mJrdu3fviqyv3Pbv38/Bgwfp3LkzLi4u7N+/n3HjxvHII4+wbNkywDhOqH79+nTv3p1JkyZx4sQJXnjhBRYuXChT+e+hrK2/kjhxIoaCAuwbNiT4009R+92bbtrKICYzhnXn17HhwgYu511f+qKRdyP6RvSlZ1hPXGxdrFihEHegIBsyYouC06Wyj5prt3+/QgkugdfDUvFWHKBcg0BaWcVdsOhstRkzZvD666/j6Oh4V0VWtMOHD/PKK68QFRVFQUEB4eHhPPfcc4wfPx47u+uzL0ouAunt7c2oUaOYNGlSuT9HwlHFyDt6lLhXXkWXnm68ae1nn2J/k3Fj1VmhvpC9CXtZc24Nv8f/bloSwN7Gnu5h3elTtw8t/VpW27FZogYxGCDvaolWp6LQlBFX9Bj7L6uJYwxPrkFFoSkU3EOMz91CrocnaXkSt2GVRSBrCglHFUcTF0fciy8Zb1rr6EjQwg9wfugha5dlFWl5aWy6uIk159ZwMfOiaX+ISwh96vahV51e+DvdujtbiCrNYICcyyVCU+z1VqiMWGOI0hX8y0WKZtoVhyX3WiUCVC1wC5Z72dVwEo4sSMJRxdJlZhI/Ziy5Bw6AUonfm1PxfOYZa5dlNQaDgeNpx1l7bi2/xPxSahB328C29Knbh84hneW+bqJm0euNq4ZfLQ5LMcbAlBlnRniiaMxTUVByK9nyFGJ8tJd/06szCUcWJOGo4hk0GpJmzCDzpzUAeD4/GN+JE1HY2Fi5MuvK1eay7dI21p5fy98pf5v2u9q68kj4I/Sp24f7vO6Tbjch9Hpjy1NmXInuutgS4SkWtLn/fh07t+tBqfjRLfh6iHL2ldXFqzAJRxYk4cgyDAYDV774kssLFwLg3KULQfPnVduZbOa6lHWJ9efXs+HCBlJyU0z767rXpXed3jxe53G8HbytWKEQlVjxvewyLhUFpqJWp8z46yEq7+q/X8fGDtyCrrc8uQWX2EKM455sK9d4XHHdPQ9HGRkZt5y9Vt1IOLKsrC1bSJw0GYNGg13DSEI+/axGzWT7Nzq9joPJB1l/fj07YneYVuK2UdjQPqg9vev0plNIJ2xtZGCqEGYpyDaGpeLWJtPzoiB1LQkM+n+/jqPXzcOTa7AxWDn7gbJmt4pbi0XD0dy5cwkLC2PgwIEADBgwgJ9++gl/f382b95MkyZN7rzyKkDCkeXlHjlC/Ksja/RMtvK4prnG1pitrDu/jmOXr68r42bnxqPhj9K7bm8aejaUbjchKoJOC1mJRaEpHrLirz/PjDeGqH9brgBAqTIuWeAWZGxpcgu6Hpxci1ql5ObAFmHRcBQeHs7y5ctp164d27ZtY8CAAaxcuZJVq1YRGxvLr7/+elfFV3YSju4NmclmnujMaDZc2MCGCxtIzU017a/rXpc+dfvwWO3HpNtNCEvLzywRmOJKh6fMeGO4+rdVxsF4mxbXwOthyRSiirdAcPCQAGUmi4YjBwcHzp49S0hICGPGjCE/P5/PP/+cs2fP0rp1a65eLUe/bRUm4eje0WVlET96zPWZbFPfwPPZZ61dVqWm0+s4kHTA1O2m0WuA691uT9R5Qma7CWEteh1kp5QISwnG27JkxRc9JhiPl4fKoShABV4PTG4lwpNrkLRA3cCi4SgwMJAff/yRdu3aUb9+fd59912eeuopzpw5wwMPPEBWVtZdFV/ZSTi6t26cyeYx+Dn8Jk2q8TPZyiNLk8Uv0b+w/vx6jqcdN+13UbvQI7wHver0oqlPU+l2E6IyKSwwtjCVCU5F+7ISITetfNeysQPXAGM3nmuAMTQVPy9+dPavMYtnWjQcjRw5ko0bNxIREcGRI0eIiYnB2dmZH374gXnz5nH48OG7Kr6yk3B07xkMBq58+RWXP/gAAOfOnQl6f77MZDNDdGY0P1/4mZ8v/kxyTrJpf4hLCE/UeYInaj9BsEuwFSsUQpSbNt84QDzrhtBU8nl5W6BQgJNP6cBkeiwOVP7GmwpX8V+kLBqOtFotixYtIi4ujiFDhtCsWTMAFi5ciIuLC8OHD7/zyqsACUfWk/XLL8aZbAUFRTPZPkXt52ftsqoUvUHPoeRDrL+wnm2XtpFXmGc61ty3Ob3r9qZbaDe5t5sQVV2hpihAJcK1RMi64fm1RLiWDDpN+a6ncjCGJJeA2z/aOVv257oLss6RBUk4sq68o0eJe3UkuitXUPn5EfLpJ9g3bGjtsqqkXG0uO2J38POFnzmQdAADxn8K7Gzs6FKrC73q9KJNQBtUSpWVKxVCWITBALlXrrc6lQxOWUnGcHUtqXxrQBWzdSkKS0Wbs1/Roz+4+F1/tHO95y1RFg1Hy5Ytw9vbm8ceewyAiRMn8sUXX9CwYUNWrFhBaGjonVdeBUg4sj5NfLxxJtuFCygcHAh6fz4uDz9s7bKqtOScZDZd3MSGCxtK3dvN28Gbx8If44k6T1DPo56MTxKiJtLmQ3ZyicCUfPNHTXb5r6lyKB2Wbnx0CwGf+hX6Y1g0HNWvX59PP/2ULl26sH//frp27crChQvZuHEjKpWKNWvW3FXxlZ2Eo8pBl5VFwthx5OzbBwoFvq+9hucLQ+XL+y4ZDAZOXTnFhgsb2By9mYyCDNOxuu51ebz24zxW+zG5Ca4QoqyCa9eDUlaScdxTdopxX8nHgnJM3PJvBC/9UaHlWTQcOTo6EhUVRa1atZg0aRJJSUl8++23nDx5kk6dOnH58uW7Kr6yk3BUeRgKC0meNYuMFT8A4Nb/SQLefhuFbc2YeWFpWp2WPQl72HhxI7/F/WZaFkCBggf8H+Dx2o/TNbSrjE8SQphHk1MUllKMLVI3e/RrCE9+VaEfa9Fw5Ovry9atW2nWrBnNmjVj/PjxPPfcc1y4cIEmTZqQnW1Gs1oVJOGocjEYDFz9bjkpc+aAXo9j69YEL/oQmxpyO5t7JUuTxbaYbWy8uJFDKYdM++1s7OgU0onHaz9O+6D2qJVqK1YphBC3ZtFw9OyzzxIVFUWzZs1YsWIFsbGxeHl5sWHDBt544w1OnDhxV8VXdhKOKqfs334jYdx49Lm52IaFEfLZp9iGhVm7rGopMTuRzdGb+fnCz6XGJ7nbudMzrCeP13mcxt6NpYtTCFGpWDQcZWRk8OabbxIXF8fLL79Mz549AZg2bRq2trZMnTr1ziuvAiQcVV75Z84S9/JLFCYmoXRzI3jxYpxat7J2WdWWwWDgdPppNl7cyOaLm7mSf8V0rJZLLdP4pFqutaxYpRBCGMlUfguScFS5FaalEffqq+QfOw4qFQEzpuP+5JPWLqvaK9QXcjDpIBsvbmRH7I5S6yc19m7Mo7UfpUdYD7m/mxDCaiwejjIyMvjf//7H6dOnAbjvvvt44YUXcHNzu7OKqxAJR5WfPj+fpDfeIGvzFgA8h72A74QJKJRKK1dWM+Rqc9kZt5ONFzayP2k/eoMeAKVCSZuANjwa/igP13oYZ9vKu1icEKL6sWg4OnToED169MDBwYFWrYxdFn/99Rd5eXn8+uuvNG/e/M4rrwIkHFUNBr2etCUfk/bJJwA4d32YoHnzUDo6WrmymiUtL42tMVvZfHFzqfu72dnY8VDwQzxW+zE6BHXA1kZmGAohLMui4ejBBx+kbt26fPnll6hUxpVzCwsLGT58OBcvXuT333+/88qrAAlHVUvmzz+T9MZUDFqt3HLEyuKy4tgUvYlNFzcRkxVj2u9q60q30G48VvsxWvi1QKmQFj4hRMWzaDhycHDgyJEjNGjQoNT+U6dO0bJlS3Jzc82vuAqRcFT15B4+QvzIkejS01H5+hL8ySc43H+ftcuqsQwGA1HpUWy6uIkt0VtIzUs1HfN19OXR8Ed5NPxRGng2kBlvQogKY873t9m/orm6uhIbG1tmf1xcHC4ushicqHwcmzcjbNVKbOvWoTA1lUvPPUfWtm3WLqvGUigURHpF8toDr/Fr/1/5X/f/8WTEk7jYupCam8o3J79hwMYB9Fnfh0+PfcqlrEvWLlkIUcOY3XI0evRo1q5dy/vvv0+7du0A2Lt3L6+//jpPPvkkH374oSXqrDSk5ajq0l27RsK48eT8YVyS3mfcOLz+O0JaJyoJjU7DnoQ9bLq4qdSK3ACRnpE8Gv4oPcN7yq1LhBB3xKLdahqNhtdff53PPvuMwsJCANRqNS+//DLvvfcednZ2d155FSDhqGozFBaSMuc9ri5fDoBrrycImDkTZTX/c1vVXNNcY1fcLjZHb+ZA4gF0Bp3pWHPf5jwS/gjdQrvh5eBlxSqFEFXJPVnnKDc3lwsXLgBQp04dHGvILCAJR9XD1RUrSH53Fuh0ODRtSvCSj1B5yxo8ldHV/Ktsu7SNzdGbOZxyGAPGf7KUCiWt/VvzSPgjPBz6MK628vdRCHFrsgikBUk4qj5y9u0jfuw49FlZqAIDCPnkE+xvmGggKpfknGS2xmxlS/QWTl45adqvVqrpENSBR8MfpWNwRxzVNeOXNSFE+VV4OOrXr1+5P3zNmjXlPrcqknBUvRRERxP/0stoLl1C4ehI0Px5uDz8sLXLEuUQmxXLlugtbInewoXMC6b9DioHOoV0okdYDzoEdcDORrpMhRAWCEdDhw4t94cvXbq03OdWRRKOqh9dZibxY8eSu/8AKBT4jB+H1/DhMlC7Cjl79Sy/RP/C5ujNJGQnmPY7q53pHNKZnuE9aRvQFrWN2opVCiGsSbrVLEjCUfVk0GpJnj2bjBU/AODWuzf+M99BaSsrN1clBoOBf9L+4ZeYX9gas5XU3OtrKLnYuvBwrYfpGdaTVgGtUCslKAlRk0g4siAJR9Vb+vLlpMyeYxyo3bw5wR8tRuUlM6KqIr1Bz9HUo2yN2cqvl34lLS/NdMzdzp2uoV3pGdaTln4tsVHaWLFSIcS9IOHIgiQcVX/Ze/eSMHYc+mvXUAcGEvzpp9jXr2ftssRd0Ol1HE49zC/Rv7A9djvp+emmY572nnQL7UbPsJ4092suty8RopqScGRBEo5qhoKLF4l7+WW0l2JROjoS+P77uHTpbO2yRAUo1BfyV/JfbI3ZyvbY7WQWZJqO+Tr40j2sO93DutPEp4kEJSGqEQlHFiThqObQZWQQP2YsuQcPgkKB72uv4fnCUBmoXY1o9VoOJh3kl+hf2Bm7k2vaa6Zjvo6+dAvtRvfQ7jT1bSpBSYgqTsKRBUk4qlkMWi3J784iY+VKANz69cN/+jQZqF0NaXQa9iXu49eYX9kVt4tsbbbpmK+DL11Du9I9rDvNfJtJUBKiCqrwcLR48eJyf/jo0aPLfW5VJOGo5jEYDFz9bjkpc+aAXo9DixYEL14kA7WrMY1Ow/7E/fx66Vd2xe4q1aLk4+BjDEqhxqAkg7mFqBoqPByFh4eX64MVCgUXL14sX5VVlISjmit7zx8kjBuHPjvbOFD7k49lRe0aQKPTcCDpAFtjtpYJSt4O3nStZWxRau7bXIKSEJVYtexWmzVrFps2beLo0aPY2tqSkZFR5pzY2Fhefvlldu3ahbOzM88//zxz5sxBpVKZztm9ezfjx4/n5MmThISE8OabbzJkyJBy1yHhqGYruHCBuFdeQXspFoWjI4Fz38O1WzdrlyXukVJBKW4X1zSlg9LDtR6me2h3mvs1R6VU3eZKQoh7rVqGo2nTpuHu7k58fDz/+9//yoQjnU5H06ZN8ff3Z/78+SQlJTF48GBGjBjB7NmzAYiOjub+++/npZdeYvjw4ezYsYOxY8eyadMmevToUa46JBwJXWYmCePGkbNvPwDeo0fh/fLLMlC7htHqtOxP2s+vMb+yM25nqaDkYedBl1pd6BbajVb+rWRlbiEqAYuHo/j4eDZs2EBsbCwajabUsQ8++MDcy5nlm2++YezYsWXC0ZYtW3j88cdJTEzEz88PgM8++4xJkyZx+fJlbG1tmTRpEps2beLEiROm9w0aNIiMjAx++eWXcn2+hCMBYCgsJGXuPK7+3/8B4PJITwJnz0bp4GDlyoQ1aHVaDiQdYNulbeyM21lqeQAXWxc6h3SmW2g32ga2lXu9CWEl5nx/m93uu2PHDnr16kXt2rWJiori/vvvJyYmBoPBQPPmze+46Lu1f/9+GjVqZApGAD169ODll1/m5MmTNGvWjP3799O1a9dS7+vRowdjx4695XULCgooKCgwvc7Kyqrw2kXVo1Cp8J/6Bnb1Ikh+ZybXtvzCpUuxBH+8BHVAgLXLE/eY2kbNg8EP8mDwg7ylf4tDyYfYfmk7O2J3cCX/ChsubGDDhQ04qhx5KPghuoV1o31gexzVjtYuXQhxE2bPR50yZQqvvfYa//zzD/b29vz000/ExcXx0EMP8dRTT1mixnJJTk4uFYwA0+vk5OTbnpOVlUVeXt5Nrztnzhzc3NxMW0hIiAWqF1WVx1NPEbr0a2w8PMg/dYropwaQe+SItcsSVqRWqmkb2Ja32r7Fjqd2sLTHUp6NfBZfR19yC3PZErOF8bvH89DKhxi3axybL24mW5P97xcWQtwzZoej06dPM3jwYABUKhV5eXk4OzvzzjvvMHfuXLOuNXnyZBQKxW23qKgoc0usUFOmTCEzM9O0xcXFWbUeUfk4tmxJ2OrV2NWvjy4tjdjBz5Oxdp21yxKVgI3Shpb+LZncajLb+m/ju0e/Y8h9QwhyDiJfl8/22O1M2jOJjis7MnLHSNadX0dGfoa1yxaixjO7W83Jyck0ziggIIALFy5w3333AZCWlna7t5YxYcKEf50pVrt27XJdy9/fnz///LPUvpSUFNOx4sfifSXPcXV1xeEWY0Xs7Oyws5MxAuL2bIODCPt+OYmTJ3Nt23aSpkyh4OxZfF+bgMJGpncLUCqUNPFpQhOfJoxvMZ7T6afZfmk72y5tIyYrht/if+O3+N+wUdjQ0q8lD4c+TJeQLvg5+f37xYUQFcrscNSmTRv++OMPIiMjefTRR5kwYQL//PMPa9asoU2bNmZdy8fHBx8fH3NLuKm2bdsya9YsUlNT8fX1BWDbtm24urrSsGFD0zmbN28u9b5t27bRtm3bCqlB1GxKJyeCFi0ibckS0j75lPSlSym4cJ6gBQuwcXGxdnmiElEoFDT0akhDr4aMajaK8xnnTWOUzlw9w8HkgxxMPsjsg7Np7N2Yh0Mf5uFaDxPqGmrt0oWoEcyerXbx4kWys7Np3LgxOTk5TJgwgX379hEREcEHH3xAaKhl/vLGxsaSnp7Ohg0bmD9/Pnv27AGgbt26ODs7m6byBwYGMm/ePJKTk3nuuecYPnx4man8r776Ki+88AI7d+5k9OjRMpVfVLisLVtInPIGhvx8bGvXJuSTj7ENC7N2WaIKiLsWx87YnWy/tJ1jl49h4Po/0XXd69I1tCtda3Wlnkc9WT5CCDNUy3WOhgwZwrJly8rs37VrF506dQLg0qVLvPzyy+zevRsnJyeef/553nvvvTKLQI4bN45Tp04RHBzMW2+9JYtACovIO3mS+FdHUpicjNLNjeCFH+DUrp21yxJVyOXcy+yK28X2S9v5K/kvCg2FpmNBzkF0rdWVrqFdaezTWO73JsS/sGg4+uuvv9Dr9bRu3brU/oMHD2JjY0PLli3Nr7gKkXAkzFF4+TLxI0eRd+wY2NjgN2kSHs/9R37jF2bLLMjk9/jf2X5pO3sT91Kgu77EiLeDN51DOtOlVhda+bfC1kZujCzEjSwajlq1asXEiRPp379/qf1r1qxh7ty5HDx40PyKqxAJR8Jc+oICkt+eRub69QC4PdkP/2nTUNrKF5i4M7naXPYl7mN77HZ+j/u91P3enNROPBj0IA/XepgOQR1wtnW2YqVCVB4WDUfOzs4cP368zCyy6OhoGjduzLVr127xzupBwpG4EwaDgfSl35D6/vug1+PQtCnBHy1GVUETEkTNpdVp+TP5T3bG7mRX3C4u5102HVMpVbQOaE2XkC50DumMj6P8eRM1l0XDkZeXFxs3biwzw2vfvn089thjXL161fyKqxAJR+JuZO/5g4QJE9BnZaHy8yN4yUc4NGpk7bJENaE36DmRdoKdsTvZEbuDmKyYUscb+zSmS0gXHq71MGFuYVapUQhrsWg4evrpp0lKSmL9+vW4ubkBkJGRQZ8+ffD19WXVqlV3XnkVIOFI3C1NTAxxr45Ec+ECCltbAt6diVuvXtYuS1RDFzMvGluUYndxPO14qWO13WrTpVYXuoR04T7v+2RAt6j2LBqOEhIS6NixI1euXKFZs2YAHD16FD8/P7Zt21btb68h4UhUBF12NomvTyR71y4APF94Ad8J42XBSGExqbmp7Irdxc64nfyZ9GepmW8+Dj48FPIQnUM60zqgtdwcV1RLFp/Kn5OTw/Llyzl27BgODg40btyYp59+GrVafcdFVxUSjkRFMej1XP7oI658+hkATu3bE/TBAmyKWmSFsJQsTRZ/xP/Bzrid7InfQ25hrumYg8qBDkEd6BTSiY5BHXG3d7deoUJUoGq5zlFlIeFIVLSsX34xLhiZl4c6tBYhH3+MXd261i5L1BAanYY/k/9kV+wudsftJjUv1XTMRmFDM99mdA7pTOeQzoS4Vu+eAVG9VXg42rBhA4888ghqtZoNGzbc9txe1XzshIQjYQn5UVHEv/Iq2sRElE5OBM6fj0uXztYuS9QwBoOBU1dOsTNuJ7vjdnP26tlSx+u616VzSGc6hXTifu/7ZZySqFIqPBwplUqSk5Px9fVFqbz1XwaFQoFOpzO/4ipEwpGwlML0dBLGjiP3zz9BocBnzGi8XnxRFowUVhN/LZ7dcbvZHbebQymH0Bmu//tecpxSK/9W2KvsrVanEOUh3WoWJOFIWJJBqyXlvblcXb4cAJeePQmcPQulo6OVKxM1XWZBJnsS9rA7bjd/JPxBjjbHdMzexp42gW3oFNyJh0IewtvB23qFCnELFgtHWq2Wnj178tlnnxEREXHXhVZFEo7EvXB19WqS35kJWi129esT/PHH2AYHWbssIQDjOKW/kv9iV9wufov/jeSc5FLHG3k34qHgh+gU0klukCsqDYu2HPn4+LBv3z4JRxKOhIXlHj5M/Ogx6NLSsHF3J2jRIpxat7J2WUKUYjAYOHP1DLvjdvNb3G+cuHKi1PEApwBTUHrA/wG575uwGouGo3HjxmFnZ8d77713V0VWVRKOxL2kTUoifuQo8k+eNN64dvJkPP7zrPwmLiqty7mX+S3+N36L+40DSQfI1+WbjjmqHGkf1J6Hgh/iweAH8bT3tGKloqaxaDgaNWoU3377LREREbRo0QInJ6dSxz/44APzK65CJByJe02fn0/SW2+T9fPPALj16YP/jOko7WShPlG55RXmcTDpILvjdvN7/O+l7vumQEETnyY8FPIQHYM7EuEeIaFfWJRFw1HnzreeXqxQKNi5c6c5l6tyJBwJazAYDKR/s4zU+fNBr8f+/vsJ/mgx6oAAa5cmRLnoDXpOXzltGqcUlR5V6niAUwAdgzvSMbijzH4TFiGz1SxIwpGwppz9+0kYNx5dRgY2Xl4Ef7gQxwcesHZZQpgtOSeZ3+N/5/f43zmQdIACXYHpmL2NPW0C2tAxpCMdgzri5+RnxUpFdXFPwtH58+e5cOECHTt2xMHBAYPBUCOaRCUcCWvTxCcQP3IkBVFRoFLhN2UyHs88UyP+/onqKa8wj7+S/+K3uN/4Lf43UnJTSh2P9IykY3BHHgp+SG6SK+6YRcPRlStXGDBgALt27UKhUHDu3Dlq167NCy+8gIeHBwsWLLir4is7CUeiMtDn5ZE09U2yNm8GwK1fP/ynvS3jkESVZzAYOHv1rHFQd/xv/HP5Hwxc/5rytPc0db+1DWiLs62zFasVVYlFw9HgwYNJTU3lq6++IjIykmPHjlG7dm22bt3K+PHjOXny5F0VX9lJOBKVhcFgIP3rpaQuWGAch9S4McGLF6H297d2aUJUmCt5V/gj4Q9+i/+NfYn7Si0+qVKoaO7XnI7BHXkw6EHC3cKlBVXckkXDkb+/P1u3bqVJkya4uLiYwtHFixdp3Lgx2dnZd1V8ZSfhSFQ22Xv3kjB+AvrMTGy8vQle9CGOLVpYuywhKpxWp+Xv1L/5Le43/kj4g5ismFLHg5yDTEHpAf8HZFC3KMWi4cjFxYXDhw8TERFRKhwdOnSIHj16cOXKlbsqvrKTcCQqI01cHPGvjqTg7FlQqfCf+gbugwbJb9GiWovNiuX3+N/Zk7CHv5L/QqvXmo7Z29jTOqC1KSwFOMvMzprOouHo0UcfpUWLFsycORMXFxeOHz9OaGgogwYNQq/X8+OPP95V8ZWdhCNRWelzc0mcOpVrW34BwK3/k/i//TZKW1mRWFR/udpcDiQdYE/CHn6P/53U3NRSx+u61zUFpaa+TVEpVVaqVFiLRcPRiRMnePjhh2nevDk7d+6kV69enDx5kvT0dPbu3UudOnXuqvjKTsKRqMwMBgNXvvqKyx8sBIMBhyZNCFq8GLWfr7VLE+KeKR7UvSdhD3vi93D08lH0Br3puIvahbaBbekQ1IEOQR3wcfSxYrXiXrH4VP7MzEyWLFnCsWPHyM7Opnnz5rz66qsE1IAF6SQciaoge88fJEyYgD4rCxsfb4IXLcaxeTNrlyWEVWQWZLI3YS97EvbwR8IfZBRklDoe6RlJh6AOPBj8II28G0mrUjUli0BakIQjUVVoLl0yrod07jyo1fi/MUXGIYkaT6fXcfLKSWNQiv+jzI1yXW1daRfYjg5BHWgf1B5vB28rVSoqmsXDUX5+PsePHyc1NRW9Xl/qWK9evcy9XJUi4UhUJfqcHBLfmMq1rVsBWQ9JiBtdybvCvsR97Infw97EvWRpskodb+jVkAeDHqRDUAcaeTfCRmljpUrF3bJoOPrll18YPHgwaWlpZS+mUKDT6cyrtoqRcCSqGoPBQPr//kfqBwuv35dt8SLUgYHWLk2ISqVQX8iJtBOmsUqn00+XOu5m50a7gHZ0CO5Au8B20qpUxVg0HEVERNC9e3fefvtt/Pxq3v1uJByJqip7714Sx09Al5mJjYcHQQs/wKlNG2uXJUSllZaXxh8Jf/BHwh/sS9jHNe21UseLxyq1D2pPY5/GqJVqK1UqysOi4cjV1ZUjR45U+1lptyLhSFRlmvgE4kePouDUaVAq8Z0wAc8Xhso4JCH+RaG+kOOXj/NHwh/sTdzLqSunSh13UbvQJrAN7QPb0z6oPf5OslJ9ZWPRcPTCCy/Qvn17hg0bdldFVlUSjkRVp8/PJ3nadDLXrwfA5ZGeBL77LkonJytXJkTVkZaXxv7E/cZWpcR9ZWbA1XWva2pVau7bHFsbWW/M2iwajnJzc3nqqafw8fGhUaNGqNWlmxFHjx5tfsVViIQjUR0YDAaurlhByuw5UFiIXUQEwR8txjYszNqlCVHl6PQ6Tl05xR+Jf7A3YS//pP1Tal0lB5UDrf1b0z6oPe0D2xPiGmLFamsui4aj//3vf7z00kvY29vj5eVVqjleoVBw8eLFO6u6ipBwJKqT3MOHiR8zBt3lNJQuLgTOm4tL587WLkuIKi0jP4MDSQdMXXBpeaUnMIW4hJiWC2jl3wpHtaOVKq1ZLH7j2dGjRzN58mSUSuVdFVoVSTgS1Y02JZWEsWPJO3IEAO+RI/F+5WUUNfDvtxAVTW/Qc/bqWVP325GUIxQaCk3HVUoVzXyb0S6wHe0D21Pfsz5KhfzdswSLhiNPT0/++usvGZAt4UhUIwaNhpT35nL1++8BcO7UicB5c7GRP+NCVKgcbQ5/Jv3J3sS97E3YS3x2fKnjXvZetAtsR7ugdrQNaIuXg5eVKq1+LBqOxo0bh4+PD2+88cZdFVlVSTgS1VnG2nUkT5uGQaNBHVqL4I8+wr5ePWuXJUS1FZsVy97EvexL2MfB5IPkFeaVOl68XEDbwLY09WmK2kaWC7hTFg1Ho0eP5ttvv6VJkyY0bty4zIDsDz74wPyKqxAJR6K6yztxkvjRoyhMTELh6EjgrHdxfeQRa5clRLWn1Wk5knrEGJYS9xGVHlXquKPKkVb+rWgb2JZ2ge0IdQ2VZTjMYNFw1Pk2gzUVCgU7d+4053JVjoQjURMUXr1Kwvjx5O4/AIDn0KH4ThiPQiU35BTiXknLS2Nf4j72JuzlQNIB0vPTSx0Pcg4yBaVW/q1ws3OzUqVVQ7W88eysWbPYtGkTR48exdbWloyMjDLn3CxBr1ixgkGDBple7969m/Hjx3Py5ElCQkJ48803GTJkSLnrkHAkagpDYSGXP/yQK1/9DwDHli0JWvgBKh8fK1cmRM2jN+g5k36GfYn72J+4n8Oph9HqtabjSoWSRt6NjOOVAttxv/f9qJTyy0xJ1TIcTZs2DXd3d+Lj4/nf//53y3C0dOlSevbsadrn7u6Ovb09ANHR0dx///289NJLDB8+nB07djB27Fg2bdpEjx49ylWHhCNR02T9+itJU95An5ODyseHoEUf4ti8ubXLEqJGy9XmcijlEPsT97MvcR8XM0svo+OsdqZ1QGvaBbajbWBbQlxkbSWLd6vdro/T0t1q33zzDWPHjr1lOFq7di19+vS56XsnTZrEpk2bOHHihGnfoEGDyMjI4JdffinX50s4EjVRwcVo4kePQnP+AqhU+E2ciMdz/5HxDkJUEknZSexP2m9qWcrSZJU6HuwcfL0LLqAVrrY17/vLnO9vs9vcmjZtWuq1Vqvl6NGjnDhxgueff97cy1W4V199leHDh1O7dm1eeuklhg69ft+o/fv307Vr11Ln9+jRg7Fjx97yegUFBRQUFJheZ2Vl3fJcIaoru9rhhK9cSdJbb5G1eQsps2eTd+wYATPfQekoC9gJYW0BzgH0i+hHv4h+phW79yXuY1/iPo5fPk58djyrz65m9dnVKBVK7ve6n7aBbWkb2FZumnsTZoejhQsX3nT/9OnTyc7OvuuC7sY777xDly5dcHR05Ndff+WVV14hOzvbdEuT5ORk/Pz8Sr3Hz8+PrKws8vLycHBwKHPNOXPmMGPGjHtSvxCVmdLJicAFC3Bo2pSUefPJ2rSJgrNnCVq8CLvwcGuXJ4QoYqO0oZFPIxr5NOLFJi+So83hr+S/2J+4n/1J+4nOjOZ42nGOpx3n8+Of46hy5AH/B0xhKdw1vMa3ClfYmKPz58/TqlUr0tPT//3kIpMnT2bu3Lm3Pef06dM0aNDA9Pp23Wo3evvtt1m6dClxcXEA1KtXj6FDhzJlyhTTOZs3b+axxx4jNzf3puHoZi1HISEh0q0marTcv/8mfuxY421HnJ0JmDMb127drF2WEKIcknOSjUEpcT8Hkg5wteBqqeN+jn6mLrjWAa3xtPe0UqUVy6Ldareyf/9+08Dn8powYcK/zhSrXbv2HdfUunVrZs6cSUFBAXZ2dvj7+5OSklLqnJSUFFxdXW8ajADs7Oyws7O74xqEqI4cW7Qg/KefSBg/nrxDf5MwajT5I4bjM2aMTPcXopLzd/Knb0Rf+kb0LT0LLmk/R1KOkJKbwrrz61h3fh0ADTwb0CagDW0D2tLMrxkOqpt/X1YnZv8r1q9fv1KvDQYDSUlJHDp0iLfeesusa/n4+OBjwWnBR48excPDwxRu2rZty+bNm0uds23bNtq2bWuxGoSortS+voQuXUrq+wtIX7aMK19+Rd4/Jwha8D4qL7nlgRBVgVKhJNIrkkivSIY1GkZeYR5HUo6YBnefvXqWqPQootKj+ObkN6iVapr5NjOGpcC2RHpGYqO0sfaPUeHM7lYbOnRoqddKpRIfHx+6dOlC9+7dK7S4kmJjY0lPT2fDhg3Mnz+fPXv2AFC3bl2cnZ35+eefSUlJoU2bNtjb27Nt2zZee+01XnvtNdOYoeKp/K+++iovvPACO3fuZPTo0TKVX4i7lLVlC4lT38SQm4vK35/gRR/i0KSJtcsSQtyltLw0/kz6kwNJB9iftJ/knORSx11sXWjt39oUlkJcQirteKVquc7RkCFDWLZsWZn9u3btolOnTvzyyy9MmTKF8+fPYzAYqFu3Li+//DIjRoxAWeLu4rt372bcuHGcOnWK4OBg3nrrLVkEUogKUHD+PPGjRqOJjga1Gv+pb+A+cGCl/YdSCGEeg8HApaxLxqCUuJ+/kv/imvZaqXMCnQJpE9iGNgFtKt14pXsSjjQaDampqej1+lL7a9WqdSeXqzIkHAlxa7rsbJKmvMG1bdsAcOvdG//p01DeYkyfEKLqKtQXcurKKdPA7qOXj1KoLyx1Tn2P+rQOMLYstfBrgaPaekt/WDQcnT17lmHDhrFv375S+w0GAwqFAp1OZ37FVYiEIyFuz2AwkP71UlIXLAC9Hrv69Qle9CG2YWHWLk0IYUG52lz+Tvnb1AV37uq5UsdVChWNfRqbWpUa+TS6p+srWTQctW/fHpVKxeTJkwkICCjTZN6kmo8zkHAkRPnkHPyThAkT0KWloXRyMk73t+C4RCFE5XIl7wp/JhvHKx1MOkhCdkKp4w4qB1r6tTS1LEV4RKBUKG9xtbtn0XDk5OTE33//XWrtoZpEwpEQ5adNSSVhgnG6P4Dn0KH4jh+HQi2r8QpR08RdizMFpYNJB8koyCh13NPek1b+rUwtS8EuwRX6+RYNRw888AALFy6kQ4cOd1VkVSXhSAjzGLRaUhd+SPrXXwPg0KIFQR98gNrP18qVCSGsRW/Qc/bqWQ4mHWR/0n4OpxwmrzDPdDzQKZBfnvylQid0WDQc7dy5kzfffJPZs2fTqFEj1Df8BljdA4OEIyHuTNa2bSRNeQN9djY2Xl4ELViAU5vW1i5LCFEJaHVajl0+xsFkY6tSXfe6vN327Qr9DIuGo+Jp8TemORmQLYT4N5qYGOLHjKXgzBlQKvEZMwavEcNRKC03zkAIUfUUZ4qKZNHbh+zateuOCxNC1Gy2YWGE/bCC5Hdmkrl2LZcXLiTvyBEC576HjZubtcsTQlQS1l4frcosAllZSMuREHfPYDCQ8eOPpMx8F4NGgzo4mKBFH+Jw333WLk0IUU2Z8/0tbdlCiHtOoVDg8dRThK74HnVwMNr4eC49/QxXV61Cfl8TQlibhCMhhNU43Hcf4T/9iHPnzhg0GpLfnmYctJ2X9+9vFkIIC5FwJISwKhs3N4I/XoLP+PGgVJK5bh0xg55GExNj7dKEEDWUhCMhhNUplEq8/zuCWl9/jY2XFwVnzhDd/ymytv5q7dKEEDWQhCMhRKXh1KY14WvW4NCyBfrsbBLGjCF59mwMGo21SxNC1CDlmq3WrFmzck+rO3z48F0XVZnJbDUhLM9QWMjlRYu48uVXANg3bkzwwg9QBwVZuTIhRFVV4bPV+vTpQ+/evenduzc9evTgwoUL2NnZ0alTJzp16oS9vT0XLlygR48eFfIDCCFqNoVKhe+ECQR/8glKNzfyjx/nYr8nuSbrrAkh7gGz1zkaPnw4AQEBzJw5s9T+adOmERcXx9dF90+qrqTlSIh7SxOfQMK4ceT/8w8AXiOG4zNmDAqV2WvYCiFqMIvePsTNzY1Dhw4RERFRav+5c+do2bIlmZmZ5ldchUg4EuLeM2g0pMybz9XvvgPAoWULghbIzWuFEOVn0UUgHRwc2Lt3b5n9e/fuxd7e3tzLCSHEv1LY2uL/5lSCPlyI0smJvEN/E923Lzn79lm7NCFENWR2u/TYsWN5+eWXOXz4MK1atQLg4MGDfP3117z11lsVXqAQQhRz7dkT+wYNiB87joKoKGKHDcf71VfxfvklFDY21i5PCFFN3NG91VatWsWiRYs4ffo0AJGRkYwZM4YBAwZUeIGVjXSrCWF9+vx8UmbNJmP1agCc2rUlcP58VF5eVq5MCFFZWXTMUU0n4UiIyiNz/XqSps/AkJeHyteXoA8W4NiypbXLEkJUQvfkxrMajYb4+HhiY2NLbUIIca+49e5N+KqV2NapQ2FqKpeeH8KVr77CoNdbuzQhRBVmdjg6d+4cDz74IA4ODoSGhhIeHk54eDhhYWGEh4dbokYhhLglu4gIwletxPWJJ0CnI/X9BcS/8iqFV69auzQhRBVl9oDsIUOGoFKp2LhxIwEBAeVeOVsIISxF6eRE4Ly5OLZsScqsWWTv3k10vyeN3WzNmlm7PCFEFWP2mCMnJyf+/vtvGjRoYKmaKjUZcyRE5ZZ/+jQJY8ehuXQJVCp8x43Fc+hQFEq5laQQNZlFxxw1bNiQtLS0Oy5OCCEsyT4ykrCffsT10UehsJDU+e8T//Ir0s0mhCg3s8PR3LlzmThxIrt37+bKlStkZWWV2oQQwtpsnJ0JXPA+/jNmoLC1Jfu334ju24/can5jbCFExTC7W01Z1DR941gjg8GAQqFAp9NVXHWVkHSrCVG15EdFGbvZYmLAxgafsWPwGjZMutmEqGHM+f42e0D2LrkrthCiCrFv0ICwH38kefp0sjZu5PKCD8j96y8C33sPlaentcsTQlRCsgikmaTlSIiqyWAwkPHjj6S8OwtDQQEqPz+CFrwvi0YKUUPckxWyc3NziY2NRaPRlNrfuHHjO7lclSHhSIiqLf/MGWM3W3S0sZtt9Gi8RgyXbjYhqjmLhqPLly8zdOhQtmzZctPjMuZICFHZ6XNySJoxg6wNPwPg1KEDgXPfk3uzCVGNWXQq/9ixY8nIyODgwYM4ODjwyy+/sGzZMiIiItiwYcMdFy2EEPeK0smJwLlzCZj1Lgp7e3L++IPoPn3J+fNPa5cmhKgEzA5HO3fu5IMPPqBly5YolUpCQ0P5z3/+w7x585gzZ44lahRCiAqnUChwf/JJwlevMt6b7fJlYocMJe3TTzFU8xZwIcTtmR2OcnJy8PX1BcDDw4PLly8D0KhRIw7LGiJCiCrGLiKC8NWrcOvTB/R6Li9aTOzw4RQW/dsmhKh5zA5H9evX58yZMwA0adKEzz//nISEBD777DMCAgIqvEAhhLA0paMjge/NIWD2bBQODuTuP8DFPn3J3vOHtUsTQliB2eFozJgxJCUlATBt2jS2bNlCrVq1WLx4MbNnz67wAoUQ4l5x79eX8J9+xK5+fXRXrhA3YgSpCxZg0GqtXZoQ4h4yOxz95z//YciQIQC0aNGCS5cu8ddffxEXF8fAgQMruj4AYmJiGDZsGOHh4Tg4OFCnTh2mTZtWZhmB48eP8+CDD2Jvb09ISAjz5s0rc63Vq1fToEED7O3tadSoEZs3b7ZIzUKIqsmudm3CVv6A+9ODALjy5Vdcem4w2oQEK1cmhLhX7nphD0dHR5o3b463t3dF1HNTUVFR6PV6Pv/8c06ePMnChQv57LPPeOONN0znZGVl0b17d0JDQ/n777+ZP38+06dP54svvjCds2/fPp5++mmGDRvGkSNH6NOnD3369OHEiRMWq10IUfUo7e0JmDaNoEWLULq4kHf0KBf79iPr11+tXZoQ4h6ositkz58/n08//ZSLFy8C8OmnnzJ16lSSk5OxtbUFYPLkyaxbt46oqCgABg4cSE5ODhs3bjRdp02bNjRt2pTPPvusXJ8r6xwJUbNo4hNInDCBvGPHAPB45ml8J01CaWdn5cqEEOaw6DpHlUVmZiaeJe6LtH//fjp27GgKRgA9evTgzJkzXL161XRO165dS12nR48e7N+//5afU1BQQFZWVqlNCFFz2AYHEfrd/+E1YjgAV79fQczAQRRcjLZyZUIIS6mS4ej8+fN89NFHvPjii6Z9ycnJ+Pn5lTqv+HVycvJtzyk+fjNz5szBzc3NtIWEhFTUjyGEqCIUajW+EyYQ8uWX2Hh6UhAVRXT//mSsXWft0oQQFmDVcDR58mQUCsVtt+IusWIJCQn07NmTp556ihEjRli8xilTppCZmWna4uLiLP6ZQojKyfnBDoSvW4tjmzYYcnNJmjKFxEmT0GXnWLs0IUQFUpn7hqVLl+Ls7MxTTz1Vav/q1avJzc3l+eefL/e1JkyYYJr5diu1a9c2PU9MTKRz5860a9eu1EBrAH9/f1JSUkrtK37t7+9/23OKj9+MnZ0ddjK2QAhRRO3rS63/fcWVL7/k8uKPyFy/gbyjxwj6cCH2kZHWLk8IUQHMbjmaM2fOTWem+fr6mr3OkY+PDw0aNLjtVjyGKCEhgU6dOtGiRQuWLl2K8oY7aLdt25bff/8dbYn1SLZt20b9+vXx8PAwnbNjx45S79u2bRtt27Y1q24hRM2msLHB+6WXCP2/b1H5+6O5dImYAQNJ/245VXSOixCiBLPDUWxsLOHh4WX2h4aGEhsbWyFF3ag4GNWqVYv333+fy5cvk5ycXGqs0DPPPIOtrS3Dhg3j5MmTrFy5kkWLFjF+/HjTOWPGjOGXX35hwYIFREVFMX36dA4dOsTIkSMtUrcQonpzbNGC8LVrcO7SBYNWS8q77xL/6kgKiyaBCCGqJrPDka+vL8ePHy+z/9ixY3h5eVVIUTfatm0b58+fZ8eOHQQHBxMQEGDairm5ufHrr78SHR1NixYtmDBhAm+//Tb//e9/Tee0a9eO77//ni+++IImTZrw448/sm7dOu6//36L1C2EqP5UHh4Ef7wEvzfeQKFWk71zJ9F9+pJz8E9rlyaEuENmr3M0adIkVq5cydKlS+nYsSMAv/32Gy+88AL9+/fn/ffft0ihlYWscySEuJX806dJGD8BTXQ0KBR4vfQiPq++ikJl9vBOIUQFM+f72+xwpNFoeO6551i9ejWqor/wer2ewYMH89lnn5VaZ6g6knAkhLgdfW4uybNnk/njTwA4NGtG4Pz52AYHWbkyIWo2i4ajYmfPnuXYsWM4ODjQqFEjQkND76jYqkbCkRCiPLI2bybp7Wnos7NRurgQMPMdXHv2tHZZQtRY9yQc1VQSjoQQ5aWJjydxwmumW4+4P9UfvylTUDo6WrkyIWqeCg9H48ePZ+bMmTg5OZWa/XUzH3zwgXnVVjESjoQQ5jBotVz++GOufP4FGAzY1q5N0AcLsG/QwNqlCVGjmPP9Xa5RgkeOHDGtH3TkyJG7r1AIIWoIhVqN79ixOLVpS+LEiWguXiTmqQH4TpyIx3+eRaFQWLtEIcQNpFvNTNJyJIS4U4VXr5L0xlSyd+0CwLlTJwLmzEZVtFCtEMJyzPn+NnudoxdeeIFr166V2Z+Tk8MLL7xg7uWEEKLGUHl4EPzJx/i9+SYKW1uyd+8muldvcg4csHZpQogSzG45srGxISkpCV9f31L709LS8Pf3p7CwsEILrGyk5UgIURHyo6KMayJdvGhcE2nECHxGjUShVlu7NCGqJYu0HGVlZZGZmYnBYODatWtkZWWZtqtXr7J58+YygUkIIcTN2TdoQPiPq3F/6ikwGLjyxRfEPPsfNBa6DZMQovzKvWyru7s7CoUChUJBvXr1yhxXKBTMmDGjQosTQojqTOnoSMDMd3Bq346kt6eRf/w40X364vfWW7j16S2DtYWwknJ3q/32228YDAa6dOnCTz/9hKenp+mYra0toaGhBAYGWqzQykK61YQQlqBNTCRx4iRyDx0CwPXRR/CfPh0b+XdGiAph0UUgo6OjCQsLq7G/0Ug4EkJYikGn48qXX3L5oyWg06EKDCBo/nwcW7SwdmlCVHkWna3WpUsXZs6cSaz0iwshRIVS2Njg/dJLhH2/HHVICIWJSVx6bjCXFy/GUM0nuwhRmZgdjsaMGcOaNWuoXbs23bp144cffqCgoMAStQkhRI3k0KQJ4WvX4Na7N+j1pH3yKZf+8xya+HhrlyZEjWB2OBo7dixHjx7lzz//JDIyklGjRhEQEMDIkSM5fPiwJWoUQogax8bZmcC57xG44H2Uzs7kHT1KdO8+ZG7YYO3ShKj27nqFbK1WyyeffMKkSZPQarU0atSI0aNHM3To0Go5LknGHAkh7jVNfAKJEyeSV/QLqOsTT+D/9lvYuLhYuTIhqg6LjjkqptVqWbVqFb169WLChAm0bNmSr776iieffJI33niDZ5999k4vLYQQogTb4CBCv12G96iRYGND1s8/E92nL7mH5V6XQliC2S1Hhw8fZunSpaxYsQKlUsngwYMZPnw4DUrcYfrEiRM88MAD5OXlVXjB1iYtR0IIa8o9fITE119Hm5AANjZ4v/Iy3i++iEJV7mXrhKiRLNpy9MADD3Du3Dk+/fRTEhISeP/990sFI4Dw8HAGDRpk7qWFEEL8C8fmzQhftxbXJ54AnY60j5ZwafDzaOITrF2aENWG2S1Hly5dIjQ01FL1VHrSciSEqCwyN2wgecY76HNyUDo74//2W7g+8US1HO8pxN2yaMtRTQ5GQghRmbj16kX4+nU4NGuGPjubxImTSJwwAV1mprVLE6JKK1fLkYeHR7l/E0lPT7/roiozaTkSQlQ2hsJC0r74grSPPzGurO3vT+B77+HUprW1SxOi0jDn+7tcI/g+/PDDiqhLCCGEBShUKnxeeQXnDh1IeP11tJdiiR06FM8XhuIzZgxKW1trlyhElXLX6xzVNNJyJISozPQ5OaS8N5eM1asBsIuMJGj+POzq1rVyZUJY1z1Z5wggPz+frKysUpsQQgjrUTo5ETDzHYKXfISNuzsFp08T/WR/0r9bjvwuLET5mB2OcnJyGDlyJL6+vjg5OeHh4VFqE0IIYX0uXbsSvmE9Th06YCgoIOXdd4n774sUXr5s7dKEqPTMDkcTJ05k586dfPrpp9jZ2fHVV18xY8YMAgMD+fbbby1RoxBCiDug9vUl5IvP8Zs6FYWtLTl79nCxV2+u7dxp7dKEqNTMHnNUq1Ytvv32Wzp16oSrqyuHDx+mbt26/N///R8rVqxg8+bNlqq1UpAxR0KIqqjg3DkSXp9IQVQUAO4DBuA3eRJKR0crVybEvWHRMUfp6enUrl0bAFdXV9PU/Q4dOvD777/fQblCCCEszS4igrBVK/F84QVQKMhYtYrovv3I++cfa5cmRKVjdjiqXbs20dHRADRo0IBVq1YB8PPPP+Pu7l6hxQkhhKg4Sltb/Ca+Tq2lX6Py80Nz6RIxTz/D5U8+wVBYaO3yhKg0zA5HQ4cO5dixYwBMnjyZjz/+GHt7e8aNG8frr79e4QUKIYSoWE5t2lB7/TpcHukJhYWkLf6IS8/+B82lS9YuTYhK4a7XObp06RJ///03devWpXHjxhVVV6UlY46EENWFwWAga+NGkt+Zif7aNRQODvhNmoT7wAFyfzZR7Zjz/S2LQJpJwpEQorrRJiaSOOUNcg8eBMDpoY4EvvsuKh8fK1cmRMWxeDjasWMHO3bsIDU1Fb1eX+rY119/be7lqhQJR0KI6sig15P+7bdc/mAhBo0GG3d3/Ge+g2u3btYuTYgKYdHZajNmzKB79+7s2LGDtLQ0rl69WmoTQghR9SiUSryGDCHsx9XYNWiALiODhFGjSXxjKrrsbGuXJ8Q9ZXbLUUBAAPPmzeO5556zVE2VmrQcCSGqO71GQ9pHS7jy1VdgMKAOCiLwvTk4PvCAtUsT4o5ZtOVIo9HQrl27Oy5OCCFE5aa0tcV3wnhC/+9b1EFBaBMSuDT4eVLffx+9RmPt8oSwOLPD0fDhw/n+++8tUcstxcTEMGzYMMLDw3FwcKBOnTpMmzYNTYm/pDExMSgUijLbgQMHSl1r9erVNGjQAHt7exo1alTtV/QWQog75diyJeHr1+H2ZD8wGLjy1f+IeWoA+WfOWrs0ISxKZe4b8vPz+eKLL9i+fTuNGzdGrVaXOv7BBx9UWHHFoqKi0Ov1fP7559StW5cTJ04wYsQIcnJyeP/990udu337du677z7Tay8vL9Pzffv28fTTTzNnzhwef/xxvv/+e/r06cPhw4e5//77K7xuIYSo6mycnQmcNQuXzp1JeuttCs6cIaZ/f3zGjcNzyPMolGb/ji1EpWf2mKPOnTvf+mIKBTvv0Q0N58+fz6effsrFixcBY8tReHg4R44coWnTpjd9z8CBA8nJyWHjxo2mfW3atKFp06Z89tln5fpcGXMkhKipCtPSSHrzLbJ37wbAsVUrAufMRh0UZN3ChCgHc76/zW452rVr1x0XVpEyMzPx9PQss79Xr17k5+dTr149Jk6cSK9evUzH9u/fz/jx40ud36NHD9atW3fLzykoKKCgoMD0Oisr6+6LF0KIKkjl7U3wp5+QsXo1Ke/NJffPP7nYqzd+b7yBW7++snCkqDaqZHvo+fPn+eijj3jxxRdN+5ydnVmwYAGrV69m06ZNdOjQgT59+rBhwwbTOcnJyfj5+ZW6lp+fH8nJybf8rDlz5uDm5mbaQkJCKv4HEkKIKkKhUOAxYAC1167BoVkz9Dk5JE2dSvyrIylMS7N2eUJUiDtaBPLQoUOsWrWK2NjYUoOiAdasWVPu60yePJm5c+fe9pzTp0/ToEED0+uEhAQeeughOnXqxFdffXXb9w4ePJjo6Gj27NkDgK2tLcuWLePpp582nfPJJ58wY8YMUlJSbnqNm7UchYSESLeaEKLGM+h0XPn6a9IWf4RBq8XGwwP/6dNx7dHd2qUJUYZFp/L/8MMPtGvXjtOnT7N27Vq0Wi0nT55k586duLm5mXWtCRMmcPr06dtutWvXNp2fmJhI586dadeuHV988cW/Xr9169acP3/e9Nrf379MCEpJScHf3/+W17Czs8PV1bXUJoQQAhQ2NniPGGFcOLJ+fXRXr5IwZgwJEyeikyEIogoze8zR7NmzWbhwIa+++iouLi4sWrSI8PBwXnzxRQICAsy6lo+PDz7lvHdPQkICnTt3pkWLFixduhRlOWZIHD16tFRNbdu2ZceOHYwdO9a0b9u2bbRt29asuoUQQlxnX78+4atXcfnjT7jy5ZdkbfiZ3IN/EjB7Fs7t21u7PCHMZna3mpOTEydPniQsLAwvLy92795No0aNOH36NF26dCEpKanCi0xISKBTp06EhoaybNkybGxsTMeKW32WLVuGra0tzZo1A4zde2+99RZfffUVQ4cOBYxT+R966CHee+89HnvsMX744Qdmz55t1lR+ma0mhBC3lnvkCEmTp6C5dAkAj2eexve111A6Olq5MlHTWbRbzcPDg2vXrgEQFBTEiRMnAMjIyCA3N/cOyv1327Zt4/z58+zYsYPg4GACAgJMW0kzZ86kRYsWtG7dmvXr17Ny5UpTMAJo164d33//PV988QVNmjThxx9/ZN26dbLGkRBCVBDHZs0IX7sGj2eeAeDq9yu42LcvuYePWLkyIcrP7JajZ555hpYtWzJ+/HhmzpzJRx99RO/evdm2bRvNmzc3a0B2VSQtR0IIUT45+/aR+MZUCpOTQanEa/hwvEe+itLW1tqliRrInO9vs8NReno6+fn5BAYGotfrmTdvHvv27SMiIoI333wTDw+Puyq+spNwJIQQ5afLyiJl1iwy1xuXVbGrX5/AeXOxr1/fypWJmsai4aimk3AkhBDmy/r1V5KnTUd39Sqo1fiMHInX8GEoSowhFcKSLDrmSAghhDCXa/fu1P55A84PPwxaLZcXLuTSM89ScDHa2qUJUYaEIyGEEPeEytub4CUfETBnDkpnZ/KOHSO6b1+ufPMNBr3e2uUJYSLhSAghxD2jUChw79uH2j9vwKl9ewwFBaS+N5dLgwejiY21dnlCABKOhBBCWIE6IICQr77Ef/p0FI6O5B36m4u9+5D+/ffSiiSsTsKREEIIq1AoFHgMGkjtDetxbNUKQ14eKe/MJHbYMLQJCdYuT9RgZs9Wy8nJ4b333mPHjh2kpqaivyHhX7x4sUILrGxktpoQQlQ8g17P1eXfk7pgAYb8fJROTvhOnoR7//4oFAprlyeqAXO+v82+t9rw4cP57bffeO655wgICJA/tEIIIe6aQqnE87n/4PxgBxKnvEHekSMkv/U217ZtI2DmTNR+ftYuUdQgZrccubu7s2nTJtrX0JsJSsuREEJYlkGnI33Zt1z+8EMMGg1KV1f8p76Ba69e8gu5uGMWbTny8PDA09PzjourKXQ6HVqt1tplCCtQq9Wlbo4shDCPwsYGrxeG4vxQRxInTyH/n39InDSZrF+3ETB9GiofH2uXKKo5s1uOvvvuO9avX8+yZctwrIF3Wf635GkwGEhOTiYjI+PeFycqDXd3d/z9/eW3XCHukqGwkCtf/Y/LH38MWi027u74v/0Wro8+au3SRBVj0duHNGvWjAsXLmAwGAgLC0OtVpc6fvjwYfMrrkL+7T9uUlISGRkZ+Pr64ujoKF+ONYzBYCA3N5fU1FTc3d0JCAiwdklCVAv5Z86QOHkKBadPA+DSowf+b7+FysvLypWJqsKi3Wp9+vS507qqPZ1OZwpGXvIXtsZycHAAIDU1FV9fX+liE6IC2NevT/iqlaR99jlpn3/Ota1byf3zT/zffguXnj3lF1FRoeTGs2a6XfLMz88nOjqasLAw0xekqJny8vKIiYkhPDwce3t7a5cjRLWSf+oUiVPeoODMGQBcunc3tiJ5e1u5MlGZyY1nrUx+gxHyZ0AIy7Fv2JDw1avwfvVVUKm49uuvXHz8CbI2b0Z+3xcVoVzhyNPTk7S0NOD6bLVbbUIIIYSlKWxt8Rk1kvDVq7Br0ABdRgYJ4yeQMHoMhUXfV0LcqXKNOVq4cCEuLi4AfPjhh5asR1Rz33zzDWPHjr3tbL7p06ezbt06jh49es/qEkJUTfaRkcaxSF98Sdpnn3Ft2zZy//oLv7fexPXRR6UVV9wRGXNkpvKMOZJxJrdWnnCUnZ1NQUGBaVD7kCFDyMjIYN26dfemyJtYs2YNs2fP5vz582i1WiIiIpgwYQLPPffcTc+XPwtC3Hv5p08bxyJFRQHg0q0r/m+/LesiCUDGHIkqztnZudLN9vP09GTq1Kns37+f48ePM3ToUIYOHcrWrVutXZoQooh9ZKRxLNKokcaxSNu2c/HxJ8jcuEnGIgmzSDgSAHTq1InRo0czceJEPD098ff3Z/r06aXOOXfuHB07dsTe3p6GDRuybds2FAqFqUVn9+7dKBSKUq1CR48eRaFQEBMTU+pa69atIyIiAnt7e3r06EFcXJzp2PTp02natKnp+bJly1i/fj0KhQKFQsHu3bvRaDSMHDmSgIAA7O3tCQ0NZc6cORb4L2PUqVMn+vbtS2RkJHXq1GHMmDE0btyYP/74w2KfKYQwn0KtxufVVwn/cTV2kZHoMjNJfO014keNovDyZWuXJ6oICUcWZjAYyNUUWmUz9zelZcuW4eTkxMGDB5k3bx7vvPMO27ZtA0Cv19OvXz9sbW05ePAgn332GZMmTbqj/ya5ubnMmjWLb7/9lr1795KRkcGgQYNueu5rr73GgAED6NmzJ0lJSSQlJdGuXTsWL17Mhg0bWLVqFWfOnGH58uWEhYXd8jOXL1+Os7Pzbbc9e/aUq36DwcCOHTs4c+YMHTt2vJP/BEIIC7Nv0IDwVSvxHj0K1Gqyt+8wtiL9vFFakcS/MnsRSGGePK2Ohm9bp+vl1Ds9cLQt///ixo0bM23aNAAiIiJYsmQJO3bsoFu3bmzfvp2oqCi2bt1KYGAgALNnz+aRRx4xuy6tVsuSJUto3bo1YAxlkZGR/Pnnn7Rq1arUuc7Ozjg4OFBQUIC/v79pf2xsLBEREXTo0AGFQkFoaOhtP7NXr16mz7uVoKCg2x7PzMwkKCiIgoICbGxs+OSTT+jWrdtt3yOEsB6FWo3PK6/g8vDDJE6ZQsGp0yS+/jpZW7bgP20aaj9fa5coKqk7Dkfnz5/nwoULdOzYEQcHBwwGg8wKqOIaN25c6nVAQACpqakAnD59mpCQEFMwAmjbtu0dfY5KpeKBBx4wvW7QoAHu7u6cPn26TDi6lSFDhtCtWzfq169Pz549efzxx+nevfstz3dxcTHNuLxTLi4uHD16lOzsbHbs2MH48eOpXbs2nTp1uqvrCiEsy75+fcJXriTtyy9J+/Qzsnfu5OJff+E3eRJu/frJd5cow+xwdOXKFQYOHMjOnTtRKBScO3eO2rVrM2zYMDw8PFiwYIEl6qyyHNQ2nHqnh9U+2xw33idPoVCg1+vL/X6l0thLW7LJWqvVmlVDeTVv3pzo6Gi2bNnC9u3bGTBgAF27duXHH3+86fnLly/nxRdfvO01t2zZwoMPPnjL40qlkrp16wLQtGlTTp8+zZw5cyQcCVEFmFqRunYlaeqb5P/zD0lT3yRr02YCZr6D+l9ajkXNYnY4GjduHCqVitjYWCIjI037Bw4cyPjx4yUc3UChUJjVtVVZRUZGEhcXR1JSkulmqgcOHCh1jk/RdNmkpCQ8PDwAbrpWUWFhIYcOHTK1Ep05c4aMjIxSf55KsrW1RafTldnv6urKwIEDGThwIP3796dnz56kp6ffdDHSiuhWu5Fer6egoMCs9wghrMu+Xj3CVnxP+rJvubx4MTn79nHxiV74vDYBj0GDUChlKK64g3D066+/snXrVoKDg0vtj4iI4NKlSxVWmKhcunbtSr169Xj++eeZP38+WVlZTJ06tdQ5devWJSQkhOnTpzNr1izOnj1707CsVqsZNWoUixcvRqVSMXLkSNq0aXPLLrWwsDC2bt3KmTNn8PLyws3NjY8++oiAgACaNWuGUqlk9erV+Pv74+7uftNr3G232pw5c2jZsiV16tShoKCAzZs383//9398+umnd3xNIYR1KFQqvIa9gHOXziS9+RZ5f/9NyjszubZ5CwGz3sX2X8YwiurP7Iick5ODo6Njmf3p6enY2dlVSFGi8lEqlaxdu5a8vDxatWrF8OHDmTVrVqlz1Go1K1asICoqisaNGzN37lzefffdMtdydHRk0qRJPPPMM7Rv3x5nZ2dWrlx5y88eMWIE9evXp2XLlvj4+LB3715cXFyYN28eLVu25IEHHiAmJobNmzebuvYqWk5ODq+88gr33Xcf7du356effuK7775j+PDhFvk8IYTl2YWHE/p/3+L35psoHB3JPXSIi737cOXrpRhu0lotag6zV8h+9NFHadGiBTNnzsTFxYXjx48TGhrKoEGD0Ov1txzzUV3ICtmlKRQK1q5dS58+faxdSqVSE/8sCFGVaeITSH77LXL27QfAvkljAt99F7uICCtXJiqKOStkm92tNm/ePB5++GEOHTqERqNh4sSJnDx5kvT0dPbu3XvHRQshhBDWYhscRMj//kfmTz+R8t5c8o8dJ7rfk3i/+gpew4ahuGHCiqjezO6DuP/++zl79iwdOnSgd+/e5OTk0K9fP44cOUKdOnUsUaMQQghhcQqFAvf+/am9aSPOnTph0Gq5/OEiogcMJP/UKWuXJ+4hufGsmaRbTZSH/FkQomozGAxkbdxIyruz0GVmgo0NXiOG4/3KKyhtba1dnrgDFr3x7NKlS1m9enWZ/atXr2bZsmXmXk4IIYSodBQKBW5PPEHtTRtx6dkTdDqufPY50X37kXv4iLXLExZmdjiaM2cO3t7eZfb7+voye/bsCilKCCGEqAxU3t4Ef7iQoMWLsPH2RnPhApeefZbkme+iy86xdnnCQswOR7GxsYSHh5fZHxoaSmxsbIUUJYQQQlQmrt27U2fTRtye7AcGA1eXL+diryfI/v13a5cmLMDscOTr68vx48fL7D927BheXl4VUpQQQghR2di4uRE4axa1vv4f6uBgChOTiPvviyRMnEjh1avWLk9UILPD0dNPP83o0aPZtWsXOp0OnU7Hzp07GTNmDIMGDbJEjUIIIUSl4dSuHbU3rMdzyBBQKsna8DMXH3uczI2bkDlO1YPZ4WjmzJm0bt2ahx9+GAcHBxwcHOjevTtdunSx6JijXr16UatWLezt7QkICOC5554jMTGx1DnHjx/nwQcfxN7enpCQEObNm1fmOqtXr6ZBgwbY29vTqFEjNm/ebLGahRBCVE9KR0f8Jk8i7IcV2EVEoEtPJ/G114h/+RW0SUnWLk/cJbPDka2tLStXriQqKorly5ezZs0aLly4wNdff42tBac3du7cmVWrVnHmzBl++uknLly4QP/+/U3Hs7Ky6N69O6Ghofz999/Mnz+f6dOn88UXX5jO2bdvH08//TTDhg3jyJEj9OnThz59+nDixAmL1S1K++abb255/7Ni06dPp2nTpvekHiGEuBsOjRsT/tOPeI8eBWo12bt3c/HxJ7i6YgUGvd7a5Yk7Zaii1q9fb1AoFAaNRmMwGAyGTz75xODh4WEoKCgwnTNp0iRD/fr1Ta8HDBhgeOyxx0pdp3Xr1oYXX3yx3J+bmZlpAAyZmZlljuXl5RlOnTplyMvLM/fHqTGWLl1qcHNzu+05165dM6SlpZleP//884bevXtbtjAzrFixwgDctib5syBEzZN/7pwheuAgw6n6DQyn6jcwRD/7rCH/wkVrlyWK3O77+0Zm3z5Ep9PxzTffsGPHDlJTU9HfkIx37txZMantNtLT01m+fDnt2rVDXbSk+/79++nYsWOp1qsePXowd+5crl69ioeHB/v372f8+PGlrtWjRw/WrVt3y88qKCigoKDA9DorK6tifxhRhrOzM87OztYu46ZiYmJ47bXXePDBB61dihCikrGrW5fQ5d9x9fsVpC5cSN6hv4nu0wfvV17Ba9gLcguSKsTsbrUxY8YwZswYdDod999/P02aNCm1WdKkSZNwcnLCy8uL2NhY1q9fbzqWnJyMn59fqfOLXycnJ9/2nOLjNzNnzhzc3NxMW0hIiHlFGwygybHOZsbAwE6dOjF69GgmTpyIp6cn/v7+TJ8+vdQ5586do2PHjtjb29OwYUO2bduGQqEwhcvdu3ejUCjIyMgwvefo0aMoFApiYmJKXWvdunVERERgb29Pjx49iIuLMx0r2a02ffp0li1bxvr161EoFCgUCnbv3o1Go2HkyJEEBARgb29PaGgoc+bMMef/jNl0Oh3PPvssM2bMoHbt2hb9LCFE1aSwscHzuf9Q5+cNOHXogEGj4fKHHxL91ADyTpy0dnminMxuOfrhhx9YtWoVjz766F1/+OTJk5k7d+5tzzl9+jQNGjQA4PXXX2fYsGFcunSJGTNmMHjwYDZu3IhCobjrWm5lypQppVqbsrKyzAtI2lyYHWiBysrhjUSwdSr36cuWLWP8+PEcPHiQ/fv3M2TIENq3b0+3bt3Q6/X069cPPz8/Dh48SGZmJmPHjr2jsnJzc5k1axbffvsttra2vPLKKwwaNOimNy5+7bXXOH36NFlZWSxduhQAT09PFi9ezIYNG1i1ahW1atUiLi6uVMC60fLly3nxxRdvW9eWLVtu2yL0zjvv4Ovry7Bhw9izZ085f1ohRE2kDgoi5MsvyNqwgZTZcyiIiiJmwAA8Bw/GZ/QolI6O1i5R3IbZ4cjW1pa6detWyIdPmDCBIUOG3Packr+he3t74+3tTb169YiMjCQkJIQDBw7Qtm1b/P39SUlJKfXe4tf+/v6mx5udU3z8Zuzs7LCzszPnx6qyGjduzLRp0wCIiIhgyZIl7Nixg27durF9+3aioqLYunUrgYHGsDd79mweeeQRsz9Hq9WyZMkSWrduDRhDWWRkJH/++SetWrUqda6zszMODg4UFBSU+v8UGxtLREQEHTp0QKFQEBoaetvP7NWrl+nzbiUoKOiWx/744w/+97//cfTo0X/56YQQwkihUODWuzdOHTqQMms2WZs3k/7NN1zbtg3/6dNxfrCDtUsUt2B2OJowYQKLFi1iyZIld91i4+Pjg4+Pzx29t3isU/F4oLZt2zJ16lS0Wq1pHNK2bduoX78+Hh4epnN27NhRqsVj27ZttG3b9i5+in+hdjS24FiD2rzfTBo3blzqdUBAAKmpqYCxBS8kJMQUjIA7/u+mUql44IEHTK8bNGiAu7s7p0+fLhOObmXIkCF069aN+vXr07NnTx5//HG6d+9+y/NdXFxwcXG5o3qvXbvGc889x5dffnnTW+cIIcTtqLy8CPpgAW69e5E0YwbahATiRozA9Ykn8JsyGZWnp7VLFDcwOxz98ccf7Nq1iy1btnDfffeZgkixNWvWVFhxxQ4ePMhff/1Fhw4d8PDw4MKFC7z11lvUqVPH9AX9zDPPMGPGDIYNG8akSZM4ceIEixYtYuHChabrjBkzhoceeogFCxbw2GOP8cMPP3Do0KFS0/0rnEJhVteWNd34/1KhUJQZcH87SqVxCJuhxFgnrVZbMcXdoHnz5kRHR7Nlyxa2b9/OgAED6Nq1Kz/++ONNz7+bbrULFy4QExPDE088YdpX/N9FpVJx5swZ6tSpcxc/jRCiJnB+6CHq/PwzlxcvJv3/viPr55/J2bMH38mTcOvd26JDRIR5zA5H7u7u9O3b1xK13JKjoyNr1qxh2rRp5OTkEBAQQM+ePXnzzTdNXV5ubm78+uuvvPrqq7Ro0QJvb2/efvtt/vvf/5qu065dO77//nvefPNN3njjDSIiIli3bh3333//Pf15qqLIyEji4uJISkoiICAAgAMHDpQ6p7gVMCkpydRad7NuqMLCQg4dOmRqJTpz5gwZGRlERkbe9LNtbW3R6XRl9ru6ujJw4EAGDhxI//796dmzJ+np6Xje5Lewu+lWa9CgAf/880+pfW+++SbXrl1j0aJF5g/SF0LUWEonJ/ymTMH18cdJevMtCs6cIWnyFLI2/Iz/jOnYyr8nlYLZ4ah4UOy91KhRo3ItEdC4ceN/HSj71FNP8dRTT1VUaTVG165dqVevHs8//zzz588nKyuLqVOnljqnbt26hISEMH36dGbNmsXZs2dZsGBBmWup1WpGjRrF4sWLUalUjBw5kjZt2tyySy0sLIytW7dy5swZvLy8cHNz46OPPiIgIIBmzZqhVCpZvXo1/v7+t1xg8m661ezt7csE6OLPkWAthLgTDo0aEf7jaq4s/Ya0jz8mZ98+Lj7RC59RI/F8/nkUKrO/nkUFMnsqPxh/89++fTuff/45165dAyAxMZHs7OwKLU5UHkqlkrVr15KXl0erVq0YPnw4s2bNKnWOWq1mxYoVREVF0bhxY+bOncu7775b5lqOjo5MmjSJZ555hvbt2+Ps7MzKlStv+dkjRoygfv36tGzZEh8fH/bu3YuLiwvz5s2jZcuWPPDAA8TExLB582ZT154QQlR2CrUa7/+OoPb6dTi2bo0hP5/U+e8TPUCm/VubwmAw7y55ly5domfPnsTGxlJQUMDZs2epXbs2Y8aMoaCggM8++8xStVYKWVlZuLm5kZmZiaura6lj+fn5REdHEx4ejr29vZUqvLcUCgVr166lT58+1i6lUqmJfxaEEHfOYDCQuWYtKfPmoc/MBKUSz+efx2fUSJn2X0Fu9/19oztaBLJly5ZcvXoVBwcH0/6+ffuyY8cO86sVQgghajiFQoH7k/2os2kjro8+Cno96UuXcvGJXmT/UXYNOGFZZoejPXv28Oabb5a5yWxYWBgJCQkVVpgQQghR06i8vQn6YAEhn3+GKjDAOO1/+HASXnudwitXrF1ejWF2ONLr9TedORQfH3/HA15F1WUwGKRLTQghKljxtH+Pwc+BQkHWxo1cePQxMn78EYMZS6yIO2N2OOrevTsffvih6bVCoSA7O5tp06ZVyC1FhBBCCGGc9u//xhuErVqJXWQk+sxMkt58i0uDB1Nw4YK1y6vWzA5H77//Pnv37qVhw4bk5+fzzDPPmLrU/u0+aUIIIYQwj0OjRoSvXoXvxIkoHBzIO/Q3F/v05fLij9AX3SVCVCyzZ6uBcSr/ypUrOXbsGNnZ2TRv3pxnn3221ADt6kpmq4nykD8LQghL0CYkkPzOTLJ/+w0A27Aw/KdPx6nN7Re5FebNVjMrHGm1Who0aMDGjRtvuZpxdSfhSJSH/FkQQliKwWDg2tatJM+ahe5yGgBuffrgO2kiqqK7E4iyLDaVX61Wk5+ff1fFCSGEEOLOKRQKXHv2pM7mzbg/PQgUCjLXrePiI4+SsXYdd9AhJG5g9pijV199lblz51JYWGiJeoQQQghRDjYuLgRMm0bo98uxq1cPXUYGSVOmEDtkKAXR0dYur0ozOxz99ddfrFmzhlq1atGjRw/69etXahPidr755ptb3v+s2PTp02natOk9qUcIIao6x2bNCP/pR3wmjEdhb0/uwYNE9+7D5U8+Qa/RWLu8KsnscOTu7s6TTz5Jjx49CAwMxM3NrdQmxN167bXXSq22PmTIEKuvpfTNN9+gUChKbTKWSAhRWSjUarxHjKD2zxtwat8eg0ZD2uKPiO7bj9y//rJ2eVWO2bf9Xbp0qSXqEMLE2dkZZ2dna5dRhqurK2fOnDG9VigUVqxGCCHKsg0JIeSrL8natJmUOXPQXLjApecG49avH76vvyYDtsvpjm5hXlhYyPbt2/n888+5du0aAImJiWRnZ1docdWBwWAgV5trlc2cQXmdOnVi9OjRTJw4EU9PT/z9/Zk+fXqpc86dO0fHjh2xt7enYcOGbNu2DYVCwbp16wDYvXs3CoWCjIwM03uOHj2KQqEgJiam1LXWrVtHREQE9vb29OjRg7i4ONOxkt1q06dPZ9myZaxfv97UYrN79240Gg0jR44kICAAe3t7QkNDmTNnjjn/a8ymUCjw9/c3bX5+fhb9PCGEuBMKhQK3xx+jzuZNuA8YAEDmmjVc7PkIGT/9JCtsl4PZLUeXLl2iZ8+exMbGUlBQQLdu3XBxcWHu3LkUFBTw2WefWaLOKiuvMI/W31tn/YmDzxzEUV3+uzkvW7aM8ePHc/DgQfbv38+QIUNo37493bp1Q6/X069fP/z8/Dh48CCZmZmMHTv2jurKzc1l1qxZfPvtt9ja2vLKK68waNAg9u4te3PF1157jdOnT5OVlWVqtfT09GTx4sVs2LCBVatWUatWLeLi4koFrBstX76cF1988bZ1bdmyhQcffPCWx7OzswkNDUWv19O8eXNmz57NfffdV86fWggh7i0bNzcC3pmBW58+JE+fTsHZsyRNfZOMtWsJmDYNu4gIa5dYaZkdjsaMGUPLli05duwYXl5epv19+/ZlxIgRFVqcuLcaN27MtGnTAIiIiGDJkiXs2LGDbt26sX37dqKioti6dSuBgYEAzJ49m0ceecTsz9FqtSxZsoTWrY2hcdmyZURGRvLnn3/SqlWrUuc6Ozvj4OBAQUEB/v7+pv2xsbFERETQoUMHFAoFoaGht/3MXr16mT7vVoKCgm55rH79+nz99dc0btyYzMxM3n//fdq1a8fJkycJDg7+tx9ZCCGsxrG5ccB2+v99x+WPPjKusN23H15Dh+L9yssoa8ACzuYyOxzt2bOHffv2YWtrW2p/8S1ERGkOKgcOPnPQap9tjsaNG5d6HRAQQGpqKgCnT58mJCTEFIwA2rZte0d1qVQqHnjgAdPrBg0a4O7uzunTp8uEo1sZMmQI3bp1o379+vTs2ZPHH3+c7t273/J8FxeXu7oxctu2bUv9vO3atSMyMpLPP/+cmTNn3vF1hRDiXlCo1Xi9MBTXnj1Inj2b7O07uPLll2Rt3ozfW2/i0qmTtUusVMwec6TX69HpdGX2x8fH39WXT3WlUChwVDtaZTN3wLBarS5Tu96Mvmml0vjHqeRYJ61Wa1YN5dW8eXOio6OZOXMmeXl5DBgwgP79+9/y/OXLl5sGet9q27NnT7k/X61W06xZM86fP18RP44QQtwT6sBAQpYsIfiTj1EFBKBNSCD+pZeJHzUabXKytcurNMxuOerevTsffvghX3zxBWD8As3OzmbatGk8+uijFV6gqBwiIyOJi4sjKSmJgIAAAA4cOFDqHB8fHwCSkpLwKJoRcfTo0TLXKiws5NChQ6ZWojNnzpCRkXHLW9LY2treNJC7uroycOBABg4cSP/+/enZsyfp6el4enqWOfduu9VupNPp+Oeff+TPvBCiSnLp0gWn1q25/MknpH+zjGvbtpGzdy8+Y0bj8eyzKFRmx4NqxeyffsGCBfTo0YOGDRuSn5/PM888w7lz5/D29mbFihWWqFFUAl27dqVevXo8//zzzJ8/n6ysLKZOnVrqnLp16xISEsL06dOZNWsWZ8+eZcGCBWWupVarGTVqFIsXL0alUjFy5EjatGlzyy61sLAwtm7dypkzZ/Dy8sLNzY2PPvqIgIAAmjVrhlKpZPXq1fj7+99ygcm77VZ75513aNOmDXXr1iUjI4P58+dz6dIlhg8ffsfXFEIIa1I6OeH3+uu49epN8vTp5B05Qsqc98hYt56AGdNxuGGoRU1idrdacHAwx44dY+rUqYwbN45mzZrx3nvvceTIEXx9fS1Ro6gElEola9euJS8vj1atWjF8+HBmzZpV6hy1Ws2KFSuIioqicePGzJ07l3fffbfMtRwdHZk0aRLPPPMM7du3x9nZmZUrV97ys0eMGEH9+vVp2bIlPj4+7N27FxcXF+bNm0fLli154IEHiImJYfPmzaauvYp29epVRowYQWRkJI8++ihZWVns27ePhg0bWuTzhBDiXrGvX4/Q5d/hP/MdlG5uFJw+TczAQSTNmIEuK8va5VmFwlCOxXCaN2/Ojh078PDw4J133uG1117D0bH8U8Srk9vd1bcm3oldoVCwdu1aq69gXdnUxD8LQoiqrzA9ndR588ksWr/OxssLv0kTcX3iiSq/8O3tvr9vVK5fs0+fPk1OTg4AM2bMkMUehRBC/H979x5XY7b/Afyz29pddE9qx66kROxCSJgYQjmkwxzXocyMOYhBv1yHKTPIcPJyneM1Mz8j88O4TBfjckyinOMSMjGchFxqqFxSVKhpP78/jD17q9iZ8mzT5/16Pa/X3s+z9l7fvWbN9J211vMs+hNqYmMDx2UxcIqLg8zVFVX37uHW7DnIHR+KJ43oBhSd1hx17NgREyZMQK9evSAIAv7xj3/Uur3DJ598Uq8BEhER0evV1LcbXBMTcG9THO5+8QXKT53C1ZC/wnZCGJpNngyDP/nskU7TatnZ2YiKikJOTg7OnDkDT09PNKlhJbtEIsGZM2caJFB9wWk10gX7AhH9WVTevImCmBiUHny6IXgTuRz28+fBPCDgjZpqq8u0mk7JkSYDAwMUFBQ02sXXTI5IF+wLRPRn8/DwYRQuXoLK3x743LS3PxwWLIBMoRA5Mt3U+5qjzp074/79+wCAqKgovdwxnYiIiBqO+dtvw3XPD7CdPAkwNERZ2hFcHTwEd774AqonT8QOr17VeUH2p59+ygXZREREjZCBiQmaT58O16QkNO3hB+HJE9xdsxbXgoei9D/VNw9/U3FBNhEREdWJkWsrKP73f/Fw/34UxixDxY0byPvgA5gHBsJ+3lwY2tuLHeIfwgXZdcQ1R6QL9gUiaiyqSktxd+1aFH37f4BKBQNTUzSbNg02746F5Lk9O8XEBdkNiMkR6YJ9gYgam8dZWShY9Cke/banppG7Oxw+WQjTrl3FDew39b4gW5NKpWq0iRH9cZs2bap1/7NnoqOj0bFjx9cSDxER1Q/jdu3gvHUL5EsWQ2plhSeXL+PGuPG4OXs2Km/fFju8OtFpzdHu3bsRFBQEQ0ND7N69+4Vlg4OD6yUwarwiIyMxbdo09fuwsDAUFxcj8bfH2YuluLgYH3/8MeLj41FUVARnZ2esWrUKgwYNEjUuIiJ9ITEwgNXw4TDr2xd3Vq9G8fYdeLD7B5SmHILdR9NgPXYsJDUsy9E3OkUYEhKinkp70R5aEokEVVVV9RUbNVJmZmZ697iIiooK9O/fH82bN8euXbvQokUL3Lhx46WjYEREjVETa2vIo6NhNfwdFHz6KR7//DMKY5ah+Pv4p1NtXbqIHeIL6TStpjmVplKpaj2YGFUnCAJU5eWiHHVZTtanTx989NFHmD17NmxsbODg4IDo6GitMpcvX4a/vz+MjY3h6emJ5ORkSCQS9YhOamoqJBIJiouL1Z/JzMyERCLB9evXtb4rMTER7u7uMDY2xsCBA5GXl6e+pjmtFh0djbi4OCQlJUEikUAikSA1NRUVFRWYOnUq5HI5jI2N4ezsjJiYmLr8o6mTjRs3oqioCImJiejZsydcXFzQu3dveHt7N1idRERvOhNlB7hs/w4Ony56OtV26RJuvDsON2fPxq937ogdXq30f2zrDSc8eoTszj6i1O1xJgOSOux/ExcXh4iICKSnp+P48eMICwtDz5490b9/f6hUKgwbNgz29vZIT09HSUkJZsyY8UpxlZeXY8mSJdi8eTNkMhmmTJmCUaNG4ejR6s/IiIyMRFZWFh48eIBvvvkGAGBjY4M1a9Zg9+7d2LFjB5ycnJCXl6eVYD1vy5Yt+Pvf//7CuPbv34+33nqrxmu7d++Gn58fwsPDkZSUBDs7O4wZMwZz5syBVCqtw68nImpcJAYGsB4xAub9++POqtUo3vHbVNuhw0+n2saM0buptjotyFapVNi4cSMGDx6MDh06QKlUIjg4GJs3b67TKMWrCA4OhpOTE4yNjSGXyzFu3DjcunVLff369evqkQXN48SJE1rfs3PnTrRt2xbGxsZQKpXYt29fg8b9JvHy8kJUVBTc3d0xfvx4dOnSBSkpT/fSOXjwIC5evIjNmzfD29sb/v7+WLp06SvVU1lZiXXr1sHPzw8+Pj6Ii4vDsWPHcPLkyWplzczMYGJiAiMjIzg4OMDBwQEymQy5ublwd3dHr1694OzsjF69emH06NG11hkcHIzMzMwXHl1eMMx79epV7Nq1C1VVVdi3bx8WLlyI2NhYLF68+JXagIiosWlibQ35omi47NgBY6USqtJSFC6NwbVhw1F++rTY4WnROVUTBAHBwcHYt28fvL29oVQqIQgCsrKyEBYWhvj4+AZdMPv2229j/vz5kMvluHnzJiIjI/HOO+/g2LFjWuUOHjyI9u3bq9/b2tqqXx87dgyjR49GTEwMBg8ejK1btyIkJARnzpxBhw4dGiRuiYkJPM5kNMh361J3XXh5eWm9l8vluP3bHQZZWVlQKBRwdHRUX/fz83uluJo0aYKuGrd2tm3bFlZWVsjKykK3bt10+o6wsDD0798fHh4eCAwMxODBgzFgwIBay5ubm8Pc3PyV4gV+n1r+8ssvIZVK4ePjg5s3b2LFihWIiop65e8lImpsnk21Fe/ahTuxK9VTbZZDg9E8MhJN7OzEDlH35GjTpk04cuQIUlJS8Pbbb2tdO3ToEEJCQrB582aMHz++3oMEgJkzZ6pfOzs7Y+7cuQgJCUFlZSUMNR4yZWtrCwcHhxq/Y/Xq1QgMDMSsWbMAAJ999hmSk5Oxbt06bNiwoUHilkgkdZraEpPhcw/rkkgkUKlUOn/ewODpQKTmKGJlZWX9BPeczp0749q1a9i/fz8OHjyIESNGICAgALt27aqx/B+dVpPL5TA0NNSaQmvXrh0KCgpQUVEBmUz26j+GiKiRqWmqrSRpNx4+u6tN5Kk2nafVtm3bhvnz51dLjACgb9++mDt3LrZs2VKvwdWmqKgIW7ZsQY8ePar9QQ8ODkbz5s3Rq1evao8dOH78OAICArTODRw4EMePH6+1ridPnuDBgwdaR2PUrl075OXlIT8/X33u+SlLu9+yfc0ymb89DEzTr7/+itMaQ6jZ2dkoLi5Gu3btaqxbJpPVuNjfwsICI0eOxFdffYXt27fj+++/R1FRUY3f8Uen1Xr27IkrV65oJYuXLl2CXC5nYkRE9Ip+n2rbDuMOHX6favvbCFE3s9U5OTp37hwCAwNrvR4UFISzZ8/WS1C1mTNnDpo2bQpbW1vk5uYiKSlJfc3MzAyxsbHYuXMn9u7di169eiEkJEQrQSooKID9c/u92Nvbo6CgoNY6Y2JiYGlpqT4UCkX9/7A3QEBAANq0aYPQ0FCcPXsW//73v/Hxxx9rlXFzc4NCoUB0dDQuX76MvXv3IjY2ttp3GRoaYtq0aUhPT0dGRgbCwsLQvXv3WqfUXFxccO7cOWRnZ+Pu3buorKzEypUrsW3bNly8eBGXLl3Czp074eDgUOut9ebm5nBzc3vhYfKCacjJkyejqKgI06dPx6VLl7B3714sXboU4eHhujciERHVyESpfHpX26JFkFpawkSphIGRkWjx6JwcFRUVVUssNNnb2+P+/ft1qnzu3Lk1LqLWPC5evKguP2vWLPz000/48ccfIZVKMX78ePUUTrNmzRAREQFfX1907doVy5Ytw7vvvosVK1bUKabnzZs3DyUlJerjRXdE/ZkZGBggISEBjx49Qrdu3fDBBx9gyZIlWmUMDQ3VCYuXlxc+//zzGhcsm5qaYs6cORgzZgx69uwJMzMzbN++vda6J06cCA8PD3Tp0gV2dnY4evQozM3NsXz5cnTp0gVdu3bF9evXsW/fPvXUXn1TKBQ4cOAATp06BS8vL3z00UeYPn065s6d2yD1ERE1NhKpFNYjR8D1X/vR/H8ixI1F173VpFIpCgoK1FMnzyssLISjo2OdnnV0584d3Lt374VlXF1da5y2+OWXX6BQKHDs2LFaFwavX78eixcvVk/zODk5ISIiQusW9KioKCQmJuo86sW91bRJJBIkJCS88OGgjVFj7AtERPqsLnur1elutbCwMBjVMsz15BXmBu3s7GpNtl7m2dqPF9WbmZkJuVyufu/n54eUlBSt5Cg5OfmV77oiIiKiPx+dk6PQ0NCXlmmoO9XS09Nx6tQp9OrVC9bW1sjJycHChQvRunVrdWITFxcHmUyGTp06AQDi4+OxceNGfP311+rvmT59Onr37o3Y2Fj85S9/wXfffYfTp0/jyy+/bJC4iYiI6M2jc3L07OnEYjA1NUV8fDyioqJQVlYGuVyOwMBALFiwQGsk67PPPsONGzfQpEkTtG3bFtu3b8c777yjvt6jRw9s3boVCxYswPz58+Hu7o7ExMQGe8ZRY9DQD/8kIiJ63XRec0RPcc0R6YJ9gYhIv9RlzVHD3NrTyDHfJPYBIqI3F5OjevTsgZTl5eUiR0Jie9YHnn9IKRER6T/92gb3DSeVSmFlZaXej8zU1BQSiUTkqOh1EgQB5eXluH37NqysrLS2GyEiojcDk6N69mxft2cJEjVOVlZWte7xR0RE+o3JUT2TSCSQy+Vo3rx5g226Svrt+Q1qiYjozcLkqIFIpVL+gSQiInoDcUE2ERERkQYmR0REREQamBwRERERaeCaozp69nC/Bw8eiBwJERER6erZ321dHtLL5KiOHj58CABQKBQiR0JERER19fDhQ1haWr6wDPdWqyOVSoVbt27B3Ny83h/w+ODBAygUCuTl5b1035fGjm2lO7aV7thWumNb1Q3bS3cN1VaCIODhw4dwdHSEgcGLVxVx5KiODAwM0LJlywatw8LCgv/y6IhtpTu2le7YVrpjW9UN20t3DdFWLxsxeoYLsomIiIg0MDkiIiIi0sDkSI8YGRkhKioKRkZGYoei99hWumNb6Y5tpTu2Vd2wvXSnD23FBdlEREREGjhyRERERKSByRERERGRBiZHRERERBqYHBERERFpYHKkJ9avXw8XFxcYGxvD19cXJ0+eFDskvRQdHQ2JRKJ1tG3bVuyw9MKRI0cwZMgQODo6QiKRIDExUeu6IAj45JNPIJfLYWJigoCAAFy+fFmcYEX2srYKCwur1s8CAwPFCVZkMTEx6Nq1K8zNzdG8eXOEhIQgOztbq8zjx48RHh4OW1tbmJmZYfjw4SgsLBQpYvHo0lZ9+vSp1rcmTZokUsTi+ec//wkvLy/1gx79/Pywf/9+9XWx+xSTIz2wfft2REREICoqCmfOnIG3tzcGDhyI27dvix2aXmrfvj3y8/PVx3/+8x+xQ9ILZWVl8Pb2xvr162u8vnz5cqxZswYbNmxAeno6mjZtioEDB+Lx48evOVLxvaytACAwMFCrn23btu01Rqg/0tLSEB4ejhMnTiA5ORmVlZUYMGAAysrK1GVmzpyJH374ATt37kRaWhpu3bqFYcOGiRi1OHRpKwCYOHGiVt9avny5SBGLp2XLlli2bBkyMjJw+vRp9O3bF0OHDsWFCxcA6EGfEkh03bp1E8LDw9Xvq6qqBEdHRyEmJkbEqPRTVFSU4O3tLXYYeg+AkJCQoH6vUqkEBwcHYcWKFepzxcXFgpGRkbBt2zYRItQfz7eVIAhCaGioMHToUFHi0Xe3b98WAAhpaWmCIDztR4aGhsLOnTvVZbKysgQAwvHjx8UKUy8831aCIAi9e/cWpk+fLl5Qesza2lr4+uuv9aJPceRIZBUVFcjIyEBAQID6nIGBAQICAnD8+HERI9Nfly9fhqOjI1xdXTF27Fjk5uaKHZLeu3btGgoKCrT6maWlJXx9fdnPapGamormzZvDw8MDkydPxr1798QOSS+UlJQAAGxsbAAAGRkZqKys1Opbbdu2hZOTU6PvW8+31TNbtmxBs2bN0KFDB8ybNw/l5eVihKc3qqqq8N1336GsrAx+fn560ae48azI7t69i6qqKtjb22udt7e3x8WLF0WKSn/5+vpi06ZN8PDwQH5+PhYtWoS33noL58+fh7m5udjh6a2CggIAqLGfPbtGvwsMDMSwYcPQqlUr5OTkYP78+QgKCsLx48chlUrFDk80KpUKM2bMQM+ePdGhQwcAT/uWTCaDlZWVVtnG3rdqaisAGDNmDJydneHo6Ihz585hzpw5yM7ORnx8vIjRiuPnn3+Gn58fHj9+DDMzMyQkJMDT0xOZmZmi9ykmR/RGCQoKUr/28vKCr68vnJ2dsWPHDrz//vsiRkZ/JqNGjVK/ViqV8PLyQuvWrZGamop+/fqJGJm4wsPDcf78ea7z00FtbfXhhx+qXyuVSsjlcvTr1w85OTlo3br16w5TVB4eHsjMzERJSQl27dqF0NBQpKWliR0WAC7IFl2zZs0glUqrrcIvLCyEg4ODSFG9OaysrNCmTRtcuXJF7FD02rO+xH72alxdXdGsWbNG3c+mTp2KPXv24PDhw2jZsqX6vIODAyoqKlBcXKxVvjH3rdraqia+vr4A0Cj7lkwmg5ubG3x8fBATEwNvb2+sXr1aL/oUkyORyWQy+Pj4ICUlRX1OpVIhJSUFfn5+Ikb2ZigtLUVOTg7kcrnYoei1Vq1awcHBQaufPXjwAOnp6exnOvjll19w7969RtnPBEHA1KlTkZCQgEOHDqFVq1Za1318fGBoaKjVt7Kzs5Gbm9vo+tbL2qommZmZANAo+9bzVCoVnjx5ohd9itNqeiAiIgKhoaHo0qULunXrhlWrVqGsrAwTJkwQOzS9ExkZiSFDhsDZ2Rm3bt1CVFQUpFIpRo8eLXZooistLdX6v89r164hMzMTNjY2cHJywowZM7B48WK4u7ujVatWWLhwIRwdHRESEiJe0CJ5UVvZ2Nhg0aJFGD58OBwcHJCTk4PZs2fDzc0NAwcOFDFqcYSHh2Pr1q1ISkqCubm5es2HpaUlTExMYGlpiffffx8RERGwsbGBhYUFpk2bBj8/P3Tv3l3k6F+vl7VVTk4Otm7dikGDBsHW1hbnzp3DzJkz4e/vDy8vL5Gjf73mzZuHoKAgODk54eHDh9i6dStSU1Nx4MAB/ehTr+WeOHqptWvXCk5OToJMJhO6desmnDhxQuyQ9NLIkSMFuVwuyGQyoUWLFsLIkSOFK1euiB2WXjh8+LAAoNoRGhoqCMLT2/kXLlwo2NvbC0ZGRkK/fv2E7OxscYMWyYvaqry8XBgwYIBgZ2cnGBoaCs7OzsLEiROFgoICscMWRU3tBED45ptv1GUePXokTJkyRbC2thZMTU2Fv/71r0J+fr54QYvkZW2Vm5sr+Pv7CzY2NoKRkZHg5uYmzJo1SygpKRE3cBG89957grOzsyCTyQQ7OzuhX79+wo8//qi+LnafkgiCILyeNIyIiIhI/3HNEREREZEGJkdEREREGpgcEREREWlgckRERESkgckRERERkQYmR0REREQamBwRERERaWByRERERKSByRERkQ5cXFywatUqscMgoteAyRER6Z2wsDD1nm99+vTBjBkzXlvdmzZtgpWVVbXzp06dwocffvja4iAi8XDjWSJqFCoqKiCTyV7583Z2dvUYDRHpM44cEZHeCgsLQ1paGlavXg2JRAKJRILr168DAM6fP4+goCCYmZnB3t4e48aNw927d9Wf7dOnD6ZOnYoZM2agWbNmGDhwIABg5cqVUCqVaNq0KRQKBaZMmYLS0lIAQGpqKiZMmICSkhJ1fdHR0QCqT6vl5uZi6NChMDMzg4WFBUaMGIHCwkL19ejoaHTs2BHffvstXFxcYGlpiVGjRuHhw4fqMrt27YJSqYSJiQlsbW0REBCAsrKyBmpNItIVkyMi0lurV6+Gn58fJk6ciPz8fOTn50OhUKC4uBh9+/ZFp06dcPr0afzrX/9CYWEhRowYofX5uLg4yGQyHD16FBs2bAAAGBgYYM2aNbhw4QLi4uJw6NAhzJ49GwDQo0cPrFq1ChYWFur6IiMjq8WlUqkwdOhQFBUVIS0tDcnJybh69SpGjhypVS4nJweJiYnYs2cP9uzZg7S0NCxbtgwAkJ+fj9GjR+O9995DVlYWUlNTMWzYMHAvcCLxcVqNiPSWpaUlZDIZTE1N4eDgoD6/bt06dOrUCUuXLlWf27hxIxQKBS5duoQ2bdoAANzd3bF8+XKt79Rcv+Ti4oLFixdj0qRJ+OKLLyCTyWBpaQmJRKJV3/NSUlLw888/49q1a1AoFACAzZs3o3379jh16hS6du0K4GkStWnTJpibmwMAxo0bh5SUFCxZsgT5+fn49ddfMWzYMDg7OwMAlErlH2gtIqovHDkiojfO2bNncfjwYZiZmamPtm3bAng6WvOMj49Ptc8ePHgQ/fr1Q4sWLWBubo5x48bh3r17KC8v17n+rKwsKBQKdWIEAJ6enrCyskJWVpb6nIuLizoxAgC5XI7bt28DALy9vdGvXz8olUr87W9/w1dffYX79+/r3ghE1GCYHBHRG6e0tBRDhgxBZmam1nH58mX4+/uryzVt2lTrc9evX8fgwYPh5eWF77//HhkZGVi/fj2Apwu265uhoaHWe4lEApVKBQCQSqVITk7G/v374enpibVr18LDwwPXrl2r9ziIqG6YHBGRXpPJZKiqqtI617lzZ1y4cAEuLi5wc3PTOp5PiDRlZGRApVIhNjYW3bt3R5s2bXDr1q2X1ve8du3aIS8vD3l5eepz//3vf1FcXAxPT0+df5tEIkHPnj2xaNEi/PTTT5DJZEhISND580TUMJgcEZFec3FxQXp6Oq5fv467d+9CpVIhPDwcRUVFGD16NE6dOoWcnBwcOHAAEyZMeGFi4+bmhsrKSqxduxZXr17Ft99+q16orVlfaWkpUlJScPfu3Rqn2wICAqBUKjF27FicOXMGJ0+exPjx49G7d2906dJFp9+Vnp6OpUuX4vTp08jNzUV8fDzu3LmDdu3a1a2BiKjeMTkiIr0WGRkJqVQKT09P2NnZITc3F46Ojjh69CiqqqowYMAAKJVKzJgxA1ZWVjAwqP0/a97e3li5ciU+//xzdOjQAVu2bEFMTIxWmR49emDSpEkYOXIk7Ozsqi3oBp6O+CQlJcHa2hr+/v4ICAiAq6srtm/frvPvsrCwwJEjRzBo0CC0adMGCxYsQGxsLIKCgnRvHCJqEBKB940SERERqXHkiIiIiEgDkyMiIiIiDUyOiIiIiDQwOSIiIiLSwOSIiIiISAOTIyIiIiINTI6IiIiINDA5IiIiItLA5IiIiIhIA5MjIiIiIg1MjoiIiIg0/D+BopGhKCOPpAAAAABJRU5ErkJggg==", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "plt.figure()\n", "for q in range(len(nqubits)):\n", @@ -1010,23 +593,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-05-02 14:03:24]: Using numpy backend on /CPU:0\n" - ] - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -1039,11 +608,7 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", -<<<<<<< HEAD "cost = DoubleBracketCostFunction.least_squares\n", -======= - "cost = DoubleBracketCost.least_squares\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" @@ -1051,32 +616,21 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, -======= - "execution_count": 45, ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "metadata": {}, "outputs": [], "source": [ "dbi_eval = deepcopy(dbi)\n", "params = np.linspace(1,2**nqubits,2**nqubits)\n", -<<<<<<< HEAD "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,1e-2, analytic=False, d_type = d_ansatz_type.element_wise)\n", "flows = 50\n", "off_diagonal_norm = np.empty((flows+1,2))\n", -======= - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,1e-3, analytic=False, d_type = d_ansatz_type.element_wise, normalize=False)\n", - "flows = 50\n", - "off_diagonal_norm = np.empty((flows+1,3))\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", "for i in range(flows):\n", " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", " dbi_eval(step_poly,d=d_opt)\n", " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", "\n", -<<<<<<< HEAD "\n", "\n", "dbi_eval = deepcopy(dbi)\n", @@ -1088,244 +642,14 @@ " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", " dbi_eval(step_poly,d=d_opt)\n", " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n" -======= - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=True)\n", - "best = np.argmin(loss_opt)\n", - "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", - "\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", - "\n", - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=False)\n", - "best = np.argmin(loss_opt)\n", - "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", - "\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,2] = dbi_eval.off_diagonal_norm\n", - "\n" ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 ] }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], "source": [ -======= - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", - "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", - "plt.plot(off_diagonal_norm[:,2],label='1-local ansatz unnormalized')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "dbi_eval = deepcopy(dbi)\n", - "\n", - "flows = 5\n", - "off_diagonal_norm = np.empty(flows+1)\n", - "off_diagonal_norm[0] = dbi_eval.off_diagonal_norm\n", - "\n", - "for i in range(flows):\n", - " params = np.logspace(1,-1,nqubits)\n", - " d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=True)\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1] = dbi_eval.off_diagonal_norm" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7548398294391669\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s = np.linspace(0,5,100)\n", - "off_diagonal_norm = np.empty(len(s))\n", - "params = np.logspace(1,-1,nqubits)\n", - "d = d_ansatz(params, d_ansatz_type.local_1, normalization=True)\n", - "for step in range(len(s)):\n", - " dbi_eval2 = deepcopy(dbi_eval)\n", - " s_poly = dbi_eval2.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=5)\n", - " dbi_eval2(s[step], d=d)\n", - " off_diagonal_norm[step] = dbi_eval2.off_diagonal_norm\n", - "plt.figure()\n", - "plt.plot(s,off_diagonal_norm)\n", - "plt.axvline(x=s_poly, color='r', linestyle='--')\n", - "\n", - "dbi_eval2 = deepcopy(dbi_eval)\n", - "dbi_eval2(s_poly, d=d)\n", - "\n", - "plt.figure()\n", - "plt.matshow(np.real(dbi_eval2.h.matrix))\n", - "\n", - "s = np.linspace(-5,5,100)\n", - "\n", - "off_diagonal_norm = np.empty(len(s))\n", - "params = -np.linspace(1,nqubits,nqubits)\n", - "d = d_ansatz(params, d_ansatz_type.local_1, normalization=True)\n", - "print(np.linalg.norm(commutator(d, dbi_eval2.h.matrix)))\n", - "for step in range(len(s)):\n", - " dbi_eval3 = deepcopy(dbi_eval2)\n", - " s_poly = dbi_eval3.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=5)\n", - " dbi_eval3(s[step], d=d)\n", - " off_diagonal_norm[step] = dbi_eval3.off_diagonal_norm\n", - "plt.figure()\n", - "plt.plot(s,off_diagonal_norm)\n", - "plt.axvline(x=s_poly, color='r', linestyle='--')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.24449019+0.j 0.24113607+0.j 0.23388354+0.j 0.23052942+0.j\n", - " 0.210949 +0.j 0.20759488+0.j 0.20034235+0.j 0.19698823+0.j\n", - " 0.13842364+0.j 0.13506952+0.j 0.12781699+0.j 0.12446287+0.j\n", - " 0.10488246+0.j 0.10152834+0.j 0.0942758 +0.j 0.09092168+0.j\n", - " -0.09092168+0.j -0.0942758 +0.j -0.10152834+0.j -0.10488246+0.j\n", - " -0.12446287+0.j -0.12781699+0.j -0.13506952+0.j -0.13842364+0.j\n", - " -0.19698823+0.j -0.20034235+0.j -0.20759488+0.j -0.210949 +0.j\n", - " -0.23052942+0.j -0.23388354+0.j -0.24113607+0.j -0.24449019+0.j]\n" - ] - } - ], - "source": [ - "print(np.diag(d))" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[76], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m plt\u001b[39m.\u001b[39mfigure()\n\u001b[1;32m----> 2\u001b[0m plt\u001b[39m.\u001b[39mplot(off_diagonal_norm[:,\u001b[39m0\u001b[39;49m],label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39melement-wise ansatz\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 3\u001b[0m plt\u001b[39m.\u001b[39mplot(off_diagonal_norm[:,\u001b[39m1\u001b[39m],label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m1-local ansatz\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 4\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mFlows Iterations\u001b[39m\u001b[39m'\u001b[39m)\n", - "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "plt.figure()\n", "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", @@ -1336,44 +660,22 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], -======= - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-05-01 21:50:45]: Using numpy backend on /CPU:0\n" - ] - } - ], ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", -<<<<<<< HEAD "nqubits = 5\n", -======= - "nqubits = 7\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "h = 3.0\n", "\n", "# define the hamiltonian\n", "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", -<<<<<<< HEAD "cost = DoubleBracketCostFunction.least_squares\n", -======= - "cost = DoubleBracketCost.least_squares\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" @@ -1381,7 +683,6 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": null, "metadata": {}, "outputs": [], @@ -1389,60 +690,12 @@ "dbi_eval = deepcopy(dbi)\n", "params = np.linspace(1,nqubits,nqubits)\n", "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 20, 1e-3, analytic=False, d_type = d_ansatz_type.local_1)\n", -======= - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.527668414752788\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dbi_eval = deepcopy(dbi)\n", - "params = np.logspace(1,-1,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=True)\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "best = np.argmin(loss_opt)\n", "\n", "\n", "plt.figure()\n", "plt.plot(loss_opt)\n", "\n", -<<<<<<< HEAD "s = np.linspace(-0.1,0.1,100)\n", "least_squares = np.empty(100)\n", "off_diagonal_norm = np.empty(100)\n", @@ -1452,243 +705,15 @@ " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", "plt.figure()\n", "plt.plot(s,loss)\n", -======= - "trials = 1000\n", - "s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", - "s = np.linspace(-3*s_poly,3*s_poly,trials)\n", - "least_squares = np.empty(trials)\n", - "off_diagonal_norm = np.empty(trials)\n", - "for i in range(trials):\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval(s[i],d=d_opt)\n", - " least_squares[i] = dbi_eval.least_squares(d=d_opt)\n", - " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(s,least_squares)\n", - "plt.axvline(x=s_poly, color='r', linestyle='--')\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "plt.xlabel('s')\n", "plt.ylabel('Least squares cost function')\n", "plt.figure()\n", "plt.plot(s,off_diagonal_norm)\n", -<<<<<<< HEAD -======= - "plt.axvline(x=s_poly, color='r', linestyle='--')\n", ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 "plt.xlabel('s')\n", "plt.ylabel(r'$||\\sigma(H_k)||$')\n", "\n", "\n", -<<<<<<< HEAD "print(np.diag(d_opt))" -======= - "print(s_poly)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-05-03 08:28:38]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 2\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", - "\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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TQOcYLP/6178wbNgwfPXVV1i/fj3Gjh2LmTNn4qeffkKdOnVEO47Q94DD2/mza/UlakCpPnKfwzlz5ngddh4XF8e77+mzev/99+PHH3/EM888gzZt2iAuLg52ux3Z2dmyDbuX4zWsUaMGunXrhg8++ABz584VrV1CO5DohBlJSUmoXr06bDYbMjMzfe77+eefo2HDhli5ciXvQjVlyhS3faOiotCnTx/06dMHdrsdjz/+ON566y1MnjwZjRs3DiqV0KhRI0yYMAETJkzAoUOH0KZNG/zrX//C+++/j3r16gG4+tdtw4YNHc85e/as21+D3F+Jly5d4g17dv3rcuvWrTh//jxWrlyJO++807H96NGjAfV5z5496N69u99zNhqN6N69O7p374558+bhlVdewQsvvIAtW7Z4fW8aNWoEu92Offv2eb3Ica/NwYMHea9NZWUljh496tZ2eno60tPT8eKLL+LHH3/EP/7xDyxevBgvvfQSgODSQK4IfQ8CoVGjRn4ngguk7/Xq1cPGjRtx+fJlXlSHS1lyr6tceOs7ly6zWCx+v8PeuHjxIjZt2oRp06bxhmRz0SIh/fBEvXr1cPDgQbftSr2GHFeuXFFFpJJQBkpdhRkmkwkDBgzAF1984fEicfbsWd6+AP+vq+3btyMvL4/3nPPnz/PuG41GR1SooqICABzzdgiZXbWsrAzl5eW8bY0aNUL16tUd7WVmZiIyMhILFy7k9c/TcgPcheH77793bCstLXWLNnk638rKSrzxxht++8xx//3349SpU3jnnXfcHrty5QpKS0sBXE0buMKJC3eOnujfvz+MRiOmT5/u9lc31+/MzExERUVhwYIFvHNZsmQJioqK0Lt3bwBXR9VZrVZeG+np6TAajbw+VKtWLeRZcYW+B4EwYMAA7NmzB19++aXbY9x5B/K569WrF2w2G15//XXe9tdeew0GgwE9e/YMuq/B4K3v7dq1Q6NGjTB37lyUlJS4Pc/5O+wNT591wPP3J9DXcMeOHbzfiNLSUrz99tuoX7++z5SrN7j6uHPnzvnd1zntznHs2DFs2rQJ7du3D/jYhD6giI5OWbp0qcc6mXHjxmHWrFnYsmULMjIyMGrUKLRo0QIXLlzArl27sHHjRsdF+K677sLKlStx9913o3fv3jh69CgWL16MFi1a8H5gH3nkEVy4cAHdunVDnTp1cPz4cSxcuBBt2rRx5ObbtGkDk8mE2bNno6ioCGazGd26dUOtWrXc+vjHH3+ge/fuuP/++9GiRQtERETgyy+/RGFhIQYOHAjgamTq6aefxsyZM3HXXXehV69e2L17N9asWeMYAsvRo0cP1K1bFyNHjsQzzzwDk8mEpUuXIikpCSdOnHDs16lTJ9SoUQNDhw7F2LFjYTAY8N577wUURn/44Yfx6aef4tFHH8WWLVvwj3/8AzabDQcOHMCnn36KdevWoX379pg+fTq+//579O7dG/Xq1cOZM2fwxhtvoE6dOrxCTlcaN26MF154ATNmzMAdd9yBe+65B2azGT///DNSU1Mxc+ZMJCUlYdKkSZg2bRqys7PRt29fHDx4EG+88QZuvfVWR83R5s2bMWbMGNx333246aabYLVa8d577zlkmKNdu3bYuHEj5s2bh9TUVDRo0AAZGRmCX5NA3oNAeOaZZ/D555/jvvvuw4gRI9CuXTtcuHABq1evxuLFi9G6dWs0atQICQkJWLx4MapXr45q1aohIyPDY81Knz590LVrV7zwwgs4duwYWrdujfXr1+Orr77C+PHjvQ6r94fBYEDnzp2xdevWgJ7nq+/vvvsuevbsiZtvvhnDhw/HjTfeiFOnTmHLli2wWCz4+uuvfbZtsVgctW9VVVW48cYbsX79eo/Ry3bt2gEAXnjhBQwcOBCRkZHo06ePx0kHJ06ciI8++gg9e/bE2LFjUbNmTaxYsQJHjx7FF198EdQsyoEML09PT0f37t3Rpk0b1KhRA4cOHcKSJUtQVVWFWbNmBXxsQicoMNKLkBBvk7Zxt5MnTzLGGCssLGRPPPEES0tLY5GRkSwlJYV1796dvf3224627HY7e+WVV1i9evWY2Wxmbdu2Zd98843bsNzPP/+c9ejRwzH5YN26ddn//d//sdOnT/P69s4777CGDRsyk8nkc6j5uXPn2BNPPMGaNWvGqlWrxuLj41lGRgb79NNPefvZbDY2bdo0Vrt2bZ8TBjLG2M6dO1lGRoajf/PmzfM4tHnbtm3stttuYzExMSw1NZU9++yzbN26dW795SYM9ERlZSWbPXs2u/nmm5nZbGY1atRg7dq1Y9OmTWNFRUWMMcY2bdrE+vXrx1JTU1lUVBRLTU1lgwYNYn/88YfHNl1ZunQpa9u2raP9zp07sw0bNvD2ef3111mzZs1YZGQkS05OZo899hhvwsA///yTjRgxgjVq1IhFR0ezmjVrsq5du7KNGzfy2jlw4AC78847WUxMjM8JAzngYXg5Y8LfA27CQFdch18zxtj58+fZmDFjHBNL1qlThw0dOpQ3dcJXX33FWrRowSIiIvxOGHj58mX21FNPsdTUVBYZGcmaNGnic8JAV1w/e5cvX2YA2MCBA728Wr7Pz1vfGbs6PcA999zDbrjhBmY2m1m9evXY/fffzzZt2uTYhxte7mkag7/++ovdfffdLCEhgcXHx7P77ruP/f333x6HcnMTcxqNRsETBiYkJLDo6GjWoUMHrxMGuk5tcPToUbfzDGR4+ZQpU1j79u1ZjRo1WEREBEtNTWUDBw5kv/76q9/nEvrFwJiKqvsIQgTq16+PLl26YPny5Up3hQhzvvvuO9x1113Ys2cP0tPTle4OQYQlVKNDEAQhEVu2bMHAgQNJcghCQahGhyAIQiJclw0hCEJ+KKJDEARBEIRu0ZTofP/99+jTpw9SU1NhMBgcCyT6YuvWrbjllltgNpvRuHFjj3UbixYtQv369REdHY2MjAzs2LFD/M4TsnHs2DGqzyEIgiAAaEx0SktL0bp1ayxatEjQ/kePHkXv3r3RtWtX5OfnY/z48XjkkUewbt06xz6ffPIJcnJyMGXKFOzatQutW7dGVlaWx/kYCIIgCILQFpoddWUwGPDll1+if//+Xvd57rnn8O233/Imxhs4cCAuXbrkmGMmIyMDt956q2OSMLvdjrS0NDz55JOYOHGipOdAEARBEIS06LoYOS8vz22K9KysLMcK15WVldi5cydvsTej0YjMzEy32X+dqaio4M0cy63sfcMNN4gyXT5BEAShTxhjuHz5MlJTU4OaQFEo5eXlqKysFKWtqKgoREdHi9KWEuhadAoKCpCcnMzblpycjOLiYly5cgUXL16EzWbzuA+3NosnZs6ciWnTpknSZ4IgCEL/nDx5UtSFc50pLy9Hg3pxKDjjedHcQElJScHRo0c1Kzu6Fh2pmDRpEnJychz3i4qKULduXdSZ9iKMQX4QWKT6MogsSvwVjA0RwbdpigzsucZI4V/ySIH9io6q8ruP2WT1u090hO92YiL8txEX6X1NLGdiTcH9VRfs85wps0WFtF9JlZl3/4rV/Ser3Brp+H+FzcPjlZFu24Tg/F5z7yn3vnHvD/cecK8V968l4upabXGm6++RxVQWVD844ozX26puvHLt36vHib/2b5zRem379UhBdQP/NeS4zCpw+dp6aSX2CBTZo/G3tQZK7GYU22Lxd0U8zlXGoaTKjIvlMaiwReBymRlVZZFgViMMlUYYy40wVhpgqAKMNsBYBRivfWyMTh9ho8vH2R7h/b7d6aNgv/bW2Z0WOWfctqjrv5nM5PR/l99S198x198gT78rvn47hPxW+PqdsJVV4Pchr/MWjhWbyspKFJyx4fAvabBUDy1qVHzZjsbtT6KyspJER42kpKSgsLCQt62wsBAWiwUxMTEwmUwwmUwe90lJSfHartlshtns/uNhjI6GMSa0D4LahEdNssMAmALsj0mg7NgBREb437cKZr+yY4UZ0X5E5eo+3tupghmxfmSoAlGCZKcKUagWEbi0WHH1ilPNJEyoXCm1mSFMc4Aqq/ueJVVmREa57seXlnJrJCKcvopWFxEqr4yEycuvXJX1+tXT03tvirp+gYiIMF3713ht/2v/Xvu+RkVwr1PkNcm52s9o0/V2K2FBfAiyE+sk0LHGq/2pdu3fuGtiU93lXwCwGNwvdMWsAtVhBOzAZXsErPYY2OzRiLWbcKHSgipbNKzGOFSgOqqMkbCyGJSVRQORJhgiI2BkRhgiDDAZjTCaACO7eoMZMF7L3hud5MQ5Q2OPBJwegj3i+n17lNP/r+3HezwScBQHOItOhGfRYVF2OBcTuEmOl98TX78bJgG/E86fHW/IUeZgqW4MWXT0gK5fgY4dO2LTpk28bRs2bEDHjh0BXM07tmvXjreP3W7Hpk2bHPvIjaHK4LipAUPl1b/cxIRZg2/PFmBfbFUm/ztdw/nC5wshEYJyD5GHQCmz+j+Oa8TDG6UeREIopTaz4ybmvmL0LViEvtdCCEYipaC6h2iOJ4rZVXG9bLfjsj0Cl+wxKLZH47I9Bpdt0bhsi0axNRql1iiUWSNRbo1EeWUkbFUmx/fPUGWAwer+G+UxmuPk63aXj7S/SA5v30jnfYVJjjNC/8DyJTlC/hgSEvUl5EVTolNSUoL8/Hzk5+cDuDp8PD8/37H68aRJkzBkyBDH/o8++ij+/PNPPPvsszhw4ADeeOMNfPrpp3jqqacc++Tk5OCdd97BihUrsH//fjz22GMoLS3F8OHDZT03T6hNeMSEZIefcvGGWmTH0YaTyHi6BdxeAH1yfS1cXz8x5DIQhKYOxYZLW1mupasSrt33RzGrcEiOJy7bolFki70qOTYzSqrMVyXHGoEq61XJ4VJWBqsBxkrD1VSVlS8zbmmqSP6//uAJTYSHbQIkxxVPkuMpmhOq5BDqRFOpq19++QVdu3Z13OfqZIYOHYrly5fj9OnTDukBgAYNGuDbb7/FU089hX//+9+oU6cO3n33XWRlZTn2eeCBB3D27Fnk5uaioKAAbdq0wdq1a90KlJWEkx2l01qc7IiVzmJWY9BpLFulMaA0lq3KJDiNVWU1yfajVm6N9FuvU2aN9JvGKqkyC7rwllqDS2PJjVB584U3GQ00msOlIbn3yd97IQXVTeVBP7eYVcDiUqfjKZpTZIvFZdtVySm8Uv1aNCfCEc0BrkV4q/iSw2H08bHyGKHxEM3xJDm85wiUHH+/UYGmwIVC0Rx1oinR6dKlC3xN++NpNtwuXbpg9+7dPtsdM2YMxowZE2r3JEdNwkOyc/VC6u+Hrdwa4bdeJ1xlR4mUldIU2WJDqtMJFudITqApK+dojtFDyspbAbIvvKWsPD7uqfg4AMkRoy6HojnaRlOpK+IqakhpiZnKYlZj0KkspdNYYtXrCEljCUHONJZUeDqHQNNWoUZztPKXeYJj9JUww+Ak5/r9GP8pqypTUCkrb/gdYeVBcoQiRV0OoX00FdEh+Cgd4VFLKkvKyI4QxIrs+ENIVCcQlIzsqFm0AoF7/QIZmVZki/W7j7eoj2t9TiBwQ8kBXI/mXJMcXykr27UBCdzvTSApK1eULj4OJmUlNJqjRjm+zCoAFtofpZeZNGk+OaGIjg5QOsIjdnQnGKSK7Oi5OBm4KhxyS4ev4wmJ5gSLmCOtvKUIuflz1AYnOa4pKwCCUlbcKCtj5fXfmUBTVkIkR6ziY08Ek7Ii9AGJjo5QUnhIdoKfmI7XhgKyA8gXYRHjOMGkrYKVHNconBKFyKHiTXLkTll5QqriY7FSVlSbow9IdHSIUsIj5pw7WpUdf4hVr6NF2fHXvhgjraTCX7G4Egipz3FOV7ninLK6Hs3xnLLiJMcZMVJWHh8XufgYkDZlBagzbUVch0RHxygpPGKgRdnRanEyh9pqZ8RKW2kZbmh5oPU5zpLjb5SVI5rjJWXFwUVzxExZSV18TCkrgkQnDFBCeEh2/Owj08zJQHCyI6bwCGlPaB/lTFsJJdglMqTi6sgq35LjmrK6UB4bcMpKCHIWH3siWMmhlJW+INEJI0h2/OyvMtkRK4UFBJcWEkN4Qnm+2qM5apiLyB+XXQpfXEdZ+UtZAXCTHKHRHLmLj0NZMJjQNyQ6YYbc0R2x6nbkkh0xUVNxMhB8DUywwiP0OVLW5gQTzfFXb6HU0g9AYMs+cJLja5SVv5SV8ygrIDjJ4W0XSXKkTFlRNEd/0Dw6YYrcc/CIMZuyHPPsCJ1jR+gyEf7m2JFz5mRA+OzJnuDExVckQ8oan2AnCQwWMUZcXbZFh7R8g2t9TqAolbJyJtTlHVyhuhzhXLbbgRADXb4K2rUCiU6YY6gyaE52gMDD1ErKjj+0JDuAuDLjLZojRtpKrNocNY648oVzJMeZYFNWHKGmrDw+LuBtlmNSQA6K5ugTEh1C1uiOWOtkBRPdUUp25Jo5ORBClR25kTuaIyaXbdFeHwsm2uMrbeVakxPoKCtjiBMDBjvCSqxJAX0hVTSHhparH6rRIRzIVb+jZN1OIDU7ei5OBpSft0YL0Ryp4QTEGW/LP/gbVu4sOf5SVmVeJgYEEHTKSsoRVoB6ozlqlmwl+P7779GnTx+kpqbCYDBg1apVbvvs378fffv2RXx8PKpVq4Zbb70VJ06ckKxPJDqEG3IVK5PsBI5eZEdpyQoWLgomx4grofU5l+0RbpLjeMzLKKtL5TEeU1YGqyGolJXUI6xClZxwq81RktLSUrRu3RqLFi3y+PiRI0dw++23o1mzZti6dSt+/fVXTJ48GdHR3iOfoUKpK8IjcqWzlCpSVmsaS+56HUBdaSxPkiZn2srbe+PttVTbHDqc5AQ6yiqUlFUgI6x4j0u8vAOHkO8u1eaIR8+ePdGzZ0+vj7/wwgvo1asXXn31Vce2Ro0aSdoniugQPpErlRUqaonsiIGcy0RwyBlhkfpYYqStONnUUiGyq+QEkrISa5RVMMXHgYyw8kYoKStCGMXFxbxbRUXgkm+32/Htt9/ipptuQlZWFmrVqoWMjAyP6S0xIdEh/CJH7Y4WZEcIapo5GVCf7KgxZaWmv+Zd63MCXfbBOZLjeZRVpNdRVq6IkbISUnzsTDCTAlLKyjsl19KaodxKrr2JaWlpiI+Pd9xmzpwZcH/OnDmDkpISzJo1C9nZ2Vi/fj3uvvtu3HPPPfjvf/8r9uk7oNQVIRiph6KrPY2ltpFYQlJYgSJlGsuf5CidtlITgY7Gch1K7m2U1aWyGK8pK+dojpAFO9VefCxUckSZIkLA91nrnDx5EhaLxXHfbA78jxb7tTl5+vXrh6eeegoA0KZNG/z4449YvHgxOnfuLE5nXaCIjogYbFd/NJwXwdMbUkd3xBiRFewsykLQe3EyoM6oi1DkGG2ltqUfQklZAb5HWXmL5qih+JiQF4vFwrsFIzqJiYmIiIhAixYteNubN29Oo660CCc8ehUftaeyApUdtY7EUqJeB7gqO2IJj5C2hERzlIIrRJYq0uUveuMtbXXJHuNVcoSmrLjfpkBGWcldfOwNSllpj6ioKNx66604ePAgb/sff/yBevXqSXZcSl3JhLPs+Cq+0xJSj8wKNZUVaBpLipFYYqDESCyOUFNZYkaHJF/ywSX1EExasNgaDUtEcEs+cPU5/oaVX7LH8CYNLLZfH5bLSY7bKCsumuMlZQUIK0B2FRh/yzs4anWCXN4BEH++HGfErM8Kh/SVEEpKSnD48GHH/aNHjyI/Px81a9ZE3bp18cwzz+CBBx7AnXfeia5du2Lt2rX4+uuvsXXrVsn6RBEdBdBbpEfqVFYoKB3Z0XJxMkew0R2hzxFrlXJfr3Ugo+ZCnaG62Brt8SYmzpEcAI5oDnd815TV5bJoQSkrX9Ecb5LjbZ9Al3fwRDApKyWjOXqtGQuEX375BW3btkXbtm0BADk5OWjbti1yc3MBAHfffTcWL16MV199Fenp6Xj33XfxxRdf4Pbbb5esTxTRURi9RHqkjO5oPbIjVnGyP4QWJwcT2QH44uItyiNWBEfMtJWcUwP4IpRojy9CSVk5I6QAmUOK5R2E1uWoPWUV7pGdLl26gDHf14ERI0ZgxIgRMvWIIjqqQg+RHqmiO2qO7AhBTfU6QOhRFC7K43oLFKH98Je2EvL6ehIeuYeWu0Z2vNXnCFmt3LkuB/AyMaBLygqA31FW/qI5coyw8oaY8+VI+d6XV0ZSdEdFUERHpXCyo8Uoj1TD0NUa2ZFztXOx6nWA4CM7UqPGaI7UM0e71uf4w7kuB4DXUVZcNMcxMWAVX3KEIPcIKyC4uhw1RHNc4WRHqQhPkT0aVnto34FSu/pe10ChiI7K0WqUR6ph6GqN7Ih1QRWrXkeuyE4oSH1sMd4TVxFUw9By17ocXymr8itRPlNW/qI5Yo+wckWo5GgdLsJDUR5lINHREFoVHtHbDHGuHSVlR23FyYCysiOEYNJWgUoO9xe32pd+cJYcQPgoK4OXiQGd8Sc5ShYfazGa4w2SHfkh0dEgWovyqLFuRw+y47eNAARGbtnxdjy1zJ0jB65pKn/Dyl0lx9coKy5lBSCoUVa+JEdNxceAtiSHUAYSHY2jFeFRYypLqhmU5ZIdMVNYgHyyo/YIkty4rm/lSrE92q0mx1/KiovmoCwi4FFWQiSH97iExccEIQYkOjpBS8Ijepsij5DyhhIjsfyhNdnx1b6nfkqZthJaGC5mIXKow8td63K8pawqSyN9jrLyhlDJkaP4GKBoDiEOJDo6QwvCoybZoeJkdyjichUhI9JKbfKtC+Y8lBzwnbJyHmUFwG2UladoTqCSw99X/OJjMYeSu6KmFesJ6SHR0SlqFx6SnavIVa8TKFLITqBtillwHQihFiIHIj+BDisHIChl5TzKylh5/bvmLZrjbXQVENryDlLMfMwRSjRHjsVfCfVA8+joHDXPxyPFbMrBzrWj5Bw7YsycLOb8OhxizrPjT3KERJykGG0FuI+4ChRPYlNqM6OayXvKKxCxcS4+dk5ZAXAfZcVFc+A9ZSVkBmQpRlgF8r2UMpoTTly2R8MW4jw6ZTSPDqEV1BzhETu6o9fIjj/ETmEBVwUl1OgOpcKCh5McZ7iU1fVoToTHZR48TQwYqORINcIKCC1lRbU5RCCQ6IQZahUevcuOGChRr8MRrPAIeY6QImQhqGVNKw7XSI+3QmRv0R3nSI6nUVaOaI5Tysp5zhxnhKxMDsg3wkqJuhwifNGc6CxatAj169dHdHQ0MjIysGPHDq/7dunSBQaDwe3Wu3dvxz7Dhg1zezw7O1uOU1EUNQqPVmVHCGqr1wl2vhqhsiNGJMiZYNa2ciWQKICUS2NwYuNtWPlle4xbJMdTyupCeax7yqrSGHTKSukRVkIRK5pDdTrhg6ZqdD755BPk5ORg8eLFyMjIwPz585GVlYWDBw+iVq1abvuvXLkSlZXXv93nz59H69atcd999/H2y87OxrJlyxz3zWb5RlIojdpqeMSu25GjZkeL9TpX9wusZodDVIERqa1AozneXmPX10PqNa784WmUlb+UFSBslJUzUoywChQlojlirD9HqB9NRXTmzZuHUaNGYfjw4WjRogUWL16M2NhYLF261OP+NWvWREpKiuO2YcMGxMbGuomO2Wzm7VejRg05TkdVqC3CI2Z0R47Ijhbrda7up776GTlHW4VaiCwlnORw0RwAglJWQkZZeSOQEVauyJGyotocIhg0IzqVlZXYuXMnMjMzHduMRiMyMzORl5cnqI0lS5Zg4MCBqFatGm/71q1bUatWLTRt2hSPPfYYzp8/77OdiooKFBcX8256gWSHj1plR+71sKRAqGSJkbZSG/5mQ+bgTQx4bc4cKVNWjm0CRljJnbKSCj18ngjfqPuX0Ilz587BZrMhOTmZtz05ORkHDhzw+/wdO3bg999/x5IlS3jbs7Ozcc8996BBgwY4cuQInn/+efTs2RN5eXkwmTx/AWbOnIlp06YFfzIqR03pLEOVQZdpLDHwl8ICpBl2LiV6KEL2BFeILHRYudvsx0GmrPyh9AgrQB0FyJzs6C2NddkeQ8PLoSHRCZUlS5YgPT0dHTp04G0fOHCg4//p6elo1aoVGjVqhK1bt6J79+4e25o0aRJycnIc94uLi5GWliZNxxVELcKjR9mRq15HKHLLjhpTZhy+CpFLrVGoFiFgjHYIONflOB/XW8rK6CNl5SuaI8UIq0AJRHLkSFvpVXjCHc2krhITE2EymVBYWMjbXlhYiJSUFJ/PLS0txccff4yRI0f6PU7Dhg2RmJiIw4cPe93HbDbDYrHwbnpGDeksNaSxxCZc63UCOY6caSuxRc/XhIHecK3L8bTMg7+UFUcwkhPICCtPaCll5Ysqq8lxI7SPOn7xBRAVFYV27dph06ZNjm12ux2bNm1Cx44dfT73s88+Q0VFBR566CG/x/nrr79w/vx51K5dO+Q+6wk1FCuLuQJ6MLITDvU6SkZa5E5biVGIXGqNQqnVQ5GLB/wOK3eSHMDTKKtInykrDmNVaJLD31f48g5aTFkJwVl6XG+ENtBU6ionJwdDhw5F+/bt0aFDB8yfPx+lpaUYPnw4AGDIkCG48cYbMXPmTN7zlixZgv79++OGG27gbS8pKcG0adMwYMAApKSk4MiRI3j22WfRuHFjZGVlyXZeWkIN6SyxUlnBpLGUGnbuD7Hqda7uJ10aSw0pq1DTEkLFhsPfiuXOqSpOcjyOsuKiOV5SVv5WJgeESU4wa1hJLTlqHW0VjOzYJJini/CNpkTngQcewNmzZ5Gbm4uCggK0adMGa9eudRQonzhxAkYj/0N08OBB/PDDD1i/fr1beyaTCb/++itWrFiBS5cuITU1FT169MCMGTPCai6dYDBYDSQ7AhCrOFnOeh3gupCIKTyBSo6/tJWaipDFrN3xlrK6XBYtaJSVNzyNrgKkWcNKi+kqQr9oSnQAYMyYMRgzZozHx7Zu3eq2rWnTpmDM8wUxJiYG69atE7N7YYXS0R09yY5Yxcn+EBrVub6/PEXKUg+FV2tEwBnvo6y8p6w8IWg9KwlHWPlDKykrQj9QDI0IGSVrd7RSsyMEtdXrXN8/9HSTGlJWcsIVIgsdVu5JcrylrJwnBhQ6Z46cI6womkOoDc1FdAh1omR0RwuRHa3W61zfP/jIjj/J8SRecqWtuNeAOzcp17jyBleX44xzyoqL5nApKwAeR1l5Q00jrCiaIy8ldjNsttAu81fs6ps1PFAookOIilLRHSUjO0IRa6VzsRb/DCayE9DQ8AD3lwMu9SdWLVOoOBcfe0tZcdEcLmXlbWVyT9EcOUdY+SNYydFC2pFQNyQ6hOgoNRRdKdkRe9i5XPPrBAsnMN4kJhDBERLN0Tre0lZuI6y8pKwqSyMdn7FA5syRc4QVQCkrQr2Q6BCSQbLjGbFkxx9yrIflLD1SRXACSVvZKo2iRc6CxbU+x5UiW6yb5BRbr/7rK2VlqHKXHG8jreQcYQVIm7JS0+g6wj/ff/89+vTpg9TUVBgMBqxatcrxWFVVFZ577jmkp6ejWrVqSE1NxZAhQ/D3339L2icSHUJStCw7gSJ3cbJSKSyxEPO4oQiOr9qjkirpp5kQO2XlKjlijbDyBtXlEM6UlpaidevWWLRokdtjZWVl2LVrFyZPnoxdu3Zh5cqVOHjwIPr27Stpn6gYmZAcJQqVxShQVkNxsj/EGHIOBF6cLBVSpK281Xgofb5+R1lx0Ry4p6yERnKCHWHliWDqcojwo2fPnujZs6fHx+Lj47Fhwwbettdffx0dOnTAiRMnULduXUn6RBEdEXFeVI9wR+7ojhiRHSmHnctVryNUHpSK7PhCzNFWYhQil1SZPUZ5uMkC/aWtfLXra2VyTnJccY7m+JKcQEdYqSFlRYQHRUVFMBgMSEhIkOwY6vtl0zjOsuP8Q0JcRe4ZlTnZCSW6o3RkR64h54B8kR2x01ZSIkX6SsgoK+dlHjiEzIAMyDPCSs7iYzG+A0RoFBcX8+6bzeaQVxAoLy/Hc889h0GDBkm6ODaJjoSQ9HhGi6msYGRHKGLIjphLREgtO94kR2+jrbwhOGXlY2VywHs0J5ARVq6IKTliR3NIdgKn2BaLyhDn0Sm3Xf3gpaWl8bZPmTIFU6dODbrdqqoq3H///WCM4c033wyli34h0ZEJTnpIeK6jRHRHTtkJZJkIMRCrXgdQR82Omte28oWvtBUnORz+UlYAPI6y4iRHSLrKdbszWpv5WOuyY6sywa6Rz7ErJ0+e5EVdQonmcJJz/PhxbN68WdJoDkCiIzsU5eFDsnMVudbDCmThTylkR+w6IE9pK+fXUY5CZNf6HG84r1IuNGXl/HshJGXFIcYIq1AimFLW5nDCq0bh0YqMB4PFYhFFSDjJOXToELZs2YIbbrhBhN75hkRHQSjKcxW5U1l6lx1/KC07Ho8jU9rK9bzlWLDUWXDESFlxKDHCSk2TAjpLhRzSo2eJEZOSkhIcPnzYcf/o0aPIz89HzZo1Ubt2bdx7773YtWsXvvnmG9hsNhQUFAAAatasiagoLxNAhQiJjgqgKM9V5IzuaFl2/CFmvQ4gnuwEEs0RkrYKpAhZrJReKASSsnIdZeUazVHjCCsOJUZakYSoh19++QVdu3Z13M/JyQEADB06FFOnTsXq1asBAG3atOE9b8uWLejSpYskfSLRURnhHuXRs+yIhRgpLEBe2fElOcFEc5Se/ZiDS1sJhYvmABA8ysofckgOQQilS5cuYMxHwbuPx6RCHb8WhBvGSkPYzssj53w7cs6iLPf8OmITbH2NGufnERt/9TmuKauyAFJWvmpzxBxG7gs1R3MIwh/6/wXSOOEa4ZGzbieUyI6a63XEjuoA16VFaHQnGMmRerSV3KPJPKesIr2OsnImGMkJZg0rQBt1OURglNjMqLKFVv9WYZO+jk1qKKKjEbgIT7hFeeSK7oQS2ZFqAVAx0jNirYfl9hxrhF+JESI5Yhch+xpxJUToykRelJSTHLdRVlw051rKyuA0ysrfMg+AMMkRY4RVIJJD0RxCrVBER4OEW5RHrrodNUZ2/KFEvQ7veQqkpQIVQLELkeMir6ap/A0rd5Uc51FWl8uiAx5l5YlAJccTVJdD6B0SHQ0TTsIj93w7wSBFcbJcQ86VwFM0Rw2TBHqK6sQGMAzdOVVVars+qRrXrpgpK0/bxCo+ppQVoRcodaUDwiWlJUcaK9Ti5EDSWHIWJ0uVwtIycsyhA8DznDk+Ula+EHOElS8ClRxKWxFqhkRHR4SL7EgtPFqVHX/oQXaUHlbOpa2E4GmUFRfN4VJWAASPshJ7hBUVHxPhAomOzqDojkjtq3DYuT/ESvPIITtC0lZiwNXncPVHYo248jd/jrvk8OfM4VJWQj/HYi7UCVBdDhFekOjolHAQHjXLjhQjseRKYQHSyo7Qtl3PJZBojlpqllxHWVWWRvpMWXmK5oi9UCfV5ciHrdLofquiy67cUDGyztF7wbLURcpyjsQSglxLRADBj8RSCqnqRAIpRAb40RwAflNWHMFKjlgjrEhyQkPptKoniq3RMIc4ZUKFBJFWuVHfO0NIgp4jPHqJ7MiZwlIqsuOtPSnSVmLjOqzcE/5SVgDcUlbe5swJVXJCWd6E8I7HKI0KJYe4Dr07YQbJTpDtq0h21LpEhJiEkrYSSrnIkwM6r2PFtR/syuTeCEVyKJoTOCQ0+oBSV2GIXtNZekljCZlMUIspLKVGdEmdfnOeKwcIfGVyT4g9jJwkxzckMPqG3t0wRo/pLDVHdsTG34+z2CksqURF7LQVV4gsSOL8RHUCqc/xlbJyLkDm8FaALNcwcjHRkihQlCb8oHeYINkJtP0gZUet9TqBEKzsBPI8qdJWoQwtF1KfA7iPsgo0ZSX3MHI9R3OolobgoHedAKC/6E64yI6cQ845ApUdtU9CKEatjrdRVr6WeQD40Rw5h5ED4kuOUiJBQkP4g2p0CB7GSoNuanfUWrOj5XodDk5efNW/CBEcsdJWoZ5/uTUy6CUh/KWsjALmzBF7GLk/pIzkcJIh5nB/EhciFEh0CDf0VKzMRXakEh45ZEcMxFrl3BXXQuVQozdip9oCKUR2lR2uPsfXsg/8SI7/lBWHmJKj1pmPSU6Up8wWBastyv+OPqi0aT/ST59EwiuUylIHah9yzhUqByo5QqI5gV4sAylEDhVnyfE1ygq4LjautTne0lJSSo6e63IIwhMkOoRP9FS7I5XsaKlexx9amLhPSUqtUby5cgD4HWXFfX/8SY7Uw8gJZWFWo+NGyIvmXvFFixahfv36iI6ORkZGBnbs2OF13+XLl8NgMPBu0dHRvH0YY8jNzUXt2rURExODzMxMHDp0SOrT0BwkO37a1YjsiDnkXGy0NMmhW12OwJQVhzfJ4W2TQHIomiMPzlJDcqM8mnr1P/nkE+Tk5GDKlCnYtWsXWrdujaysLJw5c8brcywWC06fPu24HT9+nPf4q6++igULFmDx4sXYvn07qlWrhqysLJSXl0t9OppDL9EdLcuOGKhBdqRIW4mJkPocDiGjrHzNgCxkhJUzQU9aSZIjCSQ16kdT78i8efMwatQoDB8+HC1atMDixYsRGxuLpUuXen2OwWBASkqK45acnOx4jDGG+fPn48UXX0S/fv3QqlUr/Oc//8Hff/+NVatWyXBG2oRkx0e7EsuOXCksQLtpLDnqc4RMDChkmYdAi4+9SQ6lrOSDpEZ7aOZdqqysxM6dO5GZmenYZjQakZmZiby8PK/PKykpQb169ZCWloZ+/fph7969jseOHj2KgoICXpvx8fHIyMjw2SZBsuOzXYlnT5YrhSUVoQqU6/lzw5j9jSrzh9Dh5a6jrAD4XZncE2INI6eUlbSQ2Ggfzbxr586dg81m40VkACA5ORkFBQUen9O0aVMsXboUX331Fd5//33Y7XZ06tQJf/31FwA4nhdImwBQUVGB4uJi3g0ADNIuqaM69JDKUpPshGMKi0PobMiBXGhch5ZzUuIPX8s+OI+yAsCL5nApK0/LPLiip2HkeoLSUPpE1/PodOzYER07dnTc79SpE5o3b4633noLM2bMCLrdmTNnYtq0aR4f4/6KE5Jr1wtan2RQ6okFA0Ho/DpyTSQIBDe/jq+2gkUtFx3XAuTK0sir7wcQ8MrkckgORXO8o5bPlFSU2aJQZQ1tHp0q6TPBkqOZdzkxMREmkwmFhYW87YWFhUhJSRHURmRkJNq2bYvDhw8DgON5gbY5adIkFBUVOW4nT55028dY5b5Yn57RemRHCrRQryM0haV0vY5YF6Rya4TgyA5wPYLD/d9ZcrgCZMcoK5c5c7whxjByIjgoWhOeaOadjoqKQrt27bBp0ybHNrvdjk2bNvGiNr6w2Wz47bffULt2bQBAgwYNkJKSwmuzuLgY27dv99mm2WyGxWLh3bwRTsKj5VSWmlJYgSDGj7VcsuPt+aEs4ukasZKiELmkyswTHsB7ysrbnDkcYgwjByiaIwRKQxEcmnrnc3Jy8M4772DFihXYv38/HnvsMZSWlmL48OEAgCFDhmDSpEmO/adPn47169fjzz//xK5du/DQQw/h+PHjeOSRRwBcHZE1fvx4vPTSS1i9ejV+++03DBkyBKmpqejfv7+ofQ834dEiapIdMet1xBymrXRkxxtiFSL7qs/xNmcOAJ9z5nCINYyc6nI8Q1KjPDabDZMnT0aDBg0QExODRo0aYcaMGWBM2aikpmp0HnjgAZw9exa5ubkoKChAmzZtsHbtWkcx8YkTJ2A0Xv+AX7x4EaNGjUJBQQFq1KiBdu3a4ccff0SLFi0c+zz77LMoLS3F6NGjcenSJdx+++1Yu3at28SCYhEuNTxarduRql4nmDWx5KzXEbIWFkcwNTtCozmecL1oiRGlKLdGIDpCQBEN7znel3lwlntP0Rw5R1hx6D2aQzKjPmbPno0333wTK1aswM0334xffvkFw4cPR3x8PMaOHatYvwxMadXSAcXFxYiPj0fDF16BKUBB0rvwaFF2AOkWAQ2m3kKsNIWQwuRAIiNCZcdXFEhI2sqX6LgOLef6JGQxT2fR8RfR4aI5l8pieAXIxlKTWzTHbakHhUZY6U10tC42XJTWfqUcJ3Mmo6ioyGfZQyhw16QBG4cislqIxcillfgic4Wg/t51111ITk7GkiVLHNsGDBiAmJgYvP/++yH1IxS0/cnRAXpPaVEay6VdBdNYYi/8KSSNpdZUlzP+5s8JZJkHkhzx0HIqysBF+5xuWsd1SpWKCvdZwzt16oRNmzbhjz/+AADs2bMHP/zwA3r27Cl3d3loKnWlZ/Sc0qI0ljzIOeScw1VknKM8/iQnmEkLPUVzpMTTKCuhyzyIJTnhgtZkxhm1ikxJlRmRVSFGdK59ztPS0njbp0yZgqlTp/K2TZw4EcXFxWjWrBlMJhNsNhtefvllDB48OKQ+hAqJjsrQq/BwkR2tCY8UsqN0vY4/AqnXcUXsCI6Qi59r2kosXCXHeZkHo59lHsSUHL0WH5PYaIuTJ0/yUldms9ltn08//RQffPABPvzwQ9x8883Iz8/H+PHjkZqaiqFDh8rZXR4kOipFz8JDsiOt7PhDSFQnFNkJFjkX8fRWiMzNeOxaqxPIyuTeCHfJIbHRNv6mUgGAZ555BhMnTsTAgQMBAOnp6Th+/DhmzpxJokN4x1hFsqMG1CI7QlAihSUFclwYvdXnBJKycsVTNCccJYfEJvwoKyvjjXwGAJPJBLtd2c8yiY4G0GN0h2TnWpsByo5WUlhC2tYC/lJWAD9tFehcOd7QmuRoWWoAEhux6NOnD15++WXUrVsXN998M3bv3o158+ZhxIgRivaLREdD6E14SHaC7INIsqOmFJZr2krohdNb38orIwUNMfeGt1FWgPvK5PaIq7KjpuJjMUTXX/tahsRGGhYuXIjJkyfj8ccfx5kzZ5Camor/+7//Q25urqL9ItHRIHoSHi3KjtiEW71OsNGcQEdclVde/4I4S4+/iQJ9pay8TTugxuJjsWRH61IDkNjIRfXq1TF//nzMnz9f6a7wINHRMHqp39Ga7KghhSUUqf+yVwtijbgSOsrKG2qRHEd73BIVAj4DehAaZ0huCA4SHY2jl+gOyY5y9TpyRnU8RXPkHG3li3Ir/0vE9dV5lBWHa6oKUJ/k8NrWmcR4gsTGnSvWCFRZQ7s4WK3a/0OJPhk6QQ8zLGttFmUpZk8OdOZktc6aHApSXpRd01aucuMpmuP8nnj7jildkxOuaGHmYUOVwe1GyAtFdHSG1tNZFNkJog8CIjtipbBCieyodaQVJzs8yfEzZ45rNMexPUjJ0dooK6VQs9BwkMioD/V/aoiA0Xp0J9wjO1L9UPqLlAhNIYkpLMGkrbwt5CkGnORYyyIEzZkDeP7DgiRHPNQetaFojfpR5yeHEAWSHe2i5hQWELjsCN1fqVoSX6OsfBUge0tZCYUkxx21L4ZJYqM91PcpIkRFy9EdLcmOVup1xBQJofKidMrK33w65dareShunS7HSCWXAmRX1Fx8rDXULDYASGw0jjo/VYTokOxIjxSyIwVipbAA/xIjluRIPUTe1zIPnr47VHwcGhS1IeREfZ8wQjK0Gt0JZ9lRewoL8C4zfiUohPocofibKNDbKCtf0RwxJCccozkkNoRS0KgrETHaPM+voTa0OPeOlkZjiT0SS4r5dcSeSFCMyE0gabVgRn6VWyN4suNvlBXg/ocBSU5gqFFqgPAZGVVujUQEzaNDER0pMFr5C/2pFa1FdyiyIy5iprC0AlePw8FJjpCUFUmOMNQauaGITfii8tiDtuFkR80RHq1Fd7QU2VESOdfCCrZdsaiymgIaYs7JDrcWVsDLPFBNjhtqkxogfKI2hH/U9+nUIVqI8GgpuqOVyI7SUR25R2GFQqj94EZMOeOvPsfTyuTe4P4QCEWy9RTNUWsxsZqjNgbrVZE22NTXN72j4liD/lB7hEdL0R2tRHa0UK/jD6miOkLhaolcJwoMBOeUlSOSI3DOHG/fh3BLWalJaJxRo9QA2hmFGQ6o9JKrb7QgPCQ74qG07PhDSGGymLKjhtofbyuTuxLudTlqlBsSGyJQVHqpDQ/ULDwkO/pBiVFYgSBn+szTKCsAHkdZhetcOSQ3wiG50QYqvMSGH2oVHi2lstSO0lEdtaSwlIzmOEsO1w8uZcVhj3SX/FAkRyvRHJIbYWhNbCpsEbBaQ7uw2GzirSWnFCq7tIY3ahYeNcuOVqI6SsuOP+ROYQkhlCgTV4vjugSEY2JAlzlzOMJFckhu/KM1sSE8o7JLKgGoU3jUHt0h2RGwr8pTWFLBCQ8XzQEgaGVyPUqO2uSGxIaQA3V96gkeahyWruZh6FoZdq4kYlzogk0/eXqe0PqcQKJI0VFVHhfy5FJWnpZ5cBtppaOaHLUNA1fbEHDHsG+SHN2iopgB4Q21RXjUnMrSQmSHUljy47wyOZeyckbMwmM1RHPUIjUcapEagKI24Yi6vg2ET9QU4VHzAqFaiOwoOZmgWBMJqmGYuBC8LfPg73OitUgORW589IWiNmGNSmIERCCoaeFQtUZ3tBDZERux63WEIDSyE0zaKthaIa4mB+BLjr85c4DQZj4G5I/mqEVqONQgNRxqlRot/CGmN1RyuSQCRU3pLLUWKqtddsROYYmN0MJkf7ITauRHjCHtgpZ50EhdDsmNZ7QiNsonNsMPFVwmiVBQm/CQ7ASG2kdhCYWTGVcpkSK9ZasyCVoGwnViQNcCZGfEkBypozkkOB76oEK5UVPEprwyEqaI0H6UbZXaVzMVXB4JMVCL8KhRdsINMWUn0OHmztEduWt4uIJjbiVzTxMDSiU5UqA2sQFIbjyhJrEhPEOiozPUUL+jtlRWuEV1xCYY2RHSplhwYsOJjvNK5v5SVmqTHJIbL31QkdyQ2GgP9X2r/LBo0SLUr18f0dHRyMjIwI4dO7zu+8477+COO+5AjRo1UKNGDWRmZrrtP2zYMBgMBt4tOzs7qL6pZSSSWkZnqeG14FD7j5PaR2FpDSHRHLVIjtpGSwHKj5hyHiWlBskxVhocN0I4s2bNgsFgwPjx4xXth3q+WQL45JNPkJOTgylTpmDXrl1o3bo1srKycObMGY/7b926FYMGDcKWLVuQl5eHtLQ09OjRA6dOneLtl52djdOnTztuH330UUj9JOFx6oMKXgeOcPuREvMiJefCm0BoSz/wJgb0tsyDBJITiKiQ3Hg4vkrFJtx+N8Ti559/xltvvYVWrVop3RVtic68efMwatQoDB8+HC1atMDixYsRGxuLpUuXetz/gw8+wOOPP442bdqgWbNmePfdd2G327Fp0ybefmazGSkpKY5bjRo1ROkvCc+146vkdQDULTtS/MALvWiJNbeOEKSUJm6JB+cCZFekjOS4vo7OQqNGuQFAcgMSG7EpKSnB4MGD8c4774h2PQ0FdX3jfFBZWYmdO3ciMzPTsc1oNCIzMxN5eXmC2igrK0NVVRVq1qzJ275161bUqlULTZs2xWOPPYbz58+L2ne1XOgpunMVNf+QKfmDr7YLcLA4TwzoNgOyDLVaahUaZ5SM3qhRbghxeeKJJ9C7d2/e9VpJNFOMfO7cOdhsNiQnJ/O2Jycn48CBA4LaeO6555Camsp78bOzs3HPPfegQYMGOHLkCJ5//nn07NkTeXl5MJlMHtupqKhARUWF435xcbGg46uhSFfp0VlqGZWl9gJlMRFziYhQF/30FM0JdZh7ldWEyAib28SArri+32ooPpYTJYuKlZYaDpKa4HG9zpnNZpjNZrf9Pv74Y+zatQs///yzXF3zi2ZEJ1RmzZqFjz/+GFu3bkV0dLRj+8CBAx3/T09PR6tWrdCoUSNs3boV3bt399jWzJkzMW3atKD7Eu7Co4bzVzNSjMISKjtizq0TLEJFynk+Hde1rADfF9dwkpxwFpxwF5sqqxF2q+c/2IViu/bHSVpaGm/7lClTMHXqVN62kydPYty4cdiwYQPvOqs0mhGdxMREmEwmFBYW8rYXFhYiJSXF53Pnzp2LWbNmYePGjX4Loxo2bIjExEQcPnzYq+hMmjQJOTk5jvvFxcVuHwIhqOGCr+RwdKWjO+EU1QkEsefWERvnyQK5mhwAbqOsnFHLCCu5ILlRF85pe6aSFH6gnDx5EhaLxXHfUzRn586dOHPmDG655RbHNpvNhu+//x6vv/46KioqvGZKpEQzohMVFYV27dph06ZN6N+/PwA4CovHjBnj9XmvvvoqXn75Zaxbtw7t27f3e5y//voL58+fR+3atb3u4y1kFyxKC4/S0R2SHXeUjOoIQSnZ8TYjstBlHvQuOUoWFSuJ2uRGLfWIYmKxWHii44nu3bvjt99+420bPnw4mjVrhueee04RyQE0JDoAkJOTg6FDh6J9+/bo0KED5s+fj9LSUgwfPhwAMGTIENx4442YOXMmAGD27NnIzc3Fhx9+iPr166OgoAAAEBcXh7i4OJSUlGDatGkYMGAAUlJScOTIETz77LNo3LgxsrKyZD+/cBUexc9bpbIjBWKmsAKVHalGWzlPUOg6Z044RHJIbpRHj2ITDNWrV0fLli1526pVq4YbbrjBbbucaEp0HnjgAZw9exa5ubkoKChAmzZtsHbtWkeB8okTJ2A0Xv/Re/PNN1FZWYl7772X1w6XWzSZTPj111+xYsUKXLp0CampqejRowdmzJghasQmUBS/8CuUzlIyuqNG2VH7jMliEOjIJG/rXDnPmcOhd8lRasSUUpDYEMGiKdEBgDFjxnhNVW3dupV3/9ixYz7biomJwbp160TqmfgoKTxKRndIdqRFyaiO32MG2BZvYsAq/UtOuEVvSG60j+t1WQk0JzrhSLgJj6LnqzLZkSqqI/corFDSVs4LhXrC25w5gH4kJ5yiN2qRGxIb/UCioyGUFp5wie6oTXbUDicx3qIx3iRHaNrKVXK4kVbOc+boUXJIbuRHb3JjrzIBVaEVANtDfL4aINHRIEoJj1LRnXCXHa1EdTylscSUHNdV0X2NstJybVO4CI4a5EZvYkN4hkRHw4SL8Ch2niqSHamQUnaEpquCTY15W8vK4zFUHs0huZGxDyQ3YQeJjg5QUnj0Ht1Ri+xoaQSWWMPIvdXlOFJl1+67XrC1lLKSW3BIbohwhERHRIyVgPHad9oepcDxFRAeJaI7tHSEuCi5PISv9nwVIXsrQNaK5JDgSHxslYmN84LKzPOcl4SEkOhIhLHy6r/hJDx6TWWFQ1RHzFmT/R4rhFW9PQ0ldzymcskhuZH42CqWG0JZSHQkJpyER8/RHbXIjtKoYdFPDucLuZolR++CQ3JzFRIb9UKiIxMkPBIdK8xkRw1RHdHm1hHQhmv6ylPKSq2SI6fgkNzID4mNdiDRkZlwEx65ZAeQ57zUIDtSInUKy1fayts8PNywcm7OHGfUKDkkOBIcl+QmKGxVRrAQUsUAYK+SZo06OSHRUQhOeAD5pUfWKIgOozt6lx0hyJ3C8jRnjpokh+RGguOqQG60JjaEZ0h0VIASUR49R3f0LjtSDzWXszDZcUw/syq7FiCrRXL0KjgkN4SeINFREXoXHrmiO3Kdk54jO0JkR+qojjfJcdtPAckhwRHxmArLDYmN/iHRUSEkPCIdR4bojlKyo5YJBAORHV/1Od5WQQ9lKLoUyCU4JDcSH5/kJqwg0VEx4SA8JDvqRYkUlrd+eEO2uX90JjjhJjckNuENiY4G0LPwyBHdkeNclJAdLUZ1fOEtquN1fxkkhwQnxOOR3DjgXgumgjqkcINER0MoJTwU3RHYvg4jO3JHdZzXyfKZ6pK4T3IIDsmNBMdWkdwonZ4jrhNwAvy7777DI488gmeffRYHDhzgPXbx4kV069ZNtM4RnjFW8oenS368Knm+tEar9D9UUp+HsdIg64VFjoulkIu+v1qaQGttuP09LvUgkeQYqgyOm5QYrO5rdEmBnJ9F7jdCiYs797uhtOQ4vwZqkRxmNYpy0zoBncGHH36Ivn37oqCgAHl5eWjbti0++OADx+OVlZX473//K3onCc+Q8ATZvgznoYYVm+XGm8yoXXLkkBtAHsHh5EZuwZEbNcoNoV4CShbMmTMH8+bNw9ixYwEAn376KUaMGIHy8nKMHDlSkg5qCaMVMJrkW+vJcVyZU1py1u9IXbujh1SWHLU6wS4PEYzkeJMOsSVHT/U3coq1Uhd1paUGIKHRKgFdRg4dOoQ+ffo47t9///1ISkpC3759UVVVhbvvvlv0DmoRuRe3dBxXAeHRerGy1NKmp7qdQOt1fNbYeChelqsmRy/1NyQ3MvWB5EbzBHT5sFgsKCwsRIMGDRzbunbtim+++QZ33XUX/vrrL9E7qGXCQXgouiOgbRlkRy0jsADx574RS3JIcAI8TpjKDYmN/gjoF6lDhw5Ys2aN2/bOnTvj66+/xvz588Xql65QKpcsZw2PLHUvMtTuSNa2Tmp2xJAFOdfIAuSpwZG6/kbO2hslak6UrrmhWht9E5DoPPXUU4iOjvb4WJcuXfD1119jyJAhonRMjyj1Zdaj8EjWtoT9l/oiJddwZamlwTmCE0o0R0+CIzVKXOiVlBs1jpAipCOgZEDnzp3RuXNnr4937doVXbt2DblT4YASaS09pbTkqN2Rou/cRUuqVJaaUlhC8ZbqClZy9JCi0mt6Ssm0FAlN+BLSZeLMmTM4c+YM7HZ+KLpVq1YhdSqcCAfhkXRkk4Svn5SyJmXdjppGYfltR0TJkSN6IyUkNxIcW2VyY6wEmIxTghgqjTCYQquZU9t6c8EQ1OVh586dGDp0KPbv3w/Grv4gGQwGMMZgMBhgs9lE7WQ4oGfhkWUJBgmLlaWM7uhlRFYgePrhDFWcpJQcPQgOyY0yyDnPGeGdoC4NI0aMwE033YQlS5YgOTkZBoM+Ci3VAAlPCO1LHN3RkuxoKqpzTVKCaY8Ex88xZK65UQq1yA2JjToJ6pLw559/4osvvkDjxo3F7g9xDecfDbmkR0/Co6VUlpZlRwxYJAt8jh6NCg7JjUjHVYnYACQ3nli0aBHmzJmDgoICtG7dGgsXLkSHDh0U609Qybfu3btjz549YveF8ILcIxPkGqUl6XBuCV8zKfot1egaqaMSYgmHUMmRciSVlCOo5Bg9JecIIkVGj6pklBT3+yj3Ejxa4ZNPPkFOTg6mTJmCXbt2oXXr1sjKysKZM2cU61NQf/e+++67GDp0KH7//Xe0bNkSkZH8P3H79u0rSucIPnKnteSI8Gg1naWl6I7UkR05VjinCI6X9nUevVFaagCSmUCZN28eRo0aheHDhwMAFi9ejG+//RZLly7FxIkTFelTUD//eXl52LZtm8fJA6kYWXqUEB5KZ3lpV4LaHS3KTqAIXjuLBMdz+zJGbuSG5EadFBcX8+6bzWaYzWbetsrKSuzcuROTJk1ybDMajcjMzEReXp4s/fREUKmrJ598Eg899BBOnz4Nu93Ou5HkyIec4WNKZ/loV4JwuhSpDikv7IEKiT/J0WKKSo7Zi+VK3cieLldBWkqP6SjuexTqDQDS0tIQHx/vuM2cOdPteOfOnYPNZkNycjJve3JyMgoKCmQ5Z08E9Tfu+fPn8dRTT7mdTLhjtAJGo/TrPnk6LiBPhEcv6axwje6oLbLjihYjOBS9CfJ4Kqi1IYRz8uRJWCwWx33XaI6aCern/p577sGWLVvQqFEjsfujC+Ra6NLtuCQ8wtvWUO2OVmRHrXPhkOD4OAbJDSEQi8XCEx1PJCYmwmQyobCwkLe9sLAQKSkpUnbPJ0H9zN90002YNGkSfvjhB6Snp7sVI48dO1aUzmkdEh6RjiHlyuIaie6IvXSEWiI7FMFxaZuiN+IfW0VyY7QCTOHV2aUkKioK7dq1w6ZNm9C/f38AgN1ux6ZNmzBmzBjF+hVUjc67776LuLg4/Pe//8Xrr7+O1157zXGTegXzRYsWoX79+oiOjkZGRgZ27Njhc//PPvsMzZo1Q3R0NNLT0/Hdd9/xHmeMITc3F7Vr10ZMTAwyMzNx6NAhUfusVO5ZTzU8ki62qbHaHbGQQgSEiouWa3CkQK7FcGX9TVCo7kZtw7+VXpldbnJycvDOO+9gxYoV2L9/Px577DGUlpY6RmEpQVB/yx49elTsfgiCG5+/ePFiZGRkYP78+cjKysLBgwdRq1Ytt/1//PFHDBo0CDNnzsRdd92FDz/8EP3798euXbvQsmVLAMCrr76KBQsWYMWKFWjQoAEmT56MrKws7Nu3z+tK7cHi/IWXM8ojd4SH0lku7UoQ3REzsgNA1OiOrxSW1iI4lJ4K8FgKRW7UIDQc4SI03njggQdw9uxZ5ObmoqCgAG3atMHatWsVrek1MG6xKg2QkZGBW2+9Fa+//jqAqyGxtLQ0PPnkkx7H5z/wwAMoLS3FN99849h22223oU2bNli8eDEYY0hNTcWECRPw9NNPAwCKioqQnJyM5cuXY+DAgYL6VVxcjPj4eLQc9QpMUYHJkdxpLUC+YelSD0mX8rWT4jWSor9i1u5IlcripEdLNThaT0+R3MhHoK+1raIc+958HkVFRX5rXoKFuybVnf0SjDGh/cFuv1KOE8+9KGl/pSbon/O//voLq1evxokTJ1BZyf/EzZs3L+SOuRLM+Py8vDzk5OTwtmVlZWHVqlUArkamCgoKkJmZ6Xg8Pj4eGRkZyMvL8yo6FRUVqKiocNx3nV8gEJSo45ErwiN1/Y7WojtqL1SWskhZCkhwPBxDxrSUEmhVbghlCepnfNOmTejbty8aNmyIAwcOoGXLljh27BgYY7jlllvE7iMA3+PzDxw44PE5BQUFPsfzc/8GOuZ/5syZmDZtWsDn4AsSnhDal1h4wi2VpYYiZV+Q4Li0r/PojRrkRqtiY7CFXq9msGl/0e6gipEnTZqEp59+Gr/99huio6PxxRdf4OTJk+jcuTPuu+8+sfuoOiZNmoSioiLH7eTJk6K1rUjxnkyFclL/YGmpWFns91nMQlkp13wKFq5PYvdLqgJjOQpxZfveKlBUrIZi4nArItYzQYnO/v37MWTIEABAREQErly5gri4OEyfPh2zZ88WtYMcwYzPT0lJ8bk/92+gY/7NZrNjTgEhcwsEg1LCI/kxND46S/Q2w2xUVjB90NIIKrnkRurvajjKjfNrS3KjL4ISnWrVqjnqcmrXro0jR444Hjt37pw4PXPBeXw+Bzc+v2PHjh6f07FjR97+ALBhwwbH/g0aNEBKSgpvn+LiYmzfvt1rm3Ij+4+NjNEdLQqPVqI7YqGU7EgZvdGy4EhNOMsNoV+Cqj647bbb8MMPP6B58+bo1asXJkyYgN9++w0rV67EbbfdJnYfHeTk5GDo0KFo3749OnTogPnz5/PG5w8ZMgQ33nijYw2OcePGoXPnzvjXv/6F3r174+OPP8Yvv/yCt99+G8DVBUjHjx+Pl156CU2aNHEML09NTXVMdqQW5K7h0VP9TjjW7og5waCcdTtUf+PUtk4Li5WuuSGpCT+C+qmeN28eSkpKAADTpk1DSUkJPvnkEzRp0kSSEVcc/sbnnzhxAkbj9SBVp06d8OGHH+LFF1/E888/jyZNmmDVqlWOOXQA4Nlnn0VpaSlGjx6NS5cu4fbbb8fatWtFn0NHLPQsPForVpZqZJYaC5Wllh0tCY4eiovDSW7UJjZq6084EPA8OjabDdu2bUOrVq2QkJAgUbe0RSjz6ISK3PPwyDEHj5Tz70j1eql93h2xRmWJLTskOE7t60xwSG6u4vqa2yrL8fs78syjU++Vl2EM8Y92e3k5jj//gqbn0Qm4RsdkMqFHjx64ePGiFP3RNCYFvth6rOGRMmcvZe2O6G2qsG5HDDHR6ggqKZCjRkTuwmKlam7UVG+j1PIXhGeC+ju0ZcuW+PPPP9GgQQOx+6N5ONmxSTwrsCtKpLS0ns4Kt1SWmGksIPDojtYW2dR6/U04RG/UIDWAeoXGWGmA0Rji90PiZVDkIKhRVy+99BKefvppfPPNNzh9+jSKi4t5N+Kq8Og9wkPRHS/tSjAqS7S2ZB6RJXX0RmzJkfIvcb1Fb5QaMaWWyA1FbbRDUH979urVCwDQt29fGAzXf2gYYzAYDLDZbOL0TgeEQ4RHjoLlcI/uiPl+SrEoqFxQ9MbLMWRMSymB0lIDkNBomaB+hrds2SJ2P3SPc3RHTumRW3iklh1AGuGRcmSWGlNZYsqOHJDgeGhf56kpkhtCLIL6Ce7cubPY/QgrlIjyyCU8FN3x0CbJTtBoTXAoehPiMVWQjiL0R0g/v2VlZR5XL2/VqlVInQoXSHhCaF/i6A7JjnJoTW4A/URvSG4IPRLUT+/Zs2cxfPhwrFmzxuPjVKMTGEoJj17SWVpJZUlRtyOW7ADizbUTaj9Eb5cEx/9xFCgoVgoSm/AjqFFX48ePx6VLl7B9+3bExMRg7dq1WLFiBZo0aYLVq1eL3cewQe6RWrKNzpB6pInEI7NEb1PE10KtI7ICPa6W5r+RetSPXKN55B41peRoqXAdIWWo4p97MDeDDl6zoP623Lx5M7766iu0b98eRqMR9erVwz//+U9YLBbMnDkTvXv3FrufYYXcER49pbPCMZWlxrl2hBxHsrY1Wn8j15BwOaHIzVV4f8AqvNZXOBJURKe0tBS1atUCANSoUQNnz54FAKSnp2PXrl3i9S7M0XOER7K2JfoLVYrXJtwiO3KsHq6VFesdbcscvZEDitxchfv9VmI+NYJPUKLTtGlTHDx4EADQunVrvPXWWzh16hQWL16M2rVri9pBQv4vil7SWZK0S7ITcDtSyQ2g/fSUlMiZmiK54YsNyY26CCp4Pm7cOJw+fRoAMGXKFGRnZ+P9999HVFQUVqxYIWoHiavoOZ0V7qksNaexgMCLlOWo9aH0lI9jyBkFVig1pbTUcJDQaIOgfl4feughx/9vueUWHD9+HAcOHEDdunWRmJgoWucId/QoPFLW7kg1DD1cZAfgi4ur9MhZwKzV0VMkNyIck8SGCIGgUlcAsGTJErRs2RLR0dGoUaMGhgwZglWrVonYNcIXStTvSH4MjaWywiWNxWvXKRUll+RQespL+zpPTakxJUVok6D+hszNzcW8efPw5JNPomPHjgCAvLw8PPXUUzhx4gSmT58uaie1grGKATKvaSVnhEcP0R0pIjuAiOkiFUd25ISiNz6OIWNRsdwoLTUACY0eCeon9c0338Q777yDQYMGObb17dsXrVq1wpNPPhm2ogNckx0A9kh55ySRW3i0WrujhVRWOMsOCY6X9nWcmiK5kQ6jLfT3k+lg/t+gfk6rqqrQvn17t+3t2rWD1aqCldhUgJLCo6fojpYKlUl2gkOrcgPoR3DCTW70KjaEZ4Kq0Xn44Yfx5ptvum1/++23MXjw4JA7pSeMVcwhPXIhZz5Z8h96KWsoVF63o4WanVCQsv5Cy3PfyFV7o2TdjRJQrU34EvTfjEuWLMH69etx2223AQC2b9+OEydOYMiQIcjJyXHsN2/evNB7qQOUiPDIlc7ScnRHC5EdQJxzl2vKACF9kKx9Sk/5P04YRW/UJjXGKgYm8x++WqCiogIZGRnYs2cPdu/ejTZt2ojaflA/n7///jtuueUWAMCRI0cAAImJiUhMTMTvv//u2M9gUGbtHDVjrGK6TmeR7FxrM4yGnws5nqTtk9z4Pw7JjSLIHc3XKs8++yxSU1OxZ88eSdoP6qdzy5YtYvcjrKDoTgjtSzQqS4oi5XCWHc1PR6ADwSG5kR8Sm8BZs2YN1q9fjy+++AJr1qyR5BgSlXoSQtC78FB0R/2yA4jTP7kuclRcLOAYMtfcKIFaxAYguQmFwsJCjBo1CqtWrUJsbKxkxyHRUQFKCY8eojskOyK0F8T7JPcFjqI3fo5BciMb4So2xcXFvPtmsxlmszno9hhjGDZsGB599FG0b98ex44dC7GH3gl6ZmRCfOQeoSXXCAQtDh8W++Kk5lmUHW1WCb/JgV5mLpYSOUdNKTFiSi0jpbjfZq1JTiDfaX/f97S0NMTHxztuM2fO9HjMiRMnwmAw+LwdOHAACxcuxOXLlzFp0iTJXweK6KgQuQuW5YruaLFuJ5wiO2pBy+kpit6EjtJSA4Rv1MYXJ0+ehMVicdz3Fs2ZMGEChg0b5rOthg0bYvPmzcjLy3Nrp3379hg8eLCoC4Tr8GdSH8idzpKjdkeLqSySHXnQstwA+hIckhvCExaLhSc63khKSkJSUpLf/RYsWICXXnrJcf/vv/9GVlYWPvnkE2RkZITUV1d08BOpb5QQHq1Hd0h2tIEcF26K3gg8DskNITN169bl3Y+LiwMANGrUCHXq1BH1WBr8eQxP5BQeuaI7JDsitqch2aHojYBj6FRwlJYbEpvwRCM/jQSHnPU7Ukd3pExlaUV2AHWujyU2Wo/eAPIUFssByY2yKLEqvBaoX78+GJPmvVLpzyLhC4ruCGxXA7IDiHv+apIdkhuBxyDBER01yY3J5XUnz5EflfwkEsGgt+iOVLIDiDzvjMpnUZZqFJrQ48pyLI0LDsmN+KhZbpTCWAkYQ7xEMBXUUoUKiY6IGK0AZFhTindMmaM7WpQdQBvRHS3W7ZDcBHgMHUS5XFFKbkhsCKGQ6IgM94G3ybxCtFzRHalTWVS3I77sACIuG6FA3F3rgqPH6A3JDcmNliDRkQglhIeiOwLaDUPZAdwvtv5eA6ULJrUuN4D+ojfhLjckNtpFM0tAXLhwAYMHD4bFYkFCQgJGjhyJkpISn/s/+eSTaNq0KWJiYlC3bl2MHTsWRUVFvP08TU/98ccfi9ZvU5X8XxC5fhik/uGT6kdckuUTVL5khFv7Vt83JZBjiQmpl2WQ4zWUeykOJZZgUMuSC9zvN0mOttFMRGfw4ME4ffo0NmzYgKqqKgwfPhyjR4/Ghx9+6HH/v//+G3///Tfmzp2LFi1a4Pjx43j00Ufx999/4/PPP+ftu2zZMmRnZzvuJyQkiN5/U5U+oztypLIosqNvKHoj8Bg6j94oLTUACY1e0YTo7N+/H2vXrsXPP/+M9u3bAwAWLlyIXr16Ye7cuUhNTXV7TsuWLfHFF1847jdq1Agvv/wyHnroIVitVkREXD/1hIQEpKSkSH4eSqWztJ7KItkRrz21IMdFWw+1NyQ30kNyo380kbrKy8tDQkKCQ3IAIDMzE0ajEdu3bxfcTlFRESwWC09yAOCJJ55AYmIiOnTogKVLl/qdtKiiogLFxcW8WyDIHQqVKwQs5Q8lpbG0j5ypKTnSU1IiV2pKidXB1ZCWopRUeKGJiE5BQQFq1arF2xYREYGaNWuioKBAUBvnzp3DjBkzMHr0aN726dOno1u3boiNjcX69evx+OOPo6SkBGPHjvXa1syZMzFt2rTAT8QFJdJZcqSytDYiiyI70iGXpFH0JjCUqLlRknAVGqMVMJpCa4PpYIZDRUVn4sSJmD17ts999u/fH/JxiouL0bt3b7Ro0QJTp07lPTZ58mTH/9u2bYvS0lLMmTPHp+hMmjQJOTk5vPbT0tKC6pvc6Sw5ane0WLdDsiMeepEbQF+CE26pKbXJjani6mvBVJCuCzcUFZ0JEyZg2LBhPvdp2LAhUlJScObMGd52q9WKCxcu+K2tuXz5MrKzs1G9enV8+eWXiIz0faXIyMjAjBkzUFFRAbPZ7HEfs9ns9bFgoehOYISz7ADqEx45oxEUvREOyY2ycHJDKIuiopOUlISkpCS/+3Xs2BGXLl3Czp070a5dOwDA5s2bYbfbkZGR4fV5xcXFyMrKgtlsxurVqxEdHe33WPn5+ahRo4boIiMEJaI7JDsubUq0ZISa18cK9viyHo+iNwERTqkpNckNiY060USNTvPmzZGdnY1Ro0Zh8eLFqKqqwpgxYzBw4EDHiKtTp06he/fu+M9//oMOHTqguLgYPXr0QFlZGd5//31e0XBSUhJMJhO+/vprFBYW4rbbbkN0dDQ2bNiAV155BU8//bSSpytrdEeuVJaWZAcQP7ojlewA8gkPyU2QxyC5ER21yA2JjTbQhOgAwAcffIAxY8age/fuMBqNGDBgABYsWOB4vKqqCgcPHkRZWRkAYNeuXY4RWY0bN+a1dfToUdSvXx+RkZFYtGgRnnrqKTDG0LhxY8ybNw+jRo2S78S8oLfoDsmONLID8C+koi0MquCFhARHOCQ38kNyoz00Izo1a9b0OjkgANSvX583LLxLly5+h4lnZ2fzJgpUI3JHd7RapBzusuNo38PFwNvroqZh6yQ3gSH3cHAlILEhxEIzohPOyBnd0XLdDsmOl+Op5ILhihxyA+hHcMIhekNyQ0gBiY6GkCu6o+W6HS3JDiCv8KgBkpvA0Xv0Rg1yo1exMVoBY4jTAtM8OoTs6Cm6I6XsAOE5/FytkOAEBsmNDH3QqdwQ7pDoaBQ5oztalB0gfOfaUQskN4Ejl+CEa90NyU14QqIjIqYKBsg4twnJjn9IduRFT3IDUPRGDEhu+Bir7Ep3Iewg0REZUwWDzSz9auGO48mUypK6bodkR5uyI5fYOI6nk+gNyY2Ex1aZ2AB8uSHNkR8SHQngvmhyC4/WoztaG34ulewA6hYekpvg0XNqiuTmOhS1URckOhKiRHRH67IDaGtElhSyA6gvuiO33AD6ERw9R29Ibq5CYqNuSHQkRu7ojpypLJKda21KKDuAMsKjhNgA+pEbQL/RG5Kbq5DcaAcSHZnQY3SHZMepTYlkB+BLhyRLSCgkNY7jU2FxwJDcyI8WxcZUCZhCbUTh3wcxINGRESWiO1Sk7BmtyY7jGB5+dITKj9JC44ye5AYgwRH9uCQ3hIiQ6CiAnNEdPaSytCY7gPTCwzumigTGF3LJDUDRm1AIZ7khsdEnIU4OTQSL3F9qOX68pPxBluqiItUFUc6LutoxWuWrveFuUmKqlEdyjFVMNskxVV2/yYmpgjluSmGssjtuhLz88ccf6NevHxITE2GxWHD77bdjy5Ytoh+HREdB5P6Ck+x4hmRHfDi5kVNwpISTG6kFh5MbOQSH5IbkRmnuuusuWK1WbN68GTt37kTr1q1x1113oaCgQNTjkOioAJId4UgpO1JcLMNJdpSQGykFR49yA4Sn3DiLDcmNOjh37hwOHTqEiRMnolWrVmjSpAlmzZqFsrIy/P7776Iei2p0VIKchcpy1O3QxIJObcpQpKwUequ7AfRZe6NE3Y3SNTdqFBpTBQOzKl+LFAzFxcW8+2azGWazOej2brjhBjRt2hT/+c9/cMstt8BsNuOtt95CrVq10K5du1C7y0OnP7/aRe5CZa3KDkBFykqgRISKCouDg+RGeZR/PRiMhtD6wK59ZtPS0njbp0yZgqlTpwbdrsFgwMaNG9G/f39Ur14dRqMRtWrVwtq1a1GjRo1QuuwGpa5UiJ5SWVKH5KluR3rkTEk5jkmFxUEjd2pKTWkpNaD06yEVJ0+eRFFRkeM2adIkj/tNnDgRBoPB5+3AgQNgjOGJJ55ArVq18L///Q87duxA//790adPH5w+fVrUvmv870v9Incqi4afuyNFZAdQf3RHKRmj1FTwKFFzoyRqkRpA+ddCLiwWCywWi9/9JkyYgGHDhvncp2HDhti8eTO++eYbXLx40dHuG2+8gQ0bNmDFihWYOHGiGN0GQKKjeuRKZZHseEYq2QHUJTx6lxtAf4ITbqkpkhttkJSUhKSkJL/7lZWVAQCMRn5iyWg0wm4X971WwU8s4Q85ZQfQbt2OFmUHkF941JA+o+hN8IRT9EYtckNiIz4dO3ZEjRo1MHToUOTm5iImJgbvvPMOjh49it69e4t6LKrR0Qh6q9uRCq3V7PCOYYXo9TCubSopOXLV3QD6GxYu95w3StWZqGkYuF5rbdRCYmIi1q5di5KSEnTr1g3t27fHDz/8gK+++gqtW7cW9VgU0dEQeqrbociOgOOpIPISKpSaCp5widwoLTQcJDTy0759e6xbt07y45DoaBC91O2Q7OgTkpvQkHvElBKQ3BByQqKjUUh2/EOyIx9yyg2gP8Gh6I18hJPcGK2AKcSfV6aDyDKJjogYq+yAjBdAPckOAEmER0rZAcJbeEhuQoPkRh7CSWwIz5DoiIyxyg57pHw13nLV7Wh5RJZUsgOEX3RHr3ID6FNwSG7Ug7Hy6mtitCof1Qo3SHQkgPuSyy08eonukOyoC7nlBqDoTcjHU+BCT3LjDic3hLKQ6EiIEtEdkh3vSL0YKKAf4SG5EQe9R29IbviQ2KgTEh2JkTu6Q7LjH4ruuKOE2ACUmgr5WCQ3ikJiow1IdGRCzuiOnHU7JDvuaCG6o5TYcOgteqP31BTJzXVIbrQHiY6M6DGVRbLjHTUJj9JiA1D0JuRjhZHckNgQYkKiIzMkO4GjZdkB+JIhh/SoQWo4SG5CPFYYpaZIbsTHVMFgsof2ujIZa9qkgkRHAfRYtyP18HOtyw6HJwkJVn7UJDTO6FFuAH0LDsmNfuSGcIdER0HkrtvRepGyXmTHFbUKSyDIKTcARW/EINzlhsQmfNDM6uUXLlzA4MGDYbFYkJCQgJEjR6KkpMTnc7p06QKDwcC7Pfroo7x9Tpw4gd69eyM2Nha1atXCM888A6tVvjmv5fyxkWslXikvDlpc+VyvcCuEy1lYLPdq4ZIfR+YVspVaGVwtK4EbK+2OGxE+aCaiM3jwYJw+fRobNmxAVVUVhg8fjtGjR+PDDz/0+bxRo0Zh+vTpjvuxsbGO/9tsNvTu3RspKSn48ccfcfr0aQwZMgSRkZF45ZVXJDsXV/SaypIysgNoa8kIvaDXyA1A0RspUFpsAIrcEBoRnf3792Pt2rX4+eef0b59ewDAwoUL0atXL8ydOxepqalenxsbG4uUlBSPj61fvx779u3Dxo0bkZycjDZt2mDGjBl47rnnMHXqVERFyXvF01sqS6tFylJOLKhFlIh0UWoqdMJVbtQqNsYq29V/rTaFexJ+aCJ1lZeXh4SEBIfkAEBmZiaMRiO2b9/u87kffPABEhMT0bJlS0yaNAllZWW8dtPT05GcnOzYlpWVheLiYuzdu9drmxUVFSguLubdxELuVJbkx5D4QkKpLGmQOy0F6DM1BciXMgbCNzWlxpSUscrGuxHKoYmITkFBAWrVqsXbFhERgZo1a6KgoMDr8x588EHUq1cPqamp+PXXX/Hcc8/h4MGDWLlypaNdZ8kB4Ljvq92ZM2di2rRpwZ6OXyiyExh6LVKWGz1HbgCK3ogNRW7cUZvQGKvsMCK010npySLFQFHRmThxImbPnu1zn/379wfd/ujRox3/T09PR+3atdG9e3ccOXIEjRo1CrrdSZMmIScnx3G/uLgYaWlpQbfnCTnrdkh2fKPXVJaSESs9Cg7JjfSoTW7UJjaEZxQVnQkTJmDYsGE+92nYsCFSUlJw5swZ3nar1YoLFy54rb/xREZGBgDg8OHDaNSoEVJSUrBjxw7ePoWFhQDgs12z2Qyz2Sz4uKEgV3RHjmUjtCw7gD6Eh+RGgmPJePEnuVEWEhttoqjoJCUlISkpye9+HTt2xKVLl7Bz5060a9cOALB582bY7XaHvAghPz8fAFC7dm1Huy+//DLOnDnjSI1t2LABFosFLVq0CPBspENPqSwtTyzIoSXhUbrOiORGHJSouVESkhtCTDRRjNy8eXNkZ2dj1KhR2LFjB7Zt24YxY8Zg4MCBjhFXp06dQrNmzRwRmiNHjmDGjBnYuXMnjh07htWrV2PIkCG488470apVKwBAjx490KJFCzz88MPYs2cP1q1bhxdffBFPPPGEbBEboVCRsnBkG7Ejc7GuEJyLiJXqm5xFxQAVFosJFRRf6wsVEesKTRQjA1dHT40ZMwbdu3eH0WjEgAEDsGDBAsfjVVVVOHjwoGNUVVRUFDZu3Ij58+ejtLQUaWlpGDBgAF588UXHc0wmE7755hs89thj6NixI6pVq4ahQ4fy5t1RE3qK7ADanUXZFWehkDPKozbJouhN6IRbakoNUgNQ1EbvaEZ0atas6XNywPr164Ox61/YtLQ0/Pe///Xbbr169fDdd9+J0kc5oCJl4cgpOxye5CMU+VGbzLgip9wAVFgsFiQ3JDfhhGZEh+AjZ5EyyU5oqF1WAkWvcgPoO3oT7nJDYhO+kOhoGJIdYUi5ZES4QHIjDuGUmiK5UR5TJYPJHtr7z6zKL+MRKiQ6IsJ9qORYJZyDhp8LRw3RHS0ht9wAJDhiQXKjPozl15aAsKmzf3qGREcC5IiAOKOnImWSHWUhuREPkhsZ+6BCueHEhlAeTQwv1yJKFDPK9cMq9bnJsT6WEhd0tSL3cHDg+pBwOSRH7nWY5P4uKjEkXA1DwdU4BNxYbnPcCP+8/PLL6NSpE2JjY5GQkOD2+J49ezBo0CCkpaUhJiYGzZs3x7///e+Aj0MRHQmRI93jil7qdqSO7ADhG91RUvIoeiMOSkRvKHLjDglNaFRWVuK+++5Dx44dsWTJErfHd+7ciVq1auH9999HWloafvzxR4wePRomkwljxowRfBwSHRnQaypLL7ID6L9QmeRGfMJBbgBlBYfERt9wi2MvX77c4+MjRozg3W/YsCHy8vKwcuVKEh01QrITZPsyyA6gv+iO0qk5OeUG0K/gkNwoD8mNuigqKkLNmjUDeg6JjoyQ7ATZvoyyA2g3ukNyIx3hEL0hubkOyY07xcXFvPtyLm7N8eOPP+KTTz7Bt99+G9DzqBhZZuQuHNRTgbJcF1IlinMDxbmPSvdVzvcGUGa9KTlQorBYyaJiNRUTOxcR60lynN/fUG7A1dUG4uPjHbeZM2d6PObEiRNhMBh83g4cOBDwufz+++/o168fpkyZgh49egT0XIroKISc0R25lo3Q+sSCnnAWCKUiPWoULoreiIcSI6aUQg1Sw6EnoZGDkydPwmKxOO57i+ZMmDABw4YN89lWw4YNAzr2vn370L17d4wePZq3XqVQSHQURI+pLD3KDoercIgtPmoUGmdIbsQjnFJTJDf6wGKx8ETHG0lJSUhKShLtuHv37kW3bt0wdOhQvPzyy0G1QaKjMCQ7QR5DIdlxRu1iIgZyyw2gX8EJJ7kB1CE4JDbq5sSJE7hw4QJOnDgBm82G/Px8AEDjxo0RFxeH33//Hd26dUNWVhZycnJQUFAAADCZTAHJFImOCiDZCfIYKpAdPUJyIy6UmpK5DyqVG2Ol9eq/tASEg9zcXKxYscJxv23btgCALVu2oEuXLvj8889x9uxZvP/++3j//fcd+9WrVw/Hjh0TfBwqRlYJeixSluOclLgo6xE5Zyp2HJNmLBaNcC8qVmshsbHS6rgR7ixfvhyMMbdbly5dAABTp071+HggkgNQREd1yF2krJfh5wBFdwJFKUnUm9BzhFNqiiI3niGhUSckOiqEZCfIY1Aqyy/hIDeAvgWH5EZdqFlujFU2GFlor5nRqr7XPFBIdFQKyU6Qx6DojhskN+JDciNjH1QmN2oWG8IzJDoqhubaCeE4YRzdUbpuSa+pKSA8BEdpuVGb2AAkN1qHREflyL0Cul5GZAHhFd0JJ7kBKHojyXEVLiZWEyQ2+oJERyPoLZUl5/noUXiUFhtA33IDyByZIrlRHJIb/UKioyFIdkI8noaFRw1iAyg0soiiN+Iek+QGAIlNOEGiozFIdkQ4ppM0qFV61CI2HHqO3pDcyHB8lQgOyU14QqKjQUh2RDy2wtKjNqFxRs9yA5DgSH5skhtCJZDoaBQ9yg4gX9G1xz54kA6x5EfNQuMMpabEheRGGdQsNwYZ+2assIU8D44elqwg0dEwepMdQNnojie0IiihoHe5AfRfWBzuckNiQ/iCREdEjJV2GO122KPkW0JMzkhIuMqOHlFCbgCK3oh6PJIbpbvgERIb9UGiIwHGSnllB5BPDkh2tEs4yA1A0RvJjkty4xWSG3VDoiMRJDuho4a6Ha1DciMN4RK9IbnxDImNtiDRkRCSHXGg6E5gKCU3AKWmRD0eyY2qILnRLiQ6EkOyIw4U3fFNuMgNQIIj2XEVFhy1yQ2JjX4g0ZEB7odS7iJlvckOQNEdDiXFBiC5Ef14JDeqgORGn5DoyIjc0R05ZQeQfuVzjnCN7igtN4C+BSdcCotJbq6jd7ExVtpgNIV2jjSPDhEwepUdQJnoDqBf4VGD2AD6lhsgPKI3JDfX0bvcEO6Q6CgAyY646EV41CI2gPxyA+g7ehOOqSmSG0ItyHtFCoELFy5g8ODBsFgsSEhIwMiRI1FSUuJ1/2PHjsFgMHi8ffbZZ479PD3+8ccfS34+cv/QynoRUeAiCVw9R+6mdpz7qpY+G6vsjptcyH3+xkq7rN89Y5VNdskxltscN7kxVlodN6UxVFodNyK80UxEZ/DgwTh9+jQ2bNiAqqoqDB8+HKNHj8aHH37ocf+0tDScPn2at+3tt9/GnDlz0LNnT972ZcuWITs723E/ISFB9P57QonIDqCvWZS94XzhVDrSowaJ8YbeIzdAeERvKHKj8qhNpdNaMvYwWFdGZWhCdPbv34+1a9fi559/Rvv27QEACxcuRK9evTB37lykpqa6PcdkMiElJYW37csvv8T999+PuLg43vaEhAS3feWCRmRJj6cLqxTnr2ahcYUER4LjhZHgkNwIoJKERi0ofxUSQF5eHhISEhySAwCZmZkwGo3Yvn27oDZ27tyJ/Px8jBw50u2xJ554AomJiejQoQOWLl0KxhRY5FCnqSyl0lj+8JQ6CvWmdsIpNSXX94lLTckpOWpITSmJalNSlVX8G6EaNBHRKSgoQK1atXjbIiIiULNmTRQUFAhqY8mSJWjevDk6derE2z59+nR069YNsbGxWL9+PR5//HGUlJRg7NixXtuqqKhARUWF435xcXEAZ+MdvRYpyz38nLiOkvVSciJ33Y3chHtqSnVSw0FCowkUFZ2JEydi9uzZPvfZv39/yMe5cuUKPvzwQ0yePNntMedtbdu2RWlpKebMmeNTdGbOnIlp06aF3C9P6FV2APWksvQOyY1ExwsjwSG58YLGxMZQaYXBZAqtDZsK34cAUVR0JkyYgGHDhvncp2HDhkhJScGZM2d4261WKy5cuCCotubzzz9HWVkZhgwZ4nffjIwMzJgxAxUVFTCbzR73mTRpEnJychz3i4uLkZaW5rdtoZDsEIFCciPR8UhuZIXkhpACRUUnKSkJSUlJfvfr2LEjLl26hJ07d6Jdu3YAgM2bN8NutyMjI8Pv85csWYK+ffsKOlZ+fj5q1KjhVXIAwGw2+3xcDPQuOwClskJFyfonEhyRj0dyox5IbHSHJq40zZs3R3Z2NkaNGoUdO3Zg27ZtGDNmDAYOHOgYcXXq1Ck0a9YMO3bs4D338OHD+P777/HII4+4tfv111/j3Xffxe+//47Dhw/jzTffxCuvvIInn3xSlvPyh14LlDnUWqisVpyLiZUaNUWFxSIfM0yLilVXUExFxIrw8ssvo1OnToiNjfU5rcvy5cvRqlUrREdHo1atWnjiiScCOo4mipEB4IMPPsCYMWPQvXt3GI1GDBgwAAsWLHA8XlVVhYMHD6KsrIz3vKVLl6JOnTro0aOHW5uRkZFYtGgRnnrqKTDG0LhxY8ybNw+jRo2S/HyEoufIDkDRHX8oLYNKjCaj6I1Ex1XBaClVQVKjOJWVlbjvvvvQsWNHLFmyxOM+8+bNw7/+9S/MmTMHGRkZKC0txbFjxwI6joEpMZZaZxQXFyM+Ph533DkFERHRkh1HTuFRapI9Eh7l5Qag1JQkx1QocqMkqpIblYiN1V6BjUcXoqioCBaLRZJjcNekzJtyEGEKrczCaqvAxj/mSdrf5cuXY/z48bh06RJv+8WLF3HjjTfi66+/Rvfu3YNuXzMRHULe6I7ckR2OcIzwqEFsAIreSHI8khtlUYnc6AHXaVTkqFXdsGED7HY7Tp06hebNm+Py5cvo1KkT/vWvfwU0ACh8riY6Qc4Lg5KT4ClVhyIHStfaOKPEhId6r71RakI/JetuVFVzo/Z6m/JK+Y5VVeU+kWGgt6qrr2NaWhri4+Mdt5kzZ0re/T///BN2ux2vvPIK5s+fj88//xwXLlzAP//5T1RWCn8dKaKjQeSO7ADKpbL0EOFRWmY8QdEbCY4XZtEbVUgNh1qlBgDKK/zvowFOnjzJS115i+YInR+vWbNmfo9pt9tRVVWFBQsWOOpsP/roI6SkpGDLli3IysoS1HcSHY2i9yJlV5xlQc3So0ap4SC5keB4YSY3gIoER61yoxOxccVisQiq0RE6P54QateuDQBo0aKFY1tSUhISExNx4sQJQW0AJDqaJtxkh8NVJpQQHzULjTNKpR9JcCQ4JkVvSG40gND58YTwj3/8AwBw8OBB1KlTBwBw4cIFnDt3DvXq1RPcDomOiBirbLK/ouEqO874ko5gJUgrIuMJkhuJjkdyIz8kNrrmxIkTuHDhAk6cOAGbzYb8/HwAQOPGjREXF4ebbroJ/fr1w7hx4/D222/DYrFg0qRJaNasGbp27Sr4OCQ6ImOsssEeGdraIgEfk2THK1oWlkAIF7kBSHCkhOTGByQ3opObm4sVK1Y47rdt2xYAsGXLFnTp0gUA8J///AdPPfUUevfuDaPRiM6dO2Pt2rWIjIwUfBwSHQkIF9kBlCtSJhQeFUfRG/GPSXKjLkhsJGf58uVYvny5z30sFguWLFnidUJBIZDoSEQ4yA6greiOHggnuQH0LzhhnZoiuSFkgkRHQkh2CDFQUm4Ait5Icsxwjd6Q3MhLeWXos+XZZZz3RyJIdCQmnGQHoFSWWISb3AD6F5ywlRtAXYKjZ7EhPEKiIwPhIjsARXdCgeRGhuNR9EY+SG4IlUCiIxNKyQ4g72KgAEV3AkFpuQFIcCQ5HsmN8pDcENcg0ZER7sc9nKI7AAmPM2oQG4DkRrJjKiAaJDdOqF1uKip1UfOiNUh0FCCcUllAeAuPWsQGUEZuAP0LTlhGb0huhFFBUqMGSHQUItxkBwgf4VGT3AAUvZHkeCQ3ykJyQwQAiY6ChKPsAHwR0Lr0qE1qOCh6I9Hxwi01RXLjHxIb1UOiozDhKjscWpMetYoNQHIj2fEoeqMMahUbQDtyU0Hz6AAkOqog3GWHQ03So2ahcSUcUlNAeAgOyY1K5UYrYkN4hERHJYTT8HMheBMNMQVISzLjCkVvJDoeyY38kNwQEkOioyKUkB1AndEdb2hZTkIlXOQGIMGRFDXIDaBOwSG50SUkOiqDZIdwRim5ASh6IwUUvVGZ3JDYhAUkOiqEZCe8CSe5AUhwJIXkxh2Sm7CDREelKCk7gDrrdvSMknIDUPRGCkhuVATJTVhDoqNilJIdgKI7chBucgOQ4EiK0oJDcqM6WGUFmCG0ukbGtP86kuioHKVlB6DojpgoLTeA/qM3gPyCQ3KjAjQgNqyiQhfioDVIdDSAUouBOo5PwhM0ahAbgKI3UqGI4JDcXEflcsMqVPRahTEkOhpCyegOQOksoZDcyHxMit5ID8mNYEhu1AeJjsZQg+wAFN1xRi1iwxEOgkPRG5lQi+CoWG5IbNQPiY4GUVp2ABIekptrx9W54FD0RkFIbgiRINHRKGqQHYB/wdez9KhNbACSGykJu+gNyY1PSGy0DYmOhlGL7HDoJcqjRqlxhgRHGsIuekNy4xOSG/1AoqNx1CY7gLsoqFl81C41HCQ30kHRGwUguZEF+5Vy2A2hfYftTAUTUIYIiY4OUHr4uT/UIj5akRoOpeQG0L/gkNwogArlRm9iQ3iGREdEjBU2RV9RNUZ3POFLOIKRIK0JjC/CSW4AEhxJIbnxCMlN+KHenIILL7/8Mjp16oTY2FgkJCQIeg5jDLm5uahduzZiYmKQmZmJQ4cO8fa5cOECBg8eDIvFgoSEBIwcORIlJSVB91OJiwXv+ApeKMXAWGkP+KYHjFU2RdNTcn5ujZVWx00ODJVWx002Kquu3+SmvEJ5yamoVJXksIoKx41QB8eOHcPIkSPRoEEDxMTEoFGjRpgyZQoqK/mfm19//RV33HEHoqOjkZaWhldffTXgY2lGdCorK3HffffhscceE/ycV199FQsWLMDixYuxfft2VKtWDVlZWSgvL3fsM3jwYOzduxcbNmzAN998g++//x6jR48Oqa9yXzjcjq9x2QkXOLlRat4bpQRHLmSXG0B5uVFScDi5UYngkNyomwMHDsBut+Ott97C3r178dprr2Hx4sV4/vnnHfsUFxejR48eqFevHnbu3Ik5c+Zg6tSpePvttwM6loExFtqKXzKzfPlyjB8/HpcuXfK5H2MMqampmDBhAp5++mkAQFFREZKTk7F8+XIMHDgQ+/fvR4sWLfDzzz+jffv2AIC1a9eiV69e+Ouvv5CamiqoT8XFxYiPj0fXWyYhwhTNe8werWwqSQuprHBCaQnVe+0NoEB6Kpxrb1QiNRxakJrKsmJssa1EUVERLBaLJMdwXJNM9yDCEBlSW1ZWJXl/OebMmYM333wTf/75JwDgzTffxAsvvICCggJERUUBACZOnIhVq1bhwIEDgtvVTEQnUI4ePYqCggJkZmY6tsXHxyMjIwN5eXkAgLy8PCQkJDgkBwAyMzNhNBqxfft2UfpBqSxCycgNEF7RG0XSU3KjdPRGRZEb56iNWiXHfuUK70Z4p6ioCDVr1nTcz8vLw5133umQHADIysrCwYMHcfHiRcHt6rYYuaCgAACQnJzM256cnOx4rKCgALVq1eI9HhERgZo1azr28URFRQUqnL5URUVFAACrzcsXrRSwmxWMrFz77afojnw4S41SVUTGCpvsxzdWyntM2aM3VUoVFissFSqQGg5WqU6hccZ+pdzrY9Zrw7XlSKZYUQWEeBgrrva3uLiYt91sNsNsNofWuBOHDx/GwoULMXfuXMe2goICNGjQgLcfd00vKChAjRo1BLWtqOhMnDgRs2fP9rnP/v370axZM5l6JIyZM2di2rRpbtv/t2eeAr0hCIIgtMb58+cRHx8vSdtRUVFISUnB/wq+FqW9uLg4pKWl8bZNmTIFU6dOdds3mOv6qVOnkJ2djfvuuw+jRo0Spc/OKCo6EyZMwLBhw3zu07Bhw6DaTklJAQAUFhaidu3aju2FhYVo06aNY58zZ87wnme1WnHhwgXH8z0xadIk5OTkOO5funQJ9erVw4kTJyT74EpNcXEx0tLScPLkScnzsFKih/PQwzkAdB5qQg/nAOjjPIqKilC3bl1eikZsoqOjcfToUbcRTMHCGIPBYOBt8xbNCfS6/vfff6Nr167o1KmTW5FxSkoKCgsLedu4+76u0a4oKjpJSUlISkqSpO0GDRogJSUFmzZtcohNcXExtm/f7hi51bFjR1y6dAk7d+5Eu3btAACbN2+G3W5HRkaG17a9hezi4+M1++XjsFgsmj8HQB/noYdzAOg81IQezgHQx3kYjdKWyEZHRyM6Otr/jiITyHX91KlT6Nq1K9q1a4dly5a5vSYdO3bECy+8gKqqKkRGXi2q3rBhA5o2bSo4bQVoqBj5xIkTyM/Px4kTJ2Cz2ZCfn4/8/HzenDfNmjXDl19+CQAwGAwYP348XnrpJaxevRq//fYbhgwZgtTUVPTv3x8A0Lx5c2RnZ2PUqFHYsWMHtm3bhjFjxmDgwIGCR1wRBEEQBBEYp06dQpcuXVC3bl3MnTsXZ8+eRUFBAa8+9sEHH0RUVBRGjhyJvXv34pNPPsG///1vXkZFCJopRs7NzcWKFSsc99u2bQsA2LJlC7p06QIAOHjwoKMwGACeffZZlJaWYvTo0bh06RJuv/12rF27lme5H3zwAcaMGYPu3bvDaDRiwIABWLBggTwnRRAEQRBhyIYNG3D48GEcPnwYderU4T3GFWrHx8dj/fr1eOKJJ9CuXTskJiYiNzc38LnuGBEy5eXlbMqUKay8vFzprgSNHs6BMX2chx7OgTE6DzWhh3NgTB/noYdz0BqamzCQIAiCIAhCKJqp0SEIgiAIgggUEh2CIAiCIHQLiQ5BEARBELqFRIcgCIIgCN1CoiOAl19+GZ06dUJsbCwSEhIEPYcxhtzcXNSuXRsxMTHIzMzEoUOHePtcuHABgwcPhsViQUJCAkaOHMmbF0hsAj3esWPHYDAYPN4+++wzx36eHv/4449VcQ4A0KVLF7f+Pfroo7x9Tpw4gd69eyM2Nha1atXCM888A6tVunWUAj2PCxcu4Mknn0TTpk0RExODunXrYuzYsbzpFADp34tFixahfv36iI6ORkZGBnbs2OFz/88++wzNmjVDdHQ00tPT8d133/EeF/I9EZtAzuGdd97BHXfcgRo1aqBGjRrIzMx023/YsGFur3l2drak5wAEdh7Lly9366PrZHJqfy88fY8NBgN69+7t2EeJ9+L7779Hnz59kJqaCoPBgFWrVvl9ztatW3HLLbfAbDajcePGWL58uds+gX7XCB8oOuZLI+Tm5rJ58+axnJwcFh8fL+g5s2bNYvHx8WzVqlVsz549rG/fvqxBgwbsypUrjn2ys7NZ69at2U8//cT+97//scaNG7NBgwZJdBaBH89qtbLTp0/zbtOmTWNxcXHs8uXLjv0AsGXLlvH2cz5PJc+BMcY6d+7MRo0axetfUVER7zxbtmzJMjMz2e7du9l3333HEhMT2aRJkyQ5h2DO47fffmP33HMPW716NTt8+DDbtGkTa9KkCRswYABvPynfi48//phFRUWxpUuXsr1797JRo0axhIQEVlhY6HH/bdu2MZPJxF599VW2b98+9uKLL7LIyEj222+/OfYR8j0Rk0DP4cEHH2SLFi1iu3fvZvv372fDhg1j8fHx7K+//nLsM3ToUJadnc17zS9cuCBJ/4M9j2XLljGLxcLrY0FBAW8ftb8X58+f5/X/999/ZyaTiS1btsyxjxLvxXfffcdeeOEFtnLlSgaAffnllz73//PPP1lsbCzLyclh+/btYwsXLmQmk4mtXbvWsU+grw3hGxKdAFi2bJkg0bHb7SwlJYXNmTPHse3SpUvMbDazjz76iDHG2L59+xgA9vPPPzv2WbNmDTMYDOzUqVOi912s47Vp04aNGDGCt03Il1sMgj2Hzp07s3Hjxnl9/LvvvmNGo5H3w//mm28yi8XCKioqROm7M2K9F59++imLiopiVVVVjm1SvhcdOnRgTzzxhOO+zWZjqampbObMmR73v//++1nv3r152zIyMtj//d//McaEfU/EJtBzcMVqtbLq1auzFStWOLYNHTqU9evXT+yu+iTQ8/D326XF9+K1115j1atXZyUlJY5tSrwXzgj5/j377LPs5ptv5m174IEHWFZWluN+qK8NwYdSVxJw9OhRFBQUIDMz07EtPj4eGRkZyMvLAwDk5eUhISEB7du3d+yTmZkJo9GI7du3i94nMY63c+dO5OfnY+TIkW6PPfHEE0hMTESHDh2wdOlSx8yWYhLKOXzwwQdITExEy5YtMWnSJJSVlfHaTU9PR3JysmNbVlYWiouLsXfvXlWdhzNFRUWwWCyIiOBPcC7Fe1FZWYmdO3fyPtNGoxGZmZmOz7QreXl5vP2Bq68rt7+Q74mYBHMOrpSVlaGqqsptQcatW7eiVq1aaNq0KR577DGcP39e1L47E+x5lJSUoF69ekhLS0O/fv14n20tvhdLlizBwIEDUa1aNd52Od+LYPD3vRDjtSH4aGYJCC3BrdXhfOHk7nOPFRQUoFatWrzHIyIiULNmTd5aH2L2KdTjLVmyBM2bN0enTp1426dPn45u3bohNjYW69evx+OPP46SkhKMHTtWtP4DwZ/Dgw8+iHr16iE1NRW//vornnvuORw8eBArV650tOvpveIeExsx3otz585hxowZblOhS/VenDt3DjabzePrdODAAY/P8fa6On8HuG3e9hGTYM7Bleeeew6pqam8i1B2djbuueceNGjQAEeOHMHzzz+Pnj17Ii8vDyaTSdRzAII7j6ZNm2Lp0qVo1aoVioqKMHfuXHTq1Al79+5FnTp1NPde7NixA7///juWLFnC2y73exEM3r4XxcXFuHLlCi5evBjy55TgE7aiM3HiRMyePdvnPvv370ezZs1k6lFwCD2PULly5Qo+/PBDTJ482e0x521t27ZFaWkp5syZI/jiKvU5OMtAeno6ateuje7du+PIkSNo1KhR0O26Itd7UVxcjN69e6NFixaYOnUq77FQ3wvCO7NmzcLHH3+MrVu38gp5Bw4c6Ph/eno6WrVqhUaNGmHr1q3o3r27El11o2PHjujYsaPjfqdOndC8eXO89dZbmDFjhoI9C44lS5YgPT0dHTp04G3XwntByE/Yis6ECRMwbNgwn/s0bNgwqLZTUlIAAIWFhahdu7Zje2FhIdq0aePY58yZM7znWa1WXLhwwfF8IQg9j1CP9/nnn6OsrAxDhgzxu29GRgZmzJiBiooKmM1mv/vLdQ7O/QOAw4cPo1GjRkhJSXEb0VBYWAgAqnsvLl++jOzsbFSvXh1ffvklIiMjfe4f6HvhjcTERJhMJsfrwlFYWOi1zykpKT73F/I9EZNgzoFj7ty5mDVrFjZu3IhWrVr53Ldhw4ZITEzE4cOHJbm4hnIeHJGRkWjbti0OHz4MQFvvRWlpKT7++GNMnz7d73Gkfi+Cwdv3wmKxICYmBiaTKeT3l3BB6SIhLRFoMfLcuXMd24qKijwWI//yyy+OfdatWyd5MXKwx+vcubPbCB9vvPTSS6xGjRpB99UbYr1mP/zwAwPA9uzZwxi7XozsPKLhrbfeYhaLRZKF94I9j6KiInbbbbexzp07s9LSUkHHEvO96NChAxszZozjvs1mYzfeeKPPYuS77rqLt61jx45uxci+vidiE+g5MMbY7NmzmcViYXl5eYKOcfLkSWYwGNhXX30Vcn+9Ecx5OGO1WlnTpk3ZU089xRjTznvB2NXfYbPZzM6dO+f3GHK8F85AYDFyy5YtedsGDRrkVowcyvtL8CHREcDx48fZ7t27HUOrd+/ezXbv3s0bYt20aVO2cuVKx/1Zs2axhIQE9tVXX7Fff/2V9evXz+Pw8rZt27Lt27ezH374gTVp0kTy4eW+jvfXX3+xpk2bsu3bt/Oed+jQIWYwGNiaNWvc2ly9ejV755132G+//cYOHTrE3njjDRYbG8tyc3NVcQ6HDx9m06dPZ7/88gs7evQo++qrr1jDhg3ZnXfe6XgON7y8R48eLD8/n61du5YlJSVJPrw8kPMoKipiGRkZLD09nR0+fJg3fNZqtTLGpH8vPv74Y2Y2m9ny5cvZvn372OjRo1lCQoJjtNrDDz/MJk6c6Nh/27ZtLCIigs2dO5ft37+fTZkyxePwcn/fEzEJ9BxmzZrFoqKi2Oeff857zbnv/uXLl9nTTz/N8vLy2NGjR9nGjRvZLbfcwpo0aSLp6tSBnse0adPYunXr2JEjR9jOnTvZwIEDWXR0NNu7dy/vXNX8XnDcfvvt7IEHHnDbrtR7cfnyZcc1AQCbN28e2717Nzt+/DhjjLGJEyeyhx9+2LE/N7z8mWeeYfv372eLFi3yOLzc12tDBAaJjgCGDh3KALjdtmzZ4tgH1+Yv4bDb7Wzy5MksOTmZmc1m1r17d3bw4EFeu+fPn2eDBg1icXFxzGKxsOHDh/PkSWz8He/o0aNu58UYY5MmTWJpaWnMZrO5tblmzRrWpk0bFhcXx6pVq8Zat27NFi9e7HFfJc7hxIkT7M4772Q1a9ZkZrOZNW7cmD3zzDO8eXQYY+zYsWOsZ8+eLCYmhiUmJrIJEybwhm0rfR5btmzx+BkEwI4ePcoYk+e9WLhwIatbty6LiopiHTp0YD/99JPjsc6dO7OhQ4fy9v/000/ZTTfdxKKiotjNN9/Mvv32W97jQr4nYhPIOdSrV8/jaz5lyhTGGGNlZWWsR48eLCkpiUVGRrJ69eqxUaNGyXJBCuQ8xo8f79g3OTmZ9erVi+3atYvXntrfC8YYO3DgAAPA1q9f79aWUu+Ft+8m1/ehQ4eyzp07uz2nTZs2LCoqijVs2JB37eDw9doQgWFgTIJxwARBEARBECqA5tEhCIIgCEK3kOgQBEEQBKFbSHQIgiAIgtAtJDoEQRAEQegWEh2CIAiCIHQLiQ5BEARBELqFRIcgCIIgCN1CokMQBEEQhG4h0SEIQvWUl5dj2LBhSE9PR0REBPr37690lwiC0AgkOgRBhAxjDFarVbL2bTYbYmJiMHbsWGRmZkp2HIIg9AeJDkGEIV26dMGYMWMwZswYxMfHIzExEZMnTwa3Isx7772H9u3bo3r16khJScGDDz6IM2fOOJ6/detWGAwGrFmzBu3atYPZbMYPP/yAI0eOoF+/fkhOTkZcXBxuvfVWbNy4kXfs+vXr46WXXsKQIUMQFxeHevXqYfXq1Th79iz69euHuLg4tGrVCr/88ovjOdWqVcObb76JUaNGISUlRZ4XiSAIXUCiQxBhyooVKxAREYEdO3bg3//+N+bNm4d3330XAFBVVYUZM2Zgz549WLVqFY4dO4Zhw4a5tTFx4kTMmjUL+/fvR6tWrVBSUoJevXph06ZN2L17N7Kzs9GnTx+cOHGC97zXXnsN//jHP7B792707t0bDz/8MIYMGYKHHnoIu3btQqNGjTBkyBDQUnwEQYQKLepJEGFIly5dcObMGezduxcGgwHAVWlZvXo19u3b57b/L7/8gltvvRWXL19GXFwctm7diq5du2LVqlXo16+fz2O1bNkSjz76KMaMGQPgakTnjjvuwHvvvQcAKCgoQO3atTF58mRMnz4dAPDTTz+hY8eOOH36tFsEZ9iwYbh06RJWrVoV6stAEEQYQBEdgghTbrvtNofkAEDHjh1x6NAh2Gw27Ny5E3369EHdunVRvXp1dO7cGQDcIjPt27fn3S8pKcHTTz+N5s2bIyEhAXFxcdi/f7/b81q1auX4f3JyMgAgPT3dbZtzuowgCCIYSHQIguBRXl6OrKwsWCwWfPDBB/j555/x5ZdfAgAqKyt5+1arVo13/+mnn8aXX36JV155Bf/73/+Qn5+P9PR0t+dFRkY6/s/JlqdtdrtdvBMjCCIsiVC6AwRBKMP27dt593/66Sc0adIEBw4cwPnz5zFr1iykpaUBAK8w2Bfbtm3DsGHDcPfddwO4GuE5duyYqP0mCIIIBIroEESYcuLECeTk5ODgwYP46KOPsHDhQowbNw5169ZFVFQUFi5ciD///BOrV6/GjBkzBLXZpEkTrFy5Evn5+dizZw8efPBB0aIy+/btQ35+Pi5cuICioiLk5+cjPz9flLYJgtAvFNEhiDBlyJAhuHLlCjp06ACTyYRx48Zh9OjRMBgMWL58OZ5//nksWLAAt9xyC+bOnYu+ffv6bXPevHkYMWIEOnXqhMTERDz33HMoLi4Wpb+9evXC8ePHHffbtm0LADQyiyAIn9CoK4IIQ7p06YI2bdpg/vz5SneFIAhCUih1RRAEQRCEbiHRIQiCIAhCt1DqiiAIgiAI3UIRHYIgCIIgdAuJDkEQBEEQuoVEhyAIgiAI3UKiQxAEQRCEbiHRIQiCIAhCt5DoEARBEAShW0h0CIIgCILQLSQ6BEEQBEHoFhIdgiAIgiB0y/8DIys7owHXtTsAAAAASUVORK5CYII=", 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", 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Bly5dEB4eDofDgdTUVL8Nu+f5Gr7zzjs4ePAg3njjDZf5us6fP49jx46hSZMmqFevnpSuEjqGRCfAiIqKQoMGDVBZWYnk5GSP23766ado1aoVVq9eLbpQZWVluWwbEhKCW2+9FbfeeiscDgceffRRvPHGG5g2bRratGkjKZXQunVrTJ48GZMnT8ahQ4fQpUsX/Otf/8J7772H+Ph4AFW/blu1aiXsc+bMGZdfg85fiYWFhaJhzzV/XW7btg3nzp3D6tWrceONNwrrjx496lOff/vtNwwYMKDOczabzRgwYAAGDBiAhQsXYvbs2Xj22WexdevWWt+b1q1bw+FwYN++fbVe5JyvzcGDB0WvTXl5OY4ePerSdkJCAhISEvDcc8/hxx9/xHXXXYclS5bg+eefByAtDVQTb98DX2jdujX27NnjcRtf+h4fH49vv/0W58+fF0V1nClL5+vqL2rruzNdZrPZ6vwO18Y///yDzZs3Y8aMGcjMzBTWO6NF3vTDHfHx8Th48KDLen+8hrm5uaioqMB1113n8tw777yDd955B2vWrPF6PijCOFDqKsCwWCwYNmwYPvvsM7cXiTNnzoi2BcS/rnbs2OEyCde5c+dEj81msxAVKisrAwDUr18fALyaXfXChQu4dOmSaF3r1q3RoEEDob3k5GQEBwdj0aJFov65y807LwzfffedsK60tNQl2uTufMvLy/Hvf/+7zj47ueuuu3Dy5Em89dZbLs9dvHhRmMOkoKDA5XmnuDjP0R233XYbzGYzZs6c6fKr29nv5ORkhISE4NVXXxWdy9KlS1FUVITBgwcDqBpVZ7fbRW0kJCTAbDaL+lC/fn3Zs+J6+x74wrBhw/Dbb79hzZo1Ls85z9uXz92gQYNQWVmJ1157TbT+pZdegslkws033yy5r1Kore/dunVD69atsWDBApSUlLjsV/07XBvuPuuA+++Pr6/hzp07RX8jSktL8eabb6JFixYeU6614ayPO3v2rMftRowYgTVr1rgszn6tWbMGSUlJPh+f0D8U0TEoy5Ytc1sn88QTT2Du3LnYunUrkpKSMHbsWHTs2BEFBQXYvXs3vv32W+EifMstt2D16tW4/fbbMXjwYBw9ehRLlixBx44dRX9gH3roIRQUFKB///5o1qwZjh8/jkWLFqFLly5Cbr5Lly6wWCyYN28eioqKYLVa0b9/fzRp0sSlj3/88QcGDBiAu+66Cx07dkRQUBDWrFmD/Px8jBgxAkBVZOrJJ5/EnDlzcMstt2DQoEH49ddfsW7dOmEIrJOBAweiefPmGDNmDKZMmQKLxYJly5YhKioKubm5wna9e/dGw4YNMWrUKEyYMAEmkwnvvvuuT2H0Bx54AB9//DEefvhhbN26Fddddx0qKytx4MABfPzxx9iwYQO6d++OmTNn4rvvvsPgwYMRHx+P06dP49///jeaNWsmKuSsSZs2bfDss89i1qxZuOGGG3DHHXfAarXi559/RmxsLObMmYOoqChkZGRgxowZSE1NxZAhQ3Dw4EH8+9//xrXXXivUHG3ZsgXjx4/H8OHDcdVVV8Fut+Pdd98VZNhJt27d8O2332LhwoWIjY1Fy5Ytfb5gePse+MKUKVPw6aefYvjw4XjwwQfRrVs3FBQU4IsvvsCSJUvQuXNntG7dGpGRkViyZAkaNGiA+vXrIykpyW3Nyq233op+/frh2WefxbFjx9C5c2ds3LgRn3/+OSZOnFjrsPq6MJlM6NOnD7Zt2+bTfp76/vbbb+Pmm2/G1VdfjdGjR+PKK6/EyZMnsXXrVthsNnz55Zce27bZbELtW0VFBa688kps3LjRbfSyW7duAIBnn30WI0aMQHBwMG699VZBgKozdepUfPDBB7j55psxYcIENGrUCCtXrsTRo0fx2WefSZpF2dvh5e3bt0f79u3dPteyZUuK5AQyKoz0IhSktknbnMuJEycYY4zl5+ezxx57jMXFxbHg4GAWExPDBgwYwN58802hLYfDwWbPns3i4+OZ1WplXbt2ZV999ZXLsNxPP/2UDRw4UJh8sHnz5uz//u//2KlTp0R9e+utt1irVq2YxWLxONT87Nmz7LHHHmPt27dn9evXZxERESwpKYl9/PHHou0qKyvZjBkzWNOmTT1OGMgYY7t27WJJSUlC/xYuXOh2aPP27dtZz549WVhYGIuNjWVPPfUU27Bhg0t/nRMGuqO8vJzNmzePXX311cxqtbKGDRuybt26sRkzZrCioiLGGGObN29mQ4cOZbGxsSwkJITFxsaye+65h/3xxx9u26zJsmXLWNeuXYX2+/TpwzZt2iTa5rXXXmPt27dnwcHBLDo6mj3yyCOiCQP//PNP9uCDD7LWrVuz0NBQ1qhRI9avXz/27bffito5cOAAu/HGG1lYWJjHCQOdoJbhvN6+B84JA2tSc/g1Y4ydO3eOjR8/XphYslmzZmzUqFGiqRM+//xz1rFjRxYUFFTnhIHnz59nkyZNYrGxsSw4OJi1bdvW44SBNan52Tt//jwDwEaMGFHLq+X5/GrrO2NV0wPccccd7IorrmBWq5XFx8ezu+66i23evFnYxjm83N00Bn/99Re7/fbbWWRkJIuIiGDDhw9nf//9t9uh3M6JOc1ms9cTBkZGRrLQ0FDWo0ePWicMrDm1wdGjR13O09fh5TWp7b0iAgcTYxqq7iMIDrRo0QJ9+/bFihUr1O4KEeB88803uOWWW/Dbb78hISFB7e4QREBCNToEQRAKsXXrVowYMYIkhyBUhGp0CIIgFKLmbUMIgvA/FNEhCIIgCMKw6Ep0vvvuO9x6662IjY2FyWQSbpDoiW3btuGaa66B1WpFmzZt3NZtLF68GC1atEBoaCiSkpKwc+dO/p0n/MaxY8eoPocgCIIAoDPRKS0tRefOnbF48WKvtj969CgGDx6Mfv36IScnBxMnTsRDDz2EDRs2CNt89NFHSE9PR1ZWFnbv3o3OnTsjJSVFdBsEgiAIgiD0iW5HXZlMpjpnuXz66afx9ddfiybGGzFiBAoLC4U5ZpKSknDttdcKk4Q5HA7ExcXh8ccfx9SpUxU9B4IgCIIglMXQxcjZ2dkuU6SnpKQId7guLy/Hrl27kJGRITxvNpuRnJzsMvtvdcrKykQzxzrv7H3FFVdwmS6fIAiCMCaMMZw/fx6xsbGSJlD0lkuXLqG8vJxLWyEhIQgNDeXSlhoYWnTy8vIQHR0tWhcdHY3i4mJcvHgR//zzDyorK91u47w3izvmzJmDGTNmKNJngiAIwvicOHGC641zq3Pp0iW0jA9H3mn3N831lZiYGBw9elS3smNo0VGKjIwMpKenC4+LiorQvHlztHpzEiz1rNyOYw3h8yEV2rPY697IB8KC+fxaCAvi0696QRWy26gfdKnujbwg3CK/Lw2CLnLoCb9zcmKz8G3PEw3MfF4DJTnvcL0zuLcUV3p34Si1177debv745dUBktq74K99v2k4Ol76e6zWfO7U/N7UH2f6p/F6p+ViGr/b2AWHyPcLL6XXITZff8aSIi2NDDV/ff/fIkDrbudEN04ljfl5eXIO12Jw7/EwdZAXtSo+LwDbbqfQHl5OYmOFomJiUF+fr5oXX5+Pmw2G8LCwmCxWGCxWNxuExMTU2u7VqsVVqvrB9pSzwpLPX4fBDsAawg/ObHDilBOUgEAQcF80nQVsHKRlOAg+f0pRwjCg+VfyEMssptAGYJh4yA7dgQjnKPslCMYERb/CIgdDTQrO07Bka45QLkHGXFSYg9FbZfPYnsYQmp5LqTS/TMlFaEIrqXBC/bgWp+TSm3fy6rvmbiP4ZZyAJdfk6rP/+XHVZ/jqsdVn8GqS1jVZ6Tq/5GWCwCqvoA28yXh/1XbXULNMTi1CY0U0QGq/o7ZvBAef5Q52BqYZYuOETD0K9CrVy9s3rxZtG7Tpk3o1asXgKq8Y7du3UTbOBwObN68WdjGFyrtHK5uNSgr5+uil+z82rtYUdufWHXg9Uu0pEK+rJbUcpHxleJafq37SomHX/BSKKrk0y9vkBMxUQoeffLmNZT6vtX2+fP02eYdyQFqj+ZI+TFRXdari3Z1Ea6SHPfUjOwAQGQt0Ry5FLMyYSHUR1eiU1JSgpycHOTk5ACoGj6ek5Mj3P04IyMDI0eOFLZ/+OGH8eeff+Kpp57CgQMH8O9//xsff/wxJk2aJGyTnp6Ot956CytXrsT+/fvxyCOPoLS0FKNHj5bURzvJjiR4/ZFV4o+1VHjJDi/0LjtaEB5e/eDx2vGSYKXwNUpbFc25TPVoppSIpM2N2FTHk+RIi+a4p7r0FLMynDe4/Jw8eRL3338/rrjiCoSFhSEhIQG//PKLqn3SVerql19+Qb9+/YTHzjqZUaNGYcWKFTh16pQgPQDQsmVLfP3115g0aRJeeeUVNGvWDG+//TZSUlKEbe6++26cOXMGmZmZyMvLQ5cuXbB+/XqXAmVfsNstCAriW19TVh7ENY11yR7ELY11sSKES73OBXswlxQWj3ZKKkK5pLB4UGwP45LCUoKiyjC/pbGAKtFQK5XFS7S8lRxPYupJcrQSzakNd98rT5JTE2+iOTUlx100h+DPP//8g+uuuw79+vXDunXrEBUVhUOHDqFhw4aq9ku38+hoieLiYkRERKDVimdhrlajw1t2AL41OwC41uzwKk7mITs82gCkhdhd2rDweV14yQ7Peh0AfhWd6vhLeHhGknhIDlC76GhFcjx9/3wVHSkpq7pEp66UFc+ITk3On3egdfs8FBUVwWazKXIM5zXp9MF4LsXITdod96q/U6dOxfbt2/H999/LOiZvdJW60huBlsbiBY8/vEas1+GFnlNY1VE6naWVdFlNtJ6y8oQcyfEWiuYoQ3FxsWipPpecky+++ALdu3fH8OHD0aRJE3Tt2hVvvfWWCr0VQ6KjMHqQHV4YtThZK2j5AqeW7ACXhYSHlPBsqyaBkrLypQBZjZSVUgXIWuR8jfogKYuzpiguLg4RERHCMmfOHJfj/fnnn3j99dfRtm1bbNiwAY888ggmTJiAlStX+vvURWjzimkwtF6zQ/U6nuFRr1NSGcIlhcWrXqfEHso9heXveh131BSUutJb/orY8EpZ1bqfxiVHCnJHWRF8OXHihCh15W6KFYfDge7du2P27NkAgK5du2LPnj1YsmQJRo0a5be+1oQiOn5C65EdI4/E4gGlsLxDzciOO6pHaNwt/kDpuhw9oERdTk14pqzOOxyS9zUqNptNtLgTnaZNm6Jjx46idR06dBANElIDEh0/ooTs8MSosqMlYeKB1i94WpMdNeH1WgRyyqo2pIyyCqS0lRpcd911OHjwoGjdH3/8gfj4eJV6VAWJjp/hLTuBUJzMAx5/2LUU1dHqRILEZXyRHN4pK09Qyso7KKrjO5MmTcJPP/2E2bNn4/Dhw1i1ahXefPNNPPbYY6r2i0RHBQJFdrQU1eGFlmSHF4GQwvI3PCVHitTy+JzyQEspKynRHJId37j22muxZs0afPDBB+jUqRNmzZqFl19+Gffdd5+q/TLmz3cdwLtAmfeEgrzQUnEyrwJnrcBzIkGjFicbGUpZ1T37MQ+csqPk3DpG4pZbbsEtt9yidjdE0DtnIIxenMwDSmH5l0CM7PgjmqOVlJUcakoOj5SVknPmnHc43C6E9qGIjooE0rBzHtCQc2VRIqoDBFZkJ9BSVnKiOaLnOMx+7Akli5B9lR1/ytF5hwOQeTgjyBxFdFQmUIadG7FeRyvwjOooVZwcCJEdf52j0VJWtUmOJ9xJDs2ATNQGiY4GCJRh51qSHUph+R8jy46v5+bPOXO09uPA27ocraSsCP1DoqMRtD4SixdUr+OmjQAYhWVk/Ck5UqI5SqCXlBVBACQ6mkLLsqO1+XW09itVC1AKy//wlhyP+wZIysrXu9JTNIeoCxIdjREIskMpLDdtaCyqoyRGkR0lzoNSVmIomkPwgERHg5Ds+BetXBB4yI4eojqA/mVHSv8pZVXtOU4pK4rmEN5AoqNRtCw7vOAhO5qRFI3MRAuQ7CiNEpLjcV+NzJnjy5QM/khZ+UKhQxt/Jwh10N7Vj9A8Rpxfh+bWUQe9zbGjlJzxnDPH3/Jf8zPvreR4QomUVaEjOOBu6lnkCEalQ148o4Tm0SGURMtRHSOmsIyEXqI6gH4iO1L7GWgpq+p4qsuRO8pKStqKIjuBCYkOR+zl/OfDIdnxDipMVpZAlx2lJIc3/ozmKFGX4w9IdgIPSl1xxl5uQVAI39s6BMoNQOVCKSwxPG/66Q+0msZSUnL0HM2pCY+6HCWjOdXhKTuBlg7TIxTRUYBAiuxoKapDuKKnFBagvciOWv3RegGynJRVbdQ1+7FWKXQE17oQ2oBERyH0IDu80JLsUApLWfwlO2oLj9w+yI3m1NquRgqQa6JEysoIc+aQ9GgDEh0F0brsaLFehwdakR2toIf7YLlDLdmRe1yjp6ykjrLikbKq2lefAkTCox4kOgqjhOzwRGvz6xgphUVRHfn4W3bUjiRJQaspK2/vSq7XlJVUikh2/I62rnIGhXeBslaLk3nNr3OxIgRhwfIKcY1UmMwD3oXJJfZQr+dEkYtTPpQsVOYlOP6O5mh5lFVtBELKSiuUOKxgDnk/tksdfAfXqAFFdHSK0et1eGCU9BOvqI5eU1hOlIi28KwHMnrKqiZKpKwIQglIdPxEINXr8EArKSwqTK4df88RA/ATEy0UPMtFzZSVFMmpiTvJoWgOoQQkOn4kUGSHRmFpF95RHTVkB7gsKr7IipR9vMUo0Rx38BpK7u+JAQnCibZ+xgcASkwoyBOt1evwgEetjVy0MomgEVE7MqOU5HhCzZt28hhKLieac94RqtuRV4Q6UERHBXhHdoxcr2OkFJZWMEpURwsoee7+LEBWcyg5QSgNiY5KaFl2jFivowVR0VKtDsmO/wiElJW3dTm1tuVjhOa8Q/3Xh9AP2rqiEbLgOezciCksuRhpuDkhHzXkTispK6l1OTyjOdVlh1JZ7jnvCEWlzOHlFwwwvJxER0W0Xq/DCx6yY6S5deTCq1ZHz3PrqI23kqP1aI7UlJXoOZkpKx4jrZSM8JBE6R9KXakMpbC8h1JY1drQ4HBzIDBSWDwkRwq8P7v+nP1Yz5x3hIoWQn/oTnQWL16MFi1aIDQ0FElJSdi5c2et2/bt2xcmk8llGTx4sLBNWlqay/Opqan+OBWBQJAdLU0kKBctyBIv9D6JoF7R8gzINaktZeUpeufPaI6/IeHRH7oSnY8++gjp6enIysrC7t270blzZ6SkpOD06dNut1+9ejVOnTolLHv27IHFYsHw4cNF26Wmpoq2++CDD/xxOiJIdryDojrV2qCojt9RMmXlT3ikrKoTiKOsSHjc8/rrryMxMRE2mw02mw29evXCunXrVO2TrkRn4cKFGDt2LEaPHo2OHTtiyZIlqFevHpYtW+Z2+0aNGiEmJkZYNm3ahHr16rmIjtVqFW3XsGFDf5yOC1q/AahW0MKQcy3IEi+UiOoYUXaUTln5K5rjz9mPAwESHjHNmjXD3LlzsWvXLvzyyy/o378/hg4dir1796rWJ92ITnl5OXbt2oXk5GRhndlsRnJyMrKzs71qY+nSpRgxYgTq168vWr9t2zY0adIE7dq1wyOPPIJz5855bKesrAzFxcWiRYsYNarDAy2IipGjOkaDl7hpoQDZHbzqcgIlmuMOkp0qbr31VgwaNAht27bFVVddhRdeeAHh4eH46aefVOuTbkTn7NmzqKysRHR0tGh9dHQ08vLy6tx/586d2LNnDx566CHR+tTUVLzzzjvYvHkz5s2bh//85z+4+eabUVlZ+2ioOXPmICIiQlji4uKknZQbKIXlHVpIYWlBlnhBUZ3a8eU89FqAXBMedTl1ocf6nLowcnSn5o/7srKyOveprKzEhx9+iNLSUvTq1csPvXSPNn5e+4GlS5ciISEBPXr0EK0fMWKE8P+EhAQkJiaidevW2LZtGwYMGOC2rYyMDKSnpwuPi4uLucsOz2HnPOfXIcTIHW5u9FtDBNKQ87pQuwDZn3U5QGBFc2qildtUFDnCUF4p7zJ/0VE1NUjNa1xWVhamT5/udp/ff/8dvXr1wqVLlxAeHo41a9agY8eOsvohB92ITuPGjWGxWJCfny9an5+fj5iYGI/7lpaW4sMPP8TMmTPrPE6rVq3QuHFjHD58uFbRsVqtsFqt3ndeAlqdY0dLEwlqZW4duWhlEkHe8+oYAV7RHK2mGJWqywlkyXHijOxoQXh4cOLECdhsNuGxp2tgu3btkJOTg6KiInz66acYNWoU/vOf/6gmO7pJXYWEhKBbt27YvHmzsM7hcGDz5s11hsQ++eQTlJWV4f7776/zOH/99RfOnTuHpk2byu6zltBiCosHVJhchVZvDQHoN4Xlj5SV2tEcT0ityyHEGCWd5RxF5Vw8iU5ISAjatGmDbt26Yc6cOejcuTNeeeUVP/ZWjG5EBwDS09Px1ltvYeXKldi/fz8eeeQRlJaWYvTo0QCAkSNHIiMjw2W/pUuX4rbbbsMVV1whWl9SUoIpU6bgp59+wrFjx7B582YMHToUbdq0QUpKil/OyRNartfhARUmX0btYlSl0Zvs8Oyv2gXIUlJWUm/xAHgfzSk2wMXfF5zCU+JQNhugRRwOh1c1PUqhjSuNl9x99904c+YMMjMzkZeXhy5dumD9+vVCgXJubi7MZrG7HTx4ED/88AM2btzo0p7FYsF///tfrFy5EoWFhYiNjcXAgQMxa9YsSakpVsnfG7Var2O0FJZcNJEC0+itIfSGr5Kj5QJkJW/xwINiR6ghi5IDmYyMDNx8881o3rw5zp8/j1WrVmHbtm3YsGGDan3SlegAwPjx4zF+/Hi3z23bts1lXbt27cAYc7t9WFgY9xef2c0wBTm4tmn0eh0toAlR0UitjlIYsTC5LslRO5rjDqWGkgNUm0MAp0+fxsiRI3Hq1ClEREQgMTERGzZswE033aRan3QnOnpACdnhidZGYVFUhx9aj+poXXb8kbLyhD+iOTXxx1ByT/grhUWRI/+wdOlStbvggq5qdPQEs/N9abVar2OkuXWoVsc/aLVex18pK38UIPOuy/EmZaX1aE6xI1RYiMCCIjo6glJYyiM3KkNRHX3CW760OJycV12OEVJWTtkxepTnvCMMdgefeXT0DEV0FIR3VAfgG9kx4igsLQw3lwtFdfyLlL7oMZrjiUAdSk4RnsCAREdhlJAdnmgthaUFjHBrCC3Pq+NEC7KjhOSoWYAsN2Wl5fopJSHZMTbavgobBK3X6/CCh+xQVKeKQIjqAOrKjhZEC1BejHnV5dQWzdFb2qo2KLpjXEh0dAqlsJSFojqXUTKqA6gjHFKPyTuao3TKSsm6HKNCwmM8SHT8RKDU62glhUVRHX3hT9lR6lhqFiArWZcTqJDwGAcSHT+i9XodLUFRHT7oJaoD+Ed25BxDqwXIStfleIrmGCVt5QkSHv1DV14/o+V6HYrqEGqjpOwoKTlaG06udF1OIFJ9Hh4SH32hjSsSIQue8+to6V5YWpgxWe15dXjcFkJv8+rwnj1Z1YJnlaI53kIpK+l4KzvV5+opdoTigsN/c6EVV4aivFLeZ+1SpbrzgvGAREcFtH6LCIJQG16yw0NytDqcXM2UFeE9FP1RH0pdqQSlsLxDC8PN1a7V0VJRsj9qdZyU2ENliYo/JEcKStZuSZUcqSmrwsp6vnaRIPwOiY6KaFl2tIQWCpP1jtZqSHzBF+FxbuuvdJWWojm+DCWvDqWsCKNDVxDCLVqq1eEB1erwQ617YPm71kaJAmQe0RxvPkc1JceXoeS+pqwKK+v5NPpKShQoEEZ3EcpBoqMyvOt1qDCZqA1eRcmEZ5QsQPa2Lqc6/rjFg9IprOrtk/QQvkKpKw1AKSz/QLU6/PBnrY4a6Gk4Oa+6HEAfBciFlfWoNojwCRIdwiNGK0wOdLR0gdYqciTO39EcXvPlAPqQnOqQ8BDeQlcOjRAIKSwtQLU6/FCrVkdt1JBFKXU5oucMPF+OU3YopeVKqT0UdpmSXaaxeyFKgSI6GsLoKSyK6mgDiurUjhajOf6oy9FbNMcdFOEhaoNEh/AKrd3hXA56r9XREkaq1fHmXNQaTl4TnnU5RkOrwuPsV5HDON8ZvUA/jzWGllNYPKARWPLRUvqK8IwS0RyedTmAMaI57vD3SC0tyhVRBYmOBtHqLSK0NNxcLmrX6qgNz6HmRqjV0WI0h2ddTqDjSUJqkyASF+NAoqNReMqOEQuT9R7VMVJRciDg78kBedflBELKSiokNMaHanQCBCMWJstF7VodteFZlKznWh0tFiC7Q4m6HKOmrQiiOiQ6Gob3KCxeaKUwWe8jsIw0gaBekZOyUgqedTkEQVDqii/lZkDDP2yNWJgsl0Cv1eGJEWp1fEGJaA7vuhxKWQU25+1hKJP5g6pc5z8oAYroaB6tzq1DUR0+qJ3+CuQ5dbQWzaG6HIJQBhId3pTzf0m1msLigRFqddSE0lfq4K/aHJovx3icp3l0/I5xr6BqooDs8ISiOnxROyojl0AsStZDNEcvdTnnHWEuC1FFoL42ixcvRosWLRAaGoqkpCTs3LlT1f5o+4pMCGg1qsNDdgI9qkNFyf5FrozxjuaoWZfj7YXX3QXb04U7EC/ugSo1Nfnoo4+Qnp6OrKws7N69G507d0ZKSgpOnz6tWp+0efU0AhpPYWltuLlcKKqjHfQS1fGEv6I5/riPVV14khheF2wjXfiVeo2MwsKFCzF27FiMHj0aHTt2xJIlS1CvXj0sW7ZMtT6R6CiJxlNYvDBKVEdN1BalQClK1lI0x1vJ8bYuB9B+bY7epICExjfKy8uxa9cuJCcnC+vMZjOSk5ORnZ2tWr8C++qiQ7Q6Y7IWkDtbsp6HmmttpmQtDjX3VnLUlD45dTlal5zqOIVBKxMWksDUTXFxseix1WqF1WoVrTt79iwqKysRHR0tWh8dHY0DBw4o3sfaINFRmnIzEKK9+1Y54SU7PG4NoYV5dQIZnve/0iueJEfpaI4vkmOU+1hVFwx/SE+gCU1JZTBCZIp7+f/+rMfFxYnWZ2VlYfr06bLa9he6y634Us29YsUKmEwm0RIaKv5jxRhDZmYmmjZtirCwMCQnJ+PQoUN8O805haXVwmQtILdWR89FyVpDS7U6SqWspCA36qfHlJU38EgRKV1rFMicOHECRUVFwpKRkeGyTePGjWGxWJCfny9an5+fj5iYGH911QVdXTGlVHPbbDacOnVKWI4fPy56/sUXX8Srr76KJUuWYMeOHahfvz5SUlJw6ZK2fzFpsTCZanXUlRUafeUeJVNWvr7fgViXIxVviqT1JDJFlWEoqgxDcaU+v6c2m0201ExbAUBISAi6deuGzZs3C+scDgc2b96MXr16+bO7InQlOlKquU0mE2JiYoSleu6QMYaXX34Zzz33HIYOHYrExES88847+Pvvv7F27Vq+nQ+QwmQtQFEd6fCuT9FSVEcqSgukXubLIXzDKTbOJVBIT0/HW2+9hZUrV2L//v145JFHUFpaitGjR6vWJ91cfaVWc5eUlCA+Ph5xcXEYOnQo9u7dKzx39OhR5OXlidqMiIhAUlKSMhXiGk5hUVSHHxTV0Q5aj+bIrcsxajRHb9SUmkASm5rcfffdWLBgATIzM9GlSxfk5ORg/fr1LgXK/kQ3VxQp1dzt2rXDsmXLkJiYiKKiIixYsAC9e/fG3r170axZM+Tl5Qlt1GzT+Zw7ysrKUFZWJjyuWY1OqI/aI7CIy6g1AotHNImXOPKeFJBQl0AWGW8YP348xo8fr3Y3BHQT0ZFCr169MHLkSHTp0gV9+vTB6tWrERUVhTfeeENWu3PmzEFERISw1KxG9whFdbxC71EdORgtfaV1lI7mKDUpIEVz/AdFa/SNbq4mPKq5g4OD0bVrVxw+fBgAhP3y8/PRtGlTUZtdunSptZ2MjAykp6cLj4uLi32THc7wnFvHSMiN6siB5tQR4++ojpaiOe6guhztYiSRKbWHolxmzWGFAUb56uYMeFRzV1ZW4vfffxekpmXLloiJiRG1WVxcjB07dnhs02q1ulSg+4SGC5MpqnOZQC5K1jO+SI4a0Rwe8+VQNIcfFK0xPrqJ6ABV1dyjRo1C9+7d0aNHD7z88suiau6RI0fiyiuvxJw5cwAAM2fORM+ePdGmTRsUFhZi/vz5OH78OB566CEAVSOyJk6ciOeffx5t27ZFy5YtMW3aNMTGxuK2225T9mQ4TyRIMya7J1CjOnJRYvJAf0R1eI3y4hHN8VVyXLaluhxFUFtmSu00YMDf6Ep07r77bpw5cwaZmZnIy8tDly5dRNXcubm5MJsvR0v++ecfjB07Fnl5eWjYsCG6deuGH3/8ER07dhS2eeqpp1BaWopx48ahsLAQ119/PdavX+8ysSDhO0aYLVmvRclaTF8B2ro1hJLRHCnFx97erJOiOd6jttSUuJUaff740TMmxhhTuxN6p7i4uKoo+ZWZMIf5KEicbw/Bs1aHR1RHrugAkC06ciM6ckRHbkRHzv48REeJW0IoJTq+RnNqEx1P0RxvREfupIAAiY5UtCk2YspKKvCv675CUVGR72UPXuK8Jg3d+CCC68us0Sktx+cDlynaX6XRVUSH0B9aiOqoOdRcz+krpVAiqsNLcjyhBckhxOhBbAj1IdFRG6rVIRRCq+kr3vCcfVmJkVZakBxfhECrESO1pQYgsdErJDpaQMOywwMjRHXkICeqo3ZESKk7mvOK6kiRHH9Hc6rDo/i4qDLMo4zIFYLq+6spPSQ2BC9IdAiPUFSnCr0WJWsZLRUmA/KiOXInBfQVf0mA8zhKCo8WhMaJ0cTmgj0YwTKnqqiw67+Ml0RHK1BUp070HNVRCyOnr7QSzQmEuhweUR4tCY0TNcTmvAFudKs3SHSIOqGoThVqFSWrnb5SEqlRHd53RZcazQkEyamJFoXFF/wtN66fVWN+l7UMiY6WoKgOoUGUqtNx4ovsyBEc3tEcHpJDKI/6YkOoDYkO4RVaiepQ+sp39JC+cl4cPImAUhcQRe9pVYcg6i2aowdIbIiakOhoDYrqaBpKXylLzegOt1s6qBTNqY7eU1ZahcSGqAsSHS3CWXZ4QVGdwEXp9FV1/HkhkRLNoZSV+vhTbkhs9A+JTgBAUR2+6HGmZD2kr5SCZzSHl+RQNMc3SGwIOZDoaBWK6nhE7Zt9qkGgpK/8ha/RHG9fe6rL4YMR5aakMgTlfvzzedEehAqZ8+jY7dq7DvkKiU6AoLWojtpQ+sp3/Jm+4oWUaI47PElO9WiOpzuSE54xqtgQ6kOio2U0GtXhAaWvKH2lJnLvUO7E15mPKZpzGSOKDUByo0VIdAIIXlEdo6Sv9BjVofSV9ygdzaG6HN8xotyQ2GgfEh2eVJgA3t8tiuoQGkKP6aua+FKbQ5IjDxIbQguQ6AQYFNXhhx7TV4GC1ItR9bQVr+LjQMNockNio39IdHhTYQKCOd/tlaI6ihFo6atAr9OpK5rjzevqa/Gx0aM5Rpywj+TGWJDoEJLRSlSHIKqj5EXK1+Jjo0JRG4nHqQhFhd3sl2MRlyHRUQKNR3WMNtRczaLkQExf6bVOR+49rQJ55mOjRW38KTZqcrEiBEEV8s7VXsH5WqYCJDqELHhEddROX+kRSl+5R6kLmFTJ0Wvayt9iAxhDbtQWG8I9JDpKQVEdv6LXomTCP8i5AHmSnDqPaw/1WXbqkgwl5EkNsQH0LzckNvqARIfQBGpHddQqStZr+kqL+OMXO++Zj6UIRvV9pEiPWlID6F9sAJIbPUKioyRKRHU4YrSh5oGGmpKkpzodntGc6ngrOc6ojhKCoaa0eIve5YbERjmGDBmCnJwcnD59Gg0bNkRycjLmzZuH2NhYrseh8m+9UU5vWW2Ulavn7RdlFvz5G6P98VbiYsez+FgPQsKTYnuYsChBSWWIsHBvuyJUtBDK0a9fP3z88cc4ePAgPvvsMxw5cgR33nkn9+NQREdpKKrjfRuUviI0gpy6nEBFyciNEaI2zskoK+zavR74m0mTJgn/j4+Px9SpU3HbbbehoqICwcHy7rpeHRIdPWLgCQQJwlc8XQSlXMDqkhyjDyX3Bb3KjT/FxigUFxeLHlutVlitVm7tFxQU4P3330fv3r25Sg5AqSv/UGFSuwe6wW63yNo/0NJXav4xNeLssSQ5daNkWspfKSkluGAPFi1aoKwyCJfs8payyqq/qXFxcYiIiBCWOXPmcOnj008/jfr16+OKK65Abm4uPv/8cy7tVodER69wrNVhnGbqtJfLkxQtcMmujiip8YfRCPUHvKM5nghkySG5cUWLYqMkJ06cQFFRkbBkZGS43W7q1KkwmUwelwMHDgjbT5kyBb/++is2btwIi8WCkSNHgjG+6T1KXfkLjdfqGAk159Qh9Esgz3zsDj2mpZSW90AQmtqw2Wyw2Wx1bjd58mSkpaV53KZVq1bC/xs3bozGjRvjqquuQocOHRAXF4effvoJvXr1kttlARIdPaPBCQQDvShZjckDA7WYmWc0h4qPqyC5ERPIYiOVqKgoREVFSdrX4ai6BpWVlfHsEomOX6GoDmEw9DSfTm0Eel0OyY0Ykhv/sGPHDvz888+4/vrr0bBhQxw5cgTTpk1D69atuUZzABIdohpGui2EHtNXakRmjHjfK1kTBAaI5JDcXMYfYlP9GHa7Mf7GyqVevXpYvXo1srKyUFpaiqZNmyI1NRXPPfcc19FcAImO/tHgUPNAT18RysPrYlpd8owuOXqTGz1HbSgqVDcJCQnYsmWLX45FouNvKH1leKhORz18uTjyuL2D1iG5qYLEJrDR3fDyxYsXo0WLFggNDUVSUhJ27txZ67ZvvfUWbrjhBjRs2FC4j0bN7dPS0lyGvqWmpip9Gnwx6FBzPc+pIxX6g1k3PC6wRi8+1tNQcKWGgCs99FsPQ8vLyi0oKw+Sueh/2hBdic5HH32E9PR0ZGVlYffu3ejcuTNSUlJw+vRpt9tv27YN99xzD7Zu3Yrs7GzExcVh4MCBOHnypGi71NRUnDp1Slg++OADZU+EJhDUBWrNqeNv5F5gtDJxoLfnYdTiY6XmuiG5cW1by3JDuKKrv+QLFy7E2LFjMXr0aADAkiVL8PXXX2PZsmWYOnWqy/bvv/++6PHbb7+Nzz77DJs3b8bIkSOF9VarFTExMcp2nggo1EhfBQJyL7ZGkxwlb5rJvU2dpaVIZoyDbiI65eXl2LVrF5KTk4V1ZrMZycnJyM7O9qqNCxcuoKKiAo0aNRKt37ZtG5o0aYJ27drhkUcewblz57j23S28ozqUvnKLHtNXUgnkP8zeXESNkq7yxyzF3NrTWeRG6YjNxYoQVW4VE+jo5ipw9uxZVFZWIjo6WrQ+OjpaNJ20J55++mnExsaKZCk1NRV33HEHWrZsiSNHjuCZZ57BzTffjOzsbFgs7i+yZWVlogmNat7sjCDkQIXF7pFzAfZmCL2Wozl6KipW6nYLSqH0DwQSG/XRjejIZe7cufjwww+xbds2hIZe/iKOGDFC+H9CQgISExPRunVrbNu2DQMGDHDb1pw5czBjxgzF++wzGpwpWe/obZZktdDbxIF6SFnpSW4A/oKj15QUiY320E3qqnHjxrBYLMjPzxetz8/Pr7O+ZsGCBZg7dy42btyIxMREj9u2atUKjRs3xuHDh2vdJiMjQ3RzsxMnTnh/ItUJgKJkSl/pA63f4FPO7R70lrJSKi0FaD81pceUlDMdRWkp7aIb0QkJCUG3bt2wefNmYZ3D4cDmzZs9Thf94osvYtasWVi/fj26d+9e53H++usvnDt3Dk2bNq11G6vVKtzgzNsbnRGEL0j9gxzIdTru8EZytBDN0dPdwUluQGKjM3T1Uzc9PR2jRo1C9+7d0aNHD7z88ssoLS0VRmGNHDkSV155JebMmQMAmDdvHjIzM7Fq1Sq0aNECeXl5AIDw8HCEh4ejpKQEM2bMwLBhwxATE4MjR47gqaeeQps2bZCSkqLaecqC0lfcofSVukiN5mhdcvSUmtJDWkpJyder0FTaLWAyI+IOmftrAV2Jzt13340zZ84gMzMTeXl56NKlC9avXy8UKOfm5sJsvhykev3111FeXo4777xT1E5WVhamT58Oi8WC//73v1i5ciUKCwsRGxuLgQMHYtasWdzvtVErATBTshZuCaHHe18R0tFiukpJsXHCOy3FE5IbQi10JToAMH78eIwfP97tc9u2bRM9PnbsmMe2wsLCsGHDBk49Iwh9I/cGn0oUJEuJ5nh7Dv6I5uhNbgC+gqMnuVFabJwTkFZWqncPv0BFd6JjSHhHdSh9xR01bvIpdZh5IA9Pdyc5clNWtclKzTb8ITXV0Wr0huTmMoEyu7rWoXeB8As80ldyUSt9RXU6/PH6dg+1RJiK7WG1yo63wuJvsQG0KzcAfxnRo9yQ2GgTelcI3SC3TofQF75e1H2ty1FDVKQQSKkppUZIKQWJjT6gd0kraDh9RRBaR4vFx3LRavSG5IYum3qD3jGiTnjV6WghfSWHQKjTkVuQzAtfipCNJjk857vhRSDLDYmN/qF3kNAVeh1mHih1Ov6+FYQWpIwHWozekNzoH3u5BeYgmfPocJjdXm2M8W4S7qH0FaFDvL3oezvCSssYPXqjF7lRWmyq34amspzm8/I3JDpaQsOTB9Iw8yrUSF8RruhZcoweveEtN3qN2tA99rQDvROEXwnkWZKlpq8CaT4dbyRAr5KjtegNyQ1fSGy0C70zBEHoGq1LDgmOd+hNbkhs9AO9U1qDhpkTKqOVkVd6huTGO/RWb0Nyo0/oXSO8hup0qqA6HWWQIgdai+YYVXACVW5IbIwBvYuE36E6Hf/V6RgZLUkOD8ExstwA/AVHr3JTafffcG1WaQazm2W3oXdIdAjCwKghSP6YS0cLkqOl6E2gpKb0KDd2F7GhHyz+hkRHi2i4TofSV4QS+CINakuOEQUn0ORG6aiNq9wQakKiQxASMHqdjlYLktWUHK2kp0hupOHfqA2hJfSffCN0id0A04r7GyVmmdUCXs+ErJLklFSGyJackopQ2ZJzwR7M5TPAq52LFSHCwoNL9iBh4UVZeZCw8MZutwgLIZ2vv/4aSUlJCAsLQ8OGDXHbbbdxP4bP7/4333yD1atXo1GjRnjwwQfRvn174bl//vkHw4YNw5YtW7h2kiDcEWgFyYGMvyXHaOkpXpIcyJEbEhr+fPbZZxg7dixmz56N/v37w263Y8+ePdyP41NEZ9WqVRgyZAjy8vKQnZ2Nrl274v333xeeLy8vx3/+8x/unQxIKkx82yun4F2gouVIEM/bIfCAZ/RGjuQ4oy5y3jsebTgJ1MgNRW2Uw26344knnsD8+fPx8MMP46qrrkLHjh1x1113cT+WT5+K+fPnY+HChZgwYQIA4OOPP8aDDz6IS5cuYcyYMdw7R2gTKkgm/Ik/ojlGqr/RYvSGd+RGr1Ebe7lFt3cDLy4uFj22Wq2wWq2S29u9ezdOnjwJs9mMrl27Ii8vD126dMH8+fPRqVMnud0V4dOn5dChQ7j11luFx3fddReioqIwZMgQVFRU4Pbbb+faOcLY8JhPR02MXpAsB2+GmHt1XyuFJUcLgkNy4x16lhvVKDcDFpnR/P9lA+Li4kSrs7KyMH36dMnN/vnnnwCA6dOnY+HChWjRogX+9a9/oW/fvvjjjz/QqFEjyW3XxKdPjs1mQ35+Plq2bCms69evH7766ivccsst+Ouvv7h1jCC8IdDqdPw5L47aI6+UlBwe6Sm5GFFwSG6MO9DixIkTsNlswuPaojlTp07FvHnzPLa1f/9+OBxVWYFnn30Ww4YNAwAsX74czZo1wyeffIL/+7//49RzH0WnR48eWLduHXr27Cla36dPH3z55Ze45ZZbuHWMIAh+0MzKVRhBcLQmNwBfwVFqhJRSGFVsamKz2USiUxuTJ09GWlqax21atWqFU6dOAQA6duworLdarWjVqhVyc3Nl9bUmPn2iJk2ahB9//NHtc3379sWXX36Jd955h0vHCGh64kCCUBLe0Ry1BUcr0RuSG05tB4jcSCEqKgpRUVF1btetWzdYrVYcPHgQ119/PQCgoqICx44dQ3x8PNc++fTp6tOnD/r06VPr8/369UO/fv1kd4rQPlSQTMjBk3jwkhwj1N9oLXoTqHJDYsMfm82Ghx9+GFlZWYiLi0N8fDzmz58PABg+fDjXY8n6pJ0+fRqnT58Wcm1OEhMTZXWKIPQCFST7jtKSQ4JTBcmNzHZJbhRn/vz5CAoKwgMPPICLFy8iKSkJW7ZsQcOGDbkeR9KnbteuXRg1ahT2798PxqpSKyaTCYwxmEwmVFbqdyQN4V/0PvJKDlSQzBdKT1URCIKjR7lx3kXcCHcD50VwcDAWLFiABQsWKHocSZ++Bx98EFdddRWWLl2K6OhomEycJ7cjCB/Q68irQESJaI7eBYfkxjuUkBulozZOuSHURdIn8c8//8Rnn32GNm3a8O4PQRAK4e+RV97MpeNEiuSQ4PARHJIbfmhObCpMQJDMQATvWfpVQNKncsCAAfjtt99IdAiC8JraxMRXydGz4GhFbgB+gkNyozG5IVyQ9Al9++23MWrUKOzZswedOnVCcLD4yztkyBAunSNAQ8wJQ+OL5KgpOBS9ERPIckNioz8kfVqzs7Oxfft2rFu3zuU5KkYmCMIbvJWcQBYcI0dvSG4IfyHp3Xv88cdx//3349SpU3A4HKKFJCdwoC9/FbynvfcGf96RXI4oyElXyb2TuJw7iPO6c7hUeNwtnNddwnneGZz33cDt5RbRwgtmN4sWQt9I+uSeO3cOkyZNQnR0NO/+EARhUIwewdFC9CYQIje6jdr87+aYwr+E35D0ab7jjjuwdetWtG7dmnd/CCKgkDqXjlS0fM8rtQTHCOkprQkOyQ1IaDSEpE/1VVddhYyMDPzwww9ISEhwKUaeMGECl84RBGEcahtuLjc9JRW9C47W5AbgKzgkNwQvJL0rb7/9NsLDw/Gf//wHr732Gl566SVhefnllzl3UczixYvRokULhIaGIikpCTt37vS4/SeffIL27dsjNDQUCQkJ+Oabb0TPM8aQmZmJpk2bIiwsDMnJyTh06JCSp0DUgKZaDxycdTfVF0ntqFR/49xX6v7O2hs5kmPkuhvd1duUm8WLxjBXmmC2y1wq9T+PjqR35ujRo7Uuf/75J+8+Cnz00UdIT09HVlYWdu/ejc6dOyMlJQWnT592u/2PP/6Ie+65B2PGjMGvv/6K2267Dbfddhv27NkjbPPiiy/i1VdfxZIlS7Bjxw7Ur18fKSkpuHTJ92nvjfCBCFSUuAcPwR+1BUcqvOSGl+DIRU9ywx0Niw3hHl29UwsXLsTYsWMxevRodOzYEUuWLEG9evWwbNkyt9u/8sorSE1NxZQpU9ChQwfMmjUL11xzDV577TUAVdGcl19+Gc899xyGDh2KxMREvPPOO/j777+xdu1aP54ZQRCeCHTBkQOv6I1SI6Z4EahRG6JuJH/y//rrL3zxxRfIzc1Febk4775w4ULZHatJeXk5du3ahYyMDGGd2WxGcnIysrOz3e6TnZ2N9PR00bqUlBRBYo4ePYq8vDwkJycLz0dERCApKQnZ2dkYMWKE23bLyspQVlYmPC4uLpZ6WgRhePRYgyNXbuTCo/5Gi4XFvNPUitXbkNAYCknfhM2bN2PIkCFo1aoVDhw4gE6dOuHYsWNgjOGaa67h3UcAwNmzZ1FZWekypD06OhoHDhxwu09eXp7b7fPy8oTnnetq28Ydc+bMwYwZM3w+B4IgvEMNwTFCcTHJjQyUlJvq94sywL2j9IakdzYjIwNPPvkkfv/9d4SGhuKzzz7DiRMn0KdPHwwfPpx3HzVHRkYGioqKhOXEiRNqd4kgDIHUFJWcImEjpafkoOW6G0XSUkqnpCpMlxdCVSR9M/bv348PPvigqoGgIFy8eBHh4eGYOXMmhg4dikceeYRrJwGgcePGsFgsyM/PF63Pz89HTEyM231iYmI8bu/8Nz8/H02bNhVt06VLl1r7YrVaYbVapZwGQRBuUGMOHDXTU0aM3ugicuOvqA2hKSS96/Xr1xfqcpo2bYojR44Iz509e5ZPz2oQEhKCbt26YfPmzcI6h8OBzZs3o1evXm736dWrl2h7ANi0aZOwfcuWLRETEyPapri4GDt27Ki1TcKYWEPsanchIJEbwZECj+HhUjFy9IYHikdueENRG10g6dvSs2dP/PDDD+jQoQMGDRqEyZMn4/fff8fq1avRs2dP3n0USE9Px6hRo9C9e3f06NEDL7/8MkpLSzF69GgAwMiRI3HllVdizpw5AIAnnngCffr0wb/+9S8MHjwYH374IX755Re8+eabAKpuQDpx4kQ8//zzaNu2LVq2bIlp06YhNjYWt912m8/9c1iYvoaxaYSgELo/mpYJD/Z9qoW6oAiObxg5eqOryI3OhMZUboLJLK/PpnJ9nbM7JH17Fi5ciJKSEgDAjBkzUFJSgo8++ght27ZVZMSVk7vvvhtnzpxBZmYm8vLy0KVLF6xfv14oJs7NzYXZfPkD3rt3b6xatQrPPfccnnnmGbRt2xZr165Fp06dhG2eeuoplJaWYty4cSgsLMT111+P9evXIzRUejEkQWgVtW//EEiCo5X0FMmNTHQmN4QrJsYY82WHyspKbN++HYmJiYiMjFSoW/qiuLgYERERiFswC+YwBQQp2Ke3qG5CHNyaMgXJb4tHRCcoSF4bclNXoUHS9pd6nyupwuLrfryiOf4WnECP3pDcyERBuXFcvIQTT05DUVERbDabIsdwXpNazHoBZpk/2h2XLuHYtGcV7a/S+PyNslgsGDhwIPbv30+iQxCQLjn+Rg3JIcHxHhIcL1Gq1kZBzPZq7dMM+n5H0jerU6dO+PPPP9GyZUve/SF0BI9oDmE89DYHjt7TUyQ3EvGn3BCqIukb9vzzz+PJJ5/ErFmz0K1bN9SvX1/0vF7DWwRBVCElmkOC4z1GjN4EutyQ2GgXSd+2QYMGAQCGDBkCk+nym8sYg8lkQmUljaLhhobrcwh5+LM+R8kiZBIc7zFa9IbkhuRGD0j61m3dupV3PwhCMmoXImsdpWpz9DSCSu/1N7zmu+EF93lueKOQ3JDY6BNJ374+ffrw7gdB6BJ/FyLrdXg4oD/BUVtuAG0JDsmN/jBXmGC2yOy/AYbXy/omXrhwwe3dyxMTE2V1iggcAnWyQKlpK1/hIUZy5AYgwfEVkhsfILkhvEDSN/LMmTMYPXo01q1b5/Z5qtExPjTiyv/4Ki1SJUeu2Dih+hvfIMHxEgXkRmmxqT67sBFmGtYbkj6BEydORGFhIXbs2IGwsDCsX78eK1euRNu2bfHFF1/w7iNBKIac+hy9zJ/jb9S4i7ice1DJuf+U875TUiWHxz2neN0pnOt9pnjfX0qBe0qZ7SZhUQJTuUlYCPfs3r0bN910EyIjI3HFFVdg3Lhxwl0XeCLpU7hlyxYsXLgQ3bt3h9lsRnx8PO6//368+OKLwn2mCA7QiKs6kVuIrAZS0lb+iubIIRAFRyo8bqhJcuM7SspNdbEhuambv//+G8nJyWjTpg127NiB9evXY+/evUhLS+N+LEnf1NLSUjRp0gQA0LBhQ5w5cwZXXXUVEhISsHv3bq4dJIxLoNbnKI2/JUdPKSq9p6c0l5oK8LQUCY10vvrqKwQHB2Px4sXCPSqXLFmCxMREHD58GG3atOF2LEmf0nbt2uHgwYMAgM6dO+ONN97AyZMnsWTJEjRt2pRb5whtYpT6HDXSVkpHc/wlOc4ojF4iOHKiN4C8CA7P9JQcFIne8EBHkRuK2vCjrKwMISEhohtxh4WFAQB++OEHrseS9El94okncOrUKQBAVlYW1q1bh7i4OLzyyiuYPXs21w4SRG3oMW3lK1qTHDmSInVfp9z4W3B41d/IgafgyIZnakqnchPIFBcXi5aysjJZ7fXv3x95eXmYP38+ysvL8c8//2Dq1KkAIPgFLyR9Yu+//34hj3bNNdfg+PHj+OWXX/DXX3/h7rvv5tm/wMXg9Tl6Tlv5K5qj9pw51VFTcKSg5/obHsXF3KI3JDdc2/U3pgrALHMx/e/PUFxcHCIiIoSltnrcqVOnwmQyeVwOHDiAq6++GitXrsS//vUv1KtXDzExMWjZsiWio6NFUR4eSP42L126FC+99BIOHToEAGjbti0mTpyIhx56iFvnCEIp/D0bstLz5iglRYE0B45cuZELj/obTdbecK67UaqQWEnM1V8DnU7Ad+LECdF9LK1Wq9vtJk+eXGdBcatWrQAA9957L+69917k5+ejfv36MJlMWLhwofA8LyR9szMzM7Fw4UI8/vjj6NWrFwAgOzsbkyZNQm5uLmbOnMm1k4R20Ep9jlppK38NKVc7ZUWC4x1aKC4muZGGX+XGANhsNq9u2B0VFYWoqCif2o6OjgYALFu2DKGhobjpppsk9bE2JH3DX3/9dbz11lu45557hHVDhgxBYmIiHn/8cRIduVDaSlG0Hs1RK2UlR27k7K+G4Bhh9BS3uhteBLjcGE1s/MFrr72G3r17Izw8HJs2bcKUKVMwd+5cREZGcj2OpG97RUUFunfv7rK+W7dusNtpEjVCWfQUzVFacnhIkRrRG0CdIeJ6FxzNRW9IbhRrOxDYuXMnsrKyUFJSgvbt2+ONN97AAw88wP04kr71DzzwAF5//XUsXLhQtP7NN9/Efffdx6VjhPbQStpKDlKjOf5IWell/hu5++pNcNSWG8DY0Rs9yQ2JDV/eeecdvxxHVjHyxo0b0bNnTwDAjh07kJubi5EjRyI9PV3YrqYMEXVAaSvP++toSLm/btzpC2qlpwASHCloSnBIbgidIumvwJ49e3DNNdcAAI4cOQIAaNy4MRo3bow9e/YI25lM9AExChTN8Q1/1OX4sg8JjveoLTgkN95DcuMZs71qiLgcmAGqUST9Ndi6dSvvfhBKQNEcAX8WIPur+PiCPdjjvnLlRm4bgSQ4FL2pHSXmuFECJeWmumww7UyPFTDIq8wj+MI7bcUJI0RzpOJrNMffI6x4yAzvdklwvIfkxjuUkBulozZyIykEP0h0CL+gx2iO0gXIWpr5GFBHbgB1hohTeqoanC74gS43JDbahURHKxi8CFlvKF2XoyXJIcHxYX+1BUdjcgPwFRySG0IJSHQIj/BIW+kxmuMrWhxhVRckOF7uq7bcAPzuNcUJkhtCT5DoaAGNRnO0UJujh5SVXmY+dkKC4+W+JDgiAlVuSGz0D4kOoShq3+7BV4wsOSQ4Xu6rtuBoSG4AbQsOyQ3hDSQ6amPgaI7eUlZGlRwSHC/3JcERILlRrn1/zktjLgfMMl8upr+svAskOoQmIcmRDw0R93JfGYJjpPQUyY1+2iZ8g0RHTSiawx1/SI7WIcHxcl81BUcjcgNoV3BIbghekOgYBQ0NJ1crZaXVEVb+iuYEiuBQeoqiN763yb1Jv7RN8IFERy1oFmS3+PumnUZIWUkVHDXqbwASHEloLHoTyHJDYqM/SHSMAKWsNFmXoyR6KzAG9Cc4qssNYOjoDckN4S9IdNRAo9EcuegpZeUPyVEimkOC4+W+JDiajN6Q3BBqwGk+ceUpKCjAfffdB5vNhsjISIwZMwYlJSUet3/88cfRrl07hIWFoXnz5pgwYQKKiopE25lMJpflww8/VO5EDFyALAejSQ5vLtiDZaWo5NTgyBkmLkVy7HaLZMmxl1skSw6zm+VJTrlZnuRUmC4vEjHbTcIiB1O5SVjkYq4wCQsvzBWXF54o1S7wv6He/1sI/6KbiM59992HU6dOYdOmTaioqMDo0aMxbtw4rFq1yu32f//9N/7++28sWLAAHTt2xPHjx/Hwww/j77//xqeffiradvny5UhNTRUeR0ZGKnkq/NCQ5MiJ5pDkuEfunckpguMdqkdwKHrjZXtcm1OsTVH7KkuNuQIwy/x4MgNEtnQhOvv378f69evx888/o3v37gCARYsWYdCgQViwYAFiY2Nd9unUqRM+++wz4XHr1q3xwgsv4P7774fdbkdQ0OVTj4yMRExMjPInQikr131JclwgwfFhX7UERwPpKZIb7bQpap8iNppDF6mr7OxsREZGCpIDAMnJyTCbzdixY4fX7RQVFcFms4kkBwAee+wxNG7cGD169MCyZcvAmGchKSsrQ3FxsWipE4OmrPRyiwc1JMcXYXGmpuTW4FCKqm5kpag0lJ6Si1ZTU0qkj/yVkiLJ0Sa6iOjk5eWhSZMmonVBQUFo1KgR8vLyvGrj7NmzmDVrFsaNGydaP3PmTPTv3x/16tXDxo0b8eijj6KkpAQTJkyota05c+ZgxowZvp8ILzQiOXLxVzRHi5EcuVGb6tBtGuqG0lNVaDV6o7fIDQmNvlBVdKZOnYp58+Z53Gb//v2yj1NcXIzBgwejY8eOmD59uui5adOmCf/v2rUrSktLMX/+fI+ik5GRgfT0dFH7cXFxtXeAZzRHQ5Kjh5SV2pLDU2hqEiiCE8j1N1pLT5HcEHpEVdGZPHky0tLSPG7TqlUrxMTE4PTp06L1drsdBQUFddbWnD9/HqmpqWjQoAHWrFmD4GDPF56kpCTMmjULZWVlsFqtbrexWq21PueCButySHLco4XRVd5AglM3qgqOwaI3Wq+7Ibkh6kJV0YmKikJUVFSd2/Xq1QuFhYXYtWsXunXrBgDYsmULHA4HkpKSat2vuLgYKSkpsFqt+OKLLxAaGlrnsXJyctCwYUPvRcafaOQ2D0aUHD1AglM3qhYYk+B4aItbU4q0J7SrsNiYK4wxiklv6KJGp0OHDkhNTcXYsWOxZMkSVFRUYPz48RgxYoQw4urkyZMYMGAA3nnnHfTo0QPFxcUYOHAgLly4gPfee09UNBwVFQWLxYIvv/wS+fn56NmzJ0JDQ7Fp0ybMnj0bTz75JJ+OGzBlZVTJ0XI0hwSnbvQsOCQ36rQntOsHuSHURReiAwDvv/8+xo8fjwEDBsBsNmPYsGF49dVXhecrKipw8OBBXLhwAQCwe/duYURWmzZtRG0dPXoULVq0QHBwMBYvXoxJkyaBMYY2bdpg4cKFGDt2rPwOk+SI9yXJ8RkSnLohwSHBkdRmgMiNxQ5Y5I6tNkBwXDei06hRo1onBwSAFi1aiIaF9+3bt85h4qmpqaKJArlhwLockhz/QYJTNyQ4fIaF8yLQ5UYrYkO4Rzeioxs0OF+OmsPISXK8hwSnblQTHIPIDcBPcEhulGub4AuJjpbRefExSY53kODUDQmOPLQavSG5IfyBLmZG1g1Ul3N5Pw1LjlZQYyZjqbMYA9JnMtblLMYamL2Yx8zFvGYs5jmzsCIzHys0M3H1vpLk8OeFF15A7969Ua9ePbf3mPztt99wzz33IC4uDmFhYejQoQNeeeUVn49DER0tQpLjFVIlR+1oDkVw6iZQIzgUvfGhPYW+xkoLjUW/v824U15ejuHDh6NXr15YunSpy/O7du1CkyZN8N577yEuLg4//vgjxo0bB4vFgvHjx3t9HBIdrUGS4xV6lBwSnLqRFb2Rg8zojVy0JDgkN/yxVGtfH3cH9A/OWymtWLHC7fMPPvig6HGrVq2QnZ2N1atXk+joFpIcr9Cb5JDg1A0JjnR4paZ4QXJThcVgqa6aN6/26Q4BHCkqKkKjRo182odERyuQ5HiFnmpySHDqRhXBofRUtXa4NMO9LUAZwVFSbrQoNuYKwCzzo+Kcybnm/RyzsrJc7h2pND/++CM++ugjfP311z7tR6JjIEhyasef0RwSnLohwZHYB40JDsmNcm1rjRMnTsBmswmPa4vmeHuz7vbt2/t0/D179mDo0KHIysrCwIEDfdqXREcLaGCuHJIceciRG4AEp05IcDi0IbsJZdoiudEFNptNJDq14e3Nun1h3759GDBgAMaNG4fnnnvOp30BEh31IcmpEy2nq0hwvIMER8LxDRy9IbkxLt7erNtb9u7di/79+2PUqFF44YUXJLVBoqMmASY5vgoOoF3JkSs4gDTJIcHxAhULjI0WvdF6akopuSGx8Q+5ubkoKChAbm4uKisrkZOTA6Dq/pTh4eHYs2cP+vfvj5SUFKSnpyMvLw8AYLFYfJIpEh21IMmpEx6SwzNtxUNuABIcj5DgyGxDdhNc2wFIbpw4+81IogQyMzOxcuVK4XHXrl0BAFu3bkXfvn3x6aef4syZM3jvvffw3nvvCdvFx8fj2LFjXh+HZkZWA5KcOtFKJMc5ezGvCI4/JUdXMxmrNIux3BmM5c5e7Jy5WI7k8Jq5l+vsxwrMVKzE7MSWisuLEtCsyp5ZsWIFGGMuS9++fQEA06dPd/u8L5IDUETH/5Dk1AlPyblYEeJzVIdX5MaJv+twKIJTN2pHcCh642V7CgiCvyI3hHYg0fEnJDl1olYkh7fcAPoRHEC65JDg+Hh8jQgOyQ0/tCw2lnIGC2Teg7Gc4z0cVYJEx1+oLDlSBQfQv+QoITGeIMHxgA4FRwv1N0aP3pDcEEpCoqM0GpjxOJAlx5+Q4HggAAVHK9Ebru2Q3BA6hERHSXQsOf6aIwfQv+SQ4HiABEdiG7Kb0KTcAMoUFCsFyY0xINFRigCrxwFIcryFBKcOAlRwKHrjG0qOlFISiwFqXvQGiY4SkOR4hZ4lx59RHBKcugl0wSG5kYeisyrXEBvphQSEVEh0eBNgkiNFcAD9So5e0lQkOHVDglOtHQ0LjhHkhlAXEh2ekOR4hR4lhwSnFgJMcIxUf0NywxeSG+1CoqMhSHK0BwmOB+TMZCwRvQqOVuQG0K7gkNzwx1wBmGW6uRFuWUGioxGMPnwc0JfkkOB4gATHh/1l7c6tDYCf4OgheqPY/bA0LjaEe0h0NIDRh48D+pEcqYID+LfQWFe3ayDBUa+NAIrekNwQtUGioyJ6SlUB0iVHD5DgeIAEx4f9Ze3Orw0NRm9Ibgi1INFRiUCSHC1Hc/QiOICOCo1JcPy+PxA40Ru9yo2z30aoedEbJDoqoCfJkRvF0ark+FtwAB3V4ZDg+LC/rN01JTgkN/yhmZW1AYmOnwmEomMnWpQcOYID6CNNRYLjxXHl9JnSU7WiB8EhuQk8SHT8SCAUHTvRmuSQ4HiABMfLfSXvyrcNDoJDcsMPLYuNuYLBAnnnzir0X69EouMH9JSqAowlOSQ4HlBhsr9AFBytyA3A76JMcqNY04QCkOgoDEmOOqghOIA0ySHB8QwJjvr9cKJ1wSG5IdxBoqMgeqrHAYwhOXIFB/BfFCcQhoqT4EhsQ0OCo3W5AZQTHJIbY0CioxB6qscB9D9Hjp4EB9BRFIcEx3/7Gzg9RXJTvX3917zoDRIdzgRaqsqJWtEcEpw6IMHxYl/Ju/LZX0PRG0DbgqNXuaneb33/pNQnMpL1/qWgoAD33XcfbDYbIiMjMWbMGJSUlHjcp2/fvjCZTKLl4YcfFm2Tm5uLwYMHo169emjSpAmmTJkCu13aR9FkkRfFIcnxnkv2IC51OP5MU0mRHGY3S5OccrM0yakwSZIcs90kWXJM5SbJkmOuMEmWHHOFvAuc7P3L5UuO3D44sVRcXuTi7BO3yFI5Exae8O5nTZTqN+E7uono3HfffTh16hQ2bdqEiooKjB49GuPGjcOqVas87jd27FjMnDlTeFyvXj3h/5WVlRg8eDBiYmLw448/4tSpUxg5ciSCg4Mxe/Zsxc6lJnqsx1EDHtEbJ1qvw6EITh3HVSmCQ/U3taOH6E0g38k8kNGF6Ozfvx/r16/Hzz//jO7duwMAFi1ahEGDBmHBggWIjY2tdd969eohJibG7XMbN27Evn378O233yI6OhpdunTBrFmz8PTTT2P69OkICQlR5Hyqo/d6HH9Ec0hwvEBHc+GQ4KjbD5IbfmhdbizlDBZG8+joInWVnZ2NyMhIQXIAIDk5GWazGTt27PC47/vvv4/GjRujU6dOyMjIwIULF0TtJiQkIDo6WliXkpKC4uJi7N27t9Y2y8rKUFxcLFqkQJLjGR7pKSf+TlP5iuQUFaBKFEeK5OgxRcUrPSVHcnilWAIpNaVkWopSUvpDFxGdvLw8NGnSRLQuKCgIjRo1Ql5eXq373XvvvYiPj0dsbCz++9//4umnn8bBgwexevVqod3qkgNAeOyp3Tlz5mDGjBlST4dSVR7gGb0BaD4ct/g5TRWIERytRG8AfhEcrUdvAjlyQ3hGVdGZOnUq5s2b53Gb/fv3S25/3Lhxwv8TEhLQtGlTDBgwAEeOHEHr1q0lt5uRkYH09HThcXFxMeLi4rza1yiSwzuaw1twAEpTuUCCo+i+gHYER6vpKT2lpkhujIOqojN58mSkpaV53KZVq1aIiYnB6dOnRevtdjsKCgpqrb9xR1JSEgDg8OHDaN26NWJiYrBz507RNvn5+QDgsV2r1Qqr1er1cZ3oPVXFGyXkBvBfFIcExz0kOOr0AdBm9IbkRty+EWpeePHCCy/g66+/Rk5ODkJCQlBYWOiyjcnk+jfhgw8+wIgRI7w+jqqiExUVhaioqDq369WrFwoLC7Fr1y5069YNALBlyxY4HA5BXrwhJycHANC0aVOh3RdeeAGnT58WUmObNm2CzWZDx44dfTyb2lEjigNoV3L0LjiA9DocSZDg1LGv5F1VFxwjp6f0kpryh9wQ7ikvL8fw4cPRq1cvLF26tNbtli9fjtTUVOFxZGSkT8fRRY1Ohw4dkJqairFjx2LJkiWoqKjA+PHjMWLECGHE1cmTJzFgwAC888476NGjB44cOYJVq1Zh0KBBuOKKK/Df//4XkyZNwo033ojExEQAwMCBA9GxY0c88MADePHFF5GXl4fnnnsOjz32mKSIjTuMKjmX7EE+pa+UkhuABMcFHQ0VJ8GRDsmNdEhutIGz1nXFihUet4uMjPQpe1MTXYy6AqpGT7Vv3x4DBgzAoEGDcP311+PNN98Unq+oqMDBgweFUVUhISH49ttvMXDgQLRv3x6TJ0/GsGHD8OWXXwr7WCwWfPXVV7BYLOjVqxfuv/9+jBw5UjTvjhzkpKq0LDlOPI2Kcj7Hc+SUO/yZpiLJcUXqSCq9j6KSvL8GR0/xQKlRUzxRerSU0Udj1RxpXFZW5rdjP/bYY2jcuDF69OiBZcuWgfk4ZF4XER0AaNSokcfJAVu0aCE6+bi4OPznP/+ps934+Hh88803XProxKhRnNpQUmRqg6I4NdBJmooiONLRWgRHD9EbJaVDD0JjLnfAzOTdlshcUbV/zQE3WVlZmD59uqy2vWHmzJno378/6tWrh40bN+LRRx9FSUkJJkyY4HUbuhEdvRBokuNvSHBqQIKj2L6AcQRHq+kpkhv9cOLECdhsNuFxbeUd3o6mbt++vVfHnTZtmvD/rl27orS0FPPnzyfRUYsqyQn2fT8ZggMEhuRIFRzAP6OpSHBqOR4JjiQoeuM7SqakCMBms4lEpza8HU0tlaSkJMyaNQtlZWVe19KS6KgMSU7dGDKKY3DBAaRLjlqCo7bcAMYWHJKbwMDb0dRSycnJQcOGDX0aMESioyIkOZ4xpOAAfpUcEhwv9jWI4GhRbgDtz6isdLtOzOVVtS7OmhcCyM3NRUFBAXJzc1FZWSlMAdOmTRuEh4fjyy+/RH5+Pnr27InQ0FBs2rQJs2fPxpNPPunTcUh0VIAExzOUpqqBTqI4JDi+Y1TBIbmpwik3hHsyMzOxcuVK4XHXrl0BAFu3bkXfvn0RHByMxYsXY9KkSWCMoU2bNli4cCHGjh3r03FIdPwMSU7t+FNwAEpT1YQER/ljOyHB8Q4lRITkRjusWLHC4xw6qampookCpUKi40dIctxDglMDEhxl9jVAgTHJjfbaBUhutA6Jjh+QKzgASY47/CE5ekhT6UVwqvb1734ACU51tCo4JDfKYKlwwCJzHh1m1/551gWJjsJQFMc9WhccwLhRHBIc5Y8NGFNw9BC9CXS5IVwh0VEIiuK4R47gABouNtaB4AD6SVOpOQ+OUQQnkKI3JDeEJ0h0FIAkxxV/Cw6g8TQVCY4y+5LgBEz0huSG8BYSHc6Q5LhCaaoaGLgOhwTH/8cX+qFBwdFT9EZpubH8r30j1LzoDRIdjliCpN0CwgkJjhjNRnF0kKYiwVH2uAAJjif0Er3xl9wQ6kKioxGMJDmGFRzAsGmqQCo0NoLgkNxIh+Qm8CDR0QAkOZfRrOQYVHAAfRUak+CQ4EiB5CawIdFRGaNIDglODagOh9t+AAmO1gQn0OVGL2JjLnfA7JDXV7MBaopIdFSCBKcKwxUbUx0Ot/0AEhweAkDRGz7oRW4IV0h0VMAIkiNXcACDRXF0kKbSU6ExCY52BIfkhtA7JDp+hiRHw1EcSlO52U/SbiQ4Uo5v4PSUnuQGIMExGiQ6fkTvkkNRnBqQ4PDbjwRHNiQ48lBabpz9NkLNi94g0fEDJDgGi+JQHQ63/QDpkkOCo025UaI9vcsNoS4kOgqjZ8lRS3AAP0RxDJqmCgTBkXtcEpzLaDl6Q3JD8IJER0FIcihNJQV/RnECqdCYBOcygSY4SsoNiY32IdFRABIcA0VxKE3Fbz8SHNX6wKsfSrQFkNwohbnMDnOlvOuR2a7f65kTEh3O6FVyeAgOQFEcSlO52Y8ER7U+8OqHEm0B+hIcvcgN4QqJDkesIZXQ40tKUZwaaFxwAP+mqUhwfDy+TBnQWnqK5IbQO/q7KhPcoChODShNJXsfYV8VRlKR4PDpB+92AH3JDUCCYzRIdAIQNQUHoCgOpan4HQ8gweHVD97tAPoSHKXlxlxWVdZghJoXvUGiE2BQFKcGJDh89iPB8fvxefSBdztOeEuD3uWGUBcSnQCBojhu8JPkUB0O32OS4GhTcEhu/tc+yY3mINExOLwEB9BoFEfDggNoP4qjJ8EB5EkOCQ7/dgB9CA7JTWBDomNgKIpTA40XG1OaqnZIcLQlOHqQG0BZwdGD3JjKHTBVVspro1L/hdkkOgZEb1EcwEfJ0XAUh9JU/I4HkODwEBOK3nBsWwdyQ7hComMg1BYcwBhRHEpT1diPBMevx+fRB15tOOEpDyQ3hL+ReGdDQmvwTFNJjeIoKjnlZs1Kjqnc5LPkmCtMkqM4RpYcS4V0yZH62gjHLmey5IDX8eUKCo82gCp5cC48sJQ7uEsOz/6J2i2zCwuhHC+88AJ69+6NevXqITIy0u02P//8MwYMGIDIyEg0bNgQKSkp+O2333w6jm5Ep6CgAPfddx9sNhsiIyMxZswYlJSU1Lr9sWPHYDKZ3C6ffPKJsJ275z/88EN/nBIXysqDdFmLo2iqqsLks+SY7SafJUeK4ADS01RSBUfSfuXSJEfq8Uhw+ERweAoOD5xyw1NweAuY0K7CcmMqq6xaaDJCgfLycgwfPhyPPPKI2+dLSkqQmpqK5s2bY8eOHfjhhx/QoEEDpKSkoKLC+y+dblJX9913H06dOoVNmzahoqICo0ePxrhx47Bq1Sq328fFxeHUqVOidW+++Sbmz5+Pm2++WbR++fLlSE1NFR7XZpZaQ2+CAxgnVUVpKj7HA+QJjhzUTlEZucBYiciNEigdsTGVySsENjozZswAAKxYscLt8wcOHEBBQQFmzpyJuLg4AEBWVhYSExNx/PhxtGnTxqvj6EJ09u/fj/Xr1+Pnn39G9+7dAQCLFi3CoEGDsGDBAsTGxrrsY7FYEBMTI1q3Zs0a3HXXXQgPDxetj4yMdNlWy1Atjhs0LDiA9ouN9SQ4so9rAMEJBLkBlBEckhv90K5dO1xxxRVYunQpnnnmGVRWVmLp0qXo0KEDWrRo4XU7ukhdZWdnIzIyUpAcAEhOTobZbMaOHTu8amPXrl3IycnBmDFjXJ577LHH0LhxY/To0QPLli0DY3xnCeUFzzQVELiSIyVNBUiP4vgqOWqkqaSgtzocI6SotJye4oXSqSklENJSBpac4uJi0VJWVqb4MRs0aIBt27bhvffeQ1hYGMLDw7F+/XqsW7cOQUHeXwt1ITp5eXlo0qSJaF1QUBAaNWqEvLw8r9pwWmDv3r1F62fOnImPP/4YmzZtwrBhw/Doo49i0aJFHtsqKytzedOVhrfg+KPgWFItji+SI7EWx1f8XWwsZR+qw/FwXBIcAT0IDk+UrLvRg9yYyyq4LEBVOUhERISwzJkzx+0xp06dWmt9rHM5cOCAV/2/ePEixowZg+uuuw4//fQTtm/fjk6dOmHw4MG4ePGi16+DqqmrqVOnYt68eR632b9/v+zjXLx4EatWrcK0adNcnqu+rmvXrigtLcX8+fMxYcKEWtubM2eOkFtUGp6CAwR2FMdXKE0l/1gApajUbgPglwLSQ+2NkqkpLUuN0pw4cQI2m014bLVa3W43efJkpKWleWyrVatWXh1z1apVOHbsGLKzs2E2m4V1DRs2xOeff44RI0Z41Y6qouPtCxITE4PTp0+L1tvtdhQUFHhVW/Ppp5/iwoULGDlyZJ3bJiUlYdasWSgrK6v1jczIyEB6errwuLi4WCiU4oke01SAwpP/abgWR+uCA0iP4EhFjUJjEpwqtFx/oyfBCWS5qY7NZhOJTm1ERUUhKiqKyzEvXLgAs9kMk+ny31bnY4fD+8+QqqLj7QvSq1cvFBYWYteuXejWrRsAYMuWLXA4HEhKSqpz/6VLl2LIkCFeHSsnJwcNGzasVXKAKpP19LxcKIpTCxqN4tBoKldIcNRrQ6uCoye5AUhw/EFubi4KCgqQm5uLyspK5OTkAADatGmD8PBw3HTTTZgyZQoee+wxPP7443A4HJg7dy6CgoLQr18/r4+ji1FXHTp0QGpqKsaOHYslS5agoqIC48ePx4gRI4QRVydPnsSAAQPwzjvvoEePHsK+hw8fxnfffYdvvvnGpd0vv/wS+fn56NmzJ0JDQ7Fp0ybMnj0bTz75pN/OrSYUxXEDRXEk76cXwZFzTECeIJDgiNF69EbPcmMuq4C5ksPU2QYhMzMTK1euFB537doVALB161b07dsX7du3x5dffokZM2agV69eMJvN6Nq1K9avX4+mTZt6fRxdiA4AvP/++xg/fjwGDBgAs9mMYcOG4dVXXxWer6iowMGDB3HhwgXRfsuWLUOzZs0wcOBAlzaDg4OxePFiTJo0CYwxtGnTBgsXLsTYsWMVP5+aUBSnFiiKo4s0FQmOem1oUXD0FL3xh9wQ7lmxYkWtc+g4uemmm3DTTTfJOo6JaXUstY4oLi5GREQEOn08BZZ6vqe0tBDFATQmORTFoTSVp2OS4ASE4BhRbuyVZfj2j4UoKiryquZFCs5rUvJV6QiyyCuz8Ed/lUY3ER0jotcoDqCtVJXRBEfqfiQ4yh1X7rF5tkGCI42AjNxUVACVMmeRcWjwvHyEREclKIpTCwEuOZSmquWYJDhcZCLQ5AZQVnA0KTeECyQ6foaiOLVAgiMJiuJo87i82qDojTRIbojqkOj4Eb1Kjt6jOEYsNibB0eZxebVhdMEhuSH8CYmOn6BUlRsoiiMJPaSpSHCkoTXBCeToDcmNcSDRURi9RnEAfaeqtCw4UvfzZxSH6nD8uz8Jjm/oOnpTQQLlb0h0FISiOG6gKI7v+xg8TUWCIx8tCg5Fb6pRTnKjJiQ6ChAwURxAU5KjZcGRuh+lqfgfU85xee1v5BFUeonekNwEDiQ6nAkYyaFUlbL76CBNFYh1OEYSHC3LDaCM4ASc3JSVA2ZpAysEHBL/GGkIEh2OlJVbYOH0ilKqyjuMJjiAsaM4JDhy+mB8wdFl9EZrckO4QKKjQSiK4x1GkxwSHL7HlHtcHvuT4HiH7qI3JDe6gkRHYwSi5Pij4FjLggNoX3L0VodDgnMZrQoOyQ3hL0h0NAKlqryDojgUxVHyuCQ4btqh6I1ybRN+gURHA1AUxzuMFMUxcrExCY7UPpDg+ALJDeEtJDoqYwjJoSiOb/toPIoTSGkqowgOpac4oLTclP3vi2+AUUx6g0RHJShV5R0UxTF2FEevdTgkOJ7RTfTGX3JDqAqJjgoYIooDKJqqoiiOsQVHrWPy2F+uXGhJcLQsN4BOBUdLcnOpHPDxz7oLBohAkej4GUNIDkVxvN/HoGkqqsORcnwSHG/QpdwA2hIcQgSJjp+gVFXdUBTH2FEcEhz1+iC0E2iCQ3JDgETHL2g2igMoJjkUxVH+OIA+ojgkOOr1QWiHBIcfJDe6g0RHYTQrORqJ4gAkORTF4Xc8ufsCJDjuILkhudEzJDoK4i/JCZQoDuC75JDgSD8WEFhRHBIcVwJacEhuDAOJjgIEYj0ORXGUPw6lqZTZlwTHFZ6Coyu5AZQXnEskUP6GRIczhkhVAbqWHBIc6ccCAidNZRTB0Wr9ja4ER3G5KVO2fcIjJDocqbRbYA7xfT9NSY6OBadqH593MZzkkODUjdqSQ4LjAyQ3kmHlZWAmed8VxvQfgSLRURlKVdXSPkVxJCHpWJSm8uHYxhEcTcsNoE/B0YDcEK6Q6KhIIEgOFRz7vg9FcfjvS4JzGU0LDskNoQAkOiqg11QVoKzk+CNVZTTBkXwsEhwvj02C4w5dCE4ApKYI75B7FwzCR6REcbQgOWa7SVOSY64gyZHyGgDSxEHqsaQez7mfZCErd8iSDEu5Q5bkyD0+UCU4PCTHVFbJTXLMZRV8Jae8gr/klJUrJzmXyi4vhCyOHTuGMWPGoGXLlggLC0Pr1q2RlZWF8vLL792lS5eQlpaGhIQEBAUF4bbbbpN0LIro+BFKVblpm6I4fjkOQFEc745LERx3cJcb3lBqSnccOHAADocDb7zxBtq0aYM9e/Zg7NixKC0txYIFCwAAlZWVCAsLw4QJE/DZZ59JPhaJjp9QVHI0MnScCo59w1+SQ4LjzXHVHyquNcEJ6PQUyY3ipKamIjU1VXjcqlUrHDx4EK+//rogOvXr18frr78OANi+fTsKCwslHYtER2H0Wo+j91FVJDj/O5ZOJCeQ63BIcHxEx9EbVlam2+HaxcXFosdWqxVWq5XrMYqKitCoUSOubQIkOopCqSo3bVOqyj/H0YngyNmXBKcKTQoORW8EWJl60SHHxUtwmOR9Phys6r2Mi4sTrc/KysL06dNltV2dw4cPY9GiRUI0hyckOgpBkuOmbQ2mqowmOID0YmN/HYvHvnquwyHB8QGSG81w4sQJ2Gw24XFt0ZypU6di3rx5Htvav38/2rdvLzw+efIkUlNTMXz4cIwdO5ZPh6tBoqMAJDlu2qZUlfLH0UkUhwRHHiQ4MiDBkYzNZhOJTm1MnjwZaWlpHrdp1aqV8P+///4b/fr1Q+/evfHmm2/K7aZbdDO8/IUXXkDv3r1Rr149REZGerUPYwyZmZlo2rQpwsLCkJycjEOHDom2KSgowH333QebzYbIyEiMGTMGJSUlkvpoL7coX3TsreRUmLyWHCWHjpsrTD5Jjj+GjZvLfZccKf2yVPguOXoYMi512Ldaw8W1MlRcLryGiXMdIs57eLgSQ8MVHhLOysqEhQCioqLQvn17j0tISNW9kk6ePIm+ffuiW7duWL58OcxmZZREN6JTXl6O4cOH45FHHvF6nxdffBGvvvoqlixZgh07dqB+/fpISUnBpUuXhG3uu+8+7N27F5s2bcJXX32F7777DuPGjVPiFFzQW9Gxqdzks+T4ghTBCfRUlVR5oCiOd8fVwlw4mhMcp9zwEhyn3CglOApAciMfp+Q0b94cCxYswJkzZ5CXl4e8vDzRdvv27UNOTg4KCgpQVFSEnJwc5OTk+HQs3aSuZsyYAQBYsWKFV9szxvDyyy/jueeew9ChQwEA77zzDqKjo7F27VqMGDEC+/fvx/r16/Hzzz+je/fuAIBFixZh0KBBWLBgAWJjYxU5F0pVudvep839kqrSsuAA+qjF0aPgyD02wC+CI5eAS08pmJoiqeHLpk2bcPjwYRw+fBjNmjUTPcfY5b8bgwYNwvHjx4XHXbt2ddmmLnQT0fGVo0ePIi8vD8nJycK6iIgIJCUlITs7GwCQnZ2NyMhIQXIAIDk5GWazGTt27FCkX1qQHEpV8T8GYMwojpx0UyCmqbQSwQnY9JQCUORGGdLS0sAYc7tU59ixY3VuUxe6iej4ijP8FR0dLVofHR0tPJeXl4cmTZqIng8KCkKjRo1cwmfVKSsrQ1m1D35RUREAwHHR85eBVWogVVVpgrd/xp2C4+1HylzhfdsAYKoAfPlzbv7f9cOnfcp93L7C92NYpPRLynH+Jw6+XgKlHKtqPwYpl2xnP33d1ykYUjTDUuGQdEwexwYuR3DkxIFMXGZVrpDdD4EKznLDm0vKzUfDypUTG/v/hmv7erGWdCxUeP8H3FMbOkdV0ZEyDE0LzJkzR0ilVeevJzyfC0EQBEEAwLlz5xAREaFI2yEhIYiJicH3eV9yaS8mJkYoINYjqoqOr8PQfCEmJgYAkJ+fj6ZNmwrr8/Pz0aVLF2Gb06dPi/az2+0oKCgQ9ndHRkYG0tPThceFhYWIj49Hbm6uYh9cpSkuLkZcXJzLXAl6wwjnYYRzAOg8tIQRzgEwxnkUFRWhefPmiswA7CQ0NBRHjx4V3SBTDiEhIQgNDeXSlhqoKjpRUVGIiopSpO2WLVsiJiYGmzdvFsSmuLgYO3bsEEZu9erVC4WFhdi1axe6desGANiyZQscDgeSkpJqbbu2qa8jIiJ0++Vz4u1cCVrHCOdhhHMA6Dy0hBHOATDGeSg1lNpJaGioruWEJ7opRs7NzUVOTg5yc3NRWVkpDDGrPudN+/btsWbNGgCAyWTCxIkT8fzzz+OLL77A77//jpEjRyI2Nla41XuHDh2QmpqKsWPHYufOndi+fTvGjx+PESNGKDbiiiAIgiAI/6GbYuTMzEysXLlSeOwcYrZ161b07dsXAHDw4EGhMBgAnnrqKZSWlmLcuHEoLCzE9ddfj/Xr14ss9/3338f48eMxYMAAmM1mDBs2DK+++qp/ToogCIIgCEXRjeisWLGizjl0alaxm0wmzJw5EzNnzqx1n0aNGmHVqlWy+ma1WpGVlcX9Tq7+xAjnABjjPIxwDgCdh5YwwjkAxjgPI5yD3jAxf4xxIwiCIAiCUAHd1OgQBEEQBEH4CokOQRAEQRCGhUSHIAiCIAjDQqJDEARBEIRhIdHxghdeeAG9e/dGvXr1EBkZ6dU+jDFkZmaiadOmCAsLQ3JyMg4dOiTapqCgAPfddx9sNhsiIyMxZswY0bxAvPH1eMeOHYPJZHK7fPLJJ8J27p7/8MMPNXEOANC3b1+X/j388MOibXJzczF48GDUq1cPTZo0wZQpU2C3y78DNa/zKCgowOOPP4527dohLCwMzZs3x4QJE0TTKQDKvxeLFy9GixYtEBoaiqSkJOzcudPj9p988gnat2+P0NBQJCQk4JtvvhE97833hDe+nMNbb72FG264AQ0bNkTDhg2RnJzssn1aWprLa56amqroOQC+nceKFStc+lhzMjmtvxfuvscmkwmDBw8WtlHjvfjuu+9w6623IjY2FiaTCWvXrq1zn23btuGaa66B1WpFmzZt3I4o9vW7RniAEXWSmZnJFi5cyNLT01lERIRX+8ydO5dFRESwtWvXst9++40NGTKEtWzZkl28eFHYJjU1lXXu3Jn99NNP7Pvvv2dt2rRh99xzj0Jn4fvx7HY7O3XqlGiZMWMGCw8PZ+fPnxe2A8CWL18u2q76eap5Dowx1qdPHzZ27FhR/4qKikTn2alTJ5acnMx+/fVX9s0337DGjRuzjIwMRc5Bynn8/vvv7I477mBffPEFO3z4MNu8eTNr27YtGzZsmGg7Jd+LDz/8kIWEhLBly5axvXv3srFjx7LIyEiWn5/vdvvt27czi8XCXnzxRbZv3z723HPPseDgYPb7778L23jzPeGJr+dw7733ssWLF7Nff/2V7d+/n6WlpbGIiAj2119/CduMGjWKpaamil7zgoICRfov9TyWL1/ObDabqI95eXmibbT+Xpw7d07U/z179jCLxcKWL18ubKPGe/HNN9+wZ599lq1evZoBYGvWrPG4/Z9//snq1avH0tPT2b59+9iiRYuYxWJh69evF7bx9bUhPEOi4wPLly/3SnQcDgeLiYlh8+fPF9YVFhYyq9XKPvjgA8YYY/v27WMA2M8//yxss27dOmYymdjJkye5953X8bp06cIefPBB0Tpvvtw8kHoOffr0YU888UStz3/zzTfMbDaL/vC//vrrzGazsbKyMi59rw6v9+Ljjz9mISEhrKKiQlin5HvRo0cP9thjjwmPKysrWWxsLJszZ47b7e+66y42ePBg0bqkpCT2f//3f4wx774nvPH1HGpit9tZgwYN2MqVK4V1o0aNYkOHDuXdVY/4eh51/e3S43vx0ksvsQYNGrCSkhJhnRrvRXW8+f499dRT7Oqrrxatu/vuu1lKSorwWO5rQ4ih1JUCHD16FHl5eUhOThbWRUREICkpCdnZ2QCA7OxsREZGonv37sI2ycnJMJvN2LFjB/c+8Tjerl27kJOTgzFjxrg899hjj6Fx48bo0aMHli1b5jJ5Iw/knMP777+Pxo0bo1OnTsjIyMCFCxdE7SYkJCA6OlpYl5KSguLiYuzdu1dT51GdoqIi2Gw2BAWJ5/1U4r0oLy/Hrl27RJ9ps9mM5ORk4TNdk+zsbNH2QNXr6tzem+8JT6ScQ00uXLiAiooKlxsybtu2DU2aNEG7du3wyCOP4Ny5c1z7Xh2p51FSUoL4+HjExcVh6NChos+2Ht+LpUuXYsSIEahfv75ovT/fCynU9b3g8doQYnQzM7KeyMvLAwDRhdP52PlcXl4emjRpIno+KCgIjRo1Erbh3Se5x1u6dCk6dOiA3r17i9bPnDkT/fv3R7169bBx40Y8+uijKCkpwYQJE7j1H5B+Dvfeey/i4+MRGxuL//73v3j66adx8OBBrF69WmjX3XvlfI43PN6Ls2fPYtasWRg3bpxovVLvxdmzZ1FZWen2dTpw4IDbfWp7Xat/B5zratuGJ1LOoSZPP/00YmNjRReh1NRU3HHHHWjZsiWOHDmCZ555BjfffDOys7NhsVi4ngMg7TzatWuHZcuWITExEUVFRViwYAF69+6NvXv3olmzZrp7L3bu3Ik9e/Zg6dKlovX+fi+kUNv3ori4GBcvXsQ///wj+3NKiAlY0Zk6dSrmzZvncZv9+/ejffv2fuqRNLw9D7lcvHgRq1atwrRp01yeq76ua9euKC0txfz5872+uCp9DtVlICEhAU2bNsWAAQNw5MgRtG7dWnK7NfHXe1FcXIzBgwejY8eOmD59uug5ue8FUTtz587Fhx9+iG3btokKeUeMGCH8PyEhAYmJiWjdujW2bduGAQMGqNFVF3r16oVevXoJj3v37o0OHTrgjTfewKxZs1TsmTSWLl2KhIQE9OjRQ7ReD+8F4X8CVnQmT56MtLQ0j9u0atVKUtsxMTEAgPz8fDRt2lRYn5+fjy5dugjbnD59WrSf3W5HQUGBsL83eHseco/36aef4sKFCxg5cmSd2yYlJWHWrFkoKyvz6n4u/jqH6v0DgMOHD6N169aIiYlxGdGQn58PAJp7L86fP4/U1FQ0aNAAa9asQXBwsMftfX0vaqNx48awWCzC6+IkPz+/1j7HxMR43N6b7wlPpJyDkwULFmDu3Ln49ttvkZiY6HHbVq1aoXHjxjh8+LAiF1c55+EkODgYXbt2xeHDhwHo670oLS3Fhx9+6PEehk6Ufi+kUNv3wmazISwsDBaLRfb7S9RA7SIhPeFrMfKCBQuEdUVFRW6LkX/55Rdhmw0bNihejCz1eH369HEZ4VMbzz//PGvYsKHkvtYGr9fshx9+YADYb7/9xhi7XIxcfUTDG2+8wWw2G7t06RK/E/gfUs+jqKiI9ezZk/Xp04eVlpZ6dSye70WPHj3Y+PHjhceVlZXsyiuv9FiMfMstt4jW9erVy6UY2dP3hDe+ngNjjM2bN4/ZbDaWnZ3t1TFOnDjBTCYT+/zzz2X3tzaknEd17HY7a9euHZs0aRJjTD/vBWNVf4etVis7e/Zsncfwx3tRHXhZjNypUyfRunvuucelGFnO+0uIIdHxguPHj7Nff/1VGFr966+/sl9//VU0xLpdu3Zs9erVwuO5c+eyyMhI9vnnn7P//ve/bOjQoW6Hl3ft2pXt2LGD/fDDD6xt27aKDy/3dLy//vqLtWvXju3YsUO036FDh5jJZGLr1q1zafOLL75gb731Fvv999/ZoUOH2L///W9Wr149lpmZqYlzOHz4MJs5cyb75Zdf2NGjR9nnn3/OWrVqxW688UZhH+fw8oEDB7KcnBy2fv16FhUVpfjwcl/Oo6ioiCUlJbGEhAR2+PBh0fBZu93OGFP+vfjwww+Z1WplK1asYPv27WPjxo1jkZGRwmi1Bx54gE2dOlXYfvv27SwoKIgtWLCA7d+/n2VlZbkdXl7X94Qnvp7D3LlzWUhICPv0009Fr7nzu3/+/Hn25JNPsuzsbHb06FH27bffsmuuuYa1bdtWEUmWeh4zZsxgGzZsYEeOHGG7du1iI0aMYKGhoWzv3r2ic9Xye+Hk+uuvZ3fffbfLerXei/PnzwvXBABs4cKF7Ndff2XHjx9njDE2depU9sADDwjbO4eXT5kyhe3fv58tXrzY7fByT68N4RskOl4watQoBsBl2bp1q7AN/jd/iROHw8GmTZvGoqOjmdVqZQMGDGAHDx4UtXvu3Dl2zz33sPDwcGaz2djo0aNF8sSbuo539OhRl/NijLGMjAwWFxfHKisrXdpct24d69KlCwsPD2f169dnnTt3ZkuWLHG7rRrnkJuby2688UbWqFEjZrVaWZs2bdiUKVNE8+gwxtixY8fYzTffzMLCwljjxo3Z5MmTRcO21T6PrVu3uv0MAmBHjx5ljPnnvVi0aBFr3rw5CwkJYT169GA//fST8FyfPn3YqFGjRNt//PHH7KqrrmIhISHs6quvZl9//bXoeW++J7zx5Rzi4+PdvuZZWVmMMcYuXLjABg4cyKKiolhwcDCLj49nY8eO9csFyZfzmDhxorBtdHQ0GzRoENu9e7eoPa2/F4wxduDAAQaAbdy40aUttd6L2r6bzr6PGjWK9enTx2WfLl26sJCQENaqVSvRtcOJp9eG8A0TYwqMAyYIgiAIgtAANI8OQRAEQRCGhUSHIAiCIAjDQqJDEARBEIRhIdEhCIIgCMKwkOgQBEEQBGFYSHQIgiAIgjAsJDoEQRAEQRgWEh2CIAiCIAwLiQ5BEJrn0qVLSEtLQ0JCAoKCgnDbbbep3SWCIHQCiQ5BELJhjMFutyvWfmVlJcLCwjBhwgQkJycrdhyCIIwHiQ5BBCB9+/bF+PHjMX78eERERKBx48aYNm0anHeEeffdd9G9e3c0aNAAMTExuPfee3H69Glh/23btsFkMmHdunXo1q0brFYrfvjhBxw5cgRDhw5FdHQ0wsPDce211+Lbb78VHbtFixZ4/vnnMXLkSISHhyM+Ph5ffPEFzpw5g6FDhyI8PByJiYn45ZdfhH3q16+P119/HWPHjkVMTIx/XiSCIAwBiQ5BBCgrV65EUFAQdu7ciVdeeQULFy7E22+/DQCoqKjArFmz8Ntvv2Ht2rU4duwY0tLSXNqYOnUq5s6di/379yMxMRElJSUYNGgQNm/ejF9//RWpqam49dZbkZubK9rvpZdewnXXXYdff/0VgwcPxgMPPICRI0fi/vvvx+7du9G6dWuMHDkSdCs+giDkQjf1JIgApG/fvjh9+jT27t0Lk8kEoEpavvjiC+zbt89l+19++QXXXnstzp8/j/DwcGzbtg39+vXD2rVrMXToUI/H6tSpEx5++GGMHz8eQFVE54YbbsC7774LAMjLy0PTpk0xbdo0zJw5EwDw008/oVevXjh16pRLBCctLQ2FhYVYu3at3JeBIIgAgCI6BBGg9OzZU5AcAOjVqxcOHTqEyspK7Nq1C7feeiuaN2+OBg0aoE+fPgDgEpnp3r276HFJSQmefPJJdOjQAZGRkQgPD8f+/ftd9ktMTBT+Hx0dDQBISEhwWVc9XUYQBCEFEh2CIERcunQJKSkpsNlseP/99/Hzzz9jzZo1AIDy8nLRtvXr1xc9fvLJJ7FmzRrMnj0b33//PXJycpCQkOCyX3BwsPB/p2y5W+dwOPidGEEQAUmQ2h0gCEIdduzYIXr8008/oW3btjhw4ADOnTuHuXPnIi4uDgBEhcGe2L59O9LS0nD77bcDqIrwHDt2jGu/CYIgfIEiOgQRoOTm5iI9PR0HDx7EBx98gEWLFuGJJ55A8+bNERISgkWLFuHPP//EF198gVmzZnnVZtu2bbF69Wrk5OTgt99+w7333sstKrNv3z7k5OSgoKAARUVFyMnJQU5ODpe2CYIwLhTRIYgAZeTIkbh48SJ69OgBi8WCJ554AuPGjYPJZMKKFSvwzDPP4NVXX8U111yDBQsWYMiQIXW2uXDhQjz44IPo3bs3GjdujKeffhrFxcVc+jto0CAcP35ceNy1a1cAoJFZBEF4hEZdEUQA0rdvX3Tp0gUvv/yy2l0hCIJQFEpdEQRBEARhWEh0CIIgCIIwLJS6IgiCIAjCsFBEhyAIgiAIw0KiQxAEQRCEYSHRIQiCIAjCsJDoEARBEARhWEh0CIIgCIIwLCQ6BEEQBEEYFhIdgiAIgiAMC4kOQRAEQRCGhUSHIAiCIAjD8v/vVjD8IbyzFAAAAABJRU5ErkJggg==", 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88glOnDjhtu/8+fPxySef4JprrsEDDzwAm82GF198EZ07d+bXkgKAbt26YezYsXjllVdQXl6Ofv36Ye/evXj99dcxfPhwXH/99bLv8ZZbbsHChQsxfvx49OnTBz///DM2bdokqSbIH9LS0jBr1iwsWLAAgwcPxq233oojR47gpZdewlVXXYW77roroNfn2LhxI8aPH48NGzb47DfkSs+ePQEADz/8MAYNGgSDwYCRI0eiX79+uO+++7BkyRIcOHAAAwcORHR0NI4ePYotW7bgueeew+233+713AMHDoTRaMTQoUNx3333oaqqCuvWrUN6ejrOnj3rNo41a9bgqaeeQrt27ZCenu4WeQEcv8/Lli3D+PHj0a9fP4waNQqlpaV47rnnkJOTg0cffVTW/XPMmjULr7/+Ok6cOOG1CHjgwIFu2zgJ69evH3r16qXo+gThLyQzRFC544470KFDByxZsoQXmGbNmuH666/H7Nmz0blzZ9WvOWnSJNhsNjzzzDN4/PHH0aVLF3zwwQeYM2eOz2M3b96Mhx56CKtXrwZjDAMHDsTHH38smLUEOD6MPv74Y0yfPh1z5sxBixYtsHDhQhw+fBi//vqrYN9XX30Vbdq0wcaNG7Ft2zZkZmZi1qxZmDdvnqL7mz17Nqqrq7F582a8/fbbuPLKK/HRRx9h5syZis4nh/nz5yMtLQ0vvvgiHn30UTRt2hSTJk3C008/HbRlIaqqqgA4onNy+cc//oGHHnoIBQUF+Pe//w3GGEaOHAkAWLt2LXr27ImXX34Zs2fPRlRUFHJycnDXXXfhmmuu8Xnu9u3b45133sGTTz6J6dOnIzMzEw888ADS0tIwYcIEwb5z587FqVOnsHz5clRWVqJfv36iMgM4ZuLFxcVh6dKlmDFjBt/QcNmyZap2xCYILaFjga6kI4hGzPDhw3Ho0CG3OhtCPUaMGIGTJ09i7969oR4KQRAhgmpmCEIlampqBM+PHj2K7du3863pCfVhjKGwsJDWBiKIRg5FZghCJbKysvh1nE6dOoU1a9agrq4OP/zwg1ufG4IgCEI9qGaGIFRi8ODBeOutt1BSUgKTyYS8vDw8/fTTJDIEQRABRlNppi+//BJDhw5FdnY2dDod3nvvPZ/HFBYW4sorr4TJZEK7du0EKyJzrF69Gjk5OYiJiUFubi7l3glFbNiwASdPnkRtbS3MZjN27NiBK6+8MtTDIgiCiHg0JTPV1dXo1q0bVq9eLWn/EydOYMiQIbj++utx4MABTJ06Fffeey927tzJ7/P2229j2rRpmDdvHr7//nt069YNgwYNcmtXThAEQRBEeKLZmhmdTodt27Zh+PDhHveZMWMGPvroIxw8eJDfNnLkSJSXl2PHjh0AHAvXXXXVVXjxxRcBONqYt2jRAg899FBQprYSBEEQBOEfEV0zU1RU5NZSfdCgQZg6dSoAR9v1/fv3Y9asWfzrer0e+fn5KCoq8njeuro6QfdNu92OsrIyNGvWTHJbcoIgCKLxwRhDZWUlsrOzA7pYZ21traDruT8YjUbBKu3hSETLTElJCTIyMgTbMjIyUFFRgZqaGly8eBE2m010H9dGZ84sWbIECxYsCMiYCYIgiMjn9OnTuOyyywJy7traWuS0SkDpOZsq58vMzMSJEyfCWmgiWmYCxaxZszBt2jT+udlsRsuWLfH615cjLkHdVWobI031l0I9BE2TpK/zvVOISQxBtV6i3hT8iwaZCrvv975CwmLdZrv3n5W33zFf763z++A6Xm5sZrsJZfY4AEC5LR5meywqbDEotzrW0qqsd5yjyub4cE0w1PLnaBJVh+ToaiT+tS1JX4NkQzX/74rz2L2N1fXnxP1MXMcFABV/jaPcGi8YW5XV8bjSYkSN1QgAqK6w4dfxz6NJkyaeL+4nFosFpeds+L/vLkNiE//+2Coq7fhbr99hsVhIZkJFZmYmSktLBdtKS0uRmJiI2NhYGAwGGAwG0X0yMzM9ntdkMsFkcv9jj0swIK4JyYy/1KIJmumrQz0MzZIQwNC1Wvj576siEjXwc/EHs70OTXzM6TDbgQQJ56q3ez+Pp9+xJAk/YgYrkvQmt/FyYyu3m1Brj0ccHMJgscXBYotFDACTNRoV9TEwAqisj0E0gCZRtQAcopAYVYuUaAuSDPUAopBsuIRkQy0AA+L/GjM3dm9jdf05ldtNiAdwwWVcsQDMf43tojUedYaGsdVZTYg2AhUWE6wwIsoI1FijUB/liJYEoyQhsYneb5nRChF9l3l5edi9e7dg26effoq8vDwAjjxgz549BfvY7Xbs3r2b34cgCKIxUe4jKpOsIPKXpDfxX4BDvJwx292vXW6LR7ktDmabI/px0RqPinpHZKDyr+8OkXHgEJmG/wQ5RMbxnPvPETd2XyLjDDemC/Z4wbgAeBwbF5GpsJj4iEyNNQp1luCsV9YY0ZTMVFVV4cCBA/wqxCdOnMCBAwdQXFwMwJH+GTNmDL///fffj99++w1PPPEEfv31V7z00kv4z3/+I1hZdtq0aVi3bh1ef/11HD58GA888ACqq6sxfvz4oN4bIYT7h4OQj68PIyLycJUD8X0COwZPgpDkI73nKjIX7PECYQCEsiCGs8gkGWrCWmTqLRGdEAkZmpKZ7777Dj169ECPHj0AOESkR48emDt3LgDg7NmzvNgAQOvWrfHRRx/h008/Rbdu3fCvf/0Lr776KgYNGsTvc+edd2LFihWYO3cuunfvjgMHDmDHjh1uRcFE8CGhIdTC1wcq4UBpVEZKegnwLV2cyPD722Jx0Sr8d8A1KqOWyLiiVGQqLCaPImO1RMFm1dTHrmwqKysxdepUtGrVCrGxsejTpw/27dvncf+tW7fixhtvRFpaGhITE5GXlyfoBScVzfaZCScqKiqQlJSELT92oJoZlaHaGWUoSQUEEzkfKupcL3JlRq2ojD/pJalRGW/pJVdpcBYZT+mlxL++p0RX8yIDAMmGajeR8TZO57Fw4wGUiQwA1FiNqLE6IjACkbHoYa+pxen7F8BsNiMxMdHzgPyA+0wqOdJSlQLgzPbFksd755134uDBg1izZg2ys7Px73//G88++yx++eUXNG/e3G3/qVOnIjs7G9dffz2Sk5OxYcMGrFixAnv27OEDF1KgeBcR1lywx5PQEEQYozQq46tOxpPIcISDyHBjkioyAMAiODJTU1ODd999F++//z6uu+46AMD8+fPx4YcfYs2aNaKr269atUrw/Omnn8b777+PDz/8kGSGiCxIaAhCnFBHZXyJjNle5zUqJlYnwwkDIC4yTaJq3USGH2cARIbfr5GLTEVFheC52Kze+vp62Gw2tyncsbGx+OqrryRdx263o7KyEk2bNpU1PpIZgiCIRkygZi9xoiUmXM4C4avg11VkOJxFhquTCYTIiM2okiIy1r8KfV1FRmcJntBU2OvAfEyz90Wl3fFDatGihWD7vHnzMH/+fMG2Jk2aIC8vD4sWLULHjh2RkZGBt956C0VFRWjXrp2k661YsQJVVVUYMWKErHGSzBCagKIz8ii3m8K+biZYRGq9TDBmMPlTJ+MsNGJjcq6T4V9zKfgVExlvU7Bdx6yWyDinvPwVGa0WqZ4+fVpQMyPWaw0A3nzzTUyYMAHNmzeHwWDAlVdeiVGjRmH//v0+r7F582YsWLAA77//PtLT02WNj2SGIAiikaJ0Gr/SAm65dTLOkRjA98ylcBcZANBrNNWUmJgoqQC4bdu2+OKLL1BdXY2KigpkZWXhzjvvRJs2bbweV1BQgHvvvRdbtmxxW1NRCtr8qRKNEpqqTRAOQhmVUavg11edjCuJTmKjJZHRWfQNIhPEFFOoiY+PR1ZWFi5evIidO3di2LBhHvd96623MH78eLz11lsYMmSIoutRZIbQFJRuIgjfqFH0q/zanot+lTbGE5u5FC4i41zoCzhExi0a4yQxenUWsg5bdu7cCcYY2rdvj2PHjuHxxx9Hhw4d+Ea0s2bNwpkzZ/DGG28AcKSWxo4di+eeew65ubkoKSkB4CgaTkpKknzdxqOJBEEQEYCUqIwv1J695LxUAeA5KgN4boznXPDrXCvjaQo2IK8PlVoi45AYEhlPmM1mTJ48GR06dMCYMWNw7bXXYufOnYiOdvysXJvbvvLKK6ivr8fkyZORlZXFfz3yyCOyrkuRGUJzUHRGGlQEHLnFv94IdnpJznIFzqliZ3Go8DBjSa1eMmqKDODe1RfwLjLOEqOzBH6ByVAyYsQIrzORNm7cKHheWFioynUpMkNoEqqf0S7B7v4bSQQjKqMmUgt+vRHuImOz6CWLjD7CRSaUUGSGIAgiQgh10a8Yngp+XWcuucLNXPKnKZ4/IuNrwUixZngkMqGDZIbQLJRuIhoTgY7KKBEZuXUyzgW/gLDDr2t6SY2meBzhJDL6et/jVYsKO2D3U3CrArzaulpQwJcgCCIC8Dcqo+ya4gIkpTGer4gMh7/dfUMpMnqLTigyVs/jJfyDZIbQNFQ7451g1keEG5FU/BtuURnXiIzz+KSshO0qMmLdfV2nYLuOM9giY7VEyRYZDr2VRCbQUJqJ0DyUbiIaO76iMkqkVunsJdfrSS34Vbspnr8iI6UZHiA+9dpbNKYxTM8OBRSZIQiCCGPUiMp4Q83p+2a7tILfSpfIjNpN8YItMnoLiUyoIZkhIgJKNxGNFX+iMmrOXvJU8CtlqQJAWlO8UIoMs+o9igwHiUzoIJkhIgYSGiLS8BWV8afoV6nIyF0JG4Bbh19nnGcueeslE2qRAWTMWCKRCTokMwQR4TTGIuBIKv71B7Xee9efp6eCX/66Hgp+OZFxLfj11UsmUCJTYTGpKjKuhb7O6ScisJDMEBEFRWeISMHfqIxa6SVOZKQIoqc6GcCzyPBj8jAFO5AiAwgXjORERmpXX+ep1xSNCS00m4kgCILwq06GSy95qpPxlFoChDOXlIgMh78iA8jvIcMhVWQMdQCCuFya2W5Cvd2/mEW1v133ggTJDBFx0FRtQuuEIirjC08dfl1FhsNTPxlPU7CVioxYPxtAfDVuJSKj1tRrQ+Ne8zXgUJqJiEgo3UQQ7qg1e0lOh1/n9JK3KdjO4ws3kfF36jWJTOChyAxBEEQYEaiojNqLSEop+AWENTKA714yoRYZJTOWvNXHkMgEB5IZImKhdFMD5XaTqs3RwpnGPJNJ7uwlb11+OamSuoCkrzWXAGlN8cSu44oSkZGyzpLUGUvOUDQmPCCZIQiCCBMC1VdGjsh6k0FvBb++liqQ2903SS9+v2qIjJyp15RW0gZUM0NENFQ7QzQW1IrKSLmOp78rscZ4Yk3xAPfuvnKvTSJDOEMyQ0Q8JDSEFvAnKiN39pLSadhK6mSce8kA4k3xpEAiQ3iD0kwEQRBhTjDSSw3XEq9f8baApFidjNwp2GZ7w326ppjCRWRIYsIXkhmiUUDFwI2jCFirxb/+rIwd6PSSr4Jfb7jWyXiLyDgLjFyR4dJaziIjZXkCNWYsyRUZfb28/f2hzB6HWrvBr3NcsttUGk1goTQT0WigdBOhRdSMynharsCTBEop+PXW4deXyPgSK7VERmx5ArlrLIkhW2SsvvchlEGRGYIgiBASiKiMmhE4b5EfT+klqREZs92z0IjV5wDKRAbwPvU60PUxJDGBhyIzRKOCojOEllAalRFDSXpJap2M2CrYnrr7uo7FU2rJ9bpAeIiMoY5EJhyhyAxBEESICFZURu06GSkRGQ5PvWS81ci4XheQJjJym+EpERk5kMgED4rMEI2OxhydkVssqiW0WvzrCSVTsdVILylpjCcmMlx6yduYXCVLqshUWU0BExnndZickRuNIZEJLpqTmdWrVyMnJwcxMTHIzc3F3r17Pe7bv39/6HQ6t68hQ4bw+4wbN87t9cGDBwfjVogQ0piFhggP/InKyMFXVMZsr3NausCxTU7Br7deMr7kSklEpsrq2M+byDCrXrHIiEFppfBHU2mmt99+G9OmTcPatWuRm5uLVatWYdCgQThy5AjS09Pd9t+6dSsslobfzgsXLqBbt2644447BPsNHjwYGzZs4J+bTJH1PzyCCBeUdp1tbKgVlfE0e8lxDfFPaDkFv956ybimlpxTSmJ9ZAD1RAbw3UOG0kqRhab+aVm5ciUmTpyI8ePH44orrsDatWsRFxeH1157TXT/pk2bIjMzk//69NNPERcX5yYzJpNJsF9KSkowbocIMRSdIcIRJUW/aqaXAGkFv55WwXbt5+RcI+PcII/DX5GxWqJERUbPf3GpI13ARIbSSqFHM5EZi8WC/fv3Y9asWfw2vV6P/Px8FBUVSTrH+vXrMXLkSMTHCz/ECgsLkZ6ejpSUFNxwww146qmn0KxZM4/nqaurQ11dw296RUWFzLshwgVqpkeEAqUpJjk1T0qiYJ7SS55WwpbTFM/T9QBpIlNhcezrKjKA72Z4gSz0lSsxBg+prEBQbouHxeZn0zwbNc1TlfPnz8NmsyEjI0OwPSMjAyUlJT6P37t3Lw4ePIh7771XsH3w4MF44403sHv3bixbtgxffPEFbrrpJti8vIFLlixBUlIS/9WiRQtlN0UQhCpoqfjXm8gEMr3kfH3XMXDTsJXOXJIqMkojMoEQGbUKfaVisARXZBobmonM+Mv69evRpUsX9O7dW7B95MiR/OMuXbqga9euaNu2LQoLCzFgwADRc82aNQvTpk3jn1dUVJDQaJjGFp1pDMsaNBbUmoYt7LQrb+aSL5Hxt0bGk8j4O2NJDKkiE87RmMaKZiIzqampMBgMKC0tFWwvLS1FZmam12Orq6tRUFCAe+65x+d12rRpg9TUVBw7dszjPiaTCYmJiYIvgiAIX6gdlZGCr+UKXDvtOiNW8OtLZLjaGOcvf0TGITGBFRk5jfAoGhOeaEZmjEYjevbsid27d/Pb7HY7du/ejby8PK/HbtmyBXV1dbjrrrt8Xuf333/HhQsXkJWV5feYCe1AxcCE1pAalfEkUHIb40np7it+/YbHSkQGgGKRkbLGUiDTSkTw0IzMAMC0adOwbt06vP766zh8+DAeeOABVFdXY/z48QCAMWPGCAqEOdavX4/hw4e7FfVWVVXh8ccfx7fffouTJ09i9+7dGDZsGNq1a4dBgwYF5Z6I8IGEhggkgY7KBLLg13kKtjPOU7ClLBoJyI3IRPklMs74IzJyZys1VpGx2WyYM2cOWrdujdjYWLRt2xaLFi0CY8zjMVu3bsWNN96ItLQ0JCYmIi8vDzt37pR9bU3VzNx55534888/MXfuXJSUlKB79+7YsWMHXxRcXFwMvV74F3XkyBF89dVX+OSTT9zOZzAY8NNPP+H1119HeXk5srOzMXDgQCxatIh6zRCERtBS8a9a+FvzJKfg19sUbF8i49yED5AvMoBwnSWxHjLhVB/jS2IMFgZYPH+wa51ly5ZhzZo1eP3119GpUyd89913GD9+PJKSkvDwww+LHvPll1/ixhtvxNNPP43k5GRs2LABQ4cOxZ49e9CjRw/J19Yxb8pESKKiogJJSUnY8mMHxDXxbxocEXoaSzFwKIqAA9E0TysyIyXd44pYVMbfol/XLr8nLWmiHX69LR4ZapHxtz5GKqqLDACbpRY/bPp/MJvNAau35D6T1n3f0+/PpEuVNky8cr+k8d5yyy3IyMjA+vXr+W233XYbYmNj8e9//1vyNTt16oQ777wTc+fOlXyMptJMBBEMKN1EqI2SvjKBSC+JLVfgDBeVCbXI1FmiNSMyvop8DRbGi4yWqaioEHw591rj6NOnD3bv3o3/+7//AwD8+OOP+Oqrr3DTTTdJvo7dbkdlZSWaNm0qa3yaSjMRBEFEEnK7/UqNpiXpTaK9ZAB56SVf3X3FUENkAPHlCYJVHyMVqdGYUGG2x8Ji8+9jvsZeDwBu7UfmzZuH+fPnC7bNnDkTFRUV6NChAwwGA2w2GxYvXozRo0dLvt6KFStQVVWFESNGyBonyQxBiNDYes8QgSPY6SVvaTc1REbKzKVQiUyw6mP8kRi9RqM0p0+fFqSZxOpK//Of/2DTpk3YvHkzOnXqhAMHDmDq1KnIzs7G2LFjfV5j8+bNWLBgAd5//33R9Ra9QTJDEIRmCfd6GbVWxlajTkYLIuPa1VfOjKVgppW8vy4uK4Y6x/Z66UMKK6T0VHv88ccxc+ZMvhltly5dcOrUKSxZssSnzBQUFODee+/Fli1bkJ+fL3t8JDME4YFIj85QJ+DQEagGeY5zi6eXBNs8dPiV00tG7LyBEhlnUVFbZNRKK3mLxnAiE+lcunTJbUaxwWCA3e49n/rWW29hwoQJKCgowJAhQxRdm2SGILwQ6UJDhBdqz17yVCfDIbbuEicyHM4Fv97wV2Q89ZAJZKFvsKIxjYWhQ4di8eLFaNmyJTp16oQffvgBK1euxIQJE/h9Zs2ahTNnzuCNN94A4EgtjR07Fs899xxyc3P5tRZjY2ORlJQk+do0m4kgCCIAKKmVcUWKyHhbrgCQn17ir+0yc0ns2s7XABqfyHibqeRJZCJZcF544QXcfvvtePDBB9GxY0dMnz4d9913HxYtWsTvc/bsWRQXF/PPX3nlFdTX12Py5MnIysrivx555BFZ16bIDEH4gKIzRDDwN73kihp1MsGKyKhR6BsuaSVvsmKoY5qtmZFCkyZNsGrVKqxatcrjPhs3bhQ8LywsVOXaFJkhCAlQ75nwI5yLf+VGZfxJL4ldy30BSf9ExrlOhvtyHnekiYy33jFKozGRHJEJBygyQxAEoQG81ck4C43YApJqiIwrkSwynl+jlFK4QjJDEBKJxHQTzWhSn0BFZaRdu+GczpIhdeaSN5FJ0mszIhOqIl+P24PYZ6bCFgOLLdqvc9TaZOTlQgjJDEEQhEqo0VfG354yYnUygHDmktQaGbPdfSq2WiLjT6GvFqMx3HkiuWYmlJDMEIQMIjE6QwQeOVEZVzx1+RXrJ8Odz1vBr9xiX7Gxu4rMydpUWQtGehOZYKaVIjEa01ghmSEImZDQEHLwJ70kdbkC9zoZzyIjuJ7IekvO13Qdu1i/Gm8iI7Y8QaBFJhyjMUTgIZkhCEJzhONMJn9TTGrWyYh1+OVExjkq47pMgTdIZKRv99oN2CJzdVFCEiQzBKGASIrOUBFw4FA7veQ4p/t7JbXDr9hSBVLSS9761chZnkBJoW+w0kqBjsYY6hw/UKqZCQwkMwRBEH6idlRGznIFUhrjyamT8ZVaUtrVN1AiE+xojFKJIQILNc0jCIVQIz3CG/5EZVzxVifjr8gk6Ru+xMaqVGSYNfARGanRGLkN8Dw1uSORCV8oMkMQfhBJ6SZCGYGOyvhadwnw3OHX32UK/BEZoKGHTKjqY8IxGmOotXkeFKEYkhmCIIKCnF4p3s8TPsW//jbIk5Je8rZcAScbADzOXJLaS0aNGhktiEwgC3ylSEwwa2bKrfEwWf1rmldnpaZ5BNEoiIToDBUBBx5fIiMVT3UyrsW+AJATc14gMt7O6TxOf0RG6YylSI3GUCQmOJDMEIQKRILQEOrgKSrjCymRK28Fv85w6SVXkUnW18nqIxMpIhOKaAxJTHAhmSEIglCAnFoZJekl9+u5nNNHnUwr4wU3kfE1xsYuMnKiMUrrYvR1NDk7EJDMEIRKUHSGUBqVkYpYYzzPBb8Nv4vOIhOIGhkpM5aU1MdEUjSGkxia3xQYSGYIgtAM4VL866koVwy5URnuHp2v4ZpeOmVp5nPmEgBJvWTUFplIiMYEQmKIwEJ9ZghCRaj3DOENpeklV+HgamS4GUtSe8m4olWR8dU7xm2bSN8Yj71kPKSUSGTCG4rMEITKaDXdRDOapKE0KiP1Z+sakeHP5SQczijtJSNHZMqrYz0uT6BmfQxFYwilkMwQBEEEAF+dfqX23WmQDvGZS0qWKZAqMmWX4lTrIeOvyMidch2oAl9/JEZfWw+9jWY5BQKSGYIIAFqNzhDekROVcUZKnYzruV3TS64Fv0rXW/JHZKTMWApUWkl8u/rRmEBJTCiorDehrt7o1zks9dqoRiGZIYgAQUKjLuFS/CsFOesveVp3SazgVwy1U0ul1U0UT70OdVop2CmlcJWYxgjJDEEQhAQCFZURi8gAwgUkneGKfp2nYAdbZEKZVqJoDCEGyQxBBBCtRWeoCNh/vBX9yqmT8dbh1zm9JCYycvvI/FGdGNQZS2qKDEkMAZDMqEq5LR4Wm0HQrIogCO2jJCrj/zRsoXS4R2Qc6SVf1+HOB7h3DfZXZMItGhPuKSVdnRU6mzYWbtQaJDMBgFvFlqSGALQXnSGUI6dWxhVPK2E74yoybaPKfI6HE5mTljRFIhOs+phwjcaoJTFEYCGZCSAkNQQHCY1/hLL4V2pUxp/0klhjvJO1qfzrYjOXPIkMNzati0yoJQbwLjJS00kkMsFBG3OunFi9ejVycnIQExOD3Nxc7N271+O+GzduhE6nE3zFxMQI9mGMYe7cucjKykJsbCzy8/Nx9OhRVcfs+MeJOsMSRCTgS2RcERMZZzmT0hgPgEeREevuGy4io7d6FxlPnXyDITKGWpvXaIwnkdHX1ktPKZHIBA1Nyczbb7+NadOmYd68efj+++/RrVs3DBo0COfOnfN4TGJiIs6ePct/nTp1SvD68uXL8fzzz2Pt2rXYs2cP4uPjMWjQINTW1qo+fpKaxo1WljrwJ1USachZGZvDVwG1WJRJSmM85ynY3vAmMmdrkjyKDKqjBCKjt+gcX1ZxkTHU+RYZb0hNK0ldisBgYSLHii9DQBITeWgqzbRy5UpMnDgR48ePBwCsXbsWH330EV577TXMnDlT9BidTofMzEzR1xhjWLVqFZ588kkMGzYMAPDGG28gIyMD7733HkaOHBmQ+6D0U+OF0k3aRo30krcFJD01xmtlvOBxCja3CjY3Fm8i47rOUm2VUdLUazXTSmrXxlBdjGeqbDGI9rNpntWmjZiHNkYJwGKxYP/+/cjPz+e36fV65Ofno6ioyONxVVVVaNWqFVq0aIFhw4bh0KFD/GsnTpxASUmJ4JxJSUnIzc31ek61oEgNQYQvSqIyzsitk3FdCVuKyIidC/AtMlVVsQERGSlpJfHtyqMxbueSkVLyFokBpEVjZEdiAhD1JzQUmTl//jxsNhsyMjIE2zMyMvDrr7+KHtO+fXu89tpr6Nq1K8xmM1asWIE+ffrg0KFDuOyyy1BSUsKfw/Wc3Gti1NXVoa6u4S+6oqJC6W0BoEhNY4OiM/IIl86/cqMyUs/lvBK2+FIF7itgu51DoshwC0YGYup1MIt85UiMJ/wt7pUlMDUkMIFGMzKjhLy8POTl5fHP+/Tpg44dO+Lll1/GokWLFJ93yZIlWLBggRpDFFBuiyehIYgwQG5Uxlt6iZMx7pzOs42OW9MFM5dce8nkGP+UVCPDEY4iIyca47aPhAJfNVNKJDHaRTNpptTUVBgMBpSWlgq2l5aWeqyJcSU6Oho9evTAsWPHAIA/Tu45Z82aBbPZzH+dPn1azq14hVJPjQOtFAMTDuTMYBJbrsBVjlwLfp0RExlnWTLbhUIEOH6fnEXmTG2yJJFxSIx3kfFW6KskrSReqKu8wNft/ApSSqqnk2pqSWSCjGZkxmg0omfPnti9eze/zW63Y/fu3YLoizdsNht+/vlnZGVlAQBat26NzMxMwTkrKiqwZ88er+c0mUxITEwUfKkNSU3kE85C05hnNEmJyviTXuJExlk+OJx7yfg6vzeRcV752pvIAPA5Y8kTaky59lQb43qMlFlKJDHhQU5OjltLFJ1Oh8mTJ4vuv27dOvTt2xcpKSlISUlBfn6+15YrntBUmmnatGkYO3YsevXqhd69e2PVqlWorq7mZzeNGTMGzZs3x5IlSwAACxcuxNVXX4127dqhvLwczzzzDE6dOoV7770XgGOm09SpU/HUU0/h8ssvR+vWrTFnzhxkZ2dj+PDhobpNAVRPQxChxduyBUqWLBCbucSll5x7yXB1Mp5qZLhzeRMZbz1kgplWklIbo3ZKidJJoWHfvn2w2Rrej4MHD+LGG2/EHXfcIbp/YWEhRo0ahT59+iAmJgbLli3DwIEDcejQITRv3lzydTUlM3feeSf+/PNPzJ07FyUlJejevTt27NjBF/AWFxdDr2/41+TixYuYOHEiSkpKkJKSgp49e+Kbb77BFVdcwe/zxBNPoLq6GpMmTUJ5eTmuvfZa7Nixw625XqghqYlMqBjYO8Eu/pUTlVHS5fd4fVN+5hIHJzI5MefdRMbb9UMhMmoU+QajwNdbJMYXJDH+kZaWJni+dOlStG3bFv369RPdf9OmTYLnr776Kt59913s3r0bY8aMkXxdHWNMfBUuQjIVFRVISkrCuu97Iq6JISjXJKmJLMJRaNRePVvqitHCY0IrM95qZZx/PlKnYXMFv67ppUCKDKod/2eVIjJaicYEorg3WBJTb6/DrtNrYDabA1KiADR8Jt2xewyi4/3sM1NtwZYBb+D06dOC8ZpMJphM3v8+LRYLsrOzMW3aNMyePVvS9SorK5Geno4tW7bglltukTxOTUVmiAYoUkMQ6qJUZHyf13uHX9fuvkpFprS6iaCrb71TR19AWqGvJ/wVGSXRmEiTGABAbR1g9zC9KwBUWU2ItvopM1bH70yLFi0E2+fNm4f58+d7Pfa9995DeXk5xo0bJ/l6M2bMQHZ2tqD/mxRIZjQOSU1kEI7ppnK7SbXojJKoTLghdfaSM546/HKINcVTUiMjVWScpUSKyAQjGuNPSkkTElOrboQzVIhFZnyxfv163HTTTcjOzpZ0jaVLl6KgoACFhYWySz1IZiIEkhqCUI6UqAyH1PSS2ErY4jOXvK+3JEdknLv6is1YcjwWnj9QIhPIlBJJTPCRO3P31KlT2LVrF7Zu3Spp/xUrVmDp0qXYtWsXunbtKnt8JDMRBkmNdgnH6AzhQEl6iT/2L5HhGuMBwinYzu+54zp1Ls8jT2QCkVIiiQkvNmzYgPT0dAwZMsTnvsuXL8fixYuxc+dO9OrVS9H1SGYiFJIabUJC00Cwin+lRmXkzF4Sq5PhcBaZZIOwRsY5lSVVZKqqYmXPWFJSHxMIiXHs4180JiwkhgRGgN1ux4YNGzB27FhERQk1w7WFyrJlyzB37lxs3rwZOTk5/FJCCQkJSEhIkHzNCMhkE96g5nsE4R++Gggm6U0C8fJWJ+MqMpy4cmLEiU0oRMZbN99ARWOkNL7z1PTOV8M7yc3uuEZ3SkSmto5ERoRdu3ahuLgYEyZMcHutuLgYZ8+e5Z+vWbMGFosFt99+O7KysvivFStWyLomRWYaCRSp0Q7hFJ1Rswg4HJESlRG7f+eojPM5OJEBIFgJW3A+CTOXAKHInKpphsr6GPxRnShheQL1Cn1DmVJSWhcjq1uvUkhgvDJw4EB46vpSWFgoeH7y5ElVrkky08ggqdEG4SQ0hBApM7OOW9PdRMZ55lLbqDLB/mIrYDcGkVGzLoYkpnFDMtNIIakhGjtyZjBxSKmT4RrjKZ25JFVkUB0Fg4ep1+EuMYB6dTEBlxgSGE1AMtPIIakJXxpzdCbYnX9dUZJecy74FZu5xK2C7a0pnhyRkTJjKdgiQxITXlRajIiK9u9vqT54Pf78gmSGAEBSQzQuAhGVcRYRwXEqioxzM7xAi0wwUkqNTWJYTS0Y04gdaAySGUIASU14EQ7RmcgvAva9jy+R4RaQFFtzyR+RcV5nyVVkPM1YCnQ0Ro2UUmOUGCKwkMwQopDUhA/hIDSRhK+VseXIm6eVsIGGxSMDJTJihb6hjMb4k1JSpVeM0qnVAYIEJriQzBBeIakhIgklU7E9RWW4gl+uwy+Hcy+ZttHn/BIZsa6+oUgrhSKlFNBITIAkhgQmdJDMEJIot8WT0ISQxhSdCXXxrzPe0kueOvwqFZmzNUmosJhUE5lARGPCqi6GJIZwgmSGkAxFaYhIQkqtjLdjnTv8OsOJjCfkiIyhyvFPtCeRCedoTNhIDAlMo4BkhpANSU1oaEzRmUDgq1bGFS4qw0WKuONd00vOSxW0Ml7gRUYsKuPch0ZMZJyXJzBURXmdsSRHZMIhGuOXxFAUhvAByQyhGJKa4BMqoYm0GU2uURnX+3NdrqBBaNyFJCXa8X5w3X09ndOTyIits8SJjPOMpWCkleQU+DYmiSGBCX9IZgi/Iakhwh1fURlPIuN8vLeVsDmRcZ65VG43CRapdD7Ok8hwXX3VEBlfU65JYnyjdYmpsRoRZTX6dY56q/gaS+EGyQyhGiQ1wSGS003BKP71p1ZGrDGes8h4QkpExrmrr7+FvkqjMWrUxQRVYkhgiL8gmSFUh6Qm8ESy0KiNnFoZ8aiM4ztXJ8M1xhNbb4mLyiTpgfK/jvMkMp66+koVGSVpJbWiMZEmMSQw2odkhggYJDVEqJGzbIEvkeFWwgaEvWScpZKrkzH7KTLhEo3xN6VEEkMEC5IZIuCQ1ASGYEdntF4ErDS95CoyQEMvGe53Ollfx9fHcN891ci4ikxUFbfydWBEJhQppXCWGBKYyIRkhggaJDVEuOItKsNJCb+vk8hwBb/OaSXuGNc+MsEQGSXRmLCVGIrCEDIgmSGCDkmNekRS7Yzaxb/OKSa56SWO4/VNcdKShpO1qYLUknOdDHc8F53hUksna1NRUR8jEBnnZnhyZiwFIxoTiRJDAtN4IJkhQgZJjTpEktCEElfh4UTGvcOvcOaSa10NJzJnapNF11lyFhkpjfACHY2JNIkhgWmckMwQIYekhlAbf6MyzjOXgIaC3xzjn3xqyVONTCBFxt9ojNyUEkkMoRVIZoiwgaRGORSdkY+n5Qq8Ffw618gAdQKR4dJRYiLj2gzP37RSoFNKAZeYRiww9prgFdHX1kfBYPXvY95W7z6jLRwhmSHCDpIaZQRDaLQwo0lqVEYM51RRkqGG3+48c8k1muMpteStGV4wRUaNlJJXiaEojE/sGhuvFiGZIcIWkprGg1rFv3Ia5DWITkNExnXmEiAUmQv2eKC+odjXU2rJWWQ8zVgKpsQAQpHRosSQwBDeIJkhwh6SGumEY7rJ22yhQOIpKsONh3vdeT/ud40j2XCJXwX7gj3e7btYaqmqKlYw9VpsxpIvkVGzNiZkEtPIojAkL6GFZIbQDCQ1hFR8iQz32HkmkusCktyaS1yNTLK9IRojR2R8FfpKFRm1U0rhLDEkMIRcSGYIzUFS451wjM4EAzkpJsf+ju8NIuP4vXLt7uu8L4c3kZEzY8mbyPiKxkSaxGhBYEhewheSGUKzkNR4JpBCE+5FwHKKfrnZSCctafw2V5Hh7peTHleROVuexHf1dRWZUEZjSGLUgQRGG5DMEJqHpEbbqFH8Kzcq48xxazr/WE6NzPHyVI9dfZWKjFg0JiwlhgSGCDNCVJqnnNWrVyMnJwcxMTHIzc3F3r17Pe67bt069O3bFykpKUhJSUF+fr7b/uPGjYNOpxN8DR48ONC3QQQA1+LNxg73IdyYkBuV4frJmG2xfEQmWV+HZH0d2kaV8dEtKSITVaVTRWQMdcxNZAwW5vS6nRcZQ63No8joa+vdREZXZ/UsMjW1vkWmts4vkWE1tWErMvaaWv6L0B6aisy8/fbbmDZtGtauXYvc3FysWrUKgwYNwpEjR5Cenu62f2FhIUaNGoU+ffogJiYGy5Ytw8CBA3Ho0CE0b96c32/w4MHYsGED/9xkUneNGCJ4UJRGSGOon/EVlXGevWR2WQzSuZ+M6+8Ml4ICGmpkjlRmCNZZ4prhRVUJZywFOhojty4mlJGYcJaXSKfOEgVDVLRf57BZqGme6qxcuRITJ07E+PHjAQBr167FRx99hNdeew0zZ85023/Tpk2C56+++ireffdd7N69G2PGjOG3m0wmZGZmBnbwRFAhqWkcKG2Qx627xJFj/JN/7CwxzhEZMZERm7GkNBrDb5NR4OtXSqkRSkxjEJjGimbSTBaLBfv370d+fj6/Ta/XIz8/H0VFRZLOcenSJVitVjRt2lSwvbCwEOnp6Wjfvj0eeOABXLhwQdWxE6Gj3Bbf6NNPgUg3OX/ghzNiPW6cRSbZcIlfb4lDrEZGDZExWBpERpg2Ekkv+Ugp6evqlaeUfKWTuFSSApHh0kjhJDKUPmocaCYyc/78edhsNmRkZAi2Z2Rk4Ndff5V0jhkzZiA7O1sgRIMHD8Y//vEPtG7dGsePH8fs2bNx0003oaioCAaDQfQ8dXV1qKtr+EOvqKhQcEdEMKFITWTjfTHJhrWXnFfC5vrIcCLjXB8jR2TE6mPUjMaoEokJYBQmnMQFoOhLY0UzMuMvS5cuRUFBAQoLCxETE8NvHzlyJP+4S5cu6Nq1K9q2bYvCwkIMGDBA9FxLlizBggULAj5mQn0aq9SEa+2MPzOZpMxgctTJOPY7Xt8U5bZ4JBsuARD/HeCiMictafip6jKcrUnCyfIUj83w5IiMt9oYuSklkhghJDCEZtJMqampMBgMKC0tFWwvLS31We+yYsUKLF26FJ988gm6du3qdd82bdogNTUVx44d87jPrFmzYDab+a/Tp09LvxEiLGiM6adInd3kGpVJ0runl8rtJsH7zQmNGJ5ExlAVBWOZQSAyhjrPIuOaVgI8z1RyPPedUpKcTgrQzKRwSiNR+ig8OXPmDO666y40a9YMsbGx6NKlC7777juP+2/duhU33ngj0tLSkJiYiLy8POzcuVP2dTUjM0ajET179sTu3bv5bXa7Hbt370ZeXp7H45YvX45FixZhx44d6NWrl8/r/P7777hw4QKysrI87mMymZCYmCj4IrRJY5SaSMBTVEasRoZrdueMp6iMN5GJqvpLYkRmLHHbOaTUxrhOtwZ818U4o0hiFNbDhIPAOMsLCUx4cvHiRVxzzTWIjo7Gxx9/jF9++QX/+te/kJKS4vGYL7/8EjfeeCO2b9+O/fv34/rrr8fQoUPxww8/yLq2ptJM06ZNw9ixY9GrVy/07t0bq1atQnV1NT+7acyYMWjevDmWLFkCAFi2bBnmzp2LzZs3IycnByUlJQCAhIQEJCQkoKqqCgsWLMBtt92GzMxMHD9+HE888QTatWuHQYMGhew+ieDTWNJP4ZpukoOcGUxcL5lyW5zH9BL3M5EiMoC4yHB4q40Rm26tpC5GUTpJYTFvqCFp0RbLli1DixYtBK1OWrdu7fWYVatWCZ4//fTTeP/99/Hhhx+iR48ekq+tKZm588478eeff2Lu3LkoKSlB9+7dsWPHDr4ouLi4GHp9w3/N1qxZA4vFgttvv11wnnnz5mH+/PkwGAz46aef8Prrr6O8vBzZ2dkYOHAgFi1aRL1mGimNQWrUEppQL2sgJjJmuzA64y215hyR40TmiDndbZ0lPiLjoz5Gisi4SgwgvS6msUgMCUx44jrRxWQyuX1OfvDBBxg0aBDuuOMOfPHFF2jevDkefPBBTJw4UfJ17HY7Kisr3WYd+0JTMgMAU6ZMwZQpU0RfKywsFDw/efKk13PFxsYqys0RkU9jkJpQo6T4V2rRLyA+fdx59hL3Hpfb4gQiYy6LB6qjEO2l0FepxDi2eY/GhEJiQikwJC+Bo94SBXuUfx/zdovj+BYtWgi2c0EBZ3777TesWbMG06ZNw+zZs7Fv3z48/PDDMBqNGDt2rKTrrVixAlVVVRgxYoSscWpOZggimESq1Gg93SSnQZ43nEXmbHmSYOp1VPVfXX1VEpmASwwJDBFATp8+LagPFcte2O129OrVC08//TQAoEePHjh48CDWrl0rSWY2b96MBQsW4P333xft6u8NkhmCkECkSk2kwhX9Nrxvl1BuixPuI1FkfKWVlKSUGpvEkLxoHymTXbKysnDFFVcItnXs2BHvvvuuz/MXFBTg3nvvxZYtWwS94KRCMkMQMogkqdFadIZLMYlNxXbdLlwJm0srxfFfAGSJjFrRmFBJDAkMEQyuueYaHDlyRLDt//7v/9CqVSuvx7311luYMGECCgoKMGTIEEXXJpkhCAVEitT4KzShLgL2tFyBM3yNjCGeF5mvy9vhj+pEgcgYywyS62P8SSn5JTFhHoUhgWncPProo+jTpw+efvppjBgxAnv37sUrr7yCV155hd9n1qxZOHPmDN544w0AjtTS2LFj8dxzzyE3N5efdRwbG4ukpCTJ19ZMnxmCCEeoT40y5Bb/eorKuJ6PW67glKUZAIdsJuvreOEy22LxdXk7HC9P5UVGf9Hot8hwPWO4xndi/WJcG9659YnhesSICYGM3jDBbGxHvV8IZ6666ips27YNb731Fjp37oxFixZh1apVGD16NL/P2bNnUVxczD9/5ZVXUF9fj8mTJyMrK4v/euSRR2RdmyIzBKECWo7UhHu6ydMMJsdspTp+H9flCk5ZmqHc0FAnc8rSDAcrm+N4eSrKq2NRczHGbeq1mtEYTykl0UZ3ngjDVBJJC+GNW265BbfccovH1zdu3Ch47joLWSkkMwShIlqWmnBHLCrDTb8WFvtWI9lQjR9rWsJsi8WPNS0BgBeZPy82EfSQifrrrZIqMuEkMSQwBOGAZIYgAoDWpCZcozOu6SXnGpnyv7a5Fvu2jSpzvG6M59NNYiJjvKjzmFaSEo1RXWLCLApDAkNoCZIZggggjrRH+EmCGEqFJpRFwI6ITBzMtli0Ml7gtyfpHWJzCs3cRCa6LApR1fJERnRRSB/FvWpLTKAFhuQl8rBZ9WAW/0pj7VZtlNaSzBBEgNFalCbQSC3+FSv6dX7MLVWQY/wT5TZHFOYUmqHcGM+nmQ5WNsfBPzNRVRUL20Ujov+aei1WH+OrwNfxWDwaEyiJIYEhCGmQzBBEkNCC1KidbhKbOu0vrssUNNM7lijgIjSc1Hxb1oZfMNJ20YioKj2MFx09ZORGYyJJYkhgiEiEZEZFzPZYWGzuP1JudgVBAOEvNeFaP+MsMdwYk/V1vDBdMP6JH2ta4qI1HkcqM3CyPIVfZ8lYZvBY6Guw+I7GeKuLkSQxIRQYkheiMUAyEwRc26hzkOQ0bsJdakKJ2HTsZH0dv0wB4LwitmPfclu8V5FxXZbAUzRGLKWkNYkhgSEaGyQzIcST5AAkOo2JcJSacIrOOC9X4FxofNyaLmhYeKC6JY5UZuDIuXS+GR439dpXNEZOSkkNiSGBIQh1IZkJU0h0Gh/hKDVSkTqjSUrxr6cmeUn6BqFxrZs5UN0S+y60FHT19SUyUlNKHutiQiwxJC8E0QDJjAahtFVkEy5SE4rojPMMJrHiYWehAdxXvua6+hov6hBd6ThGrMhXLBqjWGJIYAgi5JDMRBAUzYkswkFqQplucm2U5zwtu9xuki0yYtEYsboYUYmRGYVRS2BIXghCGrJlZvv27di6dSuaNm2KCRMmoEOHDvxrFy9exG233YbPPvtM1UES/kOio13CQWpCCScxXPEv11PGdZ0lrhledKX3aIynlJIakRg1JIYEhlALZtWDRfnXH4FppGmerFFu3rwZt956K0pKSlBUVIQePXpg06ZN/OsWiwVffPGF6oMkAku5LU70iwgvQrVCd8OsocDibUHJ4/VNcdyajpOWNByobino6suJDBeRcY3GGCvtMFbYBdEYo9nKR2OcRUawkrXrCtYeVq5WY5VqWnmaIPxDVmTmmWeewcqVK/Hwww8DAP7zn/9gwoQJqK2txT333BOQARKhg6I54UljitS4RmNO1TTD2ZoklFY3ESxPYLyog6HOfcq1czTGW0rJYzopgFEYEheCUA9ZMnP06FEMHTqUfz5ixAikpaXh1ltvhdVqxd///nfVB0iEJ1SEHHqCKTVSamd8zWjyNpPJOSpjtgslhu/s6yIy3PIExos6RFc5jvVWG6OGxPgbfSEIIjDIkpnExESUlpaidevW/Lbrr78e//3vf3HLLbfg999/V32AhLagaE7wCZbUBKoY2HUNpnK76a8eMg6JuWiNR0V9DI6Y01F2KY5fnoBrhhdd5T0aI1YXEyyJIYEhiOAgS2Z69+6Njz/+GFdffbVge79+/fDhhx/illtuUXVwRGRBohNYtJx+4kTmeH1TnLSk4WRtKirqY1BZH4MqqwkVFpObyJjKGtJKgZAYEhiC0A6yZObRRx/FN998I/pa//798eGHH+KNN95QZWBE44LSVuoRSKlROzpjtteJisyZ2mReYmqsRtRYowTLE0gRGZ8So5LAkLwQROiRJTP9+vVDv379PL5+/fXX4/rrr/d7UATBQdEc5QRKagLZe8Zsi0WFUzTGWWT0F438jKXoau8SAzjqYgIlMSQwBBFe+NU079y5czh37hzsdrtge9euXf0aFEFIgURHGsFMP0ld1kD0WKdp555ExlTmiMYYK+1BlxgSGIIIXxTJzP79+zF27FgcPnwYjDlmDeh0OjDGoNPpYLPZVB0kQciF0lbuqCk1gYrOXLTGo7I+xqPIGKu8p5TcJIYEhmjE6Cx66Az+Nb3TWbTRNE+RzEyYMAF/+9vfsH79emRkZECn06k9LoIICCQ5DqkJRZGw2V4nmJ7tPB2bW56goj4Gf1QnCrr6Gi/qEFPWkFZyRGXkSYxUgSF5IQhtokhmfvvtN7z77rto166d2uMhiJDgq+NxpMmOc0pHqdgoic64dvnlesrsr80RrLNUWxoH01+FvtHVDWklY6VNdYkhgSEI7aNIZgYMGIAff/yRZIZoNERyRMef9JOY0LjWzXDRGNfGeNy+YiLDzViKucgcSxFYGIxmq/sMJYUSQwJDEJGFIpl59dVXMXbsWBw8eBCdO3dGdHS04PVbb71VlcERRLgTSUXIwUo/OU/F/rHGsc7SH9WJKD7XDLaLRsSUOkQm9kJDNCbaXOceiZEhMCQvBBHZKJKZoqIifP311/j444/dXqMCYIJwoMVojpIojad0U5JI3aCYyHALRrLSGMRc1CGuxJFWMlY6FoSMMtd6lBgSGIIgAIUy89BDD+Guu+7CnDlzkJGRofaYCCKi0UI0R42aGo/ntpsEIlNSmgTjaRNMZUB8iZ0v8BVEY1zSSZ4khgSGIBonimTmwoULePTRR0lkCEJlwlF0pKSfpBYDl9tN+Ky6o5vIJJwGYs/b3FNKEiIxJDAEQSiSmX/84x/4/PPP0bZtW7XHQxCEB0KZtlKjR43ZDuyvzeFF5uzvKTCVRCPhNJB0wiKUmItmACQvBEFIQ5HM/O1vf8OsWbPw1VdfoUuXLm4FwA8//LAqgyMIwjfBjOZ4Sz85R2fK7SaU/1Ufw81s2n2pPb4ub+eIyPzWDPGnDUg8ZUfC73UNdTF/RWJcJYYEhiDko7fqofezaR6s2miap2iUr776KhISEvDFF1/gxRdfxLPPPst/rVq1SuUhClm9ejVycnIQExOD3Nxc7N271+v+W7ZsQYcOHRATE4MuXbpg+/btgtcZY5g7dy6ysrIQGxuL/Px8HD16NJC3QBBBo9wW5/XLv3PHC+QGcAiN8+ML9ngcr2/Ki8zBPzNR8lszJB02IPVgPRKPVyP6XCV05ZXARTPYRbNAZOw1tSQyBEH4RFFk5sSJE2qPQxJvv/02pk2bhrVr1yI3NxerVq3CoEGDcOTIEaSnp7vt/80332DUqFFYsmQJbrnlFmzevBnDhw/H999/j86dOwMAli9fjueffx6vv/46WrdujTlz5mDQoEH45ZdfEBMTE+xbJIigokbqyrWmxlloym3xOFDdEmdqk3Hwz0xUHk9B0nEdmh6xIKa4XFATw0kMyQtBEHLRMW5xJQ2Qm5uLq666Ci+++CIAwG63o0WLFnjooYcwc+ZMt/3vvPNOVFdX47///S+/7eqrr0b37t2xdu1aMMaQnZ2Nxx57DNOnTwcAmM1mZGRkYOPGjRg5cqSkcVVUVCApKQkrvrsWsQl+rd1JEGGLL8FxTTudtKTxzfDKLsWh8ngKkg/r0PRwDaIOHCNpIRol9cyKz+vfgdlsRmJiYkCuwX0mtVq6GHo//1Nur63FqZn/L6DjVQPFn7y///47PvjgAxQXF8NisQheW7lypd8Dc8VisWD//v2YNWsWv02v1yM/Px9FRUWixxQVFWHatGmCbYMGDcJ7770HwBFhKikpQX5+Pv96UlIScnNzUVRU5FFm6urqUFfXMMOioqLC8d0WA4stWvSYcCHJUBPqIRAaxTWKY7bFCp6fQjNctDqiMhX1MThbk4ST5Skwl8UjqtSI5OM6pB6ohP7wSRIZgiBURZHM7N69G7feeivatGmDX3/9FZ07d8bJkyfBGMOVV16p9hgBAOfPn4fNZnObDp6RkYFff/1V9JiSkhLR/UtKSvjXuW2e9hFjyZIlWLBggex7CAdcP4AIQgmctAAOcamsd/zvr8pqQoXFhBqrEX9ebMJ39G1SzJDySyXw/a+wh2rQBEEEnPnz57t9PrZv397j5/S6devwxhtv4ODBgwCAnj174umnn0bv3r1lXVdRAfCsWbMwffp0/Pzzz4iJicG7776L06dPo1+/frjjjjuUnFJTzJo1C2azmf86ffp0qIdEEKpz0Rrv8auiPgYV9TE4U5uMszVJ+KM6EcfLU3GyPAXF55rh7O8p0J2OQdJhA5odtKHZ9+XA9+L/mBEEEVl06tQJZ8+e5b+++uorj/sWFhZi1KhR+Pzzz1FUVIQWLVpg4MCBOHPmjKxrKorMHD58GG+99ZbjBFFRqKmpQUJCAhYuXIhhw4bhgQceUHJar6SmpsJgMKC0tFSwvbS0FJmZmaLHZGZmet2f+15aWoqsrCzBPt27d/c4FpPJBJPJpOQ2CCKkOEdUpFBRL8y3V9bHoHlMORKjanGkMoOPwtRYo1BVFYv66igYqqIQe1GHJsUMCWcsiDlSAtsfpR6uQBBEpBEVFeXxc9mVTZs2CZ6/+uqrePfdd7F7926MGTNG8jUVRWbi4+P5OpmsrCwcP36cf+38+fNKTukTo9GInj17Yvfu3fw2u92O3bt3Iy8vT/SYvLw8wf4A8Omnn/L7t27dGpmZmYJ9KioqsGfPHo/nJIhwwFvUxNuXM1x0xfXrTG0y/1X5V+1LpVMqKSW6GinR1UiIbqgbcxYZIy8yVhIZgogQKioqBF/OdaOuHD16FNnZ2WjTpg1Gjx6N4uJiyde5dOkSrFYrmjZtKmt8iiIzV199Nb766it07NgRN998Mx577DH8/PPP2Lp1K66++molp5TEtGnTMHbsWPTq1Qu9e/fGqlWrUF1djfHjxwMAxowZg+bNm2PJkiUAgEceeQT9+vXDv/71LwwZMgQFBQX47rvv8MorrwBwLIo5depUPPXUU7j88sv5qdnZ2dkYPnx4wO6DIFyRGzGRimtkxZVKl9errA0RxwqL43GN1QjEAwnRdWgSVetWRF5jjYLtohHRVXoYL+oQX0IiQxDhgN4C6P3teffX/J4WLVoINs+bNw/z58932z03NxcbN25E+/btcfbsWSxYsAB9+/bFwYMH0aRJE5+XmzFjBrKzswUTc6SgSGZWrlyJqqoqAMCCBQtQVVWFt99+G5dffnlAZjJx3Hnnnfjzzz8xd+5clJSUoHv37tixYwdfwFtcXAy90zvXp08fbN68GU8++SRmz56Nyy+/HO+99x7fYwYAnnjiCVRXV2PSpEkoLy/Htddeix07dlCPGcIvAiUnnvAlLRyS5eUvaqxR/GsJ0XVIjKrlp2g3iapFjTXVMVvJSWSSjtci+tSfJDIEEUGcPn1aMDXbU6nFTTfdxD/u2rUrcnNz0apVK/znP//BPffc4/UaS5cuRUFBAQoLC2V/BsvuM2Oz2fD111+ja9euSE5OlnWxSIWb0z93Tz5iEsJ7ajahjGDLiRhShQVwlxYOZ3kBGgQGaJCY2Ghhq4VEY0M4OSG6Ds1jygEA+y60RPG5ZmClMYgpFYoMu2im6dcEIUIw+8y0XqhOn5kTc/3rM3PVVVchPz+fz5qIsWLFCjz11FPYtWsXevXqJfsasiMzBoMBAwcOxOHDh0lmCM0RDlLiCTmywuFJWgDv4gIIIzDOcPLCpZVaxV4AAHxb1gZVVhOOWB2Fv55EBrV1JDIEQQAAqqqqcPz4cdx9990e91m+fDkWL16MnTt3KhIZQGGaqXPnzvjtt9/QunVrRRclCLUIZzkRQ4mwcMgRF8C7vHDpI47Y6Pq/vlsEEpNkqEGO8U8AwKnYZjhbk8SLjO50DOJKdIi56KiR4URGbKVrgiAaB9OnT8fQoUPRqlUr/PHHH5g3bx4MBgNGjRoFwL22ddmyZZg7dy42b96MnJwcvsdbQkICEhISJF9Xkcw89dRTmD59OhYtWoSePXsiPl74gRLOLY+J8EZrciKGP8LC4U1cAHF5AcTTRo7Hnv/UY6PrERttQXZ8BZpEOUQk8a9C32TDJX4l7MSoWpxFEi8ysSU6xJc4Vr0mkSEIAnCsDjBq1ChcuHABaWlpuPbaa/Htt98iLS0NgHtt65o1a2CxWHD77bcLzuOpwNgTimTm5ptvBgDceuut0Ol0/HbGGHQ6HWw2m5LTEhFEJEiJN9QQFsC3tADSxIVDisBwURjHY3eJ4fip6jIkRtXiVHQzAA01Ms4iE3u+HtHnKvnFIgmCaNwUFBR4fb2wsFDw/OTJk6pcV5HMfP7556pcnNAGkS4mYqglKxxSpIVDqbw4nrv/SddZomEyWgE0RGEAR21MVqwZXRN+ByBc6sJ5jaWK+hgcMafjxO9piCo1Ivav1FLs+fqGla8BisoQBBEyFMlMv3791B4HEUQao5yIobawcKghLoA0eXFsa/gzrrN4nk3HiQwnMa1iL6CV8QLaRp8DAFywx6PcFs8vKMmtuXS8PBUlpUkwnjbBVAY+IsOLjEt6iYp/CYIINopXzQYcnfrEVs3u2rWrX4Mi5EFy4plACQuHHHEBvMsLoCz64klgXKMxicY6t+Jeb5ytScKRc+mouRgDU0m0QGRMpdVejyUIIvToLDro9TrfO3qBWfw7Plgokpk///wT48ePx8cffyz6OtXMKIOkRD6BlhUOudICKBMXwHf0hcObxDjPTuJqYhKjapESXS2QmHJbHI4jnX9stsXiVE0zXmRqS+NgKjPAVAY+tWQqrYauzkpRGYIgwgZFMjN16lSUl5djz5496N+/P7Zt24bS0lI89dRT+Ne//qX2GDUJiYl6BEtYAGXSAvgWF8CzvADSBMZbComDExnXdBI3MwkAkg2OqMpJi2N2AScxF63xOFObjD+qE3G2PAm1pXEwOolMwhkros11ApEhCIIIBxTJzGeffYb3338fvXr1gl6vR6tWrXDjjTciMTERS5YswZAhQ9QepyYot8bDZKUOwEoIprBwKBUXIDjyAogLjNXi2C/a2DAryVViuE69OTHnkWy4xAtMM301kvUNEnLSksZHY7j6mD8vNoHtohExpQ6RMVYxxJ63IdpcB31tvWAsVPRLEEQ4oEhmqqurkZ7uCE2npKTgzz//xN/+9jd06dIF33//vaoDJCKDUMgKhz/SAkgTF8C7vAD+p484OInxVRPTyniBP6bcFo9kQzUu2OMF25zTSifLU2Aui4f+ohExF3UCkYk5VwN9bb3XqAylmAiCCBWKZKZ9+/Y4cuQIcnJy0K1bN7z88svIycnB2rVrkZWVpfYYCY0QSmHh8FdcgMDKi2O7fIFxxrUmxjkSw9XEuKaUAIe8cN/LbXE4WZvKp5XKLsXxImO8qEN0ZYPIGM1WUZGhqAxBEOGCIpl55JFHcPbsWQCOLn2DBw/Gv//9bxiNRrz++uuqDpAIH8JBVjjUkBZAurgA6soL4FlgrJYoQQqJw1MkxlVinAVGDGeROV6eivLqWNRWGQUi4yj2/Utk6pxExgMUlSEIIpQokpm77rqLf3zllVfi1KlT+PXXX9GyZUukpqaqNjgi+ISTsHCEQlwA3/ICqCMwXA2MJ6RMsXaWmLZRZQCAcnvD+Ln00ilLMz6tVFrdBH9ebIL66ihEl0UhqlqYWjLU2aGvqxfWyVBUhiCIMERxn5n169fj2WefxdGjRwEAl19+OaZOnYp7771XtcERgSEchQVQT1o4gikvjtfkR2CccS3oBSCaTnKdZu0ajRGTmHJbPA5UtxSklaqqYmG7aER0lV4gMsYKOwx1dr7gl2YvEQQR7iiSmblz52LlypV46KGHkJeXBwAoKirCo48+iuLiYixcuFDVQRLyCFdZ4VBbWgD54gL4Ly+O1+UJDBA4ieFwLfIVq4/hRCbqL5HhamSMFXYYK20w1NqEIuMERWUIQhvo6wG91b9zMPeMd1iiSGbWrFmDdevW8Ut6A45FJ7t27YqHHnqIZCYIhLuwcISLuADS5AVQFn0B5AkMhyeR8SQxrYwXBNOsAfBTrcvtJkEkBnBPK3H1MaiO4kXGOSLDiUyUuVYoMl6iMlQvQxBEqFEkM1arFb169XLb3rNnT9TXa0TjNIBWhIUjEOIChFZeHK/LFxhvdTBqRGJcRcYZsWhMjdXoJjLGizpEVzmllizMEZHhCn5FoKgMQRDhiCKZufvuu7FmzRqsXLlSsP2VV17B6NGjVRlYY0BrssIRKGkBlIsLIF1egMAIDCAtAgO4R2EAyJIYwJFOumCP57c5LxQpllaqr46CoSoKUVU66K1wExlu5hJf8CshKkMQBBEO+FUA/Mknn+Dqq68GAOzZswfFxcUYM2YMpk2bxu/nKjyNDa0KCxBYaeHQgrwAygSGw59IDODoFeMsMa44S4xzN19PIhNVDRjqAIMFMNQxN5HxlF4Si8pQiokgiHBAkcwcPHgQV155JQDg+PHjAIDU1FSkpqbi4MGD/H46nTZW21SLynoT6uq9f2iGK+EuLoC68uLYJ3ACA6iXTnJNJTnPVgKEaaWK+hi3+hhm1QtEJrrKITLGSjuMlXbBFGxBesmHyBAEQYQLimTm888/V3scRJAIhrRwBFNeAP8FxlcXXrkCA/iWGACiIuMtEuMMt7aS8yKRrvUxBoveq8gIpmADXpvjEQRBhCOK00xE+KMlcQECIy+O/ZRHYAB5aSSgQWAAzzUxgLjEAHATGddIjJS0ks2iB6qjoPcgMoY6BkOdnZ+CzSMxvQRQiokgiPCBZCYCCKa0AOqICxA6eQHUicIA6kViAPGp1s44T7s+ZWmGi9Z4Pq1UYTG5iUxUlR56i7BGhhMZvpdMnWOcvpYrIAiCCGdIZjRGsMUFCJ28AMGLvgDSBQaQJzEAFE2zTtbX8ULDiYxrWqnKauLrY6yWKNRXR0Fn0QtExjUi4ywybnUyAEVlCCJC0FsAvZ+lq8yizlgCDclMmBIKaQHUExcgsPLi2Df8BAaAV4kB4LEuptxuEgiNs8i4FvlyaSWuPsZvkaFp2ARBaBiSmRATKmkB1BUXQJm8AMFNH3GEUmIA97oYDk/rKnlKK9VZogUzlvQWIKpaB71FpEbGqSmeaJ2MCzSDiSAIrUAyE0RCKS6A9uTFsW9oBAYInMSIde0F3EXGucjXNa1UY43i62PERIbvI2Nhbr1kOLyllwiCILQEyUyAiDRxAYIjL479QycwgDyJAeDWKwaAzxlKnnCOxnhKKzmLDDf1WiwiY7A4LVNQZ+evISW95C0qQ/UyBEGEGyQzKlJli0F0iJrmhZO8AOpHX4DQCAwgLRIDQHbnXg6x2hguGuOcVnKuj3Geei1FZLzWyRAEQWgckhkNEghxAYInL4791RUYIHwlxnlWEuA+5RrwnlZyro9xLvTlesjorfCYWpIkMjKiMgRBEOEIyUyYEyhxAYIrL45jwkNgOPxJJwHiEgOIT7X21juGkxgAgrSSc32Mc6GvJ5GJrm4QGa67r2jBrx9QiokgiHCEZCbMCFd5AQIXfQECm0JyRm4kBoDHhneA5xlKnop8nZGSVnIt9HWees2JjHNqyZvIUFSGIIhIhWQmhARSXIDgy4vjGPUFBlA3AgMolxgAkqMxXBTGNSrjWuQrllZyrY9RKjIcJDIE0fgw1AEGf0+ikUmOJDNBItDiAvgvL0DkCAwQOolxxdeUa+e0kmt9jDeRcauRcVpviUO0w69CKMVEEES4og/1AKRSVlaG0aNHIzExEcnJybjnnntQVVXldf+HHnoI7du3R2xsLFq2bImHH34YZrNZsJ9Op3P7Kigo8Hu8VVaT4CsQVFhMgi8lOFIZDV/Sj4viv3xRZ4nmv3xhtUTxX/4glk5yronhFoDklh3ginuzYs18XUyr2At8SqmV8QKfUmobfQ7N9NV8zxipKSVvIvNHdSJKq5u4iQyqo2CoihIVGecaGa7gl0O0ToaiMgRBRCiaicyMHj0aZ8+exaeffgqr1Yrx48dj0qRJ2Lx5s+j+f/zxB/744w+sWLECV1xxBU6dOoX7778ff/zxB9555x3Bvhs2bMDgwYP558nJyYrGWGU1IVpBZEMqoYq8OI6T96sSzAgMh5R6GMdj75EYAKrVxQDu0RgAomklrtDXdcaS3gKvqSVPM5cAaeklKVBUhiCIcEYTMnP48GHs2LED+/btQ69evQAAL7zwAm6++WasWLEC2dnZbsd07twZ7777Lv+8bdu2WLx4Me666y7U19cjKqrh1pOTk5GZmRn4G5GBGuLCEY4CA6gjMVJTSY7H8iXG8d29X4yUtJKn2hjXtJKnQl8xkRFNLckRGREoKkMQhNbRRJqpqKgIycnJvMgAQH5+PvR6Pfbs2SP5PGazGYmJiQKRAYDJkycjNTUVvXv3xmuvvQbGmIczOKirq0NFRYXgy1/USBlxKE0dOY6Vnj4C5KWQAPXSSIC0VJLjsXs6yXmaNVcXkxNz3qlzryMa4ywyXEpJqsg4JMZ3fUx5daxkkRFLLXkt+HWFliwgCCJILF26FDqdDlOnTvW4z7p169C3b1+kpKQgJSUF+fn52Lt3r+xraSIyU1JSgvT0dMG2qKgoNG3aFCUlJZLOcf78eSxatAiTJk0SbF+4cCFuuOEGxMXF4ZNPPsGDDz6IqqoqPPzwwx7PtWTJEixYsED+jTihZuQFCF70BQhNBIbD30gM4N7wDlBe3MuhNBojVujrj8jIqZMBKCpDEERg2LdvH15++WV07drV636FhYUYNWoU+vTpg5iYGCxbtgwDBw7EoUOH0Lx5c8nXC6nMzJw5E8uWLfO6z+HDh/2+TkVFBYYMGYIrrrgC8+fPF7w2Z84c/nGPHj1QXV2NZ555xqvMzJo1C9OmTROcv0WLFt7HECby4jhW+wIDhIfEAO7RGAAhFRm1lyugehmCIORQVVWF0aNHY926dXjqqae87rtp0ybB81dffRXvvvsudu/ejTFjxki+Zkhl5rHHHsO4ceO87tOmTRtkZmbi3Llzgu319fUoKyvzWetSWVmJwYMHo0mTJti2bRuio71/KOfm5mLRokWoq6uDySQuICaTyeNrHOEkL47jAy8wgLYkBvCvuNfTlGsAQRMZDjeRcYWiMgRBBInJkydjyJAhyM/P9ykzrly6dAlWqxVNmzaVdVxIZSYtLQ1paWk+98vLy0N5eTn279+Pnj17AgA+++wz2O125ObmejyuoqICgwYNgslkwgcffICYGN8rWR84cAApKSk+ZUWMSosRUdGhL9x1HKvsrQ21wHColU4C3HvFAJ6Le5VIDOBfWkkHyBYZDtGCX4AWkSQIQlVca0M9/ae+oKAA33//Pfbt26foOjNmzEB2djby8/NlHaeJmpmOHTti8ODBmDhxItauXQur1YopU6Zg5MiR/EymM2fOYMCAAXjjjTfQu3dvVFRUYODAgbh06RL+/e9/Cwp109LSYDAY8OGHH6K0tBRXX301YmJi8Omnn+Lpp5/G9OnTQ3KfoYi+ANoRGEBdiQHUicYA6qWVgAaR0Vs995HxWvDrKjJ+RGUoxUQQ2kVfD+j9nObD/vpnxrWUYt68eW5lG6dPn8YjjzyCTz/9VFLwwJWlS5eioKAAhYWFso/XhMwAjrzalClTMGDAAOj1etx22214/vnn+detViuOHDmCS5ccH1bff/89P9OpXbt2gnOdOHECOTk5iI6OxurVq/Hoo4+CMYZ27dph5cqVmDhxYlDuyV95cZwj8gQG8CwxnMAA3iUG8DzNGoCsqdYc3op8AaiSVgKcRcZ9DD4Lfl2h2UsEQajA6dOnkZiYyD8Xi8rs378f586dw5VXXslvs9ls+PLLL/Hiiy+irq4OBoP4AgsrVqzA0qVLsWvXLp9Fw2JoRmaaNm3qsUEeAOTk5AimVPfv39/nFOvBgwcLmuUFA60JDBBZEgMoL/AFfEdjAKgmMrILfgFJ6SWqlSEIQi6JiYkCmRFjwIAB+PnnnwXbxo8fjw4dOmDGjBkeRWb58uVYvHgxdu7cKWjBIgfNyIxWCaW8AOElMBxy0klyJQYQn6EEyJcYwHM0BpBW5AtAVZHhkCQyfkZlKMVEEIQcmjRpgs6dOwu2xcfHo1mzZvz2MWPGoHnz5liyZAkAYNmyZZg7dy42b96MnJwcvt1KQkICEhISJF+bZCYAkMC4o3RmEiBPYgD/62I41EorAfA5YwmQJjKSC349iAxFZQiCCCXFxcXQOxXyrFmzBhaLBbfffrtgP7GaHG+QzKhIjdWIqBDMQALCU2AA5UW9QHhJjOO7MK0EQFJ9DADVRCZYUFSGIAg1KCws9Pr85MmTqlyHZCaE+CMvgHKBAUIfhQECJzGAMpHxNeUaEI/GAPBaHwN4nnoNeBcZDll1MhSVIQiikUEyE2QiVWCAyJMYx/eGaAwAj2klAAERGdkFvyQyBEE0QkhmgkAkCwwQeIkBhDOUAPVTSoDvIl/Ac1oJEC/0BRA4kSEIgiAAkMwEjMYqMID/EgMoq4sB1I3GAO5pJQCi9TEAVBEZDrGZS24EKCpD9TIEERkYLID4ZGgZiPS7CkdIZlSktj4KhhAU8XJoVWIA/4p7gcBEYwD3tBIgXh8DuM9YAqSLDIenmUsALVdAEAThCZKZEOKvvADBERggfCQGUJ5SArzPVAKkpZUA9/oYAH6JjK+ZSz5FhmplCIJoxJDMBBktCQwgvR4GCIzEOB4HLhrj+C6eVgLEu/kCCIrISK6TUXHJAkoxEQShRUhmgoQWUkgcSqMwjuf+SQwQ+GgMoDytBLgX+gLCGUsAfIoMh68p2G7IkA2KyhAE0VggmQkQWovAAJErMYC0aAzgnlYC4LXQF1AmMlJmLgHK00sEQRCNCZIZFamzRMEQpZ0IDEcoJAYIXF0MIK3IF/CdVgLgsdAXUF9knPFHZJREZSjFRBCEViGZCRPCSWJ81cM4nsuTGCDwdTGA72gM4DmtBECyyLhOvQaUi4wz1E+GIAhCPiQzISScBAaIHIkBlKWVAM/1MYBykeHw1EtGUZ2MylEZgiAILUMyE2RCITAcwUwnAdIkBghcSsnx3XM0BoDH+hjA84wlQNgMj3vuSWRcm+J5m7kEhKZOhlJMBBF5GCwMBjDfO3rD4ufxQYJkJghoRWAAaRIDyOvaC/iuiwECIzLO0RjAc1oJ8FzoC4RIZBRAURmCIBojJDMBJFQS401gAHk9YgD5EgNIL+4FAhuNAcTTSoDn+hjA84wlx2PlIsMhSWRo9hJBEIQkSGZUJlyjMEBoJMbxWL2UEqBOWgmA7BlLjsfyRIbDU8EvhySR8YHSqAylmAiC0DokMypSb4mCPgQ/0cYsMYD/aSVAvNAX8F9kxGYu+Sz49QQV/RIEQYhCMqNh1KyHAaRLDBDcuhhA3bQSEF4iQ+klgiAI/yCZ0SCNXWIA9dJKgPdCX8f3EIuMD/yJylCKiSCISIBkRkMEQ2IAadOsAWkpJcD/Al9AfjQGCI7IcLiKDL9dDZGhqAxBEIRXSGbCHH/rYRzPtSMxgPJoDKAsrQSIT70G4FNkxBaOBMSnYCvCh8hQrQxBEATJTNgSLhIDyCvuBZRLDCC/NgbwnlZyfJdWH+P4rkxkPE3Bdkbt9JK/UIqJICIbvYXBwPxreses1DSPUEA4SgwgrS4GCE40BpCfVgLkiwyXalIiMsFIL1FUhiAIwgHJTJgQCokBvE+zBsQlBgheNMbxWHpaCfBeHwOEmch4gupkCIIgJEMyE0KUCAwQXIlxPA58SglQnlYCvNfHAO6FvoD71GtAfZHxicLoir9RGUoxEQQRSZDMhIBwkBggNMW9HIFIKzm+B09kOLyJDM1eIgiCCDwkM0FEjVSSY1vwJAYIn2gMIC2tBEibseT4rlxkvPWSAQJX8Eu1MgRBEEJIZoJAuEkM4HuGEhAe0RhAWloJ8FwfA4SZyHgjCFEZSjERBBFpkMwECF8CA4RWYgDpKSUgsNEYx2PlaSVAXqGv47t0keEQa4onhs81l/xIL1FUhiAIwh2SGZVRqx7GsS0yJQaQl1YC5NXHAL5FxrmHDOBdZMS6+0pdPDIc+skQBEFEOiQzKhJlrIenH2k4SwwQ2JQSoE5aCfBcHwN4LvR1fMdf3wMnMq6onV5SIypDKSaCaDwY6hgMdmqaR6gASYxniQE8944BfNfHAJ4LfYHgi4zPOhmavUQQBBEQ9KEegFTKysowevRoJCYmIjk5Gffccw+qqqq8HtO/f3/odDrB1/333y/Yp7i4GEOGDEFcXBzS09Px+OOPo77ej7V0/sJktHqsiRGbYu0sMonGOl5kEqLrBCtZO3ft9TelFOpojC+RqbCYvBb6ShUZvUWnSGQ4AioyEqFaGYIgCM9oJjIzevRonD17Fp9++imsVivGjx+PSZMmYfPmzV6PmzhxIhYuXMg/j4triArYbDYMGTIEmZmZ+Oabb3D27FmMGTMG0dHRePrppxWNU40eMYDnSAwQftEYwPdMJcdjZdOuHd+918cAnkUGgCKR8dYUzxnFBb9AUKMylGIiCCJS0URk5vDhw9ixYwdeffVV5Obm4tprr8ULL7yAgoIC/PHHH16PjYuLQ2ZmJv+VmJjIv/bJJ5/gl19+wb///W90794dN910ExYtWoTVq1fDYrF4Oas4JpHiX9dIjGsUBnBPJ3lLKTl37nXuF+M81dp5CQJv0ZhkfZ0q0RgpU67LbXGCaAw3W4mrj/HWP0auyDgkJrAiI7ngVwUoKkMQhFZYs2YNunbtisTERCQmJiIvLw8ff/yxx/3XrVuHvn37IiUlBSkpKcjPz8fevXtlX1cTMlNUVITk5GT06tWL35afnw+9Xo89e/Z4PXbTpk1ITU1F586dMWvWLFy61BCxKCoqQpcuXZCRkcFvGzRoECoqKnDo0CGP56yrq0NFRYXgyxVfEsOlksTSSYDnlJIaEgMEJhrjPOVayWwloKE+xlehL9AwY4nrIeOpq6/jueO7VJHxhqyCXz+jMiQyBEFoicsuuwxLly7F/v378d133+GGG27AsGHDPH6mFhYWYtSoUfj8889RVFSEFi1aYODAgThz5oys62oizVRSUoL09HTBtqioKDRt2hQlJSUej/vnP/+JVq1aITs7Gz/99BNmzJiBI0eOYOvWrfx5nUUGAP/c23mXLFmCBQsWuG2PiapHVLRBsE1OOgnwr7jX8TxwU605lBT5AvLTSoDyGUuAfyIT8DqZIBf9UoqJIIhgMHToUMHzxYsXY82aNfj222/RqVMnt/03bdokeP7qq6/i3Xffxe7duzFmzBjJ1w2pzMycORPLli3zus/hw4cVn3/SpEn84y5duiArKwsDBgzA8ePH0bZtW8XnnTVrFqZNm8Y/r6ioQIsWLQT7hFJigMDXxQD+1cYA4tOuHd+ldfQFwkNkAgVFZQiC0DI2mw1btmxBdXU18vLyJB1z6dIlWK1WNG3aVNa1Qiozjz32GMaNG+d1nzZt2iAzMxPnzp0TbK+vr0dZWRkyMzMlXy83NxcAcOzYMbRt2xaZmZluubnS0lIA8Hpek8kEk8kk+lqgJQYIfnEvEByRkZJWAvwTGQ41RSbcozIEQRD+4lpO4e1z8Oeff0ZeXh5qa2uRkJCAbdu24YorrpB0nRkzZiA7Oxv5+fmyxhdSmUlLS0NaWprP/fLy8lBeXo79+/ejZ8+eAIDPPvsMdrudFxQpHDhwAACQlZXFn3fx4sU4d+4cn8b69NNPkZiYKPkH74xDZBxvrhoSAyifoQQEJhoDSGuAB6iXVgLUExmDRb7IeCNQIqNmVIZSTATRODFYGAxMnaZ5rtmHefPmYf78+aLHtG/fHgcOHIDZbMY777yDsWPH4osvvvD5ubp06VIUFBSgsLAQMTExssapiZqZjh07YvDgwZg4cSLWrl0Lq9WKKVOmYOTIkcjOzgYAnDlzBgMGDMAbb7yB3r174/jx49i8eTNuvvlmNGvWDD/99BMeffRRXHfddejatSsAYODAgbjiiitw9913Y/ny5SgpKcGTTz6JyZMnezROXyiVGCC86mKA0KSVgPATGVUWkCQIgtAwp0+fFswG9vYZaTQa0a5dOwBAz549sW/fPjz33HN4+eWXPR6zYsUKLF26FLt27eI/o+WgCZkBHEVCU6ZMwYABA6DX63Hbbbfh+eef51+3Wq04cuQIP1vJaDRi165dWLVqFaqrq9GiRQvcdtttePLJJ/ljDAYD/vvf/+KBBx5AXl4e4uPjMXbsWEFfGjk0MVoAOD6gAy0xQPhEY4DGJzIeCbOoDEEQhBpwU62VYLfbUVfn+d+/5cuXY/Hixdi5c6dg1rIcNCMzTZs29dogLycnB8wpnNaiRQt88cUXPs/bqlUrbN++XZUxAtqWGMD/aAygfFkCwF1iAHVEhsOXyDgTCXUylGIiCCKYzJo1CzfddBNatmyJyspKbN68GYWFhdi5cycAYMyYMWjevDmWLFkCAFi2bBnmzp2LzZs3Iycnh59JnJCQgISEBMnX1YzMaAGHyDg+rJV07QVCUxfDoeaUa0D9aIzjMfddnsgYLEySyASk4FcGFJUhCELLnDt3DmPGjMHZs2eRlJSErl27YufOnbjxxhsBOJYQ0usb/k1fs2YNLBYLbr/9dsF5vNXkiEEyozJqSIzjefhHY4Dgp5Ucj+WLDIeryDijWGR8QbOXCIJoJKxfv97r64WFhYLnJ0+eVOW6JDMqkmCoBReZUTJDyfE8dBIDqJ9WAtSvjwHki4ynrr5yZi55JEyjMpRiIgiisUAyozJakBhAfjQG8D+tBKhX6Ov8mlSR8bfgV1F6iaIyBEEQAYdkRkWaRNUBcHxYq1kXA4RHNAZQL60ERIDI+EKGyFCtDEEQhHJIZlRGqcQAoY3GAPKKfAH10kqARkUmjAWEUkwEQRgsdhjs7jM15cDq/Ts+WJDMqEhydDWA6LAq7gUCH40BgiMyesEUawhekysyzihac0nF9BJFZQiCIPyDZEZFEg21iDU0fBiGWmIA6dEYx2P1inwB6fUxQPBFRmzmkhg0e4kgCCL8IZkJAL4kBgjPaAygbloJULeHDKCuyLgSivRSIKIylGIiCKKxQTKjMqGuiwH8i8YAntNKQGAKfR2Pue/qi4wzAa+TofQSQRBE0CGZUZEkfQ0AA4DwSCkByqIxgO+0EiC9PgYIrciosuYSQNOwCYIgwhSSGZUJ9lRrjlCklRyP/S/0dTwOH5EJxmrYFJUhCIJQD5IZFXGIjIF/Hg7RGMD7lGtAnbQSEB4i44wvkREjGOmlQEL1MgRBNEZIZgJAMCQGCE5aCdCWyEidgg2Erp8MRWUIgiDUhWRGRZrqLyHeaTVQjlBGYxyPfRf5Av7VxwDypl47HntfMNL5NSlrLQVk5pIUwiQqQxAE4Yyh1gZDlB/rzgFg9f4dHyxIZgJIOEZjAGVpJcdj+YW+gLIFI51f8yYyUqdgKyr4BVRPLwUyKkMpJoIgGiskMwEgFBIDBD6tBESmyGhxuQKCIAiiAZIZldFKNAZQllYC/J96DagvMs4EpeAXoKgMQRBEmEAyoyJJ+joA7jUzSgl0NAaQl1YC5BX6Oh5z3/0TGWeUTMEGqE6GIAgiUiGZCVPkRGMczz0X+QLy0kqOx9ILfYHAi4w/vWQAletkFEAzmAiCIAIHyUyYoUY0BpCWVgLk1ccA4Ssy3lC9ToaiMgRBEGEFyUwYEYq0EhCYQl/HY+HUa+fX1RaZcOvw60ygozJUL0MQRGOHZCYMUDsaAwSmPgYIrcg4I0VkxPAqMhSVIQiC0CQkMyEm1Gklx2NtiIycKdhAAOpkFIgM1coQBBEq9HX10Ntk/Dsodo56/44PFiQzIUKuxDieBy+tBHivjwHUFRlnAiUykdhPhlJMBEEQJDMhIVRpJSBwhb6Ox8pFRu56S6qJjFQoKkMQBBG2kMwEEbWiMUBw00pA6EXGGU/rLQEKRIbSSwRBEJqHZCZIBDIaA4ROZMSmXju/LldknJEyBdtXwa9XNC4clGIiCIJwQDITYAIRjQECl1YCvBf6Oh5z39UXGaVTsAFKLxEEQTRWSGYCSCCLfAHv0RiARIamYRMEQTQOSGYCgJjEAOGXVgKkz1hyPOa+NwKRUUiwojKUYiIIgmiAZEZlwiWt5HisjsiIFfoCES4yFJUhCILQDCQzKmK2mxDvsi0Q0RhAfn0MIL3QFwi+yDjjSWTCHaqVIQginNDX2fxueqe3aePfYJKZAKFmNAYIbH0MIH3qNeC+YCTgv8j46iUDUFSGg1JMBEEQQkhmAoDUaAwgvXcMIL0+BghPkXHGVy8ZV8JdZCgqQxAEETr0vncJD8rKyjB69GgkJiYiOTkZ99xzD6qqqjzuf/LkSeh0OtGvLVu28PuJvV5QUKBsjPY4gciU2+K9ppWcZytJFZkKi8lnoa+cGUvBFBlfTfFCOgWbIAiC0CyakZnRo0fj0KFD+PTTT/Hf//4XX375JSZNmuRx/xYtWuDs2bOCrwULFiAhIQE33XSTYN8NGzYI9hs+fLjf41UzreSrPkZOoa/UqdeOx9oQGZ9EUFSGUkwEQYQrS5YswVVXXYUmTZogPT0dw4cPx5EjR7wes27dOvTt2xcpKSlISUlBfn4+9u7dK/vamkgzHT58GDt27MC+ffvQq1cvAMALL7yAm2++GStWrEB2drbbMQaDAZmZmYJt27Ztw4gRI5CQkCDYnpyc7LavP0gp8gUCk1YClBX6Oh7D6XH4iYwnGkOdDEEQRLjzxRdfYPLkybjqqqtQX1+P2bNnY+DAgfjll18QH+86PcZBYWEhRo0ahT59+iAmJgbLli3DwIEDcejQITRv3lzytTURmSkqKkJycjIvMgCQn58PvV6PPXv2SDrH/v37ceDAAdxzzz1ur02ePBmpqano3bs3XnvtNTDmu4ZDDF9pJQ4100qAsD5GTZHRW0MnMmKEY3qJamUIgiAc7NixA+PGjUOnTp3QrVs3bNy4EcXFxdi/f7/HYzZt2oQHH3wQ3bt3R4cOHfDqq6/Cbrdj9+7dsq6tichMSUkJ0tPTBduioqLQtGlTlJSUSDrH+vXr0bFjR/Tp00ewfeHChbjhhhsQFxeHTz75BA8++CCqqqrw8MMPezxXXV0d6uoaPtErKioEr0sp8gWUzVZyPJaWVgL8Exmx18VExhkxkfH8egBnLgERF5WhFBNBEFrCbDYDAJo2bSr5mEuXLsFqtco6BgixzMycORPLli3zus/hw4f9vk5NTQ02b96MOXPmuL3mvK1Hjx6orq7GM88841VmlixZggULFoi+pkbvGEBZ/xhAucj46iEDeBYZLirjSWTkNMUDtFEnA1BUhiCIxoPrf9pNJhNMJvFu9wBgt9sxdepUXHPNNejcubPk68yYMQPZ2dnIz8+XNb6Qysxjjz2GcePGed2nTZs2yMzMxLlz5wTb6+vrUVZWJqnW5Z133sGlS5cwZswYn/vm5uZi0aJFqKur8/hGzZo1C9OmTeOfV1RUoEWLFjDbY8HpixoiI6c+BlB36rXrPoESGVekikyolisgCILQCro6K3QG/6pJdDbHv7UtWrQQbJ83bx7mz5/v8bjJkyfj4MGD+OqrryRfa+nSpSgoKEBhYSFiYmJ8H+BESGUmLS0NaWlpPvfLy8tDeXk59u/fj549ewIAPvvsM9jtduTm5vo8fv369bj11lslXevAgQNISUnxapy+jDRQaSVA3TWWHI8DLzLeUL3gVw4aispQiokgiFBy+vRpJCYm8s+9fQZOmTKFn3V82WWXSTr/ihUrsHTpUuzatQtdu3aVPT5N1Mx07NgRgwcPxsSJE7F27VpYrVZMmTIFI0eO5GcynTlzBgMGDMAbb7yB3r1788ceO3YMX375JbZv3+523g8//BClpaW4+uqrERMTg08//RRPP/00pk+frmicFbYYWGwO2VAjreR47F8jPCD0IqN05lJA62QIgiAIySQmJgpkRgzGGB566CFs27YNhYWFaN26taRzL1++HIsXL8bOnTsFE33koAmZARwVz1OmTMGAAQOg1+tx22234fnnn+dft1qtOHLkCC5duiQ47rXXXsNll12GgQMHup0zOjoaq1evxqOPPgrGGNq1a4eVK1di4sSJfo013OtjHI/DR2TECGidDKCpqAxBEIQWmDx5MjZv3oz3338fTZo04SfoJCUlITbWkbEYM2YMmjdvjiVLlgAAli1bhrlz52Lz5s3Iycnhj0lISHBro+INHVM6D5ngqaioQFJSEubuyUeNKblhu4JFIh2PG5fIqFInA0Rs0S+lmAgicqhnVnxe/w7MZrPPSIdSuM+k/MsfRZTBczpICvW2Ouw6+qyk8ep0OtHtGzZs4Otj+/fvj5ycHGzcuBEAkJOTg1OnTrkd46smxxXNRGa0QLk1HiaT+mklgESG0ksEQRDhjZTYSGFhoeD5yZMnVbm2JprmaYlIFxln1BYZT6gqMhqLyhAEQRC+ociMilTWm2DkHwenPgYIrshwoiJXZFyRWvDrkyCJTKigFBNBEIRvSGZUJlTRGCC8RUbJzCUg9MsVcFBUhiAIInwhmVGRKlsMOB0JVVrJ8dz5cXiJjBhBqZPRYFSGIAjCL2prAb2fc3zs2vi3k2QmAIRDfYzjuboi44wnkXHbLwIKfkMVlaEUE0EQhDRIZlSkympC9F/CIkViAG2JjKSOvsEo+JULRWUIgiAiGpKZAOBPNAZQb8YSEBiRUWvmkqKCXyCo6SWqlSEIggh/SGZURq20EtA4RSac0kuhhFJMBEEQ0qE+MypSaXGWF//qY7QqMlIImshQVIYgCKJRQJEZlXGWGMfz0E29dt0vGCITsIJfuVCdDEEQRKOBZEZFaqxGRBm5x+ISAzRekfGLIEdJQhmVoRQTQRCEPEhmAkCgCn0dz50fa0tktJJeIgiCILQFyYzKBKrQ1/Hc+XHoRMaVgIqMXFQQGaqVIQgiIqi1+F8ZaxdZmC8MoQJgFamtD63I6C3BEZmgzVwCGs3sJQ5KMREEQciHIjMBIFCLRTqe+26GB4RGZMRQ3EsGCEl6iaIyBEEQ2oNkRkXqLFEwREWOyLjiawq2nILfcFlA0hkSGYIgCG1CMhMAlBT6AuEnMiEr+AUaZdEvpZgIgiCUQTUzKqNVkXGlsYkMRWUIgiC0C0VmVKTeEgX9Xz9RqYW+QHiITDCmYEuCpIIgCIKQCclMAFB7xhIQGSITkDqZCInKUIqJIAhCOSQzKqOk0Nfx3Plx4ETGFTV7yXgjXNNLBEEQhPYhmVERm1XPp5nCVWQCNQUb0GY/mXCIyhAEQQQCVlsLppO3ELDbORg1zWu0RJLIiKF6nYwSIigqQykmgiAI/6DIjMpILfR1PHd+HJ4iE5Q6mRCllygqQxAEERmQzKgIs+qh++snqnTGEkAiQxAEQRByoDRTAAilyLiitLtvWBb8AhEXlaEUE0EQhP9QZEZl1Ogh47qvweXz25vIBKK7ryeCthI2QRAEQXiBIjMqEqkiE5CCX4rKEARBECpBkZkA4K3Q1/E8PETGFTVEJtzTS+EEpZgIgiDUgSIzKqMlkVF7CrYW0ksUlSEIgog8KDKjInqrHjA4PQ+hyLiixswlb0gWGYrKEARBBAV7TR3sfjbNs7Pw/08qQDITMKT2kHHdVy2RUXsKNqDtOhkgvKIylGIiCIJQD0ozBQAti4wnKL1EEARBhCskMyqjdZEJWJ0MpZcIgiCIAKEZmVm8eDH69OmDuLg4JCcnSzqGMYa5c+ciKysLsbGxyM/Px9GjRwX7lJWVYfTo0UhMTERycjLuueceVFVVKRojiYwHQhwVoagMQRBEcPjyyy8xdOhQZGdnQ6fT4b333vO6/9atW3HjjTciLS0NiYmJyMvLw86dO2VfVzMyY7FYcMcdd+CBBx6QfMzy5cvx/PPPY+3atdizZw/i4+MxaNAg1NY2fLiNHj0ahw4dwqeffor//ve/+PLLLzFp0iS/xhpqkXFFqcioglKRiOCoDNXLEAQRqVRXV6Nbt25YvXq1pP2//PJL3Hjjjdi+fTv279+P66+/HkOHDsUPP/wg67o6xpjnT8EwZOPGjZg6dSrKy8u97scYQ3Z2Nh577DFMnz4dAGA2m5GRkYGNGzdi5MiROHz4MK644grs27cPvXr1AgDs2LEDN998M37//XdkZ2dLGlNFRQWSkpLQeuFiROljBa8FQmQcxyprigeoU/CrlfRSOEZlSGYIovFSz6z4vP4dmM1mJCYmBuQa3GfS9VG3I0oX7de5/BmvTqfDtm3bMHz4cFnHderUCXfeeSfmzp0r+RjNRGbkcuLECZSUlCA/P5/flpSUhNzcXBQVFQEAioqKkJyczIsMAOTn50Ov12PPnj2yr6lTKSLjilKRET1XuIsMQRAE0Wix2+2orKxE06ZNZR0XsVOzS0pKAAAZGRmC7RkZGfxrJSUlSE9PF7weFRWFpk2b8vuIUVdXh7q6Bhsxm80AAHtdw4e3vh5w1ga9peG5oc7lNZd9Dc77WpjLeRg4zTDUNTzm9hU+twuf19rgqij6epE6mTobPHUmkCwytUrTS15WzZQJUzqGAGKvidz0GUEQvqn/q29LMJIi9bACfl6mHo7xVlRUCLabTCaYTCb/Ti7CihUrUFVVhREjRsg6LqQyM3PmTCxbtszrPocPH0aHDh2CNCJpLFmyBAsWLHDbfvKZhSEYDUEQBKE1Lly4gKSkpICc22g0IjMzE/8reV+V8yUkJKBFixaCbfPmzcP8+fNVOT/H5s2bsWDBArz//vtugQZfhFRmHnvsMYwbN87rPm3atFF07szMTABAaWkpsrKy+O2lpaXo3r07v8+5c+cEx9XX16OsrIw/XoxZs2Zh2rRp/PPy8nK0atUKxcXFAfvlDDQVFRVo0aIFTp8+HbA8bjCIhPuIhHsA6D7CiUi4ByAy7sNsNqNly5ay0yhyiImJwYkTJ2CxqBPpZoxBpxOWUagdlSkoKMC9996LLVu2CMpDpBJSmUlLS0NaWlpAzt26dWtkZmZi9+7dvLxUVFRgz549/IyovLw8lJeXY//+/ejZsycA4LPPPoPdbkdubq7Hc3sKryUlJWn2D4wjMTFR8/cARMZ9RMI9AHQf4UQk3AMQGfeh1we2ZDUmJgYxMTEBvYZavPXWW5gwYQIKCgowZMgQRefQTAFwcXExDhw4gOLiYthsNhw4cAAHDhwQ9ITp0KEDtm3bBsBRRT116lQ89dRT+OCDD/Dzzz9jzJgxyM7O5iurO3bsiMGDB2PixInYu3cvvv76a0yZMgUjR46UPJOJIAiCIAgHVVVV/Ocz4JiMw312A47MxpgxY/j9N2/ejDFjxuBf//oXcnNzUVJSgpKSEr4WVTJMI4wdO5bBUcok+Pr888/5fQCwDRs28M/tdjubM2cOy8jIYCaTiQ0YMIAdOXJEcN4LFy6wUaNGsYSEBJaYmMjGjx/PKisrZY3NbDYzAMxsNvtziyElEu6Bsci4j0i4B8boPsKJSLgHxiLjPiLhHrzx+eefi35Wjx07ljHm+Czv168fv3+/fv287i8VzchMOFNbW8vmzZvHamtrQz0UxUTCPTAWGfcRCffAGN1HOBEJ98BYZNxHJNxDOKK5pnkEQRAEQRDOaKZmhiAIgiAIQgySGYIgCIIgNA3JDEEQBEEQmoZkhiAIgiAITUMyI4HFixejT58+iIuLQ3JysqRjGGOYO3cusrKyEBsbi/z8fBw9elSwT1lZGUaPHo3ExEQkJyfjnnvuEfTNURu51zt58iR0Op3o15YtW/j9xF4vKCgIi3sAgP79+7uN7/777xfsU1xcjCFDhiAuLg7p6el4/PHHUS+yblWo7qOsrAwPPfQQ2rdvj9jYWLRs2RIPP/ywWy+GQL8Xq1evRk5ODmJiYpCbm4u9e/d63X/Lli3o0KEDYmJi0KVLF2zfvl3wupS/E7WRcw/r1q1D3759kZKSgpSUFOTn57vtP27cOLef+eDBgwN6D4C8+9i4caPbGF0bqoX7eyH2d6zT6QRN1kLxXnz55ZcYOnQosrOzodPp8N577/k8prCwEFdeeSVMJhPatWuHjRs3uu0j92+t0RPSuVQaYe7cuWzlypVs2rRpLCkpSdIxS5cuZUlJSey9995jP/74I7v11ltZ69atWU1NDb/P4MGDWbdu3di3337L/ve//7F27dqxUaNGBegu5F+vvr6enT17VvC1YMEClpCQIOjFg7/6+zjv53yfobwHxhx9DCZOnCgYn3OPh/r6eta5c2eWn5/PfvjhB7Z9+3aWmprKZs2aFZB7UHIfP//8M/vHP/7BPvjgA3bs2DG2e/dudvnll7PbbrtNsF8g34uCggJmNBrZa6+9xg4dOsQmTpzIkpOTWWlpqej+X3/9NTMYDGz58uXsl19+YU8++SSLjo5mP//8M7+PlL8TNZF7D//85z/Z6tWr2Q8//MAOHz7Mxo0bx5KSktjvv//O7zN27Fg2ePBgwc+8rKwsIONXeh8bNmxgiYmJgjGWlJQI9gn39+LChQuC8R88eJAZDAZBb7FQvBfbt29n/+///T+2detWBoBt27bN6/6//fYbi4uLY9OmTWO//PILe+GFF5jBYGA7duzg95H7syGoz4wsNmzYIElm7HY7y8zMZM888wy/rby8nJlMJvbWW28xxhj75ZdfGAC2b98+fp+PP/6Y6XQ6dubMGdXHrtb1unfvziZMmCDYJuUPWA2U3kO/fv3YI4884vH17du3M71eL/jHfc2aNSwxMZHV1dWpMnZn1Hov/vOf/zCj0cisViu/LZDvRe/evdnkyZP55zabjWVnZ7MlS5aI7j9ixAg2ZMgQwbbc3Fx23333Mcak/Z2ojdx7cKW+vp41adKEvf766/y2sWPHsmHDhqk9VK/IvQ9f/3Zp8b149tlnWZMmTVhVVRW/LRTvhTNS/v6eeOIJ1qlTJ8G2O++8kw0aNIh/7u/PpjFCaaYAcOLECZSUlAgWy0pKSkJubi6KiooAAEVFRUhOTkavXr34ffLz86HX67Fnzx7Vx6TG9fbv348DBw7gnnvucXtt8uTJSE1NRe/evfHaa68FZHl7f+5h06ZNSE1NRefOnTFr1ixcunRJcN4uXbogIyOD3zZo0CBUVFTg0KFDYXUfzpjNZiQmJiIqSrjEWiDeC4vFgv379wt+p/V6PfLz8/nfaVeKiorcFowbNGgQv7+UvxM1UXIPrly6dAlWq9VtkcDCwkKkp6ejffv2eOCBB3DhwgVVx+6M0vuoqqpCq1at0KJFCwwbNkzwu63F92L9+vUYOXIk4uPjBduD+V4owdffhRo/m8ZISBeajFRKSkoAQPDhyD3nXispKXFb4jwqKgpNmzbl91F7TP5eb/369ejYsSP69Okj2L5w4ULccMMNiIuLwyeffIIHH3wQVVVVePjhh1UbP6D8Hv75z3+iVatWyM7Oxk8//YQZM2bgyJEj2Lp1K39esfeKe01t1Hgvzp8/j0WLFmHSpEmC7YF6L86fPw+bzSb6c/r1119Fj/H0c3X+G+C2edpHTZTcgyszZsxAdna24INm8ODB+Mc//oHWrVvj+PHjmD17Nm666SYUFRXBYDCoeg+Asvto3749XnvtNXTt2hVmsxkrVqxAnz59cOjQIVx22WWaey/27t2LgwcPYv369YLtwX4vlODp76KiogI1NTW4ePGi37+njZFGKzMzZ87EsmXLvO5z+PBhdOjQIUgjUobU+/CXmpoabN68GXPmzHF7zXlbjx49UF1djWeeeUbyB2ig78H5A79Lly7IysrCgAEDcPz4cbRt21bxeV0J1ntRUVGBIUOG4IorrsD8+fMFr/n7XhCeWbp0KQoKClBYWCgonh05ciT/uEuXLujatSvatm2LwsJCDBgwIBRDdSMvLw95eXn88z59+qBjx454+eWXsWjRohCOTBnr169Hly5d0Lt3b8F2LbwXRGBotDLz2GOPYdy4cV73adOmjaJzZ2ZmAgBKS0uRlZXFby8tLUX37t35fc6dOyc4rr6+HmVlZfzxUpB6H/5e75133sGlS5cEq516Ijc3F4sWLUJdXR1MJpPP/YN1D87jA4Bjx46hbdu2yMzMdJspUFpaCgBh915UVlZi8ODBaNKkCbZt24bo6Giv+8t9LzyRmpoKg8HA/1w4SktLPY45MzPT6/5S/k7URMk9cKxYsQJLly7Frl270LVrV6/7tmnTBqmpqTh27FhAPkD9uQ+O6Oho9OjRA8eOHQOgrfeiuroaBQUFWLhwoc/rBPq9UIKnv4vExETExsbCYDD4/f42SkJdtKMl5BYAr1ixgt9mNptFC4C/++47fp+dO3cGvABY6fX69evnNnPGE0899RRLSUlRPFZPqPUz++qrrxgA9uOPPzLGGgqAnWcKvPzyyywxMTEgi8EpvQ+z2cyuvvpq1q9fP1ZdXS3pWmq+F71792ZTpkzhn9tsNta8eXOvBcC33HKLYFteXp5bAbC3vxO1kXsPjDG2bNkylpiYyIqKiiRd4/Tp00yn07H333/f7/F6Qsl9OFNfX8/at2/PHn30UcaYdt4Lxhz/DptMJnb+/Hmf1wjGe+EMJBYAd+7cWbBt1KhRbgXA/ry/jRGSGQmcOnWK/fDDD/y05B9++IH98MMPgunJ7du3Z1u3buWfL126lCUnJ7P333+f/fTTT2zYsGGiU7N79OjB9uzZw7766it2+eWXB3xqtrfr/f7776x9+/Zsz549guOOHj3KdDod+/jjj93O+cEHH7B169axn3/+mR09epS99NJLLC4ujs2dOzcs7uHYsWNs4cKF7LvvvmMnTpxg77//PmvTpg277rrr+GO4qdkDBw5kBw4cYDt27GBpaWkBn5ot5z7MZjPLzc1lXbp0YceOHRNMPa2vr2eMBf69KCgoYCaTiW3cuJH98ssvbNKkSSw5OZmfBXb33XezmTNn8vt//fXXLCoqiq1YsYIdPnyYzZs3T3Rqtq+/EzWRew9Lly5lRqORvfPOO4KfOfe3X1lZyaZPn86KiorYiRMn2K5du9iVV17JLr/88oCuiiz3PhYsWMB27tzJjh8/zvbv389GjhzJYmJi2KFDhwT3Gs7vBce1117L7rzzTrftoXovKisr+c8EAGzlypXshx9+YKdOnWKMMTZz5kx299138/tzU7Mff/xxdvjwYbZ69WrRqdnefjaEOyQzEhg7diwD4Pb1+eef8/vgr/4eHHa7nc2ZM4dlZGQwk8nEBgwYwI4cOSI474ULF9ioUaNYQkICS0xMZOPHjxcIktr4ut6JEyfc7osxxmbNmsVatGjBbDab2zk//vhj1r17d5aQkMDi4+NZt27d2Nq1a0X3DcU9FBcXs+uuu441bdqUmUwm1q5dO/b4448L+swwxtjJkyfZTTfdxGJjY1lqaip77LHHBFOeQ30fn3/+uejvIAB24sQJxlhw3osXXniBtWzZkhmNRta7d2/27bff8q/169ePjR07VrD/f/7zH/a3v/2NGY1G1qlTJ/bRRx8JXpfyd6I2cu6hVatWoj/zefPmMcYYu3TpEhs4cCBLS0tj0dHRrFWrVmzixIlB+dCRcx9Tp07l983IyGA333wz+/777wXnC/f3gjHGfv31VwaAffLJJ27nCtV74elvkxv72LFjWb9+/dyO6d69OzMajaxNmzaCzw4Obz8bwh0dYwGYQ0sQBEEQBBEkqM8MQRAEQRCahmSGIAiCIAhNQzJDEARBEISmIZkhCIIgCELTkMwQBEEQBKFpSGYIgiAIgtA0JDMEQRAEQWgakhmCIAiCIDQNyQxBEGFPbW0txo0bhy5duiAqKgrDhw8P9ZAIgggjSGYIgvAbxhjq6+sDdn6bzYbY2Fg8/PDDyM/PD9h1CILQJiQzBNEI6d+/P6ZMmYIpU6YgKSkJqampmDNnDrjVTd5880306tULTZo0QWZmJv75z3/i3Llz/PGFhYXQ6XT4+OOP0bNnT5hMJnz11Vc4fvw4hg0bhoyMDCQkJOCqq67Crl27BNfOycnBU089hTFjxiAhIQGtWrXCBx98gD///BPDhg1DQkICunbtiu+++44/Jj4+HmvWrMHEiRORmZkZnB8SQRCagWSGIBopr7/+OqKiorB3714899xzWLlyJV599VUAgNVqxaJFi/Djjz/ivffew8mTJzFu3Di3c8ycORNLly7F4cOH0bVrV1RVVeHmm2/G7t278cMPP2Dw4MEYOnQoiouLBcc9++yzuOaaa/DDDz9gyJAhuPvuuzFmzBjcdddd+P7779G2bVuMGTMGtHQcQRBSoIUmCaIR0r9/f5w7dw6HDh2CTqcD4BCTDz74AL/88ovb/t999x2uuuoqVFZWIiEhAYWFhbj++uvx3nvvYdiwYV6v1blzZ9x///2YMmUKAEdkpm/fvnjzzTcBACUlJcjKysKcOXOwcOFCAMC3336LvLw8nD171i0SM27cOJSXl+O9997z98dAEESEQJEZgmikXH311bzIAEBeXh6OHj0Km82G/fv3Y+jQoWjZsiWaNGmCfv36AYBbhKVXr16C51VVVZg+fTo6duyI5ORkJCQk4PDhw27Hde3alX+ckZEBAOjSpYvbNufUFkEQhCdIZgiCEFBbW4tBgwYhMTERmzZtwr59+7Bt2zYAgMViEewbHx8veD59+nRs27YNTz/9NP73v//hwIED6NKli9tx0dHR/GNOqMS22e129W6MIIiIJSrUAyAIIjTs2bNH8Pzbb7/F5Zdfjl9//RUXLlzA0qVL0aJFCwAQFON64+uvv8a4cePw97//HYAjUnPy5ElVx00QBOEKRWYIopFSXFyMadOm4ciRI3jrrbfwwgsv4JFHHkHLli1hNBrxwgsv4LfffsMHH3yARYsWSTrn5Zdfjq1bt+LAgQP48ccf8c9//lO16Movv/yCAwcOoKysDGazGQcOHMCBAwdUOTdBENqGIjME0UgZM2YMampq0Lt3bxgMBjzyyCOYNGkSdDodNm7ciNmzZ+P555/HlVdeiRUrVuDWW2/1ec6VK1diwoQJ6NOnD1JTUzFjxgxUVFSoMt6bb74Zp06d4p/36NEDAGjGE0EQNJuJIBoj/fv3R/fu3bFq1apQD4UgCMJvKM1EEARBEISmIZkhCIIgCELTUJqJIAiCIAhNQ5EZgiAIgiA0DckMQRAEQRCahmSGIAiCIAhNQzJDEARBEISmIZkhCIIgCELTkMwQBEEQBKFpSGYIgiAIgtA0JDMEQRAEQWgakhmCIAiCIDTN/wcFLeDECXX/GgAAAABJRU5ErkJggg==", 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r1pWsH18/Awee9p/9M7+EB9Qao+N7OGfOHI+nnZc/tRpw/1196KGH8PPPP2PChAlo06YNqlWrBrvdjrS0NMVOu1fqPUxISMClS5ckbZPQDiQ6QUZMTAyqV68Om82GlJQUr+t+9dVXaNiwIVauXCk4UE2ZMsVl3fDwcPTt2xd9+/aF3W7H008/jXfffRevvPIKGjduHFApoVGjRhg/fjzGjx+Po0ePok2bNvj3v/+Njz/+GImJiQDK/nXbsGFD5zbnz593+deg41+Jly9fFpz2XPFfl1u3bsXFixexcuVK3H333c7lJ06c8GvM+/fvR48ePSrdZ6PRiB49eqBHjx6YN28eZs6ciZdeeglbtmzx+Nk0atQIdrsdhw4d8niQc7w3R44cEbw3paWlOHHihEvbSUlJSEpKwssvv4yff/4Zd9xxBxYtWoRXX30VQGBloIr4+hn4Q6NGjXDgwAGv6/gz9sTERPz444+4cuWKINVxlCwd76tSeBq7o1xmNpsr/Q174u+//8amTZswbdo0TJ482bnckRb5Mg53JCYm4siRIy7L1XoPgTJpOnnypN//QCL0A5WugoyQkBAMHDgQX3/9tduDxPnz5wXrAsJ/Xe3cuRNZWVmCbS5evCh4bjQanalQSUkJADiv2+HL1VWvXr2K69evC5Y1atQI1atXd7aXkpKCsLAwLFiwQDA+d7cbcBwYtm3b5lxWXFzskja529/S0lK/avsPPfQQzpw5g/fff9/ltWvXrqG4uBgA3P7r0iEujn10x4ABA2A0GjF9+nSXf3U7xp2SkoLw8HC89dZbgn1ZvHgxCgoK0KdPHwBlZ9VZrVZBG0lJSTAajYIxVK1aVfRVcX39DPxh4MCB2L9/P1atWuXymmO//fne9e7dGzabDW+//bZg+ZtvvgmDwYBevXoFPNZA8DT29u3bo1GjRpg7dy6Kiopctiv/G/aEu+864P734+97uGvXLsHfiOLiYrz33nuoX7++15KrJxzz4y5cuFDpuu72/Z133sH58+dVvUI3oS6U6OiUJUuWuJ0nM3bsWLz++uvYsmULkpOTMXLkSLRo0QKXLl3C3r178eOPPzoPwvfeey9WrlyJ++67D3369MGJEyewaNEitGjRQvAH9oknnsClS5fQvXt31K1bF6dOncKCBQvQpk0bZ22+TZs2CAkJwezZs1FQUACTyYTu3bujdu3aLmP8/fff0aNHDzz00ENo0aIFQkNDsWrVKuTn52PQoEEAypKp5557DrNmzcK9996L3r17Y9++fVi7dq3LhcJ69uyJevXqYcSIEZgwYQJCQkKwZMkSxMTEIDc317le586dUaNGDaSnp2PMmDEwGAz46KOP/IrRH3vsMXzxxRd48sknsWXLFtxxxx2w2Ww4fPgwvvjiC6xfvx4dOnTA9OnTsW3bNvTp0weJiYk4d+4c/u///g9169YVTOSsSOPGjfHSSy9hxowZuOuuu3D//ffDZDLh119/RXx8PGbNmoWYmBhMmjQJ06ZNQ1paGvr164cjR47g//7v/3Dbbbc55xxt3rwZGRkZePDBB3HLLbfAarXio48+csqwg/bt2+PHH3/EvHnzEB8fjwYNGiA5Odnn98Sfz8AfJkyYgK+++goPPvggHn/8cbRv3x6XLl3CmjVrsGjRIrRu3RqNGjVCdHQ0Fi1ahOrVq6Nq1apITk52O2elb9++6NatG1566SWcPHkSrVu3xoYNG/DNN99g3LhxHk+rrwyDwYAuXbpg69atfm3nbewffPABevXqhVtvvRXDhw/HzTffjDNnzmDLli0wm8349ttvvbZtNpudc98sFgtuvvlmbNiwwW162b59ewDASy+9hEGDBiEsLAx9+/Z1e9HBiRMn4rPPPkOvXr0wZswY1KxZE8uXL8eJEyfw9ddfB3QVZX9OL09MTMTDDz+MpKQkREREYPv27VixYgXatGmDf/3rX373TegE5U/0IuTE00XbHI/Tp08zxhjLz89no0ePZgkJCSwsLIzFxcWxHj16sPfee8/Zlt1uZzNnzmSJiYnMZDKxtm3bsu+++87ltNyvvvqK9ezZ03nxwXr16rF//etf7OzZs4Kxvf/++6xhw4YsJCTE66nmFy5cYKNHj2bNmjVjVatWZVFRUSw5OZl98cUXgvVsNhubNm0aq1OnjtcLBjLG2J49e1hycrJzfPPmzXN7avOOHTvY7bffziIjI1l8fDx7/vnn2fr1613G67hgoDtKS0vZ7Nmz2a233spMJhOrUaMGa9++PZs2bZrzFNdNmzax/v37s/j4eBYeHs7i4+PZ4MGD2e+//+62zYosWbKEtW3b1tl+ly5d2MaNGwXrvP3226xZs2YsLCyMxcbGsqeeekpwwcA//viDPf7446xRo0YsIiKC1axZk3Xr1o39+OOPgnYOHz7M7r77bhYZGen1goEO4Ob0csZ8/wwcFwysSMXTrxlj7OLFiywjI8N5Ycm6deuy9PR0waUTvvnmG9aiRQsWGhpa6QUDr1y5wp599lkWHx/PwsLCWJMmTbxeMLAiFb97V65cYQDYoEGDPLxb3vfP09gZK7s8wP33389uuukmZjKZWGJiInvooYfYpk2bnOs4Ti93dxmDP//8k913330sOjqaRUVFsQcffJD99ddfbk/ldlyY02g0+nzBwOjoaBYREcE6duzo8YKBFS9tcOLECZf99Of08ieeeIK1aNGCVa9enYWFhbHGjRuzF154gRUWFla6LaFfDIxxNLuPICSgfv366Nq1K5YtW6b2UIgg54cffsC9996L/fv3IykpSe3hEERQQnN0CIIgZGLLli0YNGgQSQ5BqAjN0SEIgpCJircNIQhCeSjRIQiCIAhCt2hKdLZt24a+ffsiPj4eBoPBeYNEb2zduhXt2rWDyWRC48aN3c7bWLhwIerXr4+IiAgkJydj165d0g+eUIyTJ0/S/ByCIAgCgMZEp7i4GK1bt8bChQt9Wv/EiRPo06cPunXrhuzsbIwbNw5PPPEE1q9f71zn888/R2ZmJqZMmYK9e/eidevWSE1NFdwGgSAIgiAIbaLZs64MBgNWrVqFAQMGeFznhRdewPfffy+4MN6gQYNw+fJl5zVmkpOTcdtttzkvEma325GQkIBnnnkGEydOlHUfCIIgCIKQF11PRs7KynK5RHpqaqrzDtelpaXYs2cPJk2a5HzdaDQiJSXF5eq/5SkpKRFcOdZxZ++bbrpJksvlEwRBEPqEMYYrV64gPj4+oAso+sr169dRWloqSVvh4eGIiIiQpC010LXo5OXlITY2VrAsNjYWhYWFuHbtGv7++2/YbDa36zjuzeKOWbNmYdq0abKMmSAIgtA/p0+flvTGueW5fv06GiRWQ9459zfN9Ze4uDicOHFCs7Kja9GRi0mTJiEzM9P5vKCgAPXq1cPxPQkwVrV62bJyrtjFfTGLmLi7XhfYxH2Ri5hJ1PZXbK53WZaz76IA+rti86+fYj/W93XdYmu4JG1ds3r/E3DNEub19etW76+X2Dy3X2rx/F21Wr3/S9fmZVsAsFsr/x0wi2/pq8Hi27+6DT60Z7B5XsfoZnuDxc16bv7EGN2t5+O2UmD38DHbPXw9PC331hYAMO9ft3LtSzMjg4VIP7PDfv06/pzyquDGsVJTWlqKvHM2HNudAHN1calR4RU7Gnc4jdLSUhIdHomLi0N+fr5gWX5+PsxmMyIjIxESEoKQkBC368TFxXls12QywWRyPYgYq1pRPcAvlUNwqouYH36FhaBawFsDl20RcL17jT/9R6BKoNv+IxyBbH/FXvbj81dZrtgi4e/P9ootwq9timwm+Ko5RTYTKtcXoNjq23qllcjQVWsYQisZXGglohPqRXSuW0PhTTdCvMgK8yIqVksIjN4OlJW8DpRJjsGHv34Gi9Gnv5IGi6HS9QxWA+BhXEaLAe7eLKO7ZRVkxVgKVPySGS2uyzy1JxZ7qNuhAwAMHvbX4OGr6a0texjgi5raw5ikZ9mwUHmmsSoxzcFc3ShadPSArt+BTp06YdOmTYJlGzduRKdOnQCU1R3bt28vWMdut2PTpk3OdZRAbIpzhYXgiogk57ItApdFJDlXWASuMBHbi0hxHJKjRJ9X/HyPivxIcvxZ16f2rOLbE5vmeMN7miPD0bgcUic5ciF1IiNHmuMtffGY5niRHIKQA019tYqKinDs2DHn8xMnTiA7Oxs1a9ZEvXr1MGnSJJw5cwYffvghAODJJ5/E22+/jeeffx6PP/44Nm/ejC+++ALff/+9s43MzEykp6ejQ4cO6NixI+bPn4/i4mIMHz5c9v2RQnDEIkZwysagjuAA+pEcfyj2QWB8kZyrIiTFF65XUhILFGtlJatKXpcDn0pWVv9KVr5idDPX1J0gyYGUklNpXwqXrAh9oSnR2b17N7p16+Z87pgnk56ejmXLluHs2bPIzc11vt6gQQN8//33ePbZZ/Gf//wHdevWxQcffIDU1FTnOg8//DDOnz+PyZMnIy8vD23atMG6detcJihLiVjBAcRLjpqCA6iT4gTar9yS4+v6vkiOVFCaI928nEDQQpojJTynOQarQbbyFaEMmr2ODk8UFhYiKioKJw7XqXSOjtopjljBKRuD9iQn0D61JjlSpDliJcdbmuNNcgDvoiNFmuOL6PhTspIrzfFFdPxJc6QWHSVLVjylOVIIj/3adeS+8DIKCgpgNpslGJUrjmPSuSOJkkxGrt30lKzjlRuOPVpfqC04gLZTHCB4Jcfn9jgoWYmBlzTHV9SUHH/gWXIq7Yuzr6vj86SER1uQ6MiMHspUZWPQXoojpl+eJEdLJSsxaY43xKY5WilZ+Yqac3OkRKqSldJzc7wJLFAmQp7W8XaJAakpZqUwMnGJTjGzSzQa9SDRkRFKcdRLccT0rUXJ4SHNETMBWe40xxekPstKyTQnmEtWPFKZCBHKQqIjAyQ4/7Sh8xQH0I7k+IJaE5ArQ6k0x1d4THOUgNeSFZ1pRVQGiY7EqC05Wi9TASQ5gPTlKr2mOVKdTi51yUqPaY4nAkleeD7LitAf9HWTkCt2W8BXNqYU55/tdSo5ciFVyUqraU5lSH2WlS/IWbbgMc3xuI0CJStKcwhfINHhAEpx1BGcsm2VkRwtl6wqQ+tpjq9IUbLSYpqjZMmKIOSAREdFSHD+aYMkx4nUkiNFyYrSHGlKVmJRK83xhBwlK0pzCDkg0VEBKlP9s70IwRHTfyCCA/AjOb6iRMmK1zRH6gnIUqC3NMfjNkF4lhWvXLHbALs4Ibxip9PLCT+hFOefNjSU4gB8SY5UJSu5UTPN8QUe0hxfJUeNNIdKVoReINFRCD0IDqDdFKdsWz4lx692JSxZBXOao/QEZDE37nTfnoflCpxppXbJqmx9KlsRvkOiIzNUpirXBkmOW9SYlyN2AnJl8J7m+AoPE5B5S3M8bkMlK4JTSHRkRA8pTrAKDsCX5CgNpTl8TEB2h9xpDu8lK0pzCH8h0ZEBPQgOoH6ZSuwY9CQ5lOZoE62lOVJLDqU5BA+Q6EiM2pLDg+AA6qY4ZdsrV6oKZDu1JMcXKM0J3jRHSujqxwQv0FdRQopYCKoFuC2lONKMQekUJ5Dt1DzDKpjTHKUlJ9jTnEr7C2RSM5Wt/KKIhQAi715exPi7VIO/kOhwAKU40oxBb5LjD1KVrPSc5mgdOdMcKlkReoZER0V4SHF4EBwpxqFHyeGtZFUZaqU5PJastJbmeIJu2EnoAWkvJkH4xGVbhCQpjh4k54otUnSpSsx8nEAnHWtNcqQoWfGa5kiFFKeSy4VaaY7HbRQuWZVtR2Ur3pk1axZuu+02VK9eHbVr18aAAQNw5MgRt+syxtCrVy8YDAasXr1a1nGR6CgML4IjxVwcKlX5hlyS4ytKlKz0kOb4ip7SHCpZEVLy008/YfTo0fjll1+wceNGWCwW9OzZE8XFxS7rzp8/HwaDMv+4oJBRIahMJd04xAgOoB/JkapkJTdqpjlaP8sKkC/NCURyAm2PCA7WrVsneL5s2TLUrl0be/bswd133+1cnp2djX//+9/YvXs36tSpI/u46KspMzwIDkCS4yDYJCfY0xyl0VKaEwhqlKwI7VJQUAAAqFmzpnPZ1atX8cgjj2DhwoWIi4tTZBwkOjJCZ1NJNw61BCfQbfUiOWKhNEccPKU5YkpWhPYpLCwUPDeZTDCZPP/dstvtGDduHO644w60bNnSufzZZ59F586d0b9/f9nGWhH6esoADymOXgSnrA19Sw7P6D3NkXICslbSHDUkh9IcdSiwRcBqEzdHrdhmBwAkJCQIlk+ZMgVTp071uN3o0aNx4MABbN++3blszZo12Lx5M/bt2ydqTP5CoiMxlOJINw6xggNoQ3IozXFFqTTH5/YUvjt5WZselnN4FWRvBLPkuP1ecFhy9YXTp0/DbDY7n3tLczIyMvDdd99h27ZtqFu3rnP55s2bcfz4cURHRwvWHzhwIO666y5s3bpV6mEDINGRlAJbBKoGuC0JTsU2tJXiANqSHF+gNOef9USUrCjNCR7kkF2eMJvNAtFxB2MMzzzzDFatWoWtW7eiQYMGgtcnTpyIJ554QrAsKSkJb775Jvr27Sv5mB3Q15QD9FKmAoIzxQH4kRxfCfY0R4mSlbg2PSwXkeZQyUo69C41gTJ69Gh8+umn+Oabb1C9enXk5eUBAKKiohAZGYm4uDi3E5Dr1avnIkVSQqKjIpTiVGxDvRRHzPY8SY5UJSutpjlKl6y8wVOao8ap5HqRHJIa33nnnXcAAF27dhUsX7p0KYYNG6b8gP6BREcl9JLi8CA4gDopDiC/5PiD3Dfs9BXer4IcjGlOIIg5lVwOjBaD7FdHJqkRB2P+fz6BbOMvJDoKQylOxTa0meIAykiOGiUrSnP+aS+I0xxeS1ZSyg5JTfBAoqMQJDgV21A3xRG7PW+So9QE5MrQS5rji+RoIc3R49WPy7/vlUlPsMtMETPBzsT9rq4ym0SjUQ8Ovrb6h8pUFdsJrhQH4ENypJiArOU0R6mbdvKS5gQqJWJLVkrOzQl2kSF8g0RHRijFqdhG8KU4gHYkR+9pjq/oJc3xhtZKVgQhBhIdmaAUp3wb6guO2DZ4lBylCYY0R+xtHrSQ5tD1cohgg76+EkOCU7Ed9SVH7Pa8So6SaU5lkqOXNMcXlExzAkHqeTk+98uvoxNBjrSnKSjAwoULUb9+fURERCA5ORm7du3yuG7Xrl1hMBhcHn369HGuM2zYMJfX09LSAhpbERN3QNWT5FyxRUgyF0ftUpVSkuMvPJWsKkNPaY43yZEjzfG3bCXHvBxKcwito6mv8Oeff47MzEwsWrQIycnJmD9/PlJTU3HkyBHUrl3bZf2VK1eitPTGX5WLFy+idevWePDBBwXrpaWlYenSpc7n3u7hIQd6Exwp0GqKAwQmOWqcYeUrWk5zlJqALAal5ubIWbKiNIfgGU2Jzrx58zBy5EgMHz4cALBo0SJ8//33WLJkCSZOnOiyfs2aNQXPV6xYgSpVqriIjslkcntZaiUQfcsETgSnrB31y1RStKEXyaE0x3d4THP8hUpWREWu2CJhs4k8vdym/dPLNVO6Ki0txZ49e5CSkuJcZjQakZKSgqysLJ/aWLx4MQYNGoSqVYW33ty6dStq166Npk2b4qmnnsLFixe9tlNSUoLCwkLBw1+u2CJ1IzlSlKkAaVIcNefjyC05/iDVvayCIc0ROwFZDFKlOVSyIgjPaEZ0Lly4AJvNhtjYWMHy2NhY543DvLFr1y4cOHDA5c6paWlp+PDDD7Fp0ybMnj0bP/30E3r16gWbF4udNWsWoqKinI+EhASf90MqwRErOZKMQ0LB4aFUpfR8HDUnH5etR2mOr/Cc5lQmJFSyIoKdoHH2xYsXIykpCR07dhQsHzRokPP/k5KS0KpVKzRq1Ahbt25Fjx493LY1adIkZGZmOp8XFhb6JDt6SXDK2lE/wZGqDaVLVYB8kuMrvkgOpTnyIuWZVp6gU8n5wmgBmAKfOyFEM4lOrVq1EBISgvz8fMHy/Pz8SufXFBcXY8WKFRgxYkSl/TRs2BC1atXCsWPHPK5jMplgNpsFD29QmcoVHiRHTIoD8Ck5UpWsxEJpToVlMp1ppda8HCXa1wtGy40HoQ6aEZ3w8HC0b98emzZtci6z2+3YtGkTOnXq5HXbL7/8EiUlJXj00Ucr7efPP//ExYsXUadOHdFjBqRJcahMJX0bYgQH0LbkUJrzz3oaT3PUnJdDklM5JDf8oBnRAYDMzEy8//77WL58OXJycvDUU0+huLjYeRbW0KFDMWnSJJftFi9ejAEDBuCmm24SLC8qKsKECRPwyy+/4OTJk9i0aRP69++Pxo0bIzU1VdRYeUhxpBhDWTt8CI6jHbGITXF4lBye0EKa46vk8JrmqDkvh/AOCQ5/aOqr/fDDD+P8+fOYPHky8vLy0KZNG6xbt845QTk3NxdGo/AP4ZEjR7B9+3Zs2LDBpb2QkBD873//w/Lly3H58mXEx8ejZ8+emDFjhqhr6agtOFKMoawNPq6JI1UbaqU4gPySEwxpjtI37pTrhpFyHwQDkRwp2g92SG74RVOiAwAZGRnIyMhw+9rWrVtdljVt2hSMMbfrR0ZGYv369ZKNTew1C0hw5GtHK6WqQFBScuSksjSnMpQsWfGa5gQqIZTmBA7PglPETLCJ/HCvMRnuKqsw9PXmBCnKVJKMg5OJxlK1o6UUxwHPk48rS3PElK28wdMEZHHtitte7evlUJpTBs9yQ7hCoqMylOLI0wZAkiNcV/00x1vZSutpjtv1JE5zaF6O+pDgaBP6mqsIDykOT4IjVTtiBQfgX3L8QaoLA6qV5kiFFBOQPW7jg6iIOUgGKjlSEsxpDgmOtiHRUQE9pTg8CQ6gbooDKCc5UpeseE5zKitbKXnjTjnSHDmhklXgyCU3Ut+slagcEh2FoRRH+jYA9VMcgE/JkapkRWmOl21EpjmVbU8lK2WRW3C0f4tM7UFfd4UgwZGnHSkEB1CnVAWoLzlKIOcp5XpPc+SQHL/HEARpjpylKUpw1IePv4Q6Ry+Sw5PgANpOcQA+JEftNEfsJGRfUDvNCRS55uVQmnMDvQtOkS0SVpu4D/y6jYMdEQl95WVEL4ID6K9MBehXcniC0pzAylZiZETKkpUe0xy5JxbzIDiEEBIdmdCL5FCK4x7ezqwqD6U5N9BqmlMZNC/HP5Q4a4oEh1+C+KsvDyQ48rQTjCmOA15LVpWhdpojleTwluYoMS/HWz9aQalTwklw+IdER0KKmAliNEWKO4xLAZWp3KMXyZEK3tMcOZHr4EbzcsSh9PVuSHK0QZB8/fmGUhz3aL1M5UAJyfEHSnNuwFOaw8u8HEBbaY4aF/MjwdEWJDoqw0OKo0fBAYJLcqQsWfkCpTnS4ouMKFWy0gJqXalY7GdvLAUYXWVZcUh0VEJPKY4eBQfQn+T4CqU5jtd9asbvNKciYiRHira1gpq3YZBCcAj10NHPQDtQiuMKT5KjluAA8koOpTnikTrNESs5cpxlxUvZiof7S2ldcK7YTLDYxH2g120cfBAiIdFREBIcV3gSHIAkh9Icx+s+NSMqzRGbtujxVHIe5MaBGMlRW3AIIRr9OWgPvUgOCY5nlJQcOfBFcnhOc3yVnECRMs3xVUSCYV4OT3IDkODoEe//hCJEc8UWKUpyrtgiREtOkc1EkuMFKVIcpSVHjZKVnCh5FWSp0hypt62IGvNylCpbGS03HrxgtJLkSMG2bdvQt29fxMfHw2AwYPXq1YLXi4qKkJGRgbp16yIyMhItWrTAokWLZB0TJToyQimOEL0JDqBOiqNWyaqyNEdM2Uoscl4BGXB/AJTzwKb0vBwl4ElqyqP1eTi8UVxcjNatW+Pxxx/H/fff7/J6ZmYmNm/ejI8//hj169fHhg0b8PTTTyM+Ph79+vWTZUwc/yy0CwmOEN4EBwgOyeEJb2UrLd7TSuptfUXukpXRIm2qw6vcACQ4ctGrVy/06tXL4+s///wz0tPT0bVrVwDAqFGj8O6772LXrl2yiQ6VriRGbcnhqUxVbDVxJzlXrWGaLFWVbeef5FCaI0TraU5lksNLmsNjWao8UpSoglFyCgsLBY+SkpKA2uncuTPWrFmDM2fOgDGGLVu24Pfff0fPnj0lHvENOPlp6IMiWyQC1RRKcdzDU5kKEH9TTj1KTmWImYQcbGmOVq+Xw6vUVCTY5uAU20ywijy9vMRWlockJCQIlk+ZMgVTp071u70FCxZg1KhRqFu3LkJDQ2E0GvH+++/j7rvvFjVOb5DocIAUKY5Y9Co4gPplKge8SY5UyHlKeaXb6yjN0dq8HK3IDRB8giMHp0+fhtlsdj43mQL7G71gwQL88ssvWLNmDRITE7Ft2zaMHj0a8fHxSElJkWq4Akh0VERPKY5UggNQiiPcVj7JoTTnBjwftJU+lZzn98JfaB6OdJjNZoHoBMK1a9fw4osvYtWqVejTpw8AoFWrVsjOzsbcuXNJdPQGpTiu6E1wAGUlxx/EXhjQAaU50oiB1ufl8AYJDp9YLBZYLBYYjcLffkhICOx2u2z90s9EBfQiObwJDhDckiN1yUoPaY4vkqN2gqHVeTm8QmUqdSkqKsKxY8ecz0+cOIHs7GzUrFkT9erVQ5cuXTBhwgRERkYiMTERP/30Ez788EPMmzdPtjHRT0VBSHBc0WOKA/AtOcGU5kiJHGlOoPNyCFe0Iji83EtMLnbv3o1u3bo5n2dmZgIA0tPTsWzZMqxYsQKTJk3CkCFDcOnSJSQmJuK1117Dk08+KduYSHQUQm3JoTJV5aid4pRtz4fkBHuaI/XNO/2FSla+oxXBAf75XK8r26fSdO3aFYwxj6/HxcVh6dKlCo6IREd21BYcqdrgTXAAfaU4ZdurX67yFUpz5EtzSHJ8Q0vzcNRK54oluHt5qU37l9ujn4yMqC05JDje0bLgAP5LjlJpjhiCJc0hyQkcLQkOQCVIHqCfjUyIkRxKcdzDW5kKCE7JqSzNkfMu5XpIc/Q+R0NONFemIriAREdiKMW5AQlOZW3wJTlK4K1sFQxpjpjJx8Gc5mhJcACSHN4I4p+O9FyxmQK+BQSgH8nhsUwFBKfk+AOlOa7Idd2cipDkuIfKVIQUBOnPhy/0IjgApTiVtxH4Ty4QydFKmiMVWkxzSHLco6UUhwSHb4L0J8QPepEcPQsOoH/JUTvNqaxspUaaIyU0L8d3tCQ4AEmOFtDceWMLFy5E/fr1ERERgeTkZOzatcvjusuWLYPBYBA8IiKExSXGGCZPnow6deogMjISKSkpOHr0qNy7AUCcpBTZTKIlp9hqEi05RVYTd5JzzRImaYpDkiMeLaY5UpWtaF6Obxit2pIcezhJjlbQ1M/o888/R2ZmJhYtWoTk5GTMnz8fqampOHLkCGrXru12G7PZjCNHjjifGwzCP4ZvvPEG3nrrLSxfvhwNGjTAK6+8gtTUVBw6dMhFiqSCUhwhPKY4UshNWTvifmK8lKt4T3MCvZ+VmpDklEHzcOSj2BoOi1XcgEs1nqYCGkt05s2bh5EjR2L48OFo0aIFFi1ahCpVqmDJkiUetzEYDIiLi3M+YmNjna8xxjB//ny8/PLL6N+/P1q1aoUPP/wQf/31F1avXi3LPqgtOTylOFetYdymONK0o7zk+IsUJavKUCLN8QWe0hySHGkSHJIcwhf4+AvkA6WlpdizZ4/gNu5GoxEpKSnIysryuF1RURESExORkJCA/v374+DBg87XTpw4gby8PEGbUVFRSE5O9tpmoPBQqhILbykOr2UqtSRHjZIVpTnuoXk5ntGi4JDkaBfN/LvhwoULsNlsgkQGAGJjY3H48GG32zRt2hRLlixBq1atUFBQgLlz56Jz5844ePAg6tati7y8PGcbFdt0vOaOkpISlJSUOJ8XFhZ6HTsPKY5YeBMcgL8yVVlb4n9SSkiOr4hNc3hB6TSH5uW4h8pUhBro+CcFdOrUCZ06dXI+79y5M5o3b453330XM2bMCLjdWbNmYdq0aT6tKzbFEYsUZSop4FFwAH7KVIC4UpW/kqNUmiPmAoFaTnM8EaySozXBAUhy9IRmSle1atVCSEgI8vPzBcvz8/MRFxfnUxthYWFo27Ytjh07BgDO7fxtc9KkSSgoKHA+Tp8+7XY9NSVHqrk4UqDnMlVZW/qUHEpzvG/vCZqXcwOtzsMhydEXmhGd8PBwtG/fHps2bXIus9vt2LRpkyC18YbNZsNvv/2GOnXqAAAaNGiAuLg4QZuFhYXYuXOn1zZNJhPMZrPgURG1JUcMvE025llw9Co5viAmzakMraY5NC/nBlpLcUhw9Ium/g2RmZmJ9PR0dOjQAR07dsT8+fNRXFyM4cOHAwCGDh2Km2++GbNmzQIATJ8+HbfffjsaN26My5cvY86cOTh16hSeeOIJAGVnZI0bNw6vvvoqmjRp4jy9PD4+HgMGDAhojGrOx+FlLo7ey1RlbakrOIC8Vz2WO82R6r5WleFPGiP2wErzcsrQmuAA+hWcYpsJpSJPL7fY+PsHhb9o6uf18MMP4/z585g8eTLy8vLQpk0brFu3zjmZODc3F0bjjX9F/v333xg5ciTy8vJQo0YNtG/fHj///DNatGjhXOf5559HcXExRo0ahcuXL+POO+/EunXrArqGTrHNhEA1gYcURwqkLFNJBW+CA6gjOVKWrLSe5vh6MPZVlEhySHAIfjEwxpjag9A6hYWFiIqKwpjt/WGq5v9BVQ+SQ4LjOzxLDiC/6Cg1CdmbpFQ8KHs6yPoiOiQ52pMcNQXHdv06jr3+IgoKCtxOe5ACxzGp74YRCKsqMtEpLsW3PRfLOl650cnPTLtQqeoGPJ4uXtYeHykOoH3JqQwtpjmeCIa0QGuCAwTH50IIIdFRETUlR4+CA1CK47IdZ2dO8Tg3RwxiJh9rOc0hwSG0hIZ/atqFSlVlkOD4hlKSI0WaIzdypDmBnlIejCUrsYIDBFeZiuADjf7ctAulOGXovUwFqCs5/iLVWVZyl618QYk0hyQngO1JcAiV0OhPTpuQ5FCK4w9iJEeOkpXYNEds2UqpNKcyxEiOFqEUh9A6JDoKoWXJIcHxDR4EB5CnZOULeklz5EyEtJTmkOBon2vWUFhE/r20Wu0SjUY9NPSz0y5qSY7eUhxeBQfQv+RoPc1x26bCaY5WJIcEh9AbGvnpaZdAJYdSnBvofR6OA14lxxd4T3OkOKWcJMfHNhSUHBIcwhc08vPTHlSq4k9y9Co4gLynkVOao/95OSQ4hJ4h0ZEBrZaqSHB8gyfJCVRwKM3xHbGSw3OaI4XgACQ5BN9w/BPUJiQ54pHy7uJSwpPgAMpIjtavm+O2TT9u9yD2buR6lxwSHEILcPwz1B7FNhMC/S1qWXJ4E5yytvgsUwH6kpzK0hwlroQsV5pTmeRodV4OCQ4RbHD6Uwwu1JIcXlIcEhw/2+GgXCUFlZWt5EhzPPfl3/palBytlalIcMRzzRKGUJF/o60WOr2cEEmgkkMpTsV29F2mcralkOToNc3x9SCtp8nHJDhEsEOioxLBXqqiFMfPdhRMY5SYl8NTmlMRPU0+1pLkkOAQchH46RJEwJDkSJfiSCU5pZYQyVMcXiRHjpKVXtMcvUw+Nlqlm4tDkiMtStyLTU22bduGvn37Ij4+HgaDAatXr3a+ZrFY8MILLyApKQlVq1ZFfHw8hg4dir/++kvWMZHoKIya83HESM41S5hoybluDZNEcoJJcJSWnGBNc+xh4icf84BUggMoJzhaeF/F4JBFpaRRbYqLi9G6dWssXLjQ5bWrV69i7969eOWVV7B3716sXLkSR44cQb9+/WQdEyf//ggOtDrpmLcURyp4LVMB0pSq5JIcMdfNkQq55uZ4QgslK60Jjp4JBqHxRK9evdCrVy+3r0VFRWHjxo2CZW+//TY6duyI3Nxc1KtXT5YxkegoRLBKDgmOn22pIDhSI6ZspebcHE/wLjlSCQ4g/wFar4ITDGJTWFgoeG4ymWAyib+fYkFBAQwGA6Kjo0W35QkSHQXQouToMcXhWXAAdSVHqTRHiish+zvHQcxBiGfJIcFRFy3IzXVrGEIlunt5QkKCYPmUKVMwdepUUW1fv34dL7zwAgYPHgyz2SyqLW+Q6MgMSY6YNvhMcXgUHEB+yfEFtdMcKQ/+vqCG5JDgqIMWxEZOTp8+LZARsWmOxWLBQw89BMYY3nnnHbHD8wqJjowEo+RQiuNnexqSHC2mOWLg8SCtFcnh8b0LhGCXm/KYzWbJUheH5Jw6dQqbN2+WNc0BSHS4JFDJ0cN8HBIc/+EhyQGUOaXcG1JOQuatZEWCowwkNvLjkJyjR49iy5YtuOmmm2Tvk0RHJpS+4jFJzg2CoUwFKDvpWO4zrXwpWymV5vAkOVKX4uQ6kGtZcEhupKWoqAjHjh1zPj9x4gSys7NRs2ZN1KlTBw888AD27t2L7777DjabDXl5eQCAmjVrIjxcni8SiY4MaElySHDcw7PgAOIlR+qSVWVpjhRlK29IleboVXJIcISQ3MjH7t270a1bN+fzzMxMAEB6ejqmTp2KNWvWAADatGkj2G7Lli3o2rWrLGMi0ZEYkhx/2xD/FaQylX8ocVFAf+ApzakMJSRHCymOFgWH5EYZunbtCsaYx9e9vSYXJDoSUmwNRyC//2CUHEpx/EcNyZGiZKWnNEdOSHCkhcSGcECiozIkOYFBguM/ckmO3KeUK5HmqF2y4l1ytCI4JDdCSmyhsIr8e2uz2SQajXqQ6KiI1iRHb6Uq3gUH0H6pSu40xx3+HuzUlBwSHGkgwSG8QaKjEsEmOTwJDsC/5KiV4jhQIs3xhcrSHLGioJbkyHFhQykP9rwLjlbFxmgBGCfzzYIJEh0VIMnxn2BJcaQ8ZVxuyRELj/e1UgKeUxyeBUeLcsPLJPpgh0QnCCDJKYO3m29WRGuSI/cp5YGkOf4cDJVOc3hOcUhwpIPkhj9IdBRG6TRHy5JDguM/vJ067gm10xytS47eBUdLckNiwz8kOgpCkuM7wSA5Ul/ZWKzkBEuao6Tk8Jri8Cg4JDeEXJDoKESwSA4vpSoSHP+QUnIqQ800R8uSo0fBIbmRl1JLCEJE/r2xKXirGblQZtahhCxcuBD169dHREQEkpOTsWvXLo/rvv/++7jrrrtQo0YN1KhRAykpKS7rDxs2DAaDQfBIS0uTdMwkOb5RagnhSnKslhDJr2qsdclRArnSHKUO8kYrX5JjD7/x4AVjqTYkx2i58SC0C19/4Srh888/R2ZmJqZMmYK9e/eidevWSE1Nxblz59yuv3XrVgwePBhbtmxBVlYWEhIS0LNnT5w5c0awXlpaGs6ePet8fPbZZ0rsjleCUXLEYrWGSCo5UiKH4KghOb6kOWpcO0cqxKY5PJWqeJUbngWnvNiQ3OgHTYnOvHnzMHLkSAwfPhwtWrTAokWLUKVKFSxZssTt+p988gmefvpptGnTBs2aNcMHH3wAu92OTZs2CdYzmUyIi4tzPmrUqCHZmANNcwIh2CVHCoIlxQHUSXLUKlvJXbKSK8UJ9FYWvAiO1uSG0CeaEZ3S0lLs2bMHKSkpzmVGoxEpKSnIysryqY2rV6/CYrGgZs2aguVbt25F7dq10bRpUzz11FO4ePGiJGNW43o5gUCS8087GhAcNSVHiTRHjrKVEpIjNXoRHF4huQkuNDMZ+cKFC7DZbIiNjRUsj42NxeHDh31q44UXXkB8fLxAltLS0nD//fejQYMGOH78OF588UX06tULWVlZCAlxf6AqKSlBSUmJ83lhYWEAe+QepUtWWpYcXgVHaqQ8ZTzQFEeKKyCrkebIKTk8CQ4P8Cw2AElNMKMZ0RHL66+/jhUrVmDr1q2IiIhwLh80aJDz/5OSktCqVSs0atQIW7duRY8ePdy2NWvWLEybNs1rf4GkOSQ5vhMMkiP1NXHkLlWpkeZ4Q04BUFtyeJEbgG/BIbkhAA2VrmrVqoWQkBDk5+cLlufn5yMuLs7rtnPnzsXrr7+ODRs2oFWrVl7XbdiwIWrVqoVjx455XGfSpEkoKChwPk6fPi14XQvzctSSHCnOrJJCcqSci8PzPBwHYiRH7vtZicHTQdZXEfA3zZFjLg7g3zWAeJAcnufe8FyWkuv7Q3hHM4lOeHg42rdvj02bNmHAgAEA4JxYnJGR4XG7N954A6+99hrWr1+PDh06VNrPn3/+iYsXL6JOnToe1zGZTDCZpJUZpeflBIpYyRGLVJIjBbyXqQDxKY6vklNZmlNZ2UqNg1IgkiM1Ul7kUAl4FBuAT6lxoKbYWK1GMJF/M21WzeQhHtGM6ABAZmYm0tPT0aFDB3Ts2BHz589HcXExhg8fDgAYOnQobr75ZsyaNQsAMHv2bEyePBmffvop6tevj7y8PABAtWrVUK1aNRQVFWHatGkYOHAg4uLicPz4cTz//PNo3LgxUlNTAxqjnktWakoOT4ID8F+mApSTHCXwZxKy1PNySHD4FBxe5YYSG/7QlOg8/PDDOH/+PCZPnoy8vDy0adMG69atc05Qzs3NhdF444/7O++8g9LSUjzwwAOCdqZMmYKpU6ciJCQE//vf/7B8+XJcvnwZ8fHx6NmzJ2bMmBFQYlNsM0GpXIYkx882OE1xeBScsjZ8HxdPaY7UYqCG5JDceIbkhggETYkOAGRkZHgsVW3dulXw/OTJk17bioyMxPr16yUaWWAEkuaoMS8nUEhy3MOr4JS1w0+SA/ie5kg9L0dpySHBcQ/JDSEWzYmOnlByXo7a968KBLGSw6PgAPqSHLnPtPIVKSVHrgOYGDmTG97kBuBPcEhstAuJjsYQc/XjQFCrZKVHyeFZcMrakn58Yq+d48vBRYuSQ4LjHpIbQg5IdFRCCyWrYJacYBKcsvb8HyMP97SSSnLkPqDxIjYOeBIckhtCbkh0dA5Jjv/wLDlyXPRPrjk5YichezrgOA7SUslDsBzYSG48o9fvgM0SAiby75kcl9FQGhIdFVAyzVEakpwy9JriOLdVMc3xV3C8pTl6PcCVhwTHPcHw2RNlkOjoGKXTHJKcMvSe4kghOWof8ILhIMeL4Kj9WZcnGD53whUSHYXhPc1R4wwrtSWHBEd6lJiEzGPbPMCD4JDcEDxBoqNTlL5mjhS3dwgEPUqOXDfflEJytJjmGK03yld6PuiR4NyAx8/Z8d4wDsemd0h0FESpNCdYSlY8SE4wpTi+Sg4PaU75eTl6v5Gi2oJDcuMZXt6bYIdEhwBAkhMIvKc4UpapeDiV3B94POhJidpyA/BxEOfxc+bhfSGEkOgoBO9pjtKQ5JTBu+AA/kmOXKeUE2WoLTg8HMR5+47w8J54wm4NAcT+vZTgNjxqQ6JDqJLmBApJjrf2ZDilXWNJjl4JdsEhuSHEQKKjAJTmCJHiRp2BIFZyeBWcsjb5lxw6OPiPmoKj9udFckNIhTyndxCaQek0R62SlV4lx2AxyFKqCkRyeJiErBeMpepJjtGi3kHdMXGcl++C470gyfGdM2fO4NFHH8VNN92EyMhIJCUlYffu3aqOiRIdDtHKVZCVhCSnYlvylJSoVKUuasqNmvAkNkTg/P3337jjjjvQrVs3rF27FjExMTh69Chq1Kih6rhIdGQmkLJVIARSttJKmiPVncgDQQrJCQbBETsJOdgJRsEhudEfs2fPRkJCApYuXepc1qBBAxVHVAaVrgi/UKNkJQYxaU4wSE6gZaryiC1ZAfwc9JRGjRKVmuUYXkpTVJKShzVr1qBDhw548MEHUbt2bbRt2xbvv/++2sPyX3R++OEHPPHEE3j++edx+PBhwWt///03unfvLtngghE9T0IOBDVLVmKRSnJ4modTESkkJxhRU3DUgORG+xQWFgoeJSUlLuv88ccfeOedd9CkSROsX78eTz31FMaMGYPly5erMOIb+PWX+NNPP0W/fv2Ql5eHrKwstG3bFp988onz9dLSUvz000+SD1KrKFW2CgQl72mlRpqj9rwcKSVHapSeh0PXzrlBMAkOD+lNsMsNsxgkeQBAQkICoqKinI9Zs2a59Ge329GuXTvMnDkTbdu2xahRozBy5EgsWrRI6V0X4NfRbs6cOZg3bx7GjBkDAPjiiy/w+OOP4/r16xgxYoQsAyT4Qenr5qg1N0evkiP96eJUsvIVNeRGDXj4PHmUmvJjYhyOzxdOnz4Ns9nsfG4ymVzWqVOnDlq0aCFY1rx5c3z99deyj88bfonO0aNH0bdvX+fzhx56CDExMejXrx8sFgvuu+8+yQcYTChVtqI0xzM8SA7vggP4Ljk8HnSUQo0JxsEqODx+z3gckxjMZrNAdNxxxx134MiRI4Jlv//+OxITE+UcWqX4dcQzm83Iz88XzKLu1q0bvvvuO9x77734888/JR8gwQfBkuaIgTfJUVNwghlKbxTqnzOR4G08avDss8+ic+fOmDlzJh566CHs2rUL7733Ht577z1Vx+XXX+aOHTti7dq1Lsu7dOmCb7/9FvPnz5dqXJqH5/k5ekatNIckh1B6/o0ac0/UnnfD25wb3sajNrfddhtWrVqFzz77DC1btsSMGTMwf/58DBkyRNVx+ZXoPPvss/j555/dvta1a1d8++23+PDDDyUZGFE5ei1bBZrmqH0quRikkhy5JhoHIjnBMgk5GBIctScU8wRv4+GNe++9F/fee6/awxDg11GvS5cu6NKli8fXu3Xrhm7duokeFMEXaty8U0uITXN4lhxKcTyjd8FRW0R5EgqexkL4j6h/3p87dw7nzp2D3W4XLG/VqpWoQQUjdNuHMtSYm6NmyYpXyRF93yodpzlKl6eUhtIbfsYhFoPFCEOo2L9R2r+ucECis2fPHqSnpyMnJweMMQCAwWAAYwwGgwE2m03SQRLaQ+mzrdS+OGAgSCE5vAmOntGz4JDc8DMOQnoCEp3HH38ct9xyCxYvXozY2FgYDPTHsTw0EVk7qD0BWQxSSo6UgqO3NIcER4Z+OZEKXsZByEtAovPHH3/g66+/RuPGjaUeD+EjSk1EVnJ+jhqTkNVCbJqjVcnREkoJTrDIDcDH94OHMRDKEtA/SXv06IH9+/dLPRaCUBS10hy57kAeCFSqckWp08SVPi1ZrdPCeTgFm4cxEOoRUKLzwQcfID09HQcOHEDLli0RFiZMF/r16yfJ4AhtotadyoMFXm/G6ctBhOeylR4TnGAuTfEwhooYLQzMwtQeRtARkOhkZWVhx44dbi8eSJOR/YfOuNIWaqY5WpYcXiHBkbBflb8HavfvDiOJjeoE9Bf7mWeewaOPPoqzZ8/CbrcLHiQ5hJJocX6OmkgtOf6WA+zKXa+yUvRYolKjPKV2WUjt/ititDDBg1CfgP7sXLx4Ec8++yxiY2OlHg8RpCh9/Ry1r4QcCGLTHJp0XIZScqMUas27URO1+68Ir0JjsBhgCBV7o2Lt/a2sSECJzv33348tW7ZIPRaC0DVq/sGQSnJ4+pezvyiR4ARLeqMGPCc3BN8ElOjccsstmDRpErZv346kpCSXychjxoyRZHCEdPB+arlWUOvaOXLdw8ofeDnA+IueEpxgS294+s6R0GiXgP5qf/DBB6hWrRp++uknvP3223jzzTedD7nvYL5w4ULUr18fERERSE5Oxq5du7yu/+WXX6JZs2aIiIhAUlISfvjhB8HrjDFMnjwZderUQWRkJFJSUnD06FE5d4EgNIdUBxwlD9R6SnCCNb1RG0pt9EFAonPixAmPjz/++EPqMTr5/PPPkZmZiSlTpmDv3r1o3bo1UlNTce7cObfr//zzzxg8eDBGjBiBffv2YcCAARgwYAAOHDjgXOeNN97AW2+9hUWLFmHnzp2oWrUqUlNTcf36ddn2gyCUhJdr5Sh1oCbBEdGfiuUhXkpTJDf6Q1N365o3bx5GjhyJ4cOHo0WLFli0aBGqVKmCJUuWuF3/P//5D9LS0jBhwgQ0b94cM2bMQLt27fD2228DKEtz5s+fj5dffhn9+/dHq1at8OGHH+Kvv/7C6tWrFdwz/wjkqsgEEShSHHiUOFiT4Ijoj+SG5EbHBHyy559//ok1a9YgNzcXpaXCvy7z5s0TPbCKlJaWYs+ePZg0aZJzmdFoREpKCrKystxuk5WVhczMTMGy1NRUp8ScOHECeXl5SElJcb4eFRWF5ORkZGVlYdCgQW7bLSkpQUlJifN5YWGh8//pPlcEIUTuA7YScqMEapSm1EBtqXFAUhM8BCQ6mzZtQr9+/dCwYUMcPnwYLVu2xMmTJ8EYQ7t27aQeIwDgwoULsNlsLqe0x8bG4vDhw263ycvLc7t+Xl6e83XHMk/ruGPWrFmYNm2a3/sQDNBVkfWF2IOSnAdvEpwA+wtiwQk2uTHYDKJPZDDY+Ch9iyGg0tWkSZPw3HPP4bfffkNERAS+/vprnD59Gl26dMGDDz4o9Ri5Y9KkSSgoKHA+Tp8+rfaQCIIr5Cy9yF2i0mN5Sq0SEQ+lKSpLEQGJTk5ODoYOHQoACA0NxbVr11CtWjVMnz4ds2fPlnSADmrVqoWQkBDk5+cLlufn5yMuLs7tNnFxcV7Xd/zXnzYBwGQywWw2Cx4EIRdqnVoeyMGJBKeSPqzqCI6SkNy4J8Ry40EoS0CiU7VqVee8nDp16uD48ePO1y5cuCDNyCoQHh6O9u3bY9OmTc5ldrsdmzZtQqdOndxu06lTJ8H6ALBx40bn+g0aNEBcXJxgncLCQuzcudNjmwQRCGpdLDCQM64COUiR4FTSRxClN2rBm9yUFxuSG3UJaI7O7bffju3bt6N58+bo3bs3xo8fj99++w0rV67E7bffLvUYnWRmZiI9PR0dOnRAx44dMX/+fBQXF2P48OEAgKFDh+Lmm2/GrFmzAABjx45Fly5d8O9//xt9+vTBihUrsHv3brz33nsAym5AOm7cOLz66qto0qQJGjRogFdeeQXx8fEYMGCAbPtBELwSiODIhR7m4Ch95pTSqD3vhhepcUBCwycBic68efNQVFQEAJg2bRqKiorw+eefo0mTJrKcceXg4Ycfxvnz5zF58mTk5eWhTZs2WLdunXMycW5uLozGGyFV586d8emnn+Lll1/Giy++iCZNmmD16tVo2bKlc53nn38excXFGDVqFC5fvow777wT69atQ0REhGz7QRBK4E+a4+2ApfhkWRIc3/shuVEdkhv+MTDG/PrW2Gw27NixA61atUJ0dLRMw9IWhYWFiIqKQt8NI2AxVfV7+2sW/05JD+Q6OkrdAiLQs64CualnoHcuF3NDz0BvARFo6SrQ+Tm+Sk5lBy2lr2Qsa/sKlKeUItgER09yYyu9jj2fv4SCggLZ5nc6jkmJM1+DUeQ/2u3Xr+PUi/KOV278PvqFhISgZ8+eyMnJIdEhggotSI4vguPrAUvJKxnL1raO0htAnXk3asGT3Gg1tTFaDDCGiJwfyMmV1cUQUOmqZcuW+OOPP9CgQQOpx0MQBPyXnMoEh6fkxtknCY5v/VB6owpalRvClYD+ifrqq6/iueeew3fffYezZ8+isLBQ8CAIQhmMFoNXyfF0Jkz505zVmIMjl+To6Qwqpc9iUvc+V3ycMUVnSemTgBKd3r17AwD69esHg+HGH1nGGAwGA2w2mzSjI4KCQObnKE0gZatASla+JjmVyY3H11RIbpx9U4LjWz9BUp5SW2ockNTon4BEZ8uWLVKPgyAUIZCJyEpIji+CE6jcACQ4AbdPciNxvyQ3hPIEJDpdunSRehwE4ReBnnGlBP5IjlyCo6bYOMdAglN5PyQ4ikFyE7wEfPdyALh69arbu5e3atVK1KCCCX9PLVeKQE4t5x0l0hwpJceT4PAsNwAJjk/9KDz3Rg1IbtxjsKr/vgQbAYnO+fPnMXz4cKxdu9bt6zRHhy8CuYZOIARyDR2lrp8jt+T4Kjh6lRuABMenfnQuOCQ37in/vthVHEewEtBZV+PGjcPly5exc+dOREZGYt26dVi+fDmaNGmCNWvWSD1GghCFv5JjsBh9lhyDxVCp5BisBufDHY4zpypKjrezYNQ4W8odjjOotHoWlRLvo5JnM6l3nyt1z5ji7Wyp8meRqfm+GCzC70QgDwMn76kYAvqn/ubNm/HNN9+gQ4cOMBqNSExMxD333AOz2YxZs2ahT58+Uo+T+IdAroqsBLymOYFIjm/rVd6ulOkND1JTHrqKsQ99UHojK7xIjQO13w/CMwGJTnFxMWrXrg0AqFGjBs6fP49bbrkFSUlJ2Lt3r6QDJJSH1/k5WpEcb4Kj5dIUQILjUx8kOLJBckMEQkClq6ZNm+LIkSMAgNatW+Pdd9/FmTNnsGjRItSpU0fSAWqJawrNhfEHJebnKJHmyCk5vpSqHCUqT5LjrTzlb2lKrQv5eUPO8hSgrxKV3KhRnlKzDMNzWYpwz8KFC1G/fn1EREQgOTkZu3btUnU8AR0Fx44di7NnzwIApkyZgrS0NHz88ccIDw/H8uXLJR2gnuHxjCsl0hw5Jcdfwal8ncDSG60nNw60nOBQeiO2PypNOVD7vdASn3/+OTIzM7Fo0SIkJydj/vz5SE1NxZEjR5yVIKUJSHQeffRR5/+3a9cOp06dwuHDh1GvXj3UqlVLssERQnicn+NvmsOD5PAiON4OxO4Ewx7udUiSQoJTSR8kOLLAi9yQ2ATOvHnzMHLkSAwfPhwAsGjRInz//fdYsmQJJk6cqMqYArsdM4DFixejZcuWiIiIQI0aNTB06FCsXr1awqERYvG3bOVvmsOL5DCLwSfJ8adE5fY1D+Upf0pTlZWl3JWI7OHKSY6WS1RUnhLTH5WmqCTlnYr3tCwpKXFZp7S0FHv27EFKSopzmdFoREpKCrKyspQcroCAEp3Jkydj3rx5eOaZZ9CpUycAQFZWFp599lnk5uZi+vTpkg5Sj/BYtvIHOSXHH8HxBV/kxuNrCpWmPMmFkoIja/uU4HDVz43+1Dmoqy01DvQuNVLIP/tn+4SEBMHyKVOmYOrUqYJlFy5cgM1mQ2xsrGB5bGwsDh8+LG4gIghIdN555x28//77GDx4sHNZv3790KpVKzzzzDMkOjLgb9lK7jTHH9SSHKUEJxC5qUwsSHB8aJsEJ8C+grs0pXe5kYvTp0/DbDY7n5tMJhVH4x8BiY7FYkGHDh1clrdv3x5WK6ezKoMInkpWvkoOD4Ij57wbX4VCD4Kj9VPE9Sg3Zf0Fb3pDciMes9ksEB131KpVCyEhIcjPzxcsz8/PR1xcnJzD80pAc3Qee+wxvPPOOy7L33vvPQwZMkT0oPSOv2UrOSch+yM5VmuIz5JjtYT4JDl2S4hPklPZPBzH/BtPkuPt9HA55934M+dFCcmhqxh7aV/h+TdKoca8Ex7m3fA65yaklK/xSEl4eDjat2+PTZs2OZfZ7XZs2rTJOc1FDQK+yMrixYuxYcMG3H777QCAnTt3Ijc3F0OHDkVmZqZzvXnz5okfZRAjZ8nKX8nxeV0JU5zKEhxv6Y1apSl/RUIpwZGtbUpwuOqnrK/gTG94kxrAVWz0XvPIzMxEeno6OnTogI4dO2L+/PkoLi52noWlBgGJzoEDB9CuXTsAwPHjxwGURVa1atXCgQMHnOsZDP7fSFHvyDkJWW3J4V1weJIbgASn0vZJcALoS52zptREC3ITTDz88MM4f/48Jk+ejLy8PLRp0wbr1q1zmaCsJAGJzpYtW6QeR1AgZ8lKC5LDk+AoOe/GHXJLDgmOl/YVKk8pBaU3fBDMclORjIwMZGRkqD0MJ/zds4AAoL7kKCk4PKY3csiNAzklhwTHS/skOKIhubmBFsTGaAGMAV8trwzGwWRysZDoKIQ/aY4ckqM1weFJbgASHDkhwfG3L+UnFqsJyQ0hFhIdBdCT5GhFcNSad+MOEhwP7ZPg+NFPcKU3JDeElJDoyIwckiN1qYpnwdHSvJuKkOB4aJ8Ex49+gie9Ibkh5IJER0bUlBxeBEduuQH4mHdTEbkkh65k7KV9uQWNylOywIvgkNzoFxIdmVBLcqQqU1UmODymN2rLDUCC47ZtmS/yJzeU3kgPyQ2hJCQ6MuCr5PCY4sglOErKDeD5KsVyIofkkOB4aJsEJ2DUEhySG0ItSHQkRg3JUVtwpEpvtFSaKg8JToW2SXB87IcERylIboIbEh0JuWYJQ6gPBz1fJEfLghMMcgOQ4Li0TYLjYz/KHXRJbvgipJSBKfje0HV0yiDRURAlUxwxgiO33ADamndTERKcCm2T4PjQh/7TG5Ib9/A4pmCDREchlEpxeBYctebduGvPHuAtx6SWHBIcD22T4PhNsKY3PIoEj2MKZkh0ZEbLgqP19MbTgYwHySHB8dC2Tk4R17vgkNy4wuOYiDJIdGSkMsmRW3CUSG94kxtPbQLqC45SpTW5DuYkOL70Q4IjJzzJBE9jIbwjcpqScly6dAlDhgyB2WxGdHQ0RowYgaKiIq/rP/PMM2jatCkiIyNRr149jBkzBgUFBYL1DAaDy2PFihWixnrdGiZackotIV4lx2oN8Sg5VkuIR8mxW0LcSg6zGJyP8hgsRudDuNzgIjkGq8H5cGC0GJyPG8tuPMpjtLoeTI2lNx6eKN+elJJjD5dGciobv1R4239R7br5XCRrW6YxK9X+jX6YIhIQYrnxUArHvql2l/RS5nyoDU9jIXxHM4nOkCFDcPbsWWzcuBEWiwXDhw/HqFGj8Omnn7pd/6+//sJff/2FuXPnokWLFjh16hSefPJJ/PXXX/jqq68E6y5duhRpaWnO59HR0QGN8bo1DKESCI43pE5weExvKpMCfw5caqY4lOB4aVsHCQ6lN/LBk0jwNBYiMDQhOjk5OVi3bh1+/fVXdOjQAQCwYMEC9O7dG3PnzkV8fLzLNi1btsTXX3/tfN6oUSO89tprePTRR2G1WhEaemPXo6OjERcXJ+s+iBEcNeWmbLm0E4sDLU0pITiAeMkhwfHSNgmOXygtOCQ3ZfA0FjEYrYDRt4vle4TJfOsWJdBE6SorKwvR0dFOyQGAlJQUGI1G7Ny50+d2CgoKYDabBZIDAKNHj0atWrXQsWNHLFmyBIx5/5KXlJSgsLBQ8PDEdWuoV8nxVqKSqzxVnspKU+UlR+3SlC/Yw8SVqcRIDpWovLStgxJVMJSn1ICXUhCVpfSLJhKdvLw81K5dW7AsNDQUNWvWRF5enk9tXLhwATNmzMCoUaMEy6dPn47u3bujSpUq2LBhA55++mkUFRVhzJgxHtuaNWsWpk2b5rU/pROcYC1NlUetMhUlOF7apgTHZyi9UR5exkHIi6qiM3HiRMyePdvrOjk5OaL7KSwsRJ8+fdCiRQtMnTpV8Norr7zi/P+2bduiuLgYc+bM8So6kyZNQmZmpqD9hIQEAPIIjlTlKZ5LU2IOWHoXHEC+BEcuSHB8J1gEhxep4GUchHKoKjrjx4/HsGHDvK7TsGFDxMXF4dy5c4LlVqsVly5dqnRuzZUrV5CWlobq1atj1apVCAvzflRMTk7GjBkzUFJSApPJ5HYdk8nk9rUSWyjcaYdW0hs1LuinhuAA4ktUSkGCo2z7ZX2Q4EgJD2LBwxgI9VBVdGJiYhATE1Ppep06dcLly5exZ88etG/fHgCwefNm2O12JCcne9yusLAQqampMJlMWLNmDSIiIirtKzs7GzVq1PAoOf6gpuDwfM0bsQcrMYIDBCY5SsoNoL0yFQmOb5DcBNcY3GG02NUeQtChiTk6zZs3R1paGkaOHIlFixbBYrEgIyMDgwYNcp5xdebMGfTo0QMffvghOnbsiMLCQvTs2RNXr17Fxx9/LJg0HBMTg5CQEHz77bfIz8/H7bffjoiICGzcuBEzZ87Ec889J2q8UpaneExv1JAbQB3BAbSf4AAkOJ7bp/RGStSWC7X7d4exVCg2pDnKownRAYBPPvkEGRkZ6NGjB4xGIwYOHIi33nrL+brFYsGRI0dw9epVAMDevXudZ2Q1btxY0NaJEydQv359hIWFYeHChXj22WfBGEPjxo0xb948jBw5MqAxllpCEOJGTHhJb7QoNw6UlhxKcCpplwTHJ5Q+c0oNeJALHsZQnopyQ6iLZkSnZs2aHi8OCAD169cXnBbetWvXSk8TT0tLE1woUGrkTG/0OrG4IiQ4Abar0Xk4JDj+E6yCo3b/FeFRboxWwCjyIjJ6uI6OZkRHS0ghOMFYmiqP3gUHoInGSrVd1j4JjhSoLRdq918RHuWGcIVER0KsViOYG8lxJzhUmnKPWMEB/JMcEhwf2ibBqRSlBIfkRn1IbrQHiY5MUHrjH1IIDuC75OhFcABtzsPRg+BQeqPv/stDcqNtSHQkhtIb/1BScNSQG4AER6m2y9onwRGLmoJBckPIAYmOhNgsITCWO3D7mt6ImVgs5wX95DwokeCIaJcEx037JDhiILkpg+RGn5DoSIwW0hu15MaBVJIDlO2LJ9lRS3AAbc3DIcHxDs2/0U+/7iC50T8kOhJitwoTHZ7kBtCX4HhCTbkBtJXikOB4R6+CQ+lN8MiN0QIY3U8B9Rmm8MUu5YBERwZ4Ehy15QaQXnAcCY5j39SWG4AER4m2y9onwQmUYE9vgkVuCFdIdCSEWQxgoTekJJjTG0CeBKd8mcoerr7kaElwABnHS4LjMyQ4ykFyQwCAyGsmEu4wWIwukmOwGASSY7AanA8HRovB+bix7MbDucx641EeY+mNR0XctSMX9jD5ylTlUxw9So67z1WSdmX67OX+ThktTHYxCLEoIzlK7IuDkFLmfCiJWv2Wx1hqdz54ofyYeBoXD3z//fdITk5GZGQkatSogQEDBkjeByU6EmKwGGEINZZ7rm56o1Ry40CJOTiAPgUHoDKVsG19JDiU3igDj/LA45h44+uvv8bIkSMxc+ZMdO/eHVarFQcOHJC8HxIdiVFbbjy1IydKCY7akOAo0TYJTiCoIRpqyw3An0zwNh6esVqtGDt2LObMmYMRI0Y4l7do0ULyvkh0JMRgMQjeUb1c88YTcglOxcnGakOCo0Tb8penlEBJwaH0hg94G49cFBYWCp6bTCaYTKaA29u7dy/OnDkDo9GItm3bIi8vD23atMGcOXPQsmVLscMVQHN0JMbb3JvyuJvbUNncG3coNe+mPHLOwQH4mH/jQCuSI+f3QN625Z23ouf5N0qi9twb3ua38DYeTzi+l2IfAJCQkICoqCjnY9asWaLG9scffwAApk6dipdffhnfffcdatSoga5du+LSpUui9708lOhIiMFmAP4RAL2lN0DwlKgA7QgOoM0zqSjB8Z9gK0/xJhG8jUdpTp8+DbPZ7HzuKc2ZOHEiZs+e7bWtnJwc2O1l7+dLL72EgQMHAgCWLl2KunXr4ssvv8S//vUviUZOoiM5PE4sNloBu4hPmgRHgnZJcP5pmwTHH4JNbgC+hIKnsaiN2WwWiI4nxo8fj2HDhnldp2HDhjh79iwA4Zwck8mEhg0bIjc3V9RYK0KiIyFGiwEIcfy/m9dlnljsqX2SHN/Qym0bSHBcIcHRZp8OeBIKnsaiRWJiYhATE1Ppeu3bt4fJZMKRI0dw5513AgAsFgtOnjyJxMREScdEoiMxalyxmARHHFpJcbQoOGXtyzsHR270XJ4iuSmDp7EEC2azGU8++SSmTJmChIQEJCYmYs6cOQCABx98UNK+SHQkxGDBjURHgdPCSXDEoRXBAbQpOSQ4vkOCozy8jCOYmTNnDkJDQ/HYY4/h2rVrSE5OxubNm1GjRg1J+yHRkRg50xtfDqCBSo6cgsPb6eIAlalIcDxDgiM9vEgFL+MgyggLC8PcuXMxd+5cWfsh0ZEQoxWCd1SKicW+Hjx5FJzy8CI5WklxSHBckVty9Dr/JtjTGx7GQKgLiY7EKJneAPwLDsCH5GhFcACZ0iYSHI+Q4EgLD2LBwxjcYSy1wWi1KdZfiAUIcT0R2D9UupyJlJDoSIjRAsDkZpk/behIcHghmMtUJDieIcGRFh7kgocxVMRYqpzYEO4h0ZEJXw8w/h4weRIcx9wbd1CK40d7GitTkeD4hpLCQXLDFyQ3fEGiIyFGC2D08aYaWhYcgG/J0YrgANpKcUhwfIMER//9V4TEhm9IdBQkkAMlT4LjTW4A9QUH0I7kkODcgASH774cqC0XavdfEZIb7UCiowCBHiQDkRypBacyuXGgtuSQ4MiDlk8VJ8GRBrUFQ+3+y0Nyo01IdGRCSbkB1BMcQF3J0YrgANqSHBKcylFKOkhu1IXkRvuQ6EiI0QoYQwLblgTHf7QiOSQ4ZehBcCi90WffFdGL3BisDEaDuO+R3aruTV6lgERHZbQoOAClOD61R4IDgASH574ckODoR24IV0h0VETtOTj+yo0DvUmOFspUJDiukOCIg+SmDBIc/UOiozA8JDiBCg6gnuRQiiNHu/IcWElw+O0LIMEBSG6CDRIdhSDBEdE3pTgSt6vNFIcERxxqSQbJDaE2JDoyo3XBASjFqbQ9EhwSHM76cRDs6Q3JDQGQ6MiGVuffVEQNyQlWwZGvTW0KDqDAxQpJcHTRr3AMfMqNY1xGG5/j0zM+3rBAfS5duoQhQ4bAbDYjOjoaI0aMQFFRkddtunbtCoPBIHg8+eSTgnVyc3PRp08fVKlSBbVr18aECRNgtQZ+RLSHqis59nBpJMdYSpLjtT0ZUhzp22SyzsORO8WRVdBKmSLyoVQ/DoyldlVkQ61+hWOwcSc5jjHxNq5gQzOJzpAhQ3D27Fls3LgRFosFw4cPx6hRo/Dpp5963W7kyJGYPn2683mVKlWc/2+z2dCnTx/ExcXh559/xtmzZzF06FCEhYVh5syZfo/RHgr4cxkdHhMcgATHa3tBXqaiBIevfhyoJTdqw6NA8DSmkFKGECbuu8gUugCnnGhCdHJycrBu3Tr8+uuv6NChAwBgwYIF6N27N+bOnYv4+HiP21apUgVxcXFuX9uwYQMOHTqEH3/8EbGxsWjTpg1mzJiBF154AVOnTkV4uIT2UA5eBQfQj+QE62RjEhz36FFwgrU8xZNIOOBxTMQNNFG6ysrKQnR0tFNyACAlJQVGoxE7d+70uu0nn3yCWrVqoWXLlpg0aRKuXr0qaDcpKQmxsbHOZampqSgsLMTBgwc9tllSUoLCwkLBwxfsYdJIjqM8pXXJkaNcA8iT4kg5Ti2WqeSCSlT+o0aZyNGnupOb+SoBUVlKO2gi0cnLy0Pt2rUFy0JDQ1GzZk3k5eV53O6RRx5BYmIi4uPj8b///Q8vvPACjhw5gpUrVzrbLS85AJzPvbU7a9YsTJs2zefxSzn/Rg4oxfHQngYSnLJ2tSk4ckIJjnb7FPbPl0TwNh7CN1QVnYkTJ2L27Nle18nJyQm4/VGjRjn/PykpCXXq1EGPHj1w/PhxNGrUKOB2J02ahMzMTOfzwsJCJCQkuKzHu+AA6qQ4srQbhJJDguMKCY52+7zRN18ywdt4CP9RVXTGjx+PYcOGeV2nYcOGiIuLw7lz5wTLrVYrLl265HH+jTuSk5MBAMeOHUOjRo0QFxeHXbt2CdbJz88HAK/tmkwmmEwmj69LVZ6SGz1IDgmOdJDg8NMPoLxsUHpzA57GQohHVdGJiYlBTExMpet16tQJly9fxp49e9C+fXsAwObNm2G3253y4gvZ2dkAgDp16jjbfe2113Du3DlnaWzjxo0wm81o0aKFn3tTJjgGkZJDguNHu0EmOVoUHEDm6/joTHAovVEPnsZCSIsmJiM3b94caWlpGDlyJHbt2oUdO3YgIyMDgwYNcp5xdebMGTRr1syZ0Bw/fhwzZszAnj17cPLkSaxZswZDhw7F3XffjVatWgEAevbsiRYtWuCxxx7D/v37sX79erz88ssYPXq018RGDuSYXOwOPUiO0Sqt5GhlsrEcaH2isdwoNclYzQnGasDLJF69Tyh2fH/FPrSOJiYjA2VnT2VkZKBHjx4wGo0YOHAg3nrrLefrFosFR44ccZ5VFR4ejh9//BHz589HcXExEhISMHDgQLz88svObUJCQvDdd9/hqaeeQqdOnVC1alWkp6cLrrsjN0rIjQMlJScYUxwqU5WhhzKVXhMcSm/4GQehHJoRnZo1a3q9OGD9+vXByl0YKSEhAT/99FOl7SYmJuKHH36QZIy+oqTcAPpJcSRtj8pUkkOC4zskOME3BkI9NCM6ekBpwQEoxXHbXhCmOCQ46vcBBI/g8CIWvIyDUBcSHQVQQ3AA7UsOz4IjT3vaSnFIcHyHBCe4xuAOQ2nZHzQD3dRTcUh0ZEQtwQGUkxxKcaRoT1uCA2j/TCo9Cg7JDV84xIZQHxIdiVFTbgBKcVzaC8IUhwRH3T4AEpxgGkN5SG74hERHQuxh/t29XGpIciq0RymOZJDg+AYJjv77rwjPcmO02GFk4r4nRqv6d6kXC4mOTtByqYpnwZGnPe2kOFqfh0OCI1WfJDfl4VluCFdIdHQASU659oIsxaEylTrtA8ExwVhtwVC7/4qQ4GgTEh2No4TkBJvgSN0elaluQILDb1/CftUTDJIbQmo0cQsIwhVjKUmOsz2Ob98gV4ojh+TIecsGuS8lr8Sl6pW8ZYI6t4RQ93YIPN2KwVBqdT4I+XjttdfQuXNnVKlSBdHR0S6v79+/H4MHD0ZCQgIiIyPRvHlz/Oc///G7H0p0NAgJzj/tBVmKQwmO8u0D+k9wKL0pg6RGeUpLS/Hggw+iU6dOWLx4scvre/bsQe3atfHxxx8jISEBP//8M0aNGoWQkBBkZGT43A+JjsYgyfmnPZqLIwlyJjhyQoIjRZ8kOCQ36jJt2jQAwLJly9y+/vjjjwueN2zYEFlZWVi5ciWJjl4hyaEURyq0muKQ4Ijtj+SG5CYwCgsLBc9NJhNMJpPi4ygoKEDNmjX92oZERwOQ4PzTXhClOCQ4yrcPKCceJDjKE4yCYyy1w2iX5jo6CQkJguVTpkzB1KlTRbXtLz///DM+//xzfP/9935tR6LDOSQ5lOJIAQmOd0hw9NFvRYJRbuTi9OnTMJvNzuee0pyJEydi9uzZXtvKyclBs2bN/Or/wIED6N+/P6ZMmYKePXv6tS2JDsdoUXIoxQkcraU4JDj89XOjv+AVHF7l5sZNPfkcX2WYzWaB6Hhi/PjxGDZsmNd1GjZs6Fffhw4dQo8ePTBq1Ci8/PLLfm0LkOhwi9ySQymOmLaCW3AAmofDWz9lfQV3eYpHweFxTHITExODmJgYydo7ePAgunfvjvT0dLz22msBtUGiwyFakxxKcQKHylTKtO2ABEf7/ZaHR5HgcUy8kpubi0uXLiE3Nxc2mw3Z2dkAgMaNG6NatWo4cOAAunfvjtTUVGRmZiIvLw8AEBIS4pdMkehwBJWq+JUcLQgOoM0yFQlOoH0Fp+DwKBI8jkkLTJ48GcuXL3c+b9u2LQBgy5Yt6Nq1K7766iucP38eH3/8MT7++GPneomJiTh58qTP/dCVkTlBiRSHZ8mRcny8X91YS1c1VuKKxnKi1BWGlb1qsjpXEFb7ysW8XamYrp4snmXLloEx5vLo2rUrAGDq1KluX/dHcgBKdLiASlW8tqUNwZELrQuOEug9waH0Rghv46kMY6kNRru4z9BoVb9EKRYSHZWRU3KCacIxzcWRDhIcfvop64sER014GgsRGCQ6KkKSw2NbJDhaaxsgwdFynw54EgqexkKIh0RHBahUxWtb0h2MSXDkbxsgwdFifxXhSSp4GgshHSQ6ChPMkhMMggOQ5CjRNqCMfCg5wVhpKL0pg6exEPJAoqMgWipVUYrjPyQ48rcNkOBosU8HvEgFL+MglIFERyGCVXKCQXAA7UgOCY76fZT1EzyCw5NU8DQWQjlIdBSAJIe3tijF0UK7DkhwtNNfeXiRCl7GQagHiY7MyCU5PAsOwKfk8J7iaE1w5G6bBEc7/ZWHB7HgYQxuKbEAdpkui+4Go0WC6+jY6Do6hAcoxeGtreBLcbQqOID8AkKCIy28iAUv4xBQopzYEO4h0ZEBkhx+2qIURxvtOiDB0UZ/DngQCx7G4ALJDVeQ6EiMViSH11IVpTji0GqKQ4Kjjf4cqC0XavfvFpIbbiHRkRCjBYBJpnalbE/nKU5ZW/xKDqU4N9DLPJxgEBwe5IKHMQggudEEJDqcQ5Ljbzv8Cg6grRSHBMeXPkhw9N6/CyQ3moNEh2OCQXIoxQkcLQoOoI8yFQmO/vt3gQRHs5DocAqv83EoxfEPKlPdgASH//4AdQWD5EZaDKU2GELEvacGHZxeblR7AL5y6dIlDBkyBGazGdHR0RgxYgSKioo8rn/y5EkYDAa3jy+//NK5nrvXV6xYocQuucVo0b/kSLmPwSY5IaVMFhmRq12gTEDklBC527/RjzJ/8I2lNudDKQylVudDDdTs24USy40HoQs0k+gMGTIEZ8+excaNG2GxWDB8+HCMGjUKn376qdv1ExIScPbsWcGy9957D3PmzEGvXr0Ey5cuXYq0tDTn8+joaMnH7wtUqvK3LT4lh8pUZeghwSnrRznBURq15ULt/p2Q1OgaTYhOTk4O1q1bh19//RUdOnQAACxYsAC9e/fG3LlzER8f77JNSEgI4uLiBMtWrVqFhx56CNWqVRMsj46OdllXafSe4kjZFq+CA5DkONCD5JDg6K9vF0hwggJNlK6ysrIQHR3tlBwASElJgdFoxM6dO31qY8+ePcjOzsaIESNcXhs9ejRq1aqFjh07YsmSJWBM3smYFdG75ARDqcpoYdJfnJDKVIq3X9aHMmUjpctTgLolIm7KU1SaCjo0kejk5eWhdu3agmWhoaGoWbMm8vLyfGpj8eLFaN68OTp37ixYPn36dHTv3h1VqlTBhg0b8PTTT6OoqAhjxozx2FZJSQlKSkqczwsLC/3YmxtQqcrftqQ5OFOKIz2U4PDXT3nUlBsuIKkJalQVnYkTJ2L27Nle18nJyRHdz7Vr1/Dpp5/ilVdecXmt/LK2bduiuLgYc+bM8So6s2bNwrRp00SNiVfJoVKV75DglCF3giM3JDj66dcFEhwCKovO+PHjMWzYMK/rNGzYEHFxcTh37pxgudVqxaVLl3yaW/PVV1/h6tWrGDp0aKXrJicnY8aMGSgpKYHJ5P4yx5MmTUJmZqbzeWFhIRISEipt20EwlKqkglIc/tp0QILDVz8Ogn7+Da9y46gC2GW8T1AFDKVWGEJCxLVh4+AzFYmqohMTE4OYmJhK1+vUqRMuX76MPXv2oH379gCAzZs3w263Izk5udLtFy9ejH79+vnUV3Z2NmrUqOFRcgDAZDJ5fd0bJDm+tsNnigNILzlaS3G0XqYiwdFHvy7wKDjlpjgQ6qGJOTrNmzdHWloaRo4ciUWLFsFisSAjIwODBg1ynnF15swZ9OjRAx9++CE6duzo3PbYsWPYtm0bfvjhB5d2v/32W+Tn5+P2229HREQENm7ciJkzZ+K5556TZT94lBwqVfkOpTjaTnFIcPTRrwCSG8IHNCE6APDJJ58gIyMDPXr0gNFoxMCBA/HWW285X7dYLDhy5AiuXr0q2G7JkiWoW7cuevbs6dJmWFgYFi5ciGeffRaMMTRu3Bjz5s3DyJEjJR273ufjUKnKf0hwlGn7Rh/6kxwSHI4gueEaA1P6XGodUlhYiKioKNwyfiZCTBGC13hMcQD+JIdSHP7aBEhweOsHCGLB4U1ugIAEx2ovxY9576GgoABms1mGQd04JqU0eRahIYFNs3BgtZXgx6NvyjpeudFMoqNFSHJ8bSc4JEdLggNoW3JIcLTbpwu8CQ6lN5qDREcmeJQcPc/HCTbBkbNdEhx++gGCVHBIbggJIdGRAT1LDm+CAwSf5JDguGufBEerfQrgSXBIbnQDiY6EGC2AUaKbalCpqnKCTXDkbJckR/0+HASd4PAkN4C+BKdUgoOSnbPPJwBIdDiEJKdygk1ySHDctU+Co8U+nfAkOHqSG8IFEh3OoFJV5QST5JDguGufBEeLfTrhRXBIboIGTdy9PFggyfFOiEU6yZH6buNy3BGcJKdi2/Lf7VvJO4orfTdvR3+qSA5PdwwvKSHJ4YTXXnsNnTt3RpUqVRAdHe12nV9//RU9evRAdHQ0atSogdTUVOzfv9+vfkh0OIEkxzvBluLIITnGUrtsIiJn22Xt6yfFUUtwVIE3uSHB4YrS0lI8+OCDeOqpp9y+XlRUhLS0NNSrVw87d+7E9u3bUb16daSmpsJi8f17RaUrlaH5OJUjZYojJTQXh8pU/qC0bFB5CiQ2nDNt2jQAwLJly9y+fvjwYVy6dAnTp0933jh7ypQpaNWqFU6dOoXGjRv71A8lOioiZYojhZxI1440ZSGpS1VSQikOlal8hRIcpcfAZ3rDSkqcDy1SWFgoeJQosB9NmzbFTTfdhMWLF6O0tBTXrl3D4sWL0bx5c9SvX9/ndkh0VIJKVd7htVSllbk4Wi1TkeBopz8nvMy/IblxpaRUKH4BPUoBAAkJCYiKinI+Zs2aJfvwq1evjq1bt+Ljjz9GZGQkqlWrhnXr1mHt2rUIDfW9IEWiowIkOd7hecKxlMiZ4siB1ufhkOBIDE9yw5HgqC43MnH69GkUFBQ4H5MmTXK73sSJE2EwGLw+Dh8+7FOf165dw4gRI3DHHXfgl19+wY4dO9CyZUv06dMH165d83nsNEdHYfQoObwJDiB9iiM1WhIc+duWX3CUIGjm4KgtNwBXYgNAd1LjDrPZ7NNNPcePH49hw4Z5Xadhw4Y+9fnpp5/i5MmTyMrKgvGfCx9++umnqFGjBr755hsMGjTIp3ZIdBSEJ8nRa4oD8C05NNm4fNv6mGhMgqNU//zJRDAIjr/ExMQgJiZGkrauXr0Ko9EIg8HgXOZ4brf7/reJSlcKYLRKIzlSTjqWAt4kRwulKqmRq5xEZSrf0H2Jiof5N1Sa0i25ubnIzs5Gbm4ubDYbsrOzkZ2djaKiIgDAPffcg7///hujR49GTk4ODh48iOHDhyM0NBTdunXzuR9KdGSGpxRH2nb4kxyp0ILgAJTiqNG+A6UFR3EovRFAUiMPkydPxvLly53P27ZtCwDYsmULunbtimbNmuHbb7/FtGnT0KlTJxiNRrRt2xbr1q1DnTp1fO6HREdG9Cg5vAkOEHySQ4KjfPsOSHDk7p8voSDBkZdly5Z5vIaOg3vuuQf33HOPqH5IdGSCJ8mhFKdygn3CMUmOd0hw5O6fH6EgudEfJDoyQJLjmWCQHBIcR9skOLz25UTtuTecoFe5YSUlYEZxf4+YvVSi0agHiY7ESDXpWAp4khwqVQWOXJON5YIEh9++nJDgANCv4BBCSHQkxGgFECKyDY4kh1Ic9doDKMVRsm0HJDhy9s2HVJDcBB8kOhxBkuMZkhw+27zRNqU4vPUjQC3B4UgqSHCCFxIdTqD5OJ7hVXK0IjjytkuCw1s/AoJccEhuCIBEhwv0Jjl6Fxw52gMoxVGqbQe6FRwqT3ErOOx6CRjT/uRerUGiozIkOe7hVXK0IjjytqvtFIcER46+1RcLnuWGUBcSHZWg+TiekWw8lOLI0DalODz1A4AEh4MxVIQXuWElJWAGkaeX6yCBItFRAZIczwSL5FCKo0zbDkhwpOxX/YM4yQ3hDyQ6CqM3yeFNcIDgLFVpUXCUaJ8ER8p+1T+Qk+AQgUCioyA0H8c9lOLw2q52UxwSHCn7Vf9AzpvgkNxoCxIdhSDJcQ9JDl9tlrWrXcEBlJGPoJhorLJckNwQUkGiowC8SA5P83GCpVRFKY4ybQMkONL1SYJTHhIc7UOiIzMkOa6Q5PDVZlm7JDi89AOABEdlSG70BYmOjOhJcvRcqqIJx/KICAlOAASZ4JDcyAsrKZXg9HIVL18gESQ6MkBnVrknGCSHBEf+tgEdlqlIcFRDj4JDCCHRkRheUpyydviQHCpV8dYmCY7afTghwVEFkpvgwqj2AHzltddeQ+fOnVGlShVER0f7tA1jDJMnT0adOnUQGRmJlJQUHD16VLDOpUuXMGTIEJjNZkRHR2PEiBEoKioKaIxGCf4+SpXi6E1yQkqZZGIiZVtAmYxILSRytFnWrjYlx1BqlV1AlOjDSYlFeckpKVFNclhJCReSw66XkOQEIZoRndLSUjz44IN46qmnfN7mjTfewFtvvYVFixZh586dqFq1KlJTU3H9+nXnOkOGDMHBgwexceNGfPfdd9i2bRtGjRolxy5UCk+lKt4kRyqCOcWRcy6O3JIjNyQ40uOQG7UFxyE3JDjBi2ZKV9OmTQMALFu2zKf1GWOYP38+Xn75ZfTv3x8A8OGHHyI2NharV6/GoEGDkJOTg3Xr1uHXX39Fhw4dAAALFixA7969MXfuXMTHx8uyL+7gSXKkgCRH/fZutEuTjdXsA0BQlajUFhsHvIoNK9H+vaO0hmYSHX85ceIE8vLykJKS4lwWFRWF5ORkZGVlAQCysrIQHR3tlBwASElJgdFoxM6dOxUbK0mOe4KtVCU1cqc4cqGrMlUQJjhqw2t6w8v7E4xoJtHxl7y8PABAbGysYHlsbKzztby8PNSuXVvwemhoKGrWrOlcxx0lJSUoKfeFLSgoAADYSq972sQjRgsg9pBhtDDRbYRIMA6DtUwmxB6yHVIixWEopJRJ0o4Do8Uuev8E7f0jOFJrjrHUJnmbAGC0lH1L5Lo/ukEHKZGTUqUFR52kgIeDNw9jcIe79Mb6z+najEl/WQuXvmABRHZjBZ1eLoqJEydi9uzZXtfJyclBs2bNFBqRb8yaNctZSitPzrLpKoyGIAiC0BoXL15EVFSULG2Hh4cjLi4O/837VpL24uLiEB4eLklbaqCq6IwfPx7Dhg3zuk7Dhg0DajsuLg4AkJ+fjzp16jiX5+fno02bNs51zp07J9jOarXi0qVLzu3dMWnSJGRmZjqfX758GYmJicjNzZXtiys3hYWFSEhIwOnTp2E2m9UeTsDoYT/0sA8A7QdP6GEfAH3sR0FBAerVq4eaNWvK1kdERAROnDiB0lJpUr7w8HBERERI0pYaqCo6MTExiImJkaXtBg0aIC4uDps2bXKKTWFhIXbu3Ok8c6tTp064fPky9uzZg/bt2wMANm/eDLvdjuTkZI9tm0wmmEwml+VRUVGa/fE5MJvNmt8HQB/7oYd9AGg/eEIP+wDoYz+MRnmnyEZERGhaTqREM5ORc3NzkZ2djdzcXNhsNmRnZyM7O1twzZtmzZph1apVAACDwYBx48bh1VdfxZo1a/Dbb79h6NChiI+Px4ABAwAAzZs3R1paGkaOHIldu3Zhx44dyMjIwKBBgxQ944ogCIIgCHnQzGTkyZMnY/ny5c7nbdu2BQBs2bIFXbt2BQAcOXLEOTEYAJ5//nkUFxdj1KhRuHz5Mu68806sW7dOYLmffPIJMjIy0KNHDxiNRgwcOBBvvfWWMjtFEARBEISsaEZ0li1bVuk1dCrOYjcYDJg+fTqmT/c8SbhmzZr49NNPRY3NZDJhypQpbstZWkEP+wDoYz/0sA8A7QdP6GEfAH3shx72QWsYmBLnuBEEQRAEQaiAZuboEARBEARB+AuJDkEQBEEQuoVEhyAIgiAI3UKiQxAEQRCEbiHR8YHXXnsNnTt3RpUqVRAdHe3TNowxTJ48GXXq1EFkZCRSUlJw9OhRwTqXLl3CkCFDYDabER0djREjRgiuCyQ1/vZ38uRJGAwGt48vv/zSuZ6711esWMHFPgBA165dXcb35JNPCtbJzc1Fnz59UKVKFdSuXRsTJkyA1SrffZH83Y9Lly7hmWeeQdOmTREZGYl69ephzJgxgsspAPJ/FgsXLkT9+vURERGB5ORk7Nq1y+v6X375JZo1a4aIiAgkJSXhhx9+ELzuy+9EavzZh/fffx933XUXatSogRo1aiAlJcVl/WHDhrm852lpabLuA+DffixbtsxljBUvJsf7Z+Hud2wwGNCnTx/nOmp8Ftu2bUPfvn0RHx8Pg8GA1atXV7rN1q1b0a5dO5hMJjRu3NjtGcX+/tYILzCiUiZPnszmzZvHMjMzWVRUlE/bvP766ywqKoqtXr2a7d+/n/Xr1481aNCAXbt2zblOWloaa926Nfvll1/Yf//7X9a4cWM2ePBgmfbC//6sVis7e/as4DFt2jRWrVo1duXKFed6ANjSpUsF65XfTzX3gTHGunTpwkaOHCkYX0FBgWA/W7ZsyVJSUti+ffvYDz/8wGrVqsUmTZokyz4Esh+//fYbu//++9maNWvYsWPH2KZNm1iTJk3YwIEDBevJ+VmsWLGChYeHsyVLlrCDBw+ykSNHsujoaJafn+92/R07drCQkBD2xhtvsEOHDrGXX36ZhYWFsd9++825ji+/Eynxdx8eeeQRtnDhQrZv3z6Wk5PDhg0bxqKiotiff/7pXCc9PZ2lpaUJ3vNLly7JMv5A92Pp0qXMbDYLxpiXlydYh/fP4uLFi4LxHzhwgIWEhLClS5c611Hjs/jhhx/YSy+9xFauXMkAsFWrVnld/48//mBVqlRhmZmZ7NChQ2zBggUsJCSErVu3zrmOv+8N4R0SHT9YunSpT6Jjt9tZXFwcmzNnjnPZ5cuXmclkYp999hljjLFDhw4xAOzXX391rrN27VpmMBjYmTNnJB+7VP21adOGPf7444Jlvvy4pSDQfejSpQsbO3asx9d/+OEHZjQaBX/433nnHWY2m1lJSYkkYy+PVJ/FF198wcLDw5nFYnEuk/Oz6NixIxs9erTzuc1mY/Hx8WzWrFlu13/ooYdYnz59BMuSk5PZv/71L8aYb78TqfF3HypitVpZ9erV2fLly53L0tPTWf/+/aUeqlf83Y/K/nZp8bN48803WfXq1VlRUZFzmRqfRXl8+f09//zz7NZbbxUse/jhh1lqaqrzudj3hhBCpSsZOHHiBPLy8pCSkuJcFhUVheTkZGRlZQEAsrKyEB0djQ4dOjjXSUlJgdFoxM6dOyUfkxT97dmzB9nZ2RgxYoTLa6NHj0atWrXQsWNHLFmyxOXijVIgZh8++eQT1KpVCy1btsSkSZNw9epVQbtJSUmIjY11LktNTUVhYSEOHjzI1X6Up6CgAGazGaGhwut+yvFZlJaWYs+ePYLvtNFoREpKivM7XZGsrCzB+kDZ++pY35ffiZQEsg8VuXr1KiwWi8sNGbdu3YratWujadOmeOqpp3Dx4kVJx16eQPejqKgIiYmJSEhIQP/+/QXfbS1+FosXL8agQYNQtWpVwXIlP4tAqOx3IcV7QwjRzJWRtUReXh4ACA6cjueO1/Ly8lC7dm3B66GhoahZs6ZzHanHJLa/xYsXo3nz5ujcubNg+fTp09G9e3dUqVIFGzZswNNPP42ioiKMGTNGsvEDge/DI488gsTERMTHx+N///sfXnjhBRw5cgQrV650tuvus3K8JjVSfBYXLlzAjBkzMGrUKMFyuT6LCxcuwGazuX2fDh8+7HYbT+9r+d+AY5mndaQkkH2oyAsvvID4+HjBQSgtLQ33338/GjRogOPHj+PFF19Er169kJWVhZCQEEn3AQhsP5o2bYolS5agVatWKCgowNy5c9G5c2ccPHgQdevW1dxnsWvXLhw4cACLFy8WLFf6swgET7+LwsJCXLt2DX///bfo7ykhJGhFZ+LEiZg9e7bXdXJyctCsWTOFRhQYvu6HWK5du4ZPP/0Ur7zyistr5Ze1bdsWxcXFmDNnjs8HV7n3obwMJCUloU6dOujRoweOHz+ORo0aBdxuRZT6LAoLC9GnTx+0aNECU6dOFbwm9rMgPPP6669jxYoV2Lp1q2Ai76BBg5z/n5SUhFatWqFRo0bYunUrevToocZQXejUqRM6derkfN65c2c0b94c7777LmbMmKHiyAJj8eLFSEpKQseOHQXLtfBZEMoTtKIzfvx4DBs2zOs6DRs2DKjtuLg4AEB+fj7q1KnjXJ6fn482bdo41zl37pxgO6vVikuXLjm39wVf90Nsf1999RWuXr2KoUOHVrpucnIyZsyYgZKSEp/u56LUPpQfHwAcO3YMjRo1QlxcnMsZDfn5+QDA3Wdx5coVpKWloXr16li1ahXCwsK8ru/vZ+GJWrVqISQkxPm+OMjPz/c45ri4OK/r+/I7kZJA9sHB3Llz8frrr+PHH39Eq1atvK7bsGFD1KpVC8eOHZPl4CpmPxyEhYWhbdu2OHbsGABtfRbFxcVYsWKF13sYOpD7swgET78Ls9mMyMhIhISEiP58iQqoPUlIS/g7GXnu3LnOZQUFBW4nI+/evdu5zvr162WfjBxof126dHE5w8cTr776KqtRo0bAY/WEVO/Z9u3bGQC2f/9+xtiNycjlz2h49913mdlsZtevX5duB/4h0P0oKChgt99+O+vSpQsrLi72qS8pP4uOHTuyjIwM53ObzcZuvvlmr5OR7733XsGyTp06uUxG9vY7kRp/94ExxmbPns3MZjPLysryqY/Tp08zg8HAvvnmG9Hj9UQg+1Eeq9XKmjZtyp599lnGmHY+C8bK/g6bTCZ24cKFSvtQ4rMoD3ycjNyyZUvBssGDB7tMRhbz+RJCSHR84NSpU2zfvn3OU6v37dvH9u3bJzjFumnTpmzlypXO56+//jqLjo5m33zzDfvf//7H+vfv7/b08rZt27KdO3ey7du3syZNmsh+erm3/v7880/WtGlTtnPnTsF2R48eZQaDga1du9alzTVr1rD333+f/fbbb+zo0aPs//7v/1iVKlXY5MmTudiHY8eOsenTp7Pdu3ezEydOsG+++YY1bNiQ3X333c5tHKeX9+zZk2VnZ7N169axmJgY2U8v92c/CgoKWHJyMktKSmLHjh0TnD5rtVoZY/J/FitWrGAmk4ktW7aMHTp0iI0aNYpFR0c7z1Z77LHH2MSJE53r79ixg4WGhrK5c+eynJwcNmXKFLenl1f2O5ESf/fh9ddfZ+Hh4eyrr74SvOeO3/6VK1fYc889x7KystiJEyfYjz/+yNq1a8eaNGkiiyQHuh/Tpk1j69evZ8ePH2d79uxhgwYNYhEREezgwYOCfeX5s3Bw5513socffthluVqfxZUrV5zHBABs3rx5bN++fezUqVOMMcYmTpzIHnvsMef6jtPLJ0yYwHJyctjChQvdnl7u7b0h/INExwfS09MZAJfHli1bnOvgn+uXOLDb7eyVV15hsbGxzGQysR49erAjR44I2r148SIbPHgwq1atGjObzWz48OECeZKayvo7ceKEy34xxtikSZNYQkICs9lsLm2uXbuWtWnThlWrVo1VrVqVtW7dmi1atMjtumrsQ25uLrv77rtZzZo1mclkYo0bN2YTJkwQXEeHMcZOnjzJevXqxSIjI1mtWrXY+PHjBadtq70fW7ZscfsdBMBOnDjBGFPms1iwYAGrV68eCw8PZx07dmS//PKL87UuXbqw9PR0wfpffPEFu+WWW1h4eDi79dZb2ffffy943ZffidT4sw+JiYlu3/MpU6Ywxhi7evUq69mzJ4uJiWFhYWEsMTGRjRw5UpEDkj/7MW7cOOe6sbGxrHfv3mzv3r2C9nj/LBhj7PDhwwwA27Bhg0tban0Wnn6bjrGnp6ezLl26uGzTpk0bFh4ezho2bCg4djjw9t4Q/mFgTIbzgAmCIAiCIDiArqNDEARBEIRuIdEhCIIgCEK3kOgQBEEQBKFbSHQIgiAIgtAtJDoEQRAEQegWEh2CIAiCIHQLiQ5BEARBELqFRIcgCIIgCN1CokMQBPdcv34dw4YNQ1JSEkJDQzFgwAC1h0QQhEYg0SEIQjSMMVitVtnat9lsiIyMxJgxY5CSkiJbPwRB6A8SHYIIQrp27YqMjAxkZGQgKioKtWrVwiuvvALHHWE++ugjdOjQAdWrV0dcXBweeeQRnDt3zrn91q1bYTAYsHbtWrRv3x4mkwnbt2/H8ePH0b9/f8TGxqJatWq47bbb8OOPPwr6rl+/Pl599VUMHToU1apVQ2JiItasWYPz58+jf//+qFatGlq1aoXdu3c7t6latSreeecdjBw5EnFxccq8SQRB6AISHYIIUpYvX47Q0FDs2rUL//nPfzBv3jx88MEHAACLxYIZM2Zg//79WL16NU6ePIlhw4a5tDFx4kS8/vrryMnJQatWrVBUVITevXtj06ZN2LdvH9LS0tC3b1/k5uYKtnvzzTdxxx13YN++fejTpw8ee+wxDB06FI8++ij27t2LRo0aYejQoaBb8REEIRa6qSdBBCFdu3bFuXPncPDgQRgMBgBl0rJmzRocOnTIZf3du3fjtttuw5UrV1CtWjVs3boV3bp1w+rVq9G/f3+vfbVs2RJPPvkkMjIyAJQlOnfddRc++ugjAEBeXh7q1KmDV155BdOnTwcA/PLLL+jUqRPOnj3rkuAMGzYMly9fxurVq8W+DQRBBAGU6BBEkHL77bc7JQcAOnXqhKNHj8Jms2HPnj3o27cv6tWrh+rVq6NLly4A4JLMdOjQQfC8qKgIzz33HJo3b47o6GhUq1YNOTk5Ltu1atXK+f+xsbEAgKSkJJdl5ctlBEEQgUCiQxCEgOvXryM1NRVmsxmffPIJfv31V6xatQoAUFpaKli3atWqgufPPfccVq1ahZkzZ+K///0vsrOzkZSU5LJdWFiY8/8dsuVumd1ul27HCIIISkLVHgBBEOqwc+dOwfNffvkFTZo0weHDh3Hx4kW8/vrrSEhIAADBxGBv7NixA8OGDcN9990HoCzhOXnypKTjJgiC8AdKdAgiSMnNzUVmZiaOHDmCzz77DAsWLMDYsWNRr149hIeHY8GCBfjjjz+wZs0azJgxw6c2mzRpgpUrVyI7Oxv79+/HI488Ilkqc+jQIWRnZ+PSpUsoKChAdnY2srOzJWmbIAj9QokOQQQpQ4cOxbVr19CxY0eEhIRg7NixGDVqFAwGA5YtW4YXX3wRb731Ftq1a4e5c+eiX79+lbY5b948PP744+jcuTNq1aqFF154AYWFhZKMt3fv3jh16pTzedu2bQGAzswiCMIrdNYVQQQhXbt2RZs2bTB//ny1h0IQBCErVLoiCIIgCEK3kOgQBEEQBKFbqHRFEARBEIRuoUSHIAiCIAjdQqJDEARBEIRuIdEhCIIgCEK3kOgQBEEQBKFbSHQIgiAIgtAtJDoEQRAEQegWEh2CIAiCIHQLiQ5BEARBELqFRIcgCIIgCN3y/1iBBm1ABlN/AAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "size = 100\n", - "param1 = np.linspace(-1,1,size)\n", - "param2 = np.linspace(-1,1,size)\n", - "iters = 5\n", - "for k in range(iters):\n", - " off_diagonal_norm = np.empty((size,size))\n", - " least_squares = np.empty((size,size))\n", - " for i in range(size):\n", - " for j in range(size):\n", - " dbi_eval = deepcopy(dbi)\n", - " d = d_ansatz([param1[i],param2[j]],d_ansatz_type.local_1,normalization=False)\n", - " s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", - " dbi_eval(s_poly,d=d)\n", - " least_squares[i,j] = dbi_eval.least_squares(d=d)\n", - " off_diagonal_norm[i,j] = dbi_eval.off_diagonal_norm\n", - "\n", - " param1_optim, param2_optim = np.unravel_index(np.argmin(off_diagonal_norm, axis=None), off_diagonal_norm.shape)\n", - " d = d_ansatz([param1[param1_optim],param2[param2_optim]],d_ansatz_type.local_1,normalization=False)\n", - " s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", - " dbi(s_poly,d=d)\n", - "\n", - "\n", - "\n", - " plt.figure()\n", - " plt.title('Least squares cost function, iteration: ' + str(k+1))\n", - " plt.contourf(param1,param2,least_squares,levels=50)\n", - " plt.xlabel('param1')\n", - " plt.ylabel('param2')\n", - " plt.colorbar()\n", - "\n", - " plt.figure()\n", - " plt.title('Off-diagonal norm, iteration: ' + str(k+1))\n", - " plt.contourf(param1,param2,off_diagonal_norm,levels=50)\n", - " plt.xlabel('param1')\n", - " plt.ylabel('param2')\n", - " plt.colorbar()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "False\n" - ] - } - ], - "source": [ - "def ordered(D):\n", - " #verify if a diagonal matrix is ordered\n", - " for i in range(D.shape[0]-1):\n", - " if D[i,i] < D[i+1,i+1]:\n", - " return False\n", - " return True\n", - "d = np.logspace(1,-1,10)\n", - "d = d_ansatz(params,d_ansatz_type.local_1,normalization=True)\n", - "\n", - "print(ordered(d))\n" ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 ] } ], @@ -1708,11 +733,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", -<<<<<<< HEAD "version": "3.11.7" -======= - "version": "3.9.19 (main, Mar 21 2024, 17:21:27) [MSC v.1916 64 bit (AMD64)]" ->>>>>>> eda60e957712d85e312026b475ae4e8ab9755251 }, "orig_nbformat": 4, "vscode": { diff --git a/examples/dbi/dbi_group_commutator_tests.ipynb b/examples/dbi/dbi_group_commutator_tests.ipynb index 47d1e9bc5a..82f313612e 100644 --- a/examples/dbi/dbi_group_commutator_tests.ipynb +++ b/examples/dbi/dbi_group_commutator_tests.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -12,24 +12,16 @@ "import matplotlib.pyplot as plt\n", "\n", "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCost\n", + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", "from qibo.models.dbi.utils import *\n", "from qibo.models.dbi.utils_scheduling import *" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-04-24 16:23:01]: Using numpy backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -42,37 +34,16 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", + "cost = DoubleBracketCostFunction.least_squares\n", "# initialize class\n", "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.group_commutator,cost=cost)" ] }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", "s_space = np.linspace(1e-5, 0.3, 500)\n", @@ -112,30 +83,9 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", "flows = 30\n", @@ -184,7 +134,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19 (main, Mar 21 2024, 17:21:27) [MSC v.1916 64 bit (AMD64)]" + "version": "3.11.7" }, "orig_nbformat": 4, "vscode": { diff --git a/examples/dbi/dbi_misc.ipynb b/examples/dbi/dbi_misc.ipynb index cabe5f12b0..22691d7071 100644 --- a/examples/dbi/dbi_misc.ipynb +++ b/examples/dbi/dbi_misc.ipynb @@ -1578,7 +1578,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19 (main, Mar 21 2024, 17:21:27) [MSC v.1916 64 bit (AMD64)]" + "version": "3.11.7" }, "orig_nbformat": 4, "vscode": { From 8ced0427e3300f1c83b6f67df3edd35fe9549680 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 21 May 2024 16:52:38 +0800 Subject: [PATCH 076/154] Merging from Ising branch: tests fixed, coverage undone; utils_gradients moved to utils_analytical --- ...t_functions_and_d_gradients_tutorial.ipynb | 4 +- src/qibo/models/dbi/utils_analytical.py | 199 +++++++++++++++++- src/qibo/models/dbi/utils_gradients.py | 199 ------------------ 3 files changed, 194 insertions(+), 208 deletions(-) delete mode 100644 src/qibo/models/dbi/utils_gradients.py diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index ce0217ff69..f5f1498470 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -157,7 +157,7 @@ "plt.plot(range(iters+1), off_diagonal_norm_diff_least_squares, label=r'Least squares')\n", "plt.xlabel('Iterations')\n", "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()\n" + "plt.legend()" ] }, { @@ -341,7 +341,7 @@ "metadata": {}, "outputs": [], "source": [ - "from qibo.models.dbi.utils_gradients import *" + "from qibo.models.dbi.utils_analytical import *" ] }, { diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py index e8d7b2f572..e1673adab5 100644 --- a/src/qibo/models/dbi/utils_analytical.py +++ b/src/qibo/models/dbi/utils_analytical.py @@ -1,9 +1,14 @@ import math +from copy import deepcopy +from enum import Enum, auto from typing import Optional import numpy as np +from qibo import symbols +from qibo.hamiltonians import SymbolicHamiltonian from qibo.models.dbi.utils import commutator, covariance, variance +from qibo.models.dbi.utils_scheduling import polynomial_step def dGamma_di_pauli(dbi_object, n: int, Z_i: np.array, d: np.array): @@ -130,13 +135,27 @@ def gradient_Pauli( return grad, s -def dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list): - # Derivative of Gamma with respect to diagonal elements of D (full-diagonal ansatz) - A = np.zeros(d.shape) - A[i, i] = 1 - B = commutator(commutator(A, H), Gamma_list[n - 1]) - W = commutator(d, H) - return B + commutator(W, dGamma[-1]) +def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): + r""" + Gradient of the nth gamma operator with respect to the ith diagonal elements of D. + $Gamma_{n} = [W,[W,...,[W,H]]...]]$, + $\frac{\partial Gamma_{n}}{\partial D_{ii}} = \partial_{D_{ii}} W\Gamma_{n-1}-\partial_{D_{ii}}\Gamma_{n-1} W$. + and thus is can be computed recursively. + Args: + d(np.array): D operator. + h(np.array): Hamiltonian. + n(int): nth Gamma operator. + i(int): Index of the diagonal element of D. + dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). + gamma_list(list): List of the n gamma operators. + Returns: + (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. + """ + dD_di = np.zeros(d.shape) + dD_di[i, i] = 1 + dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) + w = commutator(d, h) + return dW_di + commutator(w, dGamma[-1]) def dpolynomial_diDiagonal(dbi_object, s, d, H, i): @@ -215,3 +234,169 @@ def energy_fluctuation_polynomial_expansion_coef( ) coef = list(reversed(coef)) return coef + + +class d_ansatz_type(Enum): + + element_wise = auto() + local_1 = auto() + + +def d_ansatz(params: np.array, d_type: d_ansatz_type, normalization: bool = False): + r""" + Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. + If $\alpha_i$ are our parameters and d the number of qubits then: + + element_wise: $D = \sum_{i=0}^{2^d} \alpha_i |i\rangle \langle i|$ + local_1: $D = \sum_{i=1}^{d} \alpha_i Z_i$ + Args: + params(np.array): parameters for the ansatz. + d_type(d_ansatz type): type of parameterization for the ansatz. + normalization(bool): If True, the diagonal is normalized to 1. + """ + + if d_type is d_ansatz_type.element_wise: + d = np.zeros((len(params), len(params))) + for i in range(len(params)): + d[i, i] = params[i] + + elif d_type is d_ansatz_type.local_1: + + op_list = [params[i] * symbols.Z(i) for i in range(len(params))] + symbolHam = op_list[0] + for i in range(len(params) - 1): + symbolHam += op_list[i + 1] + + d = SymbolicHamiltonian(symbolHam, nqubits=len(params)) + d = d.dense.matrix + else: + raise ValueError(f"Parameterization type {type} not recognized.") + if normalization: + d = d / np.linalg.norm(d) + return d + + +def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): + r""" + TODO: add formula and explain terms + Gradient wrt the ith diagonal elements of D. + We make Double_bracket rotation with duration given by the minimzer of the ´polynomial_step´ function. + Gradient of the Taylor expansion of the least squares loss function as a function of $s$ the duration of Double-Bracket rotation element-wise ansatz: + $\partial_{D_{ii}} \text{Tr}(H_k@D) \approx \sum_{k=0}^{n} \frac{1}{k!!} \partial_{D_ii}\text{Tr}(\Gamma_{k}D)$. + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + d(np.array): D operator. + h(np.array): Hamiltonian. + i(int): Index of the diagonal element of D. + Returns: + derivative(float): Derivative of the polynomial expansion with respect to the ith diagonal elements of D. + """ + derivative = 0 + s = polynomial_step(dbi_object, n=3, d=d) + dD_di = np.zeros(d.shape) + gamma_list = dbi_object.generate_Gamma_list(4, d) + dD_di[i, i] = 1 + dGamma = [commutator(dD_di, h)] + derivative += np.real( + np.trace(gamma_list[0] @ dD_di) + + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s + ) + for n in range(2, 4): + dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) + derivative += np.real( + np.trace(dGamma[-1] @ d + gamma_list[n] @ dD_di) * s**n / math.factorial(n) + ) + + return derivative + + +def gradientDiagonalEntries( + dbi_object, params, h, analytic=True, d_type=d_ansatz_type.element_wise, delta=1e-4 +): + r""" + Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. + As the analytical gradient is applied on the polynomial expansion of the cost function, the numerical gradients may be more accurate. + + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + h(np.array): Hamiltonian. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + d_type(d_ansatz_type): Ansatz used for the D operator. Options are 'Full' and '1-local'. + delta(float): Step size for numerical gradient. + Returns: + grad(np.array): Gradient of the D operator. + """ + + grad = np.zeros(len(params)) + d = d_ansatz(params, d_type) + if analytic == True: + for i in range(len(params)): + derivative = derivative_scalar_product_dbr_approx_element_wise_ansatz( + dbi_object, d, h, i + ) + grad[i] = d[i, i] - derivative + else: + for i in range(len(params)): + params_new = deepcopy(params) + params_new[i] += delta + d_new = d_ansatz(params_new, d_type) + grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta + return grad + + +def gradient_descent_dbr_d_ansatz( + dbi_object, + params, + nmb_iterations, + lr=1e-2, + analytic=True, + d_type=d_ansatz_type.element_wise, + normalize=False, +): + r""" + Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. + - Declare variables + - Calculate initial loss + - Iterate, learning at each the optimal D and measure loss + - Return values + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + nmb_iterations(int): Number of gradient descent iterations. + lr(float): Learning rate. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + d_type(d_ansatz_type): Ansatz used for the D operator. + normalize(bool): If True, the D operator is normalized at each iteration. + Returns: + d(np.array): Optimized D operator. + loss(np.array): Loss function evaluated at each iteration. + grad(np.array): Gradient evaluated at each iteration. + params_hist(np.array): Parameters evaluated at each iteration. + """ + + h = dbi_object.h.matrix + d = d_ansatz(params, d_type, normalization=normalize) + loss = np.zeros(nmb_iterations + 1) + grad = np.zeros((nmb_iterations, len(params))) + dbi_new = deepcopy(dbi_object) + s = polynomial_step(dbi_object, n=3, d=d) + dbi_new(s, d=d) + loss[0] = dbi_new.loss(0.0, d) + params_hist = np.empty((len(params), nmb_iterations + 1)) + params_hist[:, 0] = params + + for i in range(nmb_iterations): + dbi_new = deepcopy(dbi_object) + grad[i, :] = gradientDiagonalEntries( + dbi_object, params, h, analytic=analytic, d_type=d_type + ) + for j in range(len(params)): + params[j] = params[j] - lr * grad[i, j] + d = d_ansatz(params, d_type, normalization=normalize) + s = polynomial_step(dbi_new, n=3, d=d) + dbi_new(s, d=d) + loss[i + 1] = dbi_new.loss(0.0, d=d) + params_hist[:, i + 1] = params + + return d, loss, grad, params_hist diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py deleted file mode 100644 index 6b5d9d7d56..0000000000 --- a/src/qibo/models/dbi/utils_gradients.py +++ /dev/null @@ -1,199 +0,0 @@ -import math -from copy import deepcopy -from enum import Enum, auto - -import numpy as np - -from qibo import symbols -from qibo.hamiltonians import SymbolicHamiltonian -from qibo.models.dbi.utils import commutator -from qibo.models.dbi.utils_scheduling import polynomial_step - - -class d_ansatz_type(Enum): - - element_wise = auto() - local_1 = auto() - - -def d_ansatz(params: np.array, d_type: d_ansatz_type, normalization: bool = False): - r""" - Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. - If $\alpha_i$ are our parameters and d the number of qubits then: - - element_wise: $D = \sum_{i=0}^{2^d} \alpha_i |i\rangle \langle i|$ - local_1: $D = \sum_{i=1}^{d} \alpha_i Z_i$ - Args: - params(np.array): parameters for the ansatz. - d_type(d_ansatz type): type of parameterization for the ansatz. - normalization(bool): If True, the diagonal is normalized to 1. - """ - - if d_type is d_ansatz_type.element_wise: - d = np.zeros((len(params), len(params))) - for i in range(len(params)): - d[i, i] = params[i] - - elif d_type is d_ansatz_type.local_1: - - op_list = [params[i] * symbols.Z(i) for i in range(len(params))] - symbolHam = op_list[0] - for i in range(len(params) - 1): - symbolHam += op_list[i + 1] - - d = SymbolicHamiltonian(symbolHam, nqubits=len(params)) - d = d.dense.matrix - else: - raise ValueError(f"Parameterization type {type} not recognized.") - if normalization: - d = d / np.linalg.norm(d) - return d - - -def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): - r""" - Gradient of the nth gamma operator with respect to the ith diagonal elements of D. - $Gamma_{n} = [W,[W,...,[W,H]]...]]$, - $\frac{\partial Gamma_{n}}{\partial D_{ii}} = \partial_{D_{ii}} W\Gamma_{n-1}-\partial_{D_{ii}}\Gamma_{n-1} W$. - and thus is can be computed recursively. - Args: - d(np.array): D operator. - h(np.array): Hamiltonian. - n(int): nth Gamma operator. - i(int): Index of the diagonal element of D. - dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). - gamma_list(list): List of the n gamma operators. - Returns: - (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. - """ - dD_di = np.zeros(d.shape) - dD_di[i, i] = 1 - dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) - w = commutator(d, h) - return dW_di + commutator(w, dGamma[-1]) - - -def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): - r""" - TODO: add formula and explain terms - Gradient wrt the ith diagonal elements of D. - We make Double_bracket rotation with duration given by the minimzer of the ´polynomial_step´ function. - Gradient of the Taylor expansion of the least squares loss function as a function of $s$ the duration of Double-Bracket rotation element-wise ansatz: - $\partial_{D_{ii}} \text{Tr}(H_k@D) \approx \sum_{k=0}^{n} \frac{1}{k!!} \partial_{D_ii}\text{Tr}(\Gamma_{k}D)$. - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - d(np.array): D operator. - h(np.array): Hamiltonian. - i(int): Index of the diagonal element of D. - Returns: - derivative(float): Derivative of the polynomial expansion with respect to the ith diagonal elements of D. - """ - derivative = 0 - s = polynomial_step(dbi_object, n=3, d=d) - dD_di = np.zeros(d.shape) - gamma_list = dbi_object.generate_Gamma_list(4, d) - dD_di[i, i] = 1 - dGamma = [commutator(dD_di, h)] - derivative += np.real( - np.trace(gamma_list[0] @ dD_di) - + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s - ) - for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) - derivative += np.real( - np.trace(dGamma[-1] @ d + gamma_list[n] @ dD_di) * s**n / math.factorial(n) - ) - - return derivative - - -def gradientDiagonalEntries( - dbi_object, params, h, analytic=True, d_type=d_ansatz_type.element_wise, delta=1e-4 -): - r""" - Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. - As the analytical gradient is applied on the polynomial expansion of the cost function, the numerical gradients may be more accurate. - - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). - h(np.array): Hamiltonian. - analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. - d_type(d_ansatz_type): Ansatz used for the D operator. Options are 'Full' and '1-local'. - delta(float): Step size for numerical gradient. - Returns: - grad(np.array): Gradient of the D operator. - """ - - grad = np.zeros(len(params)) - d = d_ansatz(params, d_type) - if analytic == True: - for i in range(len(params)): - derivative = derivative_scalar_product_dbr_approx_element_wise_ansatz( - dbi_object, d, h, i - ) - grad[i] = d[i, i] - derivative - else: - for i in range(len(params)): - params_new = deepcopy(params) - params_new[i] += delta - d_new = d_ansatz(params_new, d_type) - grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta - return grad - - -def gradient_descent_dbr_d_ansatz( - dbi_object, - params, - nmb_iterations, - lr=1e-2, - analytic=True, - d_type=d_ansatz_type.element_wise, - normalize=False, -): - r""" - Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. - - Declare variables - - Calculate initial loss - - Iterate, learning at each the optimal D and measure loss - - Return values - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). - nmb_iterations(int): Number of gradient descent iterations. - lr(float): Learning rate. - analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. - d_type(d_ansatz_type): Ansatz used for the D operator. - normalize(bool): If True, the D operator is normalized at each iteration. - Returns: - d(np.array): Optimized D operator. - loss(np.array): Loss function evaluated at each iteration. - grad(np.array): Gradient evaluated at each iteration. - params_hist(np.array): Parameters evaluated at each iteration. - """ - - h = dbi_object.h.matrix - d = d_ansatz(params, d_type, normalization=normalize) - loss = np.zeros(nmb_iterations + 1) - grad = np.zeros((nmb_iterations, len(params))) - dbi_new = deepcopy(dbi_object) - s = polynomial_step(dbi_object, n=3, d=d) - dbi_new(s, d=d) - loss[0] = dbi_new.loss(0.0, d) - params_hist = np.empty((len(params), nmb_iterations + 1)) - params_hist[:, 0] = params - - for i in range(nmb_iterations): - dbi_new = deepcopy(dbi_object) - grad[i, :] = gradientDiagonalEntries( - dbi_object, params, h, analytic=analytic, d_type=d_type - ) - for j in range(len(params)): - params[j] = params[j] - lr * grad[i, j] - d = d_ansatz(params, d_type, normalization=normalize) - s = polynomial_step(dbi_new, n=3, d=d) - dbi_new(s, d=d) - loss[i + 1] = dbi_new.loss(0.0, d=d) - params_hist[:, i + 1] = params - - return d, loss, grad, params_hist From 29b874597fa41417a5c92d9f179552477b0791cf Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Tue, 21 May 2024 17:12:18 +0800 Subject: [PATCH 077/154] Fix circular imports --- src/qibo/models/dbi/utils_analytical.py | 178 ------------------------ src/qibo/models/dbi/utils_gradients.py | 175 +++++++++++++++++++++++ src/qibo/models/dbi/utils_strategies.py | 1 + 3 files changed, 176 insertions(+), 178 deletions(-) create mode 100644 src/qibo/models/dbi/utils_gradients.py diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py index e1673adab5..fcaf28c86b 100644 --- a/src/qibo/models/dbi/utils_analytical.py +++ b/src/qibo/models/dbi/utils_analytical.py @@ -1,5 +1,4 @@ import math -from copy import deepcopy from enum import Enum, auto from typing import Optional @@ -8,7 +7,6 @@ from qibo import symbols from qibo.hamiltonians import SymbolicHamiltonian from qibo.models.dbi.utils import commutator, covariance, variance -from qibo.models.dbi.utils_scheduling import polynomial_step def dGamma_di_pauli(dbi_object, n: int, Z_i: np.array, d: np.array): @@ -85,56 +83,6 @@ def derivative_product(k1, k2): return da, db, dc, ds -def gradient_Pauli( - dbi_object, - d: np.array, - pauli_operator_dict: dict, - use_ds=False, - n=3, - **kwargs, -): - r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients - Args: - dbi_object (DoubleBracketIteration): the target dbi object - d (np.array): the diagonal operator - n_taylor (int): the highest order of the taylore expansion of w.r.t `s` - onsite_Z_ops (list): list of Pauli-Z operators - taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` - use_ds (boolean): if False, ds is set to 0 - """ - # n is the highest order for calculating s - - # pauli_index is the list of positions \mu - pauli_operators = list(pauli_operator_dict.values()) - num_paul = len(pauli_operators) - grad = np.zeros(num_paul) - coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) - s = dbi_object.choose_step( - d=d, - **kwargs, - ) - - a, b, c = coef[len(coef) - 3 :] - - for i, operator in enumerate(pauli_operators): - da, db, dc, ds = ds_di_pauli( - dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] - ) - if use_ds is True: - ds = 0 - grad[i] = ( - s**3 / 3 * da - + s**2 / 2 * db - + 2 * s * dc - + s**2 * ds * a - + s * ds * b - + 2 * ds * c - ) - grad = np.array(grad) - grad = grad / np.linalg.norm(grad) - return grad, s - - def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): r""" Gradient of the nth gamma operator with respect to the ith diagonal elements of D. @@ -274,129 +222,3 @@ def d_ansatz(params: np.array, d_type: d_ansatz_type, normalization: bool = Fals if normalization: d = d / np.linalg.norm(d) return d - - -def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): - r""" - TODO: add formula and explain terms - Gradient wrt the ith diagonal elements of D. - We make Double_bracket rotation with duration given by the minimzer of the ´polynomial_step´ function. - Gradient of the Taylor expansion of the least squares loss function as a function of $s$ the duration of Double-Bracket rotation element-wise ansatz: - $\partial_{D_{ii}} \text{Tr}(H_k@D) \approx \sum_{k=0}^{n} \frac{1}{k!!} \partial_{D_ii}\text{Tr}(\Gamma_{k}D)$. - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - d(np.array): D operator. - h(np.array): Hamiltonian. - i(int): Index of the diagonal element of D. - Returns: - derivative(float): Derivative of the polynomial expansion with respect to the ith diagonal elements of D. - """ - derivative = 0 - s = polynomial_step(dbi_object, n=3, d=d) - dD_di = np.zeros(d.shape) - gamma_list = dbi_object.generate_Gamma_list(4, d) - dD_di[i, i] = 1 - dGamma = [commutator(dD_di, h)] - derivative += np.real( - np.trace(gamma_list[0] @ dD_di) - + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s - ) - for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) - derivative += np.real( - np.trace(dGamma[-1] @ d + gamma_list[n] @ dD_di) * s**n / math.factorial(n) - ) - - return derivative - - -def gradientDiagonalEntries( - dbi_object, params, h, analytic=True, d_type=d_ansatz_type.element_wise, delta=1e-4 -): - r""" - Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. - As the analytical gradient is applied on the polynomial expansion of the cost function, the numerical gradients may be more accurate. - - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). - h(np.array): Hamiltonian. - analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. - d_type(d_ansatz_type): Ansatz used for the D operator. Options are 'Full' and '1-local'. - delta(float): Step size for numerical gradient. - Returns: - grad(np.array): Gradient of the D operator. - """ - - grad = np.zeros(len(params)) - d = d_ansatz(params, d_type) - if analytic == True: - for i in range(len(params)): - derivative = derivative_scalar_product_dbr_approx_element_wise_ansatz( - dbi_object, d, h, i - ) - grad[i] = d[i, i] - derivative - else: - for i in range(len(params)): - params_new = deepcopy(params) - params_new[i] += delta - d_new = d_ansatz(params_new, d_type) - grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta - return grad - - -def gradient_descent_dbr_d_ansatz( - dbi_object, - params, - nmb_iterations, - lr=1e-2, - analytic=True, - d_type=d_ansatz_type.element_wise, - normalize=False, -): - r""" - Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. - - Declare variables - - Calculate initial loss - - Iterate, learning at each the optimal D and measure loss - - Return values - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). - nmb_iterations(int): Number of gradient descent iterations. - lr(float): Learning rate. - analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. - d_type(d_ansatz_type): Ansatz used for the D operator. - normalize(bool): If True, the D operator is normalized at each iteration. - Returns: - d(np.array): Optimized D operator. - loss(np.array): Loss function evaluated at each iteration. - grad(np.array): Gradient evaluated at each iteration. - params_hist(np.array): Parameters evaluated at each iteration. - """ - - h = dbi_object.h.matrix - d = d_ansatz(params, d_type, normalization=normalize) - loss = np.zeros(nmb_iterations + 1) - grad = np.zeros((nmb_iterations, len(params))) - dbi_new = deepcopy(dbi_object) - s = polynomial_step(dbi_object, n=3, d=d) - dbi_new(s, d=d) - loss[0] = dbi_new.loss(0.0, d) - params_hist = np.empty((len(params), nmb_iterations + 1)) - params_hist[:, 0] = params - - for i in range(nmb_iterations): - dbi_new = deepcopy(dbi_object) - grad[i, :] = gradientDiagonalEntries( - dbi_object, params, h, analytic=analytic, d_type=d_type - ) - for j in range(len(params)): - params[j] = params[j] - lr * grad[i, j] - d = d_ansatz(params, d_type, normalization=normalize) - s = polynomial_step(dbi_new, n=3, d=d) - dbi_new(s, d=d) - loss[i + 1] = dbi_new.loss(0.0, d=d) - params_hist[:, i + 1] = params - - return d, loss, grad, params_hist diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py new file mode 100644 index 0000000000..70f3d09115 --- /dev/null +++ b/src/qibo/models/dbi/utils_gradients.py @@ -0,0 +1,175 @@ +from qibo.models.dbi.utils_analytical import * +from qibo.models.dbi.utils_scheduling import polynomial_step + + +def gradient_Pauli( + dbi_object, + d: np.array, + pauli_operator_dict: dict, + use_ds=False, + n=3, + **kwargs, +): + r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d (np.array): the diagonal operator + n_taylor (int): the highest order of the taylore expansion of w.r.t `s` + onsite_Z_ops (list): list of Pauli-Z operators + taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` + use_ds (boolean): if False, ds is set to 0 + """ + # n is the highest order for calculating s + + # pauli_index is the list of positions \mu + pauli_operators = list(pauli_operator_dict.values()) + num_paul = len(pauli_operators) + grad = np.zeros(num_paul) + coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) + s = polynomial_step(dbi_object, n=3, d=d) + + a, b, c = coef[len(coef) - 3 :] + + for i, operator in enumerate(pauli_operators): + da, db, dc, ds = ds_di_pauli( + dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] + ) + if use_ds is True: + ds = 0 + grad[i] = ( + s**3 / 3 * da + + s**2 / 2 * db + + 2 * s * dc + + s**2 * ds * a + + s * ds * b + + 2 * ds * c + ) + grad = np.array(grad) + grad = grad / np.linalg.norm(grad) + return grad, s + + +def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): + r""" + TODO: add formula and explain terms + Gradient wrt the ith diagonal elements of D. + We make Double_bracket rotation with duration given by the minimzer of the ´polynomial_step´ function. + Gradient of the Taylor expansion of the least squares loss function as a function of $s$ the duration of Double-Bracket rotation element-wise ansatz: + $\partial_{D_{ii}} \text{Tr}(H_k@D) \approx \sum_{k=0}^{n} \frac{1}{k!!} \partial_{D_ii}\text{Tr}(\Gamma_{k}D)$. + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + d(np.array): D operator. + h(np.array): Hamiltonian. + i(int): Index of the diagonal element of D. + Returns: + derivative(float): Derivative of the polynomial expansion with respect to the ith diagonal elements of D. + """ + derivative = 0 + s = polynomial_step(dbi_object, n=3, d=d) + dD_di = np.zeros(d.shape) + gamma_list = dbi_object.generate_Gamma_list(4, d) + dD_di[i, i] = 1 + dGamma = [commutator(dD_di, h)] + derivative += np.real( + np.trace(gamma_list[0] @ dD_di) + + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s + ) + for n in range(2, 4): + dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) + derivative += np.real( + np.trace(dGamma[-1] @ d + gamma_list[n] @ dD_di) * s**n / math.factorial(n) + ) + + return derivative + + +def gradientDiagonalEntries( + dbi_object, params, h, analytic=True, d_type=d_ansatz_type.element_wise, delta=1e-4 +): + r""" + Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. + As the analytical gradient is applied on the polynomial expansion of the cost function, the numerical gradients may be more accurate. + + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + h(np.array): Hamiltonian. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + d_type(d_ansatz_type): Ansatz used for the D operator. Options are 'Full' and '1-local'. + delta(float): Step size for numerical gradient. + Returns: + grad(np.array): Gradient of the D operator. + """ + + grad = np.zeros(len(params)) + d = d_ansatz(params, d_type) + if analytic == True: + for i in range(len(params)): + derivative = derivative_scalar_product_dbr_approx_element_wise_ansatz( + dbi_object, d, h, i + ) + grad[i] = d[i, i] - derivative + else: + for i in range(len(params)): + params_new = deepcopy(params) + params_new[i] += delta + d_new = d_ansatz(params_new, d_type) + grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta + return grad + + +def gradient_descent_dbr_d_ansatz( + dbi_object, + params, + nmb_iterations, + lr=1e-2, + analytic=True, + d_type=d_ansatz_type.element_wise, + normalize=False, +): + r""" + Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. + - Declare variables + - Calculate initial loss + - Iterate, learning at each the optimal D and measure loss + - Return values + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + nmb_iterations(int): Number of gradient descent iterations. + lr(float): Learning rate. + analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. + d_type(d_ansatz_type): Ansatz used for the D operator. + normalize(bool): If True, the D operator is normalized at each iteration. + Returns: + d(np.array): Optimized D operator. + loss(np.array): Loss function evaluated at each iteration. + grad(np.array): Gradient evaluated at each iteration. + params_hist(np.array): Parameters evaluated at each iteration. + """ + + h = dbi_object.h.matrix + d = d_ansatz(params, d_type, normalization=normalize) + loss = np.zeros(nmb_iterations + 1) + grad = np.zeros((nmb_iterations, len(params))) + dbi_new = deepcopy(dbi_object) + s = polynomial_step(dbi_object, n=3, d=d) + dbi_new(s, d=d) + loss[0] = dbi_new.loss(0.0, d) + params_hist = np.empty((len(params), nmb_iterations + 1)) + params_hist[:, 0] = params + + for i in range(nmb_iterations): + dbi_new = deepcopy(dbi_object) + grad[i, :] = gradientDiagonalEntries( + dbi_object, params, h, analytic=analytic, d_type=d_type + ) + for j in range(len(params)): + params[j] = params[j] - lr * grad[i, j] + d = d_ansatz(params, d_type, normalization=normalize) + s = polynomial_step(dbi_new, n=3, d=d) + dbi_new(s, d=d) + loss[i + 1] = dbi_new.loss(0.0, d=d) + params_hist[:, i + 1] = params + + return d, loss, grad, params_hist diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_strategies.py index 86be56f55f..3221f40791 100644 --- a/src/qibo/models/dbi/utils_strategies.py +++ b/src/qibo/models/dbi/utils_strategies.py @@ -3,6 +3,7 @@ from qibo.models.dbi.double_bracket import * from qibo.models.dbi.utils import cs_angle_sgn, generate_pauli_operator_dict from qibo.models.dbi.utils_analytical import * +from qibo.models.dbi.utils_gradients import gradient_Pauli from qibo.models.dbi.utils_scheduling import polynomial_step From 4cc3fd74a08aade8c4ed05116e4bbb67ee1e49eb Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Tue, 21 May 2024 12:16:30 +0200 Subject: [PATCH 078/154] reverted energyfluctuation for now, changed cost names --- ...t_functions_and_d_gradients_tutorial.ipynb | 11954 +++++++++++++++- examples/dbi/dbi_misc.ipynb | 1592 -- src/qibo/models/dbi/utils_gradients.py | 1 + 3 files changed, 11776 insertions(+), 1771 deletions(-) delete mode 100644 examples/dbi/dbi_misc.ipynb diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index f5f1498470..37c2615aa9 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,8 @@ "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" + "from qibo.models.dbi.utils_scheduling import *\n", + "from qibo.models.dbi.utils_gradients import *\n" ] }, { @@ -39,9 +40,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-05-21 12:10:15]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -62,9 +71,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.6060645454545454\n", + "100%|██████████| 100/100 [00:00<00:00, 473.50trial/s, best loss: -30.63362551504052]\n", + "hyperopt_search step: 0.5993688830433928\n", + "polynomial_approximation step: 0.5267891182131145\n" + ] + } + ], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", @@ -90,9 +110,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.6060645454545454\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -126,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -148,16 +196,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", "plt.plot(range(iters+1), off_diagonal_norm_diff_least_squares, label=r'Least squares')\n", "plt.xlabel('Iterations')\n", "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()" + "plt.legend()\n" ] }, { @@ -172,9 +241,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-05-21 12:13:04]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -187,116 +264,23 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the energy fluctuation cost function\n", - "cost = DoubleBracketCostFunction.off_diagonal_norm\n", + "cost = DoubleBracketCostFunction.energy_fluctuation\n", "# define the state\n", "state = np.zeros(2**nqubits)\n", - "state[3] = 1\n", + "state[7] = 1\n", "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)" + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate data for plotting sigma decrease of the first step\n", - "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))/2**nqubits\n", - "s_space = np.linspace(-1, 1, 1000)\n", - "off_diagonal_norm_diff = []\n", - "fluctuation = []\n", - "for s in s_space:\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval(s,d=d)\n", - " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", - " fluctuation.append(dbi_eval.energy_fluctuation(state=state))\n", - "\n", - "# grid_search\n", - "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", - "print('grid_search step:', step_grid)\n", - "# hyperopt\n", - "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", - "print('hyperopt_search step:', step_hyperopt)\n", - "# polynomial\n", - "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", - "print('polynomial_approximation step:', step_poly)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "plt.figure()\n", - "plt.plot(s_space, fluctuation)\n", - "plt.xlabel('s')\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label ='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.title('First DBI step')\n", - "plt.ylabel('Energy fluctuation')\n", - "plt.legend()\n", - "plt.figure()\n", - "plt.plot(s_space, off_diagonal_norm_diff)\n", - "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", - "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", - "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", - "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", - "plt.xlabel('s')\n", - "plt.title('First DBI step')\n", - "plt.legend()\n", - "print('The minimum for cost function in the tested range is:', step_grid)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", - "energy_fluc = [dbi.energy_fluctuation(state=state)]\n", - "iters = 10\n", - "dbi_ = deepcopy(dbi)\n", - "for _ in range(iters):\n", - " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", - " dbi_(step_poly,d=d)\n", - " off_diagonal_norm_diff.append(dbi_.off_diagonal_norm)\n", - " energy_fluc.append(dbi_.energy_fluctuation(state=state))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "\n", - "plt.figure()\n", - "plt.plot(range(iters+1), energy_fluc)\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'Energy fluctuation')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "iters = 30\n", "states = [0,1,2,3,4,5,6,7]\n", "energy = np.empty((len(states),iters))\n", - "\n", - "\n", "d = (np.diag(np.linspace(1,2**nqubits,2**nqubits)))\n", "for i in range(len(states)):\n", " dbi_ = deepcopy(dbi)\n", @@ -311,9 +295,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eigenvalues: [-9.29150262 -5.21110255 -2. -2. 1.29150262 4.\n", + " 4. 9.21110255]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Iterations')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", "print('Eigenvalues:', eigvals )\n", @@ -337,18 +350,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from qibo.models.dbi.utils_analytical import *" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-05-21 11:54:45]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -361,7 +373,7 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the energy fluctuation cost function\n", - "cost = DoubleBracketCostFunction.energy_fluctuation\n", + "cost = DoubleBracketCostFunction.least_squares\n", "# define the state\n", "state = np.zeros(2**nqubits)\n", "state[3] = 1\n", @@ -371,9 +383,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Diagonal elements')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cost = DoubleBracketCostFunction.least_squares\n", "nqubits = 5\n", @@ -381,9 +424,9 @@ "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", "params = np.linspace(1,2**nqubits,2**nqubits)\n", "\n", - "step = 1e-2\n", + "step = 1e-1\n", "iterations = 200\n", - "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", + "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step, normalize=True)\n", "\n", "plt.figure()\n", "plt.plot(range(iterations+1), loss)\n", @@ -406,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -415,13 +458,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 58, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -443,7 +486,7 @@ "off_diagonal_norm = np.empty((2,flows+1))\n", "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", - "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", + "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step,normalize=True) \n", "for i in range(flows):\n", "\n", " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", @@ -471,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -480,13 +523,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 59, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -510,7 +553,7 @@ "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", "\n", "for i in range(flows):\n", - " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", + " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step,normalize=True)\n", " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", " dbi_trained(s,d=d_trained)\n", " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", @@ -535,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -562,9 +605,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "for q in range(len(nqubits)):\n", @@ -593,9 +667,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-05-21 12:02:03]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -616,43 +698,1496 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], + "source": [ + "QIBO_LOG_LEVEL = None" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], "source": [ "dbi_eval = deepcopy(dbi)\n", "params = np.linspace(1,2**nqubits,2**nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,1e-2, analytic=False, d_type = d_ansatz_type.element_wise)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,1e-3, analytic=False, d_type = d_ansatz_type.element_wise, normalize=False)\n", "flows = 50\n", - "off_diagonal_norm = np.empty((flows+1,2))\n", + "off_diagonal_norm = np.empty((flows+1,3))\n", "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", "for i in range(flows):\n", " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", " dbi_eval(step_poly,d=d_opt)\n", " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", "\n", + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,nqubits,nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=True)\n", + "best = np.argmin(loss_opt)\n", + "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", "\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", "\n", "dbi_eval = deepcopy(dbi)\n", "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, 1e-3, analytic=False, d_type = d_ansatz_type.local_1)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=False)\n", "best = np.argmin(loss_opt)\n", "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", + "\n", "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n" + " off_diagonal_norm[i+1,2] = dbi_eval.off_diagonal_norm\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7dozo6GgmTZpE9frMBQUFTJkyhS1btrBjxw7CwsIYM2aMIRjt3r0bgEWLFpGSkmL4fvfu3aSkpJCSksLZs2fp1asX/fv3b+BVFk2RxtIFNCdOVjZAFRVcCDbSx0YI86oohjn+5j/vS+fB2sGsp/z+++8pLS3lv//9Lw4O+nP/5z//Yfz48bzzzjv4+Pjw5ptv8vTTT/Pkk08a3te9e3fD1zNnzjR8HRwczJtvvsm0adP49NNP613HK6+8UusYzzzzDEuXLq0VkHQ6HYsXL8bJyQmAe+65hw0bNvDWW2+RkpJCZWUlkyZNIigoCICIiAjDe4cMGVLrfF988QWurq7ExMQwbtw4vLy8AHB1dcXX19ewX/V2gCeffLJW6BHNm7TYGJE+2ECJUgoonMosMvzWIYQQ9TVnzhwcHR0NjzNnzlyyz7Fjx4iMjDSEGoC+ffui0+mIi4sjPT2d8+fPM3To0DrPs379eoYOHUqLFi1wcnLinnvuISsri+Li+k8w+uOPP9K3b198fX1xdHTklVdeuaTe4OBgQ6gB8PPzIz09HYDIyEiGDh1KREQEt912G19++SU5OTmGfdPS0njooYcICwvDxcUFZ2dnCgsLL3tNLueLL77g66+/5vfff68VdkTzJS02RuRsZQcUU6lUgaqCglIV2UXleDjaWLo0IW4MWnt964klzmtE06ZN4/bbbzd87+/f8FYoOzu7K76elJTEuHHjePTRR3nrrbdwd3dny5YtPPDAA5SXl2Nvf/XPtH37diZPnszs2bMZOXIkLi4uLF26lHnz5tXaT6vV1vpepVKhM0yPYcW6devYtm0ba9eu5eOPP+bll19m586dhISEMGXKFLKysvjoo48ICgrCxsaG3r17U15eftX6Nm3axOOPP84PP/xAp06drrq/aB4k2BiRncYWK0WhSqXCz00hJVvfz0aCjRBmolKZ/ZaQKbi7u+Pu7n7Ffdq1a8fixYspKioytNps3boVtVpNeHg4Tk5OBAcHs2HDBgYPHnzJ+/fu3YtOp2PevHmo1frG+59++qlBdW7bto2goCBefvllw7bTp0836BigDzp9+/alb9++vPbaawQFBbF8+XJmzZrF1q1b+fTTTxkzZgwAycnJZGZm1nq/Vqulqqqq1rb4+HhuvfVWXnrpJSZNmtTgmkTTJbeijEhVYyFMf3f9LSgZGSWEuFhhYSGxsbHExsYCkJiYSGxsbL1vrwBMnjwZW1tbpkyZwuHDhw2tE/fccw8+Pj6AfkTSvHnzWLBgASdPnmTfvn18/PHHAISGhlJRUcHHH3/MqVOnWLJkCZ999lmDPkdYWBhnzpxh6dKlJCQksGDBApYvX96gY+zcuZM5c+awZ88ezpw5w6+//kpGRgbt2rUznGPJkiUcO3aMnTt3Mnny5Etao6oDXGpqKjk5OZSUlDB+/HiioqJ4+OGHSU1NNTxE8yfBxphqzD7s5aIPNjIySghxsT179hAVFUVUVBQAs2bNIioqitdee63ex7C3t2fNmjVkZ2fTvXt3br31VoYOHcp//vMfwz5Tpkzhww8/5NNPP6VDhw6MGzeOkydPAvq+LfPnz+edd96hY8eOfPfdd8ydO7dBn+Omm27iqaeeYsaMGXTu3Jlt27bx6quvNugYzs7ObN68mTFjxtCmTRteeeUV5s2bx+jRowH4+uuvycnJoUuXLtxzzz088cQTeHt71zrGvHnzWLduHQEBAURFRZGWlsbx48fZsGED/v7++Pn5GR6i+VMpjah368KFC1m4cCFJSUkAdOjQgddee83wF3zQoEHExMTUes8jjzzSoN8y8vPzcXFxIS8vD2dnZ6PVDsDPU7kzZxtHbGy4yfdVvtvkwNgIPz6Z3MW45xFCUFpaSmJiIiEhIdja2lq6HCGEEVzp33V9f343qj42LVu25O233yYsLAxFUfj222+ZMGEC+/fvp0OHDgA89NBD/N///Z/hPfXp4GY2NVb4dravBOCUDPkWQgghzKZRBZvx48fX+v6tt95i4cKF7NixwxBs7O3ta81V0KhobAzrRdnb6nvsn87SD/mWGS+FEEII02u0fWyqqqpYunQpRUVF9O7d27D9u+++w9PTk44dO/Liiy9edb6FsrIy8vPzaz1MRmOL84UWG422DLUKisurSC8oM905hRBCCGHQqFpsQL/2Se/evSktLcXR0ZHly5fTvn17AO6++26CgoLw9/fn4MGDPP/888TFxfHrr7/Weby5c+cye/Zs8xSvtcWpSh9siioKCHC353RWMacyivBxlj4AQgghhKk1umATHh5ObGwseXl5LFu2jClTphATE0P79u15+OGHDftFRETg5+fH0KFDSUhIoHXr1pc93osvvsisWbMM3+fn5xMQEGCa4msM9y6oKCDYw4HTWcUkZRXRu7WHac4phBBCCINGdyvK2tqa0NBQunbtyty5c4mMjOSjjz667L49e/YE9BMx1cXGxgZnZ+daD5OpcSuqoLyAEE/9pFmyZpQQQghhHo0u2FxMp9NRVnb5PirVk1s1mrkJarTY5JfnG4JNogQbIYQQwiwa1a2oF198kdGjRxMYGEhBQQHff/890dHRrFmzhoSEBL7//nvGjBmDh4cHBw8e5KmnnmLAgAGNZw0Qjc0/t6LKCwiWYCOEEEKYVaMKNunp6dx7772kpKTg4uJCp06dWLNmDcOHDyc5OZn169fz4YcfUlRUREBAALfccguvvPKKpcv+h9au9q0oD32wOZ1djE6noFbLkG8hhBDClBrVraivv/6apKQkysrKSE9PZ/369QwfPhyAgIAAYmJiyMrKorS0lJMnT/Luu++ats9MQ9Vosckvz6eFmx1aKxXllTrO55VYuDghRFOnUqlYsWKF2c63ePFiXF1dzXY+YX4X/xm/8cYbdO7c2aTnHDRoEDNnzjTZ8RtVsGnyNHaGCfoKygtQqyDQXT8zstyOEkJU27x5M+PHj8ff39/sYaU5kmtoPM888wwbNmywdBnXRYKNMdVosdEpOoori2VklBDiEkVFRURGRvLJJ59YuhTRBFRUVJjtXI6Ojnh4NO3pSSTYGJPWDltFQXthWdGaQ74TM688Q7IQ4sYxevRo3nzzTSZOnHhdxzl06BBDhgzBzs4ODw8PHn74YQoLC2vt880339ChQwdsbGzw8/NjxowZhtfmz59PREQEDg4OBAQE8Nhjj13y/qt5/vnnadOmDfb29rRq1YpXX3211g/i6lsbS5YsITg4GBcXF+68804KCgoM+yxbtoyIiAjD5xg2bBhFRfpfBnfv3s3w4cPx9PTExcWFgQMHsm/fPsN7g4ODAZg4cSIqlcrwfXBwMCqV6pJHXYKDg/nwww9rbevcuTNvvPGG4XuVSsVXX33FxIkTsbe3JywsjN9//93wenR0NCqVig0bNtCtWzfs7e3p06cPcXFxtY67cOFCWrdujbW1NeHh4SxZsqTW6yqVioULF3LTTTfh4ODAW2+9ZbiO33zzDYGBgTg6OvLYY49RVVXFu+++i6+vL97e3rz11lu1jtXQP+OLb0Vd7hpWX2OAw4cPM3r0aBwdHfHx8eGee+4hMzPT8HpRURH33nsvjo6O+Pn5MW/evDrPbSwSbIxJY4MKcLqwYHp+eX6NkVEN+89CCNFwiqJQXFFs9ody4d+8ORUVFTFy5Ejc3NzYvXs3P//8M+vXr68VXBYuXMj06dN5+OGHOXToEL///juhoaGG19VqNQsWLODIkSN8++23bNy4keeee65BdTg5ObF48WKOHj3KRx99xJdffskHH3xQa5+EhARWrFjBypUrWblyJTExMbz99tsApKSkcNddd3H//fdz7NgxoqOjmTRpkuGaFhQUMGXKFLZs2cKOHTsICwtjzJgxhmC0e/duABYtWkRKSorh+927d5OSkkJKSgpnz56lV69e9O/fv4FX+VKzZ8/m9ttv5+DBg4wZM4bJkyeTnZ1da5+XX36ZefPmsWfPHjQaDffff7/hteXLl/Pkk0/y9NNPc/jwYR555BHuu+8+Nm3aVOsYb7zxBhMnTuTQoUOG9yckJLB69Wr++usvfvjhB77++mvGjh3L2bNniYmJ4Z133uGVV15h586dhuNc759x9TVMSUkhPj6e0NBQBgwYAEBubi5DhgwhKiqKPXv28Ndff5GWlsbtt99ueP+zzz5LTEwMv/32G2vXriU6OrpWMDWFRjUqqsnT6JdNcNYpZKtVF0ZGuQOQlCUtNkKYWkllCT2/72n28+68eyf2WnuznvP777+ntLSU//73vzg46H+B+s9//sP48eN555138PHx4c033+Tpp5/mySefNLyve/fuhq9rduAMDg7mzTffZNq0aXz66af1rqPmyNTg4GCeeeYZli5dWuuHp06nY/HixTg5OQFwzz33sGHDBt566y1SUlKorKxk0qRJBAUFAfqZ5asNGTKk1vm++OILXF1diYmJYdy4cXh5eQHg6upaa4Hk6u0ATz75ZK3Qcz2mTp3KXXfdBcCcOXNYsGABu3btYtSoUYZ93nrrLQYOHAjACy+8wNixYyktLcXW1pb333+fqVOn8thjjwEwa9YsduzYwfvvv8/gwYMNx7j77ru57777ap1bp9PxzTff4OTkRPv27Rk8eDBxcXH8+eefqNVqwsPDeeedd9i0aZNhAtvr/TOuvqaKonDLLbfg4uLC559/Duj/vkVFRTFnzhzD/t988w0BAQGcOHECf39/vv76a/73v/8xdOhQAL799ltatmxZr3NfK2mxMaYLwcZJVwVcuBXlpf8PJzm7mIoL60gJIcSVzJkzB0dHR8PjzJkzl+xz7NgxIiMjDaEGoG/fvuh0OuLi4khPT+f8+fOGHyiXs379eoYOHUqLFi1wcnLinnvuISsr66qLC9f0448/0rdvX3x9fXF0dOSVV165pN7g4GBDqAH9pKrp6ekAREZGMnToUCIiIrjtttv48ssvycnJMeyblpbGQw89RFhYGC4uLjg7O1NYWHjZa3I5X3zxBV9//TW///57rbBzrWrOm+bg4ICzs7Phs1xun+oJZKv3OXbsGH379q21f9++fTl27Fitbd26dbvk3BdfRx8fH9q3b49ara61rWY9xvgzBnjppZfYvn07v/32G3Z2dgAcOHCATZs21fq72rZtW0DfupSQkEB5ebkhZAG4u7sTHh7eoHM3lLTYGFN1sKmqArTkl+fj42SLrVZNaYWOszklhj43Qgjjs9PYsfPunVff0QTnNaZp06bVas739/dv8DGqf/jUJSkpiXHjxvHoo4/y1ltv4e7uzpYtW3jggQcoLy/H3v7qLVDbt29n8uTJzJ49m5EjR+Li4sLSpUsv6Ueh1Wprfa9SqdBdGGhhZWXFunXr2LZtG2vXruXjjz/m5ZdfZufOnYSEhDBlyhSysrL46KOPCAoKwsbGht69e1NeXn7V+jZt2sTjjz/ODz/8cNWJXNVq9SW3FC/XafdKn+Vy+1T367l4n6upGVivdO4r1WOMP2OA//3vf3zwwQdER0fTokULw/bCwkJDC+HF/Pz8rrjckSlJsDEmbc1gc2HIt1pFsIcDx1MLSMoskmAjhAmpVCqz3xIyBXd3d9zd3a+4T7t27Vi8eDFFRUWGH4Jbt2413JJwcnIiODiYDRs21LrFUW3v3r3odDrmzZtn+I3/p59+alCd27ZtIygoiJdfftmw7fTp0w06Buj/3Pr27Uvfvn157bXXCAoKYvny5cyaNYutW7fy6aefMmbMGACSk5NrdU4F/Q/8qgv/71aLj4/n1ltv5aWXXmLSpElXrcHLy4uUlBTD9/n5+SQmJjb4s1xNu3bt2Lp1K1OmTDFs27p1K+3btzf6uYzxZ7x9+3YefPBBPv/8c3r16lXrtS5duvDLL78QHByMRnNpnGjdujVarZadO3cSGBgIQE5ODidOnDDcqjMFuRVlTIZbUf9M0gfImlFCiFoKCwuJjY01rHeXmJhIbGxsvW+vAEyePBlbW1umTJnC4cOHDa0T99xzDz4+PoC+A+q8efNYsGABJ0+eZN++fXz88ccAhIaGUlFRwccff8ypU6dYsmQJn332WYM+R1hYGGfOnGHp0qUkJCSwYMECli9f3qBj7Ny5kzlz5rBnzx7OnDnDr7/+SkZGBu3atTOcY8mSJRw7doydO3cyefLkS1qjqgNcamoqOTk5lJSUMH78eKKionj44YdJTU01POoyZMgQlixZwt9//82hQ4eYMmUKVlZWDfos9fHss8+yePFiFi5cyMmTJ5k/fz6//vorzzzzjNHPdb1/xqmpqUycOJE777yTkSNHGq5hRkYGANOnTyc7O5u77rqL3bt3k5CQwJo1a7jvvvuoqqrC0dGRBx54gGeffZaNGzdy+PBhpk6dWuvWmSlIsDEmKxuAWssqALJmlBCilj179hAVFUVUVBSg70AaFRXFa6+9Vu9j2Nvbs2bNGrKzs+nevTu33norQ4cO5T//+Y9hnylTpvDhhx/y6aef0qFDB8aNG8fJkycBfd+W+fPn884779CxY0e+++475s6d26DPcdNNN/HUU08xY8YMOnfuzLZt23j11VcbdAxnZ2c2b97MmDFjaNOmDa+88grz5s1j9OjRgH5G+pycHLp06cI999zDE088gbe3d61jzJs3j3Xr1hEQEEBUVBRpaWkcP36cDRs24O/vj5+fn+FRlxdffJGBAwcybtw4xo4dy80330zr1q0b9Fnq4+abb+ajjz7i/fffp0OHDnz++ecsWrSIQYMGGf1c1/tnfPz4cdLS0vj2229rXcPqDuj+/v5s3bqVqqoqRowYQUREBDNnzsTV1dUQXt577z369+/P+PHjGTZsGP369aNr165G/6w1qRRLjFO0oPz8fFxcXMjLyzPNcgz/9uYrRxs+cnfl5tCb+Xfff/PT7mSe++Ug/cM8WfKA+UdsCNEclZaWkpiYSEhICLa2tpYuRwhhBFf6d13fn9/SYmNsGttLWmyqR0ZJi40QQghhWhJsjE1ra+hjY7gVdWGV73O5JZRWVNX5ViGEEEJcHwk2xnbRCt8Ano7WONpoUBT9fDZCCCGEMA0JNsamubTFRqVSycgoIYQQwgwk2BhbjT421S028M/IqKQsCTZCGNMNNv5BiGbNGP+eJdgYm8YWpwtLJxSWF6JT9F+HeOgnDZMWGyGMo3rG1YZODS+EaLyq/z1fPKNyQ8jMw8amtcXpQphRUCiqKMLJ2knmshHCyKysrHB1dTWsi2Nvb2+Yvl4I0bQoikJxcTHp6em4urpe1+SIEmyMTWOLjQI2Kg1lSiUF5QU4WTsZ+tgkZcpvl0IYS/XKwxcvQiiEaJouXqX9WkiwMbbqZRXU1pRVVZJfno8//oZgk5pfSnF5JfbWcumFuF4qlQo/Pz+8vb0vu2ChEKLp0Gq1RlnGQn66GluNYJNZVWwYGeVqb42rvZbc4gqSMotp72+CWY+FuEFZWVmZZF0fIUTTI52HjU2jXy/KSa3PjDVHRoXIyCghhBDCpCTYGJtWv+qss0rfo7u6xQYg5MIMxCfSCi59nxBCCCGumwQbY6tusblwaWsGm27B7gCsO5pm/rqEEEKIG4AEG2PTXGixuUywGdXRFyu1iiPn80nIKLRIeUIIIURzJsHG2KpbbC5Mnlgz2Lg7WNMv1BOAlQdSzF6aEEII0dxJsDG26lFRF4JNzc7DAOMj/QH44+B5mQpeCCGEMDIJNsamvRBsLrNeFMCIDj5YW6mJTy8kTjoRCyGEEEYlwcbYqltsqmqv8F3N2VbLwHAvAP44cN68tQkhhBDNnAQbY7sQbJx1VcClwQb+uR218mCK3I4SQgghjEiCjbFVB5uqSuDywWZYO2/stFaczirm0Lk8s5YnhBBCNGcSbIyt+lZUZTlw+WBjb61hSDtvQG5HCSGEEMYkwcbYqjsPV+iDTWFFIVUXbkvVNL6T/nbUqoMp6HRyO0oIIYQwBgk2xqapHWxAH24uNijcC0cbDefzStl3Jsds5QkhhBDNmQQbY7sQbLSVpdhdmIX44iHfALZaK0a09wHkdpQQQghhLBJsjO1CsKGyFCetE3D5fjbwz+ioVYdSqZLbUUIIIcR1k2BjbNp/go2zjTNQd7DpG+qJq72WzMIydp7KMleFQgghRLMlwcbYqltsqspx0joCdQcba42a0R19Af0SC0IIIYS4PhJsjO3CIpgATloHoO5gAzDuwuio1YdTqbgwW7EQQgghro0EG2O70GEYwEljD1y+83C1Xq088HS0Ibe4gi3xmSYvTwghhGjOJNgYm5UGVFYAOF24LXWlYGOlVjE24sLtKBkdJYQQQlwXCTamoNW32jhZ6YPNlW5FAYy7MDpq7ZE0SisuncxPCCGEEPUjwcYULvSzcVHrn68WbLoGuuHnYkthWSUxJzJMXp4QQgjRXEmwMYXq2YfV1sDVg41arWJcJz9AbkcJIYQQ16NRBZuFCxfSqVMnnJ2dcXZ2pnfv3qxevdrwemlpKdOnT8fDwwNHR0duueUW0tLSLFhxHQzBRgtcPdjAP5P1bTiWTnF5pelqE0IIIZqxRhVsWrZsydtvv83evXvZs2cPQ4YMYcKECRw5cgSAp556ij/++IOff/6ZmJgYzp8/z6RJkyxc9WVUB5sLnYiv1Hm4WkQLFwLd7SmpqGLDsXSTlieEEEI0V40q2IwfP54xY8YQFhZGmzZteOutt3B0dGTHjh3k5eXx9ddfM3/+fIYMGULXrl1ZtGgR27ZtY8eOHXUes6ysjPz8/FoPk6te4RsNUL9go1KpGB8pt6OEEEKI69Gogk1NVVVVLF26lKKiInr37s3evXupqKhg2LBhhn3atm1LYGAg27dvr/M4c+fOxcXFxfAICAgwffEXWmycUQH1uxUF/9yOio7LIL+0wjS1CSGEEM1Yows2hw4dwtHRERsbG6ZNm8by5ctp3749qampWFtb4+rqWmt/Hx8fUlNT6zzeiy++SF5enuGRnJxs4k+AYVSUk6IPNiWVJVTorh5Uwn2cCPV2pLxKx5rDdX8mIYQQQlxeows24eHhxMbGsnPnTh599FGmTJnC0aNHr/l4NjY2hs7I1Q+TuzD7sGONBbsLywuv+jaVSsXEqBYALNqahKLIit9CCCFEQzS6YGNtbU1oaChdu3Zl7ty5REZG8tFHH+Hr60t5eTm5ubm19k9LS8PX19cyxdblQouNpqoch3qsF1XT3T0Csbe24mhKPptPyhILQgghREM0umBzMZ1OR1lZGV27dkWr1bJhwwbDa3FxcZw5c4bevXtbsMLLuDDzMJWlOFk7AfUPNm4O1tzVIxCATzfFm6Q8IYQQornSWLqAml588UVGjx5NYGAgBQUFfP/990RHR7NmzRpcXFx44IEHmDVrFu7u7jg7O/P444/Tu3dvevXqZenSa6te4btCH2xSi1LJK8+r99sf7B/Cf7cnsTMxm72nc+ga5GaiQoUQQojmpVG12KSnp3PvvfcSHh7O0KFD2b17N2vWrGH48OEAfPDBB4wbN45bbrmFAQMG4Ovry6+//mrhqi9DU6PFRtuwFhsAPxc7Q1+bz2ISjF6eEEII0Vw1qhabr7/++oqv29ra8sknn/DJJ5+YqaJrVN1iU1mKs7W+s3JDgg3AwwNa8/Pes6w7msaJtALa+DgZu0ohhBCi2WlULTbNxoV5bK6lj021UG9HRrbXd4qWVhshhBCifiTYmIK2OtiU4WxzbS02AI8Oag3A77HnOZtTbLTyhBBCiOZKgo0pVLfYVJQYWmzqs6zCxSIDXOkb6kGlTuGrvxONWaEQQgjRLEmwMQXNPy0219J5uKZHB4YCsHT3GbIKy4xSnhBCCNFcSbAxBUOwub4WG4C+oR50aulCaYWOb7clGalAIYQQonmSYGMKhlFRZdc8KqqaSqXi0YH6vjaLtyVRWFZplBKFEEKI5kiCjSlcx8zDlzOigy+tPB3IL63kh51njFGhEEII0SxJsDGFGjMPX8+oqGpWahXTLrTafLXlFGWVVdddohBCCNEcSbAxBY1xW2wAJkT54+tsS1p+Gcv3nbveCoUQQohmSYKNKdSYebg62JRWlVJeVX7Nh7TRWPFg/xAAPt98iiqdct1lCiGEEM2NBBtTqDHzsKPWERUq4NpHRlW7q0cgLnZaEjOLWHMk9XqrFEIIIZodCTamUGPmYbVKjaPWEbj+21EONhqm9AkGYGF0AooirTZCCCFETRJsTKHGzMMoitH62QBM7ROMndaKQ+fy2BKfed3HE0IIIZoTCTamUB1sUKCqwqjBxt3Bmjt7BADw/toT0mojhBBC1CDBxhQMwQaoLDHKkO+aHhsUir21FQeSc1l9WPraCCGEENUk2JhC9agoqLVe1PV2Hq7m5WTDQ/1bAfDemjgqqnRGOa4QQgjR1EmwMQWVqtbIKGPeiqr20IBWeDpak5hZxNJdMhuxEEIIARJsTKfG7MPXuxDm5TjaaHhiaBgAH204KWtICSGEEEiwMZ0asw9f70KYdbmrRyDBHvZkFpbz1d+njHpsIYQQoimSYGMql5l92NjBRmul5tmRbQH4YvMpMgrKjHp8IYQQoqmRYGMqRl7huy5jInyJDHCluLyKBRtOGv34QgghRFMiwcZUDC02ZSa7FQWgUql4YZS+1eaHXWdIzCwy+jmEEEKIpkKCjanUmH3YFJ2Ha+rd2oPB4V5U6hTeXxNnknMIIYQQTYEEG1PR/LNelKmDDcDzo9uiUsGqQynEJuea7DxCCCFEYybBxlQMwaak1q0oUy2B0NbXmVu6tARg7p/HZKkFIYQQNyQJNqaivbTFpkJXQVmV6UYuzRreBmuNmp2J2WyKSzfZeYQQQojGSoKNqdToY2OvtUet0l9qU3Qgrubvasd9fYIBeGd1HFU6abURQghxY5FgYyo1RkWpVWoctY6AaYMN6BfIdLHTEpdWwK/7zpr0XEIIIURjI8HGVGrMPAwY+tmYsgMxgIu9lumDWwMwf90JSiuqTHo+IYQQojGRYGMqNWYeBkw6Sd/F7u0dTAtXO1LySvk8RpZaEEIIceOQYGMqWsu02ADYaq14blQ4AB9vPMnBs7kmP6cQQgjRGEiwMZUaq3uDeVtsAG6K9GdshB+VOoWZS2MpLpfVv4UQQjR/EmxM5aI+NuYONiqVircmdsTH2YZTmUXM+fOYWc4rhBBCWJIEG1OxYB+baq721sy7rTMA/9txho3H08x2biGEEMISJNiYimHmYfP3sampX5gnD/QLAeC5ZQfJLDTdBIFCCCGEpUmwMZUaMw+DZVpsqj07Mpy2vk5kFpbz/LKDstyCEEKIZkuCjanUmHkYLBtsbLVWfHhnZ6yt1Gw4ns73u86YvQYhhBDCHCTYmIqmdouNpW5FVWvr62wYAv7vlUdJyCi0SB1CCCGEKUmwMZWL+thYssWm2v19Q+gb6kFphY6nfoylokpnsVqEEEIIU5BgYyqNMNio1Srevy0SFzstB8/msWDDSYvVIoQQQpiCBBtT0dYdbCzZedfPxY45EyMA+GRTPHuSsi1WixBCCGFsEmxMxdB5uPZw70qlkpLKEktVBcDYTn5M6tICnQIzf4yloLTCovUIIYQQxiLBxlQuuhVlp7FDo9IAlr0dVW32TR1o6WbH2ZwSnlwaS3ml9LcRQgjR9DWqYDN37ly6d++Ok5MT3t7e3HzzzcTFxdXaZ9CgQahUqlqPadOmWajiK6gONroK0FWhUqkMt6MsNTKqJidbLQvuisJWq2bj8XSe+imWKp3MbyOEEKJpa1TBJiYmhunTp7Njxw7WrVtHRUUFI0aMoKioqNZ+Dz30ECkpKYbHu+++a6GKr6B6SQVoVB2Ia+oS6Mbn93RDa6Vi1cEUXvjlIDoJN0IIIZowjaULqOmvv/6q9f3ixYvx9vZm7969DBgwwLDd3t4eX19fc5fXMNUtNqCfy8baodEFG4CBbbz4+K4oHvtuHz/vPYuDjYbXx7dHpVJZujQhhBCiwRpVi83F8vLyAHB3d6+1/bvvvsPT05OOHTvy4osvUlxcXOcxysrKyM/Pr/UwCysNqC/kxotmH24Mt6JqGtXRj/dujQRg8bYk3l8bd5V3CCGEEI1To2qxqUmn0zFz5kz69u1Lx44dDdvvvvtugoKC8Pf35+DBgzz//PPExcXx66+/XvY4c+fOZfbs2eYquzaNHZQXNNpbUTXd0rUlxeWVvPrbET7ZlICDjYbHBoVauiwhhBCiQRptsJk+fTqHDx9my5YttbY//PDDhq8jIiLw8/Nj6NChJCQk0Lp160uO8+KLLzJr1izD9/n5+QQEBJiu8Jo0NheCTe1lFfLK8sxz/ga6p3cwReVVvL36OO/+FYejjYZ7ewdbuiwhhBCi3hoUbEJCQq6p78XMmTN54okn6r3/jBkzWLlyJZs3b6Zly5ZX3Ldnz54AxMfHXzbY2NjYYGNjc8l2s9Da6Z8vzFsT7BwMwPHs45appx6mDWxNUVklH2+M57XfjmBvreHWrlf+MxBCCCEaiwYFm8WLF1/TSYKDg+u1n6IoPP744yxfvpzo6GhCQkKu+p7Y2FgA/Pz8rqk2k6oeGXWhxSbKJwqA/en7URSl0XbQnTW8DYVllSzamsRzyw5gb23FmIhGeH2FEEKIizQo2AwcONBUdQD620/ff/89v/32G05OTqSmpgLg4uKCnZ0dCQkJfP/994wZMwYPDw8OHjzIU089xYABA+jUqZNJa7smhtmH9S027d3bY2NlQ05ZDkn5SYS4XD24WYJKpeK1ce0pLqvixz3JPLl0P1ZqFSM7NPKRaEIIIW54jWpU1MKFC8nLy2PQoEH4+fkZHj/++CMA1tbWrF+/nhEjRtC2bVuefvppbrnlFv744w8LV14Hw+zD+hYbrZWWCE/9Ok370/dbqqp6UalUzJkUwbhOflRUKUz7316+2ZJo0XWuhBBCiKtpVJ2Hr/ZDMyAggJiYGDNVYwSGYPPP2lBR3lHsSdvDvrR9TAqbZKHC6sdKreKDOzrjZKvlh11n+L+VR0nKKuK1ce3RWDWqTCyEEEIAjbTzcLOhrd1iA9DFpwscavwtNtW0VmrmTOxIiKc9c/48zn+3nyY5u5iP7+6Co02jysVCCCFE4+o83OxctBAmQKRXJCpUnCk4Q2ZJJp52nhYqrv5UKhUPD2hNgJs9M3+MZVNcBrd9tp1vpnbDz8XO0uUJIYQQBo2q83CzUz0qquKfYONk7UQbtzbE5cSxP30/w4OGW6i4hhsd4Yefqx0PfrubYyn53PzJVr6e0p2OLVwsXZoQQggBXGfn4YqKCpKTk4mLiyM7O9tYNTUfmup5bEprbY7y1g/73pe2z9wVXbfOAa4sf6wvYd6OpOWXcfvn21l/NM3SZQkhhBDANQSbgoICFi5cyMCBA3F2diY4OJh27drh5eVFUFAQDz30ELt37zZFrU2PYR6b2sGmi08XoOn0s7lYgLs9yx7tQ79QT4rLq3h4yR4WbU20dFlCCCFEw4LN/PnzCQ4OZtGiRQwbNowVK1YQGxvLiRMn2L59O6+//jqVlZWMGDGCUaNGcfLkSVPV3TRor9xiczz7OMUVdS/g2Zi52GlZdF937uwegE6B2X8c5aXlhyirrLJ0aUIIIW5gDepjs3v3bjZv3kyHDh0u+3qPHj24//77+eyzz1i0aBF///03YWFhRim0Sbpo5uFqvg6++Dv4c77oPAczD9LLr5cFirt+Wis1cydFEOzpwDt/Hef7nWc4ej6fhf/qIp2KhRBCWESDWmx++OGHOkNNTTY2NkybNo3777//mgtrFi6aebgmw/IKaU3zdlQ1lUrFtIGt+WZKd5xtNcQm5zL+4y1sT8iydGlCCCFuQDLLmilpLp3HploXb30/m33pTa8D8eUMbuvNH4/3o62vE5mF5fzr65189fcpmalYCCGEWRkt2Hz11VfGOlTzcZmZh6tV97M5kHGASl2lOasymSAPB5Y/1peJUS2o0im8ueoYj/+wn+Ly5vH5hBBCNH5GCzYrV65k48aNhu+Li4u58847jXX4pukyMw9Xa+3aGidrJ0oqS4jLiTNzYaZjZ23F/NsjmX1TBzRqFSsPpjDxk20kZhZZujQhhBA3AKMFm//+97+89tprHD9+nBMnTjBgwABGjhxprMM3TZeZebiaWqU2tNo09X42F1OpVEzpE8wPD/fCy8mGuLQCbvrPFpnvRgghhMldd7B56qmn+Pbbbzl16hRfffUVkydP5o477uCrr77ivvvuM0aNTZeh8/ClwQZqTNTXTPrZXKx7sDurHu9HtyA3CkorefC/e3hz5VFKymVIuBBCCNO47mAzePBgkpOTmTNnDrfddhuJiYn4+/uzZs0aVq1aZYwam64rtNjAP8Fmf/r+ZtvJ1tvZlu8f6sXUPsEAfLUlkVEfbZZRU0IIIUziupdnvummm7jpppsM35eWlnL48GEOHjzI+vXrGTt27PWeoumqY+bhah09O6JVa8ksyeRswVkCnAPMWJz5WGvUvHFTBwa28eKl5Yc4nVXMXV/u4K4egbw4pi3OtlpLlyiEEKKZaHCwcXJyIioqiq5du9KlSxe6dOlC+/btUalUANja2tKtWze6detm9GKbnDpmHq5mY2VDB48OxGbEsi99X7MNNtUGt/Vm7VMDeHv1cb7beYYfdp1h0/F05kzqyJC2PpYuTwghRDPQ4FtR77zzDmFhYWzcuJH777+fTp064eTkRJ8+fXj88cdZtGgRBw4cMEWtTU8dMw/XZJior4muG9VQTrZa3poYwdKHexHsYU9qfin3L97DzKX7yS4qt3R5QgghmjiVch2dO0pKSnBwcOD5558nOzubffv2cfjwYcrLy6mqapwdRPPz83FxcSEvLw9nZ2fTnizjBHzSHWxd4YXTl90lOjmaxzc+TrBzMH9M/MO09TQyJeVVfLD+BF/9fQqdAh4O1rxxUwfGdfIztAAKIYQQUP+f39fVx8bOTn+r5a677qJTp04AVFZWcvTo0es5bPNRjxabzl6dAUjKTyK7NBt3W3czFNY42Flb8dKYdoyN8OO5ZQeJSyvg8R/28/3OMzw9og3dgm+cayGEEMI4jL6kgkajMYScG17NmYfraBhztXWltUtr4Ma5HXWxyABX/ni8H08Na4O1lZrtp7K49bPt3PvNLmKTcy1dnhBCiCZE1ooypeqZhwGq6u4/0lwWxLwe1ho1Tw4LY9Ozg7irRyAatYrNJzK4+ZOtPLB4N4fP5Vm6RCGEEE1Ag4PNgw8+yMKFC9m9ezdlZfpbLNIfog6aGsHmMit8V6teEPNGbbGpqYWrHXMnRbDx6UHc2rUlahVsOJ7OuI+38MiSPRxPzbd0iUIIIRqxBvexOXnyJD///DMFBQVoNPq3z549m0GDBtGlSxc6d+6Mvb290QttkqysARWgXHlk1IWJ+o5mHaWksgQ7jZ156mvEAj3sef+2SB4b1JoFG07y24HzrDmSxtqjaYyN8GPGkFDa+pq487cQQogm55pHRZ08eZK9e/eyb98+wyM3NxcrKyvatGnDkSNHjF2rUZh1VBTAm776PjZPHgS3oMvuoigKw34eRnpJOt+M/Ibuvt1NX1cTczKtgA/Xn2TVoRTDtv5hnjzQL4SBbbyk1VAIIZo5k4+KCgsLIywsrNYK3omJiezZs4f9++WWioHGRh9s6pikD/S38qJ8oliTtIZ9afsk2FxGmI8Tn0zuwoyUfP6zKZ7Vh1L4+2Qmf5/MJMzbkfv7hTAxqgW2WitLlyqEEMKCGtTH5syZM1d8PSQkhNtuu405c+YAcO7cuWuvrLm4yuzD1WquGyXq1s7PmU/u7kLMs4N5sF8IjjYaTqYX8uKvh+jz9kbmrztBRkHdt/2EEEI0bw0KNt27d+eRRx5h9+7dde6Tl5fHl19+SceOHfnll1+uu8Amr3oumzpW+K5W3YE4NiOWKl3jnNywMQlwt+eVce3Z/uIQXhnbjhaudmQXlbNgw0n6vr2R55YdkI7GQghxA2rQraijR4/y1ltvMXz4cGxtbenatSv+/v7Y2tqSk5PD0aNHOXLkCF26dOHdd99lzJgxpqq76dDUr8UmzC0MB60DRRVFnMw9SVv3tmYorulzstXyYP9WTO0TzJojaXz59ylik3P5ac9Zftpzlt6tPJjaN5hh7XywUks/HCGEaO6uqfNwSUkJq1atYsuWLZw+fZqSkhI8PT2Jiopi5MiRdOzY0RS1GoXZOw9/PhBSYuHun6HNiCvu+si6R9h2fhsv9niRu9vdbframqm9p3P4essp1hxJo0qn/+vd0s2OKb2Dub1bAC72spq4EEI0NSbtPGxnZ8eoUaO49dZbr7nAG4ahj03d89hUi/KOYtv5bexP3y/B5jp0DXKja1BXzuWW8L8dp/lh1xnO5pTw1p/HmL/uBJO6tGBqn2DCfJwsXaoQQggju+aZh11cXKQPTX3UY72oatX9bPal7eM61iYVF7RwteP5UW3Z8eJQ3rklgra+TpRUVPHdzjMM/2Az93y9k7VHUqms0lm6VCGEEEZyzcO9FUXh888/Z/78+ahUKrp168bkyZPp3l2GKtdSPfvwFWYertbRsyMalYb0knTOF52nhWMLExd3Y7DVWnFH90Bu7xbAjlPZLN6WyLqjaYbh4v4uttzVI5A7egTg7WR79QMKIYRotK5rraj9+/fTpUsX+vXrx5EjR+jfvz/PPPOMsWprHgwLYV69xcZea087j3aAvtVGGJdKpaJ3aw8+v6cbMc8O5pGBrXCz13I+r5R5607QZ+5Gpn+/j+0JWdJiJoQQTdQ1t9gAfP/99wwfPtzw/cGDB5kwYQItWrTgqaeeuu7imgVDsLnyqKhq3Xy6cSjzENHJ0YxvPd50dd3gAtzteXF0O54a1obVh1P4344z7D2dw6qDKaw6mEKotyOTewYyqUtLXOyks7EQQjQV19xi4+7uTkBAQK1tnTp14j//+Q8LFy687sKaDW3Dgs3okNEAbEreRF6ZrGhtarZaKyZGteSXR/vw5xP9ubtnIPbWVsSnFzL7j6P0mrOB55cd5NBZ+bMQQoim4JqDTefOnVm0aNEl20NDQ686Q/ENpYEtNu082hHuFk6FroJVp1aZsDBxsfb+zsyZGMHOl4by7wkdaOPjSElFFT/uSWb8f7Zw03+28OPuMxSXV1q6VCGEEHW45mDz5ptvsmDBAu655x62b99OUVER6enpzJkzh5CQEGPW2LTVc+bhmm4OvRmAFfErjF+PuConWy339A5mzcwB/PRIbyZ09sfaSs3Bs3k8/8shes7ZwBu/H+FEWoGlSxVCCHGRaw42vXr1YseOHSQnJ9O/f3+cnZ3x8/Nj2bJlzJs3z5g1Nm31nHm4prGtxqJRaziWfYy47DgTFSauRqVS0SPEnY/ujGL7i0N4cXRbgjzsKSitZPG2JEZ8sJnbP9vOb7HnKKuUZTCEEKIxuK7Ow5GRkURHR5Oens7evXvR6XT07NkTT09PY9XX9DVgHptqbrZuDGo5iPVn1rMifgXP93jeRMWJ+vJwtOGRga15qH8rtiZk8t2OM6w7lsaupGx2JWXjaq9lYlQL7uweSLivTPwnhBCWck1LKjRlZl9SYcdC+OsF6HgL3PpNvd8WkxzDjI0zcLNxY8NtG9BaycicxiYtv5Slu5JZuvsMKXn/tMh1DnDlzu4BjIv0x9Hmun53EEIIcUF9f35f1zw2oh6uocUGoG+LvnjaeZJTlsPms5tNUJi4Xj7Otjw5LIwtzw9h0X3dGdXBF41aRWxyLi/8eogeb63nuWUH2Hs6R+bFEUIIM2nQr5MhISGoVA1fIXnmzJk88cQTDX5fs9CAmYdrvU2tYXyr8Sw6sogV8SsYGjTUBMUJY7BSqxgc7s3gcG8yCspYvv8sS3cncyqjyLDKeJi3I3d0D2BiVAs8HG0sXbIQQjRbDboVFRMTc00nCQ4OJigo6Jrea2xmvxV1+FdYdh8E9YP7GjZ8+1TuKSb8NgErlRXrb1uPp530XWoqFEVhz+kclu5KZtWh85RW6Nej0lqpGN7eh9u7BdA/zAsrdcN/URBCiBuRSVb3Hjhw4HUXdiVz587l119/5fjx49jZ2dGnTx/eeecdwsPDDfuUlpby9NNPs3TpUsrKyhg5ciSffvopPj4+Jq3tmjVwHpuaWrm2opNnJw5mHmRlwkqmdpxq3NqEyahUKroHu9M92J3Xb2rP77Hn+WlPMgfP5vHnoVT+PJSKv4stt3ZtyW3dAghwt7d0yUII0Sw0qj42MTExTJ8+nR07drBu3ToqKioYMWIERUVFhn2eeuop/vjjD37++WdiYmI4f/48kyZNsmDVV9HAmYcvNiF0AqCf00b6aTRNzrZa/tUriN9n9GP1k/2Z2icYFzv9GlULNsbT/91NTP5qB78fOE9phQwbF0KI69GgW1Hm7mOTkZGBt7c3MTExDBgwgLy8PLy8vPj++++59dZbATh+/Djt2rVj+/bt9OrV66rHNPutqNPbYNFo8AiFx/c2+O0F5QUM/mkwZVVlfD/meyK8IkxQpDC30ooq1h5N46fdyWyJzzRsd7bVMD7Sn1u6tiQqwPWa/r0JIURzZJJbUYsXL76mYoKDg6/pfXl5+vV53N3dAdi7dy8VFRUMGzbMsE/btm0JDAysM9iUlZVRVvbPiKT8/PxrquWaGToPX1uLjZO1E8OChrHq1CpWxK+QYNNM2GqtuCnSn5si/UnOLubnPcks23uW83mlfLfzDN/tPEMrLwdu6dKSiVEt8He1s3TJQgjRJDSqPjY16XQ6Zs6cSd++fenYsSMAqampWFtb4+rqWmtfHx8fUlNTL3ucuXPnMnv2bFOXW7fr6GNT7ebQm1l1ahWrE1fzbPdnsa0+pmgWAtztmTUinCeHtWF7Qha/7DvL6sMpnMoo4r01cby/No6+rT25pWsLRnbwxd5a5sYRQoi6NKo+NjVNnz6dw4cPs3Tp0us6zosvvkheXp7hkZycbKQK68kwj821B5sevj3wd/CnoKKAjWc2Gqkw0dhYqVX0C/Pkgzs6s+eV4bx7ayd6hrijKLAlPpOnfjxA9zfX8+zPB9hxKgudTvpcCSHExRrlr34zZsxg5cqVbN68mZYtWxq2+/r6Ul5eTm5ubq1Wm7S0NHx9fS97LBsbG2xsLDhviLbha0VdTK1Sc1PoTXx24DNWxK9gTKsxRipONFaONhpu7xbA7d0CSM4u5pd9Z/l13znOZBfz896z/Lz3LC3d7JjUpSW3dGlBkIeDpUsWQohGoVG12CiKwowZM1i+fDkbN268ZJXwrl27otVq2bBhg2FbXFwcZ86coXfv3uYut36qbxvpKqGq8poPM6G1fnTUjpQdpBSmGKMy0UQEuNszc1gbYp4dxE+P9OaObgE42mg4m1PCgg0nGfheNLd9to0fdp0hv7TC0uUKIYRFNaq1oh577DG+//57fvvtt1pz17i4uGBnp2/5ePTRR/nzzz9ZvHgxzs7OPP744wBs27atXucw+6io8mKY46f/+sVzYON4zYe6f8397E7dzYzOM3gk8hEjFSiaopLyKtYeTWXZ3rNsic+k+l+xjUbNiA6+TOrSgv6hnmisGtXvLkIIcc3q+/O7UQWbuoa2Llq0iKlTpwL/TND3ww8/1Jqgr65bURcze7DRVcH/6Ud18ewpcPC45kP9nvA7L295mZaOLVk1aRVqlfzQEpCaV8ry/ef4Zd9Z4tMLDds9HKwZH+nPhM7+dJah40KIJq5JBhtzMHuwAfg/T9BVwFNHwKXl1fevQ3FFMUN+HkJRRRHfjPyG7r7djVikaOoUReHg2Tx+2XeWlQdTyC4qN7wW7GHPhM4tuDmqBSGe0h9HCNH0yOrejYmhA3HDVvi+mL3WnpHBIwH9TMRC1KRSqYgMcOX/JnRk50tDWTS1OxM6+2OntSIpq5iPNpxk8PvRTPhkK4u2JpJZeH1/H4UQojGSFhtzeC8UijLg0W3g0+G6DrU/fT/3rr4XO40dm27fhINWfvsWV1ZUVsnao6ms2H+eLfGZVF0YJm6lVtEv1JObo/wZ0d4XB5tGOUhSCCEAE808LK6R5kKLzTXOPlxTZ6/OBDsHk5SfxJqkNUwKa8TrZIlGwcFGw8SolkyMaklGQRkrD55nRex5DiTnEnMig5gTGdhpDzOigw83d25BvzBPtNLpWAjRRMn/XuZghEn6qqlUKiaGTQTgf8f+JwtjigbxcrLhvr4h/Da9L5ueGcSTQ8MI9rCnpKKK32LPc9/i3fScs4HXfjvMvjM58vdLCNHkyK0oc1jYD9IOwb9+gdBhV9//KvLK8hi+bDgllSV8Puxz+rToY4QixY1KURQOnM1jxf5z/HHgPFk1Oh0HuNsxvpM/4yP9aevrJCOrhBAWI52HGxNt9XpRxums6WLjwi1htwCw6MgioxxT3LhUKhWdA1x546YO7HxpKIvv687EqBbYW1uRnF3Cp9EJjP7ob0Z8sJkFG06SmFlk6ZKFEKJO0sfGHIywEObF/tX+X/xw/Ad2pOzgePZx2rq3NdqxxY1LY6VmULg3g8K9KS6vZMOxdP44cJ7ouAxOphcyf90J5q87QUQLF8ZH+jGuk7+sPC6EaFQk2JhDdbAxQufhai0cWzAiaASrk1az+Mhi3u7/ttGOLQSAvbWG8ZH621D5pRWsPZLG7wfOszU+k0Pn8jh0Lo85fx6ne7Ab4yP9Gd3RDy8nC67LJoQQSB8b85x06WQ4vhLGzofuDxjtsEeyjnDnyjuxUlnx1y1/4etQv9mXhbgeWYVlrD6cyh8HzrMrKduwnINaBX1ae3JTpD8jO/jiYq+1bKFCiGZF+tg0JkZY4ftyOnh0oIdvD6qUKpYcXWLUYwtRFw9HG/7VK4gfH+nN9heG8srYdkQGuKJTYEt8Js/9cpBub63jwW9381vsOYrKrn3xVyGEaCi5FWUORhzufbGpHaayK3UXy04s45HIR3C2NlMrlBCAr4stD/ZvxYP9W3Emq5g/Dp7njwPnOZ5awPpj6aw/lo6tVs3Qtj6M7eTH4HBv7KytLF22EKIZk2BjDhrjjoqqqV+LfoS6hhKfG8+yE8u4v+P9Rj+HEPUR6GHP9MGhTB8cyom0AlYeOM/vB86TlFXMqkMprDqUgp3WiiHtvBkX4ccgCTlCCBOQW1HmYOg8XGL0Q6tUKqZ0mALAd0e/o6KqwujnEKKh2vg4MWtEOJueGcQfM/oxbWBrAtztKKmoYtXBFB79bh9d31zH4z/s56/DqZRWVFm6ZCFEMyEtNuZgwhYbgLEhY/l438ekl6TzZ+KfTAidYJLzCNFQKpWKiJYuRLR04flR4Rw6l8eqgymsPJjCudwS/jigv3XlYG3F0Hb621UD23hhq5WWHCHEtZFgYw6GCfqM32IDoLXScne7u/lw34csPrKYm1rfJDPEikZHpVLRqaUrnVq68sLothw4m8efh1JYdSHk/H7h1pWjjYah7bwZG+HHAAk5QogGkmBjDiZusQG4Lfw2vjj4BfG58Ww9v5V+LfqZ7FxCXK/q2Y47B7jy4ui2xCbnGkLO+bxSfos9z2+x+pAzrJ03Yzv50z/MU0KOEOKqJNiYgwlmHr6Ys7Uzt7S5hSVHl7D4yGIJNqLJUKlURAW6ERXoxouj2xF7NpdVB1P481AKKXmlrIjVr0buZKNhWHsfRnf0lZYcIUSdJNiYgwlmHr6ce9rdw/fHvmdnyk6OZh2lvUd7k55PCGNTq1V0CXSjS6AbL49px/5kfchZfVgfcpbvP8fy/edwsLZiSDsfxkb4MrCNjK4SQvxDgo05mKHFBsDP0Y+RwSP5M/FPvj3yLe8MeMek5xPClNRqFV2D3Oga5MYrY9uxPzmHPw+lsvqQ/nZVdcdjO60VQ9p6MzrCl8Hh3jjYyH9rQtzIZEkFczj2B/z4LwjoCQ+sNe2pso5x+8rbsVJZ8eekP/F39Dfp+YQwN0VROHA2j9UX5sY5m/NPp3wbjZpB4V6M7ujHkHbeONvKsg5CNBf1/fktv9qYg5labADaebSjp19Pdqbs5H/H/sdz3Z8z+TmFMKeaHY9fGN2Ww+fy+fOwvk/O6axi1hxJY82RNKyt1PQL82RUR1+Gt/PBzcHa0qULIcxAWmzMIXEzfDsePMNhxi6Tn27rua1MWz8Ne409625bJ8ssiBuCoigcTcnnr8OprD6cSnx6oeE1K7WK3q08GNXRl5EdfGUVciGaoPr+/JZgYw7Ju+HrYeAaBDMPmvx0iqJwyx+3cDLnJNM7T2da5DSTn1OIxiY+vYDVh1L583Aqx1LyDdtVKuge5M6ojr6M6uiLv6udBasUQtSXBJs6WCTYpByEz/uDoy88E2eWU65OXM1zm5/DTmPH7zf/jq+Dr1nOK0RjlJRZxF9H9B2PD5zNq/VaZIArozv6MrqjL0EeDhaqUAhxNRJs6mCRYJN5Ev7TDWxd4IUzZjmloijcu/peYjNiGRMyRkZICXHBudwS/jqcyl+HU9hzOoea/wO283M2hJwwHyfLFSmEuIQEmzpYJNjknoEPI/SdiF9JM885gaNZR7lz5Z0oKHw76lu6+HQx27mFaArS80tZczSNvw6nsONUNlW6f/47bO3lwIgO+j45nVq4oFbLMiVCWJIEmzpYJNgUZsD7ofqvX8/V3+Q3k9nbZ7PsxDLaurdl6dilWKllIjMhLie7qJz1R9NYfTiFLfGZVFT981+jr7Mtw9v7MKKDD71aeaC1UluwUiFuTBJs6mCRYFOaD28H6L9+Oe2fRTHNIKc0h7HLx1JQXsCrvV7l9vDbzXZuIZqq/NIKouMyWHMklejj6RSVVxlec7LVMLStNyM6+DKwjZdMCCiEmUiwqYNFgk1lObzppf/6+dNg52qe817w/bHvmbtrLq42rqycuBIXGxeznl+Ipqyssopt8VmsPZrKuqNpZBaWG16z1qgZEObFqI6+DGvnjau9zJUjhKlIsKmDRYKNosD/uYOig6fjwMm8I5QqdZXc9sdtxOfGc2f4nbzc62Wznl+I5qJKp7D/TA5rj6ax5kgqp7OKDa9p1Cp6t/ZgZAdfRnTwwdvJfC2zQtwIJNjUwSLBBuAtP6gohicPgFuw+c57wa6UXTyw9gHUKjU/jfuJcPdws9cgRHOiKApxaQUXRlilcjy1wPCaSgVdA90MEwIGuNtbsFIhmgcJNnWwWLB5JwRKsuGxneDd1nznreHp6KdZe3ot3Xy68c3Ib1CZsROzEM1d9Vw5fx1OJTY5t9ZrYd6ODG3nw9B23nQJdMNKRlgJ0WASbOpgsWAzrx0UnIeHY8C/s/nOW0NKYQo3rbiJ0qpS3hv4HqOCR1mkDiGau5S8EtYeSeOvw6nsSqo9jNzNXsugcG+GtvNmQBsvWahTiHqSYFMHiwWbjzpDTiLcvxYCe5rvvBf57MBnfBL7Cb4Ovvw24TfstdJELoQp5ZVUEHMigw3H0oiOyyCvpMLwmkatokeIO8Pa6YeSt3STf49C1EWCTR0sFmw+7Q3pR+He36DVIPOd9yKllaXc/NvNnCs8xyOdHmFG1AyL1SLEjaaySsfe0zlsPJ7O+mNpJGQU1Xq9g78zIzvo17AK83aU28VC1CDBpg4WCzZfDILz++Hun6DNSPOd9zI2nN7AzOiZWKutWXHzCgKcAixajxA3qqTMIjYcT2ftkVR2J2VT444VIZ4OjOjgw8gOvnRu6SozH4sbngSbOlgs2HwzGs5sg9u+hQ43m++8l6EoCg+ve5gdKTsYEjCEj4Z8ZNF6hBCQVVjGhmPprDmSyt/xmZRX6gyveTvZMKy9DwPCvOgb6oGT9MsRNyAJNnWwWLD5781wahNM/AIi7zDfeeuQkJvALb/fQpVSxefDPqdPiz6WLkkIcUFhWSUxcRn8dSSVTcfTKSyrNLymUavoEujGwHAvBoR50cHfWVpzxA1Bgk0dLBZsvr8TTqyG8Qug6xTznfcK3tn1Dv879j9aOLbg15t+lY7EQjRCZZVVbEvIIiYug80nMjiVWbtfjoeDNf3DPBnQxosBbbzwdLSxUKVCmFZ9f37LIifmUr0+VGWpZeuoYUbUDDae2ci5wnN8uO9DXur5kqVLEkJcxEZjxeBwbwaHewNwJquYmJP6kLMtPpOsonJWxJ5nRex5VCroFuTGiPb62Y+DPBwsXL0Q5ictNuayfBoc+AGG/x/0fdJ8572K7ee38/C6hwH4ZuQ3dPftbuGKhBD1VV6pY9+ZHGJOZBATl8HRlPxar4f7ODGigw8j2vvSsYWzjLISTZrciqqDxYLNHzNh7yIY9BIMet58562H2dtns+zEMgKcAlg2fpnckhKiiTqXW8L6o2msPZrKjlO1Jwb0d7FlRAdfhrbzpluQO3bWVhasVIiGk1tRjY2m8d2KqvZ016fZcm4LyQXJfLz/Y57v0biClxCiflq42jGlTzBT+gSTW1zOprh01h7RTwx4Pq+UxduSWLwtCWsrNVGBrvQN9aRPaw8iA1zRWqktXb4QRiEtNuay/g3Y8gH0mg6j5pjvvPW09dxWpq2fhgoVi0ctpotPF0uXJIQwktKKKrbGZ+qHkp/MJCWv9i9Y9tZW9Ahxp09rD/q09qS9n4y0Eo1PfX9+N6qIvnnzZsaPH4+/vz8qlYoVK1bUen3q1KmoVKpaj1Gjmsh6R4YWmxLL1lGHvi36MjF0IgoKr217jZJGWqcQouFstVYMbefDu7dGsu2FIWx6ZhBv3tyRsRF+uNlrKS6vIjougzl/Hmfcx1vo/tZ6nlt2gA3H0iitqLJ0+UI0SKO6FVVUVERkZCT3338/kyZNuuw+o0aNYtGiRYbvbWyayNBGzYU6K8ssW8cVPNP9Gbae38rp/NP8Z/9/eLb7s5YuSQhhZCqVihBPB0I8HfhXryB0OoXjqQVsS8hkW0IWO09lkVVUzk97zvLTnrPYW1sxsI0XIzr4MCTcBxd7mRxQNG6NKtiMHj2a0aNHX3EfGxsbfH19633MsrIyysr+CRP5+flX2NuENHb654rG2xLibO3M671fZ/qG6Sw5uoThQcPp7N3Z0mUJIUxIrVbR3t+Z9v7OPNi/FRVVOnYnZrP2aBprj6RyPq+U1YdTWX04FSu1il6t3BnRXt8JWRbtFI1Ro7oVVR/R0dF4e3sTHh7Oo48+SlZW1hX3nzt3Li4uLoZHQICF1kVqAi02AANaDuCm1jehoPDq1lcpbYSdnYUQpqO1UtMn1JM3burA1heG8MeMfjw+JJRwHyeqdApb47N4/fcj9HtnE/3e2cjTPx3g5z3JJGcXc4N12RSNVKPtPKxSqVi+fDk333yzYdvSpUuxt7cnJCSEhIQEXnrpJRwdHdm+fTtWVpcfuni5FpuAgADzdx4+sBSWPwKth8A9y8133muQV5bHxN8mklGSwX0d7mNWt1mWLkkI0QgkZRax7mgaa46ksj85t9ZwctAPKe/VyoOerdzpGeJBkIe9zJ0jjKbJz2NzuWBzsVOnTtG6dWvWr1/P0KFD63Vci42KOrIcfp4KQX3hvj/Nd95rFJ0czeMbH0etUrNk9BI6eXWydElCiEaksKySvadz2HFK3y/n4Nk8Ki8KOi1c7Rje3odRHX3pHuyOlYy0EtfhhpjHplWrVnh6ehIfH1/vYGMxjXgem8sZFDCIsa3GsurUKl7d+io/jf8JG6sm0lFbCGFyjjYaBrbxYmAbLwCKy/VBZ+epbHYmZhGbnMu53BLD3DkeDtYMb+/DyA6+9An1wEYjEwQK02jSwebs2bNkZWXh5+dn6VKurjrYVDSNYAPwQvcX2HF+B6fyTvHe7vd4uefL0qwshLgse2sN/cO86B+mDzol5VVsic/kr8OprD+WRlZROUt3J7N0dzKONhqGtPVmZAdfBoV74WDTpH8UiUamUf1tKiwsJD4+3vB9YmIisbGxuLu74+7uzuzZs7nlllvw9fUlISGB5557jtDQUEaOHGnBquupibXYALjauvJGnzd4YuMT/Bj3I/6O/tzf8X5LlyWEaALsrK0Y3t6H4e19qKjSsfNUNmuOpLLmSCrpBWX8fuA8vx84j0atIjLAlZ4h7vRq5UHXIDcJOuK6NKo+NtHR0QwePPiS7VOmTGHhwoXcfPPN7N+/n9zcXPz9/RkxYgT//ve/8fHxqfc5LNbH5vx++GIQOLeAWUfNd14j+O+R//LenvcAeLv/24xtNdbCFQkhmiqdTmF/ci5rj6Ty15FUTmcV13pdo1YR0dKFniEe9GrlTrdgdxwl6AiaQedhU7FYsEk/Bp/2AnsPeO6U+c5rJO/ufpclR5egUWtYOGwhvfx6WbokIUQTpygKydkl7EjMutAJOZtzubXn+rJSq4hs6cLojn6M6uhLgLvMnXOjkmBTB4sFm+xTsCAKrB3hpXPmO6+R6BQdz21+jjVJa3DUOrJ41GLC3cMtXZYQoplJzi5mZ2K2PugkZpGcXTvoRLZ0YUyEH2Mi/CTk3GAk2NTBYsEmPwXmtwWVFbyebb7zGlFZVRmPrHuEvWl78bbz5rux3+HrUP9ZoIUQoqHO5Zaw4Vgafx5KYVdiNjVHlEe0cGF0hC9jI/wI8nCwXJHCLCTY1MFiwaY4G94N0X/9ahZYNc17xnlleUz9ayrxufGEuoby7ehvcbY243UUQtywMgrKWHMklT8PpbDjVFatkNPW14l+oZ70CfWge7A7TrayplVzI8GmDhYLNhUl8NaF1o0Xz4KNk/nObWSpRalMXjWZ9JJ0uvl04/Phn2NtZW3psoQQN5DMwjLWHtG35Gw/lVVrFmQrtYpOLV3o09qDPq096Rrkhq1W5s1p6iTY1MFiwUang/9z03/9bAI4eJrv3CYQlx3H1L+mUlhRyMjgkbw74F3Uqia39JgQohnILipnS3wm2y+sUH7xSCtrKzVdglzpH+bF6I6+tPJytFCl4npIsKmDxYINwL+9oaoMnjoCLi3Ne24T2Jmyk2nrp1Gpq+Te9vfybPdnLV2SEEJwNqeY7QlZbE/IYltCFqn5tecPa+/nzLhIP8ZF+BPoIR2QmwoJNnWwaLCZGwhleTBjL3iGmvfcJrLq1Cpe+PsFAB6MeJAZnWdgpZYmXyFE46AoComZRWxNyGL90TS2xmfWWtOqU0sXxnXSj7Jq6SYhpzGTYFMHiwab98KgKB2mbQXfjuY9twktOryI+XvnA9DbrzdvD3gbd1t3C1clhBCXyikqZ82RVFYeTGFbQmatDshRga6M6ejHgDZetPFxlCVkGhkJNnWwaLD5MAJyz8CDG6BlN/Oe28T+SPiDf+/4NyWVJfjY+/D+wPfp7N3Z0mUJIUSdMgvL+OtwKisPnmdnYjY1fxp6O9nQL9STvqGe9AvzxMfZ1nKFCkCCTZ0sGmz+0x0yT8DUVRDcz7znNoOTOSeZFT2LpPwkNCoNz3R/hrvb3i2/9QghGr30glJWH0plw/F0diVmUVqhq/V6mLcj/cI86R/mSc8QD1nPygIk2NTBosHms/6QehD+9QuEDjPvuc2kqKKI17a+xtrTawEYFTyKN/q8gYNWJs8SQjQNpRVV7DuTw5aTmWyJz+TQubxarTnWGjUD23gxrpMfQ9v5yFpWZiLBpg4WDTZfDYezu+CO76DdOPOe24wUReG7Y98xb888KpVKQlxC+GDQB7R2bW3p0oQQosFyisrZfiqLv09msiU+o9YyD9YaNYPaeDFWQo7JSbCpg0WDzeJxkPQ33PI1RNxq3nNbQGx6LE/HPE16cTp2Gjve6P0GY1qNsXRZQghxzRRFIS6tgD8PprDyYAqnMosMr9lo1AwO92ZsJz+GtPWW21VGJsGmDhYNNt/dBifXwoRPIOpf5j23hWSVZPH838+zM2UnAKODRzOz60z8Hf0tXJkQQlwfRVE4nlrAqoMprDx4nqQaEwNaa9R0D3ajb6gn/UO96ODvjFot/Q2vhwSbOlg02Pz4Lzj2B4ydB90fNO+5LahKV8WnBz7ly4NfoqBgrbbmX+3/xYMRD+Jk3XSXlhBCiGqKonA0JZ9VB1NYdSjlktmP3ey19An1pN+Fh6xM3nASbOpg0WDzy0Nw6CcYOQd6TzfvuRuBY1nHmLdnHjtT9a03bjZuPNb5MW5tcysatTTZCiGaB0VRSMgoYsvJDLbEZ7HjVBaFZZW19gn2sKd/mBfD2/vQq5UH1hpZkuZqJNjUwaLB5rcZsH8JDHkVBjxj3nM3EoqiEHM2hnl75pGUnwRAiEsIT3d9mgEtB8jQcCFEs1NRpeNAci5/n8xka3wm+5Nzay3a6WSrYVg7H0Z28GFAGy/sreUXvcuRYFMHiwabv16EHZ9Cr8dg1FzznruRqdBVsOzEMhbGLiSnLAeAnr49eab7M7R1b2vh6oQQwnQKSivYcSqbjcfTWXc0jczCMsNrtlo1/cO8GNXBl6HtvHG1t7ZgpY2LBJs6WDTYxP4AK6ZBQE94YK15z91IFZQX8OWhL/nu6HeU68pRoWJwwGDuancXPX17SguOEKJZq9Ip7DuTw5rDqaw5mlprKLmVWkWPYHf6hXnSq5UHnVq6oLW6cW9ZSbCpg0WDTVYCfNwFrKzhxbOgsTHv+Ruxc4Xn+GjfR6xOXG3YFuISwh3hdzCh9QQcrR0tWJ0QQpieoigcSylgzZFU1hxJ5XhqQa3XHayt6B7iTp/WHvRu5Ul7f2esbqCRVhJs6mDRYKMo8F4oFGfCA+sgoId5z98EJOQmsPT4Un5P+J3iSv2oAjuNHTe1vok7w+8k1K15rIouhBBXk5RZRMyJDLYnZLH9VBZ5JRW1Xne21dCzlQd9WnswKNybEM/mPcO7BJs6WDTYAPxwN8StghFvQp/HzX/+JqKwvJA/Tv3B0uNLOZV3yrC9m0837mx7J0MChqC10lqwQiGEMB+dTuFYar4+5CRksTMx+7IjrQaFezMo3IterTyw1VpZqFrTkGBTB4sHmy0fwPo3oN1NcMcS85+/iVEUhV2pu1h6fCkbkzeiU/QL07nYuDA8aDhjQsbQxbsLVurm9Q9YCCGupLJKx+Hz+qDz98kMdidlU1H1z49zW62a3q08GNzWm8Hh3s1i3hwJNnWweLA5vQ0WjQZHH3g6DqRzbL2lFqXyU9xPLI9fTmZJpmG7t503I0NGMiZkDB08OkiHYyHEDaewrJKt8ZlEx6UTHZdBSl5prddbeTrQI8SdbsHudAtyI8jDvsn9XynBpg4WDzYVJTC3Jegq4cmD4BZk/hqauEpdJbtTd7M6cTXrT6+noOKfDnaBToGMChnFmJAxsuimEOKGVL2e1abjGUTHpbPndE6teXMAPB1t6B7sRtcgN7oHu9Pe37nRj7iSYFMHiwcbgC+HwLm9MOkr6HSbZWpoJsqrytlybgurE1cTnRxNadU/v6UEOQfR2683fVv0pbtvdxy0zbtjnRBCXE5eSQW7ErPZczqbvUk5HDybR3mVrtY+dlorogJd6dXKg96tPYhs6droZkOWYFOHRhFsVr8AOxdC94dg7PuWqaEZKq4oZlPyJlYnrmbrua1UKv90rNOoNXT26kzfFn3p7d+bdu7tUKsa1z9aIYQwh9KKKg6dy2NPUg57krLZczrnkhFXdlorugW70bu1B71beRDRwgWNhVt0JNjUoVEEm8O/wrL7wC8SHtlsmRqaucLyQnam7mT7+e1sPbeVs4Vna73uZuNGL/9e9PTtSXff7gQ4BTS5+81CCGEMOp1CfEYhOxOz2ZGgX9sqq6i81j6ONhq6B7vRq5UHnQNc6djCBQcb8y79IMGmDo0i2OSdgw/ag8oKXjgDNjL5nKkl5yez9fxWtp3fxq7UXRRVFNV63dvOm26+3fQPn24EOwdL0BFC3JB0OoWT6YVsS8g0DC2/uEVHrYIwbyc6tXShU4ArnVu6Eu7rZNLbVxJs6tAogg3A/A6Qfxam/AEhAyxXxw2oQlfBwYyDbDu/jT2peziUeYgKXe1/tJ52nnTz0YecLj5daO3aWm5dCSFuSFU6hWMp+ew4lcWuxGwOns0jNb/0kv2srdS083cmsqULN0X60y3Y3ah1SLCpQ6MJNj/fB0d+hSGvwIBnLVeHoLSylIMZB9mdtps9qXs4mHGQcl3tZlgnayc6e3UmyjuKzt6difCMwFZja6GKhRDCstLySzmQnMvBs3kcOKt/rtmq8++bO3JPL+OO+pVgU4dGE2x2LIS/XoCwkTD5J8vVIS5RVlXGwYyD7EnbY2jRKaksqbWPRq2hvXt7Onv/E3Y87TwtVLEQQliWoiicyS7mwNk8DiTnclePAEK9nYx6Dgk2dWg0webcXv2wbzs3ePYUqOU2R2NVoavgRPYJ9qfvNzwySjIu2a+lY0s6e3cm0iuSzt6dCXUNRaM2b+c6IYRoriTY1KHRBJuqCpgbAJUlMH03eLWxXC2iQRRF4VzhOfan7yc2PZZ96ftIyE1AofY/JXuNPRGeEUR6R9LZqzOdvDrhYuNioaqFEKJpq+/Pb/l10lKstNCiC5zeCsk7Jdg0ISqVipZOLWnp1JLxrccDUFBewKGMQ8RmxHIg4wAHMw5SWKEfcr4zdafhvUHOQUR4RtDJqxOdPDvRxq2NLOYphBBGJC02lrTuddj6IXS5F2762LK1CKOq0lWRkJfAgYwDxKbrw87p/NOX7GettqadRztD0InwisDfwV+GmgshxEWkxaYpCOipf07eZdk6hNFZqa1o49aGNm5tuK2NftmM3NJcDmUe4lDmIQ5mHuRQxiHyy/M5kHGAAxkHDO91t3Wno2dHOnp2pJNnJzp6dpRbWEIIUU/SYmNJRZnw3oWFGp9P0nckFjcMRVE4U3CGgxkH9WEn4yBxOXFU6iov2TfQKZCOnh2J8Iygo2dHwt3DsdPYWaBqIYSwDOk8XIdGFWwAFnSB7ASYvAzChlu6GmFhZVVlxGXHcSjzEIczD3M48zBJ+UmX7KdWqWnt2poOHh1o79GeDh4dCHcPx8bKxvxFCyGEGcitqKYioKc+2CTvkmAjsLGy0fe38epk2JZXlseRzCOG21hHso6QWZLJyZyTnMw5yYr4FQBoVBpC3UJp79Ge9u7tae/RnjbubSTsCCFuKNJiY2l7voGVT0HIQJjyu6WrEU1EenE6RzKPcCRL/ziadZTs0uxL9tOoNLR2ba0POxcebdzayKzJQogmR25F1aHRBZu0I7CwD1g7wvOnwUoa0UTDKYpCWnGaIewczTrK0ayj5JTlXLKvlcqK1q6taefejnYe7Qh3C6ete1scrWUxViFE4yXBpg6NLtjoquCdYCjLh0f+Br9OV32LEPWhKAqpRakczTqqDzvZRzmWdeyyLTsAAU4BtHVva3i0c2+Hp52nDD0XQjQKTbKPzebNm3nvvffYu3cvKSkpLF++nJtvvtnwuqIovP7663z55Zfk5ubSt29fFi5cSFhYmOWKvl5qK2jZDRI2wtldEmyE0ahUKvwc/fBz9GNo0FCgRstO1hGOZx/neNZxjuccJ7UoleSCZJILkll3ep3hGO627rR1b0u4ezht3drS1qMtQU5BWKmtLPWxhBDiihpVsCkqKiIyMpL777+fSZMmXfL6u+++y4IFC/j2228JCQnh1VdfZeTIkRw9ehRb2ybcZ6BlD32wSd4F3R+0dDWiGVOpVPg6+OLr4MvQwKGG7TmlORzPPk5cdhzHso9xPPs4SflJZJdms+38Nrad32bY19bKljZubfRhx70tbdzaEOYWhoPWwRIfSQghamm0t6JUKlWtFhtFUfD39+fpp5/mmWeeASAvLw8fHx8WL17MnXfeWa/jNrpbUQDxG+B/k8AtGJ48cNXdhTCHksoS4nPiOZ6jDzzHs49zIufEJSudV2vh2MIwKWF12Al0CpTWHSGEUTTJW1FXkpiYSGpqKsOGDTNsc3FxoWfPnmzfvr3OYFNWVkZZWZnh+/z8fJPX2mAtuwEqyEmCwnRw9LZ0RUJgp7EjwiuCCK8Iw7YqXRVnCs4Ygs7xnOOczD5Jekk65wrPca7wHJuSNxn2t7WypbVra0PQqX52t3W3xEcSQtwAmkywSU1NBcDHx6fWdh8fH8NrlzN37lxmz55t0tqum60LeLeH9CP621Htxlm6IiEuy0ptRYhLCCEuIYwKGWXYnlOao59XJ/ckJ3JOcCL7BPG58ZRWlRqGpNfkaedJmGtYrcDTyrWVzLkjhLhuTSbYXKsXX3yRWbNmGb7Pz88nICDAghXVIaDHhWCzU4KNaHLcbN3o4deDHn49DNuqdFWcLTxLXHYcJ3P1kwmeyDnB2YKzZJZkklmSyfaU7Yb91So1AU4BtHZpTWvX1oS6hhLqFkqwczDWVtaW+FhCiCaoyQQbX19fANLS0vDz8zNsT0tLo3PnznW+z8bGBhubJvBbYEAP2LtIFsQUzYaV2oog5yCCnIMYwQjD9uKKYuJz42u38OScIK8sj9P5pzmdf5qNyRv/OY7KikDnQEJdQ2nteiH0uIQS5BKEVq21xEcTQjRiTSbYhISE4Ovry4YNGwxBJj8/n507d/Loo49atjhjqF7p+/x+qCwHjfyGKpone639JctGKIpCVmkW8bnxJOQmcDLnJAm5CSTkJlBQUUBiXiKJeYm1hqJrVBqCnIMMrTvVz4HOgWjUTea/NiGEkTWqf/2FhYXEx8cbvk9MTCQ2NhZ3d3cCAwOZOXMmb775JmFhYYbh3v7+/rXmummy3FuBvQcUZ0HqwQsdioW4MahUKjztPPG086SXXy/DdkVRSC9OJz433hB6EnITSMhLoKiiiIQ8/ddrT681vEej1hDoFEgrl1aEuITQyrUVrVxaEewcjL3W3hIfTwhhRo0q2OzZs4fBgwcbvq/uGzNlyhQWL17Mc889R1FREQ8//DC5ubn069ePv/76q2nPYVNNpdK32sT9qe9nI8FGCFQqFT4OPvg4+NC3RV/D9upZlavDjiH05CVQUlnCqbxTnMo7dcnx/Bz8agWeEGf9s4zSEqL5aLTz2JhKo5zHptrf82HDbGg/AW7/r6WrEaLJ0Sk60orSDMHmVN4pw22supaSAHC1cTUEnhCXEMPXfg5+Mg+PEI1Es5vH5oZQ3c8meRcoir4VRwhRb2qV2rCMRM0WHoDc0txaYaf6+XzheXLLctmXvo996ftqvcdabU2gcyDBzsEEOQcR7BJMsLP+4WrrasZPJoSoLwk2jYl/FKg1UJACecngGmjpioRoNlxtXeli24UuPl1qbS+pLOF0/mlO5dZu5Tmdf5pyXbmhf88lx7Nx1Ycd5+BagSfAOUDm4xHCguRWVGPzxWA4vw86T4bQYeDZBjxag9bO0pUJcUOp0lWRUpRCUn4Sp/NPk5iXaPg6tajuSUFVqPB39DcEniDnIAKdAgl0DsTPwU9GbAlxjer781uCTWOz9hXY9vFFG1XgGnAh5ISBZ9iFsGMPKiv9CuFqzaXPKqsLt7NUNZ7VF22jxi2vGre+Lt522dtiF22r162zeuzTJG/BNcWaxbUqrizhTMEZkvLPkJR/mtMF+uekgtMUVhTV+T6NWkNLB38CnAIIcgogwFH/HOgUgJ+Dr4Qe0XyoNWBl3L/PEmzq0OiDTUku7F8C6cch84T+UZpr6aqEEPWgAFlqNUnWWpK0GpK0Ws5oNJzRakjWaClX1x2ANYpCy4pKAisrCayoILCikqDKSgIqKvCvrEK6MIsmZdyH0O0+ox5SOg83VXau0Ofxf75XFCjKhKyTF4LOSf0jJwmqykFXBbrKfx5KVe1tigIo/zwLIUxGBXjqdHiWltGttKzWazogzcqKM1oNp7VakjUaTmtrh54kay1J1lqg9q3n6tDTslL/CKioJKCy0rDN7sb6/VSIK5Jg09ipVODopX8E9bn+4ylK3WGn1n+OdW2/zOt17nOV99RVn1HIf/RNyg3wg1kN+F149LzoNZ2iI604nTOF5zhdkMyZwrP6R0EyyYXnKdeV1wg9l/K09SDAsQUBjv60dGhBS8cWF75vgbuNK6omeXtXNGkay80vJ7eihBCiEdMpOlKLUkkuSOZswVn9c6H+ObkgmYLygiu+315jT4BTAC2dWuqfHVvSwqkFLRxb4O/oLyO4RJMhfWzqIMFGCNGc5JXlXTbwJBckk1aUhnKV1ksvOy9aOLYwhJ2aD1/p0CwaEQk2dZBgI4S4UZRXlXOu8Jwh6JwtOMvZwrOcKzzHuYJzFFcWX/H9ViorfB18a7XyVD9aOrXEw9ZDbnMJs5HOw0IIcYOztrI2LBNxMUVRyC3L1Yec6kfBuVrfV+gqDF9zmal7rNXW+pmeHfzwd/S/5NnH3kdafITZSYuNEEKIS+gUHRnFGYZgU6u1p/Ac6cXp6BTdFY9hpbLC294bf0d//B389c/VDwd/fB18sbayNtMnEk2d3IqqgwQbIYS4fhW6CtKK0kgpSuF84XnOF50npTDF8JxSlEKFruKKx1ChwsvOC18HX8PDz8HP8Ozj4CO3u4SBBJs6SLARQgjT0yk6skqyOFd4jpSiFP1zYQrnivTP5wvPU1pVetXjWKutLwk+1Q9fR1987X2x19qb4RMJS5NgUwcJNkIIYXmKopBdmk1qUSqpRamkFKX881ycSmphKhklGVcd1QXgYuOib+Gx99E/HC56tveR8NMMSLCpgwQbIYRoGip0FaQXp5NSeCHsFKUabnNVB6HCisJ6HcvJ2gkfex98HXwNocfX3lf/7CAtP02BjIoSQgjRpGnVWsPw8roUlBcYWnrSi9NJLUolrTiNtKI00orTSC1KpbiymILyAgrKC4jPja/zWE5aJ3wcfPC298bLzgtve2987H3wsvcyPHvYemCllpW7GjMJNkIIIZosJ2snnKydCHMLq3OfwvJCQ8i5OPRUPxdWFFJQUUBB7pXDj1qlxtPWE297b8OjOgwZvrf3wUHrYIqPK+pBgo0QQohmzdHaEUdrR1q7tq5zn8LyQn2LT3Eq6cXpZBRnkFacRkZxBunF6aQXp5NZmolO0ZFekk56STpk1X1OB60DXnZeeNp56p/t9c9e9l765wvbnLROMurLyCTYCCGEuOFVh59Wrq3q3KdKV0VWaRbpxemkFacZAs/F3xdVFBkeSflJVzyvrZXtP2HnwrO3vTeedp6GW2Iedh44WztLAKon6TwshBBCGFFRRZG+hackk4ziDDJKMgzPmSWZ+ufiTAoqrryAaU1atRZPO0887TzxsPP452vbf76ufthacGVtU5LOw0IIIYQFOGgd6lzKoqbSylJD6EkvSa8dgmp8XVBRQIWuwjAa7GoctY6XhB0POw88bD1qPbvbujfLmZ8l2AghhBAWYKuxJcApgACngCvuV1ZVRlZJFpklmfrn0kzD11klWYaWoMySTMqqyiisKKSwovCqt8FAPxLM3c4dD1t90KkVgC4KQ01lOLwEGyGEEKIRs7GyMayxdSWKolBYUWgIOdW3wjJLM8ksziS7NJvs0myySrPILs2mUlepHwlWUcDp/NNXrcNeY4+HnQdutm6427jjZuum/9rWHXfbGt/b6AOSpVqDJNgIIYQQzYBKpTIMf7/abTBFUcgvz9eHnJJ/Ao+hZejC9uptpVWlFFcWU1xQTHJB8lVrea77c9zT/h5jfbQGkWAjhBBC3GBUKhUuNi642LjQyqXukWCgD0HFlcVklvzT6pNdmk1OaQ45pTlklWYZvs4pzSG7NBt3W3czfZJLSbARQgghRJ1UKhUOWgcctA4EOQdddX9FUdApOjNUdnkSbIQQQghhNCqVCiuV5ZadUFvszEIIIYQQRibBRgghhBDNhgQbIYQQQjQbEmyEEEII0WxIsBFCCCFEsyHBRgghhBDNhgQbIYQQQjQbEmyEEEII0WxIsBFCCCFEsyHBRgghhBDNhgQbIYQQQjQbEmyEEEII0WxIsBFCCCFEs3HDre6tKAoA+fn5Fq5ECCGEEPVV/XO7+ud4XW64YFNQUABAQECAhSsRQgghREMVFBTg4uJS5+sq5WrRp5nR6XScP38eJycnVCqV0Y6bn59PQEAAycnJODs7G+244lJyrc1DrrN5yHU2D7nO5mHK66woCgUFBfj7+6NW192T5oZrsVGr1bRs2dJkx3d2dpZ/NGYi19o85Dqbh1xn85DrbB6mus5XaqmpJp2HhRBCCNFsSLARQgghRLMhwcZIbGxseP3117GxsbF0Kc2eXGvzkOtsHnKdzUOus3k0hut8w3UeFkIIIUTzJS02QgghhGg2JNgIIYQQotmQYCOEEEKIZkOCjRBCCCGaDQk2RvLJJ58QHByMra0tPXv2ZNeuXZYuqUnbvHkz48ePx9/fH5VKxYoVK2q9rigKr732Gn5+ftjZ2TFs2DBOnjxpmWKbsLlz59K9e3ecnJzw9vbm5ptvJi4urtY+paWlTJ8+HQ8PDxwdHbnllltIS0uzUMVN08KFC+nUqZNh0rLevXuzevVqw+tyjU3j7bffRqVSMXPmTMM2udbG8cYbb6BSqWo92rZta3jdktdZgo0R/Pjjj8yaNYvXX3+dffv2ERkZyciRI0lPT7d0aU1WUVERkZGRfPLJJ5d9/d1332XBggV89tln7Ny5EwcHB0aOHElpaamZK23aYmJimD59Ojt27GDdunVUVFQwYsQIioqKDPs89dRT/PHHH/z888/ExMRw/vx5Jk2aZMGqm56WLVvy9ttvs3fvXvbs2cOQIUOYMGECR44cAeQam8Lu3bv5/PPP6dSpU63tcq2Np0OHDqSkpBgeW7ZsMbxm0eusiOvWo0cPZfr06Ybvq6qqFH9/f2Xu3LkWrKr5AJTly5cbvtfpdIqvr6/y3nvvGbbl5uYqNjY2yg8//GCBCpuP9PR0BVBiYmIURdFfV61Wq/z888+GfY4dO6YAyvbt2y1VZrPg5uamfPXVV3KNTaCgoEAJCwtT1q1bpwwcOFB58sknFUWRv8/G9PrrryuRkZGXfc3S11labK5TeXk5e/fuZdiwYYZtarWaYcOGsX37dgtW1nwlJiaSmppa65q7uLjQs2dPuebXKS8vDwB3d3cA9u7dS0VFRa1r3bZtWwIDA+VaX6OqqiqWLl1KUVERvXv3lmtsAtOnT2fs2LG1rinI32djO3nyJP7+/rRq1YrJkydz5swZwPLX+YZbBNPYMjMzqaqqwsfHp9Z2Hx8fjh8/bqGqmrfU1FSAy17z6tdEw+l0OmbOnEnfvn3p2LEjoL/W1tbWuLq61tpXrnXDHTp0iN69e1NaWoqjoyPLly+nffv2xMbGyjU2oqVLl7Jv3z527959yWvy99l4evbsyeLFiwkPDyclJYXZs2fTv39/Dh8+bPHrLMFGCAHof8s9fPhwrfvkwnjCw8OJjY0lLy+PZcuWMWXKFGJiYixdVrOSnJzMk08+ybp167C1tbV0Oc3a6NGjDV936tSJnj17EhQUxE8//YSdnZ0FK5POw9fN09MTKyurS3p7p6Wl4evra6Gqmrfq6yrX3HhmzJjBypUr2bRpEy1btjRs9/X1pby8nNzc3Fr7y7VuOGtra0JDQ+natStz584lMjKSjz76SK6xEe3du5f09HS6dOmCRqNBo9EQExPDggUL0Gg0+Pj4yLU2EVdXV9q0aUN8fLzF/05LsLlO1tbWdO3alQ0bNhi26XQ6NmzYQO/evS1YWfMVEhKCr69vrWuen5/Pzp075Zo3kKIozJgxg+XLl7Nx40ZCQkJqvd61a1e0Wm2tax0XF8eZM2fkWl8nnU5HWVmZXGMjGjp0KIcOHSI2Ntbw6NatG5MnTzZ8LdfaNAoLC0lISMDPz8/yf6dN3j35BrB06VLFxsZGWbx4sXL06FHl4YcfVlxdXZXU1FRLl9ZkFRQUKPv371f279+vAMr8+fOV/fv3K6dPn1YURVHefvttxdXVVfntt9+UgwcPKhMmTFBCQkKUkpISC1fetDz66KOKi4uLEh0draSkpBgexcXFhn2mTZumBAYGKhs3blT27Nmj9O7dW+ndu7cFq256XnjhBSUmJkZJTExUDh48qLzwwguKSqVS1q5dqyiKXGNTqjkqSlHkWhvL008/rURHRyuJiYnK1q1blWHDhimenp5Kenq6oiiWvc4SbIzk448/VgIDAxVra2ulR48eyo4dOyxdUpO2adMmBbjkMWXKFEVR9EO+X331VcXHx0exsbFRhg4dqsTFxVm26CboctcYUBYtWmTYp6SkRHnssccUNzc3xd7eXpk4caKSkpJiuaKboPvvv18JCgpSrK2tFS8vL2Xo0KGGUKMoco1N6eJgI9faOO644w7Fz89Psba2Vlq0aKHccccdSnx8vOF1S15nlaIoiunbhYQQQgghTE/62AghhBCi2ZBgI4QQQohmQ4KNEEIIIZoNCTZCCCGEaDYk2AghhBCi2ZBgI4QQQohmQ4KNEEIIIZoNCTZCCCGEaDYk2AghLmvQoEHMnDnT0mU0alOnTuXmm2+2dBlCiBok2Ahxg5o6dSoqleqSR3x8vKVLu8TixYtxdXU1fP/GG2/QuXNns50/KSkJlUpFbGxsre0fffQRixcvNlsdQoir01i6ACGE5YwaNYpFixbV2ubl5WWhasyvvLwca2vra36/i4uLEasRQhiDtNgIcQOzsbHB19e31sPKyuqy++bk5HDvvffi5uaGvb09o0eP5uTJkwAoioKXlxfLli0z7N+5c2f8/PwM32/ZsgUbGxuKi4tRFIU33niDwMBAbGxs8Pf354knnqhXzYsXL2b27NkcOHDA0MpU3WqSm5vLgw8+iJeXF87OzgwZMoQDBw4Y3lvd0vPVV18REhKCra0tAH/99Rf9+vXD1dUVDw8Pxo0bR0JCguF9ISEhAERFRaFSqRg0aBBw6a2osrIynnjiCby9vbG1taVfv37s3r3b8Hp0dDQqlYoNGzbQrVs37O3t6dOnD3FxcYZ9Dhw4wODBg3FycsLZ2ZmuXbuyZ8+eel0bIYQEGyFEPU2dOpU9e/bw+++/s337dhRFYcyYMVRUVKBSqRgwYADR0dGAPgQdO3aMkpISjh8/DkBMTAzdu3fH3t6eX375hQ8++IDPP/+ckydPsmLFCiIiIupVxx133MHTTz9Nhw4dSElJISUlhTvuuAOA2267jfT0dFavXs3evXvp0qULQ4cOJTs72/D++Ph4fvnlF3799VfDraWioiJmzZrFnj172LBhA2q1mokTJ6LT6QDYtWsXAOvXryclJYVff/31srU999xz/PLLL3z77bfs27eP0NBQRo4cWev8AC+//DLz5s1jz549aDQa7r//fsNrkydPpmXLluzevZu9e/fywgsvoNVq63VthBCAWdYQF0I0OlOmTFGsrKwUBwcHw+PWW281vD5w4EDlySefVBRFUU6cOKEAytatWw2vZ2ZmKnZ2dspPP/2kKIqiLFiwQOnQoYOiKIqyYsUKpWfPnsqECROUhQsXKoqiKMOGDVNeeuklRVEUZd68eUqbNm2U8vLyetW6aNEixcXFxfD966+/rkRGRtba5++//1acnZ2V0tLSWttbt26tfP7554b3abVaJT09/Yrny8jIUADl0KFDiqIoSmJiogIo+/fvr7XflClTlAkTJiiKoiiFhYWKVqtVvvvuO8Pr5eXlir+/v/Luu+8qiqIomzZtUgBl/fr1hn1WrVqlAEpJSYmiKIri5OSkLF68+MoXRAhRJ2mxEeIGNnjwYGJjYw2PBQsWXHa/Y8eOodFo6Nmzp2Gbh4cH4eHhHDt2DICBAwdy9OhRMjIyiImJYdCgQQwaNIjo6GgqKirYtm2b4RbObbfdRklJCa1ateKhhx5i+fLlVFZWXtdnOXDgAIWFhXh4eODo6Gh4JCYm1rqtFBQUdEk/opMnT3LXXXfRqlUrnJ2dCQ4OBuDMmTP1Pn9CQgIVFRX07dvXsE2r1dKjRw/DNarWqVMnw9fVt+vS09MBmDVrFg8++CDDhg3j7bffrlW7EOLqJNgIcQNzcHAgNDTU8KjZJ6ahIiIicHd3JyYmplawiYmJYffu3VRUVNCnTx8AAgICiIuL49NPP8XOzo7HHnuMAQMGUFFRcc3nLywsxM/Pr1ZQi42NJS4ujmeffbbWZ77Y+PHjyc7O5ssvv2Tnzp3s3LkT0HcuNoWat5ZUKhWA4bbXG2+8wZEjRxg7diwbN26kffv2LF++3CR1CNEcSbARQlxVu3btqKysNPzAB8jKyiIuLo727dsD+h/Q/fv357fffuPIkSP069ePTp06UVZWxueff063bt1qhQo7OzvGjx/PggULiI6OZvv27Rw6dKhe9VhbW1NVVVVrW5cuXUhNTUWj0dQKa6GhoXh6etZ5rOrP8corrzB06FDatWtHTk7OJecDLjlnTa1bt8ba2pqtW7catlVUVLB7927DNaqvNm3a8NRTT7F27VomTZp0ycg1IUTdJNgIIa4qLCyMCRMm8NBDD7FlyxYOHDjAv/71L1q0aMGECRMM+w0aNIgffviBzp074+joiFqtZsCAAXz33XcMHDjQsN/ixYv5+uuvOXz4MKdOneJ///sfdnZ2BAUF1aue4OBgEhMTiY2NJTMzk7KyMoYNG0bv3r25+eabWbt2LUlJSWzbto2XX375iqOK3Nzc8PDw4IsvviA+Pp6NGzcya9asWvt4e3tjZ2fHX3/9RVpaGnl5eZccx8HBgUcffZRnn32Wv/76i6NHj/LQQw9RXFzMAw88UK/PVVJSwowZM4iOjub06dNs3bqV3bt3065du3q9XwghwUYIUU+LFi2ia9eujBs3jt69e6MoCn/++Wet2yoDBw6kqqrK0JcG9GHn4m2urq58+eWX9O3bl06dOrF+/Xr++OMPPDw86lXLLbfcwqhRoxg8eDBeXl788MMPqFQq/vzzTwYMGMB99/1/O3ZsokAQhmH4O9cGjAQL2MjILjYWxMSFK2FL2EzYbDM7sIUtwg5MLMAa1vQMDgwODobnif8Z5s9e5jt1Xed4PObxeGS9Xv9612KxyPV6ze12y3a7Tdd1GYbhbWa5XGYcx1wul2w2m7eY++l8Pme/3+d0OmW32+V+v2eapqxWq4/2qqoqz+czbdumruscDoc0TZO+7z86DyRf8zzP//0IAIC/4McGACiGsAEAiiFsAIBiCBsAoBjCBgAohrABAIohbACAYggbAKAYwgYAKIawAQCKIWwAgGK8AK7zGsVKeTZwAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", + "plt.plot(off_diagonal_norm[:,2],label='1-local ansatz unnormalized')\n", "plt.xlabel('Flows Iterations')\n", "plt.ylabel(r'$||\\sigma(H_k)||$')\n", "plt.legend()" @@ -660,15 +2195,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-05-21 12:03:28]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", - "nqubits = 5\n", + "nqubits = 2\n", "h = 3.0\n", "\n", "# define the hamiltonian\n", @@ -683,37 +2226,10090 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], "source": [ - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 20, 1e-3, analytic=False, d_type = d_ansatz_type.local_1)\n", - "best = np.argmin(loss_opt)\n", + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", + "size = 100\n", + "param1 = np.linspace(-1,1,size)\n", + "param2 = np.linspace(-1,1,size)\n", "\n", + "off_diagonal_norm = np.empty((size,size))\n", + "least_squares = np.empty((size,size))\n", + "for i in range(size):\n", + " for j in range(size):\n", + " dbi_eval = deepcopy(dbi)\n", + " d = d_ansatz([param1[i],param2[j]],d_ansatz_type.local_1,normalization=False)\n", + " s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", + " dbi_eval(s_poly,d=d)\n", + " least_squares[i,j] = dbi_eval.least_squares(d=d)\n", + " off_diagonal_norm[i,j] = dbi_eval.off_diagonal_norm\n", "\n", + "param1_optim, param2_optim = np.unravel_index(np.argmin(off_diagonal_norm, axis=None), off_diagonal_norm.shape)\n", + "d = d_ansatz([param1[param1_optim],param2[param2_optim]],d_ansatz_type.local_1,normalization=False)\n", + "s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", + "dbi(s_poly,d=d)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+aNasGUaNGoX7778fcXFxWLRoEdLT05GXlxcp17t3b9SrVw/Dhw/HuHHj4PP58Prrr1OFTG655Ra88847uOOOO7Bu3Tr84Q9/QEVFBXbu3Il33nkHn332GXr06IEZM2bgiy++wKBBg9C8eXMcOnQIf//739GkSRNcdNFFhu23adMGf/3rXzFz5kxcfPHFuPrqqxEIBPDNN9+gcePGmDVrFtLT0zF58mRMnz4dAwYMwODBg7Fr1y78/e9/xwUXXBDJqVq7di3Gjh2La6+9Fueccw7Ky8vx+uuvR0R0mO7du+Pzzz/H3Llz0bhxY7Rs2RJZWVnEnwnNd0DD/fffj/feew/XXnstbr31VnTv3h3Hjh3D8uXLsWDBApx33nlo3bo10tLSsGDBAtStWxe1a9dGVlYWWrZsWa29K664Apdeein++te/4ueff8Z5552HVatW4cMPP8S9995rOL2BQqGohDOD5xQKa8LDpo1e+/bt0zRN0w4ePKjdddddWtOmTbWEhAQtMzNT69evn/bSSy9F2goGg9rjjz+uNW/eXAsEAlq3bt20jz/+uNrw6Pfee0/r37+/1rBhQy0xMVFr1qyZdvvtt2sHDhyI6tvLL7+stWrVSouLizMd8n/kyBHtrrvu0tq3b6/Vrl1bS01N1bKysrR33nknqlxFRYU2ffp0rVGjRlpycrLWt29fbdu2bVrz5s2rDUvftGmTlpWVFenf3LlzdYeYf/XVV9qFF16oJScna40bN9YeeOAB7bPPPqvW3z59+mjnnnuubv9LS0u1J598Ujv33HO1QCCg1atXT+vevbs2ffp0raCgQNM0TVuzZo02ZMgQrXHjxlpiYqLWuHFj7YYbbtD++9//6rZZlUWLFmndunWLtN+nTx9t9erVUWWef/55rX379lpCQoKWkZGh3Xnnndrvv/8eef+nn37Sbr31Vq1169ZaUlKSVr9+fe3SSy/VPv/886h2du7cqV1yySVacnJytSkU9IDOMH9NI/8Omjdvrg0aNKha/T59+mh9+vSJ2nb06FFt7Nix2tlnn60lJiZqTZo00YYPHx41hcWHH36odezYUYuPj48a8l/1d6xpmnbixAlt/PjxWuPGjbWEhAStbdu22uzZs6OmkjA7Rr3fnkJRk/BpmsrCUyjcSosWLdC3b9/IjMkKhUKhsAeVg6RQKBQKhUJRBSWQFAqFQqFQKKqgBJJCoVAoFApFFTwlkL744gtcccUVaNy4MXw+X2SxTDPWr1+P888/H4FAAG3atNHN5Zg/fz5atGiBpKQkZGVlYePGjeI7r1Aw8PPPP6v8I4VC4Slo76nvvvsu2rdvj6SkJHTu3BmffvqpTT01x1MCqbi4GOeddx7mz59PVH7v3r0YNGgQLr30UmzduhX33nsvbrvtNnz22WeRMm+//TYmTJiAadOmYfPmzTjvvPOQk5MTtUSFQqFQKBQKa2jvqV9//TVuuOEGjBo1Clu2bMGVV16JK6+80nLiVDvw7Cg2n8+HpUuX4sorrzQs8+CDD+KTTz6J+qCHDh2K48ePR+bVycrKwgUXXBCZUC0YDKJp06a4++67MWnSJKnHoFAoFApFLEF7T73++utRXFyMjz/+OLLtwgsvRNeuXbFgwQLb+q1HTE8UmZubW20pgpycnMgq6aWlpdi0aRMmT54ced/v9yM7O7vaDMuVKSkpiZqdN7w6/FlnnSVkKQOFQqFQxCaapuHEiRNo3Lgx/H55QZzTp0+jtLRUSFuaplW7twUCAQQCgahtLPfU3NxcTJgwIWpbTk4OUQqNbGJaIOXn5yMjIyNqW0ZGBgoLC3Hq1Cn8/vvvqKio0C0TXrNIj1mzZmH69OlS+qxQKBSK2Gffvn1CF1GuzOnTp9GyeR3kH9JfQJmWOnXqRK04AITWsXzkkUeith05coT6nmp0n87Pz+fvOCcxLZBkMXny5CjFW1BQgGbNmuGS5GsQ70swrOeroraZygQSjd9LNN63lmj8VWuJ+it5BxMMthuVT6z+NBRM0H9Cqkg0WBFdZ7sWb1DW4HCDCdXLB40/GgQNPhqj7Zbtmbxn1W4YzaKN6P2xR8m1OE9G2CP4KtgdW38ZeV1fGUF75Rbvm7Rh+p5Ju0bvGbXnL6v+fccZlPWV6/824kp12tDZFtpfsPq20urbQtv1b+b+MuObvM+gjq/U5EMrNfmwS8wdF81kbUHSMuVaGb449X7UIsKiKS0tRf6hCuz+tilS6vK5VIUngmjTYx/27duHlJSUyPaq7lEsEtMCKTMzEwcPHozadvDgQaSkpCA5ORlxcXGIi4vTLZOZmWnYrp61CADxvgTE+4wFjM9vIm5AIo4s3o8zEUhx+l+1kXAKJsbpZvAbb/frbzcQSD4dEVOR6NP9QeoJHgCAzuEGE3zQk29GujWYAN3ywXj97WZ1ACCYaPyeVbuV2ye9dQcTNKaRFlp86IYWKwFhXznDkSRRiKQkcxETxlQkJQF+o/tvwKT9gIkQMvjy/QaH5ffpCJlEfZGkWxZAnE7aqt42APBrOmIo3kAkxRuIJKPtAHxx+h+ML87kLDMLayUHADOBk5xoLZKSE6GdthZSdqRjpNT1cwukSFspKVECSY8GDRpQ31ON7tNm92C78NQoNlp69eqFNWvWRG1bvXo1evXqBQBITExE9+7do8oEg0GsWbMmUkYUviTJajvA5h45gZF7RNUGhcPiFkicIyv3Kbosm/sTFkexhBavMR1XMEEj/hxJvhuS75infTsxfDDRQcQ5LRLTa57JtZIEkkhATYXlnmp1n3YSTwmkoqIibN26FVu3bgUQGsa/devWyArakydPxrBhwyLl77jjDvz000944IEHsHPnTvz973/HO++8g/Hjx0fKTJgwAS+//DKWLFmCHTt24M4770RxcTFGjhwprN8k4ojbPWJAtnDSC7lRt0FxkTZug247S1tAyD3igbQ/NDf1yrCKCC/BenwiRZJpfcbfiGEYmPK3LeJ8okHENcAMKdcwAdda6Q/ELsbqnjps2LCoJO577rkHK1euxNNPP42dO3fikUcewbfffouxY8c6dQgR3GUtWPDtt9/i0ksvjfwdzgMaPnw4Fi9ejAMHDkTEEgC0bNkSn3zyCcaPH4+//e1vaNKkCV555RXk5OREylx//fU4fPgwpk6divz8fHTt2hUrV66sljTGii3iSLB7ZJxjRJ57RIuIJ1BRF38eF4C1TRpxREusi6KqRMKHlGG3YIJGFHILJpiH24Lx5qG2YKJxqM2qbVlUJBjnIlUrm+gzzDkiJZjo1w2zBRPjdMNpRtvN0BLjjXORAglAickBB8xDbb5AwDLUFr72k4TbYgmre2peXl7U6L3evXvjzTffxMMPP4yHHnoIbdu2xbJly9CpUyenDiGCZ+dBchOFhYVITU3FZbWGRnKQSJ8giOxaszIWdrGRQDITTqIEkt52muRsQF/00CRnh7YblTfYTvm0Dpg7A6JCa24SRxpHYrgePoqEaap2GXKTSEQSbz6SYS6SSdum7enlEFEkagMGeUhGZQ0Ekt5246RsymRtE4FkJIRMk7UBc5EkICE7Uq6SSCrXSrH25FsoKCiwzOlhJXxPOrSruZAk7YbtfpHaX7fiqRCbV7BNHFngZO6RV8NrLO6RHaE1WnEkOpymJWhRL9HIap/lcyD5rJ0ItdH+NkWE2YScg4LCbEYPaGZwXQNFjDoOl6vBITcv46kQmxewVRxxJhvqUdPDa8btM9YTEFpjEUcikCGEWPYtwl3S4jUqN4kk3MYbamNt1w04EWYzwyycJjPUBpCF24DQvcHucFuxVgq/xneNLtYbiVhDUA6SQIifJmwQR+bzHnlXF4sYvWaXe+RFcSTTJWJFVJ9o3SQRTpLpXFoCXSQRAxFEnFtuG81GhNWDpnKSaixKINmM7LAawC6AWCxsGmgunjLDa061IxPekJrbRJERIsSSaJEkA9m/ObeH2WidbEDyQ6FIkaSmCfAMSiDZiLATgyO0JnZUG3lytkzsHroc2a9L3KOaIIz04Om7yPwst7hIdiMkNG7ztcIQkmuqQJGk8AYu+XXGPsQnjkOhNdnukRE0F1knwmsynuTdII68LIyqIlsk2ZG0LaLNmhJmk+YiKZGkqIISSDZglziSAa1wMnoitDs3QXqIgnNYv2nbEsVRLAmjyrAel10iSbSLZDcyw2zG28U/tNkpkpRQ8j5KIEmE6iQRcDLZ6R7JtMZtz49wkXskSxzFqjCqCstxihRJpvVZBgFIdDydCk2LgNVFEobKN6oRuCSaHXsIF0YcoTVW3JS0LTO8Rt0Oo3vkpDiSgZYgZvivr0y82NYSNKrpAUinAbAa/s86RN9sdm0R0PTL7lm1zWAZ8m+F6bB/wHrof6Sc9RQAAPk0ADI4EawAgnzfz4mgGuavEIQU14hTHNnlHtWU8Jrhfm143KARR8InXUwIRr1ktSuqbdrjF+UksYbaWNqzExGL14oc2MHjIgkJtQFUTpJyk7yHEkgCoToBBIkjK7w255Gs8JqoUAXzYqMC3CNaccSLLEFEu2/+tsSLJBnQ/rZqSpiN1cnmH9ovViQBKuTmNZRAcgKB4ojnImB24Ym18JpsZIfW7BRHTggiM0T0R7RIsttFEoGs0Wy06ysaIdpFsoLo2qlEUo1GCSQ7CQRsFUdy8pJiJ7xml3tk2geB4ohrXiCHnCIaePtI8/mIEEksGP3GRM7+7jSihZDUUBtAJ5KU+IkplECyC6rwm3xx5KR7ZLhfm8NrouBxj6ygEUdM7btcFBnBK5SIynGG25x0kWSF2aSveShhdKytIglQIimGUALJDmwWR1awiiNR7pEn12uSgJUDIVMceVUYVYX1OESJJJ5Qm2EdSheJqm2XhNnsdJFIIBZJyk2qUXjQoPUQgoURQHYiey0x2wyai7QT4TVW98hpcRRraAlB6ukCSKcCsJoCwGrov2G9eMBvMtrc7nZiGcuh/YRlAJBPAwAQTwUgiyItDtD4fJAireY+0CoHSQa0Tw82iiPR7pEovBpec3K/1BMixohrZATL8dnhJHnhNyji/BOVlG12reFxkYQ+XNKG3JSb5Elix2pwA4FEwE95IrhEHLESS+E1ke6R+X743SMWcSQan6AkZY3BfTFt73/HSuoo0U4qKRIj98do4kjWiShZ26CZNNKwDcmTSVbGamJJ4U4SQOcmAcApiTOCKoSiHCSnoIhn2xFWc9I9MsLu8JooZIfWqIasC3SNfAla1EsUVdsVJrwojpvkM3Wzi+TF0WxmsLpIJO8Lv57SzlUX8MDiewoASiA5A8UJJepklpHgKMo9cnN4TbR7ZIYMccSDLEFEu28e7BZJLBjmwwlI1nZLmI0lWZtHJFlBel2lCrk5sJi4Qi5KINkJpWtkhziyrlszfiLC1mnjcI+sIM6X4XSNnBBEZvD2h+bz4BVJXnGRZI1mcwsk1zxS8SPVTVK4mppx93MayqcLkScujx3N+nQnAjeH19jW0+ILrdGII1bcJoyqIkIokZXjnf+Ivj6ti+QGRM2JJMtFclQkKaEUEyiBJBOGE8Ut4ogVL4bXRM2czeoQOC2O3C6MqsITfhMlklhDbXY4lXxl+UWPnQM07BZJSijVLGIstc8lMJwUNCee7LBaqL5z7lGsYeYoOCmOvCSKjPAlaNSj4EjnTLIa3WY2PxLL3Egi5jOiacPu0WymfUn0w1+q/xs2ey/0vvnINav3AYqRa5RlAdCPdhNIQUUSyiv4rtfFFbE7NYgV6k4nkkQ2x0j0EwyJOHKze+SG8BptcrZTc90A9OLIa46RFSzH4+R8ULS/FS+G2VhcJJkPZaROkjQ3CVCOkgdRAslBRLtGgBhx5Eb3yO7wmihku0cs4kg2/oQKw5dMaIUSyWfHE2qTsZhtdPuyyrp3rjIjRD4U0l6XqYVSohJJXkEJJAegPam8II5E5RfIGjEjMzmbRWTZLY5Eu0asIsgO8eQmkWSEYd6bgGRtt4xms9tFckoksZRXeAMlkGyERRi5RRyxIiI52whZ7o+okAargyBDHPFihxMksn27RZIRsl0kN2Cn42S3SJLuJilcjRJINsBy4tCUt0Mc2eEeGe7bBeE1O90jy/qE4ojXNbIrNCZr3zTHzyuSWEJttC4STRv8ZeWe06wuEtn7YnMwWa7dSizFBkogSYL1JKF1jexIyGZ1lmQmZ9MgIjmbep+S3CMaccSKU6LICBFCiQS3L+Tr5jAbywK2PCLJCtLroiw3ibeewh2ob04wrCcDbT3SE5vsQsF+MXJq8VknR43x9MHMaXBSHLlJEBlRuY/BMjrRTzodgNUUAGZD/1mG/du9+GysYDX0P1TGeng/TTngzHWaapg/Rz1eirQAghrfA/JJzf3XBlkoB0kgdjxh0Dz1iBBHrKE1Ue6Rm8Nrxm3rCxQljsTB4iqJcpJYQ200yFp8VkSYzS0uEsmDHc21kjbsphyl2EcJJIdgOVFoTmDZ4sgrOBFecwpaceS2UBoLTokkFmhzkXTLuiDMxgKr02ynSKItCyjBE+t4/w7oMViFkegnId4Lj0j3yLAPBuV5L+Yyk7Odco9oxFEsCKPK0B6PiFF9bnORpI3oFHTOmmElnkSJJFluEqCEEgs///wzRo0ahZYtWyI5ORmtW7fGtGnTUFpaSlRf0zRcfvnl8Pl8WLZsmZQ+qm/UBlhPHNqTlPwCwKeLWS1zwzoCLu5uyEmSgQxxJJp4xjbLKfOIrPAnVBDnJpHkJPHkI8kkmAj4ye4h+vUF5CyxLD1SkehDXKn+b9XsPcA654gkJylUjjzfKHw9JS0PRF/r7c438ho7d+5EMBjEiy++iDZt2mDbtm0YPXo0iouLMWfOHMv6zz77LHw+ueef5xyk+fPno0WLFkhKSkJWVhY2btxoWLZv377w+XzVXoMGDYqUGTFiRLX3BwwYIKSvrE8VtE8wdE9HJE9b4n8Wwlb+tnlpEZo+iHaPRIojUa5RfEJFtZcb2gpDc5wknx1rqM3oOxcRZqPBCReK9Vy3w0kKlZN3fa2McpXMGTBgAF599VX0798frVq1wuDBg3Hffffhgw8+sKy7detWPP3001i0aJHUPnrq23v77bcxYcIELFiwAFlZWXj22WeRk5ODXbt2oWHDhtXKf/DBB1F23dGjR3Heeefh2muvjSoX/qLCBAIB5j7ynBAsJ6Fo10hGaM20PQHJ2TTITM6mhXXywTA04ogHEcKFZV88DhOpm8TrJMl2kWQtPktX1gd/WfXfmmgXSQTh65doNylcPtQ23fkQK65SYWFh1N+BQIDrXqlHQUEB6tevb1rm5MmTuPHGGzF//nxkZmYK3X9VPOUgzZ07F6NHj8bIkSPRsWNHLFiwALVq1TJUkfXr10dmZmbktXr1atSqVauaQAoEAlHl6tWrx9Q/jXG+IZYnFNGuEUk55tEokied485Jcsg9MsPKuZAtjkS6Oqzw9kGkk8SCTBfJ7QMKzM55nocsUdeyM+XkXnurEnKVxC8UbsSJimQhLwBo2rQpUlNTI69Zs2YJ7evu3bvx3HPP4fbbbzctN378ePTu3RtDhgwRun89PCOQSktLsWnTJmRnZ0e2+f1+ZGdnIzc3l6iNhQsXYujQoahdu3bU9vXr16Nhw4Zo164d7rzzThw9etS0nZKSEhQWFka9WLDj5LRDHLFC6x65yf3hhSe0JlMcOS2KjGDtF2nIzeozNftOeJ1AK3h/yyLCbHZO7koikkSnCtgtlLzIvn37UFBQEHlNnjxZt9ykSZN0U1sqv3bu3BlVZ//+/RgwYACuvfZajB492rAPy5cvx9q1a/Hss8+KPDRDXH4bOcORI0dQUVGBjIyMqO0ZGRnVPmw9Nm7ciG3btmHhwoVR2wcMGICrr74aLVu2xJ49e/DQQw/h8ssvR25uLuLi9H/8s2bNwvTp05mOg+eEckIYAdYXLK+4R9LylwS6RyLEEasw8gLhftKG30hCblbhNpZQm9HkkUYTR/IiK8zG1hf90BxgHWojCcWRTRZJHnILlacPo1W+LtOG37xESkoKUlJSLMtNnDgRI0aMMC3TqlWryP9/++03XHrppejduzdeeukl03pr167Fnj17kJaWFrX9mmuuwcUXX4z169db9o8GzwgkXhYuXIjOnTujZ8+eUduHDh0a+X/nzp3RpUsXtG7dGuvXr0e/fv1025o8eTImTJgQ+buwsBBNmzY13b9dwihU3h3iyLSeRPeIBhHhNVpYHQcZ4sgrwqgqLEJJhEiSDZXA4RzNRtsHllwkN4gkmnJnyrPlG9UUsWRGeno60tPTicru378fl156Kbp3745XX30Vfr/5PWnSpEm47bbborZ17twZzzzzDK644grmPhvhmRBbgwYNEBcXh4MHD0ZtP3jwoGWiVnFxMd566y2MGjXKcj+tWrVCgwYNsHv3bsMygUAgoqbNVHXYgmVNvmYJp7lJHNm5ynfUfm0NSdC5R7yj1syoKeKoMrTHwBtuYwm10eYi6Za18TdtJyIewGiuebQjdHnCaDzX/5rA/v370bdvXzRr1gxz5szB4cOHkZ+fj/z8/Kgy7du3j4xWz8zMRKdOnaJeANCsWTO0bNlSeB89I5ASExPRvXt3rFmzJrItGAxizZo16NWrl2ndd999FyUlJbj55pst9/Prr7/i6NGjaNSoEXUfgwlx3CcFWyyc7sQXIY4s92GWpClo5Jqs8JoT8IbWaMSRzDyj+PgK05eUfVIeD++oPtmL2to6EarhuWi0nT4Xyeq8FiWS3CqUKtdXYukMq1evxu7du7FmzRo0adIEjRo1irzClJWVYdeuXTh58qQjfXTpc4U+EyZMwPDhw9GjRw/07NkTzz77LIqLizFy5EgAwLBhw3D22WdXy65fuHAhrrzySpx11llR24uKijB9+nRcc801yMzMxJ49e/DAAw+gTZs2yMnJse242MUU7UlOVp7kgsSad8SS1Om18BqLe2SGaHEkAlaxY1avvJzv5hGfUEEccrMKt7GG2mhzkXihCbPRTBnAAmuojahtwukBaEJptPlJoTr8IbRgghJJQGgOQqtcpRYtWkDTLAZQWLzPg6cE0vXXX4/Dhw9j6tSpyM/PR9euXbFy5cpI4nZeXl61GOauXbvw5ZdfYtWqVdXai4uLw3/+8x8sWbIEx48fR+PGjdG/f3/MnDlT+PwOevA9kbhTHLEiyj1yQ3iNFjNHQpQ44hVGstwfo32wiiWa3CQekWQ1yzYpNMnasgQObbI2r9jRg0QAyRBJ4fIAnVAK1WPLU1J4B08JJAAYO3Ysxo4dq/ueXgZ7u3btDBVmcnIyPvvsM5Hds8ROUURbR4Q4cqN7ZGfIQrR7ZIYd4sgOYWS1XxaxROom0SxPQooIF4l3hJmIEWosbfC4SKJFEkDrDvEJpVBdd4mlIi2ACs6nxlOadye35MVzAsmL8Mad3SCMSMqxiiNhy5BIWlpEt74L3CMSWMSRU6LIiHB/aIUSTcjNCLe5SLr1HQizmY5Os0EkAXCdUArVda9YUtCjBJIERCTisa6HRlvPDnHECm0CKXdZSWcDi3skIrRGK47cJoyqwiKUSESSjFCbrCVI7F56RMZcSSJEEk05gE8o0dY7Uz/6N6UEk/fwzCg2LyBilALLCAuWehWJPuKQGq84ssM9koWI8JphecbRUKLFkcwRZjKg7S/JZ2H1mYpajoTmNyJr6RFRDwI84XSSkW2irk9R+7Xp+qrfhhrJ5jWUQHIB4ZPPrhNXlGsEyBNH1MOPXRpeM5wbhzG0JkMceRW7RZIRRt8ljXNo55xIxn2g2x56T55IAsReq6L2zXm95RVLCm+gvmWH4D3RWIWRW8SRHdgZXmNdlLYqdokjO1yjxISKyEsWNMfBK5Lc7iLJqM/bjt0iyS6hVLmuEkuxi8pBshF+i5atPs1Fg7Qsb2jMC+6R3v5EJGfLmmiQRhyJgEb4mJUtFTCKLD6+gig3SUTith4icpHckKzNknNkmVNkkrRNUh+gS8ymKRvpA3e+UfR3z9KGwn0ogSQRUU8WdggjmvJET32CQ2sykeUe0SZn87hHdogjWU5Q1XZZBRNpEreVSDJL2ha1VhvvxJGykrVZ2+GdG4m0Pk1iNotQAvhGsVVtg7cdXooqklFewXeBO12hhvkrBOG0KALkCSOAXxyxQuseuSU0URUW90iEOGIVRjLDYyT7ZBFLJG6SDJEkath/VWSMJAPEukihemxD/0nqR7VFKXx4hRIgTiwBAGqu3vAcSiAJJJjo50rq4hVXTgsjgC/nQLZ75IbkbCNY81tkiSMnhJEe4X7QCiXZIokGt4bZmNrnEGvha4NVuA2AVKFEUyfSL4GOkMpZ8g7qm3IY3kS/cGKi0+KoIkGeOJLpHtmZnG3kHrGG1mSII9lJ1ayw9Ivk2FlnGTf6zmgcQt7EfrplcejaYDkfQ++JcZhpchxZlj5iuWaGqXzNVmIntlEOks2IOKF41kJzwjWiaat6PaZq1duRtDCtHqKWFbFTHLlRFOlB6yjxOklucZGoJn0U5CKZ5xzx5SORhtwAOW5S1XosdcO4Jd9IIR4lkGxA1FMGqzBiqWenOGIRJLJyjwz3J2EtNUDc0PGqxKI4qkxiQoVQkcSCiFwkNyRry1gEV5RIIm0r0iaH4BEtlgAlmLyOEkgScFoQ8dSnsrYliyPpOUkuCK8ZweMekYojLwqjytC4SVYiyW0ukh5ecpFC74sVSQCZmxRpm9FVqlyXtX4Y5S55GyWQBBJM8COYYG+itag2RAsjkjaZ8xwo3SM75z6iCa8ZuUdeEUdJ8WJsh9PlfJchUjdJhkiSNaKNN1nbuF16F8kOkQS4UyhVrs/TBnBGLAV9Km/JKyiB5AKcEkUAfW6QHeLIDtzsHrFihzgSJYqM2mQVS6RuEo9IkoWdYTYZ7YgQSQC5mxRuE2ATSoDzYskuTlQEUMY578rpCglzTHgEJZAcQIQg4m3HKWEUKsP+vt3uEQ1Oukck4ohVGMkQRST7YhFLJG4Sa04SrYukhxvCbGyzZVuJIGuRBFgLGho3iabdavsRLJZ42lG4FyWQbMKLogigm/TRKXFEC93waL6Lnij3yAlxZKcwMts/rVDiFUmiXCTeZG2aMJsdLhKvSAqVIXeTAHqhBDgjlqq2w9uWwh0ogSQJUYJIRFuyhRHpPnjEEUs93okhjdALr8l2j3igFUdOC6OqsAglmlFuehiJpJrgItkhkgDC4fuUQom2/Wr7ExhC81o4TlEdJZAEUpHog49zEddwO7ywzjskQxiFynG+L/mXKiMnyg3uEY04cpswqgqtULISSbKG/1fGzmRtWheJfUkRfpEUKkcxfJ9DKAH8YgkQ5y5V+MQn3yvkoASSS3BSFAHyhFGoLOf7Jr9SWveIN7zGO/sxrXtkhzhyuzCqCo1Q4hFJMl0kPXiTtY1gGfJvJbZIRBIg1k0C2IRS5f3Q7KvavpUjVONQAskhRIXgeEQRwLawrCjXiKQMiziixS0zZ9PgZnGUFK9/9zpdLtamS4ovly6SZGFnmI21HV6RRFomVM4eoVR5XzT7q7Z/lW9UI1ACySZE5iQ5IYpo9ytbHJnWk+Qe0UATXmNxj8ywSxwZCSHa8jzCSZRIMkKEi+SGMJuZi+QWkRQqSx52A6KvZU6JJcC9gqm4IoByzmH+JRU1d94mJZAkEguiiHbfpG4MrzgSlZhN4x7JCq/RYuYekYgjHmFEK4po22QRS6QhNzORZMeotqrouUg0YTZaF4l1dm1RIgmgc5NC5b0nlgAVjosVlEASiKgkbYBfEIWxwy0KlRdXllUc8bTJil54TaZ7RLPGmh4s4kiGKCLZF61YInGTWEWSHjRLkLjBRTJv3yKnSIBICpcD6BylUHnKofsCxRLL/qP6UvVBuebOu+g5aq535kKCCb7Ii4eKhDMv1j7Q1SEvJ1MciXKP7EzO1kNWaI1WHCXFl9kqjkTsX1bCOc13wusU0vzWDH/DRhOqmpwjIs5NUQ5y9fLs10We62HV/Yt4cBUZWVDIRTlIDuK0S8TbD5GuESBPHNnpHtFAO+8RT2iNRjg4KYr0CPeH1FGycpKccpH04E3WZoE1Hwkgc6dkuUmhOpxD9zmdpap9YO2HwhsogWQjogQRwC+KAPcII0BOWM2sXRr3iAa98JoI98gOceQ2YVQVGqEkQyTR5CLZmazNsgCt+X7EiCSATiiRlj9Tz3mxVLUfrH1RuBMlkCTiNkEE2COKaMvziiNZM2aH4Q2v6SFq1uyaIo4qkxRfJl0k0SDDRZI1J1IYq4RtESIp3A5Ak3NEV/5MPfbZs4Hq11clmBSAEkjCiSVRFKorrw5J6ItHHMl0j3iTs/UQnZhttzhKTiBr51QZ/w+b1E0inQagKiJcJD3c5CKJEEkAuVCic4f+1zbHhJCh+vyCiUcsATrXXxufRYorAijjvJGUqmH+ChFo8c4Nxa8Mr0gTvSaablmHxBEtbhnar4eZe0QijniEEakYIq3LKppI3CQzkSTTRXLLzNoyRZJV+1XbAtiEEm29M/UFTAop0F0C+O8TCvtQAslhapIoAsiFi6ywmiz3iAaa8JqRe+SEOOIRRTRt04olWSJJloukB02yNq2LZNmeIJEE0AklwHtiCRAvmBTuRQkkm3GLIAq1YV9dulmsLd63EEdOuEeykrNlQCuOZAojs/3RCCXSvCS74U3WpnWRWBO2RYik8H4A8uRw9lDamf/ziqVQG2IEkxJLsUXNDS7aROU5OEQMx+ediyM8VwmPYyTTNZIpjmSNXKPBafeIRhwlJ5TZLo549m91bGafjdFnavQd6Ala3sR7GmeS5TdrORjC6tyimeconv6hiPeaxPfAJ34OOlEPwzWBkpISdO3aFT6fD1u3bjUtm5+fj1tuuQWZmZmoXbs2zj//fLz//vtS+qUEkgRkCCI3iCLaHCOaiyTRPEkSxBEtdiZn0yJKHDktjKpC0x8ZIkkGNL8ZIyfTSCSZzhfGKZKs2tfbH4tQcotYUoLJHh544AE0btyYqOywYcOwa9cuLF++HN9//z2uvvpqXHfdddiyZYvwfimBJBC3uEShdpy50Mh6cpQljoxuMjKSs2W6RyLFkVuxQyTpQTOKUO87lpmob4QdIkmmUKq8DyfFUqgdgbNoK6FUjRUrVmDVqlWYM2cOUfmvv/4ad999N3r27IlWrVrh4YcfRlpaGjZt2iS8b54TSPPnz0eLFi2QlJSErKwsbNy40bDs4sWL4fP5ol5JSUlRZTRNw9SpU9GoUSMkJycjOzsbP/74o+zDiCDDJXLigiJTGMnKOaIVR7zJ2XrQuEesDgeJOHKba2QEaT9ZR+jRfMa8zp+ei2T0G6N1kawgEUluEUqV9yPi2uYWd8lrFBYWRr1KSkq42zx48CBGjx6N119/HbVq1SKq07t3b7z99ts4duwYgsEg3nrrLZw+fRp9+/bl7k9VPCWQ3n77bUyYMAHTpk3D5s2bcd555yEnJweHDh0yrJOSkoIDBw5EXr/88kvU+0899RTmzZuHBQsWYMOGDahduzZycnJw+vRpacfhFpeItw3aMFrl/RGVJblAS5qBmxTe5Gw9aOc9MnNESMURD7Xiy5hePPCKJJkukh5uc5EAsvOWdLSo7BC83r6cuu5Ft+NusVRcnoji8gDnK/QjaNq0KVJTUyOvWbNmcfVN0zSMGDECd9xxB3r06EFc75133kFZWRnOOussBAIB3H777Vi6dCnatGnD1R89PCWQ5s6di9GjR2PkyJHo2LEjFixYgFq1amHRokWGdXw+HzIzMyOvjIyMyHuapuHZZ5/Fww8/jCFDhqBLly547bXX8Ntvv2HZsmXC+u0ml6hqO0z1OexyorKkT688a7fZ7B7phV5EuEdOiCNRQoe3HVkiiddFkpWszeIiiRJJsoRSuA/sLrDY66Fyl4zZt28fCgoKIq/Jkyfrlps0aVK1yE3V186dO/Hcc8/hxIkThu0YMWXKFBw/fhyff/45vv32W0yYMAHXXXcdvv/+exGHGYVnhvmXlpZi06ZNUR+m3+9HdnY2cnNzDesVFRWhefPmCAaDOP/88/H444/j3HPPBQDs3bsX+fn5yM7OjpRPTU1FVlYWcnNzMXToUOb+ijpBRDggQtrguIBRlSe9EEsQR7TwJmfrwetUhJEhjnhdH9L2T1IM109OKLOcDkDUFAC0C9lWRcb6bGGM5kYKvSdo2RCLqQCq7hOgnOeo0jnNuoZcZViG/1dth7WNUDti5l1yCykpKUhJSbEsN3HiRIwYMcK0TKtWrbB27Vrk5uYiEAhEvdejRw/cdNNNWLJkSbV6e/bswfPPP49t27ZF7uPnnXce/vWvf2H+/PlYsGAB+QER4BmBdOTIEVRUVEQ5QACQkZGBnTt36tZp164dFi1ahC5duqCgoABz5sxB7969sX37djRp0gT5+fmRNqq2GX5Pj5KSkqj4a2FhIYDQCeFzaMJGGe3wzCfklDCy2reZOBLhHvEmZ+vBmphthNuEkdH+SIUSy7xJYYwmkKSZYVtv4kgZ67MB5vMi8YokgGAR2v+dp7RCCbBXLPHs26gNvnbOfC9BLfbcpcqkp6cjPT3dsty8efPw6KOPRv7+7bffkJOTg7fffhtZWVm6dU6ePAkgZI5UJi4uDsGghAdW4S26iF69eqFXr16Rv3v37o0OHTrgxRdfxMyZM5nbnTVrFqZPny6iiwBqrigCyIURwC+OzKAdtSZjaL+d7hEJdgsjo/3TCCUjkWTmItGs18brIulB6yLJEkkAnZsEkAul8P4BhkkhXSKWRLajAJo1axb1d506dQAArVu3RpMmTQAA+/fvR79+/fDaa6+hZ8+eaN++Pdq0aYPbb78dc+bMwVlnnYVly5Zh9erV+Pjjj4X30TM5SA0aNEBcXBwOHjwYtf3gwYPIzMwkaiMhIQHdunXD7t27ASBSj7bNyZMnR8Vi9+3bR3MoANwTO6+cLGlnwiRVXgNh3yzzLRhCa7zuES+07pGVOCJxj0QkUYtEVH9ohSPvvEi8Q/5ljJokOVep5i+jOI+r9oF3YIiIvCWnr78Kc8rKyrBr166Ic5SQkIBPP/0U6enpuOKKKyJ5w0uWLMHAgQOF798zDlJiYiK6d++ONWvW4MorrwQABINBrFmzBmPHjiVqo6KiAt9//33kg2zZsiUyMzOxZs0adO3aFUAoXLZhwwbceeedhu0EAoFqcVMrYsUl4ukD9YVU5CSTAkNrNO4Rb3K2HjJDa24SRlWpFV9m6SZZ5SQZOUm8LhLv+mxGLpLoUNuZMoLXV6t0brO4SgDjkiHKXYoZWrRoAU3TLLe1bdtW2szZVfGMQAKACRMmYPjw4ejRowd69uyJZ599FsXFxRg5ciSA0AybZ599dmT44YwZM3DhhReiTZs2OH78OGbPno1ffvkFt912G4DQCLd7770Xjz76KNq2bYuWLVtiypQpaNy4cUSE8eCWBGvAOVEE0AsjwHlxRIuMof20DoaZQ2KXOKoTbz43SlE53YNFVUjCbiSJ26TQ5CKRopeszYIdIgmgE0oAW/gt3J8wbhBLrP2o2o6TYqm4IoDScsaVvf9HWUVs50yZ4SmBdP311+Pw4cOYOnUq8vPz0bVrV6xcuTKSZJ2XlxeVvPX7779j9OjRyM/PR7169dC9e3d8/fXX6NixY6TMAw88gOLiYowZMwbHjx/HRRddhJUrV1abUJIUt4giEQu2ulUUAfzCCDAXR15zj3jEEaswshJDpHVYRJOVm8SSj0TjIunBm6xN6yIB1iIJgKlQoskLYhVKgLNiCXBPordylryFT6vqXymoKSwsRGpqKjqNeRxxiQ4KKw+KIoC+306Jo9B75BNDkgokGveIViDJEEcswogUWrFkFXIzEklGCdtGAknPRdJL1tYLsxkJJD0XySxZ20gkATAUSWGs3KRQGcsi0eUZRQetWIqqK0BgsPZbVD8qSk9j20sPoaCggGjYPAvhe9IVq0YhoTang1Rcio/6L5TaX7fiKQcp1nCDKOKeSZbj3JMhjELl2MWRaT1O94gXJ8WRTFGktx9SocTqJMlykfQQ4SJZ1jNxkgDykBtAfvOndZQi9QQ5S4A7QnHKFYpdlECyGW5B4rBLBNgrigC6/vKKI9rQGk3ukYyh/axD+knFkV3CyGi/JEKJJHlbD5oJJPVykXiTtY1ykVhCbQCZSAKs3SRWoQTYK5YAd4TiROUtKdyHEkg2UNNFEeC8MALYxREtMiaGpB25ZuYeuV0cVe0Dr0iiTdp22kUyg1ckAWRuUqhc6F+7Jnh0WiwByl1SRKMEkiSUKJIvikLlyUQNjziS5R7xwhJaIxFHbhBGlSF1k1hEEu8yJHa7SKH3xIgkgCw3yYnZsGNNLCmh5E2UQBJIMB7w8SQ5O5xPBDgjigDnhBHAJo6MoHGPSJOzWec9qooXxVFlSNwk1nBbVfRcJN4h/7QuEq9IAqyTt2mEUqg8/leeqPiZeoz5SoBYsQQ4k7cU1QeOJHWFvSiB5DBKFNHWoZiJWKI48qJ7ZAWrOKrNWK+YYXg/achND1kukh4iXCSATyQBZG4SwC6UQnWIqoTqceb8VL1WOZ23xCSWbLzrniqPRxnnb7u8XPwaZ15BCSQHcFoUOSWIIvVdIIwA8eJIhnskCiv3iFYcsYoiozZoxJJVyE1UPhKpi0SzPpuRiyRbJAHWbhJAL5RCdc783+511pwOxYkIwyncixJINmL3QrBR9T0oikL17BVGopExMSSteyRSHIkQRlbtkoolMzeJNtTmtIsE8IkkwHyeJIBNKAFsYskRZ0aJJYVglECSjBJFLPUYFpUVKI6ccI94kS2OZAkjs32RCCUWkcS7FIksF8kKqzmSSNwkgE4oAfa7SkDsiCUllLyNEkgS8Nq6Z1H1Y1AUAfzCCKBfb43GPSJNzmad90gPEnFkpzDS2zdLnhIrei6SjOVHALZQG0AmkgBrNwlgF0oAu1gK1SWuGqrvErGkXKWahxJIAgnGAz4bE5YjdR0URVz9Zgx10c6ELVscedE9crs4qtoHM6HkhIukB42LZAavSAqVkSeUAHaxFKp75v9eEkvCXDHnTysFIfRz2iuEEUw486Kum3jmxbTv+DMv6roc/Q7V15gdI1rXyClxJMM9ooFHHNWOL3GFOKqMVX/MjolmSRU9h07vu6BJqDf6LZgJayu3knT6CZp8u/D5RfsAEj6fWc5prmsgxzUM4LuG8vZb4Q3UV+UATrlFXnOKALZ100hvCiQ3GVHOESBnWRHaYf0k4oiFOnF09Yoq6ENnViE32ikA3OAimeUjkThJgPkCt6Fy5G5SpA6DqwR431myOwQnm1NlCYjn/I2Xl6lh/grJKFFEjkxRdKa8PHHE6x7xYuSYiBZHtKLIqC6NWGIVSbwTSPJOHGk2oo1HJAHkC9xWPkdIxVLlc7EmiCUnQ3AK96EEkmScEEY1RRQB9gsjgE0c0bhHpMnZPJNCVoVGHPEII7P2SIWSyORtPRdJxvIjAJ9IAiDETTpTnt1VApRYsqzrYldJQY4SSJJgzs9xwC2KdVEUqkNmE/OII1rc4h6RiiPRwsiofRKhZCaSZLlIetAuP8IqkgByNwmgF0qhOkosGdZlDMEpV8nbKIEkkGAC+1psXnKLnBBFgLPCCLAWR251j3jFkWxhZLQ/K6HEIpL0IHWRaIb8s45oIxFJgLmbBNALpVAdelcJ8LZYciQE567xDwoTlEByECWKCOpKFEVhnBBHdrpHRrhRHFXdN49I0sNOF4kl1AaQTSRJ4iYB0eeCbFcJcF4sec1VUrgbNczfAZiHljowLJ93SD5rwjXpEP3oekEqceRL0KSKI1pkTAxp5B5ZDpuPK3FUHNH0w+hYaJZQ0XPkSIf8s2D1myEJ5ZL+fsPQnh+hOmznIsB3DXBy2gDqehzTrSjcjXKQbMRux8hut8hupyhUj34IKulNheQmZXajk+Ue6d3Mad0jM0SNTtODZXh/uF3WulVxg4sEkDlJAIjcJMA67BaGxVUK1bPfWXLSVbLLUZLJ6fIExHP+1svL1TB/hURY3SKmfXkohOZGURRGljgyQpQzURkW94hWHPGWpxE9ZiLJKNRGOzdSVWhykWSIJIBeKAFKLEXXO/N/GrHEGn5zo1BSsKEEkkRiWRh5QRQB9gsjwFwc0bhHpMnZNO6RCHEkMvRGOx+SKJGk5yLJmDgyjJVIAkAklEgXuaV1lQBnxRJrvpKbXSUllLyPEkgScLswUqKoOqTD92WIIzvdI8PyDogjvbZ5RZIMRLhIgPXINpFuUhgWVwmwXyw55SopoaQwQwkkgQQTAB+lOGJNumaBNdmaBVZRFKrrHrcoDEkitijnCOBzj2hDaySix66EbdIh/kYiyU4XSZZIAsjcpDBKLIVgcZV4w28sQkkN8/cOahSbQ7CMmLBzJBrvCDT6ekGmUTbhkTw04sifUEHlGMkURzLcIz14Fp91YjQbj2iTsdAu7WhCs++cJD+N9HcH0P2ew9CeM2FYztFQPb6RcDSwLp7Lcn1lvY4rvIFykGzGLsfIzjBaLIXQwpDenKxudiziSEbukRFmQsTpYf60S5BYtmejiwRYO0kALCeTJHWUgJrhKtmRq8QSfmMd+aZwN0og2YSbhZEbl/yoCosoAuQJI8A+cUSDXniNJbTGKo7qxp02ff9ERRJ1m2Y5R7ShNh6M1mhjFUkA+YzbNEIJoM9VArwhlpwIvymhVHNRAskG7LBg7RJGsSiKALHCCBA7U7bIRWnDiBJHVoLIqjypYGIRSaTIHtFGIpIAazcJYBdKgDfEkmxXqaYJpZKKeJQTLo9jREWF+Fn/vYISSBKxwzWKNWHkVlEEkM9tZCWORLhHpMnZtPk4pOKIVhhZtUMilGiFkJ6LJGNepNB283XaSBa1ZRFKgPvFUk0XSk6LJAU7SiBJoCYLIzfnFYVxShgB9OLIbvfIClHCyKhdK6FkJJJkDP03cpHMRBIA03AbAKFCCaB3lQB+sSTbVeIJv9khlLzoJinoUaPYBCN7ZJodI9JY1l+yawQawDZqJzwqiDaUJtI1EjVizSn3SJY4krkPvc9A77MSuVSL1fdMGn6l+f0BbL9x4Mz5RHNOsZ67POvBEZfnGPlGXt6eEW+xzPr16+Hz+XRf33zzjW6dY8eO4e6770a7du2QnJyMZs2aYdy4cSgoKJDSR/V1CSQYD5A+i9mRZ0R7kXBzGM2OEBpAv0QIj2t05n3n3SMzcWSHMNLbn5Gb5GYX6cz71iE3wNpNAqJ/k7SuEsDmLNnlKskMv7E6SnblJ9V0evfujQMHDkRtmzJlCtasWYMePXro1vntt9/w22+/Yc6cOejYsSN++eUX3HHHHfjtt9/w3nvvCe+j+qocQHY4zY3CyM2iCJAjjADx4kiGe+QmcVR13yJEEmkuktEitrJEEkAnlAD68BtgX74Sz1pwbhJKdoTdFEBiYiIyMzMjf5eVleHDDz/E3XffDZ9P/7Pv1KkT3n///cjfrVu3xmOPPYabb74Z5eXliI8XK2mUQLIRJYzI8LJbFIYknMY7pJ8W2twjFnFUN+6U6fsnKpKp+0ArkngwEklGkIgkwDgvKUzl35YsVwngy1eS6Srx5Cm5RSixLl/iFQoLC6P+DgQCCATEnX/Lly/H0aNHMXLkSKp6BQUFSElJES6OACWQbMPr4ki2MHKrKALEC6NQOeOrqBvcIxpxZCWKjMqSiiUzkaQHj4tkhNmwfyuRFCpDJpQAdlcJkBuC43GVZIbf7BBKXnWTSsviEEchbPWo+F/9pk2bRm2fNm0aHnnkEa62K7Nw4ULk5OSgSZMmxHWOHDmCmTNnYsyYMcL6URnPJWnPnz8fLVq0QFJSErKysrBx40bDsi+//DIuvvhi1KtXD/Xq1UN2dna18iNGjKiWIDZgwABh/ZWdhM2agE1eni6pUuayHwBfsjWLY0QTSiN1jUSJIxr0hBCvOKobd4pKHBnVJ2nDqE8yZvs2+qzNcsKsvtcz5ciT9cO/PxqBzvJbZzmnWJO6aWBJ5qYqT3HdtCOJ2+3s27cPBQUFkdfkyZN1y02aNMkw+Tr82rlzZ1SdX3/9FZ999hlGjRpF3J/CwkIMGjQIHTt2FCrUKuMpB+ntt9/GhAkTsGDBAmRlZeHZZ59FTk4Odu3ahYYNG1Yrv379etxwww3o3bs3kpKS8OSTT6J///7Yvn07zj777Ei5AQMG4NVXX438Lco2lOka1TTHyI1uEUC3lprVDZQ2KdtoUVrZ8IgiszZpw29myHCRAOsJJMPfsUhHCaB3lQC+fCVZM3azht6Um+Q8KSkpSElJsSw3ceJEjBgxwrRMq1atov5+9dVXcdZZZ2Hw4MFEfTlx4gQGDBiAunXrYunSpUhIkKNGPSWQ5s6di9GjR0dilAsWLMAnn3yCRYsWYdKkSdXKv/HGG1F/v/LKK3j//fexZs0aDBs2LLI9EAhEJYvx4qZwWk0TRnaIIkCsMALMxRGNe6Q7rF2geyRDHFVt20goGYXa7M5FIpllmyTsFirHJpQAe0JwssJvbhRKKjdJDOnp6UhPTycur2kaXn31VQwbNoxI6BQWFiInJweBQADLly9HUhL98kWkeCbEVlpaik2bNiE7Ozuyze/3Izs7G7m5uURtnDx5EmVlZahfv37U9vXr16Nhw4Zo164d7rzzThw9epS5n7JdI1niSGYojXaeFdlzFgFs4YtweIQmz0iWOJLhHpmJI95wGl0/jPfDO6qOdF4kwFyYJieUWTp+pN9/qCzdbwvgC8ERl5ccfqMNvdFep2TOn0T7YBtrITdRrF27Fnv37sVtt91W7b39+/ejffv2kdSYwsJC9O/fH8XFxVi4cCEKCwuRn5+P/Px8KUuieMZBOnLkCCoqKpCRkRG1PSMjo1o804gHH3wQjRs3jhJZAwYMwNVXX42WLVtiz549eOihh3D55ZcjNzcXcXH6T0IlJSUoKTlzUQ1n92sJAKn56lXXSKZjFAtuUag82U3R6gZLm3fE6x4ZYZcwqrpPGidJz0WicZaMQm1Wo9pI3STAOux2pvyZ35usEJydrpIsR8mzbpJzM2e4koULF6J3795o3759tffKysqwa9cunDx5EgCwefNmbNiwAQDQpk2bqLJ79+5FixYthPbNMwKJlyeeeAJvvfUW1q9fH2XJDR06NPL/zp07o0uXLmjdujXWr1+Pfv366bY1a9YsTJ8+nbkvbhFHNUUYuUkUAWS5RmbiSMSs2VUxcmZoxVFdv/nV/0SQ3A4XnZcURi8XyQwSkQSAWCgB9GLJjhCcTKEkI0eJJezmhtwkTTlJUbz55puG77Vo0QKadube07dv36i/ZeOZEFuDBg0QFxeHgwcPRm0/ePCgZf7QnDlz8MQTT2DVqlXo0qWLadlWrVqhQYMG2L17t2GZyZMnR2Xz79u3j/g4ZIXUWEankSJrVJpdYTRSWMIcNGEUkrAMQC+ODMsSuke84qiu/3TkJbKsWR/0+szrlpl9tiRuHun3C9D9bkLl2UNwpLCG30hgGfVGCk3YjWXZElJY1sdUuBvPOEiJiYno3r071qxZgyuvvBIAEAwGsWbNGowdO9aw3lNPPYXHHnsMn332meH05ZX59ddfcfToUTRq1MiwDOsEWbJGqblFGJEi0zFym1sEkI9Os7oJG93Aed0jVkhFjlV9GldJFEYuktmotvD3YzWRJKmjBLjTVZLpKLkp7OaGkJtsysv90ChGMepRUe4ZH0U4njryCRMm4OWXX8aSJUuwY8cO3HnnnSguLo6Mahs2bFjU3AxPPvkkpkyZgkWLFqFFixaRZK6ioiIAQFFREe6//378+9//xs8//4w1a9ZgyJAhaNOmDXJycoT1m2VuI+KyMSyOaBeLdZNbBNA5CqziyLC8RPeIxgEiwao9WS6SkbC0+qxJc8Novn/gzO/LjsRuElgcJVJoE7lJkZnATYNyk2IDzzhIAHD99dfj8OHDmDp1KvLz89G1a1esXLkykridl5cHv/+M5nvhhRdQWlqKP//5z1HthGcAjYuLw3/+8x8sWbIEx48fR+PGjdG/f3/MnDlT4FxIShgB9MKIFJnzFoXKy3GLwpDcbM1u2DLcIytxJIu6/tOGbpKsfCQjrOZHInWTgOjfBImrBLAndstI6qZxlGTlJ8nKTaJJ4JY9Z5LCfXhKIAHA2LFjDUNq69evj/r7559/Nm0rOTkZn332maCeVSdWxVGsCyOWNdLsFkaAiQPC6R4ZIVMcVd4HjUjiHdFmlrAd/vxJhBJAJ5ZohRJAJpZoR8C5QSjRht28FnJTIsm7eE4geQUviSOnXSNZwsiLblEYkeKIBsNwlg3iqPK+ZOQlsYgkgHy2bS+7SjKFktNukhJJClaUQJJATRdHTrtGsSyMAPqwGql7xCuOSEe9kYTKjEQSj4tktp1EJAHmblIYGqEE0IslNwklGW4SzZQAMkJuSiQpwiiBJBinxZFXQmpucI1Ez11UGdpJHkUIIxkLt5JAPVeSxZIikXKSnCQjwp+vaKEE0IslGSPgaIWSU26S0yE3JZIUYZRAEkgwQSMeFhhr4kiGa6SE0RlYxZFM94h3hm1SoaRXT4aLFIZkIsnK35sMscSaq0QqlGLJTfKaSLJzJu2KsjhoFAsc60Hq6MUiSiA5QE0VR06H02QJI1miCCALp9GIIxpkiqOqbRkuK8LpIvGIJMDcTYq0ReEqAXJzlUiFkoywmyw3KSZFksITeGoepFhAiSNzSOdpkbFoLEA/dxFtfhGNY0TiGtGvq0buHhm3IX5tNtqpBfTK047KI/n8SL6HSHv/+35Jv2Pa3w/tTN1k5cjPI5r5k4jKUczCTQLNwrc0M2+TQjtXksL9KIFkI0ocmSPaNXKDMKK9aZLekK1u7LLcI5kL18pqW8RnRSOUADoxHP49iZ6AknbSSRLcLpIA8uucEkkKK1SIzSacFEdOjVRzUhiRImqm66qIDqNF2iW4mRuVIXWPRIijur7oNk5oZGEyo3CbXqiNNBcJsA6phT8zszJA9HdFE34DxI+AS04ocyTsJjrkRpqXJCN52+lwm8LdKIFkA047R6TUJHEkQxjRLgdilzAC6MNP1etbi6OqosjoPSuxxDtrtplIAsxFEKlQAujylAA5UwXQjnoTncQtOoHbibwkJZIURiiB5CJorFxSRIbVvBBSs0JG8rUstwggD5WxhNRo3CPTdkyEkVl5UlcpUo/QRbLCyk0KlwHohBIgPqlbtFCicZPcLJJEI0MkKbyPEkiSIXWPnJwE0q3iyO2ukZNhNJqyMt0jWnFUta6RSJLlIoUhFUCVP1/RrhKtUBIddhMZcrNbJMkY3UYKqUhyg4sULI8DeIf5E3xnsYpK0paIjIQ9JY6qliNzjUjEEWmirIyka4BuVBppWSNxROoeyRJHldswaoe3j3XjTluKQxmfOcA2+s0K0t8nTRI3CSTnohPJ2zSj20igSVsgToVQSdueRgkkSXghKZtmZIgVbhZHJIh2jdwgjGjEES0ixJHI9kyFHIGD5jWhRILXRRIJTookUpRI8i4qxOYwbhdHdidkk1yIRYbUZAgjovYoc4ZIy1uJAZoZsw3LChZHZuiOVKNYp+3Me//LezIJuwF0ITWasrShN6uwG01ukp15SSLDbbGUtK3wJkogScDtTwxKHIUgDaeRIEMY0ZQlcUnsco7SDPpy3EKcmOUkkWKVu0QqlAC6RG3aaQJIlzIhEUoiRBJAnpdUE0QSKV7KR1LQo0JsDuLkfEdW0KytZoWd4ogm38gK0gVkSSd2lBHCIcmzCZXjn6nairS404biiOR9QF+A0Y62I5qSgPBzA+R8d8S/G0EhN5F5SXaH22RMJmmFCrUpACWQhCN61JrXk7LtFkckiBRHJDgtjGjFkWFZA/eIRPjwlDfsj4VIohFKMvKUSBApkkiFkhV2iyQSRD6wOTnTtsJbUAukTz/9FLfddhseeOAB7Ny5M+q933//HZdddpmwznkNLc7dJ4rdSdl2h9WsILmJkCbKinSNaBOvRQgjgG7GbDNxxIpRXVIXCbAWeKRCKVRWrFAS6SaJTOB2m0hyc9K2aJSL5C2oBNKbb76JwYMHIz8/H7m5uejWrRveeOONyPulpaX4v//7P+GdjDW8HloTeUEThdVF386QmixhRAKxc8IwIWRVRLhAsp2kSBmPCCXLtlwmkkgQJZLcHGpzq4uklfmEvGoqVEnas2fPxty5czFu3DgAwDvvvINbb70Vp0+fxqhRo6R0UGGOE6E1EtwUWrMzpMa7JEhlaEQRKSSOS7VtNo5aq7zPqgnbpqPU/ndceqPbqrYRxmoiStqRbySj3kiSuEkSuEUkb4uAdMZtUYicadupUW1ujzQozkD1y/7xxx9xxRVXRP6+7rrrkJ6ejsGDB6OsrAxXXXWV8A7GGk6stRbLoTWapUOMcJs4ckIY0bYnwvmp3JbVCLcwlqPUCIVSuC1ArFDykkginQLArpFtpDNtk0A6qk2hMINKIKWkpODgwYNo2bJlZNull16Kjz/+GH/605/w66+/Cu+gwhi3jloTgShxRJJzZIXbxJFoYWTWpp57ZCaO6vrMf0cnNP2blp5IMhr2T7IMiSyhJMJNslMkWeFFkeRmF0kRW1B94z179sSKFSuqbe/Tpw8++ugjPPvss6L6FZMo9ygE6UzZZnhJHJHkoJDkvdDkF4VflmVN2qRfhNb6uycpQ9IHuz+LM2XI85Os3rcqIyInSVQ+khcRee0jxa25SAo2qATS+PHjkZSk/wTVt29ffPTRRxg2bJiQjinMcWNitptGrZHOkG2GKHFkhSjXiFQIhNtjWWfNeOQZ+c2orq9CtzzNqLbIezTJ1xRCyboMv0gigWaRYyNEnAtuG9XmxLB/Rc2DKsTWp08f9OnTx/D9Sy+9FJdeeil3p2IRr7tHduGWvCO3iCPRI9KI2pPgHJFilI9kNcs2VfI1QfiNJOxGGnKTHW5zUz6S2xCZi6SWIKl5cA0/OHToEA4dOoRgMPqm3qVLF65OKezDbe6RFXaF1qzwkjgidlgshJGew8Mjjur6KgxzkvTL/0/YWCxHQpxTZLCmW9W2eJcuESGSrLArH8mKWM9F8hq+Mj988Xy5UzU594pJIG3atAnDhw/Hjh07oGkhZ8Tn80HTNPh8PlRUeM+58BKxagl7JbTGK45EhNREj0pjEUeysBrVJlIokbpJvAncvCKJJGnbChFD/73oIolEJWvXLJgE0q233opzzjkHCxcuREZGBny+2FPeIlHhNfvcIyt4Q2si8kqssEsckYbSaPOO6vr1n+pPBKuXN3KRSIb+0wglXjeJpA0rZIukWHORSCBxkZwY8q/CbLEBkxT+6aef8NRTTyErKwstWrRA8+bNo14Kb2BneM0ORLhHvPC6R7ziiGgklu80kTgyWzONVhyZvWfUFumabSTHw7P0SuU2zN+3f0JNWuxK2BaB3TP2x6oz72Y2b96MP/7xj0hLS8NZZ52FMWPGoKioyLLejh07MHjwYKSmpqJ27dq44IILkJeXJ7x/TAKpX79++O6770T3RUFArJ7Edlx0ZbtHbhBHVvAKI17MBJQRNELJsozDIon3N0SyZpsbEOEYK2Kb3377DdnZ2WjTpg02bNiAlStXYvv27RgxYoRpvT179uCiiy5C+/btsX79evznP//BlClTDEfY88AUYnvllVcwfPhwbNu2DZ06dUJCQrQlO3jwYCGdq0mo8Jo5Xp+rxWlxRCqMSGBxj0jatErYDvePNz+JZDZunnAbyeg2JxExok0EosJsJKjRbO7j448/RkJCAubPnw+/P/R5LliwAF26dMHu3bvRpk0b3Xp//etfMXDgQDz11FORba1bt5bSRyaBlJubi6+++kp30kiVpB2NWycOq2nhNafdIx5kiyNeYQTQiaO6/jjDfCTAeLbtMKRCSaZI4sELuUhW2JWsbfdoNoV9lJSUIDExMSKOACA5OXROfvnll7oCKRgM4pNPPsEDDzyAnJwcbNmyBS1btsTkyZNx5ZVXCu8jU4jt7rvvxs0334wDBw4gGAxGvZQ4UtBiV06DU4gYzs9aV4Q4MprYMfI+g3Nkmqtksb8wVn0nyUtiJRbykRQKGgoLC6NeJSV8D4WXXXYZ8vPzMXv2bJSWluL333/HpEmTAAAHDhzQrXPo0CEUFRXhiSeewIABA7Bq1SpcddVVuPrqq/F///d/XP3Rg0kgHT16FOPHj0dGRobo/ihMiNX8IyvcHl5z0j0yrcspjkiECldYzaIuyf55RZJpXY7P3gonHUkSRJxzXsxDirVrrK/MJ+QFAE2bNkVqamrkNWvWLN19Tpo0CT6fz/S1c+dOnHvuuViyZAmefvpp1KpVC5mZmWjZsiUyMjKiXKXKhOdcHDJkCMaPH4+uXbti0qRJ+NOf/oQFCxYI//yYQmxXX3011q1bJy3up4gd7LhIOh1e40Gmi2EGiTiy3L+BwKnjS9TdXqSV6rahF26r2hezsBvJvElG4Tae4fsihv6zwhtmEzEnkqJmsW/fPqSkpET+DgT0f18TJ060TLRu1aoVAODGG2/EjTfeiIMHD6J27drw+XyYO3du5P2qNGjQAPHx8ejYsWPU9g4dOuDLL7+kOBoymATSOeecg8mTJ+PLL79E586dqyVpjxs3TkjnFAqvwxNqkekemddld42MxFH4PSORBOjPk1S5TzwiiRWeXCS3J2u7gVhP1I4lUlJSogSSEenp6UhPT6dqOxyNWrRoEZKSkvDHP/5Rt1xiYiIuuOAC7Nq1K2r7f//7XylTDDF9w6+88grq1KmD//u//8Pzzz+PZ555JvJ69tlnBXcxmvnz56NFixZISkpCVlYWNm7caFr+3XffRfv27ZGUlITOnTvj008/jXpf0zRMnToVjRo1QnJyMrKzs/Hjjz/KPIRqeHUEm1sStGsistwjWeKIpAxJyE0GPJ9lTSbWcwcV8nn++eexefNm/Pe//8X8+fMxduxYzJo1C2lpaZEy7du3x9KlSyN/33///Xj77bfx8ssvY/fu3Xj++efx0Ucf4S9/+Yvw/jEJpL179xq+fvrpJ9F9jPD2229jwoQJmDZtGjZv3ozzzjsPOTk5OHTokG75r7/+GjfccANGjRqFLVu24Morr8SVV16Jbdu2Rco89dRTmDdvHhYsWIANGzagdu3ayMnJwenTKsnSDrx+keUJv8m6MZu5RzzzG/GKI5KyPCLJXPhJmteJ4zt0Os/IK5CMpiUZlesEbh3F7BY2btyIP/7xj+jcuTNeeuklvPjii9UiULt27UJBQUHk76uuugoLFizAU089hc6dO+OVV17B+++/j4suukh4/3xaeDE1D5CVlYULLrgAzz//PIBQwlbTpk1x9913R7LfK3P99dejuLgYH3/8cWTbhRdeiK5du2LBggXQNA2NGzfGxIkTcd999wEACgoKkJGRgcWLF2Po0KFE/SosLERqaiqaPfko/MnRljrJCULqIJEkEJI6SFYXFLsWqCURSFYJo07mIPFMDskz95FZXVaBJNs9qoxeqK0yZuE2s1CbWZjNbNi/WS6RVYjNtK5FiM1sOL/VArZWa7NZDfW3ykEimQvJaqi/1ZIjAIhCbFZD/QEQDfUnCbGRLlpLGmKrOhdS8NRp5D34MAoKCohCViyY3ZNosaO/boUpBwkAfv31Vyxfvhx5eXkoLY2+2M2dO5e7Y1UpLS3Fpk2bMHny5Mg2v9+P7Oxs5Obm6tbJzc3FhAkTorbl5ORg2bJlAEJOWH5+PrKzsyPvp6amIisrC7m5uYYCqaSkJGqIY2FhIethKRSegGe0mkKhUHgRJoG0Zs0aDB48GK1atcLOnTvRqVMn/Pzzz9A0Deeff77oPgIAjhw5goqKimpTC2RkZGDnzp26dfLz83XL5+fnR94PbzMqo8esWbMwffp06mNQKBQKhcIufBU++Mr5ZvP2VdTc2cCZcpAmT56M++67D99//z2SkpLw/vvvY9++fejTpw+uvfZa0X10HZMnT0ZBQUHktW/fPqe7pFBIxWoovkKhUMQaTAJpx44dGDZsGAAgPj4ep06dQp06dTBjxgw8+eSTQjsYpkGDBoiLi8PBgwejth88eBCZmZm6dTIzM03Lh/+laRMIzf8QHvJIOvTRa5DE/BV8ODGHjnmOjju+c9b8I4VCoRAJk0CqXbt2JO+oUaNG2LNnT+S9I0eOiOlZFRITE9G9e3esWbMmsi0YDGLNmjXo1auXbp1evXpFlQeA1atXR8q3bNkSmZmZUWUKCwuxYcMGwzZlUNPm06iMHes5ycQqkVYWponBJsnIXPs0ES5WSdekZXmcKtYEbR54RK5Tvx2vISpB2wnUYrXehikH6cILL8SXX36JDh06YODAgZg4cSK+//57fPDBB7jwwgtF9zHChAkTMHz4cPTo0QM9e/bEs88+i+LiYowcORIAMGzYMJx99tmRKdDvuece9OnTB08//TQGDRqEt956C99++y1eeuklAKGFde+99148+uijaNu2LVq2bIkpU6agcePGUha+s4NgWZxtcyGVlsWpuZA8yPGKJMPRbCe0ONPRbCeCFYYJ20VaqeVoNh5xxDp6zQqnZsP2Ol5/uFEorGASSHPnzkVRUREAYPr06SgqKsLbb7+Ntm3bShnBFub666/H4cOHMXXqVOTn56Nr165YuXJlJMk6Ly8vag2X3r17480338TDDz+Mhx56CG3btsWyZcvQqVOnSJkHHngAxcXFGDNmDI4fP46LLroIK1euRFKSmgE3VigqD1gO9ZfFiYok5tm0TwSTmGfTPqElMc/9QyKSAP2RbWYiyUgckbhGPKE1HveIdRZtwHqIv4JsiL8oRM2iDdRs178mQT0PUkVFBb766it06dIlarbLmgzvPEihctZzIZEupEjiIJFMrOaWuZBIFs706lxIofdN5jSyEEis8yEBctdjI0WEOLJyj1jnPwLMBZJlXQ/PgQRYz4NE4iBZzYPktjmQALJ5kFjnQALsnQep+eOPwc/5sB88fRq/PPTXGjkPErUMjouLQ//+/fH777/L6I/CBNIJzGINkgnrnMTqZsbjJHBNUmjhnFgLizhLccKTM0QSUpMpjqzgcY+scHv+kYhzjmSSSLcRa9dYf5lPyKumwuQTdurUSeqSIoqaRU3PZeBxMSzbJhBJvEKJRSRZjVQjEUa84ogn94jXPVIoFO6HSSA9+uijuO+++/Dxxx/jwIEDKCwsjHopzuDWUQwkljSJtW1nDoEZViEDq5CDZcjC4olfpotkheXNmsBFIUlyNhMuNCLJqCyJMAJI+8onjpx0j2SH10Rg10ONl0ewKbwPkwc6cOBAAMDgwYPh850RAJqmwefzoaJCjWyixVfmJ16TzQo7R7KRcLo83jIPyYpTZQlEuUhe5URFsmk+kVXCtmX9/wkGs7yksPCwyk0ySuI2G+FWuYxRm1aIEEYAvzjyunskYg02K0StwSYKJxK03fpwrCCHSSCtW7dOdD8UhGhlPuJkbS9xujyBaOFaHqxGsxWXB0yTtYsqAqYJ2VbvW41oEyGSAPPEbVFCiUUkseYr2SWMAPniSLlH7iXW8o8U/DAJpD59+ojuh8IBysviLEezlZfHWY5ms2s+JCsX6WR5guVoNl7cLpJI2gDEOkrV+2jtJEX3Rb+sKGEEeEMc2QHJ6DU7sHP0mkLBCtcwg5MnTyIvLy8yq3aYLl26cHUq1vCV+YiG+6swm3x4XSQR2CWSAHM3CSAXSnoiyWy+JFK3iFUc0YxOs0McicAN7lEsjl4TGV4jRYXXYgOmX/Lhw4cxcuRIrFixQvd9lYMkF7vDbHa5SCRhNjtcJNmhNkCMSAKs50kSJZTMZt8WjYglQ0gFDUkytoicI9mhNRJEuEc1PbymJoisWTB92/feey+OHz+ODRs2IDk5GStXrsSSJUvQtm1bLF++XHQfFYyQPDnZaVGTPFmKuADzjmgD5I9qA6xvrCcqkokSiklv8kROipZkKEL0hAvfDNfV6/KKI+LjpPjczN+XL45IsGNiSBJEJWer8JoYfGWAn/Pli92xMZYwOUhr167Fhx9+iB49esDv96N58+b44x//iJSUFMyaNQuDBg0S3U/P40SYzW7ckotEgojlR+xwkkJlCHKKJDhKpEuVWC1NYlSHvKyYBGyAfPg+kciySRy5JbTmRfdIjV5T8MDkIBUXF6Nhw4YAgHr16uHw4cMAgM6dO2Pz5s3ieqcwROSIC1FzIpHgFhcJ4J8bCbDHSQqVEe+MsMyfZOTw0Ake9qTsam0J/lzCbVqX8Y44siu05lX3SI1eUxjBJJDatWuHXbt2AQDOO+88vPjii9i/fz8WLFiARo0aCe1gLEH6ZCEyzi3yCUrUxJEikjhJnnjdJJJIJpIkFUokiA69Ee3TasZtwokgo+sYCDJJwogkpEYyWs0OcUSC10JrIlHukYIXpjvVPffcgwMHDgAApk2bhgEDBuAf//gHEhMTsWTJEqEdVBhDmqxNMqKNZMi/nYhI2AbsSdoGyMJpIkNuYUSG3vTa0gu1WSVss+Ql6ec3GYsjK0S6RWfK8rtGpGWIhLlNeUd2htaUe6RwE0wC6eabb478//zzz8cvv/yCnTt3olmzZmjQoIGwzsUibs5FsnNeJJJh/3aJJJJ8JLtFEgBLoRQqS5hTRCCUSHKdwogc1UYTWhO1RIhoYQS4SxyRIEoc2RlaI0W5RwoRMMdyFi5ciE6dOiEpKQn16tXDsGHDsGzZMoFdU5BA+gRk91wgokJtJBdoEeE2ESPbAPLwCmluEunNWVTISa8NmlFtotDbp9nxyQgpkn7+Ir9zUeJIxHIidoojUkjcI9JrnVPuka9CCSmvwOQgTZ06FXPnzsXdd9+NXr16AQByc3Mxfvx45OXlYcaMGUI7GWuIdpFEzoskykUixW1OEgDLiSQBCHOTABA7SoC1q0TjKFmF3aLKU4xqo0GEyBIxXD+6rDjHiLScXcIIcF9YDXBvaM3r7pG/PPTiQXN+fl/HYBJIL7zwAl5++WXccMMNkW2DBw9Gly5dcPfdd9dYgeT2JwPS2bXduASJnTlJIkNugLUAIi0HkIffiNZlMxBJdoXajEfEkbtHombApllgVqQwAtwnjkiJ9dCaDHzlPsTeSpqxC1OIraysDD169Ki2vXv37igvr8FyE6ETgKic4CcYJ0JtbhrVFsau0W1A6MYmKuxGUw6gG/VmFZoyqldtm0mojcYJMitPtYSIRaiQdLg+jWMk2jVyozhyY96RE6E10e4R6b1B4R6YBNItt9yCF154odr2l156CTfddBN3p2oKTokkEkjtbDtF0unyBNtykoDQjUlUbhIgVyiRzMpt+B5hYrMVVkKJVkiF0RVrlHlU1cvIE0YiQ2qxLI5IUaE1hVMwP7ovXLgQq1atwoUXXggA2LBhA/Ly8jBs2DBMmDAhUm7u3Ln8vfQYvnIftHj3GqkiQ22kiBrZFionLtwGQFjIDbDOTQLoQ28kZQHr8BtN6MyoPEkuEmteEY17ZNiGgKVBALrlP2jKEotpwnJ25xyJFEci8468EFpTeA8mgbRt2zacf/75AIA9e/YAABo0aIAGDRpg27ZtkXI+X839UZCKJKcStu3ORwLcKZIA8rwkwDyBG5AjlGjLms2lZJSbRJO0LSNhm2bOI5qw4Jn3ap4wApQ4qoxyjxS0MAmkdevWie5HjUaJpGhEiyQAwiaUJF3DTbZQsipPM5dSpI6OSKKZQJIVmc6RqMkdWcvTzIZtt2sEKHFUFRniSLlH3kVcdqyiGjShNiWSoglfkEVMAQCID7kB1m4SwCaUAHGukpGbRBtu021bgEgyE0ek7pEd4ohWRDkljAD7842AmiWOaHBaHPnLAD/nYWl8CxF4GiWQJCMjH6mmiCSAfJ4kAELdJECOUALkuEosIokUMzHFI5JoxRFV2wTrpZEgyy0C3C2MQuWUONIvq0at1RSUQLIB0flINDglkgAImydJZMgNoMtNAsiFEuCcq2QlqPREkm4SNuUEksAZoUMqlFhDajTukRFOCyOaJUJIhRGgxJEeMmbKVuKoZqEEksuQsVabEyIJIJ9MEoDQkFuonBg3CSAXSoDzrhLJDN6VIQ21kZQTkUsU3hdfff1+kCwHQoostwhwVhiFytqbbwQ4K45UUrbCCPFBV4UuNE8UoudHAsRPJClyniSA/IJL+mRLekM4VZZAfJM5WZ5AfPMincMmDOmkgQD7jZ54XTeGUWKioJkxWzdHSbI4ovmeALrfAc3vi+Z3S+MaKXFkVE4lZcvgscceQ+/evVGrVi2kpaVVe/+7777DDTfcgKZNmyI5ORkdOnTA3/72N8t2jx07hptuugkpKSlIS0vDqFGjUFRURN0/5SDZiJNJ24C7nSSALuQGiHOTAPmOEiA2/GblJtE6SVURsQwJ9T4ZlxOxwkz8iFwOJNImpbskwzECxIfTACWOLMsqcURFaWkprr32WvTq1QsLFy6s9v6mTZvQsGFD/OMf/0DTpk3x9ddfY8yYMYiLi8PYsWMN273ppptw4MABrF69GmVlZRg5ciTGjBmDN998k6p/SiDZTE0WSQBZXhJgHXID6HKTAGeFEiBn9JuZENJ7jzQXyQwZIonWnaJxj4wQNeN1pD2PCaNQWbG5RoASRwpypk+fDgBYvHix7vu33npr1N+tWrVCbm4uPvjgA0OBtGPHDqxcuRLffPNNZEm05557DgMHDsScOXPQuHFj4v6pEJsDxGK4zcmQG03YTWbojTb8Jmq9N9oh6KSIWOuMaD8Sl0Ex+mysPjOaddJYwmiyQmmiw2kAnWtEmoxdk8WR36O5TIWFhVGvkhJ2h5qHgoIC1K9f3/D93NxcpKWlRa0Xm52dDb/fjw0bNlDtSwkkgdD88GWJJBmL2zqZl+QWoSRbLFlhdcOmEQJ6TgtreItHJAldSFfnmFjEEa0wIoXmNwG4Rxg5FVID5Azlr2niyF8m5gUATZs2RWpqauQ1a9YsW48FAL7++mu8/fbbGDNmjGGZ/Px8NGzYMGpbfHw86tevj/z8fKr9qRCbYPxlPgQJh+rLCLeFyoqdJwlwLuQG0IfdAOv8pFBZ8tAbQBd+A+SMfisuDxiG3bjzjhhDZ5UFi+VINxsSvc2wEkeW9SWNRgPowmiAnFAaICecBshZV83JeY4Ad4sj0ezbtw8pKSmRvwMB/XNh0qRJePLJJ03b2rFjB9q3b0+1/23btmHIkCGYNm0a+vfvT1WXFSWQJOA1kQRAeF4SgJgXSgBdrhIgZvJJWpFEmotkBNU6baLCbpLcIyMsw5iCJ3WsjFuEESA+CRtw1jUClDgSRUpKSpRAMmLixIkYMWKEaZlWrVpR7fuHH35Av379MGbMGDz88MOmZTMzM3Ho0KGobeXl5Th27BgyMzOp9qsEkiS8JJIAuuRtAFKEEolIAshHuwFsQilU3llXyUoomYkkHgzXXmOYQJK5D5KmGTASTSLEkZvcolB5OcII8I5rBChx5ATp6elIT08X1t727dtx2WWXYfjw4Xjssccsy/fq1QvHjx/Hpk2b0L17dwDA2rVrEQwGkZWVRbVvlYMkEdqcJNKTzlfmczQvCaC7oNHkJtHmJ8nIUQqVJ8/rANhzlawwuzkb3dj1hABpLpIZvAnTMvZB6h6xiCOSPCOWhGvaEWm0jhFtOE10EjYgJxEbkJeMTTNDthJH4sjLy8PWrVuRl5eHiooKbN26FVu3bo3MWbRt2zZceuml6N+/PyZMmID8/Hzk5+fj8OHDkTY2btyI9u3bY//+/QCADh06YMCAARg9ejQ2btyIr776CmPHjsXQoUOpRrABHhJItBM/HTt2DHfffTfatWuH5ORkNGvWDOPGjUNBQUFUOZ/PV+311ltvCeu3v8znePJ2qCy5SJKVwC1DKAFsydy0Cd0sYokEkhssi0jiwSpxWpZQsho1JxorcWSGrJFoANtvjiX52g3CiCak5qVkbECJIxKmTp2Kbt26Ydq0aSgqKkK3bt3QrVs3fPvttwCA9957D4cPH8Y//vEPNGrUKPK64IILIm2cPHkSu3btQlnZGVf+jTfeQPv27dGvXz8MHDgQF110EV566SXq/vk0TRO7+JckLr/8chw4cAAvvvhiZOKnCy64wHDip23btmHatGkYMWIEOnbsiF9++QV33HEHunTpgvfeey9Szufz4dVXX8WAAQMi29LS0pCURH4TKCwsRGpqKlpOfxx+k3qkITcAVAvc0qzfRhpyA8jyksKQhNwqQxJ2i5QlDL2FIQ2/hSEJv1WvQ7fENWkIziz0ZhRuMwq16SVs620zykWySrgWFXJjHSknyz0iEUckeDmMBsjLMwKcd41CZZ0JqQVPn8beaQ+hoKCAKKeHhfA9qf24xxEX4HugqSg5jZ3z5PbXrXgiB4ll4qdOnTrh/fffj/zdunVrPPbYY7j55ptRXl6O+Pgzh56WlkadvMWCzLwkgEwohS8gMhK4AXKhJCuRG6BL5gaibzSkYknWCLiT5QmGIqmoPKArkuzOR4q8/z9hwyqUSJwoWaPeWMQRiTBymygC5AojQE4SNuAOYQR4O9/IXw746b7Oamj0z48xgydCbKImfgor4MriCADuuusuNGjQAD179sSiRYtgZaqVlJRUmzSLFFl5SYCckBsgL+wG2BN6o7lBsOYqiZyA0uxGbHQD17vhy8pFqlb/f2E3IsFDUda0HU73SA+7xJHsEBrA9tunPb/cEE4D5LpGXhZHCn484SCJmPjpyJEjmDlzZrUJpmbMmIHLLrsMtWrVwqpVq/CXv/wFRUVFGDdunGFbs2bNikyRzkL4RPKSmwTQz5sEyHWUAO+6SqfKEkzdJBYniQejYf/Uy5AIzE+iCa3RoCsmOcSR6EVjz5R3n1sEyHOMAHmuUai8O/KNfHTReYWDOOogTZo0STdJuvJr586d3PspLCzEoEGD0LFjRzzyyCNR702ZMgV/+MMf0K1bNzz44IN44IEHMHv2bNP2Jk+ejIKCgshr3759AOh/+G5yk2QkcQNyHSXAXldJ9EzdVm4S7dBxHhfJDCcmd6TdJ+lx0ia1m30HpMnXLDNdu9UtkuEYAfJdI5n5RjTXcb8SR57CUQeJdEIpnomfTpw4gQEDBqBu3bpYunQpEhLML1RZWVmYOXMmSkpKDGcKDQQChu+FT4Ag4b2NJi8JkOcmhcrTzZsEkCdyy3SUAD5XCaB3lkROQGnmJhk5SXa6SKH3QoJF9CK1Rvsxfl/8CDoj98hKHFkhc0JHwH1uESDXMQLck2sE0IfUlDjyHo4KJNIJpVgnfiosLEROTg4CgQCWL19ONDJt69atqFevnqEAIsVfRieSALqQG0A+0o12ckmALuwGuEcoAfRJ3YDcEByJUGIRSXroJWyTzq4NWM+wzbocCQms4kiES1YVHnHkhpmuK1PThFGovDvCaaHyVMUVLsITOUiVJ35asGABysrKqk38tH//fvTr1w+vvfYaevbsicLCQvTv3x8nT57EP/7xj6hk6vT0dMTFxeGjjz7CwYMHceGFFyIpKQmrV6/G448/jvvuu09Iv2lEUqi8u9ykUHl3CSXAu67S6fIEZpGkhwwXCSATSYA4N8muEB5t7pEeIsRRrIgiwNvCCFDiSGGOJwQSEJr4aezYsejXrx/8fj+uueYazJs3L/J+WVkZdu3ahZMnTwIANm/eHBnh1qZNm6i29u7dixYtWiAhIQHz58/H+PHjoWka2rRpg7lz52L06NHC+s0ScguVl+cmAe4TSoA9rhIgTyyRCCUrN8lIJNnpIgFka7XxCiUaYUTjHvFi5B6ZiSM3CCMWUQQoYUSCEkY1E88IpPr16xtOCgkALVq0iBqe37dvX8vh+gMGDIiaIFImLG4SUHOEEmCPqwTID8GdLo/ncpNoRBKvi8QrkkLlooWOkWBidYpoxRFpcraee+S0OIoltwjwvjACvC2O/OWAn3MoVk2eB8kzAikWoHWTQnXkhd0A+4QSYI+rBNgTgrMSSqRuEq1I4sFMDBlBKpKi64gLmdEmZctwlMywEkeyhJFdogiQ7xYB8oURoFwjBT1KIDmA28JugHyhBNjjKgH2iCVRQskqL6kqpC4SzezaVsKJRSSJwEwc0QghWe6RCHFEOzSfFrtEEWCPMArVcVc4LVSHuorCAyiBJBA/pRXptrAbYK9QAuS6SoD8UXA0QolWJNnpIrlJJFm5RjShNV5YxJFXhRGLKAJqrmMUqkNZvrxmh6y8hhJIgvGXA0GKT5U17Baq4z6hFKpjj6sE2BOCEyWUWESSHjJcJIBMJAHGi9uKgFUcGcHrHtEiUhzJXBMtjJtFUaiOfGEE2BNOo32AVjiPEkgSCJ8Idggl2vwkQK5QCtWxx1UC7BFLNOG30rI4ZpGkh1tykSojw03inQDSDe6RlTiSIYzcHkIDlDBSwsi7KIEkETuEEq2bBNgvlEL17BNLMkNw5eVx0kSSnS4Sa6gtTGVBwyqWZCdgy3CPZIoj2cLI7aIoVE8JI4V7UALJBuwUSqE6dKE3gH56AMBeseQmV4kk9GYlkozQE0kyXCQzaB2mqkLHeLkSdpfITBzZ6R6xYCWOSIWR20NogLvdIqDmCSN/GeBn+6giaDU4AV0JJBuxM/QWquM+VylUzxshOF6hZJaXRBtq00OWixR+DwBTKE7kemlW4sfofRGzZleFxT0SIY5kL/sRRrlFVesx1HGROFLwwzmFlIIFlpPIX8YwYoJypWkgdAGivQiFV8umHn5b5o+8aAiv/E17QaddYZx09XKrm5zRTdLoxqp3I+Z1NIxWsWcVIHZgR9/0wmt6nzWtOCotizMVR+XlcZa/G9LfXxja3zfAfi6xnrvM1wqG6xLL9S98nWUZnabEUeyhHCSHYHGTAPsdJYDNVQLcm69E6yqVl8URuUk0M3OHEeEkVUX0Gm28Sd2s+2TFSAyKhnbpkDAiXaNYzSsC7Aujheow7Yr5YVfhDZRAEoi/LDStezCRoo4DQilUT4klgDyxmyTsZiaSaHOSSHOReNdoA8gEEE/IjQZSYUQTWgPIk7Np3CMjrJwjM2QJI6+IIsD9YTSAURiVhv6lf4xSOIUSSBIInwgsQgmQn6MUqkfvKgFsuUqA+5O7RQklFpHkBheJ1CWSJZRoHCNad4k390gPs9CaESLEkRuX/Yiu6363KFSPoQ7jKRq+Hyi8hxJIEmERSgBfMjfgblcJ4E/uDtWVsxYcjVASKZL0sNNFAuhCaaKEErXYMSlPE1qT4R6xiiPRwkiJIqN6TNWUMKrBKIFkA3YKJcA5VwmwJwQXqit3LbhgWZw0kaSHnS6SKJEULl8Vs/o8uUUs4shO98gIu8SRncLIblEEKGHEgr9Mg99Hd22tilbGV9/LKIFkI04JJcAeVwnwRr4SqVAicZNYRJLTLhJgLZIAdndIxsg3kW2KWlakMkbuEY84kiWMlCiyqBsDwkghBiWQHKDyiWRHnhLA7yqF6saWWKIRSqJFkh5uyEWKquvA6DW9Plghwj0iDa+xjlyrighxRCOMaoIoCtVlrKdEkUIHJZAE4i8DQPmg6xVXKVTXe2JJlFCycpNIpgKoDOtM22aIdJHCOCmSeMSREW5wj3jFkWxhVFNEEWC/MPKX1eyZqb2GEkiCYXZqOIUSoMSSfh06ocTjJhmJJF4XiXf5ETMXiVQkAfKH+FfdnxVm4shp98hpccQygSMLNUkUAXzCSOE9lECSBK9QApRYMq1HORKOVCiRukk0C+IaYaeLxCuSAPlCiSbPiEUc2eke0eIlYaREEUVdJYw8jRJIkuESHoyuEsAeggO8JZZoXSUaocQiktzqIgFiRBIQLWREiCXaBGyRM2W7yT0ygkQc0QijmiKKAOUWKfhQAslGvOYqAc6LJVmukq/Mzy2SjKARSW7IRQpDI5LCsIglmcuIuNU9MhNHZu6RSHFkpzCqaaIIUMIoFlECyQFEuEpAzRBLPK6SlVAicZPMRJKoUBspNC4SS6gNYBNJkbYlL2zLKo6M4F0AWA+SddYqwyOOZAkjJYoo6rpcFMWVAXHsX0sIlx+jTJRAchhWVwmouWJJhlASLZJ4XSQZQ/7DkIgkAMxCSTQkITUzcUTjHpGG10S4R7LFkR3CqCaKIsC5fCiFvSiBJBB/OeD/3zXPziRpoGaJJVpXiUQoWblJIkWSaGhdJIBsfiQeN0kUssSR0+6RTHFEI4yUKKKoL6jvarFa78C+AI/CFH85xxwbZWdeTPVLz7yo65ZHv6jrc/TdX+ajvvj6yn3EF3mSG4fZzYd1RXQS9BwJPeeCdmV5M/eEJCRVXB4QmhRNCul+RS4lYqd7ZITZb8xX5hcmjmjOmzAs52eoHuf1jOd6xHEtBJzte03gscceQ+/evVGrVi2kpaWZlj169CiaNGkCn8+H48ePE7VfUlKCrl27wufzYevWrdT9UwJJMrwniJNiCXBGLIUvxDQXY9ILvq/MZ3kTYRFJRo6A3g2SNk+FBFZHhFRg2CWUaPZj1XevuUdW4sgKogcASmHEci6G6nE8KPE+pClR5BlKS0tx7bXX4s4777QsO2rUKHTp0oWq/QceeACNGzdm7Z4SSHaixBK7WCKFRiiZv++OU4PGRTK68Vvl4NC4MLKEEm27osWRjGVFWIf1V4XENbL8PTMKIxpEiSIWlCjyJtOnT8f48ePRuXNn03IvvPACjh8/jvvuu4+47RUrVmDVqlWYM2cOc/9UDpJDOJn3A/DlLAF8/WfpO22ukq/cZ5mf5CvzWeYl6eUkGeUj0eQi2TXkP4zV0H/aNduqihmWXCVWocUqjkSgF16T7R6ZQeoakcAaPmOFV0w4nVME1CxBVFhYGPV3IBBAICDfVf7hhx8wY8YMbNiwAT/99BNRnYMHD2L06NFYtmwZatWqxbxvdzwm13CUs0Rbh+zpluSpmXWJBdpQGw8iXCRArJNUlbADRPNigaePNO4RLzTuEWtoTaRrxOoW0eJ1pwgQcAw2iipfuQZ/Gd/LVx56GGzatClSU1Mjr1mzZknvf0lJCW644QbMnj0bzZo1I6qjaRpGjBiBO+64Az169ODav3KQBOIvB/z/u56xuDrhNsI47SwB9o2IY3WVRLhJZk4SyYSSrNjtIpFA6yTZCYk4EuUekSZnixq5pgevOCKBRhjxhJ94cNopEiFoKvfBq6PY9u3bh5SUlMjfRu7RpEmT8OSTT5q2tWPHDrRv395yn5MnT0aHDh1w8803E/fzueeew4kTJzB58mTiOkYogSQJXqECOC+WAL5QHK9YIulz+AJvJZRkiCSaUJuMIf96E0cC5kuQkMyyHRYibhFKpK6RmTjymnskWxzJFkZODscH3CeKYoGUlJQogWTExIkTMWLECNMyrVq1Itrn2rVr8f333+O9994DEHKHAKBBgwb461//iunTp+vWyc3NrSbgevTogZtuuglLliwh2jegBJItuE0ssfbDbrEkWihZTTTplJNUFZqJI2WJJMB5oUQTTmMRR0bwDu3XQ1TolVccyRRGXp6jCFCiSBTp6elIT08X0tb777+PU6dORf7+5ptvcOutt+Jf//oXWrdurVtn3rx5ePTRRyN///bbb8jJycHbb7+NrKwsqv0rgWQzbhBLIvohQizJEko8bpJV4nZVaNZrI03WFoUIkQQ4I5REiSMzeN0jvfCaTPfILnFklzBSokiRl5eHY8eOIS8vDxUVFZG5itq0aYM6depUE0FHjhwBAHTo0CEyb9LGjRsxbNgwrFmzBmeffXa1XKU6deoAAFq3bo0mTZpQ9U8JJAcRLZYA5/OWaMQSrdAjFUqkuUlGGIkkmlAb7zptIlwkK2gXtZUtlFgSsK3EEW1ojXdovx407hHr9BIixJEdwkiJIkVlpk6dGhXy6tatGwBg3bp16Nu3L1EbJ0+exK5du1BWJv5LUQJJIP4yAAxD5iN1K+GGUJwTYkmUULIKuZFMAyADN7lIAL1IAqKFDK9Y4hmVxiqOaOFNztaDdli/mXtkJo5ECyMviiI3CSJ/GaApcRVh8eLFWLx4MXH5vn37RvKQzLZVpkWLFqbvm+GZYf7Hjh3DTTfdhJSUFKSlpWHUqFEoKioyrdO3b1/4fL6o1x133BFVJi8vD4MGDUKtWrXQsGFD3H///SgvZz+jRAwjFdWOyOkDmBI1GYbj0vSXpE9mNwizG4vRDYnmCd+uIf+h7ew5OCfLE5jDVEXlAd0XazkSSPprdsyy3CO98JoI98gN4ojlOsK83BHndQfgv/ZV7QcPotpR2I9nHKSbbroJBw4cwOrVq1FWVoaRI0dizJgxePPNN03rjR49GjNmzIj8XXnSqIqKCgwaNAiZmZn4+uuvceDAAQwbNgwJCQl4/PHHufsszBXyeCiOxVUidZRI3STZThJNLpIeIob8m4XarJwkgM1NMkLkGmmVIRFyLOLICN7kbD1ErelnhzhiEUUsxFLozE1CKK5UQxyjexJGK7PfaXcLnhBIO3bswMqVK/HNN99EJn567rnnMHDgQMyZM8d0rZVatWohMzNT971Vq1bhhx9+wOeff46MjAx07doVM2fOxIMPPohHHnkEiYmM8TIDhAgdj4fiwhdQGqFEGnYTLZJoc5H0kDHk3ygXyQq7RZJoeMWRGTTuEWlytkz3iEcc0bhGpDghjNwiiES2o3AXngix5ebmIi0tLWpWzOzsbPj9fmzYsMG07htvvIEGDRqgU6dOmDx5Mk6ePBnVbufOnZGRkRHZlpOTg8LCQmzfvt2wzZKSEhQWFka9aHGTfSsqFEdVh8J+J+2bVR9Ywm00M22LcgYqY+RgsITaADIBwRNykwFpf6yOjTa05rR7xDLLuwhxRBXmZgmjO7hmG+/+ZbSjcC+ecJDy8/PRsGHDqG3x8fGoX78+8vPzDevdeOONaN68ORo3boz//Oc/ePDBB7Fr1y588MEHkXYriyMAkb/N2p01a5buBFWsyAjFsbZj+xprFI4SiZvE4yTRwDsvksxkbatRbSROEnDGsXHKUaIRaaziiBbeof160I5co1l0tjIiQ2osoogV7pm4XeYS+WtwyMprOCqQSKckZ2XMmDGR/3fu3BmNGjVCv379sGfPHsNJpkiYPHkyJkyYEPm7sLAQTZs2Da1d4wv9+IMJ7G6CiFCciHZEiCXRQokkN8lKJBlBG2rTw64h/4B5qE2USALsF0q07hXPaDVZ7pFeeE2Ee8QaWrN0V10mjGJZFHl1qZGaiKMCiXRK8szMTBw6dChqe3l5OY4dO2aYX6RHeBbN3bt3o3Xr1sjMzMTGjRujyhw8eBAATNslWcW46lMCq2ByS6K3XWus0QglVpHkFhdJD5lD/qtCI5KAaOEiWiyxhvRIxJFM94gX1nmPqiJbHNkhjNwgipRLpKiMowKJdEryXr164fjx49i0aRO6d+8OILTeSjAYpJo6PDxLZ6NGjSLtPvbYYzh06FAkhLd69WqkpKSgY8eOlEdjTuUTxml3iVd02bLGWqkzIkmEi6QHb7K2DBcJoBdJYaoKGlrBJCLHiVcciXCPeJOz9aB1j9wijmxfs83FLpEiNvBEDlKHDh0wYMAAjB49GgsWLEBZWRnGjh2LoUOHRkaw7d+/H/369cNrr72Gnj17Ys+ePXjzzTcxcOBAnHXWWfjPf/6D8ePH45JLLkGXLl0AAP3790fHjh1xyy234KmnnkJ+fj4efvhh3HXXXZYOEQ+x5C7RLhsS3p8oN4lHJBlBM/Rfz0WSMeQfkCuSADAJpTB2J3XLEkdGyJgYUpR7ZASvOKJxjWxbnkS5RFSoYf58eGIUGxAajda+fXv069cPAwcOxEUXXYSXXnop8n5ZWRl27doVGaWWmJiIzz//HP3790f79u0xceJEXHPNNfjoo48ideLi4vDxxx8jLi4OvXr1ws0334xhw4ZFzZtkB/4yLfLia0fAiDbGNmhHl9Dsw+pCbXmhN9gPzcKdANuoosroOQu8Cb5hzG74pCEmUTNPy+RUWYLUsBqve8SLSPfIDFHiiOo8ZhyB5uR1rXo7Yq7VCm/gCQcJAOrXr286KWTV6cSbNm2K//u//7Nst3nz5vj000+F9DGuDIjzARVcidXucZdoE60j9QQvGwJYh9xI50uqvn+6UBspdrtIVpCu18YacrMDUgFnJY5oQ2s07hFvcjYNrKE1EeJI9vIktPsQWTe6HXFCKC7cJ3eeXgodPCOQvERclRPAbYKJWvCwTgpJKZRkiiTWkW1V0ctFkjHknwWrCSRpRBLAF3ITCY2zxSqOaLFzaD/tsH63iCOmddtiSBRVvQ8ovIcSSDZQ+UThEUuAmGRvu8WS0GVDJIgkO10kmiH/LC6SKJEEOC+UaEN+POJIlntEA00I18g9kimO3CiMxOQkSXCJFDGBEkg2I8tdclIs0QglIcuGSAq38WC3i2SXSAKihYpsscS+VIg786d4k7NZJ4WsihvFUSyIIiWIYhslkBxGlGASEYpjDqXRDN+ncJN4RBJtuzRzI8kY8m+ErHmRaEVSGBliiTc5nEQciXSP7EzO1oPFPTJtT4A4ki2MRCRXi0AJopqFEkgC8ZVrAOf6tqLCcbzuEkuCtmihxCOSROUj2ZmsbaeLFCoT+hBYhBJgLGzMhJPIkXKkrhGLOKJFRnK2He6RneLIbmHkRlHkL9MQLLdvBJy/LAi/xjeZrb9c7GS4XkIJJMGISqoG5LhLtP1hyjmiFEpWIsmsLVaRpL8vPhdJxszagLmLxCuSQuXY3CQj7JguQIQ4MkOWe6QXXpPpHskUR7KEkdOiSLQgUngXJZAkI0swOSmWaISSCJFkWZ8h3Oa0i2QUZjNykVhDbTQiCWB3k+yCJtfIShzJdI94EeUeGeFGccSek+QeUaQEUWyhBJLNiBJMItwlVrFE5RCRznPEOSO2kUiS6SLpIctFMsNqbiRSkRQqK9ZNEold4kiEeyRj5myR7pH5fizeJ1meRLIw4hUiyiVSkOCZmbRjFVEzs8aVnXnx9IOuDsXoFgG5DKJXBqdpT++JXsbM2oDxjdhsyLmVG0ITajpdnuCqUWG0/ZEhjoywc2g/DayhNTvFEdOM/RzXSt5rZNU+qBm1Yx/lILkIGe4SrbPE4ioRu0Qk8xxZJG+bLkDLOLKt+j74XCQ9eGfWJkGkkxQq72zYjUWkyQqr8bpHNNCE16gFhkRxJPJhKbo8+3kjwikSLYLiSpWo8gpKIAmk6sKAFYl8F0oR8xyJEEs0Qok07MYzqzWLSJI1NxJvsrZduUhhaEVSqM6ZD1u2WOJxrnhnyaYNrdG4R7LCa7plJUzU6IQ44nGKeBE6eWQVQcQY+VQ4gBJIEql6YvAIJhHuEqtYohFKNG6SlZPEk5NEitPJ2iywjmoLExYStEIpVDf6w+IVTCJCeaTCSGRozQga90gvvOaEe2SHOJItjESEzUShHKLYQQkkG5ElmHjEkkyhJFskGdYT4CLZmaxN6yIB/CIJYHOTqrfhbK6SCHFkRqy4RyRrrNG0F3lfoDiyWxgJmyfJxYLIXxqEP6jmQWJFCSQHqXxiOSWWZAolopwjDpHkFheJZmZtO3KRADqRBLC5SU4jShyJco9o4E3OFrcwq8l7nOJIpmvEMxhFBG4WRQpxqFFsLiGuVIt6scI6uoJlhAfpPngvpKYjcoyGOBuFE6iWROBMtqVwDmhHtAHWN3Ya10TUKvd2cLo83hZxROseOT1zNq175AZxRHOtYh2FJmLEmajrs8JbeOeq6AEqJ2nzJmiLcJdok6wj+6ZwlWjcJFlOkgh43Sje9dnMYA21AeROEuB+N4lWxMkQR7TICK+Jco8M92mDOKIRK065RSKFUOW2NDU1gGdQAkkSIvONeNtiDcHRCiXZIom2nqwRbbKStWkWsa2MSJEEuE8osbhbombJrooI94g3OZsG1sRsw/ZcLo7cIoqUyxQbKIFkEzIEE0sbLK5SXJk4N4lHJMl2kfT7446ZtWkXsq1KWDB4SSixhv1IxJFX3SPDsoK+IpZwNun7oTLk4TQaeMNnvChBFJsogeQQIkJoPG3QCiWRbpIMkUTrIrH0ywoRydpmLhJPqC0MrZsERAsV2WKJJxeK1DViFUey3CMaqBKfKd0jmeLIbcJIiSIFCUoguQAR7hKrWJIllGSKJBHwDvmnCbPRukiyQm1hWERSmKoChn+aADGXIBHiSCQ07pGs5Gy7cEIcMU0JwClovCiI/KUV8AfZnWcA8Jfz1fcySiAJxF8WhF8LIpjIZ63zukssITgWoSRbJBnWi3EXyQyrUBuNSALoQm56uGH0myhxJNs9ooF3BKVd7pEIceRmYSRSFPlLQw9JNXleIa/h/NUtBgmfCGF4BBOPWGKpSyOUZIskN7lIejjlIokSSQCfm+Q0NInYMsQRLW6Z+0jUPu0UR9TTlrhAFFW9Dyi8h5oHyQb8pcGoFys883DQ1iOdN4RkXhKrdlguwoZzvlDOi0QK7xO9EawOhFUiMU0oqbQsTtqoLxnQ9ldWWM3ou+MNr8lKzmZxj2jbOvO+GHFEO38R7/WRaw46Qdf5msRjjz2G3r17o1atWkhLSzMst3jxYnTp0gVJSUlo2LAh7rrrLtN28/PzccsttyAzMxO1a9fG+eefj/fff5+6f8pBcoDKJw+ru8Q6ko22HsnwfcDaTSJtxw3ICrPZlYsUhnZhWy+4SbRCjkQcxZp7xPswwLLP6HrmIkOGa8QqbHjdIiWE+CgtLcW1116LXr16YeHChbpl5s6di6effhqzZ89GVlYWiouL8fPPP5u2O2zYMBw/fhzLly9HgwYN8Oabb+K6667Dt99+i27duhH3Twkkh+EVS6whOBqhRBp24xFJLKE22lwk3bYdnhMJMM9F4gm1AWwiCeDPTRINi8MlUxzJco9okOkesYbW7BZHrG4RD0oUiWP69OkAQg6RHr///jsefvhhfPTRR+jXr19ke5cuXUzb/frrr/HCCy+gZ8+eAICHH34YzzzzDDZt2kQlkFSITSC8Jw6vPctiEdPUEWGbm7UhfYZgG8NstI6BrFAbEBIKtGGmcBjLydAbTx94xZEZtN8VzW9BVnK2KGSLI6qlRyRe66oiKnSmwm90rF69GsFgEPv370eHDh3QpEkTXHfdddi3b59pvd69e+Ptt9/GsWPHEAwG8dZbb+H06dPo27cv1f6VQBKMqDg0TxusQomoX4JHpZAi8gmYZ3960DgBLJMHWoV5SG/0rLk4dool3n2RikGrz4wltMY7MaQRsh4cZJ07epCKI6K2KK9vrMJI9HXc68KosLAw6lVSUiJ9nz/99BOCwSAef/xxPPvss3jvvfdw7Ngx/PGPf0RpqfHTwDvvvIOysjKcddZZCAQCuP3227F06VK0adOGav9KIEnGSbEk60LCK5KcdJF09ylpAVuRLpLTIilMZQEjQjCJbI/02HjEkSj3yA3hNep9Mp7TosSRHcLI6Ydb0fjLKkJzIfG8ykLh9qZNmyI1NTXymjVrlu4+J02aBJ/PZ/rauXMnUf+DwSDKysowb9485OTk4MILL8Q///lP/Pjjj1i3bp1hvSlTpuD48eP4/PPP8e2332LChAm47rrr8P3331N9fioHyUZEJGeH26CpT5uYHVeqWZYlSbo2y0liyUeizUXSbdvGOZGMYF1+xCppm3Q5krCQoMlNMsIto99EiSNWRLhHdobXqEeBcoTWLPtCKI5IYXWLeHCDGJLNvn37kJKSEvk7EAjolps4cSJGjBhh2larVq2I9tmoUSMAQMeOHSPb0tPT0aBBA+Tl5enW2bNnD55//nls27YN5557LgDgvPPOw7/+9S/Mnz8fCxYsINo3oASSY/CKJZb6NEJJlEiSWd+6feeTtWlHtLFMHlkZmjXbaBO43QiNI0YijkS6R0bQuEd2hoplwDsFCEAueOwWRjVBFFUmJSUlSiAZkZ6ejvT0dCH7/MMf/gAA2LVrF5o0aQIAOHbsGI4cOYLmzZvr1jl58iQAwO+Pvi/GxcUhGKT7zlSIzQXwWrK0dUmtZ5JyokatVG+XcjtNyMGlcyKF4Qm1AXQuCUsCtxug7bdMcWTkHslKztb7rYtIzhbtHvGKI5oQGdUcbxzXWzeFz2KBvLw8bN26FXl5eaioqMDWrVuxdetWFBUVAQDOOeccDBkyBPfccw++/vprbNu2DcOHD0f79u1x6aWXAgD279+P9u3bY+PGjQCA9u3bo02bNrj99tuxceNG7NmzB08//TRWr16NK6+8kqp/SiAJpHLclr0N/pOXFFEXHx6RxGvPW+HWZG2APSwjWiQB3hJKtP3kFUdm0H6HdrpHhu0KWrtNtjgigWoUroOiqGpejyLE1KlT0a1bN0ybNg1FRUXo1q0bunXrhm+//TZS5rXXXkNWVhYGDRqEPn36ICEhAStXrkRCQig8UFZWhl27dkWco4SEBHz66adIT0/HFVdcgS5duuC1117DkiVLMHDgQKr++TRN894KfC6jsLAQqamp6Nvzr4iPT6r2fjCR78bDEoKjrUMSdrMqYxUuM8pHMqtnFCLT224UItMta5CHpNeGcR+qnzpGcyIZrc9mlotkFWojmUSSNNxWrZ4LQ28sAk6EOBLpHhkJJD0HSU8gGeYO6ThING6rSPfITnFECqsw4sFICJWXn8b6jY+hoKCAKGTFQviedFm3SYiPq35PoqG84jTWbnlCan/dispBsoHKJwqLWGLJN6JN5ibJObIqw5pTJCoXSVayNg20E0eaJWxb5SORzLRNk5MUVU9gIjcPPK6WU+KIFjckZ1Pvk9H5FSGOZAojWaJI4U2UQLIZUWJJhlCSLZJYRrXRjmjTb5tvNJqI0WxGydq8kIokgM1NqixQ7BJLIkJ9skarkSBiaL8bkrNZ2uAJp4sUR3YKIzeLIl9pBXxxfKrYV+He45ONZ3KQjh07hptuugkpKSlIS0vDqFGjIolcevz888+GczC8++67kXJ677/11lt2HBJXTJo2Jk5aVsRFindOFB54n5BlzYlkhpkTIWrkFMAvGsJ5SjJylUS1XV4WR3ycXnOPRLk/1ftAW1680HdKHLHnJal8opqAZxykm266CQcOHMDq1atRVlaGkSNHYsyYMXjzzTd1yzdt2hQHDhyI2vbSSy9h9uzZuPzyy6O2v/rqqxgwYEDkb7NVhc3wl1Uwf6LhE43WVaJxiEjLinCSWKB1kbj3J2lOJNoh/6H35IbawrCG3Kq1YyJkjJwm2UngNAKQRxyZIWtiSCNkhdfsdI9EiSMZD4zV6/GdO/7SCvhrsCPjNTwhkHbs2IGVK1fim2++QY8ePQAAzz33HAYOHIg5c+agcePG1erExcUhMzMzatvSpUtx3XXXoU6dOlHb09LSqpVlhT+ExlbfXxqkCrvJFkmsoTYaZIXZqHKZyny6ydpGsIbaRIskgD2B27J9m0fD0TpjvOJI1pIiYewMr4lyj2JZHIkQRQpv4okQW25uLtLS0iLiCACys7Ph9/uxYcMGojY2bdqErVu3YtSoUdXeu+uuu9CgQQP07NkTixYtgqiBfbw2LG1dGruYpBz3qtcMVrwI+15WKIIGFseA98ZLO1SdJhzlVkSLIyvMviMRI9eMsDN0LBLe81mkOKJPS+C/ditx5G084SDl5+ejYcOGUdvi4+NRv3595OfnE7WxcOFCdOjQAb17947aPmPGDFx22WWoVasWVq1ahb/85S8oKirCuHHjDNsqKSmJWqivsLDQcv88zhJt+I00lEZSzspJYg210bpIIpK1ddt1YOkRWaE24IwAIHWTAHFhNzthEXYk4kh0aI0WKvdHwOSQNH1gHdZvhJX4ES2OSOERRYrYwlEHSeSidmacOnUKb775pq57NGXKFPzhD39At27d8OCDD+KBBx7A7NmzTdubNWtW1KJ9TZs2peoPe2I2vaMkohzPhUz2RJA0UN18BCRry8g7Ib2Bx6qbxNLPYFmcEHHE4vDZ7R7JDK/RwhNasxwIQugG0bnq9lyXFd7BUQeJdFG7zMxMHDp0KGp7eXk5jh07RpQ79N577+HkyZMYNmyYZdmsrCzMnDkTJSUlhovxTZ48GRMmTIj8XVhYSC2SAJ58I3JHiTQ3yaqcDCfJyEUSkazt9gVsWV0kgHy9Npq8pDCVxYdbXCUe4UYqFHnEkSj3SBYiBJZo98gMEnFEgmzXyAuiyFdaDl8cZ1i5wgU5Cw7hqEAiXdSuV69eOH78ODZt2oTu3bsDANauXYtgMIisrCzL+gsXLsTgwYOJ9rV161bUq1fPUBwBoVWM9d73lVYAyZa70IVlFJu/tIJYJIXatg658Ygkw3bdtCitrBFxBsnatBNHRuo5KJLCyE7mJt0/K3aII9N6lO6RneE1t7pHIsSRTGHEK4p8peU1el4hr+GJHKQOHTpgwIABGD16NBYsWICysjKMHTsWQ4cOjYxg279/P/r164fXXnsNPXv2jNTdvXs3vvjiC3z66afV2v3oo49w8OBBXHjhhUhKSsLq1avx+OOP47777mPuq6/0jNrWEuk/Xvp8I7FuEo9IYhnVRusi2QmNC8UC7+SRNCIJoMtLqkxVoSJt9JvAEJ8ocWSFbPdI9tB8snbFuUduEUd2CqPK9wSFt/CEQAKAN954A2PHjkW/fv3g9/txzTXXYN68eZH3qy5YF2bRokVo0qQJ+vfvX63NhIQEzJ8/H+PHj4emaWjTpg3mzp2L0aNHC+kzj1hiEUp2iSQncXOYjcVF4gm1AeQiCeBzkyqjJ2SYZumWkPNEk3tFIo5YQ2tucY+cEFgy8g6dEkeswkiJothALVYrgPDCgP3OvR/xccahOYDNVaId9UZS3koAWb1vFmoze8/IFaJZyNZrC9gCxovYAsYL2Z553/rCTyqSAHYnye3YKY5C78sXSLoLyxoIJNLFbWkXptUTPCzzHsl0j9wijkiEUXlFCdZsn23LYrXZbcdb3pOsKK8owec/PlMjF6t1p00Qw/hKy6mfLuhHr1mX5b3g8FzseJGdP8HTB5YRbVZhGpIcGJpQEemoLq9AezxeFkdG2B1eoy3vtDiiuYayjEpjua4r3I8SSA4hWyjZIZJYsHM0DEB5k5E0r0wYN4kkwPtCiaX/Ita2c3LUmtvDa6IRJY7I9qWEkSIaJZAEwnKisAolUeV4LkB2uEg0T7Cy5oAxutHQukh2wCIAvCaUWPtL+tnwzGguYt4jQJ64oStL9zAj2j2yWxzRwCqMlKDyFp5J0vYKrInZ4XqkdUiTuEmSt62Sss3el7FoLQmykrVlw5qwHXrfOmkboEvcrgzvaDfZ8Ig4UeKIJbRmhhucG6f6wPMQ5ZQ4YhVFjlFaBvg5fZCgC36kDqEcJImwPC3Q1iFzifidJBaMLoB2h9loMEyApbzGsbpIIkJtQEgQsIaTwg6NW1wl3r7YIY7MEOUoygqv0SDSPTLD7HokShzRhNTsuJYr3IcSSDbAenKRIkokmddnC7WJwA1hNpFt8C5BQhMC4s25cUooiRBpNCKRd6Fgke6Rm8NrouAJrVkh+nqohFHNRQkkG6E9cWjKi7goyEjapnWRaJC2tpqgZG0z54AnYTtUxj6RBEQLFhmCSXT7NMdM8ll6yT2ialfIQ4JY98h0X5bXKHHiSOb1WuENXJKZUbOgzTfylZYTlSXLNzIvwzpJpKhcJKOZtd2AUS6TjOVLSGbZJs1JAs4IBpbcJD3cEn6rCq0YFCGOnHKPDNu2ObxG3Q6je2S3OCJFiaLYRTlIDkLzxEFaVraTJNJFosHNYTbz9tmTekU7SYAYN8mtuE0cyR7N6NXwmuF+XSCOWFwjakpdkGypIEIJJJGUlgEl9D9+0SekHTlJetAKIRHJ2naH2WiH/FvhlEiKJaHEcjy8OUc80LpHsRRekxN2EyeOSGEKp5Ww3R8UzqFCbDIInwQB8rgLTdiNJORGujabcX3jUJub12pzI0ZrtImEJtwWRnTYzW5YRR6pOHKze2S4X5eH14wQ7VpH1xcnjpgcIydFUUkpvw0SlDyDrotRdzmZMDwxiDxRrUNp9rlIIpK17Q6ziXSRZIfaQuXYTmevOUo8/bVDHJlB69rQrLsmYp8iwmtuco8cFUfKMfI8SiDZAeWJ4haRJPOpzgo3hNnsRKRIilWhxCuM7BJHLO6R3cnZNOcM7dxHtLBeZ1wrjpQwihmUQLITihOHNMbtpEgyws4nSFGIStZmdZFIoBlqzpNjExYibhBLIvpC81nwrrNm9h3b8RuLJXgewkSJI6pcIyWMYg4lkJxAsJskKnGbFlEukhvCbIZtCJpZmwSSsI1dIimME2JJ5D5FiyPeST71kJmcLSu8Jio5m/UaYv5QJ04cEaOEUUyiBJJISiiuaJRukogyZoh2kUTg1TAbj4skQySJGrFVWbiIEjAy2gToj1uEOLLDPTJs3+bwmmx4QmtWCL2e0rpGJSV09wmFoyiBJJqSEsry9okkO12kWAqzsbhIbhJJofJyTnU9gUPzkgHtscoWR6b1KN0jN4fX7HKPzLC6xgkXRzTQ3hsUjqMEkgxKSuhOBpeIJCddJDeH2ZxAlkhycv4f2bAcH2/OEQlO/QbtDq+JgtU9cq04or0fKFyDmgdJJuGTIhAgKEs2dxLJHEikS5PowTJ/ktG8SCKWH6FZeoRmyQ/aZUP8pUAwkbwdq/6ImhuJZEmS6nVC3xXtvEluhVX0EY8MlBRaExXqlRVeiyWEiSNaYeQwWkkJND/fdUZT8yAppCLYTZKZb2ReT8wN1c1hNjsREWoD2F2QWHCU3CyOWHFLeI0m/0hUeE2We2RFLIojBT/evjp6CRqbVYBIkhVqk4ndYTY7nuB5b2o0IqmmCKVwf1n6TPM58Y5Ys8M9Ytl39bLuCa8Z7ldiaE2oOFLhtJjCO1fFWMFFIokF2ic/EW6RrNFstG3IWBOLxIGguVHz5NS4XSjx9o9q9B/BZ+4G98itE58C9iZnG2G7OFJQ8dhjj6F3796oVasW0tLSdMusWbMGvXv3Rt26dZGZmYkHH3wQ5eXG39uxY8dw9913o127dkhOTkazZs0wbtw4FBQUUPfPvVfDWMYlIslJF8nuMJuTw6yt9u0mkRSqz+7QiEZUX+wWR066R4b7lTS8X9S5zCKceK5TTokjTQmpCKWlpbj22mtx55136r7/3XffYeDAgRgwYAC2bNmCt99+G8uXL8ekSZMM2/ztt9/w22+/Yc6cOdi2bRsWL16MlStXYtSoUdT982maphJCOCksLERqair61RuOeH8ifCRJ2QBZ8jZAtOitWVK22XtmCdlG7xktVGu03ShR22h7MKH6dqNEbb2yRonRhtsNPh7D8jrJ2lZtmbV35n3rU1GLpztdaRO4zduy58lftDBzkzgCxA3tp10TUG+74YSPNGUl5x+xPshxu0eSxFF5sBRrfl+CgoICpKSkENeloeo9iQc7+rt48WLce++9OH78eNT2hx56CKtXr8Y333wT2fbRRx/huuuuw6FDh1C3bl2i9t99913cfPPNKC4uRnw8+QAm5x8RYxDiJwTSeDXnLK2iXSQnwmw0xPJIHdq8GJHD2Cu7OaIcJhltnmmbLi9LxizZVXFzSEwkIpOzjXBcHFHkG2klJZ53jgoLC6NeJTYcT0lJCZKSkqK2JScn4/Tp09i0aRNxO2FxRyOOACWQpEF1MggQSTLyjWRDI5xEzOgrbC0sk1wk2aE2gE0kyZrvR0/g0LxkQT1HFOFnKiu0Jiq/jcY9MsKJ8JoRMkL9wsQRIU4KI62kBNppztf/+t+0aVOkpqZGXrNmzZLe/5ycHHz99df45z//iYqKCuzfvx8zZswAABw4cICojSNHjmDmzJkYM2YM9f6VQJII1VODZJHE6iIZ1xH3JFitDZtH1dCGKrj25ZBIAuyZFNFpWMSgHeKIFZnJ2U6NSBMBq3sk5EGyBrlGldm3bx8KCgoir8mTJ+uWmzRpEnw+n+lr586dRPvs378/Zs+ejTvuuAOBQADnnHMOBg4cCADw+63lS2FhIQYNGoSOHTvikUceIT7WMGqiSBvQSkrI8pJKSsjzkhhgmUCSZeJIPURMGkkDzaSRTO0bTBwJmE8eSdQ24SSSvnIfdU5SWDyIzE1yA8xTHAgSR5b1bXCPDNsX4r5SuL025B5Jw8o9ohBHsUZKSgpRDtLEiRMxYsQI0zKtWrUi3u+ECRMwfvx4HDhwAPXq1cPPP/+MyZMnW7Zx4sQJDBgwAHXr1sXSpUuRkEB/Q1ACySaEiaSSMtOkbdZZtEUJIVpohBPNrNpGGM6UTTmztow+RJeRJ5KA2BFKXNMaCBRHbnCPRPTBTeE1I6S5R0ocCSE9PR3p6elC2/T5fGjcuDEA4J///CeaNm2K888/37B8YWEhcnJyEAgEsHz58mp5TKSoEJuNCAu3SQq1GSEqWVsWzk1eZ/KexcdMcjOTGW6L1JWYnyQT3n7bKY6cdI8M9+uS8JpI94grfMY5ECZMTRdHtOTl5WHr1q3Iy8tDRUUFtm7diq1bt6KoqChSZvbs2fj++++xfft2zJw5E0888QTmzZuHuLjQA/3+/fvRvn17bNy4EUBIHPXv3x/FxcVYuHAhCgsLkZ+fj/z8fFRU0P2ulINkM8ROkhUWThILsl0kGrfIX6bpDuGnQZT7w9qOVahNtJME0E8DEKnvEUeJe44nG0aqiUDYgAKbw2uGbQh6aGINu3HnHhEIH1JxpETUGaZOnYolS5ZE/u7WrRsAYN26dejbty8AYMWKFXjsscdQUlKC8847Dx9++CEuv/zySJ2ysjLs2rULJ0+eBABs3rwZGzZsAAC0adMman979+5FixYtiPunBJJAtJISINl6zgkikcSZj2QWauNZzFYGbg+zmbZlkoskCpqFbVlDbpH6lQSIW8SSKIeLVhy50T1yS3jNsA2Hwm5Oh9aIxdFpJY4qs3jxYixevNi0zNq1a03fb9GiBSpP59i3b1+Imt7RPXfJGCF8AviSzMWNEJFko4tkvD2oO0Gk0XZZiHCczNt3zkUKlbNPJEXacVAsiQ77uU0c8bTLiwhHSIQQckVydoyLI62kFJqP77vStBieWM4ClYMkCZKTgejE4shHkj7clQGaC6sQa59yXhiWmxpvHgnpDZFmJJWv3Cc0nBTO+an88kTbDJ+DHUnZdrhHKrzGm5ck5hoenk9I4T2UgyQR7XSJGCdJEYWIMBsLZm4P77B/GU4SIM5N0m3b5cndLAJRlDhiDa05NQt8rIbXTOFMzCYVRwrv4hkHiWTV36pomoapU6eiUaNGSE5ORnZ2Nn788ceoMseOHcNNN92ElJQUpKWlYdSoUVEZ9LwIcZIkuUhGGI9aoxvNRoMQy17QyBwZy0GIbJN2Th7RbpLbYT1e3rmOIu24+Pfj1fCacPeIM7SmxFHNwDMCyWrVXz2eeuopzJs3DwsWLMCGDRtQu3Zt5OTk4PTp05EyN910E7Zv347Vq1fj448/xhdffME0JbkZtogkBmSH2eyeVdu4DbrtLG0B1mETEUP/z5RlmEk7xoUSjzAi/TxlhtZE/h5jLbxmhJRrmBJHiv/hmRDb9OnTAcAy4z2Mpml49tln8fDDD2PIkCEAgNdeew0ZGRlYtmwZhg4dih07dmDlypX45ptv0KNHDwDAc889h4EDB2LOnDmRialEQBJu48IkYVvkqDURUwGImFVbdpiNdzZs5v1SJIPThtvC8E4J4DZ4RB+N0OQNrcUyToXXjOByjzhR4ih28IyDRMvevXuRn5+P7OzsyLbU1FRkZWUhNzcXAJCbm4u0tLSIOAKA7Oxs+P3+yDwKIrE6cdzkItFa2m4Ps3nFRbJqv3pZvskSvewo8fbfbnHE6h6JGtpvdG7YPXu2qPCaW90jRezgGQeJlvz8fABARkZG1PaMjIzIe/n5+WjYsGHU+/Hx8ahfv36kjB4lJSUoqXSiFBQUAADKCYZD+oIWF+VTpeZJ26dKgYBBNvCpEiDRwH44VWLoImkV+heaYLm+UxT0G23X0dvlQDCh+vYKn/7noLu9DNDiq2+vMLhWBzX9toNG5Q20XdBEIxrVAQCUWLtAlu7UaUAjdchOg8lJqooW5y4XwAhfxf8cMI42/GU+kEp6Xxlg9bjgL7cuY3Z5MH3PQAcYbfcb9FczCpkZ7DtYXr28URt62/1lQd3P2F9uIJDKDQSSwezHPqPtZsKp1OSDLjF/uiEKrRGUKTtdHCoraK4eM8pRxneihNuooTgqkCZNmoQnn3zStMyOHTvQvn17m3pExqxZsyIhv8p8cep968onJXRIoVAoFJ7i6NGjSE1NldJ2YmIiMjMz8a/8j4S0l5mZicREyTPiuhBHBZLoVX8rk5mZCQA4ePAgGjVqFNl+8OBBdO3aNVLm0KFDUfXKy8tx7NixSH09Jk+ejAkTJkT+Pn78OJo3b468vDxpP3jZFBYWomnTpti3bx/Ris1uJRaOIxaOAVDH4SZi4RiA2DiOgoICNGvWDPXr15e2j6SkJOzduxelpWIW+0tMTGRe8NXLOCqQZKz6G6Zly5bIzMzEmjVrIoKosLAQGzZsiIyE69WrF44fP45Nmzahe/fuAELTmgeDQWRlZRm2HQgEENAJg6Wmpnr2pA2TkpLi+WMAYuM4YuEYAHUcbiIWjgGIjePw66UkCCQpKalGihqReCZJm2TV3/bt22Pp0qUAAJ/Ph3vvvRePPvooli9fju+//x7Dhg1D48aNceWVVwIAOnTogAEDBmD06NHYuHEjvvrqK4wdOxZDhw4VOoJNoVAoFAqFt/BMkjbJqr+7du2KJEwDwAMPPIDi4mKMGTMGx48fx0UXXYSVK1dGqeo33ngDY8eORb9+/eD3+3HNNddg3rx59hyUQqFQKBQKV+IZgUSy6m/VUQE+nw8zZszAjBkzDOvUr18fb775JlffAoEApk2bpht28wqxcAxAbBxHLBwDoI7DTcTCMQCxcRyxcAw1BZ9mx1hDhUKhUCgUCg/hmRwkhUKhUCgUCrtQAkmhUCgUCoWiCkogKRQKhUKhUFRBCSSFQqFQKBSKKiiBRMBjjz2G3r17o1atWkhLSyOqo2kapk6dikaNGiE5ORnZ2dn48ccfo8ocO3YMN910E1JSUpCWloZRo0ZFzeskGtr9/fzzz/D5fLqvd999N1JO7/233nrLFccAAH379q3WvzvuuCOqTF5eHgYNGoRatWqhYcOGuP/++1FeLm9pdtrjOHbsGO6++260a9cOycnJaNasGcaNGxc1rQUg/7uYP38+WrRogaSkJGRlZWHjxo2m5d999120b98eSUlJ6Ny5Mz799NOo90nOE9HQHMPLL7+Miy++GPXq1UO9evWQnZ1drfyIESOqfeYDBgyQegwA3XEsXry4Wh+rTiLo9u9C7zz2+XwYNGhQpIwT38UXX3yBK664Ao0bN4bP58OyZcss66xfvx7nn38+AoEA2rRpoztCm/ZcU0hAU1gydepUbe7cudqECRO01NRUojpPPPGElpqaqi1btkz77rvvtMGDB2stW7bUTp06FSkzYMAA7bzzztP+/e9/a//617+0Nm3aaDfccIOko6DfX3l5uXbgwIGo1/Tp07U6depoJ06ciJQDoL366qtR5Sofp5PHoGma1qdPH2306NFR/SsoKIg6zk6dOmnZ2dnali1btE8//VRr0KCBNnnyZCnHwHIc33//vXb11Vdry5cv13bv3q2tWbNGa9u2rXbNNddElZP5Xbz11ltaYmKitmjRIm379u3a6NGjtbS0NO3gwYO65b/66istLi5Oe+qpp7QffvhBe/jhh7WEhATt+++/j5QhOU9EQnsMN954ozZ//nxty5Yt2o4dO7QRI0Zoqamp2q+//hopM3z4cG3AgAFRn/mxY8ek9J/1OF599VUtJSUlqo/5+flRZdz+XRw9ejSq/9u2bdPi4uK0V199NVLGie/i008/1f76179qH3zwgQZAW7p0qWn5n376SatVq5Y2YcIE7YcfftCee+45LS4uTlu5cmWkDO1no5CDEkgUvPrqq0QCKRgMapmZmdrs2bMj244fP64FAgHtn//8p6ZpmvbDDz9oALRvvvkmUmbFihWaz+fT9u/fL7zvovbXtWtX7dZbb43aRnJREAHrMfTp00e75557DN//9NNPNb/fH3XDeOGFF7SUlBStpKRESN8rI+q7eOedd7TExEStrKwssk3md9GzZ0/trrvuivxdUVGhNW7cWJs1a5Zu+euuu04bNGhQ1LasrCzt9ttv1zSN7DwRDe0xVKW8vFyrW7eutmTJksi24cOHa0OGDBHdVVNoj8Pq2uXF7+KZZ57R6tatqxUVFUW2OfFdVIbk/HvggQe0c889N2rb9ddfr+Xk5ET+5v1sFGJQITYJ7N27F/n5+cjOzo5sS01NRVZWFnJzcwEAubm5SEtLQ48ePSJlsrOz4ff7sWHDBuF9ErG/TZs2YevWrRg1alS19+666y40aNAAPXv2xKJFi6pN2ikCnmN444030KBBA3Tq1AmTJ0/GyZMno9rt3LkzMjIyIttycnJQWFiI7du3u+o4KlNQUICUlBTEx0fP9yrjuygtLcWmTZuiftN+vx/Z2dmR33RVcnNzo8oDoc81XJ7kPBEJyzFU5eTJkygrK6u20Oj69evRsGFDtGvXDnfeeSeOHj0qtO+VYT2OoqIiNG/eHE2bNsWQIUOiftte/C4WLlyIoUOHonbt2lHb7fwuWLA6L0R8NgoxeGYmbS+Rn58PAFE33PDf4ffy8/PRsGHDqPfj4+NRv379SBnRfeLd38KFC9GhQwf07t07avuMGTNw2WWXoVatWli1ahX+8pe/oKioCOPGjRPWf4D9GG688UY0b94cjRs3xn/+8x88+OCD2LVrFz744INIu3rfVfg90Yj4Lo4cOYKZM2dizJgxUdtlfRdHjhxBRUWF7ue0c+dO3TpGn2vlcyC8zaiMSFiOoSoPPvggGjduHHXzGjBgAK6++mq0bNkSe/bswUMPPYTLL78cubm5iIuLE3oMANtxtGvXDosWLUKXLl1QUFCAOXPmoHfv3ti+fTuaNGniue9i48aN2LZtGxYuXBi13e7vggWj86KwsBCnTp3C77//zv07VYihxgqkSZMm4cknnzQts2PHDrRv396mHrFBehy8nDp1Cm+++SamTJlS7b3K27p164bi4mLMnj2b+KYs+xgqi4jOnTujUaNG6NevH/bs2YPWrVszt1sVu76LwsJCDBo0CB07dsQjjzwS9R7vd6Ew5oknnsBbb72F9evXRyU4Dx06NPL/zp07o0uXLmjdujXWr1+Pfv36OdHVavTq1Qu9evWK/N27d2906NABL774ImbOnOlgz9hYuHAhOnfujJ49e0Zt98J3ofAONVYgTZw4ESNGjDAt06pVK6a2MzMzAQAHDx5Eo0aNItsPHjyIrl27RsocOnQoql55eTmOHTsWqU8C6XHw7u+9997DyZMnMWzYMMuyWVlZmDlzJkpKSojWG7LrGCr3DwB2796N1q1bIzMzs9oIkYMHDwKA676LEydOYMCAAahbty6WLl2KhIQE0/K034URDRo0QFxcXORzCXPw4EHDPmdmZpqWJzlPRMJyDGHmzJmDJ554Ap9//jm6dOliWrZVq1Zo0KABdu/eLeWmzHMcYRISEtCtWzfs3r0bgLe+i+LiYrz11luma2yGkf1dsGB0XqSkpCA5ORlxcXHc369CEE4nQXkJ2iTtOXPmRLYVFBToJml/++23kTKfffaZ9CRt1v316dOn2ogpIx599FGtXr16zH01QtRn9uWXX2oAtO+++07TtDNJ2pVHiLz44otaSkqKdvr0aXEH8D9Yj6OgoEC78MILtT59+mjFxcVE+xL5XfTs2VMbO3Zs5O+Kigrt7LPPNk3S/tOf/hS1rVevXtWStM3OE9HQHoOmadqTTz6ppaSkaLm5uUT72Ldvn+bz+bQPP/yQu79GsBxHZcrLy7V27dpp48eP1zTNO9+FpoWuw4FAQDty5IjlPuz4LioDwiTtTp06RW274YYbqiVp83y/CjEogUTAL7/8om3ZsiUyxH3Lli3ali1booa6t2vXTvvggw8ifz/xxBNaWlqa9uGHH2r/+c9/tCFDhugO8+/WrZu2YcMG7csvv9Tatm0rfZi/2f5+/fVXrV27dtqGDRui6v3444+az+fTVqxYUa3N5cuXay+//LL2/fffaz/++KP297//XatVq5Y2depUVxzD7t27tRkzZmjffvuttnfvXu3DDz/UWrVqpV1yySWROuFh/v3799e2bt2qrVy5UktPT5c+zJ/mOAoKCrSsrCytc+fO2u7du6OGMZeXl2uaJv+7eOutt7RAIKAtXrxY++GHH7QxY8ZoaWlpkdF/t9xyizZp0qRI+a+++kqLj4/X5syZo+3YsUObNm2a7jB/q/NEJLTH8MQTT2iJiYnae++9F/WZh8/9EydOaPfdd5+Wm5ur7d27V/v888+1888/X2vbtq0Ucc16HNOnT9c+++wzbc+ePdqmTZu0oUOHaklJSdr27dujjtXN30WYiy66SLv++uurbXfquzhx4kTkngBAmzt3rrZlyxbtl19+0TRN0yZNmqTdcsstkfLhYf7333+/tmPHDm3+/Pm6w/zNPhuFPSiBRMDw4cM1ANVe69ati5TB/+afCRMMBrUpU6ZoGRkZWiAQ0Pr166ft2rUrqt2jR49qN9xwg1anTh0tJSVFGzlyZJToEo3V/vbu3VvtuDRN0yZPnqw1bdpUq6ioqNbmihUrtK5du2p16tTRateurZ133nnaggULdMs6cQx5eXnaJZdcotWvX18LBAJamzZttPvvvz9qHiRN07Sff/5Zu/zyy7Xk5GStQYMG2sSJE6OGzzt9HOvWrdP9DQLQ9u7dq2maPd/Fc889pzVr1kxLTEzUevbsqf373/+OvNenTx9t+PDhUeXfeecd7ZxzztESExO1c889V/vkk0+i3ic5T0RDcwzNmzfX/cynTZumaZqmnTx5Uuvfv7+Wnp6uJSQkaM2bN9dGjx5ty42M5jjuvffeSNmMjAxt4MCB2ubNm6Pac/t3oWmatnPnTg2AtmrVqmptOfVdGJ2b4b4PHz5c69OnT7U6Xbt21RITE7VWrVpF3TvCmH02CnvwaZqE8dgKhUKhUCgUHkbNg6RQKBQKhUJRBSWQFAqFQqFQKKqgBJJCoVAoFApFFZRAUigUCoVCoaiCEkgKhUKhUCgUVVACSaFQKBQKhaIKSiApFAqFQqFQVEEJJIVCoVAoFIoqKIGkUChcz+nTpzFixAh07twZ8fHxuPLKK53ukkKhiHGUQFIoFNxomoby8nJp7VdUVCA5ORnjxo1Ddna2tP0oFApFGCWQFIoaSN++fTF27FiMHTsWqampaNCgAaZMmYLwykOvv/46evTogbp16yIzMxM33ngjDh06FKm/fv16+Hw+rFixAt27d0cgEMCXX36JPXv2YMiQIcjIyECdOnVwwQUX4PPPP4/ad4sWLfDoo49i2LBhqFOnDpo3b47ly5fj8OHDGDJkCOrUqYMuXbrg22+/jdSpXbs2XnjhBYwePRqZmZn2fEgKhaJGowSSQlFDWbJkCeLj47Fx40b87W9/w9y5c/HKK68AAMrKyjBz5kx89913WLZsGX7++WeMGDGiWhuTJk3CE088gR07dqBLly4oKirCwIEDsWbNGmzZsgUDBgzAFVdcgby8vKh6zzzzDP7whz9gy5YtGDRoEG655RYMGzYMN998MzZv3ozWrVtj2LBhUEtFKhQKp1CL1SoUNZC+ffvi0KFD2L59O3w+H4CQ2Fm+fDl++OGHauW//fZbXHDBBThx4gTq1KmD9evX49JLL8WyZcswZMgQ03116tQJd9xxB8aOHQsg5CBdfPHFeP311wEA+fn5aNSoEaZMmYIZM2YAAP7973+jV69eOHDgQDXHaMSIETh+/DiWLVvG+zEoFAqFIcpBUihqKBdeeGFEHAFAr1698OOPP6KiogKbNm3CFVdcgWbNmqFu3bro06cPAFRzgnr06BH1d1FREe677z506NABaWlpqFOnDnbs2FGtXpcuXSL/z8jIAAB07ty52rbKYT2FQqGwEyWQFApFFKdPn0ZOTg5SUlLwxhtv4JtvvsHSpUsBAKWlpVFla9euHfX3fffdh6VLl+Lxxx/Hv/71L2zduhWdO3euVi8hISHy/7BI09sWDAbFHZhCoVBQEO90BxQKhTNs2LAh6u9///vfaNu2LXbu3ImjR4/iiSeeQNOmTQEgKmHajK+++gojRozAVVddBSDkKP38889C+61QKBR2oBwkhaKGkpeXhwkTJmDXrl345z//ieeeew733HMPmjVrhsTERDz33HP46aefsHz5csycOZOozbZt2+KDDz7A1q1b8d133+HGG28U5gL98MMP2Lp1K44dO4aCggJs3boVW7duFdK2QqFQVEU5SApFDWXYsGE4deoUevbsibi4ONxzzz0YM2YMfD4fFi9ejIceegjz5s3D+eefjzlz5mDw4MGWbc6dOxe33norevfujQYNGuDBBx9EYWGhkP4OHDgQv/zyS+Tvbt26AYAa6aZQKKSgRrEpFDWQvn37omvXrnj22Wed7opCoVC4EhViUygUCoVCoaiCEkgKhUKhUCgUVVAhNoVCoVAoFIoqKAdJoVAoFAqFogpKICkUCoVCoVBUQQkkhUKhUCgUiioogaRQKBQKhUJRBSWQFAqFQqFQKKqgBJJCoVAoFApFFZRAUigUCoVCoaiCEkgKhUKhUCgUVVACSaFQKBQKhaIK/w+1UpDQW8levwAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "plt.figure()\n", - "plt.plot(loss_opt)\n", - "\n", - "s = np.linspace(-0.1,0.1,100)\n", - "least_squares = np.empty(100)\n", - "off_diagonal_norm = np.empty(100)\n", - "for i in range(100):\n", - " dbi_eval(s[i],d=d_opt)\n", - " least_squares[i] = dbi_eval.least_squares(d=d_opt)\n", - " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", - "plt.figure()\n", - "plt.plot(s,loss)\n", - "plt.xlabel('s')\n", - "plt.ylabel('Least squares cost function')\n", - "plt.figure()\n", - "plt.plot(s,off_diagonal_norm)\n", - "plt.xlabel('s')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.title('Least squares cost function')\n", + "plt.contourf(param1,param2,least_squares,levels=50)\n", + "plt.xlabel('param1')\n", + "plt.ylabel('param2')\n", + "plt.colorbar()\n", "\n", - "\n", - "print(np.diag(d_opt))" + "plt.figure()\n", + "plt.title('Off-diagonal norm')\n", + "plt.contourf(param1,param2,off_diagonal_norm,levels=50)\n", + "plt.xlabel('param1')\n", + "plt.ylabel('param2')\n", + "plt.colorbar()" ] } ], @@ -733,7 +12329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.9.19" }, "orig_nbformat": 4, "vscode": { diff --git a/examples/dbi/dbi_misc.ipynb b/examples/dbi/dbi_misc.ipynb deleted file mode 100644 index 22691d7071..0000000000 --- a/examples/dbi/dbi_misc.ipynb +++ /dev/null @@ -1,1592 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCost\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Normalization of D" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-04-01 22:40:49]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "# hamiltonian parameters\n", - "nqubits = 7\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "factor = np.array([1,2**nqubits])\n", - "s_space = np.linspace(1e-5, 1.0, 100)\n", - "off_diagonal_norm_diff = np.empty((len(factor)+1,len(s_space)))\n", - "potential = np.empty((len(factor),len(s_space)))\n", - "for i in range(len(factor)):\n", - "# generate data for plotting sigma decrease of the first step\n", - " d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/factor[i]\n", - " for s in range(len(s_space)):\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval(s_space[s],d=d)\n", - " off_diagonal_norm_diff[i,s] = (dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", - " potential[i,s] = dbi_eval.least_squares(d=d)\n", - " \"\"\"\n", - " # grid_search\n", - " step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", - " print('grid_search step:', step_grid)\n", - " # hyperopt\n", - " step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", - " print('hyperopt_search step:', step_hyperopt)\n", - " # polynomial\n", - " step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", - " print('polynomial_approximation step:', step_poly)\n", - " \"\"\"\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "step = 1e-2\n", - "iterations = 200\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Least squares cost function')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "plt.figure()\n", - "plt.plot(s_space, off_diagonal_norm_diff[0,:],label= 'No normalization')\n", - "plt.plot(s_space, off_diagonal_norm_diff[1,:],label= 'Normalization')\n", - "plt.xlabel('s')\n", - "plt.ylabel(r'$||\\sigma(H_0)||-||\\sigma(H_k)||$')\n", - "plt.legend()\n", - "\n", - "plt.figure()\n", - "plt.title('D not normalized')\n", - "plt.plot(s_space, potential[0,:],label= 'No normalization')\n", - "plt.xlabel('s')\n", - "plt.ylabel('Least squares cost function')\n", - "\n", - "plt.figure()\n", - "plt.title('D normalized')\n", - "plt.plot(s_space, potential[1,:],label= 'Normalization')\n", - "plt.xlabel('s')\n", - "plt.ylabel('Least squares cost function')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "dbi_eval = deepcopy(dbi)\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "flows = 50\n", - "off_diagonal_norm = np.empty((flows+1,3))\n", - "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", - " dbi_eval(step_poly,d=d)\n", - " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", - "\n", - "\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", - "dbi_eval = deepcopy(dbi)\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", - " dbi_eval(step_poly,d=d)\n", - " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", - "\n", - "dbi_eval = deepcopy(dbi)\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "step = 1e-2\n", - "iterations = 200\n", - "d, loss, grad, diags = gradient_ascent(dbi_eval, d,step, iterations)\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", - " dbi_eval(step_poly,d=d)\n", - " off_diagonal_norm[i+1,2] = dbi_eval.off_diagonal_norm\n" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(off_diagonal_norm[:,0],label= 'No normalization')\n", - "plt.plot(off_diagonal_norm[:,1],label= 'Normalization')\n", - "plt.plot(off_diagonal_norm[:,2],label= 'Gradient Ascent')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hyperopt does it get stuck" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-04-01 22:42:14]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 7\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:14<00:00, 35.54trial/s, best loss: 334743.9033516353]\n", - "100%|██████████| 500/500 [00:14<00:00, 34.22trial/s, best loss: 332247.1783501899] \n", - "100%|██████████| 500/500 [00:10<00:00, 48.63trial/s, best loss: 330843.2174669805] \n", - "100%|██████████| 500/500 [00:12<00:00, 40.89trial/s, best loss: 329875.86478824326]\n", - "100%|██████████| 500/500 [00:09<00:00, 52.81trial/s, best loss: 329131.1483035261] \n", - "100%|██████████| 500/500 [00:12<00:00, 41.02trial/s, best loss: 328494.15894863644]\n", - "100%|██████████| 500/500 [00:12<00:00, 41.42trial/s, best loss: 328037.3084475153]\n", - "100%|██████████| 500/500 [00:31<00:00, 15.94trial/s, best loss: 327764.40651092166]\n", - "100%|██████████| 500/500 [00:26<00:00, 18.99trial/s, best loss: 327524.34657881147]\n", - "100%|██████████| 500/500 [00:30<00:00, 16.47trial/s, best loss: 327316.4158512455]\n" - ] - } - ], - "source": [ - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "dbi_eval = deepcopy(dbi)\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "flows = 10\n", - "off_diagonal_norm = np.empty(flows+1)\n", - "off_diagonal_norm[0] = dbi_eval.off_diagonal_norm\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d)\n", - " dbi_eval(step_poly,d=d)\n", - " off_diagonal_norm[i+1] = dbi_eval.off_diagonal_norm\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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npKend2Cl7dM8HBWfyflsiIiIdMmggk11dTVMTDRLMjU1hUql0vqcnJwclJSUwN3dXd/l6cxQv+4A2GdDRESkawYVbCZNmoS33noLv/76K65cuYKdO3diw4YNeOCBBwAAlZWVWL58OeLi4nDlyhXs378fkydPRkBAAMaNGydy9a0X6tUNFqYmKFQocaWkWuxyiIiIjIZB9di8//77WLVqFZ5++mkUFRXBw8MDTzzxBFavXg2g6erNmTNnsG3bNpSVlcHDwwNjx47FG2+8YVB9NH9Hs8+mBL5ONmKXREREZBQM6q6ojtDarmp92/BHKjbuT8PkAR74z8yBotVBRETUGbT2+9ughqK6EvbZEBER6R6DjUjYZ0NERKR7DDYisTQ3xQC5A4CmqzZERETUfgw2Irp5OIqIiIjaj8FGRFw3ioiISLcYbEQ08KY+m6vssyEiImo3BhsRWVmwz4aIiEiXGGxExj4bIiIi3WGwEdmffTal7LMhIiJqJwYbkTX32RQoatlnQ0RE1E4MNiJjnw0REZHuMNgYAPbZEBER6QaDjQEIZ58NERGRTjDYGIBQr24wN5Wwz4aIiKidGGwMAPtsiIiIdIPBxkA03/Ydn1kqciVERESdF4ONgeC6UURERO3HYGMgmvts8strkVXKPhsiIqK2YLAxEOyzISIiaj8GGwNy8/IKREREdOcYbAwI+2yIiIjah8HGgLDPhoiIqH0YbAyIlYUp+ns6AGCfDRERUVsw2BgY9tkQERG1HYONgVFP1Mc+GyIiojvGYGNgQr0dYG4qQV55LbJLa8Quh4iIqFNhsDEw1hZm7LMhIiJqIwYbA3Tzbd9ERETUegw2Bojz2RAREbUNg40BYp8NERFR2zDYGCD22RAREbUNg42BCvfrDoDBhoiI6E4YVLBpbGzEqlWr4OvrCysrK/j7++ONN97Q6DMRBAGrV6+Gu7s7rKysEBUVhbS0NBGr1g/22RAREd05gwo269atw6ZNm/DBBx/g4sWLWLduHdavX4/3339ffc769euxceNGfPzxx4iPj4eNjQ3GjRuH2tpaESvXvUHe3WBmwj4bIiKiO2FQweb48eOYPHkyJk6cCB8fH0ybNg1jx47FiRMnADRdrXnvvffw8ssvY/LkyQgJCcHnn3+OvLw87Nq1S9zidczawgz95Q4AgLhMDkcRERG1hkEFm2HDhmH//v1ITU0FAJw+fRpHjx7FhAkTAACZmZkoKChAVFSU+jkymQzh4eGIjY1t8TWVSiUUCoXG1lkMZZ8NERHRHTGoYLNixQrMnDkTvXv3hrm5OQYOHIglS5Zg9uzZAICCggIAgKurq8bzXF1d1cf+as2aNZDJZOpNLpfr94fQoT/XjSplnw0REVErGFSw+e677/DVV19h+/btOHnyJLZt24b/+7//w7Zt29r8mitXrkR5ebl6y87O1mHF+tXcZ5NbVoOc6+yzISIi+jtmYhdws+XLl6uv2gBAv379cPXqVaxZswZz586Fm5sbAKCwsBDu7u7q5xUWFmLAgAEtvqZUKoVUKtV77frQ3GeTdPU6YjNKIO9uLXZJREREBs2grthUV1fDxESzJFNTU6hUKgCAr68v3NzcsH//fvVxhUKB+Ph4REREdGitHYV9NkRERK1nUFdsJk2ahLfeegteXl4IDg7GqVOnsGHDBsyfPx8AIJFIsGTJErz55psIDAyEr68vVq1aBQ8PD0yZMkXc4vVkqJ8jPjx4Wd1nI5FIxC6JiIjIYBlUsHn//fexatUqPP300ygqKoKHhweeeOIJrF69Wn3O888/j6qqKjz++OMoKyvD8OHDsWfPHlhaWopYuf78tc+Gw1FERETaSYQudruNQqGATCZDeXk57O3txS6nVR786BhOZpVh/bQQzAjrPHd1ERER6Uprv78NqseGWnbzbd9ERESkHYNNJ3DzulFERESkHYNNJ3Bzn012abXY5RARERksBptOwEZqhhBPGQBetSEiIrodBptO4s/hKPbZEBERacNg00mwz4aIiOjvMdh0EuyzISIi+nsMNp2EjdQM/dhnQ0REdFsMNp0I+2yIiIhuj8GmE2GfDRER0e0x2HQiYd7dYMo+GyIiIq0YbDqRm+ezic/kcBQREdFfMdh0MhyOIiIi0o7BppNhsCEiItKOwaaTae6zybnOPhsiIqK/YrDpZNhnQ0REpB2DTSfE4SgiIqKWMdh0QuG+3QEw2BAREf0Vg00nFObTXd1nk3OdfTZERETNGGw6IVupGfr1uNFnw+UViIiI1BhsOin22RAREd2KwaaTGup3o88mk8GGiIioGYNNJ9XcZ5Ndyj4bIiKiZgw2nRT7bIiIiG7FYNOJsc+GiIhIE4NNJ8Y+GyIiIk0MNp0Y+2yIiIg0Mdh0YrZSM/Rlnw0REZEag00npx6OYp8NERERg01n19xAzJW+iYiIGGw6vTDvbjA1kSCrtBq5ZTVil0NERCQqBptOzs7S/KY+Gw5HERFR12ZQwcbHxwcSieSWbdGiRQCA0aNH33LsySefFLlq8bHPhoiIqImZ2AXcLCEhAY2NjerH586dwz333IPp06er9y1cuBCvv/66+rG1tXWH1miIhvo5YnNMBuJ4ZxQREXVxBhVsnJ2dNR6vXbsW/v7+GDVqlHqftbU13NzcOro0g/bXPpseDlZil0RERCQKgxqKulldXR2+/PJLzJ8/HxKJRL3/q6++gpOTE/r27YuVK1eiuvr2E9MplUooFAqNzdiwz4aIiKiJwQabXbt2oaysDPPmzVPv+8c//oEvv/wSBw8exMqVK/HFF1/g4Ycfvu3rrFmzBjKZTL3J5XI9Vy6Oob7ssyEiIpIIgiCIXURLxo0bBwsLC/zyyy9azzlw4ADGjBmD9PR0+Pv7t3iOUqmEUqlUP1YoFJDL5SgvL4e9vb3O6xbLwUtFeHRrAry6W+Pw83eJXQ4REZFOKRQKyGSyv/3+Nqgem2ZXr17Fvn37sGPHjtueFx4eDgC3DTZSqRRSqVTnNRqaMJ9uMJEAWaXVyCurgQf7bIiIqAsyyKGo6OhouLi4YOLEibc9Lzk5GQDg7u7eAVUZNjtLc/Rr7rPhat9ERNRFGVywUalUiI6Oxty5c2Fm9ucFpcuXL+ONN95AUlISrly5gp9//hmPPPIIRo4ciZCQEBErNhzNyyvEXeZt30RE1DUZXLDZt28fsrKyMH/+fI39FhYW2LdvH8aOHYvevXvjX//6F6ZOnXrbHpyuRh1seMWGiIi6KIPrsRk7dixa6meWy+WIiYkRoaLOo7nP5moJ+2yIiKhrMrgrNtR27LMhIqKujsHGyLDPhoiIujIGGyMT3rwgJq/YEBFRF8RgY2TCfLpr9NkQERF1JQw2Rsb+5nWjeNWGiIi6GAYbI9TcZxOfwT4bIiLqWhhsjNBQPy6ISUREXRODjRFq7rO5UlKN/HL22RARUddxRxP0+fr6QiKR3PGbLFmyBIsXL77j51HbNPfZnMkpR3xGKaYM7CF2SURERB3ijoLN1q1b2/QmPj4+bXoetd1QP0ecySlHXEYJgw0REXUZdxRsRo0apa86SMeG+nXHlsMZ7LMhIqIuhT02Rop9NkRE1BUx2Bgpe0tzBHvcmM+Gt30TEVEXweZhIzbUrzvO5rLPhoiIug42DxuxoX6O+ORIJuIzecWGiIi6BjYPG7HmPpvM4ioUlNfCTWYpdklERER61a4em/r6emRnZyMlJQWlpbwqYGhkVjf12XDdKCIi6gLuONhUVFRg06ZNGDVqFOzt7eHj44OgoCA4OzvD29sbCxcuREJCgj5qpTbg8gpERNSV3FGw2bBhA3x8fBAdHY2oqCjs2rULycnJSE1NRWxsLF555RU0NDRg7NixGD9+PNLS0vRVN7VS84KYcbwzioiIuoA76rFJSEjA4cOHERwc3OLxIUOGYP78+fj4448RHR2NI0eOIDAwUCeFUtuwz4aIiLqSOwo2X3/9davOk0qlePLJJ9tUEOlWc5/N2dxyxGeWYPIA3vZNRETGixP0dQHhvuyzISKirkFnwebTTz/V1UuRjrHPhoiIugqdBZvdu3fjwIED6sfV1dWYOXOmrl6e2mGwb3dIbvTZFCpqxS6HiIhIb3QWbD7//HOsXr0aly5dQmpqKkaOHIlx48bp6uWpHZr6bOwBcDiKiIiM2x01D7dk6dKlGDBgAPr3749PP/0Us2fPhkqlQnR0NAYMGKCDEkkXhvo64lyuAnEZpWwgJiIio9XuKzZ33XUXsrOz8fbbb2P69OnIzMyEh4cH9u7di19//VUXNZIONPfZxPOKDRERGbF2X7G5//77cf/996sf19bW4ty5czhz5gz27duHiRMntvctSAea+2wybvTZuNpzPhsiIjI+dxxs7OzsMHDgQAwaNAihoaEIDQ1Fnz59IJFIAACWlpYICwtDWFiYzoultmvus2kajuJ8NkREZJzuONisW7cOSUlJOHDgAD744AOoVCpYWVkhJCREI+z0799fH/VSO7DPhoiIjN0dB5unn35a/fuamhrY2Njgn//8J0pLSxEXF4dPP/0UdXV1aGxs1Gmh1H5D/Rzx6dFM9tkQEZHRalePjZWVFQBg1qxZCAkJAQA0NDTgwoUL7a+MdI59NkREZOx0vqSCmZmZOuSQYZFZmaOPO+ezISIi42VQa0X5+PhAIpHcsi1atAhA0x1XixYtgqOjI2xtbTF16lQUFhaKXHXnwuUViIjImN1xsFmwYAE2bdqEhIQEKJVKAFDfEdVeCQkJyM/PV29//PEHAGD69OkAmiYD/OWXX/D9998jJiYGeXl5ePDBB3Xy3l2Fej6bTF6xISIi43PHPTZpaWn4/vvvUVFRATOzpqe/9tprGD16NEJDQzFgwABYW1u3qRhnZ2eNx2vXroW/vz9GjRqF8vJyfPbZZ9i+fTvuvvtuAEB0dDSCgoIQFxeHoUOHtviaSqVSHcAAQKFQtKk2YzHE50afzbUqFClq4cI+GyIiMiJ3fMUmJiYG5eXlSElJweeff47nnnsOZWVlWL16NYYPHw6ZTIbg4OB2F1ZXV4cvv/wS8+fPh0QiQVJSEurr6xEVFaU+p3fv3vDy8kJsbKzW11mzZg1kMpl6k8vl7a6tM5NZ39Rnk8nhKCIiMi5tvisqMDAQgYGBGit4Z2ZmIjExEadOnWp3Ybt27UJZWRnmzZsHACgoKICFhQUcHBw0znN1dUVBQYHW11m5ciWWLVumfqxQKLp8uBnq54jzeU0T9d3f30PscoiIiHTmjoJNVlYWvLy8tB739fWFr6+vuicmNzcXPXq0bSK4zz77DBMmTICHR/u+eKVSKaRSabtew9gM9XPEZ0czeWcUEREZnTsaiho8eDCeeOIJJCQkaD2nvLwcn3zyCfr27Ysff/yxTUVdvXoV+/btw4IFC9T73NzcUFdXh7KyMo1zCwsL4ebm1qb36ar+2mdDRERkLO7ois2FCxfw1ltv4Z577oGlpSUGDRoEDw8PWFpa4vr167hw4QLOnz+P0NBQrF+/Hvfee2+bioqOjoaLi4vGApqDBg2Cubk59u/fj6lTpwIAUlJSkJWVhYiIiDa9T1fV3GdzPk+BuMxSDkcREZHRuKMrNo6OjtiwYQPy8/PxwQcfIDAwEMXFxUhLSwMAzJ49G0lJSYiNjW1zqFGpVIiOjsbcuXPVd10BgEwmw2OPPYZly5bh4MGDSEpKwqOPPoqIiAitd0SRduG+zfPZcDiKiIiMR5uah62srDB+/HhMmzZN1/Vg3759yMrKwvz582859u6778LExARTp06FUqnEuHHj8NFHH+m8hq5gqF93/PcY+2yIiMi4SARBENryRFNTU3z33XfqYaHOQqFQQCaToby8HPb29mKXI5qy6joMfOMPCAJw4qUxcLHjfDZERGS4Wvv93eYlFQRBwObNmxEZGYnhw4djyZIlt20qJsPiYG2BILemPxjxXF6BiIiMRLvWijp16hRCQ0MxfPhwnD9/HiNGjMBzzz2nq9pIzyL8m/ps1vx2ERfzu/aMzEREZBzaPEEfAGzfvh333HOP+vGZM2cwefJk9OjRA0uXLm13caRfC0f44eClImQUV2HapuPYOGsgxgS5il0WERFRm7X5ik337t1vmcE3JCQEH3zwATZt2tTuwkj/3GSW2Pl0JIb5O6KqrhELPk/Ep0cy0Ma2KyIiItG1OdgMGDAA0dHRt+wPCAhAVlZWu4qijiOzNse2+UMwa4gXBAF489eLWLnjLOoaVGKXRkREdMfaHGzefPNNbNy4EXPmzEFsbCyqqqpQVFSEt99+G76+vrqskfTM3NQEbz/QF6vu6wMTCfBNQjbm/vcEyqrrxC6NiIjojrQ52AwdOhRxcXHIzs7GiBEjYG9vD3d3d/zwww945513dFkjdQCJRILHhvvi07lhsLEwRWxGCR746DgyrlWKXRoREVGrtXkem5sVFRUhKSkJKpUK4eHhcHJy0kVtesF5bP7epQIFHtuaiNyyGsiszLFpdiiGBRjuf1MiIjJ+rf3+1kmw6UwYbFrnWoUSj3+RiFNZZTAzkeCNKX0xa4j2ld2JiIj0Se8T9JFxc7aT4uuFQ3F/fw80qASs3HEWb+6+gEZVl8rBRETUydzRPDa+vr6QSCR3/CZLlizB4sWL7/h5JC5Lc1P8Z+YABLjYYsMfqfj0aCYyi6vwn1kDYStt1xRIREREenFHQ1ExMTFtehMfHx94e3u36bm6xqGottl9Jg//+u40lA0q9Hazw6dzw+DZzVrssoiIqItgj40WDDZtl5xdhgXbElFcqYSTrQW2PBKGUK9uYpdFRERdAHtsSOcGyB3w0zORCHK3R3FlHWZuicPPp/PELouIiEjtjq7YGEOPDa/YtF+VsgHPfpOMfRcLAQCLxwRiaVRgm/5sEBERtYZehqLYY0PNGlUC1u+5hM2HMwAA94W44/+m94eluanIlRERkTFij40WDDa69V1CNl7ceRYNKgH95Q745JFBcLGzFLssIiIyMuyxoQ4xY7AcXzwWDgdrc5zOLsOUD47hQp5C7LKIiKiLYrChdovwd8SupyPh52yDvPJaTPv4OPZdKBS7LCIi6oIYbEgnfJxssPOpSEQGOKK6rhELv0jEJ4cz0MVGOomISGQMNqQzMmtzbH10CP4R7gVBAN767SJW/HgWdQ0qsUsjIqIugsGGdMrc1ARvTemL1ff1gYkE+DYxG4/8Nx5l1XVil0ZERF0Agw3pnEQiwfzhvvhs7mDYSs0Ql1GKBz46joxrlWKXRkRERo7BhvTmrt4u+PGpYejhYIXM4ipM+fAYjqUXi10WEREZMQYb0qtebnb46ZlIDPLuBkVtA+b+9wS2x2eJXRYRERkpBhvSOydbKb5aEI4pAzzQoBLw4s6zeP2XC2hU8Y4pIiLSLQYb6hCW5qZ496EB+Nc9PQEA/z2WiYWfJ6Kitl7kyoiIyJgw2FCHkUgk+OeYQHz4j1BIzUxw4FIRpm2KRXZptdilERGRkWCwoQ43McQd3z0RAWc7KVIKK/DAR8eQdPW62GUREZERYLAhUfSXO+CnRZHo426P4so6zPokDj8l54pdFhERdXIMNiQaDwcrfP9kBO7p44q6BhWe/SYZG/6XAhWbiomIqI0YbEhUNlIzbH54EJ4Y5QcA2HggHf/85hRq6xtFroyIiDojgws2ubm5ePjhh+Ho6AgrKyv069cPiYmJ6uPz5s2DRCLR2MaPHy9ixdReJiYSrJwQhPXTQmBuKsGvZ/Lx0JY4FClqxS6NiIg6GTOxC7jZ9evXERkZibvuugu///47nJ2dkZaWhm7dummcN378eERHR6sfS6XSji6V9GBGmBxe3a3x5JdJOJ1dhskfHsOnc8MQ7CETuzQiIuokDCrYrFu3DnK5XCO0+Pr63nKeVCqFm5tbR5ZGHWSonyN+WhSJ+VsTcPlaFaZ/HIv/zByIe/q4il0aERF1AgY1FPXzzz8jLCwM06dPh4uLCwYOHIhPPvnklvMOHToEFxcX9OrVC0899RRKSkq0vqZSqYRCodDYyLB5O9pgx9ORGBHohOq6Rjz+RSK2HL4MQWBTMRER3Z5BBZuMjAxs2rQJgYGB2Lt3L5566iksXrwY27ZtU58zfvx4fP7559i/fz/WrVuHmJgYTJgwAY2NLTebrlmzBjKZTL3J5fKO+nGoHWRW5vjvvMF4eKgXBAF4+7dLeOHHM6hrUIldGhERGTCJYED/DLawsEBYWBiOHz+u3rd48WIkJCQgNja2xedkZGTA398f+/btw5gxY245rlQqoVQq1Y8VCgXkcjnKy8thb2+v+x+CdEoQBGw7fgWv774AlQCE+3bHxw8PQjcbC7FLIyKiDqRQKCCTyf72+9ugrti4u7ujT58+GvuCgoKQlaV9NWg/Pz84OTkhPT29xeNSqRT29vYaG3UeEokE8yJ98dm8wbCVmiE+sxRTPjqGC3kcUiQiolsZVLCJjIxESkqKxr7U1FR4e3trfU5OTg5KSkrg7u6u7/JIRHf1csGPTw2DZzcrXC2pxn3vH8HKHWdxrUL5908mIqIuw6CCzdKlSxEXF4e3334b6enp2L59O7Zs2YJFixYBACorK7F8+XLExcXhypUr2L9/PyZPnoyAgACMGzdO5OpJ33q52WHXokhM7OcOlQB8fSILo/99EB8eTOeEfkREBMDAemwAYPfu3Vi5ciXS0tLg6+uLZcuWYeHChQCAmpoaTJkyBadOnUJZWRk8PDwwduxYvPHGG3B1bd3twK0doyPDlnClFG/svoAzOeUAgB4OVnhhQm9MCnGHRCIRuToiItK11n5/G1yw0TcGG+OhUgn46XQu1u9JQX550yzFA70csOq+Pgj16vY3zyYios6EwUYLBhvjU1PXiE+OZGDTocuouTEkdX9/Dzw/vhc8u1mLXB0REekCg40WDDbGq1BRi//bm4IfTuZAEACpmQkWjPDFU6MDYCs1qEm2iYjoDjHYaMFgY/zO5ZbjzV8vIC6jFADgZCvFc2N7YnqYHKYm7L8hIuqMGGy0YLDpGgRBwB8XCvH2bxdxpaQaANDbzQ6r7uuDyAAnkasjIqI7xWCjBYNN11LXoMIXcVfxn32pUNQ2AACiglyw8t4g+DvbilwdERG1FoONFgw2XdP1qjr8Z38avoy7igaVADMTCR4e6o1nxwRyeQYiok6AwUYLBpuu7fK1Sqz57SL2XSwC0LTY5uIxgZgz1BsWZgY1XyUREd2EwUYLBhsCgKNpxXjz1wu4VFABAPB1ssHKCb1xTx9XTvBHRGSAGGy0YLChZo0qAd8nZuP//peK4sqmNaci/Bzx0sQg9O0hE7k6IiK6GYONFgw29FeVygZsOpSOT45koq5BBYkEmBbqieXjesHF3lLs8oiICAw2WjHYkDY516uxfk8Kfj6dBwCwtjDFk6P8sXCEH6wsTEWujoioa2Ow0YLBhv7OyazreHP3BZzMKgMAuMss8fz4XpjcvwdMOMEfEZEoGGy0YLCh1hAEAbvP5GPt75eQW1YDAOjvKcPL9/XBYJ/uIldHRNT1MNhowWBDd6K2vhH/PZaJjw5eRqWyaYK/if3c8cL43vBy5AKbREQdhcFGCwYbaotrFUps+CMV3yZkQSUAFqYmeDTSB4vuDoC9pbnY5RERGT0GGy0YbKg9LhUo8ObuiziaXgwAcLSxwNJ7emLmYDnMTDnBHxGRvjDYaMFgQ+0lCAIOphThrV8v4vK1KgBAT1dbvDSxD0b1dBa5OiIi48RgowWDDelKfaMK2+Oz8O6+VJRV1wMARvV0xssTgxDoaidydURExoXBRgsGG9K18up6vH8gDdtir6C+UYCpiQSzhsixNKonHG2lYpdHRGQUGGy0YLAhfblSXIU1v1/E3vOFAAA7qRmeuTsA8yJ9IDXjBH9ERO3BYKMFgw3pW1xGCd789QLO5SoAAPLuVlg5IQgT+rpxgU0iojZisNGCwYY6gkolYMepXPx77yUUKpoW2OzXQ4bHR/phQl833kFFRHSHGGy0YLChjlRd14DNMRnYcjgDNfWNAADPblZYMNwXMwbLYW1hJnKFRESdA4ONFgw2JIbSqjp8HnsFn8deRWlVHQDAwdocc4Z645EIHzjbscmYiOh2GGy0YLAhMdXUNeKHkzn49EgGrpZUAwAszEwwNdQTC0b4wt/ZVuQKiYgME4ONFgw2ZAgaVQL+d74Amw9nIDm7DAAgkQBRQa54YqQfwrjQJhGRBgYbLRhsyJAIgoCEK9ex5fBl7LtYpN4f6uWAx0f6454+rjA14Z1UREQMNlow2JChSi+qwCeHM7HzVC7qGlUAAF8nGywY4YupoZ6wNOdcOETUdTHYaMFgQ4auqKIW245fwRexV6GobQDQtNjm3GE+mDPUG91sLESukIio4zHYaMFgQ51FpbIB3yZk479HM5FbVgMAsDI3xYwwTywY4Qd5d2uRKyQi6jgMNlow2FBnU9+owm9n87E5JgMX8ptmMzaRABP6ueOJkX4I8XQQt0Aiog7AYKMFgw11VoIg4PjlEmw+nIHDqdfU+4f6dccTI/0xupczl2wgIqPV2u9vg5vXPTc3Fw8//DAcHR1hZWWFfv36ITExUX1cEASsXr0a7u7usLKyQlRUFNLS0kSsmKhjSCQSRAY44fP5Q/D7syPw4MAeMDORIC6jFI9uTcC49w7j+8Rs1DWoxC6ViEg0BhVsrl+/jsjISJibm+P333/HhQsX8M4776Bbt27qc9avX4+NGzfi448/Rnx8PGxsbDBu3DjU1taKWDlRxwpyt8eGhwbg8PN3YeEIX9hKzZBaWInlP5zBiPUH8HHMZShq68Uuk4iowxnUUNSKFStw7NgxHDlypMXjgiDAw8MD//rXv/Dcc88BAMrLy+Hq6oqtW7di5syZf/seHIoiY1ReU4+vT2Qh+limetFNW6kZZg6WY/5wX3g4WIlcIRFR+3TKoaiff/4ZYWFhmD59OlxcXDBw4EB88skn6uOZmZkoKChAVFSUep9MJkN4eDhiY2NbfE2lUgmFQqGxERkbmZU5nhzljyPP341/TwtBT1dbVCob8OnRTIxcfxBLv03GxXz+2Sci42dQwSYjIwObNm1CYGAg9u7di6eeegqLFy/Gtm3bAAAFBQUAAFdXV43nubq6qo/91Zo1ayCTydSbXC7X7w9BJCILMxNMD5Nj75KRiJ43GEP9uqNBJWDnqVxM+M8RzPksHkfTimFAF2qJiHTKoIaiLCwsEBYWhuPHj6v3LV68GAkJCYiNjcXx48cRGRmJvLw8uLu7q8+ZMWMGJBIJvv3221teU6lUQqlUqh8rFArI5XIORVGXcSanDJsPZ+D3s/lQ3fi/PdjDHo+P9MO9/dxhbmpQ/74hImpRpxyKcnd3R58+fTT2BQUFISsrCwDg5uYGACgsLNQ4p7CwUH3sr6RSKezt7TU2oq4kxNMBH/4jFIeeuwtzI7xhaW6C83kKPPtNMkb/+xA+O5qJKmWD2GUSEemEQQWbyMhIpKSkaOxLTU2Ft7c3AMDX1xdubm7Yv3+/+rhCoUB8fDwiIiI6tFaizsbL0RqvTe6L2BVjsOyennC0sUBuWQ3e2H0BEWv2Y/2eSyhS8O5CIurcDGooKiEhAcOGDcNrr72GGTNm4MSJE1i4cCG2bNmC2bNnAwDWrVuHtWvXYtu2bfD19cWqVatw5swZXLhwAZaWln/7HrwriqhJbX0jfjyZg0+PZCKzuAoAYGFqggcG9sDCkb4IcLETuUIioj912pmHd+/ejZUrVyItLQ2+vr5YtmwZFi5cqD4uCAJeeeUVbNmyBWVlZRg+fDg++ugj9OzZs1Wvz2BDpKlRJeCPC4XYcvgyTmaVqfdHBbng0UhfRPg5wsSEMxoTkbg6bbDRNwYbIu0Sr5Ri8+EM7LtYiOa/GXo4WGHqIE9MC/WElyMX3iQicTDYaMFgQ/T3Ll+rRPSxTPyUnIeK2j8bi8N9u2N6mBwT+rrBRmomYoVE1NUw2GjBYEPUerX1jdh7vgA/JOXgaHqx+iqOjYUp7u3njulhcgz26cbFN4lI7xhstGCwIWqb3LIa7EjKwQ8nc3C1pFq938fRGtMGeeLBUE8u3UBEesNgowWDDVH7CIKAhCvX8X1iNn49m4/qukYAgEQCDA9wwrRBnhgX7AZLc1ORKyUiY8JgowWDDZHuVCkb8NvZfPyQlIP4zFL1fjtLM9zf3wPTBnligNyBQ1VE1G4MNlow2BDpx9WSKvyYlIMfT+Yit6xGvT/QxRbTBnnigdAecLH7+7mmiIhawmCjBYMNkX6pVAJiM0rwfWI2fj9XAGWDCgBgaiLB6J7OmDbIE2OCXGFhZlATnxORgWOw0YLBhqjjKGrrsft0Pn5IytaY/K+btTkmD+iBaYM80beHTLwCiajTYLDRgsGGSBzpRZX4ISkHO07moKhCqd4f5G6P6YM8MWVgD3S3sRCxQiIyZAw2WjDYEImroVGFI+nF+CExB39cKERdY9NQlbmpBGN6u2LaIE+M7uUMM1MOVRHRnxhstGCwITIc16vq8PPpPPyQlIOzueXq/U62UjwY2gPTB3ki0JWLcRIRg41WDDZEhulivgI/JOVg16lclFTVqff3lztg2iBP3B/iAZm1uYgVEpGYGGy0YLAhMmx1DSocTCnCD0k5OHipCA2qpr+iLMxMMC7YDdMHeSIywAmmXHGcqEthsNGCwYao87hWocRPybn4PjEHKYUV6v3uMks8GNoD0wbJ4etkI2KFRNRRGGy0YLAh6nwEQcDZ3HL8kJSDn5LzUF5Trz422Kcbpg+S494Qd9hyxXEio8VgowWDDVHnVlvfiH0XC/F9Yg6OpF3DjZEqWJmbYkI/N8wIkyPctzuXcSAyMgw2WjDYEBmPgvJa7DiVgx8Sc5BRXKXe7+tkg4cGyzE11BPOdlIRKyQiXWGw0YLBhsj4CIKAk1nX8X1iDn45nYeqGyuOm5lIEBXkiplD5BgR6MyGY6JOjMFGCwYbIuNWqWzAr2fy8PWJbCRnl6n393CwwvQwT8wIk8PDwUq8AomoTRhstGCwIeo6LhUo8M2JbOw8latuOJZIgFE9nTFzsBfGBLnAnDMcE3UKDDZaMNgQdT219Y3Ye74AX5/IQlxGqXq/k60U0wZ5YuZgOXx42ziRQWOw0YLBhqhryyyuwrcJ2fghKQfFlX8uxjnUrztmDfHCuGA3WJqbilghEbWEwUYLBhsiAoD6RhX2XyzENwnZiEm9hua/CWVW5nhgYA/MGuKFXm5cp4rIUDDYaMFgQ0R/lVtWg+8Ts/FdQjbyymvV+wfIHTBriBz3hXjAhpP/EYmKwUYLBhsi0qZRJeBw2jV8eyIb+y4WqtepsrEwxf0DPDBzsBdCPGWc/I9IBAw2WjDYEFFrFFXUYsfJXHybkI3Mmyb/C3K3x8zBckwZ0IOrjRN1IAYbLRhsiOhOCIKAuIxSfJuQhd/OFaCuQQUAkJqZ4N5+7pg5WI4hXMKBSO8YbLRgsCGitiqrrsPOU7n45kS2xmrjfs1LOAzyhJMtl3Ag0gcGGy0YbIiovQRBQHJ2Gb5NyMbPp/NQfdMSDvf0ccXMIV4YHuDEJRyIdIjBRgsGGyLSpUplA345nYdvErJx+i9LOMwIk2PGYE+4y7iEA1F7MdhowWBDRPpyMV+BbxOyseNkDhS1DQAAk+YlHIZ44e7eXMKBqK0YbLRgsCEifautb8Sec01LOMRn/rmEg7Pdn0s4eDtyCQeiO8FgowWDDRF1pIxrlfg2MRs/JuWguLJOvT/CzxEzh8i5hANRK7X2+9ugrom++uqrkEgkGlvv3r3Vx0ePHn3L8SeffFLEiomIbs/P2RYrJwTh+Iox2DQ7FKN6OkMiAWIzSvDsN8kYumY/Xtp5Fv87X4CK2nqxyyXq9AxujvDg4GDs27dP/djMTLPEhQsX4vXXX1c/tra27rDaiIjaysLMBBP6uWNCP3fkXK/G94k5+C4xG/nltfgqPgtfxWfB1ESCUC8HjAh0xohAJ4R4OvDOKqI7ZHDBxszMDG5ublqPW1tb3/b4XymVSiiVf67gq1Ao2lUfEVF7eXazxtJ7emLxmEAcSbuGA5eKcCStGJnFVUi4ch0JV65jwx+pkFmZIzLAUR10PLvxH3JEf8fggk1aWho8PDxgaWmJiIgIrFmzBl5eXurjX331Fb788ku4ublh0qRJWLVq1W2v2qxZswavvfZaR5RORHRHTE0kGN3LBaN7uQAAskurcSStGEfSruFoejHKa+rx29kC/Ha2AEDTRIAjAp0wItAZQ/0dYcuFOYluYVDNw7///jsqKyvRq1cv5Ofn47XXXkNubi7OnTsHOzs7bNmyBd7e3vDw8MCZM2fwwgsvYMiQIdixY4fW12zpio1cLmfzMBEZtIZGFc7kluNIalPQOZVdhkbVn39dm5lIEOrdDSNvBJ2+PWQctiKjZhR3RZWVlcHb2xsbNmzAY489dsvxAwcOYMyYMUhPT4e/v3+rXpN3RRFRZ6SorUfs5RIcSbuGw6nFyCqt1jjezdockQFOGBnojOGBTvBw4KSAZFxa+/1t0NcxHRwc0LNnT6Snp7d4PDw8HADuKNgQEXVG9pbmGBfshnHBTT2GV0uqcDitGEdSryH2cgmuV9dj95l87D6TDwAIcLHFiMCmoBPu1x3WFgb91z2Rzhj0n/TKykpcvnwZc+bMafF4cnIyAMDd3b0DqyIiEp+3ow3mONpgzlBv1DeqcDq7rCnopF3D6ewypBdVIr2oEtHHrsDcVIIw7+4Y0bMp6PRxt4cJh63ISBnUUNRzzz2HSZMmwdvbG3l5eXjllVeQnJyMCxcuQKFQYPv27bj33nvh6OiIM2fOYOnSpfD09ERMTEyr34NDUURk7Mqr63H8cjEOpxXjcOo15JbVaBzvbmOB4QFO6kZkN5mlSJUStV6nHIrKycnBrFmzUFJSAmdnZwwfPhxxcXFwdnZGbW0t9u3bh/feew9VVVWQy+WYOnUqXn75ZbHLJiIyKDJrc/WcOYIg4EpJtbo3J/ZyMUqr6vDz6Tz8fDoPANDT1VZ9S3m4ryOsLDgTMnVeBnXFpiPwig0RdWX1jSqcyiprCjppxTiTU4abvwUsTE0w2LebOugEuXHYigyDUdwVpQ8MNkREf7peVYfj6rutriGvvFbjuJNt87BVU9BxseewFYmDwUYLBhsiopYJgoCM4iocSb2GI2nFiM0oQXVdo8Y5vd3sMCLQCRH+jujbQwYXOwYd6hgMNlow2BARtU5dgwons67jSFpT0DmbW46/fmM420nR18MewR4y9O3R9KtnNytIJBy+It1isNGCwYaIqG1Kq+pwLL3pTqtT2WW4fK3ylqADADIrcwR72CPYwx59e8gQ7GEPXydbzoxM7cJgowWDDRGRblTXNeBifgXO55XjfK4C5/LKkVpYgfrGW79WrMxNEeRupw46wR4y9HS1g4WZiQiVU2fEYKMFgw0Rkf7UNaiQWngj7OQpcC63HBfzK1BT33jLueamEvR0tUNfDxmCbwxjBbnbcZZkahGDjRYMNkREHatRJSCzuFIddM7lKnA+rxyK2oZbzjWRAH7Otk3DWB5/Xt2RWZuLUDkZEgYbLRhsiIjEJwgCcq7X4Hzen0HnXJ4C1yqULZ4v726FYPc/G5SDe9jzjqwuhsFGCwYbIiLDVaSoVV/ZOZ/X1LeTc72mxXN5R1bXwmCjBYMNEVHnUlZdhwt5CnXQOZdbjoziKt6R1cUw2GjBYENE1PlVKRtwqUChcXXn7+7ICvaQobe7HXq72aGnqx3sLNm305kw2GjBYENEZJyUDY1IK6zU6Nu5kK9Abb2qxfM9u1mht5s9ervZqQOPj6MNzEx5C7ohYrDRgsGGiKjraL4j61yuAhfzFbhYUIGUAgUKFS03KVuYmSDQxRa93OwQ5GaPXjdCj7OtlL07ImOw0YLBhoiIrlfV4dKNkHOpoOLG71uebwcAuttYoLebnUbg6elqBysL0w6uvOtisNGCwYaIiFqiUgnIvl6Ni/lNIedSgQIpBRXILGm5UVkiAXwcbdDL9c+hrN5u9vDqbg0TNivrHIONFgw2RER0J2rqGpFW1HRV51J+BVIKFbiUX4GSqroWz7cyN0VPNzv0vhF4et0IPN1tLDq4cuPCYKMFgw0REenCtQql+qrOxRuBJ7WwEnUNLTcru9hJm4ay3O3VV3kCXGwhNeNwVmsw2GjBYENERPrS0KjClZJq9VDWpRu/Zpe2PMmgqYkEfk42twSeHg6caPCvGGy0YLAhIqKOVqlsQMqNBuWUgua7sypQXlPf4vl2UjP0vNGs3NPFFj1d7RDgatul785isNGCwYaIiAyBIAgoUNT+2btz4wrP5WuVLU40CAAO1ubo6dIUcnq62CLQ1Q6BXSTwMNhowWBDRESGrK5BhYziyhvDWRVIK6xEelEFrpZWt3h3FtC0lERPV1sEuNihp6stAm/86mxnPIGHwUYLBhsiIuqMausbcflaJdIKK5FWVIHUwkqkFVYgq7QaqtsEnsDmKzs3hrQCXW3h0gkDD4ONFgw2RERkTJoDT3pRJVILK24En0pcLanSGnjsLc0Q6Prn1Z1A16bQY8iBh8FGCwYbIiLqCmrrG5FxrQppRU1hJ7WwAulFlbjSisDz16s8rvbiBx4GGy0YbIiIqCurrW9EZnGVOuikFlbcuMJTjUYticfO0qwp7Nx0dSfQ1RZu9pYdFngYbLRgsCEiIrqVsqE58FQivfBGD09RBa7cLvBIzW7codUUdJqv8rjLdB94GGy0YLAhIiJqPWVDI64UV9/o32m6upNaePvA8/z4Xnh6dIBO62jt97eZTt+ViIiIjIrUzBS9bkwWeLO6BhUyi6vUd2il3/j1SnEV/JxsRaqWwYaIiIjawMLMRGvgERODDREREemMhZmJqO8v7rsTERER6RCDDRERERkNgwo2r776KiQSicbWu3dv9fHa2losWrQIjo6OsLW1xdSpU1FYWChixURERGRIDCrYAEBwcDDy8/PV29GjR9XHli5dil9++QXff/89YmJikJeXhwcffFDEaomIiMiQGFzzsJmZGdzc3G7ZX15ejs8++wzbt2/H3XffDQCIjo5GUFAQ4uLiMHTo0I4ulYiIiAyMwV2xSUtLg4eHB/z8/DB79mxkZWUBAJKSklBfX4+oqCj1ub1794aXlxdiY2O1vp5SqYRCodDYiIiIyDgZVLAJDw/H1q1bsWfPHmzatAmZmZkYMWIEKioqUFBQAAsLCzg4OGg8x9XVFQUFBVpfc82aNZDJZOpNLpfr+acgIiIisRjUUNSECRPUvw8JCUF4eDi8vb3x3XffwcrKqk2vuXLlSixbtkz9WKFQMNwQEREZKYO6YvNXDg4O6NmzJ9LT0+Hm5oa6ujqUlZVpnFNYWNhiT04zqVQKe3t7jY2IiIiMk0EHm8rKSly+fBnu7u4YNGgQzM3NsX//fvXxlJQUZGVlISIiQsQqiYiIyFAY1FDUc889h0mTJsHb2xt5eXl45ZVXYGpqilmzZkEmk+Gxxx7DsmXL0L17d9jb2+Of//wnIiIieEcUERERATCwYJOTk4NZs2ahpKQEzs7OGD58OOLi4uDs7AwAePfdd2FiYoKpU6dCqVRi3Lhx+Oijj0SumoiIiAyFRBAEQewiOpJCoYBMJkN5eTn7bYiIiDqJ1n5/G9QVm47QnOM4nw0REVHn0fy9/XfXY7pcsKmoqAAA3vJNRETUCVVUVEAmk2k93uWGolQqFfLy8mBnZweJRKKz122eHyc7O5tDXHrGz7pj8HPuGPycOwY/546hz89ZEARUVFTAw8MDJibab+rucldsTExM4OnpqbfX51w5HYefdcfg59wx+Dl3DH7OHUNfn/PtrtQ0M+h5bIiIiIjuBIMNERERGQ0GGx2RSqV45ZVXIJVKxS7F6PGz7hj8nDsGP+eOwc+5YxjC59zlmoeJiIjIePGKDRERERkNBhsiIiIyGgw2REREZDQYbIiIiMhoMNjoyIcffggfHx9YWloiPDwcJ06cELsko7JmzRoMHjwYdnZ2cHFxwZQpU5CSkiJ2WUZv7dq1kEgkWLJkidilGKXc3Fw8/PDDcHR0hJWVFfr164fExESxyzIqjY2NWLVqFXx9fWFlZQV/f3+88cYbf7veEN3e4cOHMWnSJHh4eEAikWDXrl0axwVBwOrVq+Hu7g4rKytERUUhLS2tQ2pjsNGBb7/9FsuWLcMrr7yCkydPon///hg3bhyKiorELs1oxMTEYNGiRYiLi8Mff/yB+vp6jB07FlVVVWKXZrQSEhKwefNmhISEiF2KUbp+/ToiIyNhbm6O33//HRcuXMA777yDbt26iV2aUVm3bh02bdqEDz74ABcvXsS6deuwfv16vP/++2KX1qlVVVWhf//++PDDD1s8vn79emzcuBEff/wx4uPjYWNjg3HjxqG2tlb/xQnUbkOGDBEWLVqkftzY2Ch4eHgIa9asEbEq41ZUVCQAEGJiYsQuxShVVFQIgYGBwh9//CGMGjVKePbZZ8Uuyei88MILwvDhw8Uuw+hNnDhRmD9/vsa+Bx98UJg9e7ZIFRkfAMLOnTvVj1UqleDm5ib8+9//Vu8rKysTpFKp8PXXX+u9Hl6xaae6ujokJSUhKipKvc/ExARRUVGIjY0VsTLjVl5eDgDo3r27yJUYp0WLFmHixIkaf65Jt37++WeEhYVh+vTpcHFxwcCBA/HJJ5+IXZbRGTZsGPbv34/U1FQAwOnTp3H06FFMmDBB5MqMV2ZmJgoKCjT+/pDJZAgPD++Q78UutwimrhUXF6OxsRGurq4a+11dXXHp0iWRqjJuKpUKS5YsQWRkJPr27St2OUbnm2++wcmTJ5GQkCB2KUYtIyMDmzZtwrJly/Diiy8iISEBixcvhoWFBebOnSt2eUZjxYoVUCgU6N27N0xNTdHY2Ii33noLs2fPFrs0o1VQUAAALX4vNh/TJwYb6nQWLVqEc+fO4ejRo2KXYnSys7Px7LPP4o8//oClpaXY5Rg1lUqFsLAwvP322wCAgQMH4ty5c/j4448ZbHTou+++w1dffYXt27cjODgYycnJWLJkCTw8PPg5GykORbWTk5MTTE1NUVhYqLG/sLAQbm5uIlVlvJ555hns3r0bBw8ehKenp9jlGJ2kpCQUFRUhNDQUZmZmMDMzQ0xMDDZu3AgzMzM0NjaKXaLRcHd3R58+fTT2BQUFISsrS6SKjNPy5cuxYsUKzJw5E/369cOcOXOwdOlSrFmzRuzSjFbzd59Y34sMNu1kYWGBQYMGYf/+/ep9KpUK+/fvR0REhIiVGRdBEPDMM89g586dOHDgAHx9fcUuySiNGTMGZ8+eRXJysnoLCwvD7NmzkZycDFNTU7FLNBqRkZG3TFmQmpoKb29vkSoyTtXV1TAx0fyqMzU1hUqlEqki4+fr6ws3NzeN70WFQoH4+PgO+V7kUJQOLFu2DHPnzkVYWBiGDBmC9957D1VVVXj00UfFLs1oLFq0CNu3b8dPP/0EOzs79TitTCaDlZWVyNUZDzs7u1v6lmxsbODo6Mh+Jh1bunQphg0bhrfffhszZszAiRMnsGXLFmzZskXs0ozKpEmT8NZbb8HLywvBwcE4deoUNmzYgPnz54tdWqdWWVmJ9PR09ePMzEwkJyeje/fu8PLywpIlS/Dmm28iMDAQvr6+WLVqFTw8PDBlyhT9F6f3+666iPfff1/w8vISLCwshCFDhghxcXFil2RUALS4RUdHi12a0ePt3vrzyy+/CH379hWkUqnQu3dvYcuWLWKXZHQUCoXw7LPPCl5eXoKlpaXg5+cnvPTSS4JSqRS7tE7t4MGDLf6dPHfuXEEQmm75XrVqleDq6ipIpVJhzJgxQkpKSofUJhEETr9IRERExoE9NkRERGQ0GGyIiIjIaDDYEBERkdFgsCEiIiKjwWBDRERERoPBhoiIiIwGgw0REREZDQYbIiIiMhoMNkTUotGjR2PJkiVil2HQ5s2b1zFTxBNRqzHYEHVR8+bNg0QiuWW7ef0XQ7F161Y4ODioH7/66qsYMGBAh73/lStXIJFIkJycrLH/P//5D7Zu3dphdRDR3+MimERd2Pjx4xEdHa2xz9nZWaRqOl5dXR0sLCza/HyZTKbDaohIF3jFhqgLk0qlcHNz09hMTU1bPPf69et45JFH0K1bN1hbW2PChAlIS0sDAAiCAGdnZ/zwww/q8wcMGAB3d3f146NHj0IqlaK6uhqCIODVV1+Fl5cXpFIpPDw8sHjx4lbVvHXrVrz22ms4ffq0+ipT81WTsrIyLFiwAM7OzrC3t8fdd9+N06dPq5/bfKXn008/ha+vLywtLQEAe/bswfDhw+Hg4ABHR0fcd999uHz5svp5vr6+AICBAwdCIpFg9OjRAG4dilIqlVi8eDFcXFxgaWmJ4cOHIyEhQX380KFDkEgk2L9/P8LCwmBtbY1hw4YhJSVFfc7p06dx1113wc7ODvb29hg0aBASExNb9dkQEYMNEbXSvHnzkJiYiJ9//hmxsbEQBAH33nsv6uvrIZFIMHLkSBw6dAhAUwi6ePEiampqcOnSJQBATEwMBg8eDGtra/z444949913sXnzZqSlpWHXrl3o169fq+p46KGH8K9//QvBwcHIz89Hfn4+HnroIQDA9OnTUVRUhN9//x1JSUkIDQ3FmDFjUFpaqn5+eno6fvzxR+zYsUM9tFRVVYVly5YhMTER+/fvh4mJCR544AGoVCoAwIkTJwAA+/btQ35+Pnbs2NFibc8//zx+/PFHbNu2DSdPnkRAQADGjRun8f4A8NJLL+Gdd95BYmIizMzMMH/+fPWx2bNnw9PTEwkJCUhKSsKKFStgbm7eqs+GiAB0yBriRGRw5s6dK5iamgo2Njbqbdq0aerjo0aNEp599llBEAQhNTVVACAcO3ZMfby4uFiwsrISvvvuO0EQBGHjxo1CcHCwIAiCsGvXLiE8PFyYPHmysGnTJkEQBCEqKkp48cUXBUEQhHfeeUfo2bOnUFdX16pao6OjBZlMpn78yiuvCP3799c458iRI4K9vb1QW1ursd/f31/YvHmz+nnm5uZCUVHRbd/v2rVrAgDh7NmzgiAIQmZmpgBAOHXqlMZ5c+fOFSZPniwIgiBUVlYK5ubmwldffaU+XldXJ3h4eAjr168XBEEQDh48KAAQ9u3bpz7n119/FQAINTU1giAIgp2dnbB169bbfyBEpBWv2BB1YXfddReSk5PV28aNG1s87+LFizAzM0N4eLh6n6OjI3r16oWLFy8CAEaNGoULFy7g2rVriImJwejRozF69GgcOnQI9fX1OH78uHoIZ/r06aipqYGfnx8WLlyInTt3oqGhoV0/y+nTp1FZWQlHR0fY2tqqt8zMTI1hJW9v71v6iNLS0jBr1iz4+fnB3t4ePj4+AICsrKxWv//ly5dRX1+PyMhI9T5zc3MMGTJE/Rk1CwkJUf++ebiuqKgIALBs2TIsWLAAUVFRWLt2rUbtRPT3GGyIujAbGxsEBASot5t7Yu5Uv3790L17d8TExGgEm5iYGCQkJKC+vh7Dhg0DAMjlcqSkpOCjjz6ClZUVnn76aYwcORL19fVtfv/Kykq4u7trBLXk5GSkpKRg+fLlGj/zX02aNAmlpaX45JNPEB8fj/j4eABNzcX6cPPQkkQiAQD1sNerr76K8+fPY+LEiThw4AD69OmDnTt36qUOImPEYENEfysoKAgNDQ3qL3wAKCkpQUpKCvr06QOg6Qt6xIgR+Omnn3D+/HkMHz4cISEhUCqV2Lx5M8LCwjRChZWVFSZNmoSNGzfi0KFDiI2NxdmzZ1tVj4WFBRobGzX2hYaGoqCgAGZmZhphLSAgAE5OTlpfq/nnePnllzFmzBgEBQXh+vXrt7wfgFve82b+/v6wsLDAsWPH1Pvq6+uRkJCg/oxaq2fPnli6dCn+97//4cEHH7zlzjUi0o7Bhoj+VmBgICZPnoyFCxfi6NGjOH36NB5++GH06NEDkydPVp83evRofP311xgwYABsbW1hYmKCkSNH4quvvsKoUaPU523duhWfffYZzp07h4yMDHz55ZewsrKCt7d3q+rx8fFBZmYmkpOTUVxcDKVSiaioKERERGDKlCn43//+hytXruD48eN46aWXbntXUbdu3eDo6IgtW7YgPT0dBw4cwLJlyzTOcXFxgZWVFfbs2YPCwkKUl5ff8jo2NjZ46qmnsHz5cuzZswcXLlzAwoULUV1djccee6xVP1dNTQ2eeeYZHDp0CFevXsWxY8eQkJCAoKCgVj2fiBhsiKiVoqOjMWjQINx3332IiIiAIAj47bffNIZVRo0ahcbGRnUvDdAUdv66z8HBAZ988gkiIyMREhKCffv24ZdffoGjo2Orapk6dSrGjx+Pu+66C87Ozvj6668hkUjw22+/YeTIkXj00UfRs2dPzJw5E1evXoWrq6vW1zIxMcE333yDpKQk9O3bF0uXLsW///1vjXPMzMywceNGbN68GR4eHhph7mZr167F1KlTMWfOHISGhiI9PR179+5Ft27dWvVzmZqaoqSkBI888gh69uyJGTNmYMKECXjttdda9XwiAiSCIAhiF0FERESkC7xiQ0REREaDwYaIiIiMBoMNERERGQ0GGyIiIjIaDDZERERkNBhsiIiIyGgw2BAREZHRYLAhIiIio8FgQ0REREaDwYaIiIiMBoMNERERGY3/ByKbDlBCFMkIAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(off_diagonal_norm[:],label= 'Hyperopt')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Difference between numerical gradients and analytic ones\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-04-01 22:50:13]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 7\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "nqubits = [3,4,5,6,7]\n", - "iterations = 50\n", - "step = 1e-2\n", - "differences = np.empty((len(nqubits),iterations+1))\n", - "loss_max = np.empty(len(nqubits))\n", - "for q in range(len(nqubits)):\n", - " # define the hamiltonian\n", - " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", - "\n", - " # define the least-squares cost function\n", - " cost = DoubleBracketCost.least_squares\n", - " # initialize class\n", - " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - " loss_max [q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", - " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", - " d_analytic, loss_analytic, grad_analytic, diags_analytic = gradient_descent(dbi, params, iterations, step)\n", - " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", - " d_numerical, loss_numerical, grad_numerical, diags_numerical = gradient_descent(dbi, params,iterations,step, analytic=False)\n", - " differences[q,:] = loss_analytic - loss_numerical\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "for q in range(len(nqubits)):\n", - " plt.plot(differences[q,:],label= 'nqubits = {}'.format(nqubits[q]))\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel('Difference in analytic and numerical loss function')\n", - "plt.legend()\n", - "\n", - "plt.figure()\n", - "plt.title('Normalized difference')\n", - "for q in range(len(nqubits)):\n", - " plt.plot(differences[q,:]/loss_max[q],label= 'nqubits = {}'.format(nqubits[q]))\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel('Difference in analytic and numerical loss function')\n", - "plt.legend()\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Difference in optimization moments\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.5|INFO|2024-03-21 14:44:56]: Using qibojit (numba) backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"qibojit\", \"numba\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 7\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "flows = 100\n", - "dbi_eval = deepcopy(dbi)\n", - "dbi_eval2 = deepcopy(dbi)\n", - "d_not_optimized = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "step = 1e-2\n", - "iterations = 100\n", - "d_optimized, loss, grad, diags = gradient_descen(dbi_eval, d,step, iterations,analytic=False)\n", - "off_diagonal_norm = np.empty((flows+1,3))\n", - "off_diagonal_norm[0] = dbi_eval.off_diagonal_norm\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", - " dbi_eval(step_poly,d=d)\n", - " step_poly = dbi_eval2.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_not_optimized)\n", - " dbi_eval2(step_poly,d=d_not_optimized)\n", - " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", - " off_diagonal_norm[i+1,1] = dbi_eval2.off_diagonal_norm\n", - "\n", - "dbi_eval3 = deepcopy(dbi)\n", - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "for i in range(flows):\n", - " d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - " d_opt, loss, grad, diags = gradient_ascent(dbi_eval3, d,step, 20,analytic=False)\n", - " step_poly = dbi_eval3.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt)\n", - " dbi_eval3(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,2] = dbi_eval3.off_diagonal_norm " - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(off_diagonal_norm[:,0],label= 'Optimized at beginning')\n", - "plt.plot(off_diagonal_norm[:,1],label= 'Not optimized')\n", - "plt.plot(off_diagonal_norm[:,2],label= 'Optimized at each step')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-04-01 23:01:13]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 7\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "end = np.linspace(1.1,10*2**nqubits,100)\n", - "loss = np.empty(100)\n", - "spacing = np.empty(100)\n", - "for i in range(100):\n", - " dbi_eval = deepcopy(dbi)\n", - " d = np.diag(np.linspace(1,end[i],2**nqubits))\n", - " spacing[i] = d[1,1] - d[0,0]\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n = 3)\n", - " dbi_eval(step_poly,d=d)\n", - " loss[i] = dbi_eval.off_diagonal_norm" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Loss function')" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(spacing,loss)\n", - "plt.xlabel('Spacing')\n", - "plt.ylabel('Loss function')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def d_poly_spacing(nqubits,degree):\n", - " d = np.empty(2**nqubits)\n", - " for i in range(len(d)):\n", - " d[i] = 1 + i**degree\n", - " return np.diag(d)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "degrees = np.linspace(0.1,5,100)\n", - "nqubits = [3,4,5,7]\n", - "h = 3.0\n", - "\n", - "loss = np.empty((100,len(nqubits)))\n", - "best_degree = np.empty(len(nqubits))\n", - "for q in range(len(nqubits)):\n", - " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", - " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - " for i in range(100):\n", - " dbi_eval = deepcopy(dbi)\n", - " d = d_poly_spacing(nqubits[q],degrees[i])\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n = 3)\n", - " dbi_eval(step_poly,d=d)\n", - " loss[i,q] = dbi_eval.off_diagonal_norm\n", - " best_degree[q] = degrees[np.argmin(loss[:,q])]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.18888889 1.13939394 1.13939394 1.13939394]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(degrees,loss[:,3],label = f'{nqubits[q]} qubits')\n", - "plt.xlabel('Degree')\n", - "plt.ylabel('Loss function')\n", - "plt.legend()\n", - "print(best_degree)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n_qubits = 7\n", - "d = d_poly_spacing(n_qubits,best_degree[2])\n", - "step = 1e-2\n", - "iterations = 50\n", - "H_TFIM = hamiltonians.TFIM(nqubits=n_qubits, h=h)\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "d_optimized, loss, grad, diags = gradient_ascent(dbi, d,step, iterations,analytic=False)\n", - "\n", - "flows = 50\n", - "off_diagonal_norm = np.empty((flows+1,2))\n", - "off_diagonal_norm[0,:] = dbi.off_diagonal_norm\n", - "for i in range(flows):\n", - " step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_optimized)\n", - " dbi(step_poly,d=d_optimized)\n", - " off_diagonal_norm[i+1,0] = dbi.off_diagonal_norm\n", - "\n", - "d = d_poly_spacing(n_qubits,1)\n", - "dbi2 = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "d_optimized, loss, grad, diags = gradient_ascent(dbi2, d,step, iterations,analytic=False)\n", - "for i in range(flows):\n", - " step_poly = dbi2.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_optimized)\n", - " dbi2(step_poly,d=d_optimized)\n", - " off_diagonal_norm[i+1,1] = dbi2.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(off_diagonal_norm[:,0],label= 'optimal poly degree')\n", - "plt.plot(off_diagonal_norm[:,1],label= 'linear spacing')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "metadata": {}, - "outputs": [], - "source": [ - "def delta_to_d(delta):\n", - " d = np.empty(len(delta)+1)\n", - " d[0] = 1\n", - " for i in range(len(d)-1):\n", - " d[i+1] = d[i] + delta[i]\n", - " return np.diag(d)\n", - "\n", - "def d_to_delta(d):\n", - " delta = np.empty(len(d)-1)\n", - " for i in range(len(d)-1):\n", - " delta[i] = d[i+1,i+1]-d[i,i]\n", - " return delta\n", - "\n", - "def polynomial(x, coefficients):\n", - " y = np.empty(len(x))\n", - " for i in range(len(coefficients)):\n", - " y += coefficients[i]*x**i\n", - " return y\n", - "\n", - "def gradient_delta_polynomial(dbi, coefficients,h=1e-5):\n", - " grad = np.empty(len(coefficients))\n", - " dim = dbi.h.matrix.shape[0]\n", - " delta = polynomial(np.linspace(0,1,dim),coefficients)\n", - " d = delta_to_d(delta)\n", - " dbi_eval = deepcopy(dbi)\n", - " angle = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", - " dbi_eval(angle,d=d)\n", - " norm = dbi_eval.off_diagonal_norm\n", - " for i in range(len(grad)):\n", - " new_coeff = np.copy(coefficients)\n", - " new_coeff[i] += h\n", - " new_delta = polynomial(np.linspace(0,1,dim), new_coeff)\n", - " new_d = delta_to_d(new_delta)\n", - " dbi_eval = deepcopy(dbi)\n", - " angle = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=new_d)\n", - " dbi_eval(angle,d=new_d)\n", - " new_norm = dbi_eval.off_diagonal_norm\n", - " grad[i] = (new_norm-norm)/h\n", - "\n", - " return grad\n", - "\n", - "\n", - "def optimize_poly(dbi, step, iterations, degree):\n", - " coefficients = np.random.rand(degree)\n", - " dim = dbi.h.matrix.shape[0]\n", - " delta = polynomial(np.linspace(0,1,dim),coefficients)\n", - " d = delta_to_d(delta)\n", - " loss = np.empty(iterations)\n", - " grad = np.empty(degree)\n", - " for i in range(iterations):\n", - " dbi_eval = deepcopy(dbi)\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", - " dbi_eval(step_poly,d=d)\n", - " loss[i] = dbi_eval.off_diagonal_norm\n", - " grad = gradient_delta_polynomial(dbi_eval, coefficients)\n", - " coefficients -= step*grad\n", - " delta = polynomial(np.linspace(0,1,dim),coefficients)\n", - " d = delta_to_d(delta)\n", - " return coefficients, loss, grad\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 207, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "H_TFIM = hamiltonians.TFIM(nqubits=7, h=5.0)\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "matrix = dbi.h.matrix\n", - "eigenenergies = np.diag(np.linalg.eigh(matrix)[0])\n", - "delta = d_to_delta(eigenenergies)\n", - "\n", - "plt.figure()\n", - "plt.plot(delta,'.')\n", - "plt.xlabel('Index')\n", - "plt.ylabel(r'$\\Delta$')\n", - "\n", - "coefficients, loss, grad = optimize_poly(dbi, 1e-3, 100, 2)\n", - "plt.figure()\n", - "plt.plot(loss)\n", - "poly = polynomial(np.linspace(0,1,2**7),coefficients)\n", - "plt.figure()\n", - "plt.plot(poly)" - ] - }, - { - "cell_type": "code", - "execution_count": 286, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 286, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nqubits = 7\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=10.0)\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "matrix = dbi.h.matrix\n", - "eigenenergies = np.diag(np.linalg.eigh(matrix)[0])\n", - "delta = d_to_delta(eigenenergies)\n", - "def remove_zeros(vector,epsilon = 3):\n", - " return vector[vector>epsilon]\n", - "delta_plus = remove_zeros(delta)\n", - "x = np.linspace(0,1,len(delta))\n", - "p1 = np.polynomial.Polynomial.fit(x,delta,4)\n", - "plt.figure()\n", - "plt.plot(x,delta,'.')\n", - "plt.plot(x,p1(x))\n", - "x = np.linspace(0,1,len(delta_plus))\n", - "p2 = np.polynomial.Polynomial.fit(x,delta_plus,4)\n", - "x = np.linspace(0,1,100)\n", - "plt.plot(x,p2(x))\n", - "plt.xlabel('Index')\n", - "plt.ylabel(r'$\\Delta$')\n", - "\n", - "flows = 200\n", - "off_diagonal_norm = np.empty((flows+1,2))\n", - "x = np.linspace(0,1,2**nqubits-1)\n", - "delta = p2(x)\n", - "d = delta_to_d(delta)\n", - "d /= d[-1,-1] \n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "off_diagonal_norm[0,:] = dbi.off_diagonal_norm\n", - "for i in range(flows):\n", - " step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", - " dbi(step_poly,d=d)\n", - " off_diagonal_norm[i+1,0] = dbi.off_diagonal_norm\n", - "\n", - "delta = p1(x)\n", - "d = delta_to_d(delta)\n", - "d /= d[-1,-1]\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "for i in range(flows):\n", - " step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d)\n", - " dbi(step_poly,d=d)\n", - " off_diagonal_norm[i+1,1] = dbi.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(off_diagonal_norm[:,0],label='Polynomial over great differences')\n", - "plt.plot(off_diagonal_norm[:,1],label='Polynomial over small differences')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "c = np.linspace(-10,10,100)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pauli Ansatz" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-04-03 10:06:29]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 7\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", - "\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def Z_decomposition(matrix):\n", - " Z = np.array([[1, 0], [0, -1]])\n", - " params = np.empty(int(np.log2(matrix.shape[0])))\n", - " for i in range(len(params)):\n", - " Z_i = np.kron(np.eye(2**(i)), np.kron(Z, np.eye(2**(len(params)-i-1))))\n", - " params[i] = np.trace(matrix@Z_i) \n", - " return params\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "nqubits = [7]\n", - "iterations = 100\n", - "step = 1e-3\n", - "differences = np.empty((len(nqubits),iterations+1))\n", - "loss_pauli = np.empty((len(nqubits),iterations+1))\n", - "loss_full = np.empty((len(nqubits),iterations+1))\n", - "loss_max = np.empty(len(nqubits))\n", - "for q in range(len(nqubits)):\n", - " # define the hamiltonian\n", - " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", - "\n", - " # define the least-squares cost function\n", - " cost = DoubleBracketCost.least_squares\n", - " # initialize class\n", - " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - " loss_max[q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", - " params = np.linspace(2**nqubits[q],1,2**nqubits[q])\n", - " d_full, loss_full[q,:], grad_full, diags_full = gradient_descent(dbi, params, iterations, step, analytic=False, ansatz = 'Full')\n", - " params = np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])/2**nqubits[q])\n", - " params = Z_decomposition(params)\n", - " params = np.linspace(nqubits[q],1,nqubits[q])\n", - " d = d_ansatz(params, type='Pauli')\n", - " d_pauli, loss_pauli[q,:], grad_pauli, diags_pauli = gradient_descent(dbi, params, iterations, step, analytic=False, ansatz = 'Pauli')\n", - " differences[q,:] = loss_full[q,:] - loss_pauli[q,:]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Parameters')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.title('Full ansatz')\n", - "plt.plot(loss_full[0,:],label='nqubits = '+str(nqubits[0]))\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel('Loss for Full ansatz')\n", - "plt.legend()\n", - "\n", - "plt.figure()\n", - "plt.title('Pauli ansatz')\n", - "plt.plot(loss_pauli[0,:],label='nqubits = '+str(nqubits[0]))\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel('Loss for Pauli ansatz')\n", - "plt.legend()\n", - "\n", - "plt.figure()\n", - "plt.title('Evolution of parameters')\n", - "for i in range(nqubits[0]):\n", - " plt.plot(diags_pauli[i,:])\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel('Parameters')" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [], - "source": [ - "nqubits = 7\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,2**nqubits,2**nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent(dbi, params, 100,1e-2, analytic=False, ansatz = 'Full')\n", - "flows = 50\n", - "off_diagonal_norm = np.empty((flows+1,2))\n", - "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", - "\n", - "\n", - "\n", - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(7,1,7)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent(dbi, params, 30, 1e-3, analytic=False, ansatz = 'Pauli')\n", - "best = np.argmin(loss_opt)\n", - "d_opt = d_ansatz(diags_opt[:,best], type='Pauli')\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(off_diagonal_norm[:,0],label='Full ansatz')\n", - "plt.plot(off_diagonal_norm[:,1],label='Pauli ansatz')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "def random_hamiltonian(nqubits):\n", - " n = 2**nqubits\n", - " h = np.random.rand(n,n)\n", - " h = h + h.T\n", - " return h" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ - "tests = 100\n", - "nqubits = 5\n", - "flows = 50\n", - "losses = np.empty((tests,flows))\n", - "losses_pauli = np.empty((tests,flows))\n", - "for i in range(tests):\n", - " h = random_hamiltonian(nqubits)\n", - " H = hamiltonians.Hamiltonian(nqubits,h)\n", - " dbi = DoubleBracketIteration(H,mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - " dbi_eval = deepcopy(dbi)\n", - " params = np.linspace(1,2**nqubits,2**nqubits)\n", - " d_opt, loss_opt, grad_opt, diags_opt = gradient_descent(dbi, params, 100,1e-2, analytic=False, ansatz = 'Full')\n", - "\n", - " for j in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " losses[i,j] = dbi_eval.off_diagonal_norm\n", - " \n", - " H = hamiltonians.Hamiltonian(nqubits,h)\n", - " dbi = DoubleBracketIteration(H,mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - " dbi_eval = deepcopy(dbi)\n", - " params = np.linspace(nqubits,1,nqubits)\n", - " d_opt, loss_opt, grad_opt, diags_opt = gradient_descent(dbi, params, 5, 1e-3, analytic=False, ansatz = 'Pauli')\n", - " for j in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " losses_pauli[i,j] = dbi_eval.off_diagonal_norm\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(np.mean(losses,axis=0),label='Full ansatz')\n", - "plt.plot(np.mean(losses_pauli,axis=0),label='Pauli ansatz')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'Average $||\\sigma(H_k)||$')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-04-03 10:15:40]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 2\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCost.least_squares\n", - "\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Step')" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "evals = 300\n", - "par1 = np.linspace(-10,10,evals)\n", - "par2 = 1\n", - "par3 = 2\n", - "par4 = 4\n", - "steps = np.empty(evals)\n", - "loss = np.empty(evals)\n", - "off_diagonal_norm = np.empty(evals)\n", - "for i in range(evals):\n", - " dbi_eval = deepcopy(dbi)\n", - " d = d_ansatz([par1[i],par2,par3,par4],type='Full')\n", - " poly_step = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", - " dbi_eval(poly_step,d=d)\n", - " steps[i] = poly_step\n", - " loss[i] = dbi_eval.least_squares(d)\n", - " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(par1,loss)\n", - "plt.xlabel('Parameter')\n", - "plt.ylabel('Loss')\n", - "\n", - "plt.figure()\n", - "plt.plot(par1,off_diagonal_norm)\n", - "plt.xlabel('Parameter')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "\n", - "plt.figure()\n", - "plt.plot(par1,steps)\n", - "plt.xlabel('Parameter')\n", - "plt.ylabel('Step')\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Step size')" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "evals = 100\n", - "par1 = np.linspace(-10,10,evals)\n", - "par2 = -3\n", - "steps = np.empty(evals)\n", - "loss = np.empty(evals)\n", - "off_diagonal_norm = np.empty(evals)\n", - "for i in range(evals):\n", - " dbi_eval = deepcopy(dbi)\n", - " d = d_ansatz([par1[i],par2],type='Pauli')\n", - " poly_step = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", - " steps[i] = poly_step\n", - " dbi_eval(poly_step,d=d)\n", - " loss[i] = dbi_eval.least_squares(d)\n", - " off_diagonal_norm[i] = dbi_eval.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(par1,loss)\n", - "plt.xlabel('Parameter')\n", - "plt.ylabel('Loss')\n", - "\n", - "plt.figure()\n", - "plt.plot(par1,off_diagonal_norm)\n", - "plt.xlabel('Parameter')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "\n", - "plt.figure()\n", - "plt.plot(par1,np.log(steps))\n", - "plt.xlabel('Parameter')\n", - "plt.ylabel('Step size')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "size = 100\n", - "par1 = np.linspace(-7,7,size)\n", - "par2 = np.linspace(-7,7,size)\n", - "\n", - "loss = np.empty((size,size))\n", - "\n", - "for i in range(size):\n", - " for j in range(size):\n", - " dbi_eval = deepcopy(dbi)\n", - " d = d_ansatz([par1[i],par2[j]],type='Pauli')\n", - " poly_step = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", - " dbi_eval(poly_step,d=d)\n", - " loss[i,j] = dbi_eval.least_squares(d)\n", - "\n", - "plt.figure()\n", - "plt.contourf(par1,par2,loss,levels=100)\n", - "plt.xlabel('Parameter 1')\n", - "plt.ylabel('Parameter 2')\n", - "plt.colorbar()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 70f3d09115..43fe3befdd 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -1,5 +1,6 @@ from qibo.models.dbi.utils_analytical import * from qibo.models.dbi.utils_scheduling import polynomial_step +from copy import deepcopy def gradient_Pauli( From b5b457e567411d7576d408801f88861588bdddf9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 23 May 2024 01:34:17 +0000 Subject: [PATCH 079/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/utils_gradients.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 43fe3befdd..6c8a173194 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -1,6 +1,7 @@ +from copy import deepcopy + from qibo.models.dbi.utils_analytical import * from qibo.models.dbi.utils_scheduling import polynomial_step -from copy import deepcopy def gradient_Pauli( From e2232c0cd0e4de0d5297c74cd40084cb04256a0c Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 23 May 2024 15:14:34 +0800 Subject: [PATCH 080/154] Revert current branch changes --- src/qibo/models/dbi/double_bracket.py | 34 ++++----------------------- tests/test_models_dbi.py | 23 ------------------ 2 files changed, 5 insertions(+), 52 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 7d520a7625..a506d8aa38 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -104,34 +104,12 @@ def __init__( def __call__( self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None ): - r"""We use convention that $H' = U^\dagger H U$ where $U=e^{-sW}$ with $W=[D,H]$ (or depending on `mode` an approximation, see `eval_dbr_unitary`). If $s>0$ then for $D = \Delta(H)$ the GWW DBR will give a $\sigma$-decrease, see https://arxiv.org/abs/2206.11772.""" - - operator = self.eval_dbr_unitary(step, mode, d) - operator_dagger = self.backend.cast( - np.matrix(self.backend.to_numpy(operator)).getH() - ) - self.h.matrix = operator_dagger @ self.h.matrix @ operator - return operator - - def eval_dbr_unitary( - self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None - ): - """In call we will are working in the convention that $H' = U^\\dagger H U$ where $U=e^{-sW}$ with $W=[D,H]$ or an approximation of that by a group commutator. That is handy because if we switch from the DBI in the Heisenberg picture for the Hamiltonian, we get that the transformation of the state is $|\\psi'\rangle = U |\\psi\rangle$ so that $\\langle H\rangle_{\\psi'} = \\langle H' \rangle_\\psi$ (i.e. when writing the unitary acting on the state dagger notation is avoided). - - The group commutator must approximate $U=e^{-s[D,H]}$. This is achieved by setting $r = \\sqrt{s}$ so that - $$V = e^{-irH}e^{irD}e^{irH}e^{-irD}$$ - because - $$e^{-irH}De^{irH} = D+ir[D,H]+O(r^2)$$ - so - $$V\approx e^{irD +i^2 r^2[D,H] + O(r^2) -irD} \approx U\\ .$$ - See the app in https://arxiv.org/abs/2206.11772 for a derivation. - """ if mode is None: mode = self.mode if mode is DoubleBracketGeneratorType.canonical: operator = self.backend.calculate_matrix_exp( - -1.0j * step, + 1.0j * step, self.commutator(self.diagonal_h_matrix, self.h.matrix), ) elif mode is DoubleBracketGeneratorType.single_commutator: @@ -144,13 +122,11 @@ def eval_dbr_unitary( elif mode is DoubleBracketGeneratorType.group_commutator: if d is None: d = self.diagonal_h_matrix - - sqrt_step = np.sqrt(step) operator = ( - self.h.exp(sqrt_step) - @ self.backend.calculate_matrix_exp(-sqrt_step, d) - @ self.h.exp(-sqrt_step) - @ self.backend.calculate_matrix_exp(sqrt_step, d) + self.h.exp(-step) + @ self.backend.calculate_matrix_exp(-step, d) + @ self.h.exp(step) + @ self.backend.calculate_matrix_exp(step, d) ) operator_dagger = self.backend.cast( np.matrix(self.backend.to_numpy(operator)).getH() diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index fb55db7cae..1fd60480d8 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -51,29 +51,6 @@ def test_double_bracket_iteration_group_commutator(backend, nqubits): assert initial_off_diagonal_norm > dbi.off_diagonal_norm -@pytest.mark.parametrize("nqubits", [3]) -def test_double_bracket_iteration_eval_dbr_unitary(backend, nqubits): - r"""The bound is $$||e^{-[D,H]}-GC||\le s^{3/2}(||[H,[D,H]||+||[D,[D,H]]||$$""" - h0 = random_hermitian(2**nqubits, backend=backend) - d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) - dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.group_commutator, - ) - - for s in np.linspace(0.001, 0.01, NSTEPS): - u = dbi.eval_dbr_unitary( - s, d=d, mode=DoubleBracketGeneratorType.single_commutator - ) - v = dbi.eval_dbr_unitary( - s, d=d, mode=DoubleBracketGeneratorType.group_commutator - ) - - assert np.linalg.norm(u - v) < 10 * s**1.49 * ( - np.linalg.norm(h0) + np.linalg.norm(d) - ) * np.linalg.norm(h0) * np.linalg.norm(d) - - @pytest.mark.parametrize("nqubits", [1, 2]) def test_double_bracket_iteration_single_commutator(backend, nqubits): """Check single commutator mode.""" From 9e1dafe06d390820592f58a8c2d0153c15989fd6 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 23 May 2024 16:16:08 +0800 Subject: [PATCH 081/154] Minor simplification of scheduling --- src/qibo/models/dbi/utils_scheduling.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 50112b7a5e..9d98c61d87 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -124,11 +124,8 @@ def polynomial_step( ] # solution exists, return minimum s if len(real_positive_roots) > 0: - sol = min(real_positive_roots) - for s in real_positive_roots: - if dbi_object.loss(s, d) < dbi_object.loss(sol, d): - sol = s - return sol + losses = [dbi_object.loss(step=root, d=d) for root in real_positive_roots] + return real_positive_roots[losses.index(min(losses))] # solution does not exist, return None else: return None From 7ffd986e7ffbf1fd937acc2f60e0f1b62c13d27e Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Thu, 23 May 2024 10:42:44 +0200 Subject: [PATCH 082/154] removed analytical gradients --- ...t_functions_and_d_gradients_tutorial.ipynb | 12549 +--------------- src/qibo/models/dbi/utils_gradients.py | 111 +- 2 files changed, 838 insertions(+), 11822 deletions(-) diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index 37c2615aa9..c604ea1fd2 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -24,8 +24,7 @@ "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_gradients import *\n" + "from qibo.models.dbi.utils_scheduling import *" ] }, { @@ -40,17 +39,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-05-21 12:10:15]: Using numpy backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -71,20 +62,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.6060645454545454\n", - "100%|██████████| 100/100 [00:00<00:00, 473.50trial/s, best loss: -30.63362551504052]\n", - "hyperopt_search step: 0.5993688830433928\n", - "polynomial_approximation step: 0.5267891182131145\n" - ] - } - ], + "outputs": [], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", @@ -110,37 +90,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.6060645454545454\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -174,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -196,30 +148,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", @@ -241,17 +172,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-05-21 12:13:04]: Using numpy backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -264,23 +187,116 @@ "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the energy fluctuation cost function\n", - "cost = DoubleBracketCostFunction.energy_fluctuation\n", + "cost = DoubleBracketCostFunction.off_diagonal_norm\n", "# define the state\n", "state = np.zeros(2**nqubits)\n", - "state[7] = 1\n", + "state[3] = 1\n", "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)\n" + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate data for plotting sigma decrease of the first step\n", + "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))/2**nqubits\n", + "s_space = np.linspace(-1, 1, 1000)\n", + "off_diagonal_norm_diff = []\n", + "fluctuation = []\n", + "for s in s_space:\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval(s,d=d)\n", + " off_diagonal_norm_diff.append(dbi_eval.off_diagonal_norm - dbi.off_diagonal_norm)\n", + " fluctuation.append(dbi_eval.energy_fluctuation(state=state))\n", + "\n", + "# grid_search\n", + "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search,d=d)\n", + "print('grid_search step:', step_grid)\n", + "# hyperopt\n", + "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt,d=d, max_evals=100, step_max=0.6)\n", + "print('hyperopt_search step:', step_hyperopt)\n", + "# polynomial\n", + "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", + "print('polynomial_approximation step:', step_poly)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(s_space, fluctuation)\n", + "plt.xlabel('s')\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label ='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.title('First DBI step')\n", + "plt.ylabel('Energy fluctuation')\n", + "plt.legend()\n", + "plt.figure()\n", + "plt.plot(s_space, off_diagonal_norm_diff)\n", + "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n", + "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n", + "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n", + "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n", + "plt.xlabel('s')\n", + "plt.title('First DBI step')\n", + "plt.legend()\n", + "print('The minimum for cost function in the tested range is:', step_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", + "off_diagonal_norm_diff = [dbi.off_diagonal_norm]\n", + "energy_fluc = [dbi.energy_fluctuation(state=state)]\n", + "iters = 10\n", + "dbi_ = deepcopy(dbi)\n", + "for _ in range(iters):\n", + " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", + " dbi_(step_poly,d=d)\n", + " off_diagonal_norm_diff.append(dbi_.off_diagonal_norm)\n", + " energy_fluc.append(dbi_.energy_fluctuation(state=state))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "\n", + "plt.figure()\n", + "plt.plot(range(iters+1), energy_fluc)\n", + "plt.xlabel('Iterations')\n", + "plt.ylabel(r'Energy fluctuation')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "iters = 30\n", "states = [0,1,2,3,4,5,6,7]\n", "energy = np.empty((len(states),iters))\n", + "\n", + "\n", "d = (np.diag(np.linspace(1,2**nqubits,2**nqubits)))\n", "for i in range(len(states)):\n", " dbi_ = deepcopy(dbi)\n", @@ -295,38 +311,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eigenvalues: [-9.29150262 -5.21110255 -2. -2. 1.29150262 4.\n", - " 4. 9.21110255]\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Iterations')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", "print('Eigenvalues:', eigvals )\n", @@ -350,14 +337,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from qibo.models.dbi.utils_gradients import *" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-05-21 11:54:45]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-05-23 10:27:34]: Using numpy backend on /CPU:0\n" ] } ], @@ -366,14 +362,14 @@ "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", - "nqubits = 3\n", + "nqubits = 5\n", "h = 3.0\n", "\n", "# define the hamiltonian\n", "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", "\n", "# define the energy fluctuation cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", + "cost = DoubleBracketCostFunction.energy_fluctuation\n", "# define the state\n", "state = np.zeros(2**nqubits)\n", "state[3] = 1\n", @@ -383,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -392,13 +388,13 @@ "Text(0, 0.5, 'Diagonal elements')" ] }, - "execution_count": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -426,7 +422,7 @@ "\n", "step = 1e-1\n", "iterations = 200\n", - "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step, normalize=True)\n", + "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", "\n", "plt.figure()\n", "plt.plot(range(iterations+1), loss)\n", @@ -449,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -458,13 +454,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -486,7 +482,7 @@ "off_diagonal_norm = np.empty((2,flows+1))\n", "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", - "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step,normalize=True) \n", + "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", "for i in range(flows):\n", "\n", " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", @@ -514,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -523,13 +519,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -553,7 +549,7 @@ "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", "\n", "for i in range(flows):\n", - " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step,normalize=True)\n", + " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", " dbi_trained(s,d=d_trained)\n", " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", @@ -569,93 +565,6 @@ "plt.ylabel(r'$||\\sigma(H_k)||$')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The numerical gradients may be preferred as they decrease more the loss at each iteration and are computationally faster. They may be more precise as the previous analytic since the analytic computations use the polynomial approximation as a starting point" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "nqubits = [3,4,5,6]\n", - "iterations = 30\n", - "step = 1e-2\n", - "differences = np.empty((len(nqubits),iterations+1))\n", - "loss_max = np.empty(len(nqubits))\n", - "for q in range(len(nqubits)):\n", - " # define the hamiltonian\n", - " H_TFIM = hamiltonians.TFIM(nqubits=nqubits[q], h=h)\n", - "\n", - " # define the least-squares cost function\n", - " cost = DoubleBracketCostFunction.least_squares\n", - " # initialize class\n", - " dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - " loss_max [q] = dbi.least_squares(d=np.diag(np.linspace(1,2**nqubits[q],2**nqubits[q])))\n", - " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", - " d_analytic, loss_analytic, grad_analytic, diags_analytic = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", - " params = np.linspace(1,2**nqubits[q],2**nqubits[q])\n", - " d_numerical, loss_numerical, grad_numerical, diags_numerical = gradient_descent_dbr_d_ansatz(dbi, params,iterations,step, analytic=False)\n", - " differences[q,:] = loss_analytic - loss_numerical\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "for q in range(len(nqubits)):\n", - " plt.plot(differences[q,:],label= 'nqubits = {}'.format(nqubits[q]))\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel('Difference in analytic and numerical loss function')\n", - "plt.legend()\n", - "\n", - "plt.figure()\n", - "plt.title('Normalized difference')\n", - "for q in range(len(nqubits)):\n", - " plt.plot(differences[q,:]/loss_max[q],label= 'nqubits = {}'.format(nqubits[q]))\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel('Difference in analytic and numerical loss function')\n", - "plt.legend()\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -667,14 +576,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-05-21 12:02:03]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-05-23 10:29:12]: Using numpy backend on /CPU:0\n" ] } ], @@ -698,1483 +607,271 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "QIBO_LOG_LEVEL = None" - ] - }, - { - "cell_type": "code", - "execution_count": 29, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:12]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:13]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:14]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:02:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" ] } ], "source": [ "dbi_eval = deepcopy(dbi)\n", "params = np.linspace(1,2**nqubits,2**nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,1e-3, analytic=False, d_type = d_ansatz_type.element_wise, normalize=False)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,lr=1e-2, d_type = d_ansatz_type.element_wise)\n", "flows = 50\n", - "off_diagonal_norm = np.empty((flows+1,3))\n", + "off_diagonal_norm = np.empty((flows+1,2))\n", "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", "for i in range(flows):\n", " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", " dbi_eval(step_poly,d=d_opt)\n", " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", "\n", - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=True)\n", - "best = np.argmin(loss_opt)\n", - "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", "\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", "\n", "dbi_eval = deepcopy(dbi)\n", "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100, 1e-2, analytic=False, d_type = d_ansatz_type.local_1,normalize=False)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, lr=1e-3, d_type = d_ansatz_type.local_1)\n", "best = np.argmin(loss_opt)\n", "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", - "\n", "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,2] = dbi_eval.off_diagonal_norm\n", - "\n" + " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2187,7 +884,6 @@ "plt.figure()\n", "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", - "plt.plot(off_diagonal_norm[:,2],label='1-local ansatz unnormalized')\n", "plt.xlabel('Flows Iterations')\n", "plt.ylabel(r'$||\\sigma(H_k)||$')\n", "plt.legend()" @@ -2195,14 +891,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-05-21 12:03:28]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.7|INFO|2024-05-23 10:35:55]: Using numpy backend on /CPU:0\n" ] } ], @@ -2211,7 +907,7 @@ "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", - "nqubits = 2\n", + "nqubits = 5\n", "h = 3.0\n", "\n", "# define the hamiltonian\n", @@ -2226,10070 +922,487 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:30]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:31]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:32]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:33]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:34]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:35]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:36]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:37]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:38]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:39]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:41]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:42]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:43]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:44]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:45]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:46]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:47]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:48]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:49]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:50]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:51]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:52]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:56]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:57]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:58]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:03:59]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:01]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:02]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:03]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:05]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:06]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:07]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:08]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:09]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:10]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-21 12:04:11]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" ] } ], "source": [ - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "size = 100\n", - "param1 = np.linspace(-1,1,size)\n", - "param2 = np.linspace(-1,1,size)\n", + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,2**nqubits,2**nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,lr=1e-2, d_type = d_ansatz_type.element_wise)\n", + "flows = 30\n", + "off_diagonal_norm = np.empty((flows+1,3))\n", + "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", + "\n", + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,nqubits,nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, lr=1e-1, d_type = d_ansatz_type.local_1)\n", "\n", - "off_diagonal_norm = np.empty((size,size))\n", - "least_squares = np.empty((size,size))\n", - "for i in range(size):\n", - " for j in range(size):\n", - " dbi_eval = deepcopy(dbi)\n", - " d = d_ansatz([param1[i],param2[j]],d_ansatz_type.local_1,normalization=False)\n", - " s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", - " dbi_eval(s_poly,d=d)\n", - " least_squares[i,j] = dbi_eval.least_squares(d=d)\n", - " off_diagonal_norm[i,j] = dbi_eval.off_diagonal_norm\n", + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", + "\n", + "dbi_eval = deepcopy(dbi)\n", + "params = np.linspace(1,nqubits,nqubits)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, lr=1e-1, d_type = d_ansatz_type.local_1,normalize=True)\n", "\n", - "param1_optim, param2_optim = np.unravel_index(np.argmin(off_diagonal_norm, axis=None), off_diagonal_norm.shape)\n", - "d = d_ansatz([param1[param1_optim],param2[param2_optim]],d_ansatz_type.local_1,normalization=False)\n", - "s_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d,n=3)\n", - "dbi(s_poly,d=d)" + "for i in range(flows):\n", + " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", + " dbi_eval(step_poly,d=d_opt)\n", + " off_diagonal_norm[i+1,2] = dbi_eval.off_diagonal_norm\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 53, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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lnxMkSJAgwfjH66+/zvbbb9+UbQ8PDzN/bhdr11di2d7MmTNZvXr1mCc945rwNAtLly5lyZIlxv2tW7fyjne8g/cpH0ZNRe/ImsplPZblgo9nbR9rxrrdaq6+vGpbV8vUZ3TWsorptj6uqSnp8komVRszLVfFNmvLTG+NscxYx7wP1mVVY526+1rN6re1dG1MLMvUZ9ROqfptRa2PpTP6CSBbG8tmysayXLp+O6+WACio9bGO2hhAIV1X+LrSJdM65vFh0+36OECXMiy9LdCtDNnujzjW6VLkv966Uu4nuW5FPmO3GZ0p9+NwNDFQ9f+12qe5v/b+qvy192vO19unOb9XfZp1hvR+LS+9DdBfyZlu15cNls3j9YN+yLT+YNk0Xq5/P4dr4yOV+lixpN8ulk3fxVLtu1obq5qWUardLunfSaWi/58q1r+3Sm1ZqnboK/WvAErJ9r/nOvXvq7FesT6Wri1XipppPdNjihXHcn279c84Vaw/cWqkbFmPku14KdqWA9UR5/fKa7y+PLpyUq6W+JP2c7q7uyNvww/FYpG16yu8+Nc59HQ3piL19mnstP/rFIvFhPC0M2bOnMm6dessY+vWraOnp4dCoUA6nSadTkvXmTlzput2c7kcOQnRUFOZhggPxaorsaFYJZWXLCtJHlMGsqb9qADZrGW5QXoqVtKjqebbJsKjmohQjcBQrZOelGVMvy3O5+naY/T/qa1vWqYiXZayESSFOukRhAfVFEGzkZ6UiegotTFBeNJqpfbycwbpUU2vUa299hI5g+hkTGQva3rerOk6mjM9Zz5dPzGXyVgIUEGpv88VuuhOWwlOp42YdEqk7y4XObw75R7L6w4goXel2rOWQakG2C/NI5Lo8viq5hzXNOv736sV6DDd76sUMNOfsmY9lZZNZKZkul02kZlsJWsar9/O1NYZLGdQa1/tYdPtco0EFUsqafE9Kuv7Wy6lUTKglRSU2jKD8JQU/YxfShlnfqVcIzdKjQCVUpCuERSxjpnk5Ez/l9G/uIA4rJQSkK2RndpLSpeATI3siP0tVWtjmjGmlKr15yxWQNUXKJrpe1wsQ1pfKVUsQ21S3NRI/bb+5hRBMZ0XiyVQbD/8RkYcYwDV4RHwIP3VkRHrySkiWhF/6OlWGiY84wnj+p1YsGABK1assIw99NBDLFiwANB9yf3228+yjqZprFixwlin1fD6ZVEdDvFrpFiy3bf+IjH/GjL/SlIst80nmvrF2/zLTSBt/jVX+xWXLjlWc/xC9FomX8f6i1ScsC0oSX7Z1lCu/fotlp2/9ofL3vzf/Mvb/EvdDX2V+H4N9WnObfVJlIk40B9ASUngjv4GPnfzMSaD7BiVHcsCsu+AgEF2iu4XXq/vYpB1ZOeAtOT8YVGDzOeaoo3s1JCSqDUG7LkV+7kQD2XH5Rzr97gEYwNjivD09/fz1FNPGTM2r169mqeeeorXXnsN0K2mE0880Vj/zDPP5OWXX+bzn/88zz33HN/5znf48Y9/zAUXXGCss2TJEm6++WbuuOMOnn32Wc466ywGBgY45ZRTWvrazGgV6TEjKukxL5edyNISebu+jdr/HueuQOsI0lNyP3FrJfdDXdgCZgz7XHjM6Df9Uh8IQIRATmASuKNZJExGGsN+Nl7rm0nvgMXOqj+vH3mWHYuyY1aQeemxbrOyZBA/Jgz7yetHiec6zh889R9BZmJjtar0sfBkx2JlJWQngQfGFOH561//yr777su+++4L6GRl33335eKLLwZgzZo1BvkBmD9/Pr/85S956KGH2HvvvfnmN7/J9773PRYtWmSs8/GPf5yrr76aiy++mH322YennnqKBx980BFkbjVaQXrsnndY0iNbXh9z/xUnPUnaTqDevyj9f5nGqfJYlZ3oUrbXL/++ijUf0mvLi4RBn0tWJUE02D8L+2fVDMiOs8jqjoQA2dUdxZMIWf/3XkfyvZedC8znFOPHU8xkR4KE7GzbGLN9eNoJvb29TJgwgcPT/9ZYhkcC10wPyDM9ssdkbfuUDRFiNud7ankdLSvJ85iWVyxj1jyPZf2M7X+vALNjHVuWB9BEriZAlkc1wsv1k6zI8uRNQWURXjYHlq23zSHl+gm30zTebQkv129328LK9hxPjyO4LAs3y0/y3Q0Gl7vaLLgcVOFxCy27kcAgCo8f4TGvbya1dktTpvCY1R0ZsTarO8Om7I5A0ZTdAau64yA8JacNbCc8Xj883NQd6zpWdcc3pOxjZSl2guMVUvZRd9qF7JSrJX5fuYetW7fS09MT67YFxDVp/fNzYwktT9/l1abub6swphSebRGxKD0R8zxuCJPnqY+5rx9MJrevM3oqT1yI0zpJ4F2h1bznDPaZuNlZMjRb3RFwIzsyRLWyBKLmdqz7O/bJTqsxUC3S3+BfkOrIsYKE8IwBjCbpaTTP00xrq76/tl+uTc7yhA0vmxG3rdWs4PK2gDjyO3FBTnJiyO4I2NSdMEHlRq0sWW6nkZByQnYSREVCeMYB2oH0yJbXx6JVbbmtY1d5pBDLWqjyuIWX46zWStBahMnvuNlZYcLKrVJ3BIIElY11A/wYkVlZxuNjCCmHJTtuSMjOtomE8IwR+DbC8vkCG4iZ9Bhjkl9xXqXqUa0t52MClKnXIC4Ecas8jaBZikISXB47CHosxa3uhAkqN2plueV2jLEWlp8nZGfbRUJ4xhCikJ5ml6vHkecJpe5EKVP3uBjEofLEaWv5IcnxhEezyF8jn0WUUnSBUOqOlwpag5tyE6eVVX+c3MqqjzW3/DwhO9s2EsIzxtAK0uOQjX0QZ54nSm+eQAHmGqKqPDLIyE+jtlac5emNYFtsPuhHYOK0s2TwsrMiqzs1uKk7Xt+z+mNr/zdoZYUNKZuRkJ0EcSAhPGMQrSY9rcrzGOsEKH91g2uAOaLKI+DXiDCsyhMnwgaXR6OaqRE0g3wFec9aQTaDhpUFoqg7XjavgJ+SGsTK8itBNx7XaG7HjITsJAiBhPCMUTSN9JgQlfT45XkE4rS2PAPMNoRReRoNL7uvY1IEEpuqYbSaxIX9zOIKKwvY1R2vKSQEwvTccYPdymp2bsc3pGxDQnYSeCEhPGMYsZEeM5oQYhaI29pybsMlwNxElceP/MRhazmWJwSp5WjUzpIhbCm617FpwEXdCWL3CkRRWMPmdlpVft6OZCeVa10riT6tEsvfeEFCeGJEKw9kgVhITwyVW/Ux7zxPfb3Gra0opMheph6nytMsWyuqtZJUagV/D/y6K8eF0VJ3BPzUnUatLGMdn9xOfSyeiqwoxGV0yM7oWd8JEsITO0bjgG4F6TFDdmIKmucJUqoeTlqv/e8SYA7SjFAgTpUnyISicdpaSQPC4Gj0vWrEzvJCs9WdMD137IhqZRmPb2JIOUpjwYTsbJtICE8T0I6kJ9BjIlRuxZXnMdbxsbbCnKQdcGlGGKTKJS6VJy5bK0F7IE47SyCSuhPgGHaD33et0RL0hkLKCdkZc5g3bx6pVMrxd/bZZ7s+5ic/+Qm77ror+XyePffck1/96leW5f39/Zxzzjlsv/32FAoFdt99d5YvXx563xLC0yS0G+mJoxtzM/M8cVpbYZoR2mFXeYI2IpQh6C/7KGhcERo/vnyzERf5DGpnCYRSdwRCqjtRfljYrSy/qSPsy/TbCdkZr3j88cdZs2aN8ffQQw8BcOyxx0rXf+SRRzjuuOP41Kc+xd/+9jeOOeYYjjnmGJ5++mljnSVLlvDggw/ygx/8gGeffZbzzz+fc845h/vvvz/UviWEp4nY1klP1DxPGGvLsa2g1lZMKo9A1PByUFtrtPrxjAU0St7C5HeCEE2/3jteYeWG1J0QZehB4WZlhc3tNKP8XIaE7Iw+pk2bxsyZM42/X/ziF+y4444ceuih0vW//e1vs3jxYi688EJ22203rrjiCt797ndz/fXXG+s88sgjnHTSSRx22GHMmzePM844g7333pvHHnss1L4lhKfJSOVyLT/oo5AeBxosV6+PBcvzCLiRnqAqTxwIovIItDK8HAXbcnC52a/dzc6SoSXqjg2NqjteVpZA0NxOfSxCRVYMvXZaTXZG47zfbPT29lr+RgK8p8VikR/84AeceuqppFJyIr5q1SoWLlxoGVu0aBGrVq0y7h988MHcf//9vPnmm1SrVX7/+9/zz3/+kyOOOCLUa0gIT4vQ7qTHt0dPwMotgbB5HtkvRmPdkKSnFSpPlPByWIS1UuIOLo/HbsuhmzQ2YGeNproTlex4wcvKEvDL7QiEqsiyYSyQnXZBfzVNX4N//bUfDnPmzGHChAnG37Jly3yf/7777mPLli2cfPLJruusXbuWGTNmWMZmzJjB2rVrjfvXXXcdu+++O9tvvz3ZbJbFixdzww038P73vz/U+xGsh36CWJDK5Vr65auOjLh++arDI6TyucDrA/qJKOt9wVCKZbSsWrutoWWV2u0KWjaNUqqiZaxMP12qUsmkUIpVtGyKdAkqGaTrtgrlUho1U6FYTpNVKxRLKtmM/CQ9WM7QoZZsYzk6VOtnPVDO0Vkb66vk6U4PA7pS0FW73afl6VaGpc/TqxXoUYaM+17rjhe0E+lys7PChJUFmq3uRIWbuhPWyrIv02/Lvz+N5nbGGtlJ5XIw2IKdiRmvv/46PT09xv1cgNd6yy238KEPfYjZs2c39NzXXXcdjz76KPfffz9z587lj3/8I2effTazZ892qENeSAhPjEjlcqSUrOcXcMyRnmIJsqaTs4n0pEbKVHP6IZQqlqkaREdOegQEkZEucyE9Sgm0DPX/y6CpOMcdy1NomSqpYopqtopSTqGpVf0Xcaaq/0LOaFTLCilVQyspKBnvstnhskpeLTNczpBX/b20/kqWrnT7XLgT1BG1/06YCWC9LM4gAfhWqTt+ZMcMu5UVS25nnJMd41yrjc1zQU9Pj4Xw+OHVV1/lt7/9Lffcc4/nejNnzmTdunWWsXXr1jFz5kwAhoaGuOiii7j33ns58sgjAdhrr7146qmnuPrqq0MRnsTSagLsJMKxfKzZW00IMduXeZWqG+tGtLaiIkqWJ0x4OQiS8vSxiaB2loB9ktDRUHf8YLeyYs3tbCtkZxvCbbfdxvTp0w2S4oYFCxawYsUKy9hDDz3EggULACiVSpRKJRTFSlfS6TSa5t/TyYyE8DQJQUhPK4nPaJOe+ljwPI89wBwVzcjyCHj9Qg/TkyeuJoSyjIpbeHc8lKY38zUEIZt+dpZXWHksqTtRczsJ2dk2yY6madx2222cdNJJqKr1h+KJJ57I0qVLjfuf+cxnePDBB/nmN7/Jc889x6WXXspf//pXzjnnHEBXlg499FAuvPBCVq5cyerVq7n99tv5/ve/z0c/+tFQ+5UQniYiyIG+rZAevxCzgB/paVqA2QZxgXFTecKWqMfVk8duuSTzaskhI3l+geWg5eh+dtZoqTvNJjtm2K2sMCHlMOXn7Ux2gvxo3RbJDsBvf/tbXnvtNU499VTHstdee401a9YY9w8++GDuvPNObrrpJvbee29++tOfct9997HHHnsY69x1110ccMABHH/88ey+++587Wtf46tf/SpnnnlmqP1KMjxNhjjg2yXXE3umxwS3TI9A2DyPHUHzPJFgy/KEgcjyRAkvJxg9xEkW4wgr2zEafXe84JXbEfALKYcpP293suO7jgvZaacqrmbhiCOOoFqVK/MrV650jB177LGujQlBz/ncdtttDe9XovDECK8DuZ1yPbEqPT4lpOCf57E3JQzThVn6fDGXqcel8sgQxNZKcjytR1g7S4YgYeXI6s4oWVleuZ0gIWULxinZSeVzbUN2+rUsfQ3+9Y+jefoSwhMzvGTObYH0xB1ijtvaaja8iE4zp5pI0DzEZWfFqu4EQKtzO8aysLmdAF2UxxLZifrYBM1HQniahIT01G5HCDE71wlGeoKgWSpP2PBy2GotM/xKp8MEl7cVNJLf8UPQsLKAm7rji5BB5TAIUhgQJLcTKaRsg/08M9bJznjsujxWkRCeJqIR0tOqL0gs01AIRAwxG8tcrK2gCBtgbha8ftHLFIBGba3Gqrq8q5zaqfFfUAQhd17vWRx2lkAYVUeQaVd1xwONWlkCflaWX25HIK6KrHYgO42EkxOi015ICE+cyDp/qXmRnnZRe8KQnjgqtwQayfOEtbZkGMsqz7YAP7LVirL6Ru0sAXspelB1J2gZuhdandsZb2THc3nYvE42I71OJGgNEsITNyQVTGMh19MK0iMQNM8jEJb0yBA4wBwzwvzCD4Nk9vT2gJedFYe644WgQWUZ4sjthKrIitBrxw3tRHZCPdalujVB65AQnmbArWx7WyI9JogTX9g8j+yXZRA0FGCOSeUJG152s7Ws6wS3teKeSHQ8Iyx5lNlZUdQdNzRShh7UygoKr9xOfZ0AFVlmNFCR1e5kx/XHbUJ22gKJttYsiAPc9uV267mTyufaoleP7wSiXuuae/S4TDQqm3PLa5JRscxvgtEovXlc59mKGaI/T5SePNvCBKHNhp38Bc3v+NlZYcPKdthL0QWZ9kKYoHJcVpZXbqfRiqzxRnYcGGWi06fl0LTGChYGxkFHdoFE4YkTGcmvPBeLSwa/XE+rwsxuJ5ZmVm6FCTFHsbZCBZhDqjwCcao8zcC2UKnVLq8xSik6+Ks7MjTDygqa2xHYFshOpLyOYywrv04kaAkSwhM3JKrGWMz1tJL0CDSS55HBz9oyI+iUEzIELismuAIQZDbuJMfTOJptZwn4NRpsRN2xrBOTlVXfnn9uZzyQHb8fk7FYWLJrQ4KWIiE8zUA26zy4s5nQao8XxhPp8QoxB83zBK3asjzGJcBsgY/K44cgv/T9qrWaXZ4+HiYRjQNx2VlRg+ph1B23oLJlnZisLK/cznghO57Lw5acJ2SnbZEQnmYihNojw5gnPWaEqNyqj8lJTzOsLcs6LmXqMkQpURdotq01noPLQUlamPxOFDRSih5I3QlQhh5EwYyD7MhyOwJBGwuOa7Ij+1Er+fEr5htM0HokhCdGVHOq82COgfSMdrvyyKQnQLl63HkeGYJkHDxVnhqiqjwCdbUnPlsrQevgRUabpu6Y0EhQWYYguZ36ut4hZVeYzgHjnuw4xpxEJyE7o4sxR3huuOEG5s2bRz6f56CDDuKxxx5zXfewww4jlUo5/o488khjnZNPPtmxfPHixQ3to5T0yCwuG6LmesYU6ZHAi/R45Xlk8LO2rOt6LItR5QlyEYxqa4XOoLRJqLcd4PbeRbGz7GiGumOGF4kPamW5wSu3IxDHHFntSnbcfmQGzuskqk7bYkwRnrvvvpslS5ZwySWX8OSTT7L33nuzaNEi1q9fL13/nnvuYc2aNcbf008/TTqddkxDv3jxYst6P/rRjxreV+kB3sRcTysquMKQHgtiCjHHYW15yf9BmhE2qvIICEVgNKq1wmKsTC/RCJnzm04ialg5CKKoO2Y008pqVki5nclO4Me4WVg22K8F1aTT8qhhTBGea665htNPP51TTjmF3XffneXLl9PR0cGtt94qXX/y5MnMnDnT+HvooYfo6OhwEJ5cLmdZb9KkSZH2z34gN8vi8kK7kJ5WhZj9SI8ZYQLMFsSo8oQNL4e1tcZLA8JmkKy48zsCdjurIXXHo8mgV1A5iJXVLiHl0SI7jVRiORDRwmo12enTCvQ2+Nc3jqpBxwzhKRaLPPHEEyxcuNAYUxSFhQsXsmrVqkDbuOWWW/jEJz5BZ2enZXzlypVMnz6dXXbZhbPOOouNGzd6bmdkZITe3l7Ln0A1q0qJjwUNWlzbGumxw4/0WNe1/V8OtqwVKo9AUKXA7YKdlKfLEYXsBbWzmqbumBAmqOy1TEb+ozYXFBiLZMdzeRPIjuW+5LqQoPUYM4Rnw4YNVCoVZsyYYRmfMWMGa9eu9X38Y489xtNPP81pp51mGV+8eDHf//73WbFiBVdddRV/+MMf+NCHPkSl4p4dWbZsGRMmTDD+5syZ41jHl/RAQxbXaIeZm0l6BKLmedysLcu2wwaYm6DyeIWX/WytIDN6h8FYKk2PY1/NJDGKnSXQTHXHDFlQORiZl1tZbrBbWbKQ8ngiO6HyOhGrsOzXAi2T5OhGC2OG8DSKW265hT333JMDDzzQMv6JT3yCo48+mj333JNjjjmGX/ziFzz++OOsXLnSdVtLly5l69atxt/rr78OOA9kO6tvtcU1VklPkBBzFGvLS/YPFWAmfpVHIA5bK0FzEPfs9mHVnbBWVqO5nW2B7AR+TEwWlpaoPKOKMUN4pk6dSjqdZt26dZbxdevWMXPmTM/HDgwMcNddd/GpT33K93l22GEHpk6dyosvvui6Ti6Xo6enx/InoGVVx0Edp8Ulw3giPQJxhpj1dW3/e/xS1m8Hb0ZoRtC+PHaVp1FbK8w647FSy+81hc3veJFM8VkFbS8QVd2RBZXDWFmWZRFCyvX9kPwKCNFrZ1shO5b7th+7sutCgtZjzBCebDbLfvvtx4oVK4wxTdNYsWIFCxYs8HzsT37yE0ZGRjjhhBN8n+eNN95g48aNzJo1q6H9DUJ6fNUeiYQaJdfT7AquyKTHjNoJNGiex4/0WNYNUbUF8gCz15QTYVSeMH1awthaXjmesRpcbgRer9nNzpKRoiAVc352lgxSdSdAUNmMMFaW5XEeuZ36Oj4hZTN8eu3I0G5kJ86Scz9VR0yUnKD1GFPv/JIlS7j55pu54447ePbZZznrrLMYGBjglFNOAeDEE09k6dKljsfdcsstHHPMMUyZMsUy3t/fz4UXXsijjz7KK6+8wooVK/jIRz7CTjvtxKJFi0Lvn5ZVLAezndUHDjTbESLX44W2Iz0ufTq8SI/fFBMCUau2AgeYI6g8fhDKQWJrtR+i2lmh1B0TggaVo1hZju26WFlhK7KCNhZsJtmJUokVteTc/qPVT9WxXx8StB5jSmP7+Mc/zttvv83FF1/M2rVr2WeffXjwwQeNIPNrr72GolgPqOeff56HH36Y3/zmN47tpdNp/v73v3PHHXewZcsWZs+ezRFHHMEVV1xBrgFyoGUVy4VZy6oWlaKaVS0nk2pOtZ5QxBfLfGLJZhwEIZXLOU4e4gvtJiPLHhMXqiMj0pNHdXjEcqKxrFcs1U8sxaLx2lMjZQcZVIrl2nup1d7jClo2jVKqomVSxni6VKWSSaEUq2jZFOkSVGpPoZRAy5j+L4OmWpdZnrOUQsvYLhSlFNjGtJKCktEol9KoGXmodricIa+WGCxn6LD8n6NDbc1s0An8EcbOarW6E1SljCO3IzCWyI7rskYsrAi9ddpF1enX8lQqjV3mh7QAHbXHCMYU4QE455xzOOecc6TLZEHjXXbZhWpV/sumUCjw61//OrZ907Jp0MRt/QAXJxLxBRAnFPEFEcRHfIEcxCcC6QH9C95OpMdzPR/SkyqWjfcrKukB6utIiU19TEaGUsUU1WwVpZxCU2vHU0mBjEa1rJBSrcpTsZwmq1YollSymfAnjIFyjk51hL5Knu70sGVZn5anW9HH+ioFutNDgG7V9ChDjnWCok+r0K3Ibbn+apGu1Ni0xqLmnhptAOml7siaDEYNKlvGQpSgxxVS3pbIjqzaNgzZ0bJpNJfvWILmI9HXYoaWTdvuK7b7EQLNlvvx5XqahUgnwBCVWwJhZlYPUrVlGfMLMEvK1L2yPEFL1L3Cy43YWo5JNNs4uOzVdFBWkh7mtYQpRxew996JQ92xwKMMXb9t/R/CWVn67fAh5TinjGgnshOq5NyEsMFkZ8Shfb9z2woSwtMEaNm05eCWZXvMaGYVV7uTHgsCkh6vpoRmhKnach3zCjCbECTLExXtNtVEu6ORkHbcWak41B1o3Mrygyy3U38RjZWftxvZcaDJwWTH9UB1n6A4QXOREJ4YYT+QvdQev0Bz4CouG8I2KWxmBVcQ0hO1XD1qfx7LOr6/mK3L9OePX+Vxg1dQtlnTJGyL8LKzgoaV3RoNmuGn7niVoZtv+1lZUXM70pDyGCQ7Xs0EHWhyMNlxDcgkZGc0kRCemKFlUpaDWqb2WNZvksUlw2ioPXGRniCVW0FK1eOwtvR13ZsRWl5KAJWnUVvLrTw9IUXu70HYTtV+dpYMYdUd8A4qm8e9rCz9dgxkx/Ji2ofseP1IC5zXiWhhmRFK1bFdFxKMDhLCEyMsB3zGXe0JW77uUHsCWFztlOtpmPTUEGb6CVmeR1aqHtbaAu9mhBBO5QmKxNZqHH5zjjVqZwVWdzyaDIK32mi+7WVlmRFLRZYJo012XJeFyetY7oezsBpVdZLS9NFD8s7HDDvpabnaY8OYJj0CEUPMZgTJ80CQi010lUcGv/BynLbWWAouB4X9NYTN7wSxs4KGlc3wVHfAswxdv13/v1Eryw+NVGS1O9lxIAYLywznOdxd1RmNPjz9Wp6+Bv/6x5FSnBCeJsDB+BtUe8yIy+Lyy/U0A6FJT4Mh5rB5Hq8Li3lcKaU8mxGCXOUJ24jQDX62ViNo50lE23nfQqs7JvgFlc3jzbKy4iI7zUKryI7lvoeFJVN17GTHbd1KYm2NGhLCEyPsB3Jcak9cFpcM4430xJXncbvtG2AmuMrjVqLuhqC2VpLj0WF+7X7l6IJEBn2P41J3ggaVzbf9rKwouZ36zjdGdpqh7jQcTrb/CLSdL6MEk83wU3XMSMjO6CIhPDGjkklZDupG1R4zGrW4wuZ6mlXBFRfpEQhLevTbwa2twAFmiEXlaaatlaAOr/fOz86SIYy64xZU9suPma0srxL0OCuy2pXsyNYNlNcxoZWqzrZCdt58801OOOEEpkyZQqFQYM899+Svf/2r6/onn3wyqVTK8feud73LWGfZsmUccMABdHd3M336dI455hief/750PuWEJ4YITr6gpz4GLdDqD1BAs0WjJFcTxykJ0zllhlxWVv688ar8gRF1IBtQz1qPBoCbouQNRoMqu5AsJ47XlYWeOd2QoWU24zshK3EitvCikvVcV4HUpbrxHjD5s2bOeSQQ8hkMjzwwAP83//9H9/85jeZNGmS62O+/e1vs2bNGuPv9ddfZ/LkyRx77LHGOn/4wx84++yzefTRR3nooYcolUocccQRDAwMhNq/MTe1RLvDmMagdiKqZFLGycgx3UQmZTl5adm0cZKSTU3hNh+XY1oK+1xcxrQN9ROZ2zxcozEdhRlh593ymnPLMuYy9QRgzLUl1tHXd0414bxdn2dLNuUEyOfYEtNNDJdV8qr1ImSfX6u/kqUrXfScakLAPM1EUPRV03Sn2icb0wipCkLmothZQUrRG1V39Nt43tbJePDcjhukuZ36CzFujibZcV0W1MKy3G9M1bEuc6/A8rOvxjPREbjqqquYM2cOt912mzE2f/58z8dMmDCBCRMmGPfvu+8+Nm/ebEwKDvDggw9aHnP77bczffp0nnjiCd7//vcH3r9E4YkRmul70yq1x4w4LK5WhpnjmmHdrXIrTmvLa9xP5TFf+LxUnqiQWTPbQo7Hq8rMLb8TBHY7S4Zmqzte43HkdqQhZY8pI0ab7MjOS3FbWK1SdbQxKjP09vZa/kYkn/3999/P/vvvz7HHHsv06dPZd999ufnmm0M9zy233MLChQuZO3eu6zpbt24FYPLkyaG2nRCemFExfd/sB7pftsdOfFzXDRtoNqNBi2u0SI8F4sTcYIg5jLXlViZsnmfLLcsjIMvymBE0vCwQ9zQI2xLCksBWqzthrSzz8sgVWSaYv5ftQHYCrRuC7HiVm8dVgeU8/1uvD61AfyVHfyXf4J/+Xs+ZM8dQYyZMmMCyZcscz/fyyy9z4403svPOO/PrX/+as846i/POO4877rgj0P6+9dZbPPDAA5x22mmu62iaxvnnn88hhxzCHnvsEer9GKNcs70hDmrxS0zLpiwWl77M3+YSXzKzzeU1+7pZ4ajm1KZZXHHbW24zrJvtLddZ2D1mV/ebWR2wzKoOqXisLYBMFUoKVSClaoa1BfWZ1AHD1houZ8ir1l/YrbK1HNvwmDV9LCOIneUWVpahEXXHLajciJWl3463ImtMkJ0WWVheDQS97Cs7yRmrHZdff/11enp6jPs5yWemaRr7778/V155JQD77rsvTz/9NMuXL+ekk07yfY477riDiRMncswxx7iuc/bZZ/P000/z8MMPh34NicITI+wqjZ/aE8bmMq8XRu2xIELpeqsquIIoPXFWbpnHZKXqEL+1BcFVnrDws7Ws60YPLo8W2qEHj6wUPay6E5eV5ZXbibMiS4Y4yU7D4eQmWVhxqjrmbYxVsgPQ09Nj+ZMRnlmzZrH77rtbxnbbbTdee+013+1Xq1VuvfVWPvnJT5K1X6tqOOecc/jFL37B73//e7bffvvQryEhPE2AnfQ0YnPVl3lne8xoxOIazVxPHKTHa/oJy5hPnsfP2hLL6reDl6mDNcsjK1EPOr9WUFtrvOZ4BMwkzi+/E/S98CpFD6TumKaQiBJU9uqmrN+W53bcEEdFVtxkRzoeQ14nTMdkr6khzCTFq9Tcz76ynMtbbGu1EocccoijXPyf//ynZx5H4A9/+AMvvvgin/rUpxzLqtUq55xzDvfeey+/+93vfIPQbkgIT4zQPNi8jPjUl0VXe+q3QwSa7Y0KQ3RnlqEVpMd1HQ/SEybE7JXn0W9juS0rHdafM7rKE6REXVguXg3ywnZdHotTTMSxz352lgxB1J0oQWV3Um0i4XHkdsYA2fFdN0AjQTOiqDphQ8n1Zd6qzngmOwAXXHABjz76KFdeeSUvvvgid955JzfddBNnn322sc7SpUs58cQTHY+95ZZbOOigg6S5nLPPPpsf/OAH3HnnnXR3d7N27VrWrl3L0FA4+z4hPDFDy/gTH2NZA2qP63qjaHHFhTh79AQNMUPj1lazVJ4EjSPq1BuysLKfugMEDiqHsbL02+FzO+OO7FjuxxdMNm43wb6yXxfGKw444ADuvfdefvSjH7HHHntwxRVXcO2113L88ccb66xZs8ZhcW3dupWf/exnUnUH4MYbb2Tr1q0cdthhzJo1y/i7++67Q+1fElqOEZoK4mtjDrfq9+th5LChZnOgWd9mNXKgWdqzx3wizGYcYWawnvBaEWaWhZQD9+gR+xMwxAxY+vOA/pnon0+tf4/xOdYvTK4BZqCaFT15UnqAGV3lSan6ZyTrywNECi/3V/J0pYfp0/J0K9Ywc69WoEdx/grq07J0K+O7kWAUO8srrBynuhPUygpSgm4Z86rIqu+8cXO8kB0/VceMIETHvl7UULKd5GgZ0ILN6TpmcdRRR3HUUUe5Lr/99tsdYxMmTGBwcND1MdWqd2+poEgUnpjhVHjq971srjiyPZHVnggWVyvCzJGVnhDl6uCd5zEv129jue0VYG5E5Qlboh4EYXI8biHhduy27Jbf8YKbnSWDm7ojUC6lA5eheyuE5mXyEnSv3E7YXjvtSnYc55AGLaz67XhVnTD21bai8LQ7EoUnRngrPFa1R19WV3zMag/oJ7mgao++LB1J7bGcFAOoPXalB+QnyrjUnshKj6lc3dgnm9IDhCpVh1T9cy1jNBAzf8b6/XqZOhCryhMUcZSntxpuZKrRCq2wdpYsrOw1SahAkDJ08M6A+VlZ+jry3I4rRpnstNLCEohb1bF3SXYLJOv35bdBP2e0suCwv5Kn1GDzn5HK2Mv4uSFReGKGpmLtuOzB8qOEmuPK9hi3I1Rx2dHsXE+jSo9U1kee5wlSqu5pRzRB5THDPqGoUCgMxSJiNdZYCi5H3Ve398YrrOzXaNBP3XELKkNwKyu23E4N44HsyKqwBBpVdcKGkuu33cmO/bqQYHSQEJ4YYT/AzePNsLnq2zOXTgYrX2/E4nIrXZdh1EiPQMgQs3lMv+1ubYUKMEes2ILGba2wUyuMB3i9Zr+5syB4KbqAXd0JE1T2s7L023Urq9GQsmW/xwLZ8bCw/ILJ9dvBK7Dq48FCyV7nd/C+FiRoLRLCEzO8WL3nL4CA1VyNqj1e5euNqj3tQHoMRKjcAmuex1yqnjb9+va+YFl3w1GmHkDlMWPQQnz8y6bNkFk5ZpVjLDYgtMMvvxPVzjLDLazspe6Af1DZWZouL0GPFFKO2EW51WQnUF7HvH8eqo44rzVT1QmT0/FS+xOMDhLC0wQEOfDrCk90m6s+biI0zVB7zAhAegLPfRMBfqSnkXL1sKXqfgFm1zJ13FUeCF6iHretNZ4h3pMgvXeClKJ7TSHhF1T2Om6C5nb022Of7FgQ0MJqlaoTl33ltW6C1iIhPDEiiLQZJN8TRe3Rl8Wr9hiQWVwmBM31xFXBFZn0iP1wIT36bXmeR9aFWV9P9kvdfD8VSuVxs7UEvOZ28kIUW6sdpnNoJcKWojvUnYBBZfC3soLkdvTnCV9+3mqy49Y52YIQFpaAXxNB43YAVScO+yoI0UnIzugiITxNQBjiEzTfEyXUHFXtMW57WVyjnOuJRHok5epmhM3z+FlbfgHmoCpPkPByHGjH4LKddAXZx7Dkzi+sHDi7g1PJi2plQbDcjgM+FVnNIjtuP2Z81V4PCytKMNlN1fErNa+PR7evXJd5rJugtUgIT4zQVJ8DPeSXpH5bTnzChJrr4/5qT9wWlwztQHrAO8Tsl+eB8NYWYFECwKYUEC287GZrbQs5HvDO79iXefXecStF983ueKg7EN7Kaii3MwpkRzoeJK9jRoBgclRVR8BN1QlqX0X98Sq73woMlnMMNPgXVVVuRySEpwkIcuAbtxvI9xjjAULNcag9BkJaXO1KesKEmMGZ5wljbenP46/yBClRDxteHq8IS9a8Jlo1n9RDZXd8ytCDWFmyEnSxTB+vjAuyY0FEC6t+O5yq4xdKNsZ97Cvjdqj2I9b71UThGTUkhCdGVEN4uHHke8KEmuvbcld76rflak9Yi8uMZoaZm0F69NveeR5jmccFrT7mXqYOjak8QW2tbaE8vRE7K4i6A87Pyv5Z2oPK+pi7lQVOKytySLmG0SI7kfI6Yp8iBpON2w2EkuPI6YTNcCZoPRLC0wSECa+FlUjrt/1trrBqT5hmhQY8LK6guZ52JD1B8jxBrC3PADM0rPKYEcbWGu9we80yOytOdccrqCwbcytBh4AhZUlFlh+ZaSbZsa8TJq9j3A5Qbh5W1QljX5lvhwkkuy5zPDaeeaEShEdCeGKElqlaDuZGvySyZWFsrvp4Y2qPgGugOQaLK44KrlCkR8CF9IB/ngf8rS3XsZhUnqi2llvpepBQcLvNp+VVhm8vRzcjyHsXRN0JGlR2s7LAmduRhZSjkJ1AzTlDIGg4uRkWVv12eFWnPl7fDTdVJ2ogOQjRScjO6CIhPE1AGOITVj6Vb6O5ak+kQHMAi0uGlpEejx49ECzPYy9Vl1lbsgCzrEw9qMrjhkaqtbyyMK0qTQ9CouKqIgsSVg6j7oB3UNnPypLldvRlPhVZxg62juxIx2O2sJqt6kTJ6TSWy0yITrsgITwxopqtUs2aiE4A4mO+HYT4NNPmMrbTaKDZjFHI9QSW9F3a7fs1JXQrVdeX+9sY+nM4lYFqWfFUeaBxW2ss5Xi8yJaMpLm9Nq+pJNzsLAiu7vgFlcHbygJnbidsSHk0yI5vXieihVW/HVzVabZ9JVvP/3HW87+4PpivEQlaiyRC1QSIA1qcCMVBL34BGrNsl+tfFueM2/X7xm2XxzkfkzJOnvaZ2MVJtpJJOWZhN8/ALk4w+mziSu22ZpyQlNrM4/rrLBsnsNRIuX5iEydl06zr4oRon3XdfoJudLZ1+yzrrjOs22ZXT43or8VrZnXxXoBimVUdqqb3sL4v9s8HJLOpo0CmTqSEopBV9Yu+UBzELOqD5Qwdtdv9lSxd6faymUYTbuXoZgQJK4OHuhMiqBzUyoKQIeWA82M1i+x4rtMiogPB7av6beS3bVdDt/Wk9z0yOqNJcPorGbINVnMWx1H/0TGn8Nxwww3MmzePfD7PQQcdxGOPPea67u23304qlbL85fPWk2C1WuXiiy9m1qxZFAoFFi5cyAsvvBBp37S080D3U3zqy8J5w37rxWlzGc/ViNoTMdfTCILMCg2EDjH75Xn8rC2oW1txqjxhba3xMAVF2NcQNKwsU3dkTQbtIXRZWL0+7m1lmXM7cZWfjzeyE5d9FT0z6WV7yRUdMzS16rhOJGgdxhThufvuu1myZAmXXHIJTz75JHvvvTeLFi1i/fr1ro/p6elhzZo1xt+rr75qWf71r3+d//qv/2L58uX85S9/obOzk0WLFjE8PBxpHzW1iqYGJz5RK7riyPcI+IWag2Z7jNcbY66nmaQnTOUWyEPM4F6q7mZruAaYbUTHnuWBYOFlu61lh8z6GS8NCO2Q2VlR1R3AU91x67kjs7LMU0fIcjswBslOAxZW0KyOQJyqTrC4gPvj9OUBiI6aEJ3RxpgiPNdccw2nn346p5xyCrvvvjvLly+no6ODW2+91fUxqVSKmTNnGn8zZswwllWrVa699lq+9KUv8ZGPfIS99tqL73//+7z11lvcd9994XfQot7ET3xky8LkewSapfa49uzxqOKyV3245QIaIT5xkB63ELNXqbq+jtPWAJcAs9gnF5XHb7oJkKs8YcvT23KKCdM+eeV3gthZdjSi7ji6aONtZYXJ7cDokJ1IlVguVVhBg8kCrVJ1gk/v4090xDk8ENHJVC3XiQStxZghPMVikSeeeIKFCxcaY4qisHDhQlatWuX6uP7+fubOncucOXP4yEc+wjPPPGMsW716NWvXrrVsc8KECRx00EGe2/SE7YAWB7z5oJcRn/r60eRVty9wozZX/bn81Z7662s/i6tR0gPyELO4H8bacg0wR1B5wDu8nKAONzsLwqk7bmXoXlZW1NyOvjPhGgs2SnYcY7YfIZ5TRLRA1YmL6Ihl5ttRS8wTojM2MGYIz4YNG6hUKhaFBmDGjBmsXbtW+phddtmFW2+9lf/5n//hBz/4AZqmcfDBB/PGG28AGI8Ls02AkZERent7LX+AJXQqO8C9iE+jpeyyxwUlPsZjJTZXGLUnUIfmUbS4ApMeAfNkoy6dmO15nrDWlkwhEGXqQVQev548XtMpQDAlRFYt1Q69ePz2PYydBfLyf5m6A/UydHf1LsbcjoCkIqsVZMd1nQAWVjNUHYFm5HSiEB3Lj1c/opPRrNeJBC3FmCE8UbBgwQJOPPFE9tlnHw499FDuuecepk2bxne/+92Gtrts2TImTJhg/M2ZM6e+0H5At4j4+H255bdHSe3xsbjMGBXSY6t+CRpihnqeR9aFWXZx1JenrCqPDWFVniC2VjuVp8vIU5j+P0HsLK+wsn0aCT91R2ZDen3GXlaWcT9C+fmokx3jtruFJSALJvupOkHtK2O7DfwI9HqMvsxpXRnnbImKnxCd9sSYITxTp04lnU6zbt06y/i6deuYOXNmoG1kMhn23XdfXnzxRQDjcWG3uXTpUrZu3Wr8vf766wCkVDPRcSE+HjmfsMTHfDuMTx3F5tLHoqs9xmsMYHEFzfVERSjSEyDEDO55HvOFrb6ue4DZQECVB/zDy0ExFoLLYffR7T0Ipe7Y++/gDCqHsbJkuR1oX7LTiIXl1UQQnETHTHYE4s7pBI0G6MvcrStpEDkA0bFcJ5qMoYo+23kjf0OVZLb0liObzbLffvuxYsUKY0zTNFasWMGCBQsCbaNSqfCPf/yDWbNmATB//nxmzpxp2WZvby9/+ctfPLeZy+Xo6emx/AmkVM2b+EBsxCdKRVdUm8uvYaFd7ak/PkSgOaTFNZqkJ0qex43gmAPMYVUev0aE4G9rydBOweW49sVu+ckaDUrVHbCoO1IbEn8ry57bCTRtRIvJjiyvY4GE7HgFkwXcVB37ci9VRzxmtIiOXdGxwIfoiOtCK8lOAifGDOEBWLJkCTfffDN33HEHzz77LGeddRYDAwOccsopAJx44oksXbrUWP/yyy/nN7/5DS+//DJPPvkkJ5xwAq+++iqnnXYaoFdwnX/++XzlK1/h/vvv5x//+Acnnngis2fP5phjjmloX0eb+JhvBzkR1NcPpvbo4+5qT8Pl6zUEIT1RiU8jpAecIWavPA84bQ+ZMmDANOWEo8MvTkXCfvH2srWCjo9FyObO8gsrh1V3wPkZellZbrkdCFaR1Uqy47rc/GPEZmEZ+xIgmAz+qk5U+8pPBTffjoXoSFR7N6JjhpKQnlGD85vexvj4xz/O22+/zcUXX8zatWvZZ599ePDBB43Q8WuvvYai1E9Qmzdv5vTTT2ft2rVMmjSJ/fbbj0ceeYTdd9/dWOfzn/88AwMDnHHGGWzZsoX3vve9PPjgg44GhUGgqBpKRrNcoMTBbgQfzaRHrCe+MEYn5tqFs1YNYu7cHLRrs/k26PfNHZuF0iDGrY+td1wWJ5t0ST8RKcWqcWJKl6rGyUp0apZ1aRa/ZLVa52J7h2bjRJ/NunZnblZnZr+OzIC0GzMg7cQMacuJWdaF2VjmIDsp9P7NOH9BgkV5yGbqF0pz12Wwdl7uq+TpTlt7SvVVCnSnh+jVCvQoQ843ZQxAEBt7fseyjkdYuRF1x62ZJFitLFluxy2kHKSLclxkp+G8jnj+EFkdYyxgT51GuiR7ER3Z+voy9+7IUjXHjgC2lVJbR3P7kZOg6UhVq9WkVq5B9Pb2MmHCBObd+iWUDitRsv86l1V8OH5F2hub2cpgLXP3ONY13S4FuB1off0QsQRxTSdzcxM1+2Psdo/+v+lXrewXLrie+L1+7dqXh4Hbr13piT+bdZzsdRVLsf6KzepTT5hP4vqv29qvTLX+a7N+vx6M1FTxC1L/lahkNNRMhaxaMQhPXi2TV0t01P4AOtQRg/B0qiMG4elKD9Ot6Le70zrREYRHjOu39cd2p6zB4W7FSga6UvFkfvxCy7IePG6Ep0/LO6qzBso5+itZQ90RhMes7pgJj0XdMZWhywiPU7Gr53bMVpY9tyMNKXv02mkV2QlqYRn74aLq1Mfc7Suwqjr1MZyPaUeiIwkhexEdY7uDw7xy6lfYunWrJQ4RJ8Q16aMPnUKms7HvaWmgyL3/cltT97dVGFMKT7sjnamQzlQsvxLrrF7/kjsUH6h/cVqk+FhuB1o/JVV7AEPxCaP22OfjEid9qdojTrgStUecqM0Xg7iVHsu4SelJif01KVZ1JStdu8AJvcZ6Qne1sfRX4DgJC2glBXMEyK7yABalR4Y+LW8hN+0Itwotv8CyX3UWyBs2eqo7NfiRHTv8cjv6NiXl5+1CdkIGkwXiVnXiIjrOxzSX6NhJDoCa0Y/rSmYcTU41xjCmMjxjBWqmYhzcAkpGs3wJpCE2n5L2sBkf47ZrUFnuZcvyPX5TVMhK2INke/TXYQ00G2gg1xMFbpkeaWNCwoWYvaq2jHVNvXnszQilyiB6lkdWou5XrWUvT5eRhXYILsv2wb6vYewscJaimyFTd7yCygJ2K0uW2wkUUq7BjdTEqWBGJTuyYHKQrA7IQ8le1VcC4QownOQoyHxXYUvLZedw+3ke5NeDBKODhPDEiKzqf6DLvhDNIj5RevjYTyjOdZ3VXEFK2PUxZyWXLNDsWcUl3rMWkR7XcY/KLcDRn0cfq0qrtjzDyzZoJcWo2LJPgQDe4eUo1VpeGO3mg7JeQl5hZVkpehB1RyColWWsb8vt6GPRQ8px27XS5QGqsMD6vfWqwAoSShbrB6m+8lou7hu3m0x0zAhKdLJqxXGdSNA6JJZWzMiq9QNclBGbD3pxYjV/OVztrpisLrNtBVgDzDYbyx5sDmtzyULNwuLSH6vbXMLiAgybK1Cg2WRxiRO12eKy21vm5UFhtrHMIWZjXBJiFhcE/YKm1l63HmIW1pZZxtfJkJdqYAow64+ginsPj2GJpWUOL5vRTrZWs0lTXOpOFCvLq7lgO5CdRoPJQbM6rbavmmVdBcnnyJQc8zWh1RgsZ8i4HO9BUSqPn5hvovDECHOmAgSbtx7sYewuA01QfNx+GTVqczVd7RFwUXvi6szse+GRlKvLOjGDsDQ0aRfm0PtVK1M3qzyysmp7+bWXreUYHwMNCO0w21z21yrrTxRW3XGDzMqS5XZaTXZk7RqaQXbiVHXE/1Htq1YpOuJ8bT5nu6s5trFM2XGdSNA6JApPzBAHs9luEAe9uXGc+HKECjjbS9pjUHyiBJv9ytjNoWaBqGqPI9AsTshC7WlimDms0qO/1/IQs/l9qO0REF7l8WpPb1YuzErPQDlHp6q/9v5Kni5bmbooT/dTfvq0iqNSq1WwV2cZ4ybS5mZngbwU3Ywo6k6Y3A5YKxKbTXYs9yNYWMbzBwgme6k6Uaqv2lXR8QoimyFTdBKS0x5IFJ4mQcbkBeM3fyHELwPzFyd0wDlmxcfvF5T7uv6h5jBqj77vPoFmU0M02ZQUZjRb6fEKMdunngB5F2bnhVUeYA6q8riFl4M2G2x1cDnoHFpB5gKTTaYqStHDqjtBM1duuR2v8vNRITs+jQS9gsleqo5sok9Z88CoOZ3YFR1xHq2dS4MEkWXnbNm5XX+bndeBXHp8k58333yTE044gSlTplAoFNhzzz3561//6rr+ww8/zCGHHGKsv+uuu/Ktb32r4e3KkCg8MSKXLqOqacvFx3ywh1V9Qud8zL9YSqmmKj5u6wrFQqb2yErYDe1CovaIw9Os9ogTsqH2+DQqlOV6mqn0mMvVFeoXC3ueB5wqWFwIWqLe7ghKtvzsLC9lB4KpOzLIrCxZc0Gv8vNWkZ12VHVkKk3LFJ0m5nPc1Jy8qo+XozfHbnts3ryZQw45hMMPP5wHHniAadOm8cILLzBp0iTXx3R2dnLOOeew11570dnZycMPP8ynP/1pOjs7OeOMMyJvV4aE8DQB4sAGpORHRnzAGXKOZHdJAs5xEx+//j1mm0u3bYKFmr369rgGmgNaXI2GmQOTHjBCzIL0iBCz/h5Z+/NEtbbEVmSn1ryN6AilQ2Zria7LYxFuGSTZtBrmRoN+TQajWFl29c6c2wFbRVabkh2/rI6xTFJqXn+cM6dTf5z1f5DbV7ESnVG2rczXgm0BV111FXPmzOG2224zxubPn+/5mH333Zd9993XuD9v3jzuuece/vSnPxmEJ8p2ZUgsrRhhv9DoY2XHQS9kTr+Qcxi7y4BHwDkuqytsGTsECzW79e3R/5cHmutvntziMt6nGGZcD2Rvucy5Ze/PAzj68/hVAIWFnlvxDy+Dsx9POwWX3borm2EvuTd3Vo4TbiXo+rJ6bsczpGxDI2QnUjjZxcIC6/fMra+OVyg5iH0F3ucYx1gU6ypiEFkgqG3ldi6Xnff18ZL0OjEW0Nvba/kbkRyr999/P/vvvz/HHnss06dPZ9999+Xmm28O9Tx/+9vfeOSRRzj00ENj3S4khCd2iAPaflCLL4Ab+bGMBajuGm3i4xjzyPd49e7xalgosgJuzQrBv4or7lyP39QWgLRyC7zzPDJEzfLYZ1EXsCsf42HSULud5RZWjkvdMUNmZdlDyn4VWY2SHct9E6m3HPf2vA7uvXX0/72zOmDO5LlXX0nPBZF/TLWW6JgRNJvjdo53uya0AkNl1fgORP0bqrkUc+bMYcKECcbfsmXLHM/38ssvc+ONN7Lzzjvz61//mrPOOovzzjuPO+64w3dft99+e3K5HPvvvz9nn322Mcl3o9s1I7G0mghxgNv7fogvhF/WJ4jdZc/5mL/M1bLSNKtLIGi+J6jNJTi43eaSTk1Re0QcFlej9pZXjx6xr7o1Jz4fa54nqLVVyVY9lsshlA5zT57xYmuBXN1pFGGsLK+QMtAysiNdZiM6IFd1BNyyOl7zX8Wd0xkN60pGcuzwy+Y4x+UEpzBGba7XX3/dMpdWTvJjUdM09t9/f6688kpAt6uefvppli9fzkknneS5/T/96U/09/fz6KOP8sUvfpGddtqJ4447ruHtmpEQnhhRUMtkVP3EYP6FbT7why3j4bM+QZoZSnM+TSA+bsFmAXeCZG1aaASdbaFmWbbH2I6kfF0aaG4D0qO/l9YQs/5e+Od57O9fqpgKnOURsIeXZTOoA66zp/dV08ZEoq0qTfcLLLvldwREWNkxSWhAdceOMLmdIBVZ7UZ2wmR1gjQPbDSQHAfRiZLPaTSb40ZyzN/B0hgNLff09PhOHjpr1ix23313y9huu+3Gz372M9/ti0zOnnvuybp167j00ksNwtPIds1ICE+TIA5wu7XgR36Cqj5Bqrs8iQ84evmEIT5BKroEZATJq5rL9Mja//JKrkBqj6Q7s1u/nmaRHnvllvja2UmcDM4sjynA7DLJqBvs4WWQd10WY31a1pg53XWb1WJss6bLStLtWSJzfsfPzrJDNhWHGWE6Ktcf48ztQHCyExaByE6DweQooeSg/XSaQnQaVHOgMaLjZVWN9UrJsDjkkEN4/vnnLWP//Oc/mTt3bqjtaJpmyQjFtd2E8MSIDrVEiaxjTMCN/ERRfcLYXaFK2gMSH4OwhKjoEghjc8kaFup2kGl7LmqPm8XlNSVF2AqusKRH3199+on6awlubVney1KKoCoPQIdqfU2jbWuFmVbCLW/kFlZ2K0X3UncEwlpZ0pByQLIT9DhrN1XHy76Ko/KqFUSnFWqObFlpHJOgCy64gIMPPpgrr7ySj33sYzz22GPcdNNN3HTTTcY6S5cu5c033+T73/8+ADfccAPveMc72HXXXQH44x//yNVXX815550XartBkBCemOFFcNyWRVF9gthdcREf0MlPFOJjPF5CfCzLXGwu01r6vz7ZHnP5urGlJltcgUiP2A/0C09Y0mO8t2V9uaVMXX5t94SbrTUWYLez3CrPZGFlO/x67gQhOwKWkDKMOtnx6q0TVNVp1L4ab0QnKsnZVnDAAQdw7733snTpUi6//HLmz5/Ptddey/HHH2+ss2bNGl577TXjvqZpLF26lNWrV6OqKjvuuCNXXXUVn/70p0NtNwhS1Wo1fAoygQW9vb1MmDCBjz50CplOubzv9ovTbdxtgkNZR127VC87sds7ygryAybiI2BaJqatEBCqD2D8KlZM65jzD+LiYb6IGGMe64lJF0Xpr1IU900ErKhZ1jU3e9P/r//iBuSlwT79UOzL/CCrBrNWypgqZGrlv5Y5iWolv6L6pdRRr3QxV8BpGSgX9CoVTa1VpWT06hMlo6Fm9KqSnsIwebVER+0PdJWnK12kUx2hOz1sKDzdyrCh8PQoQ4bNJSwtkeHRx6zHUlRLy67wmC0tkeExl6QbpfOVgqH49Ffyhp0lLDtZfqdYUhkcyrqqO+pQyjOonBnwby7o1WtntMhOM1SdsDmdVhCdKLZVM9UcL5KjDvdx5wfuZOvWrb6ZmKgQ16T3//w/UTsba89QHhjhjx/+TlP3t1VIFJ4YUUiPkK19We09QNzUnWaoPmHtLs/KLo/5uoyy0Npjw1R0GWMe+R5hc9nVHrdQs5vaE8biiqMzM7goPeCo3DI3JbRDNqN6XUXzV3ns5Fh2EpbNrQXOHI85uNxs2APLQe0scCc7jh8BtqCygJuVJYMr2anB7biJlew0YGFFJTrm23ERnaBdkdtZzfFTcoSlHNzIbRzD5Qxqg7Oll8v+LTTGChLC0ySIg1vW/Mwt0ByG/MiyPkFCzpECzj7TVsRBfOzLwthc9We2K1vWQLOxFR+LK2qY2Wxtice7kR7AEWL2s7Y01W65mMrUS84sj9eEhSLga7a12q08Xdb8MKidZYcju1ODPahcHw+W23ErP2+0i7Jb76g4VB0/oqOPYV03RE6nHYlOo2pOVMvKnptLMLpICE+TYT7gg6o+5mVhgs5hVB97WXscJe1RiY9XvsetmsuvhD2w2uNTxRU2zGw81i3TA56VW26kR8s4y6U11VmmjunC4FWVZO7JA/JqrXaFXfERdlYUdUdAVpUVJKQMzoqsppOdAFVYcas6YQPJoYhOxNLyOG2rONUcP5LT5SYZJmg6EsITI7rSJcyVxvYGaFHIT6tVH7dGhmFK2oMQH69gs4wc+dlcQdQeac8esdhmccU6+ahp7q2wlVvGxKu2c6SWsQaYqb3/1bISWOURsNtabv14BJrRi8dvlvQgs6O7QabuyILKditLQNZcUN+GvCJLICzZaYaF1Uz7qlVEJw7bavRJTv0HRistrQRWJISniRAHuazzazPIT1TVJ0zOJ0xJuxfxwRjzsLds5Mhuc4VVe+wWl6vaE0Oux25vWcZspAdwVJYZ70FRWFru1pZOflKhVR4BN1urHVQfr/yOPawMPpVZpqAy2AmOvorMyjJDmtuBhhsLRiE7rVZ1mkJ0Gggix6nmNJvkJGgPJIQnRnSoI5Ql6VFrS/9o5CeI5RVW9YlidwUNOMdJfJyIrvZEtbii5HrClqtHsbZ0ImhtRmhWeWS/ft3gRnBaGVz26rDs1V3ZrfeORd2pwRxUjmJlhanIaoTsRM3rNIvoeK2jj0UjOmHyOaOt5jRKcjrVEdRtqEy93ZAQnphh7mIr6/walfw0Q/WJancFDjg3QHyCESB/4uM+PYVLoFncCGBxxUJ6QFq5pcNKeiqZlFPZMe7XAsymZoRkNEd+xe1EHtbWajbcZmvv0/LS6iwzwqg75ttmK2s0yE6zVZ04cjqtIjrtpObEQXIStAcSwtNEtAP5aVT1iZzzaZD4YCyzBpv98j3OCUljUHtaRHrAvXKrkknXyqPr1pZivuiYA8xitvuS4lB53Po+tXMTQll+x2xn2cPKAnZ1x9Jk0MfKClSR1YZkp1n2ld80EFGJTqO2VUJyvDFSUSlLereFQaXSmnYUrUBCeGJEV3qYkkvb27jIT9i8T1DLy031cbO7fHM+MRMfO9zyPUFsriBqT5hcT5AKriCkx3XOrYxqypLIrS0loy/TA8zuKo/XvD9Qz8z45XjMweU459Ny3S+fyUIFvNQdqJeh67fN6o57bscMI6TcANlp1MIKouqMZaITRc0JY1lFzeXEQXK608NkkiqtUUNCeGKGNQAql97tXww7AfIiP2HDzkEtr7CqT5CcT6PER67m+MHd5hKh5iBqj31rBmy5njBh5jCkxwzZrOqai7VlCTBnnCqPrIN3pzpiHKuyJoTNgLnLsluFlj2w7GZnhVJ3zPaVrf9OoNyOpLFgrGQnZlWnnYnOWFBz4iI5CdoDCeFpIuwHehACFDf5acTy8lN9/HI+loCzZe9qF24f4mNYNxGJj/tM7MHUniAWV9gwsyfpAUvllleeRyg6ULe2zAFm1KrRjNBP5Rko56QnbnOOp6XBZVN+J4idZYZZ3TFPISELKtdv6+pOYLJj6rUTlexEtbCiqjqNVF61G9EZTZKTEJyxjYTwxIiutLxKSyCs+hMH+YlieQVVfbzsrmD9fKIRH79Ac315g2qPbbspiCXX40p6wFGu7pfnMVtb5gBzNaDK43biH42uy24VWn52lrkUHerqDuBehi6xsoC2IjthVZ0w9lWQyqvARKcJtlUjak47k5yu9DBq2r83VoLmICE8McNsCXhVk7QL+WlU9YnF7jJRC2PEg/gI2Cu63Ds2e1Vzeas9jkCz25YluZ5mkB57nsdsbRm2ljnALFF5zBcT2dQngCW30y79eOy9d+yl6FJ1BxxBZagTH3sJOnj32omL7DSq6jTDvmoG0WmVmtPuJCdBeyAhPE2E/UB3I0BBrK92IT921SdMyDlMzicM8QkOM/GRhZplXZqDB5pjJz3iucTeZbOOPI/M2hJj5jJ1Ta2rPOZmlH4THo4W3BoOyuCn7tjnyzKrO+C0sgBpRVYryY6fqhOHfRWK6MRoW0VRc8YSyUkITvsiITwtRFzqT7PIT1TLK4zqM9rEx7uaK5jaY3n2AKQH3C+MnqQHLFMICAXCMvVEyVm1ZSg9pjJ18f7aVR7x+couBMJKsud4jOVNmWLCO79jhggrg1zdsQeVQW5luc2TZa/IguaSnbhUnXYhOq1Uc5pFcpqh4nSNkTnrxiMSwhMjupRhCrWf2X6/UKOoP80mP81WfRx2l2l/zG6/cYptquLjZ3N5qz1+FpdAELVHRnoAS+WWeF5znkfLiuyJ3NpSVGuZukzlEZ+vrBVCGBsramm63xxafZWC8V2ShZW91B3A2VFZZmV55XZM5edByY4n0QGd7MSk6sjsq1YQnaD5nNGyrJpNcsKqOObvUiuTccWSSjrA9DJeqJSSPjwJfGC/WIQhQKNFfhqxvIKoPqHtLrFyy4iPXO3RYVN7zFuIweKykx6xvhfpUUyNFd2sLaNM3UXlqX+23hcBc46n0Uotc0m623M5HuNSji7gp+7YrSxLCXqryE7AYLKbqhPUvoqb6DTDtmqGZdVOJGe0M28J5EgIT4sQhgAFUX/8cj9RyE+UvE+cqo8v8QHEBb45xEeu9ojpKXyzPdBS0qNk63keYW1Bqk50Sv4qT974HPXn9StPbxa85tAywx5WFo0GDXXHVobuVpVlze14kx0z4iA7fsFkN1UnaE6nlUTHy7aKoua0iuQkBGfbhL3ytu1xww03MG/ePPL5PAcddBCPPfaY67o333wz73vf+5g0aRKTJk1i4cKFjvVPPvlkUqmU5W/x4sWR9q1LGQ5cytutDFv+PLebHjb+XLeXHjb+7OhURyx/1m0XjT8zOtQR4886XrKclMR981heLVlObnm1bDn5ZTNl48SYVSvGSVPNVIyTqZLRjJNsStXqJ9+MVvurQqaKplaNk3Y1W6WaraJlan9q/Rev40+6LIWW0S8sWjZVu8ikKHWm9V/eteVaNm26QOm/0qtZVb+QZbO1v1rouHbBk5YiS+B5gS0WSY3oF2alWNaVCeOCXcuklKpW+6ZMLbSbglLKaMRXLKcpllSGTTkY0MlxX6VWEaXlHSXhbnNcxY1erWDYWebqLAFzo0GLugPyoLLNypKFlP0aCzZEdmrHhd3CEmRHP6bShqojyE79OKwfn/7HsX1Z1SA7xndEfG8y4k8z/szfN/P30Pz9NH9vxfdZfKft33f7+UB2zrDf18fk5yC3cxbgep4D73Nkfdv+51pjewHP3/XnH6I7PZRkeEYRY0rhufvuu1myZAnLly/noIMO4tprr2XRokU8//zzTJ8+3bH+ypUrOe644zj44IPJ5/NcddVVHHHEETzzzDNst912xnqLFy/mtttuM+7nPC5IQWAnPUHa4tvLgN3QTOsrjPITxPIKqvrY7S4zZDkfWUl7vIpPqqaQeNlc+gVKv2C6qD0uz14dHgmk9PhWbhXreR57qbqK/t6ZA8yiGaFM5fEKL7cKQchUYHXHz8qy53Z8ys+DkJ1Ggsl2VaeRnE4oRccniByXmtNo+NjtuGylVRVFxWl1L6sE3hhThOeaa67h9NNP55RTTgFg+fLl/PKXv+TWW2/li1/8omP9H/7wh5b73/ve9/jZz37GihUrOPHEE43xXC7HzJkzm7bfYQlQUPsrrPU1GuTHz/KKZHfV1o1CfMSUFTI4l3lVc1mzPeZmhZZAs4fF5VXB5Up6wLiA6tVE+ldYBegU85vp+6tkrAFm0YywmlHQgGImTTZTNpSSDrVkfNayC0Ur+vF4kX3zcShVd8xl6BKykxlwz+3oG5OXnzdKdtyCyfasThj7KmhOJwzRCWJbNZrNaQeSkxCcbRdjhvAUi0WeeOIJli5daowpisLChQtZtWpVoG0MDg5SKpWYPHmyZXzlypVMnz6dSZMm8YEPfICvfOUrTJkyJdb9N6MVBCjO3E8j5Mcv6BxE9fEjPmDO+SiOZfb4cTjFJ0io2SPQbN5UyFyPF+nxCjGLPI9SSlkDzKZmhKga5VKaoqqvIFQe8ZnKyLE5xyOCy3GUptvzO6IcXWZn2UvRLeoOONUdk5UVJLcTlOwEsrDwV3XcQsluZeZxEZ2w+ZxG1Jxmk5zRVHESgjO2MGYIz4YNG6hUKsyYMcMyPmPGDJ577rlA2/jCF77A7NmzWbhwoTG2ePFi/u3f/o358+fz0ksvcdFFF/GhD32IVatWkU7LT+QjIyOMmE6Evb29AHQrQ3TWTv5+PUTMMH9p4rK/GlV/4iQ/YS0vu+rjBrGGPOAsmbZCrI8I9foTn/rEnF42l4aWUY1GdkEsrmaQHhFi1omPbm0ptQuiPcAsU3k61JJneDlueJWky74H9lL0oOpOZrAq6bfTBLITUNXxCiX79dNpBtGJouZEsaxaSXLajeCYfyykldaVeRfLCulyYz9IKuUxF/V1xZghPI3ia1/7GnfddRcrV64kn69f3D/xiU8Yt/fcc0/22msvdtxxR1auXMkHP/hB6baWLVvGZZdd5vl89qqWoASoHdSfuMhPFMsrkupj2lfD7jJGJNNWWO47SY959nEndPWkkknV5rOyb9Ga7XGr4gqT6wlKepSiAqTJDFQokUbJgDqYotxRIwA1q6taTklVHnOJuvic26FrrOi9A7iqO44ydI/cjtpfbJjs+FlYYVQdN/sqLNHxmv4hKtFplpozWiRnNAhOgvbBmCE8U6dOJZ1Os27dOsv4unXrfPM3V199NV/72tf47W9/y1577eW57g477MDUqVN58cUXXQnP0qVLWbJkiXG/t7eXOXPmeG53NAhQVPXHy/oKQ36iWF6NqD7SnI9YKOvlA1SyVVJFXRGxw0x8zPkeLZOqjdea/mVTtQaAsoaFVrXHz+JqiPTYQsx2a0sdhHKHCDB7qzzi8+yv1HM75mPW3HFZIEjzQbcePLLAsrCzoB5WdlN3UsUU6lDKYmOpg7YS9Fpupylkx2Zh+ak6XvaVLJAciOh4lJY3Ylu1K8lphoqTEJzxjTFDeLLZLPvttx8rVqzgmGOOAUDTNFasWME555zj+rivf/3rfPWrX+XXv/41+++/v+/zvPHGG2zcuJFZs2a5rpPL5Rqu5GoFAWq2+tNK8mNGkAovw+6q/e/Zywdv4uMOp82lz2Qu+uMEy/bYLa6gpAew9OhRsvUQs5JJkQHoVKTWllB5qmCoPKIRoQgvi89XHDeOY6/BBoR29Gl5Szm6QBB1R2ZlmUvQ1YGyZdoItykjopIdZ7l5OFXHLafTDKITRc1pFclptYqTEJxtC2OG8AAsWbKEk046if33358DDzyQa6+9loGBAaNq68QTT2S77bZj2bJlAFx11VVcfPHF3HnnncybN4+1a9cC0NXVRVdXF/39/Vx22WX8+7//OzNnzuSll17i85//PDvttBOLFi0KvX/dygidihJq4kMB2RcoCAmKmwCFUX/cyA9gskQaIz9ellcYuytIwNmN+FiUHultN5vLme3xsrjCkh6wNSYsqtI8T62Y3WJtCZVHNCMsZtJGd2wRXm6FreXXcNAcVgZc1R1BctQhq5WVGTD3KjLndpxdlEORHR8Ly03V8bKvIhOdGG2rsGpOs0nOeCI4Yj8VRfNZM0GzMKYIz8c//nHefvttLr74YtauXcs+++zDgw8+aASZX3vtNRSlfkm58cYbKRaL/Md//IdlO5dccgmXXnop6XSav//979xxxx1s2bKF2bNnc8QRR3DFFVc0pODIvoBxkKBmEqAo6k/Y3E9Y8hNG9fFDkJxPVOKj35fYXJmUQ+3Rsln94lt7nFe/HgFZM0IZ6THPrK72Q7krY/TnUWohbUW1qTzoZep2lcccXhafuyVcb5tING7Y7SzQya6vulOzsowSdLeQMv5kJ6yFJVN1ZKHkIDmduIjOaJKcZlpVYZr9hUWc5CZBe2FMER6Ac845x9XCWrlypeX+K6+84rmtQqHAr3/965j2zBth59aSoZkEqFH1J2zuJwj5CWp5BVF9BDxzPhGtLmv/Hlk1l6JP9JlJOdSealbVL8Sm7QVRe+Skp+gIMYv+PFqpXrWlDppeb6eGVrKqPOYS9YFyzvhsxbET98ncnN+R2VmiFF2oO1pJoTqkOpsMmqwsc27HLaTcCNmRWVheqo5XTqdRohPVtgpjWTVKchq1qhKCkyAOjDnCM14QhwrUTgQoivUVB/mJQ/UJS3ysAWa70uO0ubSSPdTsofaEsLgcpMe8HeohZiWT8rW2zCrPsGoNL/dV8pbP2t6AMJZePLX8jhkD5ZxU3RFWVnpQQR1KoQ7qVpY6WLeyzLmdWMiOxMKSBZPdQsmynE5cREdmW0VRc1pJcsYywRlL5KZSSlMtNfbd1Bp8fDshITxthEZJUJQcUBQC1E7kx4wgqo8McRIfq+VltbmgHmqWqT0A5FTDmjKrPlFIjwgxq7VXKErVpdaWTeWxh5dltlYj8OrBI+wsc1jZrO64WVmim7IjtxOR7ASxsLxCyUFyOnEQnWarOe1EclpNcMYSuUngj4TwxIgupUiXKUMUx0SLjVphYVWgIATIb5+CWl+NkB+/vE8UGJeHjGb0qdEnpAxOfECe7xF2kj3UbFV7VKOKyCA7tdJ1O8wXazfSo/QB3XlEiNkoVR+sGpkjL5XHzdaSdVyOAllgWRxL5rCyTN3xtLJKVdT+kj7Bat9wILJjbyYos7D8sjpe9lWriE47kZzRUHHaUb2xZ91SSWh51JAQnibCLdTZCBGKWwVqNQEKkvsJQn4atbxCqT6C+GTQJ9+sLZMSH3uw2ZbvkYWa3dSeMLkeP9Jj5HkAOhVTjgc0D5VHZmv59eMJgz4taxwv4thy9t7xVnfUQb2bcmZAM/Xbqc2RFYLsBLGwZPYVOEPJspyO1dLyJjpx2VZhLKugJCeqitNuBKcV5GZbxJtvvskXvvAFHnjgAQYHB9lpp5247bbbPNvCrFy5kiVLlvDMM88wZ84cvvSlL3HyySdb1rnhhhv4xje+wdq1a9l777257rrrOPDAA0PtW0J4RgGyL8VokaBGCFAU+yus+tMo+TEjSt5Hanf5EB+QqDwGGaqpPbZQs7VvjwpZFSVbJlW0WlxRSE+qqNaqtrJGnsdsbalDNVXCReXpV+u2VrMkfnHcieNBhJXd1B2R2dHL0D1yO3hXY7mSHZ9gskzVccvpBCI6TVJzmk1yWm1TRSU4cR+3CbGRY/PmzRxyyCEcfvjhPPDAA0ybNo0XXniBSZMmuT5m9erVHHnkkZx55pn88Ic/ZMWKFZx22mnMmjXLaA9z9913s2TJEpYvX85BBx3Etddey6JFi3j++eeZPn164P1LCE+boNkkqBkEqFXqTzPIT1h4Ep9MVZ/LCZ34aMWUMU+XPdtTH6vbXFrJpvZkq46pKeyBZJDnemSkxyhXL9bzPIaVVrO21CG9J08lU5WqPAPpIt3pYT2wLLlAieBykG7Lbvkds53lpe4YHZVNVpY5t5PuH7bkdszvjRleeR0/VccvpxM30ZGpOUEtq1aSnPFIcBJyExxXXXUVc+bM4bbbbjPG5s+f7/mY5cuXM3/+fL75zW8CsNtuu/Hwww/zrW99yyA811xzDaeffrrRc2/58uX88pe/5NZbb+WLX/xi4P1LCE8bI04SFFUFChOEbpQAuak/MutL1ugwKPmJUuWVVSt6w7tMRe8DUxs3B5yDEh/rPF11m0tsx6721MvXVVJ9g8ajoE56oH5Bl5IeOkkzDF15S55HWFu6/ZaiOqSguag8wtaKWp7uNq0EWO0s87xsjsqsAUVuZdVyO3ayI9QdM9mR5nVCWFhu9pWs8spBdELmc6KqOXGSnFYRnITctDfEJNkCshkH7r//fhYtWsSxxx7LH/7wB7bbbjv+8z//k9NPP911u6tWrbJM6A2waNEizj//fACKxSJPPPEES5cuNZYrisLChQtZtWpVqNeQEJ4Y0ZWq0J3ST2h+HWSjoh1JUFwEKE71x4v8xIIa8ZEFnL2ID5jIj93mclV7lHr5endHoCouB+mplbqnsX7mlYyCUiM+mggwD6bRVM3SfRlqk3im67aW25QTYWAuRzd/xoadZVJ30oMKStnFyqqFlN0qsgRcyc4oEJ041JwwJKdVKk4zCc5YJTeWYH+qdaFlrax/dxreBjjmixQNfM14+eWXufHGG1myZAkXXXQRjz/+OOeddx7ZbJaTTjpJuv21a9cazYMFZsyYQW9vL0NDQ2zevJlKpSJd57nnngv1WhLC0yS4Va40gwjFGY6OYoXFRYAaVX9Gi/xIA85uxEdNOSu6fIhP/Usa3uIyj4v1lWxZJ1GZFJkBoUDVSI8IMJfrKo85vAw4bK2olVqy49NuZwl1RxmqEzOd9NRL0PXcjn9FVlQLq9SpBAok+xGdRtSc0SQ544HgtIrcxDm3XLvh9ddfp6enx7gvm41A0zT2339/rrzySgD23Xdfnn76aZYvX+5KeFqJhPC0GKNNhMKSoCgqUKsJUBzkJ0reJ5spG92ci+V0eOKTsdtbAjabqzNtU3tsgWZTh2UzuTHDPLu60mf/4tcu7uYAs0XlkdtajVTImI930V1Z2FlC3ekfyhnqjggqCytLlKCrA2Wjk7KbsuNnYWnd+ciqTjOITlg1JyrJaTeCEwe5SYhN89DT02MhPDLMmjWL3Xff3TK222678bOf/cz1MTNnzmTdunWWsXXr1tHT00OhUCCdTpNOp6XrzJw5M9RrSAhPm6BVRGg0SFDQHFAYAhSG/OjbynuSHzP8yE9eLTFcm8zSDGnOx1TSrl+83YlPEJsriNpjt3HMY+bKLZ046XmeTK03D+gX92o2hTakUs5ojvCysLV6tULDHZfNdpaXuiOCysLKygxoqAOVekVW36CrjSUQxMIqd6abQnRaTXLiVHGCEJzRUG9aQW62RWLTCA455BCef/55y9g///lP5s6d6/qYBQsW8Ktf/coy9tBDD7FgwQIAstks++23HytWrOCYY44BdCVpxYoVrtNMuSEhPG2OVhChZpCgOFQgLwLk9nxBra+g5Kch2HI+orIrKvFhAKnak2bYk/S4VW6Z8zwVYWkZ1paCplbRSgr9QzljUtF+VT8uhK2lTzMR/L0zV2gZ/XdMx4q5FF0rKTCYNoLKeim6bmWpAxVbSNlpYwXJ65S7slJVp9QRgeiEyOfYiU6zSE67EJx2JzcJsYkHF1xwAQcffDBXXnklH/vYx3jssce46aabuOmmm4x1li5dyptvvsn3v/99AM4880yuv/56Pv/5z3Pqqafyu9/9jh//+Mf88pe/NB6zZMkSTjrpJPbff38OPPBArr32WgYGBoyqraBICM8YRbOJUKMkqFEVqBkEqJnkR6g+ebVsNDV02F0NEh9whppVyrXKq7rFlcpmjCyP3eKyV24p2TJqv+gObLe2FCoZp8ojs7XC5Hjsdpb4HOp2lsrgUFZXd8qpelB5sF6Cbgkp9w+42lhRVZ1yR+NEJ4ya02yS00qC087kJiE2zcUBBxzAvffey9KlS7n88suZP38+1157Lccff7yxzpo1a3jttdeM+/Pnz+eXv/wlF1xwAd/+9rfZfvvt+d73vmeUpAN8/OMf5+233+biiy9m7dq17LPPPjz44IOOILMfEsIzztBMItRoODqMChQnAfJSfxolPx21mcX94LC7RGVXRglMfMBpcwGG2qP268/lZ3GZSY9e+ZVHHRDTJaQMG0cppaiUUlSGVPozOeOC7azWyoa+SJmPGbOdJVN3RFWWKEG3hJQlZEcgqKojCyWXC8GIThTbKm6S0wwVp1UEp1nkJiE2o4ejjjqKo446ynX57bff7hg77LDD+Nvf/ua53XPOOSe0hWVHQnhiRLeSpluRlwB6TZbYCrSaCAUhQWFUoCA5IDcCFFT98WqZHwaCBLmpPoAz5xOA+IDE5qI+GSlgDTT3DQYmPSqgZdNkBjQqGcV08debEZZLeon6huEOOtSRmsqTd+R4wqBXk4WVa+rOkFqbDd1sZWk1K6toVGSFITuVrjxaVqHcqfraV0LhAZ3sBCU6YSyroCSnFSpOUIIT9bPe1oiNZ5ZNSbkvS9BUJISnRfALc44WIfI6YTRChkaDBDVCgMKqPzJ0qCPGpKZ2mIPOFrsrk67nfFTFk/iAt80FNrWn1rMnCOlJ5VTUfv1CnMmkDGtLDzDr1pZQeczhZfzFrUAQYWWh7oigsrCyzLkdUZFV7euvvxZTZscgO12dFgur3JVBy6QodaYN+6osyI6LfSUjOmFsK7ua40Zyoqg4UQnOWCQ37UZswobzRwvVslKrFm1sG+MFCeFpE3h9gdqJDLUDCYqDAIWxv6KQHwG75SVVfVTVanfZiA9qVe+NkzFNq1AjPiJkrGRAy6p612GT2qMHmjsduR4zqiMjpPoGjd7OWk35MFQeVUErKIbKYwkvVwr0ifddKdId4D0RE4b2VQomOyvDluGCoe4oxZTNyqqgFCt6SLlv0CA7BmnL5erzYXV3eao6pc6Ub04nDNEJY1k1i+SMFsGJm9y0E7EZK6QmQXCEJjy/+tWvuOeee5g8eTKnnnoqu+66q7Fs8+bN/Pu//zu/+93vYt3JbR3tpA7FbY1FIUFBVSAvAtSI+mN9nJz8dKWLlikSoE5+7JYXILe7asQHagFnVS8X13vS1ImPHjLWba5KRiEzoGdT1AH9s/KyuCxBX/QJLcReVzKqQXoqQ3WVZzBvDi9b3/c+rUKXx6FgP07MdlaxpFIZUlGGFLK9KZuVVSazadAoP3fsu8nCqnZ3WFQdcyhZ2FeWjI6J6FQ6al1wbUTHzbZqNslpFsEZbXLTDsQmITTbHkIRnjvvvJMTTzyRxYsX8/zzz3Pdddfxve99z0hgF4tF/vCHPzRlRxO4ox0IUZzWWJRwdBAS5JUDiosAyeBFfsCp+gC66lOzuxwB5xrxSQ8oRr5Hy4geNXWbC2oK0IAoX8fT4qJYMpQeNauQ36KgZdJoKpQHFDQ1TbmQZstwgQ61pE/gmiVwjkc/DvVjQeR3Bso5Ngx3GeoOJaUeVLZYWUXXXjt2C8uu6ohQcllGdsxER1JxFVbN8SM5o0FwwpKbuIjNaJOahNAksCMU4fnGN77BNddcw3nnnQfAj3/8Y0499VSGh4f51Kc+1ZQdTNA4Rtsui0sVCqsGBbHC3FSgIAQoDvLjq/qYiI8956OpVZRynfhoGSy9dNzUHgV9bi2B6vCINc/TN4iaVdGyaXIZPdZsBJg7VIqFmq1Vyeql/hks/Xj8ZkwXn0O/i7ojgsrCylL7S7WQspXs2C0s0THZrOrY7atyhzvRkdlWQdScZpAcN4ITt3oTB7kZTWKTkJoEYRCK8Lzwwgt8+MMfNu5/7GMfY9q0aRx99NGUSiU++tGPxr6DCZqL0VSH4iBCYUhQFBXIiwA1qv4I8iPCznbyY1Z9impN+RGqjwvxEROTaiootdxPZrCqWzpC7cmqehYGDHtIQExBofQN66SnZgWVO3QbrTKYZrCQZUtGV3lm5PqMfjx9WtZ4n9xmRhefTV+lQF8lb5SiDw5lYTBNZqtCthdyW6vkNpfJbi6ibuw3ys/BRHRMFpZZ1RGhZLt9Vc/syImO3bYyqzmNkJxmEpxtgdwkpCZBXAhFeHp6eli3bh3z5883xg4//HB+8YtfcNRRR/HGG2/EvoMJRhejoQ41SoTiJEFRCVBc5MdQfdSMNeQcgPjoHZN1xUcp1YLINbVHyyqoWRWlTyc+5glHBenRTw5dQC3Po6bQ1DSlTJZiochgOcNAOWfMeB704mu1szroHcpTEWXohpWl53bS/fXyczPsFpZQdcz2VRii46XmNIPkNJvgNEJuWk1qEkKToFUIRXgOPPBAHnjgAd7znvdYxg899FB+/vOfezYbSjD+0Gp1qBEiFCYX5EWC3HJAQSdUBP1iaO76LCY6rS+3kh/AID+AQX78iI+mKijler5Hy6R0m8s0S7oKdYurZhlBjfRkM6T7h1GzCpmBdE3l0QPM/b15spky6/PdzMxtpU/Rq7X6PCq1+qppS3VWfyVrqDtKr4o6lNLVnS0a2c1FI6RsLj+HOtnRuvOUu7KGqmO2rwyyUwhGdNzUHC+S02yCE4TcjAVikxAaKzo97N7YUVJAbbCsvLSNlqVfcMEFPPLII9Jlhx12GD//+c+N+TG2RXSmsnSlgh0cbpL/eEKr1KG4iVAQEuSnAoUhQMZzBCA/FsurRnwoQLGkl7aXS2kqQ6qD+BhWl+ixU1N7tGwaVVhctSouo6dNLcScQW9KqGUzlAspygMKpYJK/1COwa6MtFrLD32VPBuGu3R1pzdLZkAhu1WvysptsVZkiXJ6u4VlVnVKHcGITraj5Aghu1lWYUhOowSnmeSmFcRmWyc1Xpk1O3rRmrgnCbwQivAceuihHHrooa7LDz/8cA4//PCGd2pbQJgvCIw/gtQKdSgqEQpCghohQLL8j4BQf8zkp1MdMaa56FezDssL7KpPffoKbUhFU6tUOutWV7mgWz6ljjSZToVMZxq1K0NWWFw1tQcwSE+2luepZFQgRaVToTiYYdNwB+vy3XSna7Ona8PgMR2H2c7aNNyhqztDem4n2wsd68tkNw1Zys/BGky2qzrFCSkjkGwnOqlCWarmBLGsGiU5jag3UchNM4nNtkhowp6jE7Q/Gmo8uH79etavX4+mWRnrXnvt1dBOJXAizJdvPJCjZhKiKETIjwSFtcGCQGZ9daojDNQuyIL8QN3ycthdtQlLtbJeVm4mPiLfI2wuKFjUHjPpUfqGddKT1UvVi1sVRgoqvUN5NuS7jPAy6mbX19OnZemrFFhTnMj6kW5D3cltVchvhI63K2Q3F42KLAO16SHKU7ood2UYmahS6kwZOZ2wRMeN5MjyOHEQnLjJTbOIzXgnNQmBSRCJ8DzxxBOcdNJJPPvss1Sr+qR6qVSKarVKKpWiUhn9plLbMqJ8sccaSWoGIQrbS8gvF+RGgnyDqekhSxm8ufuzWf3xJT+1uamE3VVUMxbiUx7QlZW6zZUhl0npmR0wZiLXn2CAdE4lm1WALOUOvSdPfyHPYHeGdSPdzMpu0TM62pD089GX1cPKhrqztZ7bMYeU7RZWcVKWkUmqxb7S/68RnY6KxbZys6zMdpVdxXEjODKLSvY5ek73MIrEZryRmYS8JIiCSITn1FNP5Z3vfCe33HILM2bMIJVKJkMb6wh6AhkrxCju/FCYaTbsFzYvJUhGgIJkgIKQH6fqU5ESn3SN+Og2V4bcljTlTlUnIDm9QzPZLKmRMmq//hzZiXmyPSmGOjKs7+lman6QNcWJzM5sBja7vsf9lTzrR7rZMlyg0pulsEEhv7FKYf2I3lxwpKw/V3eXJaszMlFlZKISiejYw8dRSU6zCE5CbOpIiEyCZiIS4Xn55Zf52c9+xk477RT3/iRoc4yH7FFcZCioNeZlh4XugitRf9zIj5jvykJ+yhmGa4HjcqEWcC4oVDoVMlsV1AKUOxSyW/VQc9ZUvg56f55MsUxHp0q5I0O5kKavo4MN3R1Mz+Usc5TZ0asVWDsygQ3DHWza2EVmg0p+I+S3aPUZ0GvQpvRQ7spSnJRleKJCcUJKSnTShTJqpkJXYSQSyQlDcBolN3EQm7FIaBISk6BdEInwfPCDH+R///d/E8KTwBdjjSDFYZUFIUJedpgjK2KytUC/EJv7AZlD0KJaqr/2f18lrys/OWpdjWvkJ69P1lks1OyuwQwjBZXy1jTqUEqvxupIU8jkyJkruUbKpEb0hoCFzjTlgkKlU+WNzROZmh+kryPvqnzp5ehZ1vd1k9qUJb9RbzBo5HZqMGd1RiYqDE+xEp1UT4l0RqOjUCSbKTMxP+RKcoISnKDkptnEpt0JTUJexhhKKVAbdGBK48fBiUR4vve973HSSSfx9NNPs8cee5DJZCzLjz766Fh2LsG2h3YnSI0QoiAZITc1yEtdcCNAhvpjIz9C+ZmaH2TDcIdF9SkWMowMqZSGFModCuWCQqkzQ35LWld7+osoG3tJ9w+T25Kh1Jmh3KEwVCjwRvcE1nROhE75fq4pTeS1/kn0beqgsKHWUXlzzSYrFql2d1Ca3GFRdYanWIlOrmZb9RSGDTVnal7v0+NHcsISHD9yE4XYtBuhSQhMgm0JkQjPqlWr+POf/8wDDzzgWJaElhO0Eu2WPXK7oPkpQ14ZoUCqgkkFEgRIXOD7FJ0MdaXzDvJj7u8zmM+wpVDQiU9N8SlNUChOUChuTJOdmCe/JUu2S3/PlWKFjvUptEyGYk+aDX2drJvYzVvlHimBWDsygfV93Si9qlGVpQ6UKXdloWuqhegUe6DYU6UyoUKqUCbXUTJsq8n5QUv42E5yZCqOH8Hxeo/HIrFJiEyCBE5EIjznnnsuJ5xwAl/+8peZMWNG3PuUIEHsGO2y/ijKkJc15qs+uBAgM/khq4eI+yp65VR/JcvU/CCDXXp/nd6hPINDWUZ6VEoTVHIbFIoTBPHRq6rUgTJdb8LwlAwDU/WZz3u1gqE4mbF+pJv+3jz5DQpdb+kl6ADlTpWRSSojNUVHEJ10T5HuQtFQc6bmBx0kx03FMROcoOQmDLEZDUKTkJgECRpDJMKzceNGLrjggoTsJBiXGI2y/jCEyO3C3J0ekuaEzJZYn4kI9GoFfabzSsEyg3lfJc+6fDeDXXr5+JbhAv09OYZ6chQ3ZMjX8j1dZA3Sk9+YYahXZdNwB2+VJkn3743+CVR7M+Q3YpCd4qQs/bPTFHtgeIpOdHJThphYGGFifsggOTNyfQ4Vx4/cyIhNUFLTbEKTkJcECVqPSITn3/7t3/j973/PjjvuGPf+JEgwJtFsBSloZZn0gm668HuRoF6tQJ9SYFYGZmW30F/JszY3gf6uLBuGu9jU3cGGnk4GJhQY3ppmeEqajrUFut7Ue9wU1iisndHDmhkTLd2JBTb0dVJYq7+OcqdK/3aZmqIDpallclOGmNk9wOT8IFPz/XSli8zMbbWoON3pocAEx4/cxE1qEhKTIEF7IxLheec738nSpUt5+OGH2XPPPR2h5fPOOy+WnUuQYDwiyIUxDCnyyw0FIUGCADnJj95McE1uIgOdOdZ3dbNhUgdvbJ7I8GCGoZkZ+udkUAf1bWWf6eDezF50FayzmxdLKjzTjVLSCc7a92QoTdDQesrkekbYrnuA7bu2Mj3XZ7Gr7CTHsK5CkJs4iE1CZhIkGPuIXKXV1dXFH/7wB/7whz9YlqVSqaYSnhtuuIFvfOMbrF27lr333pvrrruOAw880HX9n/zkJ3z5y1/mlVdeYeedd+aqq67iX//1X43l1WqVSy65hJtvvpktW7ZwyCGHcOONN7Lzzjs37TUkSOCHOBQjzwu9LTNUDzjr25pNr9ErqE/LMzuzWbe+OvK68jNhAutHunmjfwK92+fp29RB5/NZlBLwVDf9Geuc6UoJas2OGdhBV3Nm1UhOhzrCTh1vOwiOjNy42nkRSE1CYhKMdyiVFEq5wbLyyjZelr569eq49yMQ7r77bpYsWcLy5cs56KCDuPbaa1m0aBHPP/8806dPd6z/yCOPcNxxx7Fs2TKOOuoo7rzzTo455hiefPJJ9thjDwC+/vWv81//9V/ccccdzJ8/ny9/+cssWrSI//u//yOfDzcDdIIEo4EoF+4uEz8wEyYLTUkL+2sI0OfIEhmhPi3Lm+VJ9E0psKY0kbXbT+AX6h50PJNHKcH0J0rmLbFhL10FHtilyOHveo6dOt5mVmYL3ekhtlM3G6TGTGjkJKY+lhCWBAkShIEy2jsQBtdccw2nn346p5xyCrvvvjvLly+no6ODW2+9Vbr+t7/9bRYvXsyFF17IbrvtxhVXXMG73/1urr/+ekBXd6699lq+9KUv8ZGPfIS99tqL73//+7z11lvcd999LXxlCRKMHrpSWU/yYCYegpB0K0XDZupKD9OpjqBk9EmEpz9RJPebJy1/M1cNoZQgpWp0pYtGLkdXcoKSnWD7myBBggQyRJ4t/Y033uD+++/ntddeo1i0SurXXHNNwztmR7FY5IknnmDp0qXGmKIoLFy4kFWrVkkfs2rVKpYsWWIZW7RokUFmVq9ezdq1a1m4cKGxfMKECRx00EGsWrWKT3ziE9LtjoyMMDJSzyj09vZGfVkJEmxzMM9KniBBggStQiTCs2LFCo4++mh22GEHnnvuOfbYYw9eeeUVqtUq7373u+PeRwA2bNhApVJxlMLPmDGD5557TvqYtWvXStdfu3atsVyMua0jw7Jly7jssstCv4YECRIkSJAgweggkqW1dOlSPve5z/GPf/yDfD7Pz372M15//XUOPfRQjj322Lj3se2wdOlStm7davy9/vrro71LCRK0DcqdaZQJPZa/cqeKVivmHCjnRncHEyRIsE0iksLz7LPP8qMf/UjfgKoyNDREV1cXl19+OR/5yEc466yzYt1JgKlTp5JOp1m3bp1lfN26dcycOVP6mJkzZ3quL/5ft24ds2bNsqyzzz77uO5LLpcjl0tO2gnGLoKUvdu7P5sDy31anjdLk/WS9eJEVg9MpdKr52o27ZqmktnF8titO+q/raq9GV4ZmEynOkJ/2txDZ9hSKm+uIrPneWT7nmR6EiRI4IdIhKezs9PI7cyaNYuXXnqJd73rXYBuPTUD2WyW/fbbjxUrVnDMMccAoGkaK1as4JxzzpE+ZsGCBaxYsYLzzz/fGHvooYdYsGABAPPnz2fmzJmsWLHCIDi9vb385S9/aQppS5AgTkTt7uw2r5e5S7O5JL1XK9BX0aeKWFOaSF8lz+qBqUYX5i29HVQ25uh8vdZUsAPWHWgtZVVKkO2FztfTPFOex//NmMnEnkGjm/L8zg3S3jvdyjBU/EvT+xiK3G8nIUsJEmwbiER43vOe9/Dwww+z22678a//+q989rOf5R//+Af33HMP73nPe+LeRwNLlizhpJNOYv/99+fAAw/k2muvZWBggFNOOQWAE088ke22245ly5YB8JnPfIZDDz2Ub37zmxx55JHcdddd/PWvf+Wmm24C9J5B559/Pl/5ylfYeeedjbL02bNnG6QqQYJWIq55vLwmKzUTG33drOl23pgHS0w50V/Js3ZkAv2VWsfl4Q7Wbuqh0pslvTVNfmOd3BR7oGefDeTVsuN51/7vDPIbUxTWKLCmgy1TCmycUOGVniIv9kxhdlevo8OybAoJt+aD9t5CZniRoTDveUKOEowlpIopUkpjfXRSxW28D88111xDf38/AJdddhn9/f3cfffd7Lzzzk2p0BL4+Mc/zttvv83FF1/M2rVr2WeffXjwwQeN0PFrr72GotRjSQcffDB33nknX/rSl7jooovYeeedue+++4wePACf//znGRgY4IwzzmDLli28973v5cEHH0x68CSIHc2YlNRvFnY7udEfYyU4gJTkiElF1490O9SczNY0HRshv7FK15tFhqZn2LpDCmWXPv5lu+elU0vcNZSn+L8T6Xm5Ss/qYQa2zzMwU6U4QWXjlByberqYPKWfifkh1ue7HV2X+xR9AtRerUCPMkSflrdODOoxd5bb+xRWFQryGSakKEGC9kSqWq1WwzygUqnw5z//mb322ouJEyc2abfGFnp7e5kwYQLrn59LT/eYam2UoEE0g8TY4UdqQE5s9Mc6yQ1YCY5YZp85Xag5w2VVJzq9WTIbVH3yz17oerOEOqArOVveWWDzHhqzdlvPx+Y84ZihHOCHbx7IS8/OZtIzaSY/o6s1Yk4tffJQKE2okJ4yQkdtlnTzvFr2KSfAf3b0RmZGb8WM6Ak52vbQ26cxfZdX2bp1Kz09Pc15jto1ad4VX0Vp8Me7NjzMK1/+f03d31YhtMKTTqc54ogjePbZZxPCk2BcoRXkRYYghMZYNwCx0e87yQ04CY4+ZiU5g+WMPkP6UI7iYIbqkEp6a5rOjSmyvZDbWiW3uWyQneKkLIMzoTq5yPZdW5md0bsy99hmZp+aH+SVKSMMzuyg8HaOwvoR1IEyXW/CyKCKOpSi2JumuLWDvgk5+nvybOjo5K1CDxPzQ3SoJTrUkqH8ABb1R78/ZLxeoQIBTgIWgAgF+VwaJUVRjrmEJCVoZ1x66aWOti277LKLa/sYgC1btvD//t//45577mHTpk3MnTuXa6+91jINlMDXvvY1li5dymc+8xmuvfbaUPsWydLaY489ePnll5k/f36UhydI0DKMFokxIwyhMR7jQmz07TkveEEIDmCoOIBBcgbLOTYMdzBczjBcVukfyjHSm4PBNOlBhWxvivzGOtHJbi6iZXUlszgpS//sNMWeKl09w0zP9bGdutmxf92KvqyjUGRgQp6B2QpQJz3ZzUVyk7KMTFIZmaATn+EpCiMdGUY6cmwpdBiqz4bhDqbmB+lQRxhI5+hU86xBJz9mgtNXKRgKkLDBvNCtFF3fdzdFyOuzbZZClGSOErQ73vWud/Hb3/7WuK+q7lSjWCzyL//yL0yfPp2f/vSnbLfddrz66qtSQeXxxx/nu9/9LnvttVek/YpEeL7yla/wuc99jiuuuIL99tuPzs5Oy/KxLnslaH+0A5GxIwqxAW9yo2/Xm+CAP8nRx/MWkjNYzhhqTrGkMjiUpTKkWohOthcKG+pEJ90/TKpvkGp3B0NzehiclmZ4ClSmlpje3cfM3FZXG2lmbivTu/t4eXKe4a151EGFzIBKft0gysZe0v0dZDfnDeKT7U0xPCVNsUeh0pGmr0NlsJClo1Bky3DBovqYyQ9Av1B+TDkfMwGyo1sZlr7P4rXIPiM/Wyyu3FAjCPo9SYhRgjihqqpruxg7br31VjZt2sQjjzxCJqM365o3b55jvf7+fo4//nhuvvlmvvKVr0TbrygPEjLT0UcfTSpVT3BXq1VSqRSVSrQTf4JtF+1IYOyISmjAn9To25dfdIKQG/N6ZoIDSEmOWc0pl9IWoqMOpchu1XM6uS0auS1l1P4S6ka9UIFsltLkDkYmqRR7YGSqRvfkQabmB405suxkoK+aZlZmC+/o2qxXZ/VmUAfTZAZV1IEs2b4sqZEy6kg/SjGPOpAlM5BFKaVRB1OUO6zEJ10o0z+Uo6swQl4tkzeIj5z8ABYCJN43LwUoCgky1vEgQ37HUSsJkUDY719CkLY92KdQ8upH98ILLzB79mzy+TwLFixg2bJlvOMd75Cue//997NgwQLOPvts/ud//odp06bx//1//x9f+MIXSKfr34Wzzz6bI488koULF7aW8Pz+97+P9GQJth2MBQJjRyOExthGAGKjP1cwcgP+BAecKo4+ZiU5gKHmFMtpPZ9TVixERx3UiY46WKVjvZ7TUfuLKH31DIzWnac4KcvIhBTDU/TszvTuPqbn+pid2Sy92HenKnSnh5iR62NifogtUzoYHiygDqXIDGRR+/PGcyh9w2SKZZSihlLMMjKo21zqYIrihDTlQZ34VDoqFAczZDtKZNUK2UzZlfwAdKp5utPD9FfyBjELqgAZr8OHBBmfTUhrzPLYNiREdiQEaWxAKaVQ0g2WlZf0x8+ZM8cyfMkll3DppZc6Vj/ooIO4/fbb2WWXXVizZg2XXXYZ73vf+3j66afp7u52rP/yyy/zu9/9juOPP55f/epXvPjii/znf/4npVKJSy65BIC77rqLJ598kscff7yhlxKJ8Bx66KENPWmCsYmxSGLMiIPQGNsKSGz053U/2fsRHPBWcfTlTpIDONQcYVtpJYXqkAqllIXoqEOQ3VolM1Alt6VMdtOQTkKKRcjqr0HrzlOcXGB4oqJXVU0tM2VKP1Pzg8zMbfV8H3qUIbrTw0zNDzI8OcObQyrDQ1nUwTTqQIEsGKQn1TeIOlJGKeZRSgUyA2mGJ+r7WuxJUR4SxEdjZEilWCijZDQ6CkWGMyrDaoZBtURH7X+AjkrWQn4ABwHy23/752WQpQAkCBojQsY2xgAhsiMhSGMfr7/+uiWu4qbufOhDHzJu77XXXhx00EHMnTuXH//4x3zqU59yrK9pGtOnT+emm24inU6z33778eabb/KNb3yDSy65hNdff53PfOYzPPTQQw23i4k8WzrA4OCgdLb0qIGiBK3HWCcxMsRJbKC55AaCERzwJjmAQ80ZLmfoHcpTLKcpl9KeREcd1O2rzEDFktWhWDKes9rdQbkry8hEleEpKYo9VdI9RSbmh5ie66MrPWxqDijJvCh6rmZ6ro8Nwx2kC2VKE1SKWxVGJqooxSwqoGzs1Z+3WEIpFskVy6hdedSBDCMT1ZrSk6JcqBGfQpXqkEKloNFfUlAyGmqmIlF9BAkaob+SpStdZKCco1Mdoa/irf7IICNA4nXqjw9GgiAeImRsqw2yQ40iyR61H3p6eiLlcydOnMg73/lOXnzxRenyWbNmkclkLPbVbrvtxtq1aykWizzxxBOsX7/eMjF5pVLhj3/8I9dffz0jIyOWx3ohEuF5++23OeWUU3jggQeky5MMz+hhPBIYM+ImM8Z2Q5AafT+8T7RByQ0EJziAK8nRb1vVHC+io5Qw7KvMYJXMgIY6ULGqOiayQzZLpatmZU1UKE6AyoQK3YUiU/ODeo8cpdYZ2ePC2q0MMzO3lfX5brb0FNg4pDI8JYs6VKvaAjLFDlIba2pRsWRRe6BAupQmXVIodaQoD6ETn44U5WLKID6VjEa5UNatO1V1qD6AL/kRn0FQAgTRSJC+fPSIkHU/xg4pgqSsfyygv7+fl156iU9+8pPS5Ycccgh33nknmqYZjYP/+c9/MmvWLLLZLB/84Af5xz/+YXnMKaecwq677urI+fghEuE5//zz2bJlC3/5y1847LDDuPfee1m3bh1f+cpX+OY3vxllkwlcMN4JjAzNIjUQntiAP7nR14lGcOyPbYTkAM58TkkxiI5SShlqjv6/bl9lBirWrE7/gHWHsxkjtyOqskoTNNI9epl4hzrCrOyWAPmXIt1pnRBMz/WxId/BlkIHpQlpir1plJJCupRFKWqoI+X6fgi1B8gVyyiTO1CKKkoxrWcUSjqBU0optEyN+GQVKiWFSkajqGqG3dVL3lB9gpCfMDD3ABIQipebFSYQRg2CeImQdT/Gnl0WFklZf3Pxuc99jg9/+MPMnTuXt956i0suuYR0Os1xxx0HOKeAOuuss7j++uv5zGc+w7nnnssLL7zAlVdeyXnnnQdAd3e3ZXYE0OfznDJlimPcD5EIz+9+9zv+53/+h/333x9FUZg7dy7/8i//Qk9PD8uWLePII4+MstltBtsiiTGjmYTGeI4IxAYaIzcQnuBAcJKj/2+1rKRqjg/RUUpWVUftL1ksrOqIfqFP5XKQzVDt7qA4ucDgdL0qq9gDWkGju1Bkcn6QGbk+oJbR8bhIi3X6lAKd6ogeKi4U6etQGZ6i1IiLglLM6tkdgP6B+v4AqWKGDNSyPRkyAylGJqmog1AeSlEuOImPVtColOt2l131yaslBssZOtQMHWqJwXLOlfyY1R8/hCFA0DgJguYRofo+tb7v0GgiIUfh8cYbb3DcccexceNGpk2bxnvf+14effRRpk2bBjingJozZw6//vWvueCCC9hrr73Ybrvt+MxnPsMXvvCF2PctEuEZGBhg+vTpAEyaNIm3336bd77zney55548+eSTse7gWMJAtYhSTaaWgNaQGohObCAYudHXa4zg2LchIzjgTXLE/8MG2XEnOsqQQqqYMoiOUqrndATRUYqaQ9URxMJAjeyUJncwMlHVbaMOqHTW1Z2p+X66TVM9+EFMBqqHl/vZVOhgsJCl0pGmOCGNUk6RLuml6plimVQxQwqojozo+zcyQqpYMiwuLauilKqUO9OUSjXSVAZNdSc+WkmhnElbiE9eLTNcFpaXIEBO8mP9nN3Jj7n03RjzIEDgrwLp64QnQdB8IgTbHhmyIyg5GqhqTd6T0cVdd93luXzlypWOsQULFvDoo48Gfg7ZNoIgEuHZZZddeP7555k3bx5777033/3ud5k3bx7Lly9n1qxZkXYkwdhCqwiN8XwtIDb6uuHIDfgTHGiM5IBVzQFCER09r1O3r5RS1VXVEUh1dxlkx8jt9ECxp4rWU6a7UCSvluhKF/W5rdJDOplJVQD3i2tf7XZXepiutK4Q9Rby9HWolAcVyoUUpY4UxUn6+5FBr9jCtH/VkRFSgFIskuruMMb1Mnbd5ip36MRHKD7lQoqUID5qlUpHxUJ8+smRVSuG6gOEIj9+CEqAIJwKpK8XLhdkeWwLiBBsG1ZZOyJVBqXc2DaqDT6+nRCJ8HzmM59hzZo1gF6Lv3jxYn7wgx+QzWa54447Yt3BBKODVhMa43kbIDYQH7mB+AiO/phoJEf/36rmAK5ERynrSo6Z7MhUnVSx7E52cjkjpFzuVCl1KpQ70NWdDo10oVxTd+phZb9pG8yw21o9hWFd5ZmgUCyptTyOAphIj20/BelJ9Q2SBpRimXJXFqVUJV3S1Z5KRpAd/X3QMjXik6lSLad04pOpUslopFTNovpkM2WGy3bLKx7yA+4ECJzHnZcKJLYlQ1Q1CFpHhIznSwhRghYgEuE54YQTjNvvfve7efXVV3nuued4xzvewdSpU2PbuQTNxWiRGmic2EA4cqOvH43gQPwkB/wtK8Azn6OUU1KiI25nBqsoRYmqUwsECwJRHa5lZPI5nex0dVKe0qUrO2Juqx49qExHhY6autNRm71cXKjNF1NZnqEPPePTp+UNW6tDHdG3VSjSX1KodCqUhxTKQyndoiqqeuflkXLd2hqu73cqnyNVLJHq6iRTLFPpyqMUKyhFlVJnmrQRZsYgPuWOFFqpRnyyVbRSimpGcdhdWbVCsWS1vPJqyfG66p9/MPIjOj4LmBsfmhGEAEEwK6y+bnQSBK0nQsbzbuN2WYJ4ELkPzy233MK3vvUtXnjhBQB23nlnzj//fE477bTYdi5BYxhNQmPsQwzEBuInN9A4wQF3qwqCkxxwqjkgt61kREeUmNdJT5V0CSOUrBQrUlVHEAcwkZ1aRVa5K0OpM62Xfhd0sqAVdHUnmynTYbKzwHqR9QtvdivD9GoFw9aamh/U8zNDWSoFjXJBMawo3aLKGCFm0TPWTHqgHmhOA0pWf/9Etkcppih1pmpWlyA9oJX9iY+aqQRWfazHRTTlx/xemo9BcwVcWAJk3qYMjZIgGD0iBIk6lCA4IhGeiy++mGuuuYZzzz2XBQsWALBq1SouuOACXnvtNS6//PJYdzKBO8YTqTG212JyA9EIjv44p4qjrxeO5EBdzQECEx2zdSXsK7uqoxTLwcnOlB7KXVk9BNyZqltZnRrUSrsn5oeYmu83KpeC2lndSpq+2uFqsbUqWYfKUywrtdeYIl1K15sS4k56EDZXVl9XE8QnkwLSaNm6wiOCzUGIj5LRKJfqqo/4zMyqjxf58XxPas0OBezqj/6+Oe0v/bFyBUi8v2bEQYL0x8RDhKA1ZAgSQpSgjkiE58Ybb+Tmm2826upBn0h0r7324txzz00IT4xoB0IjMNrEpv645hAct23HQXLMt+19cwDXfI4X0bGSHT2UnC5VDVVHKWp1C6tYpNqnT/5pJzuAUZGlkx2R29HVnWJPVaruyKqz/C4e3akK1Gwt0C/4A+kcU/ODbBkuoGQ0XeUp6lNIQIp0zdoC/YSlFIs6ucnnHPZWta+fVHcXSh+kcqol21PuTNfIj57n0TIYwWYt4018UqpGUWJ3CdUnrzqTnY2SH3C3v8BdAQJ/FQjCWWH1xzSuBhnbGkVVyLIfiV22zSAS4SmVSuy///6O8f32249yeRxFuluEdiI1AmOJ3EBzCY7++OAkR78dXs0BAhMdmX2lFLXAqg7UyY6oyKp05dGyaauV1QHVbJVUoVzL7uiEp1MdsVZnKUXPCi07RHl6n5anU83TUckyMT9EsaTqKk8pRbmURilBqUO3tkCvxEp1d+h5nr5+B+kxXlsup5e0m9QepVih3KnqXZo79VCzltGbFhqkx4P4kNHQwCA+wu5yU30aRVD1B9zzPwJRCJB5+16IkwSB/NzTahIkkJCh8YVIhOeTn/wkN954I9dcc41l/KabbuL444+PZcfGE9qR0ED8pMbYbpPJDcRLcMBbxYFoJAfcszlgs60gMNGx21deqo4b2RFIdXfVK7K6Mg4rq1yoqTsZjWymzOT8IB0hOxD7oTs9zDq66VBLZDP6JKCVTBUtU9Urqzp0awv0PA/otEqQHjvMuR7AUHvoyqNSRqspaUomhZaFSkZfWyuDVgpGfCplxbC7AFfVx255BYWY4sL8HtnJD7irNHEQINn2zc/hhVaQIGO7bUiGoD0IkThfNIJtviwd9NDyb37zG97znvcA8Je//IXXXnuNE088kSVLlhjr2UnReEafVgGtOtq74UCziA1EJzf6Y5tPcNyex0/FgXAkx3xbpuaAi20FoYmOTNWxBJMDkJ1UPmeQHa07j5ZVamSnbmWVC3oZul3dMdtZYcrR7RA5Hn2+qiKDakl/DpHlMak8OuGhluepoGRVUtms/hrA8RrtpEenS8MoWdWS7dFJi9XmCkJ8yFSpgoX4lEu66hM3/MgPyAmQm/2lb8ObAEFwEhS06WTcJMjYbptYY3a4EaI+bXw3HmxnRCI8Tz/9tDFz6UsvvQTA1KlTmTp1Kk8//bSxXiqVkj4+QXPQTGID7UNuIDzBgcZIDvjncgTc1Byw5XNASnRAHkgWREem6ijFsmXST09VxwgpZ2sVWfXcjtnK0oPKVZSauiNK0YWdJRD2omXO8dhtrbxaYjijku0oMVJWqJRTlMtK7VdqvVQdanmePjxJj1G6brK46M6j9oOWVdBKaUuoWdhcoYhPqZ7zKdc+a7Pd5ab6uEH095FBRn6AUOoP+BMgaL4KpD82nnC0dNttSoQSjB4iEZ7f//73ce9HghBoNrGBxsiN/vjWERy35wtCcCAYydFve1tWgKttBXgSHbCrOJhKzq1ER5rVCWphmXrtVHO62iFyO5UMhpWlqaCpzuyOUHfAepGMchER5en6tvTwcodaYljNUFRViqqGplYpF6oGGRR5HqVU1ZWa7nzNstI/JyPPMzLiGLNbXELt0bL6ZyLUHjvxsSv6ip34qFVAsRCfosnuagRhyI94H4OoP9A8AuT3XEHQLDUIEiK0LSOypZWg+RgLxEbfRnByA80jONA8kmO+bVdzwN22guhEx67qiFJzs6oTiexkM1RzqiS3kzLCu5VOvcmgVd2pqxLiAhb2QtatpB1Sf7eiX6RF5+VBm8pTLdfmxKo1Dix16qXqAGq/Tl5SdFrK1QXZwTQGVosL9DJ3UI1sj7C56FSAKpDSQ882tcfYigvxMQeczXaXOeQMuFZ4yeBFfiCc+gPu9hc0nwDJntMPzSRBkBChbQEJ4WkDtILYwOiQG2g9wdG3525V6Y8LR3IgmJoDknwOWIgOEDinY1d1pBaWvjPByU53B9WasiFyO4JQ6B2J9Yt3StVQMxVD3bHbWfYLXJSQZo8yRF9Fb0LYV8nToY7QoWasKk+tTB1SFmvLjDTDxkSj4LS3zGOp2vslLC6z2lPuygCaPiO7RO1x2Fy1wLOD+JTSloCzsLsE7MQnLOzkR8zqLhCE/OiP81d/IH4CJHtO2fP6odkkCBIiNJ6QEJ4WolXEBuIhN/p22ovgQHSSoz82ePgY/NUc8CA6hoITL9GRqTpgtXOgbvPIyI7I7VQyKcPKEtMvVLNVQ93pKowY6o6bnRUF5olExbb6a7bWoEnlUTL6DOeVjhRKKa1XbBnWVqpGShS9u3J3B6m+QWdjQtP7YSldt/2vgCPb42ZzBSI+AKW0JeBsJz4yhFF9IDz5gXDqD8RDgGD8kCBIiNBYREJ4moBWEhtof3JjbD9mkuNnVdUfKyc5+v1wag545HMgFNHR12mc7Jgv4l5kh2y2puyotZBy3coyug5n9DJ0mboj4Gg2GPEE321qQCggOi8LlUdUbFUzCpVODaWsWKwtc98ftV/vsgw47K3qyIhjklTzeqli0VXtEcZXrRDe9Cj985MRHw1IF1OexMfP7nKD6Ohcv50zLfMnP0Ao6wu87S99e+5NEM0IqwLZn9vt+f230RoSBAkRamckhCdG9FfTUFX8V4yIuIiNvq3w5AZGl+Do2/QnOV52lX4/HjUHwhEd/X48qg7YJtKUXcztZCdXv5BrWQUtW8/GiKBypUOfQkLJaGTViiW7Y7ezRMPBRmDuGyNsLVGibs/yaKWUEWAuF0Dvwqx/FlopbQkx60t1mEmPeK/Mao+5ljSK2mOGIshPWSeRXsQH9OXgDEZHQbPIj/7Y6OqPvl05sYmDAMn2IQiaWSEmfb4aEeqvtq56WSmB0uAlqdp4D822QUJ42hRxkht9e+1NcKAxkqM/PnouR7/treZA/EQHiJXsuCo7ppCylknVS9A76sqEKEMX6k42U78Mx2FndaWy9FeL8uByrTy9Oz3MQDknzfJUMwrVbJVyQc/yaDVrq67AmEPMtfej9r8v6cnljPfWHmjWkcas9ihF5DaX6Yyq76MP8TEFnCEe4gPxkh8Irv5A6xUg2T647UewbbVODUrQWiSEpw3QLuQG4iU4fvviRnD0bcdLcuz3g6g54EJ0TEFkkBMd/baT6NTHQ6g64JvXARyVSXayo2WVGtlJ1y7UtfJzU1DZqu5Yw8rgfhERgWW/mdK9YK7WEp+rvS+PKAcvd4j3WycdSqae56Err4eYxftS+9+P9JjXTQGpkbKjfN1Qe7JOm6uu7tRtLrGPrsRHX8MgPkFzPnbkHTZX/fg2kx9hTYYlPxBM/YFw9pe+3fgJkH0/3PYl+LYSEjQekBCeFmOskhtonOBAOBUHwpMc/X5wNQdC2lYQmuhYx5xER18eTNUBOdkxw1B3gGpONVVkqVIrywgq29Qdc1gZnBe2uOBpa5Uz5NUyRVWlXChTKSlG0z8RshbWVqmW51Ep6yFm6hVZ4n1xNCd0Iz3FEmQzrmqPUqpIbS63fE99zJv4ANKcjwyimaEX/PI+EIz8QGvUH33bwQkQjI4KpG8vIUFjDQnhaRLiJjb17Y4PgqNvv/kkB2KyrSAU0dHXC29fAdHyOj4hZS2bRssqUitLy3irO2C9wMWW3zE6Lmdrz1G3tYB6eLn2+amZCpWMZqg8WrluJZU6UmSo53nKXVnU2jRboly9Wpth3f4eyqq4ZBZXuli2NSu02lw68apK8z2G+iNUn4DEB+LN+QhEIT8QzvrSt9FcAgTxqkBu+xR8e63NBSUIh4TwxIh+LUtViy+03Ai5geYQHIif5MgIjr6d4CTHfj+omgPxER3jdgD7Sl/WmIUl4EZ29DmyVCO3I0rQzVaWpnqrO4DFzrJffOKuPBG2loC5EWGxnCZdKFMBKoBSSuuvoUYmKhlqDQOpz5COtXLLjfSI+36l6+Ct9lQyKaQ2l4T8gJz4UE4ZTQwhHPExqz5iGgv9djDLS78vJz9AqNyPvg139Qf87S99+60jQPZ98tqvcNusv5cDyVxao4aE8LQJGiU3MHYIDjSf5Oj3/dUcaA3RAaSqDuC0sPSdbojs1KeNqOd2Sp2pesA2gLoDNM3OkgWXBcRzDaRzDJat001Ard9RrUzdHFiGFAxWrXkenJVbFkXHg/Sk8vpyN4tLDy7L1R5zqFnQJRG0FsFmN+Ijtt8I8QmKsORHHA/gT34gfvVHf47RJUBe+5agvZEQnlFCq9UbCE5woL1Ijj4WzrKCYLYVBCc6+pi88spy38W+0tdxqjpApLyOX0WWNLdTy+zo/1drZehOdQeQ2lkg6cMTocOyHeYcj4DoyQNYVB7RjFBmbWm1PI1WUijXpowANVDlloCU9NgsrtRImWpOdVd7bKFmndzY8j0xER9wkh8xaSkEU30gGPmRqT4gJz8Qn/oDzSNA0B4qUDOQlKVbkRCeFmA01BtoHcHRn6u5JMd+PwzJAQ/bChzTPwCBiI55vL4sgqoDrmTHNZjsQXZESFnkdvysLHl2Rw8r2+0scWGIM5PQbcvxCBjhZQ+VxxxgtltbeqZH/9wtIWaCkR4ZDMqSzRqfnSA9SlFzqD3pUrVGgJw2lzf0Z5ISH1tJu3m9qKqP3fICJPdbQ3707UWzv/TnCUeAIFGBthWMGcKzadMmzj33XH7+85+jKAr//u//zre//W26urpc17/kkkv4zW9+w2uvvca0adM45phjuOKKK5gwYYKxXirlbAL1ox/9iE984hOR9jMOcgOjT3Cg+SQHwllWECPRkag5EJ7o1Md1ogPOULI+5hFM1l9A8CosO9kBG9mphZRruR2jm7KLlZUulF3VHWhedZYfzD159Gotf5Wn9u4a1hadaUuep7bU8r8b6QEPi8u2jXSxTDWrYld7dFXHGWq22Fzm8nXHbQnxgUgB57hUH6h/b73yPhCM/EDz1B/9uVpPgCAhQe2IMUN4jj/+eNasWcNDDz1EqVTilFNO4YwzzuDOO++Urv/WW2/x1ltvcfXVV7P77rvz6quvcuaZZ/LWW2/x05/+1LLubbfdxuLFi437EydOjLSPfVqOzkiPjEZwoLUkx43gQOtIjn4/mG0F8REd8X8Q+wpoSNUx37dDTnYyNrJTz+0IK0uUcMuCyoCh7gCWsLL9QmRGHIHl7lTFcyoW0ZNHXHhFiTo4VZ5yB6iDVmsLQMmkKHdlUPuxkp5iqV6+HwPpAX+1xxxqNttcbqpPo8RHTF3hBq/S9iiWF4QnP9CY9QXB1R/9uUaHAIG+nwMu2bUEzceYIDzPPvssDz74II8//jj7778/ANdddx3/+q//ytVXX83s2bMdj9ljjz342c9+Ztzfcccd+epXv8oJJ5xAuVxGVesvfeLEicycObP5L6SGqOQG2kfFgcZIjj7WXDUHohEd/b430dFvu9tX4KHqQKxkx1p+bs3tOKwsSVDZ3FXZHlYGp53VCES3ZRnsFypxbIp96VD148FN5QGrtWUuVQfNCDEr1DJT2UxjpMeU6wF7h2bwUnv0ULNzbi6w5nuMsQaJD9RVHzFvlx1uqo9+393yEmOtJj/6NhtTf/Tna5wAQXQSlKB1GBOEZ9WqVUycONEgOwALFy5EURT+8pe/8NGPfjTQdrZu3UpPT4+F7ACcffbZnHbaaeywww6ceeaZnHLKKVKrS2BkZIQR00mxt7fX83nbieDA2CQ5MDpERyzzs6/023VVx7gvCybrL8baCyYE2TEgq8iy5XYcVpaHuiNK0c1hZfsFxav/TmNdlouOHiZGtZZhn/ioPGAJMEO9CzMoRp7HHmK2NyYMQnrMSEGNPBU9ytdBqD062ZHbXNYAs5P42J41MPFJqZrx/Qhjd4G/5QX+eR+In/zo2wyn/kA4AhRmGpW4VKAEzcOYIDxr165l+vTpljFVVZk8eTJr164NtI0NGzZwxRVXcMYZZ1jGL7/8cj7wgQ/Q0dHBb37zG/7zP/+T/v5+zjvvPNdtLVu2jMsuu8x1easIDsSj4ujP2x4kR78fXM2B+IiO+N+L6ACBVB39tsTC0l+UQ9WB4GTHM6Rcy+2YrSxzULnSqQVSd+wXntGE2BcjvFzOkM2UHSqPAvUOzNR68WRSlDqBAYw8D8hDzKJHTxDSAwS2uET5ug5x3MptLucWrHBaX+GIj2VdH7sLgqs+4J/30ceCkx+InvvRt9s4AYqi/ggkBKj9MKqE54tf/CJXXXWV5zrPPvtsw8/T29vLkUceye67786ll15qWfblL3/ZuL3vvvsyMDDAN77xDU/Cs3TpUpYsWWLZ/pw5c+jTClS0cCW6zSA40BwVR99uNJIjG2tEzQH/iiuITnQcYy6hZLHMruroz+kMJkNwCwuCkp16SFlYWaWOlNTKAhzqDiANKwtEnSzUD169eMRzWpsQ1sPLgEPl0cAIMOvjdRvInOeBtGuIOSjpAQJbXOCu9piXetlc/ghGfMhoxvdFRnzMdlcY1QdkKk9j5Aear/5A8/M/AoIADbYww6OUodGuEdU4W3yPMkaV8Hz2s5/l5JNP9lxnhx12YObMmaxfv94yXi6X2bRpk2/2pq+vj8WLF9Pd3c29995LJuNdD3rQQQdxxRVXMDIyQs4+GWMNuVzOdZkfRovg6M/dXiRHvy9XcyCEbQUNER3xvxvRAfdQsn7bqeqA08KCZpEdxSA7pU6lntuRWFkydcdcii4g7CxxAbD/OhWB5Vh68NiCyz3KkHFx6LI0rssa+yhTeaAeYAYc1hakpHkefUn9/yCkB3xyPSaLq5pTSRXLLqQHzA0LxV642VwC7qXtAYkPwXM+ZuID4VUfaC/yo287fvtLf+5ExWlnjCrhmTZtGtOmTfNdb8GCBWzZsoUnnniC/fbbD4Df/e53aJrGQQcd5Pq43t5eFi1aRC6X4/777yef9ycFTz31FJMmTYpMaOwIS3CgfUkORLOsID41ByS2FcRGdIwxD/tKX8dH1YHWkR1Hvx25uqOTHXd1B7ztrFaU1HYrw67Hv7gAGiXqZdV4DVpJn3FcU6uOADOIaqiqNM8TF+kxw7uKS3/eOpxqj2lPJFsJAh/ik6ka35+gOR+IrvqAe95HH2sd+dG3HV79gYQAjXXEN/FTE7HbbruxePFiTj/9dB577DH+/Oc/c8455/CJT3zCqNB688032XXXXXnssccAnewcccQRDAwMcMstt9Db28vatWtZu3YtlYp+kvz5z3/O9773PZ5++mlefPFFbrzxRq688krOPffcyPvaVylY/gI9Rstb/rzQX8kbf/Lnz1v+7Bgo54w/57azxp8Zg+Wc8Vcfyxh/9jGB4XLG+NPvqw5Fxzy3lbms3KzomK2ralnRT9RmRafWMFB0Rk4VU/oUELVpIKyTeNr+pMt0+8qs6lgaCFq6JZvmwZJVYdXyOmLyz0bJjoAWNLfj0mTQrO4AlrCyF5o5CWLQbQs1Kq+WjfyRktH0JnyZqq7yZKqW168JIpitvVeZlEEU6/ka9Pc4m5F/Bi7wDJ8XS7VjoUhqpH6cKMWy6fipmI4rzXS8mY7DUjXgsWtaVsb4Hhjfi9r3RHxvAOP7ZHy/sH7vzN9H8/fU/P0F6/fb/t0H5/lBdg7Rx3OOH1Zu5ybwPqf5nQ/1bed9z6vG9gKep+vPH/56MNZx6aWXkkqlLH+77rqr6/o333wz73vf+5g0aRKTJk1i4cKFxnVcoL+/n3POOYftt9+eQqHA7rvvzvLly0Pv25gILQP88Ic/5JxzzuGDH/yg0Xjwv/7rv4zlpVKJ559/nsHBQQCefPJJ/vKXvwCw0047Wba1evVq5s2bRyaT4YYbbuCCCy6gWq2y0047cc0113D66adH2sd+LU+QQzpMc8JGVRxonpIjG/OyrCCYbQXNVXTsy93sK/12MFUHgllY5jEZ/C60Qt0BHLkdNyvLUHdAqu6Yf2mL3jvCzoqrHD0svJ5THJtSlQc9uJwupgyiA+7WlrCVjBCzIKzZTCSlB/C1uATCqj32poXhEF7xsazvkvOxw0v10e83ZnlBdOUHRlf90Z+/wJA2jkIxLnjXu97Fb3/7W+O+vTLajJUrV3Lcccdx8MEHk8/nueqqqzjiiCN45pln2G677QBYsmQJv/vd7/jBD37AvHnzjAKj2bNnc/TRRwferzFDeCZPnuzaZBBg3rx5VKv1r+phhx1muS/D4sWLLQ0Hm4nxTHIgHNFxIzngE0QGX6KjL6v971Jibrlvs68geFZHf/7gFpZ5TAY7uXH02nHJ7fhZWX7qDugXFPvFo5Uw53i8bC2oX/xEiTpgyvIodZUHLAFmcFpbgvQ4QszFYtNIj3laCnALNIMz2yP2LqrNVX+Me8bHNCoJOIPV7vIKOQOWrI9+P7jlZR0bffKjP0e8BGi8QlXVwL3tfvjDH1ruf+973+NnP/sZK1as4MQTTwTgkUce4aSTTuKwww4D4IwzzuC73/0ujz322PgkPGMNcRMcaB3J0cdHWc0BRxAZnEQHoud09P+t1VcQQdUBaSNBiEHZCZDb8bOyRFDZru4AjrCyG8wnb1mH5TA9eLyaD7o+Jj3sOP7rAWa5yiPK1MGk9NiqtgTpkYaYawRWkJwopMcMOz1xBpoheLYHglZzaZl6yFn09hHfGQfxUaum75uT+JhzPkGquyCY6gNu2Z5geR9oPvnRnyOY+gPhqr/GAuz95ryKd1544QVmz55NPp9nwYIFLFu2jHe84x2BnmdwcJBSqcTkyZONsYMPPpj777+fU089ldmzZ7Ny5Ur++c9/8q1vfSvUa0gIT4zo1/KU3TuFWdeNQcWB1pMcGFtERyyXER1w2lfQWlUHIig7ttyOzMoCaxk6YKg7gNFoUECEle3VWc2GV2k61EOffVrecSEaNNQCucojytRFgBmc1hZQKwd3DzGLbsxhSA8Q2uKqZtXaMeam9kB8oebmEx9oruqjjzeX/EDj1hdYyc9wCy0tpUzjs6XXdnfOnDmW8UsuucTR5gX0Sufbb7+dXXbZhTVr1nDZZZfxvve9j6effpru7m7f5/vCF77A7NmzWbhwoTF23XXXccYZZ7D99tujqiqKonDzzTfz/ve/P9RrSQhPixCXigNjj+TA6BEd+7iffaWvZ1V19P/LclVHf/H6a2mQ7EinjHAhO9bcTl3dAX91x9xo0CusbM/v2EPFcZSkO57T1HHZXJ4ug7lEHXCoPJTSup1XMhEem7Uly/P4kR4BL9IDjVlcWlY1jj0d+ueuj7mpPZY9NhQdPzSD+EC8qg80n/xAa6yvsYrXX3+dnp4e476buvOhD33IuL3XXntx0EEHMXfuXH784x/zqU99yvM5vva1r3HXXXexcuVKS1X1ddddx6OPPsr999/P3Llz+eMf/8jZZ5/tIEZ+SAhPE9EKq0p/nsZJjtt4XGoOBA8iQ/xER/9fbl/p962qjjEWwMKCeMiOAVtFloCZ7FQywqqRW1nmMnTwVnf8MBpyvFuOx3zBMYeXBYTKo4GlGaG9n019Xipnngd01QdqlpOx8ZLDrmqY9JgQzOICP5tLy6SMYz4MBPHRMtV6Ns5YJiE+yAPO4G13AYFVH33M3/JyjocnPzA61tdYQU9Pj4XwBMXEiRN55zvfyYsvvui53tVXX83XvvY1fvvb37LXXnsZ40NDQ1x00UXce++9HHnkkYBOpJ566imuvvrqhPCMFvorOcoVOZmwo1kkB5qv5oB7CBlCqjnQEqID7vaVWEem6uj74m9hQeNkxyg/F9uwqTuAJbdjtrJAElQGV3UH6qXoo2VnhYHYJ3vnZQG7ylMsKXqZOoojwAx1a0uW5xFTQSjUia5BQG3zbkF00iMe62ZxGXsUWO3RX09joeb643yJj6myq1G7C+SqDwSzvMzj9kyaOB/alcw4yQ8EV39U+qXrjVf09/fz0ksv8clPftJ1na9//et89atf5de//rVlzkzQK7BLpRKKzZtLp9NomjVu74eE8LQIjVpV0FqSo4810baC2IiOWMeL6IB7KFlfFlDVAU8LyzzuBl+y42FlAVIry6zuAL7qjl9QeTQaptk7Lpv3xa+HiTHdRO0YNVSeWjNCc4BZKdXfJ6UUMM/jUa4O4UkP4GtxhVV7KplU7Rh3t7nCoXnEB4hV9YHolhc0Tn4gjPoTT1PbdsXnPvc5PvzhDzN37lzeeustLrnkEtLpNMcddxwAJ554Ittttx3Lli0D4KqrruLiiy/mzjvvZN68ecb8mF1dXXR1ddHT08Ohhx7KhRdeSKFQYO7cufzhD3/g+9//Ptdcc02ofUsITxPRTiTHbTysmgMRbCuIheiYbzdqX4l1vFQdsPXWgVElO7ISdHAGlc1l6G7qjoBXZ2VZfkdWoRU3/HI8souJOLbNr9Gs8lBO1ULc9fcNbNaWMYu5R57HpVwdgpEesR4Es7iMx9YCzeHVHjATH3nFVr2SS47mEB8IZ3eBu+oD8Vhe0FryMx7xxhtvcNxxx7Fx40amTZvGe9/7Xh599FFjVoXXXnvNotbceOONFItF/uM//sOyHXMo+q677mLp0qUcf/zxbNq0iblz5/LVr36VM888M9S+JYQnZoxFkqOPRVdzoDlERx9z+T+AfQXuoWT9fvRgMoSzsMCD7AgEIDv65JL2vI4kqIx+oTGrO1BvNGgPK7fazjKXprtVatlzPDJbS8B8MfNSecwBZut8VE5ry5P04CxXN495IYzFJe/Zo++TllVqx7B7tkfLpkjXVCz9uPdWeyyEyEGA4iM+EN7uguiqj/d4Qn7ixF133eW5fOXKlZb7r7zyiu82Z86cyW233dbAXulICE+M6K/kcaMyzSA5+rJglhU0ruaAu20F7Ud09PsRVR1oHdkxVWS5wZrbsVpZUA8qA4a6IyDUHXOjQS+Y7Sw3AmSu0ArTgycqgthaAkFUHsCi9NitLTPpEceJ6HDtVa4O0UmPZdzD4pKXr4OZ+GhZxTj2sex1OJvLrgiZHysjPvqz1+BBfIBY7C4IpvpAuLyPvqw55GekEn91Y4JgSAhPE9FKkuM2HkXNgYi2FTSd6MjXCRZK1u+HUHWgZWTH2F7AEnSw996p+qo7YC1FN4eV2xl2W8uv6sV8fEuzPOjHo5u1RW0aB2OZaEpYO05klVtxkB7wyPXgpCgytUfLpmvHuqySy7yVaPkeP+JTzVaN77of8YHodheEU30gnOWlL2su+WkFlBIoUSNcNVQjVPy1KxLCEzPGCsmBGG0raBrRMa83GqoORM/rQHCy42dlgUnZkfTcAVNQ2UXd8SpFt5MIr/47rYA5x2Md9552AnAoWWaVRwNjni0Bu7Wlz1nlLFUX1pascisO0gMSi0ssiEHtEaFmu80lytjd8j0yRCI+gIWAeeR8AE+7C8KpPvpYNMtLXxYP+QFQGUcMYowhITwxYrCcQ0ZnvEiOeJx8PDjJgVEgOiU76RFEpk5uZERHX0eMWf+3jMVAdPT78VpY9mVuiIvsCHUHnLkdfcxUho67uiPg2mjQJ7/TksCyS6UWuNta9ouM/XtgqDxgNCPUj4SUjex4W1s6bJVbTSQ9lmUBLC4RaI5b7fEKNociPjHkfMDd7gJ31UcfC2952cf1ZdHJj/3xCVqLhPA0CVFJjr6s+WoONC+fA8GJjvm2H9HRb0crNdf/9yE60DqyI7Yl6bUDsn47LlZWCHXHPG+Wm50VJL/TDAQNLguYFSn7L2iZklU0Lq7V+pScJautpZSd1lZtCa0gPWJdCEB6cNITefk6eDcsNG8pmM3VKuID8pwPxK/6gFeux5/8yH5EBCE/CVqLhPDEiP5KhmwLLCtojZoDo0N0pMtapOpAY3kdCEh2TBVZZpj77RhjHlaWrMmgl7ojg5edJUMzppTwgznHE8TWssOu8oAIeTt78+iol6q7wQgxm3r0NEJ6IGCY2Xiu8GqPKGEPa3PJIKvokk5QGjPxAUKHnKFx1ce8LIzqA1byk00srVFDQniajHYgOdDeREe2XtxER9/H0SE7BmxkRyCqlQXm+bLMFTnu6o4IK9vRjt2VgyJoKFSm8sgCzGC6gGc9VJ4awQBrjx7z8RQH6TG2E7B0Xeyt2E8roqk98motK6TKT0zEB/xzPhDO7oLgqg/EY3mB/sM4weggITxNQBSS47UsLjUH4g0iQ/OIjn67yaoOxGZhgTvZsTcWNLYbILdjtrLA3n/HRHJCqjt+dpYxNgqBZfNz92lZi6JjzvEIZSpI7yuwqTy1MnWwKzshrS175ZatMSE0TnogmMXVLLVHh111dA82N4P4AIEDzkAguwu8VR99PB7LS182etVaCXQkhCdGDFVylMtuE3mOETUHYiE6+rj1f8uYJyFqjOjotxu3sOzLvGBpIOg27hNSNsPSb8ek7ghYytADqjsyuJV42xWfVgSWzc/lFlw21pHYWkFyEkXTsW0uUxcBZn3c/IhgeR5L5ZatMSE0mfRAgCoufV+tCKb2BLG5ZMHmVhIfkOd8ILrqo49Hs7zky/TPrlRpsE48BJRSFSX1/7d35uFRVOkaf7vTWQhJCAxLwEEUdAgOQXAhgijMkAcQruJy77gwIg4XriOMuwLugEpcrjMj16sMiruTO4vgMoqCio6KqBEcBOQKg4LKckeGhLAkvZz7R6Wqq6pPnTqn6lRvnN/z5Ektp6qrqruq3/6+9zuHuDdkQKL+ts8mlOAJEBkiR1uef0KH1s4udACJUR0gbWKHtyLLjr03ZSA1lWX28QCwDCHhFt0x971jxixu3Pw7mYQ2zIROx0ira5cQQGqURy9TB5CS1jK2YVgujD562uedTMxAgKKn/bVYKS6v0Z72s9L3YplnpblowsfczpfwAaT4fLTLJifqA/D7fRSZQwkeyQSZsgL8p60AvtJyQK7QMbeVmb6yrBNIYQFpFDv6Pm0mZZFUFpBqVNY7GbTD0+8OTzorW7GXp5cXHE5Ja7G+ZNrs94OpTF3H/NnUom/0KI/5c2gxMTuIHjMiokdvD6SKHmOdYIrLCj3a4yXNlbKMUdHlSfgAUgzOgLyoD+At5aVIL0rwSORgrBCFxanLaAQdzQHSL3TM0yyhY23HFjraMrGoDhC8XwcQEDu2iixe346TURmwlqGbozvmX69Aas/KZnjTWUFhHk/LDVZ5unlsLf0cWT46WpTHbGAGaNEePj+PSOUWwC96AGczs2WdS4pLP3IA3NGe1OuQmuYyQ/P3sCq6ALnCB2D7fIDMRn2isfxJEeUaSvAEACvK4yeaA0hOWwFZI3QAyVEdILAUFiAudnRYvh0zTqkseyeDorDSWXZ4DMsyx9Ey98Wj+3jsPS47lafrEStaWosW6YoBiNvvDaQamK3QhpzgMzEHIXoA8RSXl2iPk6m5/Qi0Nhz+HsuyAIUPwPb5AP5NzoBY1AdQ0Z1sQAkeiRyKRRDl7BxQW+4tmgNkn9AxT/P4dAA5UR3tmNOXwqLBI3ZonQsC4EplAZRUlkN0x6kU3YzT2FlO/h2zYTkTffDQsFdr2UdQN5+z0w+QUCRBjfKY++axY+6F2R79MPt5Qm2x7BI9ANPQ7BbtQft1sp6vc5rL2bfjbGwOWvgA7ukugM/kDIhFfYDk57AQqhPCTKEET0CIiBwg/4WONk1PX2nLvEd1AP4UFpC+yI6xf5tJWTSV5WSmFYGVzsol/44begTLqadz8xdWDDAGFqVBv+5sP0+2iB59f4boMY4hNcXlZGjW0DtpdDc186S5UpbJFj6Aa2UX4Cx8AO/pLoBP/Bxy+A5QBI+68pJJRzQH8F5xBfAJHa2dvty0jNOQbJ4WSV+Z2wuVmwPCKSz7elHcxA6vSdmMWypLJLpjRySdlY3QfDz2tJabeZn6g8M0sKg9ykPH3cScKHIoV5csevRtAHFfD5Ca4tLQvWbu0R7RNBfL30OdFhU+AHdJO5AqfADvJmeAHvXRltNTXkETjgEFPqvgiUNHk7mIEjwSORwrRKTYPB9sNAeQI3QA96iOH6EDpD+qA8hPYTF7UdahiB033w4rleUHmlnZLZ2li6BMdjhoh+Xj0RE1L+tfXEaUBwBsZeqiaO+tds/qn9uUyi3JogeQl+LSj1c02mO5BhxpLkv7dAsfgFnZBYilu7TLKhb1icXsnilFulCCJwCyKW0F+BM65mma0HFcH2BUB8gesePU146OiG8nXdGdfElnOY2eTuuTx35NaPeiDq1MPZXUKI/+mU0UMSq3TB0TAnAUPaIIix5AyNDMivawTM20NBfL32Neb28LJKNDgEfhAzAruwBvPh/t0opFfRTpRwkeibTGI4jZxE7a0lZAYELHPO0uiryZkrVlAsZkwDWFBaRR7Oj7dzAps3w7TrDEjhtuZuVcSGfx9LgMsNNa+jWgGZdZUR4/qS39M234ecyVW7Y+eix7s3VMCIh9XoV8PYAlxQWA29CswR/taT+aZHvXfnlMy11K2QE5wgfw5/MB+KM+8Xj6ei1XWFGCJyDyTeiY28tIX5m3SUdUx76eF26xQ6nI0jGLHRpejMpO0R07PMMtsEh3hZa5NJ0Fb1rLbl42XyNWlMcL8cIQ3Pw8hugBUlJbAAKJ9Oj7TVnvIcWlnY812qNBj/Zoy1LTXCx/DwDhii5Ae7bp48sFKXwAf+kuReZQgkcibdEICtLlzwGyQuho0+7pK22ZxKgOkFVix3gdB5OyjqxUlhO0cbOc0lk6dv9ONkIbSBTgT2vp14QW6YkUxqVEecwC3+7nSRE9ANXPA6RGekQ/u3YzMyAnxQXQoz2sDgtF01zGtARjMxCM8AH8pbsKoAYRzRRK8ASE12gOkH1Cx7ycJnSANER1gLT2ryMqdpxMyjq8/e3wYo/u2OFJZzn5d9JpWBbpbZmFU1qLZl62R8Jo96obbgOM2v08ZhOzW+UW4F/0AHJTXPqxBxnt0abF/T3GdJqEDyAn3aVIP+rKS0Za2grIKqFjnqYJHcB7VEeb9pfCArJL7OiwStC19XKiOzSzst90lhsye1mWhTmtFWSUx61TQpafh6dyCwhe9KSsZ6S4AHiK9iSvBZh99wD8aS5jeYaEDyAn3ZUOwm0EYeJvKItw1N/22QTdVKDwRJtNtMSiBdTUlVnskFiYHtGxl5fbOgzUb9ZQWyilLx39Jg/HrOkoHkMyrb11Opm+Mkd1wm0EBVFiierQys1pKSxa3zq5JHZ0eHw7PKksN1jRHbtZ2S2dlYvoESqRkd3tES9Aq5opKoy5fgmRIoJEIXEQpvofRcwWhds/AwXJedNnxPjs6CnR9s+W62dQENq9Yr5fLOvNRur2e898P5p/kOj3reVe1+/vKDHd99o66/OB8hzR1zk9e7ieT07tk89F7TySz039eWr8gLQ9b1Oex0h9btuf6wD9+d8WK0j5nlCkD3XlA8D+QddvBqbQ0W8qyUJHW2eddno42NvbhQ6tp2RaqblZ6NBSWLS+dfz4dYIQO8zlNrFjNinz+nZYyI7u0Pre0dNZLP+O2bCcKXiOQT8X8zmYz9ke6SqNRKkG70hhHGG9Cq4wARQ6j0hvxix64oVof69DpvUhW3ozGRHMlOgBwBY9+v1mLqFvTb1fwxYBlBQ9ZuGjrzMLH20ZsQgfvT2P8DFek/EjTlT4aG05hA/jOU571uvfB7RIvyK9KMEjkbiD0DHjKHTMSBQ6Yr+CrG20/1ahQys1d/Pq8ER1AFsKy1xybhoiIqiek12HjAAcy8913Hw7ZrwODmqO7thhmZV13IzJ2dDhoFM1GO+xmSNZ9mtgv0auUZ520cOK8tgxix5LerOowNRNAUP0tMMtwDnhET3UaI/pfqRFe8z3szVN7S/aYxY+xj4Foj0p2/oVPoyoDy1S7xT1iSvhkzGUhycAZBuRAX6PjraOY5rrV5Jz9ZW23FnoaMuDMSYDGRQ7lIoslm/HDC2V5QRPdIdWik5L3QD+01nZMmioH0ojrYZ5WRc+9qFgUrw8DuNsAU7vX8hyD5hL1cNR4mhippWrA84dE/rx9OjbG8t4fD2AZ0OzNu3eYaG2TLyaC+D39wD2bZw9Plpb+QZnRebImQjP3r17MWnSJFRUVKCyshJTp05FS0sLc5tRo0YhFApZ/q644gpLm+3bt2PChAkoLS1F9+7dceONNyIW89ZnQiLmIaLDiOYA6Y3osNJXADuqY09fCUd1AF9+nXSKHSeTMs234zoKOsuobMMpumMvRRdNZ+Uidh8PreLMKa1F64naS5THDi21Zaxr9/NY5otSo4T21BaQ/CzahzPxGukBBD1wFF8PYL13ZUZ7vKa5RPw9qdsIRHwA7oiPm89HkV5yJsIzadIk7Ny5EytWrEA0GsXll1+O6dOn4/nnn2duN23aNMybN8+YLy0tNabj8TgmTJiAqqoqfPDBB9i5cycmT56MwsJC3HPPPZ6P1W/FFcCO6Gjtzevo0ynrfFZfacu9R3UAsb51gODMyYCz2LEgaFLWEUllOeIjuiOSzsrm/ndE0cvTnaq1zFEeIFmibh4Oxi3Ko0UDUk3K5vtL+ywk2+n3jd4/j2VkdZfKLTOyIj2Ah04KAWoVF2DtsweAr2iPtj297x79GtLK2AHYhp6wtqFN69sAwUR8AEp/Psq0nDFy4spv2rQJy5cvx2OPPYba2lqMGDECCxcuRENDA7777jvmtqWlpaiqqjL+KioqjHVvvPEGNm7ciGeffRaDBw/GWWedhfnz5+Phhx9GW5u4l8FvxRXAjuho7flz116qrwBnU7LfqI6oMTkoczLAFjtuFVk0k7IOrQTdnsqy4ze6Y4ZWiu4lnZUNhmUdP8fiZl424xjlMRmYnfxVdiFrWefg50lOe6/csq8TxbOvB+AyNMuM9tirubTp5H/a887cJqUdJeKTbCc34gM4fD8o0kpOXP3Vq1ejsrISp5xyirGsrq4O4XAYa9asYW773HPPoWvXrhg4cCDmzJmDgwcPWvZbU1ODHj16GMvGjh2L5uZmbNiwwXGfra2taG5utvylkCaho8+bp/2kr9xMyW796rB6TAYy79cB/IsdHbPYofl2nFJZNKNyCi7RHTO0FA3gPDK6E9lgWHaDdYysgVBZ5uWSSDTlmloqtmzQDMxm7KktHV306JFAbhNzhkVPShu76DEZmo3j4Shfd6vkspuateUwtvGa5tLnzdPOYikY4ZMu9Ovo9y9fyImU1q5du9C9e3fLskgkgi5dumDXrl2O211yySXo06cPevXqhb/97W+YNWsWNm/ejBdeeMHYr1nsADDmWftdsGAB5s6dS1+ZptSVfd6rIRlwT19py52Fjnm9Nu3NqwPkhthxMikb8xypLDv2MnQzenTHjL0UnTedlY3+HVpvyzxjaunpK/u4WrS0lhm7edncEaEeQUvpQLRQ64YwgdR72P5+OqW2ksbc1E4JHU3MnB0TAnLTWwCHmRlwHZZCPydaZ4UAYO+l2Tw8BQAuU7OxnQ9jM+C8jbZOUqpLkVEy+i7Mnj07xVRs//viiy8873/69OkYO3YsampqMGnSJDz99NNYunQptm7d6uu458yZg6amJuNvx44d2oqAIjraeus0X1jWaVosfSUS1ckbsaO/HkPsOPl2dFipLLcydFp0x6mjQR0v6Swe/w6tQisbe1l2gte8zBvlsRuYLeschK1b/zw0E3PWR3oAIUMz4BztMdZzRHt401wp0z7tANo6nxEf4IgSP/X19QiFQrjmmmsc20SjUcybNw/9+vVDSUkJTjzxRCxfvtzSZsGCBTj11FNRXl6O7t2749xzz8XmzZuFjyejEZ7rr78eU6ZMYbbp27cvqqqqsGfPHsvyWCyGvXv3oqqqivv1amtrAQBbtmxBv379UFVVhY8++sjSZvfu3QDA3G9xcTGKnR4sEiM62nr3aft2mYzqANmXwgI8iB2H8nMdmm9HhzeVZVknGN0xQytFF01nZTvloTj2E3+l8U7mZXuJOi3KE4okjCEn7Pe4+b0032/J9z9Zqp4UxFpUx2xi1tYnTcwp5erawQUe6dH3YSyjmJktbRil64CzoVmbpg9NASSjPdpyuqkZANfYXPo0AC5js76OZmzW1vmM+BwBfPzxx1i0aBEGDRrEbHfrrbfi2WefxeLFi1FdXY3XX38d5513Hj744AMMGTIEAPDOO+9gxowZOPXUUxGLxXDzzTdjzJgx2LhxIzp27Mh9TBkVPN26dUO3bt1c2w0bNgz79u1DY2MjTj75ZADAW2+9hUQiYYgYHtatWwcA6Nmzp7Hfu+++G3v27DFSZitWrEBFRQVOOOEEwbOB9qHWb4iAhE7KugCFjn1b3gosgCF0gOwUO2YYFVk0306yTbIE3VjG6HPHYlQ2Hx9HdMfJt+M1nWX2xmSTYZmGPnK6GT2tpY+ezkprlRW0oSWubW9PaZVEYvSKLXulVoQY42yZ72Xz+0xLbSXTMVr/PLRBRhOmFJCBntoyEZToAXymuExVXIBznz1AMsWlTdMruYCkMLKPwq619ZfmAmzihrOiS2vrQfjYIz55SEtLCyZNmoTFixfjrrvuYrZ95plncMstt2D8+PEAgF/+8pdYuXIl/vM//xPPPvssAKREfJ588kl0794djY2NOPPMM7mPKydiawMGDMC4ceMwbdo0fPTRR3j//fcxc+ZMXHTRRejVqxcA4Ntvv0V1dbURsdm6dSvmz5+PxsZGfPXVV3jppZcwefJknHnmmYbiHDNmDE444QRceuml+Oyzz/D666/j1ltvxYwZM5wjOC7ISF35qbzS55PTYqXmvBVY2rSHqE62ih3Oiiwds9ihlaDTIjlMo7KH6I5b3zv5MnaWDHgiXvbImf3am4ecMOPUNw8rtWW0ofTPo/13NzEDwaW3gPSmuLRp90ouXlOzvm1ymv5cdXuO8vTho60XTHXlOTNmzMCECRNQV1fn2ra1tRUlJdYfJR06dMB7773nuE1TUxMAoEuXLkLHlROCB9CqraqrqzF69GiMHz8eI0aMwO9+9ztjfTQaxebNm40qrKKiIqxcuRJjxoxBdXU1rr/+elxwwQV4+eWXjW0KCgrwyiuvoKCgAMOGDcPPf/5zTJ482dJvjwjhuByho8/T1vF7e5yrr3iHhdCWp6awWOXm2eLXAeSIHR1R344Zpz53ZER33MzKLPKh/x3RczBfo1LLtLlqK2Yp/9e9PBYcytRp7zVtgFE3P49Oroge3tJ1nvJ1lrdHb6PD4+3x4++xz0sRPvHcFD72yuRWh+dzQ0MDPv30UyxYsIBrv2PHjsWDDz6IL7/8EolEAitWrMALL7yAnTt3UtsnEglcc801OP300zFw4EChc8iJKi1AU3KsTgaPOeYYEJL8YPfu3RvvvPOO63779OmDV199VcoxAv5SV/b5dPp07NtK8+oAwlEdWjtRZIkdUd+Osd4hlcWK7pjNsV6jO3byQdSI4ietVRKJWoabKIrE6V4ec6+6RQTaOxeypbG0/7ypLVanhKzKLSAz6S39tajt7L4eICXFBUCat0e/ln7SXAAc/T36PC3tpW8H8Ke60kW4LYEw8TekRTiqbd+7d2/L8jvuuAN33nmnZdmOHTtw9dVXY8WKFSlRGyd++9vfYtq0aaiurkYoFEK/fv1w+eWXY8mSJdT2M2bMwOeff86MADmei/AWCkf8RnR4fkWwU15ZJnY8pLBo7UTh6kEZ4BY7PL4dY57VL4vp54VTJ4O06I4Zp+iOjlM6KxvL0d1gjd9F64/H7dxoaS0ZUR4ztDQWb2orOZ3aKWE2RHo89dfDmeKSGe3RlstJc7HaUecFIz65xI4dOyzVyXPmzElp09jYiD179uCkk05CJBJBJBLBO++8g4ceegiRSATxeKo/sFu3bli2bBkOHDiAr7/+Gl988QXKysrQt2/flLYzZ87EK6+8grfffhs//OEPhc8hZyI8uYKfiI59nr8PniwQOkDGU1iAu9hxKj83joEidnREUlnOndJRytBdojs8HQ2KprPMMDvzS9OgobS+eFKORUKlFmCt1tKRFeWhGZjt0QBa1VZBlFj656GZmHkiPWZkR3oAD2ZmgKuKC8hMtAdwjtzwGpup8y4Rn1ykoqLCMlIBjdGjR2P9+vWWZZdffjmqq6sxa9YsFBQ4378lJSU46qijEI1G8ec//xk/+9nPjHWEEPzqV7/C0qVLsWrVKhx77LGezkEJHomEoyHA9H7yChb7PH9pOl3oANmbwgKyTOy4VGRp/5O+HS+pLGYZuglWdMduqNVhpbNYOI6rxVGhlW198OipK8sywbSWDq0jwrao9uZRK7YKiaUzQvM4W05fgtqyZGrLLHr0dfZOCamVW2Yo5epAZkWP/noApKW4tOl4UhBSKrmAZIeF2nKxNJe2X7p4cRU6PMInj6u0ysvLU3w1HTt2xA9+8ANj+eTJk3HUUUcZHp81a9bg22+/xeDBg/Htt9/izjvvRCKRwE033WTsY8aMGXj++efx4osvory83OgYuFOnTujQoQN4USmtAOA1FlPnuUOpzlEdng4EnSqw3IzJgPcUFpDdYsfJpGxeFjeED38qKzlPKUMXiO6IprPs5eiK5DVyMi+bMV97x4qtdlgG5mQbemorOZ1MlwqbmAFqx4SA/PQW4Hxv+0lxiRqaRSq5tHXe01wAuI3NTtsqNLZv324xJB8+fBi33norTjjhBJx33nk46qij8N5776GystJo88gjj6CpqQmjRo1Cz549jb//+Z//EXptFeGRSIghdOzzvIbk1O3E0lfauuxMYdHaecHtAc7T1w7LpEzz7RjTDqksM45GZRO80R2aWZk3nZVL/h2v2IeZcIPHvMyM8rQPOQFLKkuL8hi/7m2//pPLtEhOXJ9us0Z1tGkBE7N2sBmN9AAeUlxAINEeQH6aC4Crsdk+nxLtSbli+c2qVauY8yNHjsTGjRuZ+zAXJPlBRXgCwF9Zo9N2RFjs+InqAC4prCwWO34rsuwmZR2zb8cplWWZdzIq60iM7nglFwYMdcLSWSKjGk1fx+qXiGVe1qFGeUzYozxOqU3rMnqpun2Q0WQbFxOz+fi8/BAQxOkeFuqvB+Dus0ck2qO302FFe9wGJbXPe+1WBNB+GCsygxI8EgnH3MOclrYe01ci41/Zt+eN6nD3rWNLYeWD2NFh+XaSbShfam5GZVsng16iOzp6dEcknXWklKo7pfJoaS0d+yjqOsyKLVtnhPo4Wzpu6U7aqOrJdvROCbXX8Ve5ZV/vFZEKLr8pLsD6rKJ1VgjwpbnMz09tHejbCz7HzdOs+XQQjhHjXDz/xXLXaG1HCZ4AkPfLwL3zQG2dsynZLaqjzctPYQHOfp1Mix07biblZLvkF5NbKsvRqGw+Xg/RHR2vZmUW2TikhL1KjOcYRVN3tGvpKcpjK1NnDS5qntcxl6q7+XmyTfQAHn09QKrokRTtAew/9pyjPbROC1O2l+XvURGejKEEj0S8GpLt8yLpK79RHW5jsmAKKyhzMuBf7IiYlAF6CXpymp7KSs7Lie7oeE1nefHvpKskPR3Y01qsPnk8R3lM8BqYk+usqa3kdGr/POZlVLJM9AByU1xBRHu0de5pLm0elmkvz3hFZlCCJwC8+nS0eff0lbYuuKgOb8k5kF6/DuBR7JiPg9OkrONUgu6WyrIble1l6CL97gDe0lkK9wo1/RqmK8pD+9wkp+mpLWE/j5lcEz0cKS4gNdqTnPYW7ZGR5vISxVekFyV4JMIjdFg+HVr6CgguqgMIprAy6NcBfIgdn74dWgm6Me2SyqJ1Mqjj1qsyQDcr86az3Pw7uWxY1uE1LvNCi6B5ifLwGpjN8zr21FayTVL0cKe2tIM2JrNR9PhJcdHK17V5sWgPwGdq1veRnGZYElyEjyL9KMETAF59OgBfnzraOv9RHVkpLCD3xQ7Lt2NMC6ayjGkP0R03s7IIR0I5uh3WOdvTWm7mZR2nKE/YVm0nYmAWTW15MjEDWS16qG1dUlzZFO3R5mGZ5o3wK9KLEjySEcnhek1f0UrNtXXeojp+U1iZFjuOCIgdHZpvxy2VZcatDF1HNLqjY+97R1Y6KxsMy7w9OHs9Vrdro19bpxJ1HXuUx4ItymPGS2rLXqputBE1MWsHbkymU/RIMTMDKc8mHkOzNu8/2sMyNfOkuQAos3IWoASPRER8OqLpK22dc/pKtNwcEEthAWJ+nXSKHZ7yczt2scPy7RjbMH+Zm9tRjMr6sdqiOzRo0R1Rs3I+9a7s1zzt9VrQzMuyojzmaVZqKznt7Oexix7jdV1Ej5kgRQ/gI8VF8/V4NDT7ifZo6+SnuYJGP1e/f/mCEjySkZG+0tZ5i+rwGJMB8RRWuv06gH+xYzkuDpOy3bdjtHNJZRnTNKOyjkN0R6QUXcce3eFFxL/jJDKybRwtwJuPhyetpSMS5aENOWFMUwzMyXb01BaPn8cOKaKIfI6OCbNF9FDb+khxBRXt0daBug9t3tmzqcgMSvBIhJ3OYvw68GhKzkQKC8gdsSNqUjaWCaaymEZljugObym6k1mZlc46Ev07OrRz95LW0qG9N/YojwGlM0IdmoGZPi3RzwO4lqsDuSt6AO/l66xoD6uEnSfNpc3DMq1SW5lDCR7JiKavRE3J2jr+qI7MFBaQ5WLHfDwefTs6rFSWeTnTqKzjMbrj1aycT+ksWfhNazmVqOukRHl0KGXqyWlbVMclteXbz2Mml0UPZ4oL4DM0a/P0aI/eVkdWmkuRGZTgkYjs9JW+n+Q6iVEdRgoLyIw5GfApdgRNyjTfjlNvyuYvJFqljQ4ruqOLHZHojo6TWVkG2WBYFkXGMTultXijPPp7x4zytEMrUzemOVNbOqzUlgwTM5BZ0SNcug5IS3HxRHsAOWkuRfpRgkcyNKHj1ZQceFQH8O3XyXaxY4fm20muc/btuE87lKHbzaz64XJ2NOhmVuatzsr3zgidfDxe0lp23ErUdVhRHh03AzN9OjW1ZR96QluXe6InW1Jc6Yj2mPejyAxK8EjE/oHOZFQHcDEmA1mTwgL4HqS8YscMj2/HXoKebGeaZvwi1+EpQ3eL7vjtc8eMk38nFzsclDHMhd9UX6lRqeU9ymNMmwzMrIihU2rLWE/x87DINtEDZDbFlY5oj1OaS5F+lOAJgHRHdQAPxuQcEDsiHQua8evbsaeyjG0cPBe8ZejGIbtEd3TsZmWedJYM/04+jaHlBm9aS3aUR8fJEyaS2hLx81jIF9EDCKe40hHtYaW50oV+Pn7/8gUleCQSjmUmqiM7hQVksdgxI1CR5ebb0aGlsthVNfxGZafojo6oWVmNneUdr9eMFeUxwxvlSU6DY5qe2rKvFzIxc4oeGukUPX59PUD6oj28aS5F+lGCJwBoQifoqA4gL4WVTWInBQ/l5zy+Hb+pLAMP0R2WWdkr5nRWvgoilnHZzcfDgse8rEPriNBvlIe13N4Ls7Heq58HoEZJAffemO1tZMB6xshKcTkZmgE50R6AneZSZemZQwkeybgJHS9RHW2eHdUJMoXl1N4PvGJHtPzcCZpvR4cnlUWfDi66k650VrZVaPnp2JDHm0S7Rva0lhOsCFxQUR57akuHVaruS/Q49MQMpFf0BJniAvgNzdq8/GiPIjMowSMRp/SVto4vqpPSVsSYDLhGdQBxv07WiR0BkzLLtyOSypId3dHxGt0p81iW7sewnI29LPvBb+SLNdyESJTHycDsltpy8/OwkNlHD62dDERFT9ApLpFoD6+pWZFelOCRjN+oji9jcpb7dYBgxI4f3469N2UdejrBalROthWP7vCUotujOzTyNV0lG960Fq95WVaUx09qy1jmwc9jRkblFq2dDERED7U9TfR4NDRr83zRHntb+/eCIjMowSMRN6HjJ6ojbEzOMr8OIE/smJHt2xFJZcmK7vCalHk7G8xX/45o9ZjTubPSWqL4ifJ4NTCLlqrzVG7lqujx5OsBsiLao0g/SvAEQIr/RlJUBwg+heXU3i8yxY5Xk7KTb0fHnMqyL7MvN9IRkqI7ItC+nGUNJ3EklaTzwmNe1uGO8ggYmO29fCfXu6e2RERP8iRyR/QAwfh6ALahWZv3Hu1RZAYleCQSdFRHNIUFZIfYcUKG2HGDx7dDEzXOXgpKKktCdCcd6Sy7fyfbDMtesJ+DqEeJdg3dRlC3v2esQUWpUR4BA7Of1JZbp4QyytWzXfQEkeJyKl/X5vmjPekgHI0bx+z5L5r7zwkdJXgCIq1RHcC3X0ffJgiEh4ww4yJ2eHw7ybbeU1muRuWAozsyx846UnHz8XhNa+no761rlEfHJcrjJ7UVp37+rVEeM7kkeqT4eiRGe2gpLpESdkX6UIJHMiJRHW1eQgpLgl8nq8QOo/zcTLpTWY5GZf08OKM7Om7RHRpu6ax89e94RcTHQ4PXvCw7yuP+WTSvtwp5L34eM6J99ADpFT2ABF8PwCV6ZEV7gNQ0lyL9KMEjkXBMLKqT7yks32LHR0WWn1RWsp15mmFUFozu8Jais/reUWJGHqy0ligyojw8ffOkLKNEdPz4edzK1YHsFT2AT19P0NGemBI+mUIJngAIPKoDZHUKC5Andsw4iR07TmKHN5Xlxahsju7oYocV3VE447e/H199DXEIHZ4SdR3RKA+PgZm1zCm1FUTlFsAveoIiEF8PINXQrM2rZ0E2oASPRKRHdQDPKSwgP8QOj0lZxLfDm8pyMiobUJZRf8G3Y4/ueDEri6SzUtr5NCxnS6eDtCoykXOxpPw8prWcsJeos6I8XsrU/aS2ePBSuQXwiZ6gojyAuOihbsPp6xFNcbGiPYr0owRPQEiJ6nhMYQHZLXZSEBA7Xnw7xraCqSwZ0R3al54IXtNZXlJe+VqS7vV6yTKK06I8BhSRzGtgZqW2vJSqW4+B38QMZL/oyVSKS5tX0Z5sIWcEz969ezFp0iRUVFSgsrISU6dORUtLi2P7r776CqFQiPr3xz/+0WhHW9/Q0OD5ON2iOoCcFBYgbk522kYWvGJHpPzcjFffjp9UllsZuo7M6I4is7DSWrzmZZlRHtHUlkipOsvELCp67GRC9ATi6wG4RI9ItCdd6Mfl9y9fyBnBM2nSJGzYsAErVqzAK6+8gnfffRfTp093bN+7d2/s3LnT8jd37lyUlZXhrLPOsrR94oknLO3OPfdcT8fIE9WRlcLy4tfJRbHjZlLWcRM7XlJZyWOgpbQoX1r6qfiM7vCmsxTO+PHx0GD1h8QDV5SH08DsFqHU8evnMeOlXB1Iv+gBJPp6BFNcgHi0R5FeckLwbNq0CcuXL8djjz2G2tpajBgxAgsXLkRDQwO+++476jYFBQWoqqqy/C1duhQ/+9nPUFZWZmlbWVlpaVdSUuL7mI+UFBbA/wBzG/kcYIsdGk6dqgGpYof3i4I3uqOLHVpllh2n6A4Lt3SWV//OkY6Tj0c0rcUb5aFBjfK0Q+vywG9qi9fPw125lWeix3EbSSkuVvm6In3khOBZvXo1KisrccoppxjL6urqEA6HsWbNGq59NDY2Yt26dZg6dWrKuhkzZqBr164YOnQolixZAkK8lw26GZMBuSksIDvFDnfHggBX+bk2LV6CbmzLiOCwjMpuZeg6Tt4dt44Gvfa9w8LNs5IPPSzbcTsnUU8TT1qLF/2zICPKoyOa2tJxi/JYXoO3XB04skQPIGxoBlJ/BB8J1NfXIxQK4ZprrnFs8+STT6bYSmhBh02bNuGcc85Bp06d0LFjR5x66qnYvn270PHw9c+fYXbt2oXu3btblkUiEXTp0gW7du3i2sfjjz+OAQMGYPjw4Zbl8+bNw09/+lOUlpbijTfewJVXXomWlhZcddVVjvtqbW1Fq+nGaG5uBqB1442C5CV1FToAV1QHEO9fx2kbmXgWO5zl52a8+nZYqSxPRmX9nGzRHVn4TZ0ogqNjpBUHYuwv6dJIFAdjhSiJRHE4xva4hAsTSETDCEUSIDHrb89EhCAcC4EUEYTa2j/bESAc0z6rYdtjgrUuXggURLV7I9xGEC8MoSBKkCgKI9yWQKIwhHCUIFFUgHBb3FhuhhRFUr0cRUX0H2vQ7nme5w9vO6+Q1lbH55T+/LQ/r6jb6M9l87OrrS3l+RVqjVlEYagtZnmehdtiORJm8M/HH3+MRYsWYdCgQa5tKyoqsHnzZmM+FLKK9a1bt2LEiBGYOnUq5s6di4qKCmzYsEE4G5PRSz979mxHY7H+98UXX/h+nUOHDuH555+nRnduu+02nH766RgyZAhmzZqFm266Cffffz9zfwsWLECnTp2Mv969e6e0kZnCOhLEjkhFlhNOYoeVynIzKhv4iO64mZVpuFUI8aazeMmlCi0Zx+o3reVkXrZjNy+LRnl0eAzMIqmtdJiYM90xoY7bs5Db1wN4TnFZDM15NDaVEy0tLZg0aRIWL16Mzp07u7YPhUIWW0mPHj0s62+55RaMHz8e9913H4YMGYJ+/frhnHPOSQmEuJFRwXP99ddj06ZNzL++ffuiqqoKe/bssWwbi8Wwd+9eVFVVub7On/70Jxw8eBCTJ092bVtbW4tvvvnGEsGxM2fOHDQ1NRl/O3bsMNZ5MiYD0lJYrO1kEZTYMcPqXNBLCTpvKss4pjRGd3hTJEdS78oy+v3x612ipbVkR+BSvDyMzgjdDMxO61g+Nxb5JnoymeICUn8E5xrNzc2WP9Z35IwZMzBhwgTU1dVx7bulpQV9+vRB7969MXHiRGzYsMFYl0gk8Je//AU/+tGPMHbsWHTv3h21tbVYtmyZ8DlkVPB069YN1dXVzL+ioiIMGzYM+/btQ2Njo7HtW2+9hUQigdraWtfXefzxx3HOOeegW7durm3XrVuHzp07o5hxAxYXF6OiosLyB3hMYQFS/Tq5LHZYJmUvJejGtgKpLKZRWWJ0h4aM6iy7KJJhWM6WTgf94kcwyjAvc0V5dChl6jp8n+PUNqJRHstreqzcArJH9ACSfT0uVVyAu6E5aHTh5fcP0CqfzZmNBQsWUF+zoaEBn376qeN6O/3798eSJUvw4osv4tlnn0UikcDw4cPxzTffAAD27NmDlpYW1NfXY9y4cXjjjTdw3nnn4fzzz8c777wjdD1ywsMzYMAAjBs3DtOmTcOjjz6KaDSKmTNn4qKLLkKvXr0AAN9++y1Gjx6Np59+GkOHDjW23bJlC9599128+uqrKft9+eWXsXv3bpx22mkoKSnBihUrcM899+CGG27wfcxeozpAdpqTgfSLHb++HdGoDtWorGP7Je43usMyK6uR0b1RHopjP/GW7iovOIT98Q7adPgw9ifcvQFlBW1oifsXgylensIEELX+FtW9PIlCgnDUGrHRfTvG/3Y/j7WN5tPx4+dJFEUQbvek6F/cpDiSfNaZPT1FhZbnm92rEyoppj7Pgvb0AN59PfrxWWiLpvp6AMtzjubryUV27Nhh/LgHQA0K7NixA1dffTVWrFjB7a8ZNmwYhg0bZswPHz4cAwYMwKJFizB//nwkEtrnb+LEibj22msBAIMHD8YHH3yARx99FCNHjuQ+h5yxTz333HOorq7G6NGjMX78eIwYMQK/+93vjPXRaBSbN2/GwYMHLdstWbIEP/zhDzFmzJiUfRYWFuLhhx/GsGHDMHjwYCxatAgPPvgg7rjjDs/H6TeFlW2dCeqkQ+yYcRI7TjiJHZ4+dxIU2c/qZFBHNLrDwi1V4qUcnbqfPKzQ8grPtRNNawUZ5WH1wCyS2vLq5zEjWq4O5E6kB0hPiivXsGc1aIKnsbERe/bswUknnYRIJIJIJIJ33nkHDz30ECKRCOJx9+dPYWEhhgwZgi1btgAAunbtikgkghNOOMHSbsCAAflZpQUAXbp0wfPPP++4/phjjqGWk99zzz245557qNuMGzcO48aNk3aModYYUGD7hZnjfh2AX+yk4NITK8A2KdPgKUE32jJSWUYbWyoryOgOj1nZjOpsMLsoLziM/XH6r9bSSCsOulRwsXCM8hQSICWio0V56JEca5THHv0RxR7p0aM8AJwjPWZyNNIDaM9e2jPOsYrL/rxzqOLKnW9dcUaPHo3169dbll1++eWorq7GrFmzUGD/fqQQj8exfv16jB8/HgBQVFSEU0891VLFBQD/+7//iz59+ggdXx5f+gwTcAqLtZ1MRMQOs2NBwYosbTq4VBaXUVlCdIeF7HSWV/9OLlVo6ZSHC7A/4f5rsTzchv2JItM8X7qKF1Zay16iXhKJ4XAsgqLCGNqiERRF4miLFSBSGEcsWmCIHhq0MnVjnT2lxRA3oqkty7YuosfAXq5uEz128kr0AFwprnylvLwcAwcOtCzr2LEjfvCDHxjLJ0+ejKOOOsrw+MybNw+nnXYajjvuOOzbtw/3338/vv76a/z7v/+7sY8bb7wRF154Ic4880z85Cc/wfLly/Hyyy9j1apVQseXMymtnMJnCgvIcbFjh7Miy7rMn9hhpqsYRmUDm1HZa3THq1nZjFM6SyEXp/J03rSWW4k6LykVWxxl6jzDTvhNbVleX8TEbENkgOF0pbe8mpm9priOZLZv346dO3ca8//85z8xbdo0DBgwAOPHj0dzczM++OADSwrrvPPOw6OPPor77rsPNTU1eOyxx/DnP/8ZI0aMEHrtEPHTrbACgFau16lTJ9T1uwaROCXFkmMpLMCn2JFsUjav4xE8bhUr5vX2VJZTZZZZ8PD0qiwyUKj+5WmO7pi/ZHn9OzwRHpqHxy3Ck6kqrRbC/pKgRXhopmVzhEebt0Z4mhMdkuviHajtWkypLD2tZe6EUI/y6Gmtg+2dDh40dT6od0R4OKZ9MNui7f9j2jHHou2f+2g42RGhHvFpT2uFY9p/PcoTjoaMtJYe1Un5T1mvR28K9GVt+jxpnzcNgGwsi6eua4/u2LviMDB/0VPGBLQj5J0JADeBJSTMaCn9oiLE4q1YufU3aGpqspiAZWL5TirwJxrTcbzpQkV4ZBJ1H/gTUGKHx6RMW+cllWXsgyPyY+AzuuNl3Kx0cCQYltN5jiLmZVGcojw8ZeqstK2xDWeUx7JNwCZmILORHiAAMzOtdN3+PREk0bZk54he/9J5vAGjBE9Q5KBfB0iv2PHSuaBoKkvEqEz7MrHjFN3hgWZWdut7hzeddSR1SiiKPdLFulYiaS0aPIZ0t4otHmFt74yQ7/Nu/y+W2rLsk9EpIaBEjwWGf0mRXpTgCYIc9OsAwYgdMzSxY8bNt+MGbyqLui1HKouFSCl6OvreUSOk8yNc2s8YaoKGvUSdF7coD7WisB23HwAy/DxK9NC34/b1KNKOEjwyaYsJpbCAPBQ7NtxMyiL97chMZTHL0B2wR3d48GNWZiFj/KxsJxd6ePZrXnaL8hiih4GbgZl1f4jAa2KmpaxTyBPR4zvFpUgrSvAEDCuqk+lhInREHiCuYseHSdlMEKksOyJGZRoiHQ166XsnHWmqbDUs8xBUOb3XtJZIlMdscOfCJcpDrTS04TfKY9kXxc8jXLmVI6JHfoorf8vSsx0leAIk21NYAPvBIdSLMuDJpGxZJjhOFn/Y3r6eP6qjix1mj7gO0ISP13SWX//OkWBY1uE5V79ikjcFKTXKw+Ht4b8H7P/F/Tws0cOV2gJyQvQAAfh6FBlBCZ4AyAW/DpB+sSPSuSALL6M/uxqVHaI7drx0NMhrVubFLZ2l/DupuF0TPylCVlqLhucoj45LlIdn2AkRREzMZmSKHidyRfQo4ZMdKMEjmVzw6wBiYscVAbFjhuXb8ZvK8vSwd0hliUR3RM3KiuzHb1pLpESdO8rDKFN3wmuUh74viSZmCrzjbtHaBol0X48irSjBIxGvfh3WtkEgKnZkVmSlw7fDa1TmGULCDKujQTu8/a+IdjaoSA8819z83vmN8oh0b0CDN8ojK7VlRsTEzFu5RSNXRA+QRSkuvZDG11/+eI6U4AkYN6ED5I/YEa3IApx9O0HDa1Tmie6ImpX9pLPsqP53vJOJaxdklEfEwCyCXz8PE0nl6k7tgyIQM7MicJTgCRCeqE7Oih0bMkzK1vbBRndE8RvdcUtn8XZs58Vrks+GZadKLfoQGt59PLLSWjRkRXnsyIryUPfN6edxNTEDOSt6AO++HkVmUIInILIphQUEIHYkmZTNZCyVlaHojlOlj5/Ig4hhORdHSU8nQaW1eDoilBXlCSq1ZYbl5zHDFD1mlOhRBIQSPJLJNr8OkFmxY0bEt5NNBB3dYaH8O7lJtkR5ZJB2Pw+Ql6JHCZ/MowSPRLjMbEeY2PHq28ml6I5feNNZCvnYo2m8KUOLwdzl/eMxL7PIdJSHhh8/z5EqegAV7ck0SvCkkXwVO2ZoYseyXoJvJ53wDBKarnSW/cv4SDAsZ1NPzzzXm9eQTvvMOA0qKhNZqS3LPgX9PMARLnqUaTljKMGTBjLR8ZRvsWOH0W+Gm0nZi2+HRSajO6yOBjOdzpLd4WA2iQ0veDEup7T3kVLkTWulM8rjBxl+HuHKLSAnRU+2iBr9WPz+5QtK8ARMJj4sUsQOZ/k5r0nZDE8JeqarsgC50R0zTn3vBEE+V2hlGr9pLZanx1OUJ42pLZafR4qJWbBjQiC7RA+gIjnZiBI8AZIXYseGV5Oyn1SWDDIR3THDk+pQ6Sxv+K02E/HxiKa1RKM8LEO8nzG2ePFSqh6IiRlw7ZhQiR6FKErwBEQuiB0qkkzKZmhiRySVle4elQE50Z1srs5SJen8yHovAo3ySDIws/Dq5/FsYrajRI/CJ0rwBECuiJ2gKrK0aX7fjhnRVJYnfER3/JLOdJaCjkyvk1Nayy2ix1OiTsMxyuMB0dQWdR8cfh5re7kmZieU6FHQUIJHMvkudpzw49vxk8ryalRmwYrueDEr+01n8aBGSE8fou+VW6SP1RFhkFEeHvymtkRMzLJHV1eiR2FHCR6JkNb0f+kELXbsBO3bsbTxmMriwW90x4tZ2Ste/TvKsMx/Ddx8PEGmtWjIjvJ4NTDTEEltWZexTcwpx5ynoicT3xMKDSV4chgvN6yo2PHbuaAZnt6U3cLrPMiI7tDwa1bmTWep3pVzC560lp8SdZ4oj1uZOg9eUlusUnWWn8fyug5RnhTyRPSkE9LaavTy7PkvjyJTSvDkKG43qt+OBQH/JmXzelmpLBll6DzRHdYwErLNyqriyj/pNmH7ec94zcsiUR4WvAZm5j48lKpbt5fg5wE8ix4W+S56FEmU4MlBpIgdF7yalM3QxI7MVBaLbIjupAvhzvQ4xEE2dToYxLHwXDMvI9OLko4oDwuZqS0zgfl5AOkdEzpto8g/lODJMaSJHQ8VWWacxI6Ib8eyTuBBa2zjZlSmEER0x4yTWdlrOisdX7oKDbeoDSvV6DWtZSYTUR7mNj5SW5b9CPh5PJWrU1CiR0FDCZ4cIt1ixw6vSdmMSAk69TU9/PI0cOhkkIZodMdMUGZlGsqwLE5Q10JWWkt0uAlZUR43AzN9G/t/vtQWr5+HhWi5uhI9CjtK8OQI6RA7dtLh27Huo/2/0ANYvJNBnuiOG7LSWcq/k5+I9MljhmdQUTNcUR6HMnUWslNbtPZAUvTwRHkAJXoU/lCCJwdIl9iR0blgkKksT2XoHqM7fs3KXtJZdlQ6K1i8+Hh401rObZKv6RYZlBbloeDFwOw1tcXy81j2zyl6LCjRoxBACZ4sx4vYoRKQ2DHDGjrC0s5HKotGkNGddJqVvZSjqw4H5SIacZMVoeP9TMmK8tAQKRIQSW1ZlwXg51GixxHS2iZhtPT8ecYowZPFeBU7QZef8/p2rMuc24v0uSNUhh5wdMcJ3hRGpr5c85VsGx8saPOy7CgPDZ4ILKsXZi9+nqBED40jQfQokijBk6VkQuzw4CR2eErQLfsRepA6tzHapim6Y4an751sGzsr20RBukiXiVv0PfZiXhaO8gh0RugltWVZJ8HPwyKIPnqU6DlyyBnBc/fdd2P48OEoLS1FZWUl1zaEENx+++3o2bMnOnTogLq6Onz55ZeWNnv37sWkSZNQUVGByspKTJ06FS0tLQGcAT/SxI4L9geGX5OyZb2kVBa9DUcZekDRnXSls/z4d1SFllyCGmaChdQoDwWeMnUvqS0zvKXqIn4eO0r0ZDf19fUIhUK45pprHNssXrwYZ5xxBjp37ozOnTujrq4OH330kaUNz3c5DzkjeNra2vBv//Zv+OUvf8m9zX333YeHHnoIjz76KNasWYOOHTti7NixOHw4+Sts0qRJ2LBhA1asWIFXXnkF7777LqZPnx7EKXAhVewIlJ8H5dvxm8oSMiozOhlMV3QnqHQWfR/5k1t3I6iOEGnXUGaqkSet5cW87DfKQ4NncFGR1JZlHSO1ZW0nltoC5PTRAyjRI5uPP/4YixYtwqBBg5jtVq1ahYsvvhhvv/02Vq9ejd69e2PMmDH49ttvjTY83+U85IzgmTt3Lq699lrU1NRwtSeE4De/+Q1uvfVWTJw4EYMGDcLTTz+N7777DsuWLQMAbNq0CcuXL8djjz2G2tpajBgxAgsXLkRDQwO+++67AM+GTrrEjp2gfTtmvPT1QUOkk0EavNEdM6LRHZnprHT6d7Kpl+VcxM977RZN5I3y8AwsyipT56nIkpHakuHnScGjiRlQokcWLS0tmDRpEhYvXozOnTsz2z733HO48sorMXjwYFRXV+Oxxx5DIpHAm2++CYDvu5yXnBE8omzbtg27du1CXV2dsaxTp06ora3F6tWrAQCrV69GZWUlTjnlFKNNXV0dwuEw1qxZk9bjTafY4TEpm/Hr22GlssQerOmP7qRj+Ag1WGj2E0RaS2TMNdEoDxVGlMeLgZl3HatU3bqP9JqYlegJjhkzZmDChAmW719eDh48iGg0ii5dugDg+y7nRSwWmEPs2rULANCjRw/L8h49ehjrdu3ahe7du1vWRyIRdOnSxWhDo7W1Fa2mEWSbmpoAADHi/csxRJxHEAeAUCJ1fai4GEjYHppFESBhCpEXFgHx5DwpjgDx5EORmKYTseQDNFEUBtpnEyajawKmPjXaTzcOUyqrfVmi/bmZKAwBbbZlEQCt7f/bXz6RsP03rSO64ImbfpEeBhIFxHg9xNp3Hkv+sjXejqi2LG76MohH9GXJc44VaNMx05dL1PSLOtq+vENBq35KKCuIGtOlkVaYkxOFBcn9RAqS+ykLH4b567IgbP2SKg8fwgFYCYdThVyIskxbwShPDrM/ZwCQYG2fQQ4Q9+Pan3Bu0+Jwjx2gbHMgkSocDtqWHUok39MIWtCSKDHmD8eT730homiJl7S3i+JgLPmF2RbXt2/DoXhxe/s2HIxpX9CxWPLY9OkIWtEa126KeFTbQQGAtlh7tCTa3j9WLCkUiD4d1f+337MAwnFtOtQWSlkXimm3Ybj9VPV7ihj3vmldjLIuSizrEALCbe3L2tdpy9qHfIklBVc4FjdNJ69D2PTMCsVNoi8ChFpN8wUAom2W9WizicTCEHU0cELYYpT4eNbr3xOE8I9k7/m1EAV8vkys/SHb3NxsWV5cXIxiivhraGjAp59+io8//tjT682aNQu9evUyBA7PdzkvGRU8s2fPxr333stss2nTJlRXV6fpiPhYsGAB5s6dm7L8r4mXve/0oM/1CoVCocgZvv/+e3Tq1CmQfRcVFaGqqgp/3eXjO8lEWVkZevfubVl2xx134M4777Qs27FjB66++mqsWLECJSUlEKW+vh4NDQ1YtWqVp+3dyKjguf766zFlyhRmm759+3rad1VVFQBg9+7d6Nmzp7F89+7dGDx4sNFmz549lu1isRj27t1rbE9jzpw5uO6664z5ffv2oU+fPti+fXtgH+CgaW5uRu/evbFjxw5UVFRk+nA8kw/nkQ/nAKjzyCby4RyA/DiPpqYmHH300UbKJghKSkqwbds2tLXJKWwghCAUskZIadGdxsZG7NmzByeddJKxLB6P491338V//dd/obW1FQUF9O4HHnjgAdTX12PlypUWozPPdzkvGRU83bp1Q7du3QLZ97HHHouqqiq8+eabxkVpbm7GmjVrjEqvYcOGYd++fWhsbMTJJ58MAHjrrbeQSCRQW1vruG+nUF6nTp1y9ibUqaioyPlzAPLjPPLhHAB1HtlEPpwDkB/nEQ4Ha6EtKSkJJErCYvTo0Vi/fr1l2eWXX47q6mrMmjXLUezcd999uPvuu/H6669bPLUA33c5Lznj4dm+fTv27t2L7du3Ix6PY926dQCA4447DmVlZQCA6upqLFiwAOedd55R+3/XXXfh+OOPx7HHHovbbrsNvXr1wrnnngsAGDBgAMaNG4dp06bh0UcfRTQaxcyZM3HRRRehV69eGTpThUKhUChyj/LycgwcONCyrGPHjvjBD35gLJ88eTKOOuooLFiwAABw77334vbbb8fzzz+PY445xvDllJWVoaysjOu7nJecETy33347nnrqKWN+yJAhAIC3334bo0aNAgBs3rzZMBADwE033YQDBw5g+vTp2LdvH0aMGIHly5dbVO9zzz2HmTNnYvTo0QiHw7jgggvw0EMPpeekFAqFQqE4gti+fbsluvXII4+gra0N//qv/2ppZ/YI8XyXc0EUvjl8+DC54447yOHDhzN9KJ7Jh3MgJD/OIx/OgRB1HtlEPpwDIflxHvlwDrlKiJA01MYpFAqFQqFQZJC87XhQoVAoFAqFQkcJHoVCoVAoFHmPEjwKhUKhUCjyHiV4FAqFQqFQ5D1K8HBw9913Y/jw4SgtLUVlZSXXNoQQ3H777ejZsyc6dOiAuro6fPnll5Y2e/fuxaRJk1BRUYHKykpMnToVLS0tAZyBt9f76quvEAqFqH9//OMfjXa09Q0NDVlxDgAwatSolOO74oorLG22b9+OCRMmoLS0FN27d8eNN96IWIw9eno6z2Pv3r341a9+hf79+6NDhw44+uijcdVVV1m6YQCCfy8efvhhHHPMMSgpKUFtbS0++ugjZvs//vGPqK6uRklJCWpqavDqq69a1vPcJ7IROYfFixfjjDPOQOfOndG5c2fU1dWltJ8yZUrKNR83blyg5wCInceTTz6Zcoz2kt5sfy9o93EoFMKECROMNpl4L959912cffbZ6NWrF0KhENcI3qtWrcJJJ52E4uJiHHfccXjyySdT2ojeawoOMlojliPcfvvt5MEHHyTXXXcd6dSpE9c29fX1pFOnTmTZsmXks88+I+eccw459thjyaFDh4w248aNIyeeeCL58MMPyV//+ldy3HHHkYsvvjigsxB/vVgsRnbu3Gn5mzt3LikrKyP79+832gEgTzzxhKWd+TwzeQ6EEDJy5Egybdo0y/E1NTVZznPgwIGkrq6OrF27lrz66quka9euZM6cOYGcg5fzWL9+PTn//PPJSy+9RLZs2ULefPNNcvzxx5MLLrjA0i7I96KhoYEUFRWRJUuWkA0bNpBp06aRyspKsnv3bmr7999/nxQUFJD77ruPbNy4kdx6662ksLCQrF+/gLA2qgAADnZJREFU3mjDc5/IRPQcLrnkEvLwww+TtWvXkk2bNpEpU6aQTp06kW+++cZoc9lll5Fx48ZZrvnevXsDOX6v5/HEE0+QiooKyzHu2rXL0ibb34vvv//ecvyff/45KSgoIE888YTRJhPvxauvvkpuueUW8sILLxAAZOnSpcz2f//730lpaSm57rrryMaNG8nChQtJQUEBWb58udFG9Noo+FCCR4AnnniCS/AkEglSVVVF7r//fmPZvn37SHFxMfn9739PCCFk48aNBAD5+OOPjTavvfYaCYVC5Ntvv5V+7LJeb/DgweQXv/iFZRnPTS4Dr+cwcuRIcvXVVzuuf/XVV0k4HLZ8ATzyyCOkoqKCtLa2Sjl2M7Leiz/84Q+kqKiIRKNRY1mQ78XQoUPJjBkzjPl4PE569epFFixYQG3/s5/9jEyYMMGyrLa2lvzHf/wHIYTvPpGN6DnYicVipLy8nDz11FPGsssuu4xMnDhR9qEyET0Pt2dXLr4Xv/71r0l5eTlpaWkxlmXivTDDc//ddNNN5Mc//rFl2YUXXkjGjh1rzPu9Ngo6KqUVANu2bcOuXbuM4e0BbZyt2tparF69GgCwevVqVFZWWsYNqaurQzgcxpo1a6Qfk4zXa2xsxLp16zB16tSUdTNmzEDXrl0xdOhQLFmyBCSA7p38nMNzzz2Hrl27YuDAgZgzZw4OHkwOP7969WrU1NSgR48exrKxY8eiubkZGzZsyKrzMNPU1ISKigpEItYO04N4L9ra2tDY2Gj5TIfDYdTV1RmfaTurV6+2tAe066q357lPZOLlHOwcPHgQ0Wg0ZeDHVatWoXv37ujfvz9++ctf4vvvv5d67Ga8nkdLSwv69OmD3r17Y+LEiZbPdi6+F48//jguuugidOzY0bI8ne+FF9zuCxnXRkEnZ4aWyCX0sUDMX6D6vL5u165d6N69u2V9JBJBly5djDayj8nv6z3++OMYMGAAhg8fblk+b948/PSnP0VpaSneeOMNXHnllWhpacFVV10l7fgB7+dwySWXoE+fPujVqxf+9re/YdasWdi8eTNeeOEFY7+090pfJxsZ78U//vEPzJ8/H9OnT7csD+q9+Mc//oF4PE69Tl988QV1G6frar4H9GVObWTi5RzszJo1C7169bJ8GY0bNw7nn38+jj32WGzduhU333wzzjrrLKxevdpxsEQ/eDmP/v37Y8mSJRg0aBCamprwwAMPYPjw4diwYQN++MMf5tx78dFHH+Hzzz/H448/blme7vfCC073RXNzMw4dOoR//vOfvj+nCjpHrOCZPXs27r33XmabTZs2obq6Ok1H5A3e8/DLoUOH8Pzzz+O2225LWWdeNmTIEBw4cAD3338/95ds0OdgFgU1NTXo2bMnRo8eja1bt6Jfv36e92snXe9Fc3MzJkyYgBNOOMEYa0bH73uhcKa+vh4NDQ1YtWqVxfB70UUXGdM1NTUYNGgQ+vXrh1WrVmH06NGZONQUhg0bhmHDhhnzw4cPx4ABA7Bo0SLMnz8/g0fmjccffxw1NTUYOnSoZXkuvBeKzHHECp7rr78eU6ZMYbbp27evp31XVVUBAHbv3o2ePXsay3fv3m0Mb19VVYU9e/ZYtovFYti7d6+xPQ+85+H39f70pz/h4MGDmDx5smvb2tpazJ8/H62trSguLnZtn65zMB8fAGzZsgX9+vVDVVVVSgXE7t27ASDr3ov9+/dj3LhxKC8vx9KlS1FYWMhsL/peONG1a1cUFBQY10Vn9+7djsdcVVXFbM9zn8jEyznoPPDAA6ivr8fKlSsxaNAgZtu+ffuia9eu2LJlSyBfsn7OQ6ewsBBDhgzBli1bAOTWe3HgwAE0NDRg3rx5rq8T9HvhBaf7oqKiAh06dEBBQYHv91fhQKZNRLmEqGn5gQceMJY1NTVRTcuffPKJ0eb1118P3LTs9fVGjhyZUhHkxF133UU6d+7s+VidkHXN3nvvPQKAfPbZZ4SQpGnZXAGxaNEiUlFREcgAf17Po6mpiZx22mlk5MiR5MCBA1yvJfO9GDp0KJk5c6YxH4/HyVFHHcU0Lf/Lv/yLZdmwYcNSTMus+0Q2oudACCH33nsvqaioIKtXr+Z6jR07dpBQKERefPFF38frhJfzMBOLxUj//v3JtddeSwjJnfeCEO05XFxcTP7xj3+4vkY63gsz4DQtDxw40LLs4osvTjEt+3l/FXSU4OHg66+/JmvXrjVKsteuXUvWrl1rKc3u378/eeGFF4z5+vp6UllZSV588UXyt7/9jUycOJFalj5kyBCyZs0a8t5775Hjjz8+8LJ01ut98803pH///mTNmjWW7b788ksSCoXIa6+9lrLPl156iSxevJisX7+efPnll+S///u/SWlpKbn99tuz4hy2bNlC5s2bRz755BOybds28uKLL5K+ffuSM88809hGL0sfM2YMWbduHVm+fDnp1q1b4GXpIufR1NREamtrSU1NDdmyZYul7DYWixFCgn8vGhoaSHFxMXnyySfJxo0byfTp00llZaVR3XbppZeS2bNnG+3ff/99EolEyAMPPEA2bdpE7rjjDmpZutt9IhPRc6ivrydFRUXkT3/6k+Wa6/f+/v37yQ033EBWr15Ntm3bRlauXElOOukkcvzxxwc6GrboecydO5e8/vrrZOvWraSxsZFcdNFFpKSkhGzYsMFyrtn8XuiMGDGCXHjhhSnLM/Ve7N+/3/hOAEAefPBBsnbtWvL1118TQgiZPXs2ufTSS432eln6jTfeSDZt2kQefvhhalk669oovKEEDweXXXYZAZDy9/bbbxtt0N7/iU4ikSC33XYb6dGjBykuLiajR48mmzdvtuz3+++/JxdffDEpKysjFRUV5PLLL7eIKNm4vd62bdtSzosQQubMmUN69+5N4vF4yj5fe+01MnjwYFJWVkY6duxITjzxRPLoo49S22biHLZv307OPPNM0qVLF1JcXEyOO+44cuONN1r64SGEkK+++oqcddZZpEOHDqRr167k+uuvt5R7Z/o83n77bepnEADZtm0bISQ978XChQvJ0UcfTYqKisjQoUPJhx9+aKwbOXIkueyyyyzt//CHP5Af/ehHpKioiPz4xz8mf/nLXyzree4T2YicQ58+fajX/I477iCEEHLw4EEyZswY0q1bN1JYWEj69OlDpk2blpYvJpHzuOaaa4y2PXr0IOPHjyeffvqpZX/Z/l4QQsgXX3xBAJA33ngjZV+Zei+c7k392C+77DIycuTIlG0GDx5MioqKSN++fS3fHTqsa6PwRoiQAOqHFQqFQqFQKLII1Q+PQqFQKBSKvEcJHoVCoVAoFHmPEjwKhUKhUCjyHiV4FAqFQqFQ5D1K8CgUCoVCoch7lOBRKBQKhUKR9yjBo1AoFAqFIu9RgkehUCgUCkXeowSPQqHIeg4fPowpU6agpqYGkUgE5557bqYPSaFQ5BhK8CgUCt8QQhCLxQLbfzweR4cOHXDVVVehrq4usNdRKBT5ixI8CsURyKhRozBz5kzMnDkTnTp1QteuXXHbbbdBH2nmmWeewSmnnILy8nJUVVXhkksuwZ49e4ztV61ahVAohNdeew0nn3wyiouL8d5772Hr1q2YOHEievTogbKyMpx66qlYuXKl5bWPOeYY3HXXXZg8eTLKysrQp08fvPTSS/i///s/TJw4EWVlZRg0aBA++eQTY5uOHTvikUcewbRp01BVVZWei6RQKPIKJXgUiiOUp556CpFIBB999BF++9vf4sEHH8Rjjz0GAIhGo5g/fz4+++wzLFu2DF999RWmTJmSso/Zs2ejvr4emzZtwqBBg9DS0oLx48fjzTffxNq1azFu3DicffbZ2L59u2W7X//61zj99NOxdu1aTJgwAZdeeikmT56Mn//85/j000/Rr18/TJ48GWqoP4VCIQs1eKhCcQQyatQo7NmzBxs2bEAoFAKgiZeXXnoJGzduTGn/ySef4NRTT8X+/ftRVlaGVatW4Sc/+QmWLVuGiRMnMl9r4MCBuOKKKzBz5kwAWoTnjDPOwDPPPAMA2LVrF3r27InbbrsN8+bNAwB8+OGHGDZsGHbu3JkS0ZkyZQr27duHZcuW+b0MCoXiCEJFeBSKI5TTTjvNEDsAMGzYMHz55ZeIx+NobGzE2WefjaOPPhrl5eUYOXIkAKREak455RTLfEtLC2644QYMGDAAlZWVKCsrw6ZNm1K2GzRokDHdo0cPAEBNTU3KMnMaTaFQKPygBI9CobBw+PBhjB07FhUVFXjuuefw8ccfY+nSpQCAtrY2S9uOHTta5m+44QYsXboU99xzD/76179i3bp1qKmpSdmusLDQmNZFF21ZIpGQd2IKheKIJpLpA1AoFJlhzZo1lvkPP/wQxx9/PL744gt8//33qK+vR+/evQHAYiBm8f7772PKlCk477zzAGgRn6+++krqcSsUCoUXVIRHoThC2b59O6677jps3rwZv//977Fw4UJcffXVOProo1FUVISFCxfi73//O1566SXMnz+fa5/HH388XnjhBaxbtw6fffYZLrnkEmlRmo0bN2LdunXYu3cvmpqasG7dOqxbt07KvhUKRf6jIjwKxRHK5MmTcejQIQwdOhQFBQW4+uqrMX36dIRCITz55JO4+eab8dBDD+Gkk07CAw88gHPOOcd1nw8++CB+8YtfYPjw4ejatStmzZqF5uZmKcc7fvx4fP3118b8kCFDAEBVcikUCi5UlZZCcQQyatQoDB48GL/5zW8yfSgKhUKRFlRKS6FQKBQKRd6jBI9CoVAoFIq8R6W0FAqFQqFQ5D0qwqNQKBQKhSLvUYJHoVAoFApF3qMEj0KhUCgUirxHCR6FQqFQKBR5jxI8CoVCoVAo8h4leBQKhUKhUOQ9SvAoFAqFQqHIe5TgUSgUCoVCkfcowaNQKBQKhSLv+X8kCSDWvpm2AgAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, @@ -12298,18 +1411,14 @@ ], "source": [ "plt.figure()\n", - "plt.title('Least squares cost function')\n", - "plt.contourf(param1,param2,least_squares,levels=50)\n", - "plt.xlabel('param1')\n", - "plt.ylabel('param2')\n", - "plt.colorbar()\n", + "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", + "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", + "plt.plot(off_diagonal_norm[:,2],label='1-local ansatz normalized')\n", + "plt.xlabel('Flows Iterations')\n", + "plt.ylabel(r'$||\\sigma(H_k)||$')\n", + "plt.legend()\n", "\n", - "plt.figure()\n", - "plt.title('Off-diagonal norm')\n", - "plt.contourf(param1,param2,off_diagonal_norm,levels=50)\n", - "plt.xlabel('param1')\n", - "plt.ylabel('param2')\n", - "plt.colorbar()" + "print(np.linalg.norm(d_opt))" ] } ], diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 6c8a173194..3e024181fc 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -4,99 +4,16 @@ from qibo.models.dbi.utils_scheduling import polynomial_step -def gradient_Pauli( - dbi_object, - d: np.array, - pauli_operator_dict: dict, - use_ds=False, - n=3, - **kwargs, -): - r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients - Args: - dbi_object (DoubleBracketIteration): the target dbi object - d (np.array): the diagonal operator - n_taylor (int): the highest order of the taylore expansion of w.r.t `s` - onsite_Z_ops (list): list of Pauli-Z operators - taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` - use_ds (boolean): if False, ds is set to 0 - """ - # n is the highest order for calculating s - - # pauli_index is the list of positions \mu - pauli_operators = list(pauli_operator_dict.values()) - num_paul = len(pauli_operators) - grad = np.zeros(num_paul) - coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) - s = polynomial_step(dbi_object, n=3, d=d) - - a, b, c = coef[len(coef) - 3 :] - - for i, operator in enumerate(pauli_operators): - da, db, dc, ds = ds_di_pauli( - dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] - ) - if use_ds is True: - ds = 0 - grad[i] = ( - s**3 / 3 * da - + s**2 / 2 * db - + 2 * s * dc - + s**2 * ds * a - + s * ds * b - + 2 * ds * c - ) - grad = np.array(grad) - grad = grad / np.linalg.norm(grad) - return grad, s - - -def derivative_scalar_product_dbr_approx_element_wise_ansatz(dbi_object, d, h, i): - r""" - TODO: add formula and explain terms - Gradient wrt the ith diagonal elements of D. - We make Double_bracket rotation with duration given by the minimzer of the ´polynomial_step´ function. - Gradient of the Taylor expansion of the least squares loss function as a function of $s$ the duration of Double-Bracket rotation element-wise ansatz: - $\partial_{D_{ii}} \text{Tr}(H_k@D) \approx \sum_{k=0}^{n} \frac{1}{k!!} \partial_{D_ii}\text{Tr}(\Gamma_{k}D)$. - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - d(np.array): D operator. - h(np.array): Hamiltonian. - i(int): Index of the diagonal element of D. - Returns: - derivative(float): Derivative of the polynomial expansion with respect to the ith diagonal elements of D. - """ - derivative = 0 - s = polynomial_step(dbi_object, n=3, d=d) - dD_di = np.zeros(d.shape) - gamma_list = dbi_object.generate_Gamma_list(4, d) - dD_di[i, i] = 1 - dGamma = [commutator(dD_di, h)] - derivative += np.real( - np.trace(gamma_list[0] @ dD_di) - + np.trace(dGamma[0] @ d + gamma_list[1] @ dD_di) * s - ) - for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list)) - derivative += np.real( - np.trace(dGamma[-1] @ d + gamma_list[n] @ dD_di) * s**n / math.factorial(n) - ) - - return derivative - - def gradientDiagonalEntries( - dbi_object, params, h, analytic=True, d_type=d_ansatz_type.element_wise, delta=1e-4 + dbi_object, params, d_type=d_ansatz_type.element_wise, delta=1e-4 ): r""" - Gradient of the DBI with respect to the parametrization of D. If analytic is True, the analytical gradient of the polynomial expansion of the DBI is used. - As the analytical gradient is applied on the polynomial expansion of the cost function, the numerical gradients may be more accurate. + Gradient of the DBI with respect to the parametrization of D. A simple finite difference is used to calculate the gradient. Args: dbi_object(DoubleBracketIteration): DoubleBracketIteration object. params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). h(np.array): Hamiltonian. - analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. d_type(d_ansatz_type): Ansatz used for the D operator. Options are 'Full' and '1-local'. delta(float): Step size for numerical gradient. Returns: @@ -105,18 +22,11 @@ def gradientDiagonalEntries( grad = np.zeros(len(params)) d = d_ansatz(params, d_type) - if analytic == True: - for i in range(len(params)): - derivative = derivative_scalar_product_dbr_approx_element_wise_ansatz( - dbi_object, d, h, i - ) - grad[i] = d[i, i] - derivative - else: - for i in range(len(params)): - params_new = deepcopy(params) - params_new[i] += delta - d_new = d_ansatz(params_new, d_type) - grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta + for i in range(len(params)): + params_new = deepcopy(params) + params_new[i] += delta + d_new = d_ansatz(params_new, d_type) + grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta return grad @@ -125,9 +35,8 @@ def gradient_descent_dbr_d_ansatz( params, nmb_iterations, lr=1e-2, - analytic=True, d_type=d_ansatz_type.element_wise, - normalize=False, + normalize=True, ): r""" Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. @@ -140,7 +49,6 @@ def gradient_descent_dbr_d_ansatz( params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). nmb_iterations(int): Number of gradient descent iterations. lr(float): Learning rate. - analytic(bool): If True, the gradient is calculated analytically, otherwise numerically. d_type(d_ansatz_type): Ansatz used for the D operator. normalize(bool): If True, the D operator is normalized at each iteration. Returns: @@ -150,7 +58,6 @@ def gradient_descent_dbr_d_ansatz( params_hist(np.array): Parameters evaluated at each iteration. """ - h = dbi_object.h.matrix d = d_ansatz(params, d_type, normalization=normalize) loss = np.zeros(nmb_iterations + 1) grad = np.zeros((nmb_iterations, len(params))) @@ -164,7 +71,7 @@ def gradient_descent_dbr_d_ansatz( for i in range(nmb_iterations): dbi_new = deepcopy(dbi_object) grad[i, :] = gradientDiagonalEntries( - dbi_object, params, h, analytic=analytic, d_type=d_type + dbi_object, params, d_type=d_type ) for j in range(len(params)): params[j] = params[j] - lr * grad[i, j] From 29c7907d83b6d403d771952168bd8a59658c3ceb Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 23 May 2024 08:43:22 +0000 Subject: [PATCH 083/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/utils_gradients.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 3e024181fc..6fffbf995e 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -70,9 +70,7 @@ def gradient_descent_dbr_d_ansatz( for i in range(nmb_iterations): dbi_new = deepcopy(dbi_object) - grad[i, :] = gradientDiagonalEntries( - dbi_object, params, d_type=d_type - ) + grad[i, :] = gradientDiagonalEntries(dbi_object, params, d_type=d_type) for j in range(len(params)): params[j] = params[j] - lr * grad[i, j] d = d_ansatz(params, d_type, normalization=normalize) From f568363c3c16cd46f450b5444cd39af58334bbd6 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Fri, 24 May 2024 18:00:32 +0800 Subject: [PATCH 084/154] Coverage for all dbi files and passing tests. TODO: fix notebooks + documentation --- src/qibo/models/dbi/double_bracket.py | 2 +- src/qibo/models/dbi/utils.py | 20 ++ src/qibo/models/dbi/utils_analytical.py | 224 ---------------------- src/qibo/models/dbi/utils_gradients.py | 220 +++++++++++++++++++-- src/qibo/models/dbi/utils_scheduling.py | 6 - src/qibo/models/dbi/utils_strategies.py | 86 +++++---- tests/test_models_dbi.py | 126 +++++++++++- tests/test_models_dbi_strategies.py | 63 ------ tests/test_models_dbi_utils_scheduling.py | 30 --- 9 files changed, 402 insertions(+), 375 deletions(-) delete mode 100644 src/qibo/models/dbi/utils_analytical.py delete mode 100644 tests/test_models_dbi_strategies.py delete mode 100644 tests/test_models_dbi_utils_scheduling.py diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index a506d8aa38..bcc0dcc732 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -5,7 +5,7 @@ import numpy as np from qibo.hamiltonians import Hamiltonian -from qibo.models.dbi.utils_analytical import ( +from qibo.models.dbi.utils_gradients import ( energy_fluctuation_polynomial_expansion_coef, least_squares_polynomial_expansion_coef, off_diagonal_norm_polynomial_expansion_coef, diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 53b6e07d2c..83d377c86d 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -163,3 +163,23 @@ def generate_Pauli_operators(nqubits, symbols_pauli, positions): else: terms = [symbols_pauli(pos) for pos in positions] return SymbolicHamiltonian(math.prod(terms), nqubits=nqubits).dense.matrix + + +def element_wise_d(params: np.array, normalization: bool = False): + r""" + Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. + If $\alpha_i$ are our parameters and d the number of qubits then: + + element_wise: $D = \sum_{i=0}^{2^d} \alpha_i |i\rangle \langle i|$ + local_1: $D = \sum_{i=1}^{d} \alpha_i Z_i$ + Args: + params(np.array): parameters for the ansatz. + d_type(d_ansatz type): type of parameterization for the ansatz. + normalization(bool): If True, the diagonal is normalized to 1. + """ + d = np.zeros((len(params), len(params))) + for i in range(len(params)): + d[i, i] = params[i] + if normalization: + d = d / np.linalg.norm(d) + return d diff --git a/src/qibo/models/dbi/utils_analytical.py b/src/qibo/models/dbi/utils_analytical.py deleted file mode 100644 index fcaf28c86b..0000000000 --- a/src/qibo/models/dbi/utils_analytical.py +++ /dev/null @@ -1,224 +0,0 @@ -import math -from enum import Enum, auto -from typing import Optional - -import numpy as np - -from qibo import symbols -from qibo.hamiltonians import SymbolicHamiltonian -from qibo.models.dbi.utils import commutator, covariance, variance - - -def dGamma_di_pauli(dbi_object, n: int, Z_i: np.array, d: np.array): - """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. - - Args: - dbi_object (DoubleBracketIteration): the target dbi object - n (int): the number of nested commutators in `Gamma` - i (int/tupple): the index of onsite-Z coefficient - d (np.array): the diagonal operator - - Returns: - (list): [dGamma_0_di, dGamma_1_di, ..., dGamma_n_di] - """ - nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) - dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) - Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) - W = dbi_object.commutator(dbi_object.backend.cast(d), dbi_object.h.matrix) - dW_di = dbi_object.commutator(dbi_object.backend.cast(Z_i), dbi_object.h.matrix) - for k in range(n + 1): - if k == 0: - continue - elif k == 1: - dGamma_di[k] = dW_di - else: - dGamma_di[k] = dbi_object.commutator( - dW_di, Gamma_list[k - 1] - ) + dbi_object.commutator(W, dGamma_di[k - 1]) - return dGamma_di - - -def ds_di_pauli( - dbi_object, - d: np.array, - Z_i: np.array, - taylor_coef: Optional[list] = None, -): - r"""Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients\ - Args: - dbi_object (DoubleBracketIteration): the target dbi object - d (np.array): the diagonal operator - i (int): the index of onsite-Z coefficient - taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}`, from the highest order to the lowest. - onsite_Z_ops (list): onsite Z operators of `dbi_object.h` - Returns: - floats da, db, dc, ds - """ - # generate the list of derivatives w.r.t ith Z operator coefficient - dGamma_di = dGamma_di_pauli(dbi_object, n=4, Z_i=Z_i, d=d) - Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) - - def derivative_product(k1, k2): - r"""Calculate the derivative of a product $\sigma(\Gamma(n1,i))@\sigma(\Gamma(n2,i))""" - return dbi_object.sigma(dGamma_di[k1]) @ dbi_object.sigma( - Gamma_list[k2] - ) + dbi_object.sigma(dbi_object.sigma(Gamma_list[k1])) @ dbi_object.sigma( - dGamma_di[k2] - ) - - # calculate the derivatives of s polynomial coefficients - da = np.trace(3 * derivative_product(1, 2) + 3 * derivative_product(3, 0)) - db = np.trace(2 * derivative_product(1, 1) + 2 * derivative_product(0, 2)) - dc = np.trace(2 * derivative_product(1, 0)) - - ds = 0 - if taylor_coef != None: - a, b, c = taylor_coef[len(taylor_coef) - 3 :] - delta = b**2 - 4 * a * c - ddelta = 2 * (b * db - 2 * (a * dc + da * c)) - - ds = (-db + 0.5 * ddelta / np.sqrt(delta)) * a - (-b + np.sqrt(delta)) * da - ds /= 2 * a**2 - - return da, db, dc, ds - - -def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): - r""" - Gradient of the nth gamma operator with respect to the ith diagonal elements of D. - $Gamma_{n} = [W,[W,...,[W,H]]...]]$, - $\frac{\partial Gamma_{n}}{\partial D_{ii}} = \partial_{D_{ii}} W\Gamma_{n-1}-\partial_{D_{ii}}\Gamma_{n-1} W$. - and thus is can be computed recursively. - Args: - d(np.array): D operator. - h(np.array): Hamiltonian. - n(int): nth Gamma operator. - i(int): Index of the diagonal element of D. - dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). - gamma_list(list): List of the n gamma operators. - Returns: - (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. - """ - dD_di = np.zeros(d.shape) - dD_di[i, i] = 1 - dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) - w = commutator(d, h) - return dW_di + commutator(w, dGamma[-1]) - - -def dpolynomial_diDiagonal(dbi_object, s, d, H, i): - # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) - # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation - derivative = 0 - A = np.zeros(d.shape) - Gamma_list = dbi_object.generate_Gamma_list(4, d) - A[i, i] = 1 - dGamma = [commutator(A, H)] - derivative += np.real( - np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s - ) - for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) - derivative += np.real( - np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) - ) - - return derivative - - -def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = dbi_object.commutator( - dbi_object.backend.cast(d), dbi_object.sigma(dbi_object.h.matrix) - ) - Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) - sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients for rotation with [W,H] and H - c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] - c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] - # product coefficient - trace_coefficients = [0] * (2 * n + 1) - for k in range(n + 1): - for j in range(n + 1): - power = k + j - product_matrix = c1[k] @ c2[j] - trace_coefficients[power] += 2 * np.trace(product_matrix) - # coefficients from high to low (n:0) - coef = list(reversed(trace_coefficients[: n + 1])) - return coef - - -def least_squares_polynomial_expansion_coef(dbi_object, d: np.array = None, n: int = 3): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients - coef = np.empty(n) - for i in range(n): - coef[i] = np.real(exp_list[i] * np.trace(d @ Gamma_list[i + 1])) - coef = list(reversed(coef)) - return coef - - -def energy_fluctuation_polynomial_expansion_coef( - dbi_object, d: np.array = None, n: int = 3, state=0 -): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - # coefficients - coef = np.empty(3) - coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1], state)) - coef[1] = np.real(2 * variance(Gamma_list[1], state)) - coef[2] = np.real( - covariance(Gamma_list[0], Gamma_list[3], state) - + 3 * covariance(Gamma_list[1], Gamma_list[2], state) - ) - coef = list(reversed(coef)) - return coef - - -class d_ansatz_type(Enum): - - element_wise = auto() - local_1 = auto() - - -def d_ansatz(params: np.array, d_type: d_ansatz_type, normalization: bool = False): - r""" - Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. - If $\alpha_i$ are our parameters and d the number of qubits then: - - element_wise: $D = \sum_{i=0}^{2^d} \alpha_i |i\rangle \langle i|$ - local_1: $D = \sum_{i=1}^{d} \alpha_i Z_i$ - Args: - params(np.array): parameters for the ansatz. - d_type(d_ansatz type): type of parameterization for the ansatz. - normalization(bool): If True, the diagonal is normalized to 1. - """ - - if d_type is d_ansatz_type.element_wise: - d = np.zeros((len(params), len(params))) - for i in range(len(params)): - d[i, i] = params[i] - - elif d_type is d_ansatz_type.local_1: - - op_list = [params[i] * symbols.Z(i) for i in range(len(params))] - symbolHam = op_list[0] - for i in range(len(params) - 1): - symbolHam += op_list[i + 1] - - d = SymbolicHamiltonian(symbolHam, nqubits=len(params)) - d = d.dense.matrix - else: - raise ValueError(f"Parameterization type {type} not recognized.") - if normalization: - d = d / np.linalg.norm(d) - return d diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 6fffbf995e..7c8741bef8 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -1,12 +1,209 @@ +import math from copy import deepcopy +from typing import Optional -from qibo.models.dbi.utils_analytical import * -from qibo.models.dbi.utils_scheduling import polynomial_step +import numpy as np +from qibo import symbols +from qibo.hamiltonians import SymbolicHamiltonian +from qibo.models.dbi.utils import * -def gradientDiagonalEntries( - dbi_object, params, d_type=d_ansatz_type.element_wise, delta=1e-4 + +def dGamma_di_pauli(dbi_object, n: int, Z_i: np.array, d: np.array): + """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. + + Args: + dbi_object (DoubleBracketIteration): the target dbi object + n (int): the number of nested commutators in `Gamma` + i (int/tupple): the index of onsite-Z coefficient + d (np.array): the diagonal operator + + Returns: + (list): [dGamma_0_di, dGamma_1_di, ..., dGamma_n_di] + """ + nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) + dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) + Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) + W = dbi_object.commutator(dbi_object.backend.cast(d), dbi_object.h.matrix) + dW_di = dbi_object.commutator(dbi_object.backend.cast(Z_i), dbi_object.h.matrix) + for k in range(n + 1): + if k == 0: + continue + elif k == 1: + dGamma_di[k] = dW_di + else: + dGamma_di[k] = dbi_object.commutator( + dW_di, Gamma_list[k - 1] + ) + dbi_object.commutator(W, dGamma_di[k - 1]) + return dGamma_di + + +def ds_di_pauli( + dbi_object, + d: np.array, + Z_i: np.array, + taylor_coef: Optional[list] = None, +): + r"""Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients\ + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d (np.array): the diagonal operator + i (int): the index of onsite-Z coefficient + taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}`, from the highest order to the lowest. + onsite_Z_ops (list): onsite Z operators of `dbi_object.h` + Returns: + floats da, db, dc, ds + """ + # generate the list of derivatives w.r.t ith Z operator coefficient + dGamma_di = dGamma_di_pauli(dbi_object, n=4, Z_i=Z_i, d=d) + Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) + + def derivative_product(k1, k2): + r"""Calculate the derivative of a product $\sigma(\Gamma(n1,i))@\sigma(\Gamma(n2,i))""" + return dbi_object.sigma(dGamma_di[k1]) @ dbi_object.sigma( + Gamma_list[k2] + ) + dbi_object.sigma(dbi_object.sigma(Gamma_list[k1])) @ dbi_object.sigma( + dGamma_di[k2] + ) + + # calculate the derivatives of s polynomial coefficients + da = np.trace(3 * derivative_product(1, 2) + 3 * derivative_product(3, 0)) + db = np.trace(2 * derivative_product(1, 1) + 2 * derivative_product(0, 2)) + dc = np.trace(2 * derivative_product(1, 0)) + + ds = 0 + if taylor_coef != None: + a, b, c = taylor_coef[len(taylor_coef) - 3 :] + delta = b**2 - 4 * a * c + ddelta = 2 * (b * db - 2 * (a * dc + da * c)) + + ds = (-db + 0.5 * ddelta / np.sqrt(delta)) * a - (-b + np.sqrt(delta)) * da + ds /= 2 * a**2 + + return da, db, dc, ds + + +def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): + r""" + Gradient of the nth gamma operator with respect to the ith diagonal elements of D. + $Gamma_{n} = [W,[W,...,[W,H]]...]]$, + $\frac{\partial Gamma_{n}}{\partial D_{ii}} = \partial_{D_{ii}} W\Gamma_{n-1}-\partial_{D_{ii}}\Gamma_{n-1} W$. + and thus is can be computed recursively. + Args: + d(np.array): D operator. + h(np.array): Hamiltonian. + n(int): nth Gamma operator. + i(int): Index of the diagonal element of D. + dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). + gamma_list(list): List of the n gamma operators. + Returns: + (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. + """ + dD_di = np.zeros(d.shape) + dD_di[i, i] = 1 + dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) + w = commutator(d, h) + return dW_di + commutator(w, dGamma[-1]) + + +def dpolynomial_diDiagonal(dbi_object, s, d, H, i): + # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) + # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation + derivative = 0 + A = np.zeros(d.shape) + Gamma_list = dbi_object.generate_Gamma_list(4, d) + A[i, i] = 1 + dGamma = [commutator(A, H)] + derivative += np.real( + np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s + ) + for n in range(2, 4): + dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) + derivative += np.real( + np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) + ) + + return derivative + + +def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + W = dbi_object.commutator( + dbi_object.backend.cast(d), dbi_object.sigma(dbi_object.h.matrix) + ) + Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) + sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients for rotation with [W,H] and H + c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] + c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] + # product coefficient + trace_coefficients = [0] * (2 * n + 1) + for k in range(n + 1): + for j in range(n + 1): + power = k + j + product_matrix = c1[k] @ c2[j] + trace_coefficients[power] += 2 * np.trace(product_matrix) + # coefficients from high to low (n:0) + coef = list(reversed(trace_coefficients[: n + 1])) + return coef + + +def least_squares_polynomial_expansion_coef(dbi_object, d, n: int = 3): + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients + coef = np.empty(n) + for i in range(n): + coef[i] = np.real( + exp_list[i] * np.trace(dbi_object.backend.cast(d) @ Gamma_list[i + 1]) + ) + coef = list(reversed(coef)) + return coef + + +def energy_fluctuation_polynomial_expansion_coef( + dbi_object, d: np.array = None, n: int = 3, state=0 ): + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + # coefficients + coef = np.empty(3) + state_cast = dbi_object.backend.cast(state) + state_dag = dbi_object.backend.cast(state.conj().T) + + def variance(a): + """Calculates the variance of a matrix A with respect to a state: + Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" + b = a @ a + return state_dag @ b @ state_cast - (state_dag @ a @ state_cast) ** 2 + + def covariance(a, b): + """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, + applied to two operators A and B with respect to a state: + Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ + """ + + c = a @ b + b @ a + return ( + state_dag @ c @ state_cast + - 2 * state_dag @ a @ state_cast * state_dag @ b @ state_cast + ) + + coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1])) + coef[1] = np.real(2 * variance(Gamma_list[1])) + coef[2] = np.real( + covariance(Gamma_list[0], Gamma_list[3]) + + 3 * covariance(Gamma_list[1], Gamma_list[2]) + ) + coef = list(reversed(coef)) + return coef + + +def gradientDiagonalEntries(dbi_object, params, delta=1e-4): r""" Gradient of the DBI with respect to the parametrization of D. A simple finite difference is used to calculate the gradient. @@ -21,11 +218,11 @@ def gradientDiagonalEntries( """ grad = np.zeros(len(params)) - d = d_ansatz(params, d_type) + d = element_wise_d(params) for i in range(len(params)): params_new = deepcopy(params) params_new[i] += delta - d_new = d_ansatz(params_new, d_type) + d_new = element_wise_d(params_new) grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta return grad @@ -35,7 +232,6 @@ def gradient_descent_dbr_d_ansatz( params, nmb_iterations, lr=1e-2, - d_type=d_ansatz_type.element_wise, normalize=True, ): r""" @@ -58,11 +254,11 @@ def gradient_descent_dbr_d_ansatz( params_hist(np.array): Parameters evaluated at each iteration. """ - d = d_ansatz(params, d_type, normalization=normalize) + d = element_wise_d(params, normalization=normalize) loss = np.zeros(nmb_iterations + 1) grad = np.zeros((nmb_iterations, len(params))) dbi_new = deepcopy(dbi_object) - s = polynomial_step(dbi_object, n=3, d=d) + s = dbi_object.choose_step(d=d) dbi_new(s, d=d) loss[0] = dbi_new.loss(0.0, d) params_hist = np.empty((len(params), nmb_iterations + 1)) @@ -70,11 +266,11 @@ def gradient_descent_dbr_d_ansatz( for i in range(nmb_iterations): dbi_new = deepcopy(dbi_object) - grad[i, :] = gradientDiagonalEntries(dbi_object, params, d_type=d_type) + grad[i, :] = gradientDiagonalEntries(dbi_object, params) for j in range(len(params)): params[j] = params[j] - lr * grad[i, j] - d = d_ansatz(params, d_type, normalization=normalize) - s = polynomial_step(dbi_new, n=3, d=d) + d = element_wise_d(params, normalization=normalize) + s = dbi_object.choose_step(d=d) dbi_new(s, d=d) loss[i + 1] = dbi_new.loss(0.0, d=d) params_hist[:, i + 1] = params diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 9d98c61d87..b74347099a 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -5,12 +5,6 @@ import hyperopt import numpy as np -from qibo.models.dbi.utils_analytical import ( - energy_fluctuation_polynomial_expansion_coef, - least_squares_polynomial_expansion_coef, - off_diagonal_norm_polynomial_expansion_coef, -) - error = 1e-3 diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_strategies.py index 3221f40791..10e8749044 100644 --- a/src/qibo/models/dbi/utils_strategies.py +++ b/src/qibo/models/dbi/utils_strategies.py @@ -1,9 +1,8 @@ import hyperopt from qibo.models.dbi.double_bracket import * -from qibo.models.dbi.utils import cs_angle_sgn, generate_pauli_operator_dict -from qibo.models.dbi.utils_analytical import * -from qibo.models.dbi.utils_gradients import gradient_Pauli +from qibo.models.dbi.utils import * +from qibo.models.dbi.utils_gradients import * from qibo.models.dbi.utils_scheduling import polynomial_step @@ -74,6 +73,53 @@ def select_best_dbr_generator( return dbi_eval, idx_max_loss, step_optimal, flip +def gradient_Pauli( + dbi_object, + d: np.array, + pauli_operator_dict: dict, + use_ds=False, + n=3, + **kwargs, +): + r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients + Args: + dbi_object (DoubleBracketIteration): the target dbi object + d (np.array): the diagonal operator + n_taylor (int): the highest order of the taylore expansion of w.r.t `s` + onsite_Z_ops (list): list of Pauli-Z operators + taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` + use_ds (boolean): if False, ds is set to 0 + """ + # n is the highest order for calculating s + + # pauli_index is the list of positions \mu + pauli_operators = list(pauli_operator_dict.values()) + num_paul = len(pauli_operators) + grad = np.zeros(num_paul) + coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) + s = polynomial_step(dbi_object, n=3, d=d) + + a, b, c = coef[len(coef) - 3 :] + + for i, operator in enumerate(pauli_operators): + da, db, dc, ds = ds_di_pauli( + dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] + ) + if use_ds is True: + ds = 0 + grad[i] = ( + s**3 / 3 * da + + s**2 / 2 * db + + 2 * s * dc + + s**2 * ds * a + + s * ds * b + + 2 * ds * c + ) + grad = np.array(grad) + grad = grad / np.linalg.norm(grad) + return grad, s + + def gradient_descent_pauli( dbi_object: DoubleBracketIteration, d_coef: list, @@ -147,37 +193,3 @@ def func_loss_to_lr(lr): d_coef = [d_coef[j] - grad[j] * lr for j in range(nqubits)] d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) return s, d_coef, d - - -def gradientDiagonal(dbi_object, d, H, n=3): - # Gradient of potential function with respect to diagonal elements of D (full-diagonal ansatz) - grad = np.zeros(len(d)) - for i in range(len(d)): - s = polynomial_step(dbi_object, n=3, d=d) - derivative = dpolynomial_diDiagonal(dbi_object, s, d, H, i) - grad[i] = d[i, i] - derivative - return grad - - -def gradient_ascent(dbi_object, d, step, iterations): - H = dbi_object.h.matrix - loss = np.zeros(iterations + 1) - grad = np.zeros((iterations, len(d))) - dbi_eval = deepcopy(dbi_object) - s = polynomial_step(dbi_object, n=3, d=d) - dbi_eval(s, d=d) - loss[0] = dbi_eval(d) - diagonals = np.empty((len(d), iterations + 1)) - diagonals[:, 0] = np.diag(d) - - for i in range(iterations): - dbi_eval = deepcopy(dbi_object) - grad[i, :] = gradientDiagonal(dbi_object, d, H) - for j in range(len(d)): - d[j, j] = d[j, j] - step * grad[i, j] - s = polynomial_step(dbi_object, n=3, d=d) - dbi_eval(s, d=d) - loss[i + 1] = dbi_eval.least_squares(d) - diagonals[:, i + 1] = np.diag(d) - - return d, loss, grad, diagonals diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 1fd60480d8..637dd2c9f7 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -3,6 +3,7 @@ import numpy as np import pytest +from qibo import set_backend from qibo.hamiltonians import Hamiltonian from qibo.models.dbi.double_bracket import ( DoubleBracketCostFunction, @@ -10,6 +11,13 @@ DoubleBracketIteration, DoubleBracketScheduling, ) +from qibo.models.dbi.utils import * +from qibo.models.dbi.utils_gradients import gradient_descent_dbr_d_ansatz +from qibo.models.dbi.utils_scheduling import polynomial_step +from qibo.models.dbi.utils_strategies import ( + gradient_descent_pauli, + select_best_dbr_generator, +) from qibo.quantum_info import random_hermitian NSTEPS = 1 @@ -77,7 +85,6 @@ def test_double_bracket_iteration_single_commutator(backend, nqubits): [ DoubleBracketScheduling.grid_search, DoubleBracketScheduling.hyperopt, - DoubleBracketScheduling.polynomial_approximation, DoubleBracketScheduling.simulated_annealing, ], ) @@ -95,6 +102,61 @@ def test_variational_scheduling(backend, nqubits, scheduling): assert initial_off_diagonal_norm > dbi.off_diagonal_norm +@pytest.mark.parametrize( + "cost", + [ + DoubleBracketCostFunction.off_diagonal_norm, + DoubleBracketCostFunction.least_squares, + ], +) +def test_polynomial_cost_function(backend, cost): + nqubits = 4 + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + cost=cost, + scheduling=DoubleBracketScheduling.polynomial_approximation, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + for i in range(NSTEPS): + s = dbi.choose_step(d=dbi.diagonal_h_matrix, n=5) + dbi(step=s, d=dbi.off_diag_h) + assert initial_off_diagonal_norm > dbi.off_diagonal_norm + + +def test_polynomial_energy_fluctuation(): + set_backend("numpy") + nqubits = 4 + h0 = random_hermitian(2**nqubits, seed=seed) + state = np.zeros(2**nqubits) + state[3] = 1 + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0), + mode=DoubleBracketGeneratorType.single_commutator, + cost=DoubleBracketCostFunction.energy_fluctuation, + scheduling=DoubleBracketScheduling.polynomial_approximation, + ref_state=state, + ) + for i in range(NSTEPS): + s = dbi.choose_step(d=dbi.diagonal_h_matrix, n=5) + dbi(step=s, d=dbi.off_diag_h) + assert dbi.energy_fluctuation(state=state) == 0.0 + + +@pytest.mark.parametrize("nqubits", [5, 6]) +def test_polynomial_fail_cases(backend, nqubits): + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + scheduling=DoubleBracketScheduling.polynomial_approximation, + ) + with pytest.raises(ValueError): + polynomial_step(dbi, n=2, n_max=1) + assert polynomial_step(dbi, n=1) == None + + def test_energy_fluctuations(backend): """Check energy fluctuation cost function.""" nqubits = 3 @@ -108,7 +170,7 @@ def test_energy_fluctuations(backend): def test_least_squares(backend): """Check least squares cost function.""" - nqubits = 3 + nqubits = 4 h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), @@ -119,3 +181,63 @@ def test_least_squares(backend): step = dbi.choose_step(d=d) dbi(d=d, step=step) assert dbi.least_squares(d=d) < initial_potential + + +@pytest.mark.parametrize("nqubits", [2, 3]) +def test_select_best_dbr_generator(backend, nqubits): + scheduling = DoubleBracketScheduling.grid_search + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + scheduling=scheduling, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + generate_local_Z = generate_Z_operators(nqubits) + Z_ops = list(generate_local_Z.values()) + for _ in range(NSTEPS): + dbi, idx, step, flip_sign = select_best_dbr_generator( + dbi, Z_ops, scheduling=scheduling, compare_canonical=True + ) + assert dbi.off_diagonal_norm < initial_off_diagonal_norm + + +@pytest.mark.parametrize("nqubits", [2, 3]) +def test_gradient_descent_pauli(backend, nqubits): + scheduling = DoubleBracketScheduling.grid_search + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + scheduling=scheduling, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + pauli_operator_dict = generate_pauli_operator_dict( + nqubits=nqubits, parameterization_order=2 + ) + d_coef = decompose_into_Pauli_basis( + dbi.h.matrix, list(pauli_operator_dict.values()) + ) + d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) + step, d_coef, d = gradient_descent_pauli(dbi, d_coef, d) + dbi(d=d, step=step) + assert dbi.off_diagonal_norm < initial_off_diagonal_norm + + +@pytest.mark.parametrize("nqubits", [2, 3]) +def test_gradient_descent_d_ansatz(backend, nqubits): + scheduling = DoubleBracketScheduling.polynomial_approximation + cost = DoubleBracketCostFunction.least_squares + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.single_commutator, + cost=cost, + scheduling=scheduling, + ) + params = np.linspace(1, 2**nqubits, 2**nqubits) + step = 1e-1 + + d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, 25, step) + + assert loss[-1] < loss[0] diff --git a/tests/test_models_dbi_strategies.py b/tests/test_models_dbi_strategies.py deleted file mode 100644 index 89aad6ea9f..0000000000 --- a/tests/test_models_dbi_strategies.py +++ /dev/null @@ -1,63 +0,0 @@ -"""Testing DoubleBracketIteration strategies""" - -import numpy as np -import pytest - -from qibo.hamiltonians import Hamiltonian -from qibo.models.dbi.double_bracket import ( - DoubleBracketCostFunction, - DoubleBracketGeneratorType, - DoubleBracketIteration, - DoubleBracketScheduling, -) -from qibo.models.dbi.utils import * -from qibo.models.dbi.utils_strategies import ( - gradient_descent_pauli, - select_best_dbr_generator, -) -from qibo.quantum_info import random_hermitian - -NSTEPS = 1 -seed = 5 -"""Number of steps for evolution.""" - - -@pytest.mark.parametrize("nqubits", [2, 3]) -def test_select_best_dbr_generator(backend, nqubits): - scheduling = DoubleBracketScheduling.grid_search - h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.single_commutator, - scheduling=scheduling, - ) - initial_off_diagonal_norm = dbi.off_diagonal_norm - generate_local_Z = generate_Z_operators(nqubits) - Z_ops = list(generate_local_Z.values()) - for _ in range(NSTEPS): - dbi, idx, step, flip_sign = select_best_dbr_generator( - dbi, Z_ops, scheduling=scheduling, compare_canonical=True - ) - assert dbi.off_diagonal_norm < initial_off_diagonal_norm - - -@pytest.mark.parametrize("nqubits", [2, 3]) -def test_gradient_descent_pauli(backend, nqubits): - scheduling = DoubleBracketScheduling.grid_search - h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.single_commutator, - scheduling=scheduling, - ) - initial_off_diagonal_norm = dbi.off_diagonal_norm - pauli_operator_dict = generate_pauli_operator_dict( - nqubits=nqubits, parameterization_order=2 - ) - d_coef = decompose_into_Pauli_basis( - dbi.h.matrix, list(pauli_operator_dict.values()) - ) - d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) - step, d_coef, d = gradient_descent_pauli(dbi, d_coef, d) - dbi(d=d, step=step) - assert dbi.off_diagonal_norm < initial_off_diagonal_norm diff --git a/tests/test_models_dbi_utils_scheduling.py b/tests/test_models_dbi_utils_scheduling.py deleted file mode 100644 index e928ca9771..0000000000 --- a/tests/test_models_dbi_utils_scheduling.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Unit testing for utils_scheduling.py for Double Bracket Iteration""" - -import numpy as np -import pytest - -from qibo.hamiltonians import Hamiltonian -from qibo.models.dbi.double_bracket import ( - DoubleBracketGeneratorType, - DoubleBracketIteration, - DoubleBracketScheduling, -) -from qibo.models.dbi.utils_scheduling import polynomial_step -from qibo.quantum_info import random_hermitian - -NSTEPS = 1 -SEED = 10 -"""Number of steps for evolution.""" - - -@pytest.mark.parametrize("nqubits", [5, 6]) -def test_polynomial_fail_cases(backend, nqubits): - h0 = random_hermitian(2**nqubits, backend=backend, seed=SEED) - dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.single_commutator, - scheduling=DoubleBracketScheduling.polynomial_approximation, - ) - with pytest.raises(ValueError): - polynomial_step(dbi, n=2, n_max=1) - assert polynomial_step(dbi, n=1) == None From dfb70a4676ad5152b498af1f6910c35aa78ea2ba Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 27 May 2024 11:24:11 +0800 Subject: [PATCH 085/154] Fix hamiltonians indentation --- src/qibo/hamiltonians/hamiltonians.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibo/hamiltonians/hamiltonians.py b/src/qibo/hamiltonians/hamiltonians.py index c54bdae6e9..13366aa101 100644 --- a/src/qibo/hamiltonians/hamiltonians.py +++ b/src/qibo/hamiltonians/hamiltonians.py @@ -373,7 +373,7 @@ def dense(self): "Calculating the dense form of a symbolic Hamiltonian. " "This operation is memory inefficient." ) - self.dense = self.calculate_dense() + self.dense = self.calculate_dense() return self._dense @dense.setter From 34a85c4e8036ba68f5d95608ca18f5134f6bcafd Mon Sep 17 00:00:00 2001 From: MatteoRobbiati Date: Mon, 27 May 2024 11:02:34 +0200 Subject: [PATCH 086/154] feat: add callback to sgd optimizer --- src/qibo/optimizers.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/src/qibo/optimizers.py b/src/qibo/optimizers.py index 0b5c46c778..8898353085 100644 --- a/src/qibo/optimizers.py +++ b/src/qibo/optimizers.py @@ -90,7 +90,7 @@ def myloss(parameters, circuit): backend = _check_backend(backend) - return sgd(loss, initial_parameters, args, options, compile, backend) + return sgd(loss, initial_parameters, args, callback, options, compile, backend) else: from qibo.backends import _check_backend @@ -213,7 +213,15 @@ def newtonian( return m.fun, m.x, m -def sgd(loss, initial_parameters, args=(), options=None, compile=False, backend=None): +def sgd( + loss, + initial_parameters, + args=(), + callback=None, + options=None, + compile=False, + backend=None, +): """Stochastic Gradient Descent (SGD) optimizer using Tensorflow backpropagation. See `tf.keras.Optimizers `_ @@ -225,6 +233,7 @@ def sgd(loss, initial_parameters, args=(), options=None, compile=False, backend= initial_parameters (np.ndarray): Initial guess for the variational parameters. args (tuple): optional arguments for the loss function. + callback (callable): Called after each iteration. options (dict): Dictionary with options for the SGD optimizer. Supports the following keys: @@ -265,6 +274,7 @@ def opt_step(): for e in range(sgd_options["nepochs"]): l = opt_step() + callback(vparams) if e % sgd_options["nmessage"] == 1: log.info("ite %d : loss %f", e, l.numpy()) From d45cdce9c77be05e89e78cc012275199cb377ca4 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Mon, 27 May 2024 20:34:35 +0800 Subject: [PATCH 087/154] change names --- ...t_functions_and_d_gradients_tutorial.ipynb | 542 +++++++++--------- .../dbi/dbi_gradient_descent_strategies.ipynb | 0 src/qibo/models/dbi/double_bracket.py | 2 +- src/qibo/models/dbi/utils_gradients.py | 19 +- 4 files changed, 280 insertions(+), 283 deletions(-) create mode 100644 examples/dbi/dbi_gradient_descent_strategies.ipynb diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index c604ea1fd2..de926d7ca0 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -39,9 +39,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.9|INFO|2024-05-27 11:26:20]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -62,9 +70,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.6060645454545454\n", + "100%|██████████| 100/100 [00:00<00:00, 1014.35trial/s, best loss: -30.63379156124853]\n", + "hyperopt_search step: 0.5998426993650685\n", + "polynomial_approximation step: 0.5267891182131145\n" + ] + } + ], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", @@ -90,9 +109,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.6060645454545454\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -126,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -148,9 +195,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Hjhyhffv2DBo0CIvFQuvWrW3nhoaaYyYDAwOJiIg4Zx01XWPRokXs3LmTQ4cOER0dDcCkSZMYOXJknX7Wli1b8uijj9qeP/jgg8yfP58vv/ySvn372o63a9eOF1980fb8H//4R433KC8vj7KyMsaMGWOru2vXrnWqralQy009K7W2AMDI1/5SIiL1Zf369SxduhRfX1/bo2PHjgC2rqf9+/dz22230aZNG/z9/YmLiwPMQALmgOVNmzbRoUMHHnroIRYsWFDnOmq6xs6dO2nVqpUt2AD079+/zp9RXl7O888/T7du3QgODsbX15cFCxbYfo4qvXv3rvb8t+5R9+7dueyyy+jatSs33ngj7777LpmZzrmivlpu6pnhZYYbCjPsW4iISBV3b7MFxV6fXQ8qKioYNWoUU6ZMOeO1yMhIAEaNGkVMTAzvvvsuUVFRVFRUkJCQYBts26tXLw4ePMi8efNYtGgRN910E8OHD+err76qdR01XeNs3TsWi6XacxcXs03h9HNLS6svHTJ16lT+/e9/88orr9C1a1d8fHyYOHHiGYOGfXyqt4j91j1ydXVl4cKFrFy5kgULFvD666/z1FNPsXr1alsQdBYKN/XM4hMMgGuRwo2IOAiLpV66huypV69efP3118TGxuLmduafrvT0dHbu3Mk777zDJZdcAsDPP/98xnn+/v7cfPPN3Hzzzfzud7/jyiuvJCMjg6CgINzd3SkvL//NWs51jc6dO3PkyBGOHz9OVJTZDfjLL79Ue29V91dycjItWpj/Z3jTpk3Vzlm+fDnXXXcdd9xxB2CGlr1799KpU6cLukdghq2BAwcycOBA/vGPf9C6dWvmzJnDI4888ps/d1Oibql65uZr/uJaS7LsW4iISBOUnZ3Npk2bqj2OHDnCAw88QEZGBrfeeitr1qzhwIEDLFiwgLvvvpvy8nJatGhBcHAw//nPf9i3bx9Lliw54w/2v//9b2bNmsWuXbvYs2cPX375JREREQQGBgLmjKnFixeTkpJyzu6amq4xfPhwOnTowF133cXmzZtZvnw5Tz31VLX3t2vXjpiYGJ5++mn27NnD999/z9SpU884p6qFZefOnfzxj38kJSXlN+/db92j1atXM2nSJNatW8eRI0eYPXs2J0+e/M3Q1BQ5VLiZPn063bp1w9/fH39/f/r378+8efNsr48bNw6LxVLt0a9fPztWfCZPf22eKSJyvhITE+nZs2e1xz/+8Q+ioqJYsWIF5eXlXHHFFSQkJPDwww8TEBCAi4sLLi4uzJo1i/Xr15OQkMCf//xnXnrppWrX9vX1ZcqUKfTu3Zs+ffpw6NAhfvjhB1tX0dSpU1m4cCExMTH07NnzrPXVdA0XFxfmzJlDcXExF198MX/4wx94/vnnq73f3d2dzz77jF27dtG9e3emTJnCc889V+2cv//97/Tq1YsrrriCoUOHEhERUavFDX/rHvn7+/PTTz9x1VVXER8fz9/+9jemTp1a5wHPTYHFcKA5YN9++y2urq60a9cOMKe5vfTSS2zcuJEuXbowbtw4Tpw4wYwZM2zv8fDwICgoqNafkZOTQ0BAANnZ2fj7+9f7z5C+8TuC/3c72yti6fzMpjP6W0VEGlJRUREHDx4kLi4OT09Pe5cjmF1Bc+bMsevqy01FTb+/dfn77VBjbkaNGlXt+fPPP8/06dNZtWoVXbp0AcBqtdY4Te/XiouLKS4utj3Pycmpn2LPwbeFub9UgCWPnMIyArzdG/TzREREpDqH6pY6XXl5ObNmzSI/P7/aVLrExETCwsKIj49n/PjxpKam1nidyZMnExAQYHvExMQ0aN1V+0sFkUt6fvFvnC0iIiL1zaFabgC2bt1K//79KSoqwtfXlzlz5tC5c2cARo4cyY033kjr1q05ePAgf//737n00ktZv349Vqv1rNf761//Wm1QWU5OTsMGHG+zi8zbUkxmdg6E+jbcZ4mIiMNzoNEfzYbDhZsOHTqwadMmsrKy+Prrrxk7dizLli2jc+fO3HzzzbbzEhIS6N27N61bt+b7779nzJgxZ72e1Wo9Z/BpEFZ/ynDFjXJyM1MBO60KKiIi0kw5XLeUh4cH7dq1o3fv3kyePJnu3bvz6quvnvXcyMhIWrduzd69exu5yhpYLORV7i9VmF1zl5mISENRa4E0RfX1e+tw4ebXDMOoNiD4dOnp6SQlJdlWp3QURW5muNH+UiLS2NzdzUkMBQUFdq5EpO6qfm+rfo/Pl0N1Sz355JOMHDmSmJgYcnNzmTVrFomJicyfP5+8vDyefvppbrjhBiIjIzl06BBPPvkkISEhXH/99fYuvZoSjxZQfJDSXO0MLiKNy9XVlcDAQNtkC29vby1JIQ7PMAwKCgpITU0lMDAQV1fXC7qeQ4WbEydOcOedd5KcnExAQADdunVj/vz5jBgxgsLCQrZu3cp///tfsrKyiIyMZNiwYXz++ef4+fnZu/Rqyj0DIReMAm2eKSKNr2q5jN+aTSriaH5rV/bacqhw8/7775/zNS8vL3788cdGrOb8GV7m/lIuhc6526qIODaLxUJkZCRhYWFnbMoo4qjc3d0vuMWmikOFG2fh4mtuweBWrHAjIvbj6upab38sRJoShx9Q3BR5+JktN56lWfYtREREpBlSuGkAngHmKsXeZdmajikiItLIFG4agE9gOACB5JBbXGbnakRERJoXhZsGULW/VAtLHhl5JXauRkREpHlRuGkIXi0AaEEu6fkKNyIiIo1J4aYheJsDin0sxWTl5Nq5GBERkeZF4aYheAZQXnlr87NO2LkYERGR5kXhpiFYLBTYNs/UFgwiIiKNSeGmgRR5BALaPFNERKSxKdw0kLLKcFORr3AjIiLSmBRuGki5VxAARoG2YBAREWlMCjcNxFI5Y8qtSDuDi4iINCaFmwbiVrl5pkdJln0LERERaWYUbhpI1SrFnqXaX0pERKQxKdw0EK9As+UmwMghv6TcztWIiIg0Hwo3DcTqFwZAoCWP1JwiO1cjIiLSfCjcNJTKAcVB5HIko8DOxYiIiDQfCjcNxducCt7CksvhdIUbERGRxqJw01Aqw42vpYikk1rrRkREpLEo3DQUawAVFlcAMk6m2LkYERGR5kPhpqG4uFDi0xIAI32fnYsRERFpPhRuGpAR1gmAwLx9lFdorRsREZHGoHDTgDwjuwDQzkjieFahnasRERFpHhRuGpAlvDMA7V2Ocig9387ViIiINA8KNw0prCMAHSxJHEpTuBEREWkMCjcNKbg9FbgSYCkgI/mQvasRERFpFhRuGpK7J7k+rQAoP7HLzsWIiIg0Dwo3DawkKB4A76zddq5ERESkeVC4aWAekQkAhBQeoELTwUVERBqcwk0D843pCkA7kkjR7uAiIiINTuGmgblGVE4HtxzlUFqunasRERFxfgo3DS2oDaW442MpJu2otmEQERFpaAo3Dc3VnXSv1gAUH99u52JEREScn8JNI8gPaAeAW5qmg4uIiDQ0hZtGYAk1x90E5KpbSkREpKEp3DQC72hzOnhEySEMQ9PBRUREGpLCTSMIiusBQFuOcTK7wL7FiIiIODmFm0bgERJHER5YLaUkH9ph73JEREScmsJNY3Bx4bi7OWMq78hWOxcjIiLi3Bwq3EyfPp1u3brh7++Pv78//fv3Z968ebbXDcPg6aefJioqCi8vL4YOHcr27U1jenWWrzljykhVy42IiEhDcqhwEx0dzQsvvMC6detYt24dl156Kdddd50twLz44otMmzaNN954g7Vr1xIREcGIESPIzXX8lX9LgzsC4JW1186ViIiIODeHCjejRo3iqquuIj4+nvj4eJ5//nl8fX1ZtWoVhmHwyiuv8NRTTzFmzBgSEhKYOXMmBQUFfPrpp/Yu/Td5RJrTwUMKD9i5EhEREefmUOHmdOXl5cyaNYv8/Hz69+/PwYMHSUlJ4fLLL7edY7VaGTJkCCtXrjzndYqLi8nJyan2sIcWsd0BiCo7hlFWbJcaREREmgOHCzdbt27F19cXq9XKfffdx5w5c+jcuTMpKSkAhIeHVzs/PDzc9trZTJ48mYCAANsjJiamQes/l4iYtuQYXrhbyslK2mmXGkRERJoDhws3HTp0YNOmTaxatYo//elPjB07lh07Tg3CtVgs1c43DOOMY6f761//SnZ2tu2RlJTUYLXXxNPDjcMurQDIPLTZLjWIiIg0B272LuDXPDw8aNfOnFnUu3dv1q5dy6uvvsoTTzwBQEpKCpGRkbbzU1NTz2jNOZ3VasVqtTZs0bV00qsNFOym+Pg2e5ciIiLitByu5ebXDMOguLiYuLg4IiIiWLhwoe21kpISli1bxoABA+xYYe3lBVbOmDq5xc6ViIiIOC+Harl58sknGTlyJDExMeTm5jJr1iwSExOZP38+FouFiRMnMmnSJNq3b0/79u2ZNGkS3t7e3HbbbfYuvVbKovvCcQjP3oxRVoLFzcPeJYmIiDgdhwo3J06c4M477yQ5OZmAgAC6devG/PnzGTFiBACPP/44hYWF3H///WRmZtK3b18WLFiAn5+fnSuvnUEDh5C12odA8tm6bjld+11m75JEREScjsVoZttU5+TkEBAQQHZ2Nv7+/o3++TunXUOnnOV85n83tz7y70b/fBERkaaoLn+/HX7MjbOJ7DEcgIjM9Ww8kmnnakRERJyPwk0jC+x0KQB9XHYzfcluO1cjIiLifBRuGlt4F8qtAfhaijixezU7k+2zYrKIiIizUrhpbC6uuMYOBKCvy07eXLrPzgWJiIg4F4Ube4gdBEA/l518vzWZ/Sfz7FyQiIiI81C4sYfWZstNf7c9uBjlTE/cb+eCREREnIfCjT1EdAVrAF5GAZ0th/lm4zEy8kvsXZWIiIhTULixBxdXaG1uGXGl717KKgx1TYmIiNQThRt7qRxU3N9lJwBHMwvsWY2IiIjTULixl8pBxZ1Kt+NCBUczCu1ckIiIiHNQuLGXiG5g9cerIp/OlkMkqeVGRESkXijc2IuLK7TqD5hTwo9mquVGRESkPijc2FNl11RfhRsREZF6o3BjT7Zws4uUrHzKK5rVBu0iIiINQuHGniK6YVj98bcU0ME4SEpOkb0rEhERafIUbuzJ1Q1LZevNQJdtHM3QoGIREZELpXBjb22GAma4SdK4GxERkQumcGNvcUMAuNhlN8lpGXYuRkREpOlTuLG30A7ke4RgtZTifnydvasRERFp8hRu7M1iISPc3GcqIn2VnYsRERFp+hRuHEBZ7GAA4vPX27kSERGRpk/hxgH4dRwOQMeK/ZTladyNiIjIhVC4cQBBkbHsM1riYjHI2rHY3uWIiIg0aQo3DsDFxcIWjx4AlO1bat9iREREmjiFGwdxOOBiAHyO/WznSkRERJo2hRsHkRPRl3LDgl/+YchKsnc5IiIiTZbCjYMIDQlls9HWfHJwmX2LERERacIUbhxEdAtvfq5IMJ8cSLRrLSIiIk2Zwo2DiGnhxYryruaTA4lgGHatR0REpKlSuHEQ0S282Wi0o8CwQv5JSN1h75JERESaJIUbBxHi64GLu5U1FR3NA+qaEhEROS8KNw7CYrFUH3ezZ759CxIREWmiFG4cSHQLL36s6G0+OfQz5KXatyAREZEmSOHGgUS38CLJCCfFpxMYFbBzrr1LEhERaXIUbhxITAtvAFZ7DzEPbJtjx2pERESaJoUbBxJdGW7mGf3MA4dXQG6KHSsSERFpehRuHEh0Cy8ANub4QXQfwIAd/7NvUSIiIk2Mwo0DqQo3J3KKKes02jy4bbb9ChIREWmCFG4cSJCPB94ergAcb3mFeTBpFWQfs2NVIiIiTYvCjQMx17oxW28OlQRCq/7mCzu+sVtNIiIiTY3CjYOpGlR8NLMQulxvHtyuWVMiIiK15VDhZvLkyfTp0wc/Pz/CwsIYPXo0u3fvrnbOuHHjsFgs1R79+vWzU8X1L6ay5eZoZgF0vg6wwNG1kHXEvoWJiIg0EQ4VbpYtW8YDDzzAqlWrWLhwIWVlZVx++eXk5+dXO+/KK68kOTnZ9vjhhx/sVHH9iwkyW24OpeeDXwTEDjJfUOuNiIhIrbjZu4DTzZ9ffT+lGTNmEBYWxvr16xk8eLDtuNVqJSIiorHLaxSdI/0B2HI02zzQZTQcWm6Gm4EP268wERGRJsKhWm5+LTvb/AMfFBRU7XhiYiJhYWHEx8czfvx4UlPPvQdTcXExOTk51R6OrGt0ABaLOeYmLa8YOl0HFhc4vhHS99u7PBEREYfnsOHGMAweeeQRBg0aREJCgu34yJEj+eSTT1iyZAlTp05l7dq1XHrppRQXF5/1OpMnTyYgIMD2iImJaawf4bz4ebrTLtQXgM1JWeAbCm2GmS9unmW/wkRERJoIhw03EyZMYMuWLXz22WfVjt98881cffXVJCQkMGrUKObNm8eePXv4/vvvz3qdv/71r2RnZ9seSUlJjVH+BekeEwhUhhuAHreZXzd/BhUVdqlJRESkqXDIcPPggw8yd+5cli5dSnR0dI3nRkZG0rp1a/bu3XvW161WK/7+/tUejq5HZbjZWBVuOl4N1gDIToLDP9utLhERkabAocKNYRhMmDCB2bNns2TJEuLi4n7zPenp6SQlJREZGdkIFTaOHqe13BiGAe5ekFC55s2mT+1XmIiISBPgUOHmgQce4OOPP+bTTz/Fz8+PlJQUUlJSKCwsBCAvL49HH32UX375hUOHDpGYmMioUaMICQnh+uuvt3P19adDhB9WNxdyiso4lF5gHuxxu/l1x1wozrNfcSIiIg7OocLN9OnTyc7OZujQoURGRtoen3/+OQCurq5s3bqV6667jvj4eMaOHUt8fDy//PILfn5+dq6+/ri7upDQMgCATUmZ5sHoPhDUFkrztVO4iIhIDRxqnRvDMGp83cvLix9//LGRqrGv7tGBrD+cyeakbK7vGQ0WizmweMmzZtdUz9vtXaKIiIhDcqiWGzmle0xVy03WaQdvASzmoOLMQ/YoS0RExOEp3DionjEtANhxPIfisnLzYEA0tBlifq81b0RERM6qTt1ScXFxWCyWOn/IxIkTeeihh+r8vuYsJsiLFt7uZBaUsis517b2DT1uhwOJZtfU4MfBRflURETkdHUKNx9++OF5fUhsbOx5va85s1gsdI8JJHH3STYfzToVbjpeAx5+kHUYjvwCsQPtWqeIiIijqVO4GTJkSEPVIWfRozLcbDqSxV39Kw96eJubaW78CNbPULgRERH5FfVpOLCq1ppNR7Oqv9DnD+bXrV9ByrZGrUlERMTRKdw4sO7RgQAcOJlPdmHpqReiekCXMYABi5+xR2kiIiIOSwOKHViQjwetg705nF7AlqNZXNI+9NSLl/4Nds6FvQvg4HKIu8R+hYqIiDgQDSh2cN2jAzmcXsDmpF+Fm+C2cNE4WPseLPon/GGxudCfiIhIM6cBxQ6ue0wgczcfZ1NS9pkvDn4cNn0Gx9abrTidr2v8AkVERBzMBY25KS0tJSkpid27d5ORkVFfNclpqnYI31S1Q/jp/MJhwATz+8X/gvJSREREmrs6h5u8vDzeeecdhg4dSkBAALGxsXTq1InQ0FBat27N+PHjWbt2bUPU2ix1ifLHzcVCWl4xRzMLzzyh/wTwDoH0feb0cBERkWauTuHm3//+N7Gxsbz77rtceumlzJ49m02bNrFnzx5++eUX/vnPf1JWVsaIESO48sor2bt3b0PV3Wx4urvSs1UgAMv2nDzLCf4w5HHz+8QXoDi38YoTERFxQBbjt7biPs2NN97IP/7xD7p27VrjecXFxbz//vt4eHjwhz/84YKLrE85OTkEBASQnZ2Nv7+/vcuplTeX7uOlH3dzWccw3h/X58wTykrgzT7mZpq974FrpjV6jSIiIg2pLn+/6xRunEFTDDc7k3MY+epyPN1d2PSPy/F0dz3zpAPL4L/Xmt/fNffUBpsiIiJOoC5/v+ttEb8ZM2bU16XkVzpG+BEZ4ElRaQWrDqSf/aQ2Q8xWG4C5E6A4r/EKFBERcSD1Fm5mz57N0qVLbc8LCwu5/fbb6+vyzZrFYmFohzAAEnefZdxNlRHPQEAryDpirn0jIiLSDNVbuPn444956qmn2LVrF3v37uWSSy5h6NCh9XX5Zm9YB3MBvyW7Us+cEl7F6gfXvWF+v/Y9OPhTI1UnIiLiOOq0iN/ZPPLII/To0YMePXrwwQcfcNttt2EYBh988AE9e/asjxoFGNguBA9XF45kFLD/ZD7twnzPfmKbIdD7blj3AfzvAfjTL2A9x7kiIiJO6IJbboYMGcLhw4d59tlnufHGGzl8+DBRUVEsWLCA77//vj5qFMDH6kbfNkEAJO5OrfnkEf861T214KlGqE5ERMRxXHC4ue666/j73//Ol19+ydatWzl69ChPP/00ISEhLFq0qD5qlErDKsfdLNn1G+HG1j1lgfUfwtavGrw2ERERR1HnqeCGYZzXzuCOoilOBa9yKC2foS8n4u5qYcPfR+Dn6V7zG5Y8Bz+9BB6+cG8ihLRvlDpFRETqW4NOBff19WXgwIE89NBDzJw5k23btlFRUXHexUrtxYb4EBfiQ2m5wYp9ab/9hqF/hdhLoCQPvhwHpWfZvkFERMTJ1DncvPTSS3Tq1Inly5czfvx4unfvjp+fH/3792fChAnMmDGDzZs3N0StQh26pgBcXOGG98AnFE5sg3mPN3B1IiIi9ndBKxQXFxfj5eXFk08+SUZGBhs2bGDLli0UFxdTXl5en3XWm6bcLQWwfO9J7nx/DaF+VtY8eVntuggPJMJ/RwMGXP8f6H5zA1cpIiJSv+ry9/uCpoJbrVYAbrrpJrp16wZAeXk527dvv5DLSg0ujgvC28OVk7nFbD+eQ0LLgN9+U5uhMOQJWPYCfDcRIhIgvEtDlyoiImIX9baIXxVXV1db0JH6Z3VzZWC7EKCWXVNVhjxuhpzSAvjsVijIaJgCRURE7Kzew400vOGdzHE3324+fu7Vin/NxRV+NwNaxELWYfhyLJSXNlyRIiIidlLncDN+/Hjefvtt1q1bR3FxMUCTnhreFI3sGonVzYW9qXlsPppd+zd6B8Etn5lTww/+BAv+1nBFioiI2Emdw83u3bt54oknuPjii/Hz8wPgmWee4Y033mDlypUUFBTUe5FSnb+nO1d1jQTgi3VJdXtzeGe4/h3z+9Vvw4aP6rk6ERER+zrv2VJ79+5l/fr1bNiwgfXr17Nx40aysrJwdXUlPj7eYQcVN/XZUlVW7k/jtndX42d1Y81Tw/HycK3bBRKnQOIkcHGHcd9Bq34NU6iIiEg9aJTZUu3bt6d9+/bccssttmMHDx5k3bp1bNy48XwvK7XULy6YmCAvkjIKmbctmTG9out2gcGPmWvf7JwLn94EY7+FyO4NU6yIiEgjqlO31JEjR2p8PS4ujhtvvJFJkyYBcOzYsfOvTGrk4mLhxotigPPomjIvANe/DTH9oCjbXAfnhGO2tomIiNRFncJNnz59GD9+PGvWrDnnOdnZ2bz77rskJCQwe/bsCy5Qzu2Gi6KxWGDVgQwOp+fX/QIePnD7l9DyIijMgP9eByf31H+hIiIijahO3VI7d+5k0qRJXHnllbi7u9O7d2+ioqLw9PQkMzOTHTt2sH37dnr37s1LL73EyJEjG6puAVoGejGoXQjL96bx1fqj/N/lHep+EU9/uONrmHktpGyBmaPg9z9AcNv6L1hERKQRnNeA4qKiIn744QeWL1/OoUOHKCwsJCQkhJ49e3LFFVeQkJDQELXWC2cZUFzl283HefCzjUQGePLzE5fi6nKe0/Lz02HmNZC6A/yj4a7/QUi7+i1WRETkPNXl7/d5z5bKyclpkuHA2cJNcVk5fSctJquglA9/34ehlRtrnpe8VPjwakjbA94hcMdXENWz/ooVERE5T3X5+33eKxS3aNGCr7/++nzfLvXE6ubK6B4tAfhy3dELu5hvGIz7ASJ7QEEafHiNuemmiIhIE3Le4cYwDKZPn07fvn3p168fEyZMYPXq1fVZm9TSjb3NaeALdqSQnld8YRfzDTXXvYkbAiV58MmNsH1OPVQpIiLSOC5ob6nNmzdz8cUXM3ToUHbv3s2QIUP485//XF+1SS11iQqgW3QApeUGM385fOEXtPqZs6g6XwflJfDl72H1O3B+PZgiIiKN6oLCzaeffsrrr7/OCy+8wMKFC1m7di1z585l6tSp9VWf1NIfB5uzm2auPER+cdmFX9DNam602ftuwIB5j8O3D0NZyYVfW0REpAGdd7gJDg4mJiam2rGuXbvy2muv8fbbb5/XNSdPnkyfPn3w8/MjLCyM0aNHs3v37mrnGIbB008/TVRUFF5eXgwdOtRht3poTFcmRBAb7E12YSmz1p7Hon5n4+IKV0+DEc8CFtgwE/57LeSdrJ/ri4iINIDzDjfdu3fn/fffP+N4u3btSEo6vz+uy5Yt44EHHmDVqlUsXLiQsrIyLr/8cvLzTy1Q9+KLLzJt2jTeeOMN1q5dS0REBCNGjCA3N/d8fxSn4Opi4d7K1pv3lh+gpKyifi5sscDAh+C2L8DqD0d+gXeHQfKW+rm+iIhIPTvvqeCrVq1i2LBhXH/99dx///1069aNwsJCHn/8cdasWcPOnTsvuLiTJ08SFhbGsmXLGDx4MIZhEBUVxcSJE3niiScAKC4uJjw8nClTpvDHP/7xjGsUFxdTXHxqkG1OTg4xMTFOMxX8dEWl5Vzy4lJO5hbz0u+6cWPvmN9+U12c3AOf3QIZ+8HdG659Hbr+rn4/Q0RE5CwaZSp4v379WLVqFcePH2fo0KG0aNGCqKgovvrqq3obc5OdnQ1AUFAQYG7MmZKSwuWXX247x2q1MmTIEFauXHnWa0yePJmAgADb49ddac7E092VewbFAfD2sv1UVNTzAODQeBi/GNpeCqUF8PU9MO8JKC+t388RERG5AOfdcnO61NRU1q9fT0VFBX379iUkJOSCCzMMg+uuu47MzEyWL18OwMqVKxk4cCDHjh0jKirKdu69997L4cOH+fHHH8+4TnNquQHILSplwAtLyC0q4z93XsTlXSLq/0MqymHp87C8MsTG9IObZoJfA3yWiIgIjdRyc7qwsDBGjhzJ1VdfXS/BBmDChAls2bKFzz777IzXLJbqWwwYhnHGsSpWqxV/f/9qD2fm5+nOnf1aA/BW4n7qIbueycUVLvsH3PKpOQ4naRW8MxgOraj/zxIREamjegk39e3BBx9k7ty5LF26lOjoaNvxiAizZSAlJaXa+ampqYSHhzdqjY7s9wPj8HBzYVNSFqsPZjTcB3W8Gu5NhLDOkHfC3Jtq6WQor4ep6CIiIuepTuEmLi6ONm3a1Pnx2muv1er6hmEwYcIEZs+ezZIlS4iLizvj8yMiIli4cKHtWElJCcuWLWPAgAF1+VGcWqiflRsvMkPhK4v2NEzrTZXgtvCHRdD9NjAqYNkLZsjJqqfp6CIiInXkVpeTP/zww/P6kNjY2Fqd98ADD/Dpp5/yv//9Dz8/P1sLTUBAAF5eXlgsFiZOnMikSZNo37497du3Z9KkSXh7e3PbbbedV23O6k9D2/Ll+qOsOpDBwh0nGmbsTRUPH7h+ujnQ+Ls/m9PF3x5ozqbqfF3Dfa6IiMhZ1MuA4vpyrnEzM2bMYNy4cYDZuvPMM8/wzjvvkJmZSd++fXnzzTdJSEio1Wc4267gNXlx/i7eStxPXIgPP04cjIdbI/RCZhw0Z1EdW28+73UXXDEZrL4N/9kiIuK06vL326HCTWNoTuEmt6iUYS8nkpZXwt+v6WybJt7gykvN2VQ/vwIYENQWbngPWvZqnM8XERGn02DhJi4u7pytKzWZOHEiDz30UJ3f1xCaU7gB+GzNEf46eysBXu4se2wogd4ejffhB3+C2X+E3OPg4gbDnoKBD5uzrUREROqgwcLNsmXLzqug2NhYWrdufV7vrW/NLdyUVxhc/dpydqXkMm5ALE9f26VxCyjIgO8mwo7/mc9bDYDRb0JQm8atQ0REmjR1S9WguYUbgJ/3pnHH+6txc7Hw458H0za0kce/GAZs+gR+eBxK882tG0b8C3rfAy4OuRqBiIg4mEZfxE8c26D2IVzaMYyyCoPJP1z4nl91ZrFAzzvg/pXQepC5dcMPj8JHozVlXERE6p3CTTPx5FWdcHWxsGhnKot2nLBPES1iYey3cOUUcPOCg8vgrf6wfqbZuiMiIlIPFG6aiXZhvrbZUk/O2Up2gZ02u3RxgX73wZ9WQExfKMmFbx+CT34H2cfsU5OIiDgVhZtm5JER8bQJ9SE1t5hnvt1u32KC28Lv58Hlz4GrFfYtMltxNn2qVhwREbkgCjfNiKe7Ky/f2B0XC8zeeMx+3VNVXFxhwINw38/QsjcUZ8M3f4JPb9JYHBEROW8KN81Mr1YtGH+JOQ37r3O2klVQYueKgNB4uPtHGP4MuHrA3gXwVj9Y/R+oqLB3dSIi0sQo3DRDfx4RT9tQH07mFvPMtzvsXY7J1Q0GTTRbcWL6QUkezHsMZlwJJ3fbuzoREWlCFG6aodO7p+ZsPMaC7Sn2LumU0A7mWJyrXgYPX0haDW8PgsQpUOYArUwiIuLwFG6aqZ6tWjB+sNk99dhXWzicnm/nik7j4gIXj4cHVkP7K6C8BBInwX+GwNF19q5OREQcnMJNM/bIiHh6tgoku7CUP360noKSMnuXVF1ANNz2OdzwPngHQ+oOeG84zPsLFOfZuzoREXFQCjfNmNXNlbfvuIhQPyu7UnJ57KstONxuHBYLdP0dPLAWut0CGLB6ujngePc8e1cnIiIOSOGmmQv392T67b1wc7Hw/ZZk3vnpgL1LOjufYBjzDtz+NQS0guwk+OwWmHW7Fv8TEZFqFG6E3rFB/LNyt/AX5+/ipz0n7VxRDdoPhwdWwcCHweIKu76DNy+GX96CcgfrVhMREbtQuBEA7ujbipt7x1BhwIOfbWRfqgOPafHwMXcVv285RF9sThv/8a/w7jA4tt7e1YmIiJ0p3AgAFouFZ67rQo8Yc4Dx2A/WkJxdaO+yahbexVz875pXwDMAUrbAu5fB949CUba9qxMRETtRuBEbT3dX3h/bmzYhPhzLKmTsB2scYwXjmri4QO/fw4R10O1mwIC178IbF8O22dqnSkSkGVK4kWqCfa38956LCfe3sudEHvfMXEdhSbm9y/ptvmEw5j9w5zcQ1BbyUuCr38NHoyFtr72rExGRRqRwI2eIbuHNf+/ui7+nG+sPZ/LApxsoLW8iezy1HQZ/WglD/2ruNn4g0dxtfNEzUOJACxWKiEiDUbiRs+oQ4ccH4/rg6e7Ckl2p/N8XmylrKgHH3ROG/uXUCscVpfDzNLOrasdcdVWJiDg5hRs5p96xQbxVuQbO3M3HefjzTU2nBQcgKM5c4fiWz8y1cXKOwhd3wsc3QNo+e1cnIiINROFGanRpx3Deur0X7q7mIn8PfrqRkrImFHAsFuh4ldmKM/gxcPWA/Ythen9Y/C91VYmIOCGFG/lNl3eJ4J07L8LD1YX521O4/5MNFJc1gUHGp/Pwhkv/BvevgnbDzc04l081u6q2z1FXlYiIE1G4kVq5tGM4747tjdXNhUU7T/CnjzdQVNrEAg5AcFu4/Su4+RMIiDG7qr4cB/+9FlJ32rs6ERGpBwo3UmtD4kOrDTK+6/01ZBeW2rusurNYoNM18MAaGPKEOavq4E8wfaC543hhlr0rFBGRC6BwI3UysF0I/727L35WN9YcyuDmd34hNafI3mWdHw9vGPYkTFgDHa8Bo9zccfz1i2D9h1DRBFumRERE4Ubq7uK4ID7/Y39CfK3sSsnlhrdXcjCtCQ/MbRELt3wCd8yGkHgoSINvH4b/DIXDv9i7OhERqSOFGzkvnaP8mf2nAbQO9iYpo5DfTV/J1qNNfD+ndpeZCwBeMRmslXtVzbgSvrobso/auzoREaklhRs5b62CvfnqvgF0jvQnPb+Em//zC4t3nrB3WRfG1R363w8PbYBeYwELbPsaXu8NiVOg1ME3ExUREYUbuTChflZm/bEfA9sFU1BSzvj/rmPmykP2LuvC+YTAta/BH5dBqwFQVgiJkzR1XESkCVC4kQvm7+nOh7+/mJt7x1BhwD/nbudf3+6gvMIJAkBkd/j9D/C7D8A/GrKPmFPHP7gSjq6zd3UiInIWCjdSL9xdXXjhhq48dkUHAD5YcZD7Pl5PfnGZnSurBxYLJNwAE9bCkL+AmxckrYL3LoMvfw+Zh+xdoYiInMZiGM2rfT0nJ4eAgACys7Px9/e3dzlO6dvNx/m/LzdTUlZBxwg/3hvbm+gW3vYuq/7kHIclz8OmTwDD3NKh7x/N7R08A+xdnYiIU6rL32+13Ei9G9U9is/G97NNFb/ujRWsOZhh77Lqj38UjH4T/vgTxA0xt3JY+Tq81gvWzdD6OCIidqZwIw3iotYtmDthIAktzZlUt7+3illrjti7rPoV2Q3u+h/c9gUEtzfXx/luIrwzGA4ss3d1IiLNlrqlpEEVlpTz6Feb+X5LMgB39W/N367ujIebk+Xq8lJY+x4kToaiyvV+2g03N+uM6mnf2kREnEBd/n4r3EiDMwyDN5fu4+UFewDo3boFb93eizB/TztX1gAKMsyAs+4DqKgcTN3pWhj2FIR1tG9tIiJNmMJNDRRu7GfxzhNM/HwTuUVlhPpZmX57L3rHBtm7rIaRcRASX4AtnwMGWFyg+61mS45/lL2rExFpcprsgOKffvqJUaNGERUVhcVi4Ztvvqn2+rhx47BYLNUe/fr1s0+xUmeXdQpn7oRBxIf7cjK3mFv+s4r//nIIp8zXQXEw5h24/xfoNAqMCnN21Wu9YOkkKM6zd4UiIk7LocJNfn4+3bt354033jjnOVdeeSXJycm2xw8//NCIFcqFigvxYc79A7m6WyRlFQb/+N92Jn6+yTnWwzmbsE5w88fwhyUQ089c6XjZFHPn8Q3/hXIn/blFROzIYbulLBYLc+bMYfTo0bZj48aNIysr64wWnbpQt5RjMAyD95Yf5IX5uyivMGgX5sv023vRPtzP3qU1HMOAnXNh4T9OLfzXIhYGToQet4Gb1Y7FiYg4tibbLVUbiYmJhIWFER8fz/jx40lNTa3x/OLiYnJycqo9xP4sFgvjB7fh83v7EeHvyb7UPK59YwWzNzjx7tsWC3S+Dh5YA5c/D97BZsj5biK82h1+eRNK8u1dpYhIk9ekws3IkSP55JNPWLJkCVOnTmXt2rVceumlFBcXn/M9kydPJiAgwPaIiYlpxIrlt/SODeL7hwZxSfsQCkvLeeSLzTzx1RYKS5x4ITw3KwyYABO3wpUvgF8U5CbDj0/Caz3NhQDVXSUict6aVLfUryUnJ9O6dWtmzZrFmDFjznpOcXFxtfCTk5NDTEyMuqUcTHmFwRtL9vHK4j0YBrQP8+X123rSMaIZ/G9UVgybZ8HP0051V4V2hBH/gvaXmy0+IiLNnFN3S50uMjKS1q1bs3fv3nOeY7Va8ff3r/YQx+PqYuHh4e355J6+hPlZ2Zuax3VvrODjVYedczbV6dyscNFYeGAtXDkFvFrAyV3w6U0wcxTsX2qO1xERkVpp0uEmPT2dpKQkIiMj7V2K1JMB7UKY9/AlDO0QSnFZBX/7Zhv3f7KBrIISe5fW8Nw8oN998NAmGPgwuFrh0HL4aDRMH2DOriottHeVIiIOz6HCTV5eHps2bWLTpk0AHDx4kE2bNnHkyBHy8vJ49NFH+eWXXzh06BCJiYmMGjWKkJAQrr/+evsWLvUq2NfKB2P78LerO+HuamHethSufGU5K/el2bu0xuEVaHZJPbgOLv4juPtA6g6Y+yD8uwsse1EDj0VEauBQY24SExMZNmzYGcfHjh3L9OnTGT16NBs3biQrK4vIyEiGDRvGs88+W6dBwpoK3rRsOZrFw7M2cTAtH4sF7r2kDY9cHo/VzdXepTWewizY+BGs/g9kV24+6hdprnbc/VZwaUb3QkSaLW2/UAOFm6anoKSMZ7/byWeVu4p3jvTn1Vt6OPeaOGdTXgY7voHF/4Ksw+ax8K5w+bPQZqgGHouIU1O4qYHCTdO1YHsKT3y9hcyCUjzcXHjs8g7cPSgOV5dm9ke9rBhWvwM/vQzFlTuQR/WEi++FLmPA3Qk3JBWRZk/hpgYKN01bak4Rj3+9hcTdJwG4ODaIl27sRutgHztXZgf56eZWDus/hPLK5Q68gqDXXXDxeAiItmt5IiL1SeGmBgo3TZ9hGHy+Nolnv9tBfkk53h6uPHlVJ27v2wpLc+yayU8zZ1Kt+wCyk8xjbp7mtg4DHwYPb7uWJyJSHxRuaqBw4zySMgp47KvNrDqQAUD/NsG8cEPX5tmKA1BRDnvmw8o34MhK81hADFz+nLntQ3MMfiLiNBRuaqBw41wqKgxm/nKIKfN3UVRagae7C49e3oHfD2yGY3GqGAbs+B8s+NuplpzYS2DoX6D1QIUcEWmSFG5qoHDjnA6n5/OXr7fyy4F0AHq2CuTFG7o1vxlVpyspgBWvwopXoKzIPBbRDfrdDwljtAu5iDQpCjc1ULhxXoZh8NmaJCb9sJO84jLcXS3cN6QtDwxrh6d7M14LJvOwGXA2fQZllSsc+4ZD/wfMoOPqbtfyRERqQ+GmBgo3zi85u5C/zdnG4l2pAMQGe/Pc6K4Mah9i58rsrCDDnFm15l3IPW4eC+0Eo16BVv3sWZmIyG9SuKmBwk3zYBgGP25P4Z9zt3Mix5wmPbpHFE9e3Ykwv2a+Dkx5KWz5HBb+AwrMbjx6jYURz5ibdoqIOCCFmxoo3DQvuUWlTF2wh5m/HMIwwM/qxsQR8Yzt3xo3V4faWq3xFWSYAWfjR+Zzn1AY9Ii5Q7lHM51xJiIOS+GmBgo3zdPmpCz+/r9tbDlqrujbIdyPZ67rQr82wXauzAEcXgnfToS03eZzryBzLM7Ff1BLjog4DIWbGijcNF/lFQZfrEvixfm7yCwoBWBU9yieuLID0S2a+UJ3ZSWw+VP4+RXIPGge8/CDfn+CSx4Bdy+7licionBTA4Ubycwv4eUFu/l0zREMA6xuLoy/pA1/GtoWH6ubvcuzr6rNOZdPg9Tt5rHgdjDqNYgdaNfSRKR5U7ipgcKNVNl2LJvnvt9hW+E41M/KY1d04IZe0c13AcAqVQsBznsC8lLMY73vhuHPgKf+uxGRxqdwUwOFGzmdYRgs2HGCST/s5HB6AQAdI/x44sqODO0Q2jz3qjpdYZY56HjDTPO5XxRc+hR0u1nr44hIo1K4qYHCjZxNcVk5/115mNeX7CWnqAyAvnFB/GVkR3q20qBaDv4E3z4MGQfM5y1iYfBjCjki0mgUbmqgcCM1yS4o5a3EfcxYeYiSsgoAruwSwZ9HxNMhohlv5QBQWmguALjiVShIM48Ftoahf4Xut2jPKhFpUAo3NVC4kdo4nlXItIV7+HrDUQzD/Lt9bfcoJg6PJy6kma8BU5IP6z4wQ07+SfNY7CVwzSsQ0s6upYmI81K4qYHCjdTFnhO5/HvhHuZtMwfVurpYGNOzJRMubUfrYIUcVr8Dy14096xytZpdVQMfBjcPe1cnIk5G4aYGCjdyPrYdy+bfC/fY9qtydbFwXY8oJgxrR5tQXztXZ2eZh+C7R2D/YvN5aEdzVlX8FeqqEpF6o3BTA4UbuRAbjmTy2uK9JO42u2NcLHBNtygmXNqO+PBmPCbHMGDb1+bU8arxOBHdYMgT0PFqhRwRuWAKNzVQuJH6sDkpi9eX7GPRzhO2Y8M7hXHfkLb0jg2yY2V2VpABK16BNe9Bab55LLwrDP2LQo6IXBCFmxoo3Eh92n48mzeX7mPethSq/kvqE9uC+4a0ZViHMFya62KA+emw6k1Y/R8oyTWPxQ2GkS9CWCf71iYiTZLCTQ0UbqQhHDiZx7vLD/D1+mOUlJtTyNuG+nDPoDaM6dUST3dXO1doJwUZ8MsbsPINKC8GiytcPN5sydGmnCJSBwo3NVC4kYZ0IqeID34+yKerj5BbbC4GGOTjwR19W3Fn/1hC/ax2rtBOMg/Bj0/Bru/M597BMOAh6P178Aywa2ki0jQo3NRA4UYaQ25RKV+sO8oHPx/kWFYhAO6uFq7pFsXYAbH0iAm0b4H2sn+pOeg4bbf53MPPDDj9/gT+UfatTUQcmsJNDRRupDGVlVewYMcJ3lt+gA1HsmzHu8cEMm5Aa67qGonVrZl1WZWXwpYvYOVrcHKXeczF3VzleMgTEBhj3/pExCEp3NRA4UbsZcvRLD5ceYjvNifbxuUE+XhwY+9obr+4Na2Cve1cYSOrqIB9C82Vjg+vMI+5ekDve+CS/wPfUPvWJyIOReGmBgo3Ym9pecV8tvoIn645QnJ2ke344PhQbu/biks7huHu6mLHCu3gyGpY8iwcWm4+d/eB/vdD3z+BT7B9axMRh6BwUwOFG3EUZeUVLNmVyserj/DTnpO246F+Vm68KJpb+rRqXq05hgEHEmHxv+D4BvOYqxUSxkCf8RB9kV3LExH7UripgcKNOKLD6fl8uuYIX68/Slpeie34wHbB3NQ7hiu6RDSf6eSGYc6q+ullSN506nhUT7j4j5Bwg/auEmmGFG5qoHAjjqykrILFO0/w2doklu89aVsY0M/TjWu7R3FT7xi6RQdgaQ4r/RoGHFsPa96F7bOhvDL0+beEfvfDRWPB2oy3vBBpZhRuaqBwI01FUkYBX60/ylfrj9qmkwO0D/PlhouiGd2jJREBnnassBHlp8GG/5q7kOeZO7RjDYA+90DvuzXDSqQZULipgcKNNDUVFQa/HEjni3VJzN+WQnGZOdPKYoFB7UIY06slV3SJwNvDzc6VNoKyYtg8y5xGnr6v8qAF4i6BHrdDp1Hg4WPXEkWkYSjc1EDhRpqy7MJSftiazOwNR1l7KNN23NvDlSu6RHBdjygGtQvBzdlnW1VUwO7vzZacqhlWAB6+0OV6c2HAlhqALOJMFG5qoHAjzuJwej5zNh5j9oZjHMkosB0P8fXgmm5RXNsjip4xgc4/PifzMGz5HDZ9Ym7zUCWyh9ll1fV3as0RcQIKNzVQuBFnYxgGG5Oy+GbjMb7bkkxG/qnZVq2CvBnVPZJru7ekQ4STD741DDi8EtZ/CDu+OTUA2epvdld1uR7ihmimlUgTpXBTA4UbcWal5RUs33uSuZuOs2DHCQpKym2vxYf7MqpbFNd0jyIuxMlbMvLTzZacdR9A5sFTxz0DoOM1ZtBpMwxcm8E4JREnoXBTA4UbaS4KSspYvDOV/206zrI9qZSWn/pPPaGlP9d0i+LqrpHEBDnxQoEVFXBkJWz/BnbOhbwTp17zCYWE30G3m8w1dJy9+06kiVO4qYHCjTRH2QWl/Lgjhe+2JLNiXxrlFaf+s+8eHcDV3SK5qmsk0S2cOeiUw5FVsH2O+ShIO/VaSLzZdRV/pTkQ2aWZLJgo0oQo3NRA4Uaau/S8YuZtS+GHrcmsOpDOaTmH7jGBXN01wvmDTnkp7F9qDkTe9T2UnVpHCO9gaH+5GXTaDQerr/3qFBGbJhtufvrpJ1566SXWr19PcnIyc+bMYfTo0bbXDcPgmWee4T//+Q+ZmZn07duXN998ky5dutT6MxRuRE45mVvM/O0pfL/lOKsPZnD6vwbdowO4qqvZouPUXVfFubB7PuyZB/sWQVH2qdfcPM2A02kUxF8BXi3sV6dIM9dkw828efNYsWIFvXr14oYbbjgj3EyZMoXnn3+eDz/8kPj4eJ577jl++ukndu/ejZ9f7WaCKNyInF1qbhE/bkvh+63JrDmYUa1Fp2vLAEZ2jWBkQqRzD0YuL4Wk1bB7nrm/1elTy13cIKYvtBlqDkaO6qkBySKNqMmGm9NZLJZq4cYwDKKiopg4cSJPPPEEAMXFxYSHhzNlyhT++Mc/1uq6Cjciv62qRWfeWbquOkb4cVXXSEYmRNA+3ImnlxsGnNgGO7+FHXPh5M7qr1sDoM1gswur/eXgF2GfOkWaCacMNwcOHKBt27Zs2LCBnj172s677rrrCAwMZObMmWe9TnFxMcXFxbbnOTk5xMTEKNyI1FJ6XjELdpzgh63JrNyfXm0wcttQH67qGsmVCRF0jvR37gUDMw6Y43QOLIWDP1XvvgKI6GZ2XcUNhug+4O5lnzpFnFRdwk2TaVNNSTE3ywsPD692PDw8nMOHD5/zfZMnT+aZZ55p0NpEnFmwr5VbL27FrRe3IjO/hIU7TzB/WwrL955k/8l8Xl+yj9eX7KNVkDcjEyK4MiGCHs64MnJQG/PR5x5z5tXxjeYYnT0/wvENkLLFfPz0Eri4m7OuYgdC64HQqj94OPG4JREH02RablauXMnAgQM5fvw4kZGRtvPGjx9PUlIS8+fPP+t11HIj0jByikpZsjOVeduSSdx90rahJ0BkgCdXdIlgZEIEvWODcHVxsqDza3mpsG8x7FsIh1ac2rm8iqtH9fE6kd01Xkekjpyy5SYiwuzPTklJqRZuUlNTz2jNOZ3VasVqtTZ4fSLNjb+nO6N7tmR0z5YUlJSRuPsk87alsGTnCZKzi/hw5SE+XHmIEF8rl3cJZ2RCBP3aBOPujJt6+oZBj1vNh2GYXViHV8LhFWYXVs4xc4PPQ8thybPg7m227ET3gZiLIfpi8Am2908h4jSaTLiJi4sjIiKChQsX2sbclJSUsGzZMqZMmWLn6kSaN28PN9u08aLScpbvTWPetmQW7ThBWl4xn64+wqerjxDo7c7wTuFc2SWCQe1D8HR3wsXyLBYIbms+et1php30/eZYnQOJZsApyj4VdqqEdqrsxhoArQeB37n/T5uI1MyhuqXy8vLYt28fAD179mTatGkMGzaMoKAgWrVqxZQpU5g8eTIzZsygffv2TJo0icTERE0FF3FQJWUV/HIgnfnbUliwPYX00zb19LW6MaxjGCMTIhgSH4qPtcn8f60LU1EBaXvg6Bpz2nnSWkjbfeZ5Aa2gZU+I6gUte5m7nHvq3yxpvprsbKnExESGDRt2xvGxY8fy4Ycf2hbxe+edd6ot4peQkFDrz1C4EbGP8gqDtYcymL8thfnbUkjJKbK9ZnVzYUh8KFcmRHBZp3ACvNztWKkd5Ked6sY6tMKcgs6v/2m2QHgXc+xOq35md1Zga+2JJc1Gkw03jUHhRsT+KioMNh/NYv62FOZtS+FIRoHtNTcXCwPahXBllwhGdA4n1K8Zjpkryobjm8xZWMc2mDOzspPOPM8rCCK7mdPQI7ubj6C24OKE45qk2VO4qYHCjYhjMQyDncm5zN+ewvxtyew5kWd7zWKBPrFBXNklgisSImgZ2IzXjslJruzGquzOSt4MFaVnnucZYHZlRfeGlr3NwOMXoRYeafIUbmqgcCPi2PafzOPH7WbX1Zaj1RfK69oygCsTIriiSzjtwpx4deTaKC2C1B3m2jrJlWvspGyFsqIzz/UKMru0whMgoitE9YCQDpqOLk2Kwk0NFG5Emo5jWYX8uC2F+dtTWHuo+saebUN9uKJLBFd0iaBbdIDzLRp4PspL4cR2OLYOjq6HY+shfS8YFWee6+ZVGXR6msEnrDOEdtCgZXFYCjc1ULgRaZpO5hazaOcJftyewop9aZSWn/qnK8Lfk8u7hHN55wj6tglyzrV0zldpIZzcZYaelG1m607yZijJPfv5/tEQ3rlyHE/leJ4WserWErtTuKmBwo1I05dTVMrSXaks2H6CxN2p5JeU217z93Tj0o5hjOgcwZAOofg2lynmdVFRARn7zYHKxzeZ3Vsnd0Fu8tnPtwZAWEcIiYfQjmYLT2gHCIhR6JFGo3BTA4UbEedSVFrOL/vT+XF7Cot2niAt79RaOh6uLgxoF8zwTuEM7xRORICnHSttAgozIXWXORU9ZYvZwpO6E8pLzn6+NaByLE8XiEgwg09we622LA1C4aYGCjcizqu8wmDjkUwW7jjBgh0nOJiWX+31ri0DuKxTGMM7hdMlysl3Ma8vZSXmooMnd1V+3W1+Tdt79tlaAF4tzJATGm+O5QnrBGFdzG0qdM/lPCnc1EDhRqR5MAyD/SfzWLDjBIt2nGBjUla1AckR/p5c2imMyzqGMaBtCF4eTrgVREMqKzEHK6dsgxNbzTE9aXvPvh5PFe9gM/QEt4PgNuaaPCGVz92a4XpGUicKNzVQuBFpnk7mFrN0VyoLd57g571pFJaeGqdjdXNhQNtghnUMY1iHMGKCvO1YaRNXUmCO50nba7bypO4wHxkHzj5rC8DiCkFxZrdWWCcz7AS1gRZx4BOi1h4BFG5qpHAjIkWl5aw6kM7inaks2ZXKsazCaq+3DfVhWIcwhnQIpU9skHNu8NnYSgvNsJO+z9xINGO/+TVtLxRnn/t9Hn5m8AmJPzWQOaSDeUytPc2Kwk0NFG5E5HSGYbD7RC6Ju0+yZFcq6w9nUl5x6p9FT3cX+rcJZkh8KIPjQ4kL8dFYnfpkGJCbAid3Vrb07DRbeTIOQs4xztxjq4oF/KPMaeotYs1WnpB2ld1ebcG9Ga9m7aQUbmqgcCMiNckuLGX53pMs232Sn/ae5EROcbXXWwZ6cUn7EAa1D2Fg2xBa+HjYqdJmoLQIsg6brT1VA5mrvpbk1fBGCwTGmF1bga3MDUYDW5shKLgteAc11k8g9UjhpgYKNyJSW1WtOst2n2TZnpOsO5RJSfmpcSMWC3SO9GdguxAGtguhT2wLvD20rk6DMwwoSIfMQ5WPg5B+wBzgnLbH3Hi0Jl4tKsf1tDUDT2ArMwwFxEBANLg2s13pmwiFmxoo3IjI+SooKWPNwQyW701j+d6T1Tb5BHB3tdAjJpD+bYLp1yaYXq1baLxOYzMMyE8zg07mIcg6ApmHK79WdXXVwOJihpzgtmbLT1BbaNHaDD0BMWYwUrekXSjc1EDhRkTqS2pOESv3p7NiXxor96efMTDZw9WFHjGBXBwXxMVxQfRq3UIrJttbSYE5pid9n/nIOgxZSeYU9qwkKC+u+f1uXmYrT9U4n6A482tgKwhoae7KLg1C4aYGCjci0hAMw+BwegGrDqSz6kA6vxxIP2O8jquLhYQof3rHBtG7dQsuim1BmJ9WTXYYFRWQd6JyQHPVY39l+DkK+am/fQ0PPzPkBERXjvNpfWrQs1p+LojCTQ0UbkSkMRiGwcG0fNYczGDNoQzWHMzgaGbhGee1CvKmd+sW9GwVSM9WLegY4YebNv50TGXFZrdW1hFzNlfmwVNfs4+a21f8FjdP8IsE/5bmbK/AVmYAqhr47N8S3DRI/WwUbmqgcCMi9nIsq5C1BzNYdziDdYcy2X0il1//C+zl7kq36AB6tAqkR3Qg3WMCiQzw1PTzpqAkH3KOm0En+6jZ5WUb9HwI8k/W7jreIZUBKNL8GhBjdoUFtjK/94sE1+bXvalwUwOFGxFxFDlFpWw8ksWGw5lsOJLJpqQscovKzjgv1M9K9+gAEloG0K3yq7qzmqDSInPn9dzkyhCUZLYCnT7o+bfG/ABgAZ9Q8Is49aia6VX18IsCd+f6HVG4qYHCjYg4qooKcz+sDUcy2Xw0m81JWexKya22qGCVcH8rCVEBdInyp3NUAAkt/WkZ6KUWnqbMMKAgA3KPmwsbVoWgrCTIrgxB2cfOvWHpr3kGgG8E+IWbX/2jzODj39IcF+QXZa7549I0ZvQp3NRA4UZEmpLCknJ2JGez5Wg2W49ms/VYNvtO5p3RnQUQ4OVOxwg/OkX60ynS/No+zE+bgjqTinJzjZ/cFPORl3JaV1jSqS6xsqLaXc/iau7f5RsGvuGnxgMFVI4J8o0wX/MOtnsIUripgcKNiDR1+cVl7EjOYfuxbLYfz2H78Rz2puZSWn7mP+cWC8QG+9Ah3I/4CD86RvjRPsyX2BAf3DVw2TkZhjm4OS/VnP2Vd+K0rrCj5qDo7GOVY4BqGwEsZsDxDTttPFDUqXFBPqHm6z6h4OHTIDPCFG5qoHAjIs6ouKycfal57EzOZVdyDjtTctiZnEtGfslZz3dzsRAX4kN8uB9tw3xpF+ZL21Af2ob6auHB5qK8DArSKgNQ6mldYZXhJ+eY+VpBBrUPQZhrAQ2YAJf+rV7Lrcvf7+Y33FpExAlZ3VzpEhVAl6hTi8gZhkFaXgm7U3LZfSKX3Sk57DmRx77UPPKKy9ibmsfe1OqrLFssEN3CizYhvrQJ9aFNiA9tQs3vw/08cXHRmB6n4ep2akByTcrLzK6w/JOntQIlm2ODco6bwSg/zVwHqKwIygrB1b7T2dVyIyLSzBiGwfHsIvaeyGVvZdjZd9L8ml147sGqnu4uxAb7EBfiQ2yID7HB3rQO9iE22IcwP6uCT3NnGOZ0+II0cPcB39B6vby6pWqgcCMicnaGYZCeX8L+1DwOpOVz4GQeB07mcyAtn6SMAsrOMmuriqe7CzEtvGkd7E2rIB9aBXnROtiHmCAvolt4q6tLLpi6pUREpM4sFgshvlZCfK30bRNc7bXS8gqOZhZyKM0MO4fS8jmcUcDh9HyOZhZSVFpx1m6uKmF+VloFeRMT5E10Cy9iWphfo1t4ExnoqcHNUq/UciMiIhektLyCY5mFHM4o4EhGAUfS8zmSUcDh9AKOZhaSV3zmwoSns1ggwt+TloFetGzhZfsaFehFdKD51UcbjjZ7arkREZFG4+7qYo7BCfE54zXDMMgsKCWpMvgczSzkaKb5Nanya0lZBcnZRSRnF7Hu8Nn3Zwr0dicywIuWgZ5EBXoRGeBFVKAnkQFeRAZ4EhGg1h85ReFGREQajMViIcjHgyAfD7rHBJ7xetWMrqOZBRzLKuRoZiHHswo5llnIsSzzkVtURlZBKVkFpexMzjnH50Cor9UWdE4PPVXfh/lbsbpp7E9zoG4pERFxaDlFpSRnFXE8q5Dj2Wb4Sc4q4lhWIcnZRaRkF1FSXlGrawX7eBAR4EmEv6fta/hpz8P9PfH3dNM2Fg5I3VIiIuI0/D3d8Y9wp0OE31lfr6gwZ3mlZBdxPLuw2teU7CJScswur5KyCtLzS0jPL2H78bO3AIG5M7sZdKyE+1cGINvDPKZWIMemcCMiIk2ai4uFUD8roX5WukYHnPUcwzDIyC8hJaeIE5Vh5/TwcyLH/D6nqIzC0nIOpuVzMC2/xs9t4e1+ltDjSbif1fzqb84801igxqdwIyIiTs9isRDsayXY11ptFedfKywpN4NOZeAxH8Wk5BSRetr3JWUVZBaUkllQyq6U3Bo+1+wKC/Uzw06YX2UIqgxAVc9D/RSC6pPCjYiISCUvD9dzzvyqYhgG2YWlnMgptgWhk7nF1cLQicpjZRXmgOm0vBJ2Jtf82WYIOhV6bI9fhSAtiPjbFG5ERETqwGKxEOjtQaC3xznHAYE5FiijoITUyrCTmltkfm/7WszJnCJO5hVTWm7YxgPV1BIE4O/pZoYgP3PsT1jl96G2MGQl1K95D4xWuBEREWkALi6nVnzuHHXu2T0VFQZZhaWVAaiY1F9/zS22BaPisgpyisrIKSpj/8maxwRZ3VxOBZ7Twk9oZQCqOhbs44Gbk3WJKdyIiIjYkYvLqbWAOkWe+zzDMMgpLONknhl0Tg89J/OKK4+ZgSi3qIzisorKRRMLa/z8qnFBIb5mF1ior9U2QDvUz0qob1VrkBU/a9NoDVK4ERERaQIsFgsB3u4EeLvTLuzc3WEARaXlnKwMPyerWn9yim3HUnPN79PyiqkwsI0L+q0usarWoKrQU/V9iK/1jOP2HBukcCMiIuJkPN1diancqLQm5RXmFPmq0JOWV1ItEKXlmq1CJ+vYGjRuQCxPX9ulPn+kOlG4ERERaaZcT1sjqDM1r/pbWGK2BlWFnaqvaVXPc08dD/WzNtJPcHZNKtw8/fTTPPPMM9WOhYeHk5KSYqeKREREmgcvD1daBXvTKrjm1iDDMCivsO/OTk0q3AB06dKFRYsW2Z67umq+v4iIiKOwWCy4udp30HGTCzdubm5ERETU+vzi4mKKi4ttz3Nyzr2fiIiIiDR9TW5i+969e4mKiiIuLo5bbrmFAwcO1Hj+5MmTCQgIsD1iYmIaqVIRERGxB4thGPbtGKuDefPmUVBQQHx8PCdOnOC5555j165dbN++neDg4LO+52wtNzExMbXaMl1EREQcQ05ODgEBAbX6+92kws2v5efn07ZtWx5//HEeeeSRWr2nLjdHREREHENd/n43uW6p0/n4+NC1a1f27t1r71JERETEQTTpcFNcXMzOnTuJjKxhvWoRERFpVppUuHn00UdZtmwZBw8eZPXq1fzud78jJyeHsWPH2rs0ERERcRBNair40aNHufXWW0lLSyM0NJR+/fqxatUqWrdube/SRERExEE0qXAza9Yse5cgIiIiDq5JdUuJiIiI/BaFGxEREXEqCjciIiLiVBRuRERExKk0qQHF9aFqQWZtoCkiItJ0VP3drs3GCs0u3OTm5gJoA00REZEmKDc3l4CAgBrPadJ7S52PiooKjh8/jp+fHxaLpV6vXbUpZ1JSkvatakC6z41D97lx6D43Dt3nxtGQ99kwDHJzc4mKisLFpeZRNc2u5cbFxYXo6OgG/Qx/f3/9x9MIdJ8bh+5z49B9bhy6z42joe7zb7XYVNGAYhEREXEqCjciIiLiVBRu6pHVauWf//wnVqvV3qU4Nd3nxqH73Dh0nxuH7nPjcJT73OwGFIuIiIhzU8uNiIiIOBWFGxEREXEqCjciIiLiVBRuRERExKko3NSTt956i7i4ODw9PbnoootYvny5vUtq0iZPnkyfPn3w8/MjLCyM0aNHs3v37mrnGIbB008/TVRUFF5eXgwdOpTt27fbqWLnMHnyZCwWCxMnTrQd032uH8eOHeOOO+4gODgYb29vevTowfr1622v6z5fuLKyMv72t78RFxeHl5cXbdq04V//+hcVFRW2c3Sfz89PP/3EqFGjiIqKwmKx8M0331R7vTb3tbi4mAcffJCQkBB8fHy49tprOXr0aMMUbMgFmzVrluHu7m68++67xo4dO4yHH37Y8PHxMQ4fPmzv0pqsK664wpgxY4axbds2Y9OmTcbVV19ttGrVysjLy7Od88ILLxh+fn7G119/bWzdutW4+eabjcjISCMnJ8eOlTdda9asMWJjY41u3boZDz/8sO247vOFy8jIMFq3bm2MGzfOWL16tXHw4EFj0aJFxr59+2zn6D5fuOeee84IDg42vvvuO+PgwYPGl19+afj6+hqvvPKK7Rzd5/Pzww8/GE899ZTx9ddfG4AxZ86caq/X5r7ed999RsuWLY2FCxcaGzZsMIYNG2Z0797dKCsrq/d6FW7qwcUXX2zcd9991Y517NjR+Mtf/mKnipxPamqqARjLli0zDMMwKioqjIiICOOFF16wnVNUVGQEBAQYb7/9tr3KbLJyc3ON9u3bGwsXLjSGDBliCze6z/XjiSeeMAYNGnTO13Wf68fVV19t3H333dWOjRkzxrjjjjsMw9B9ri+/Dje1ua9ZWVmGu7u7MWvWLNs5x44dM1xcXIz58+fXe43qlrpAJSUlrF+/nssvv7za8csvv5yVK1faqSrnk52dDUBQUBAABw8eJCUlpdp9t1qtDBkyRPf9PDzwwANcffXVDB8+vNpx3ef6MXfuXHr37s2NN95IWFgYPXv25N1337W9rvtcPwYNGsTixYvZs2cPAJs3b+bnn3/mqquuAnSfG0pt7uv69espLS2tdk5UVBQJCQkNcu+b3caZ9S0tLY3y8nLCw8OrHQ8PDyclJcVOVTkXwzB45JFHGDRoEAkJCQC2e3u2+3748OFGr7EpmzVrFhs2bGDt2rVnvKb7XD8OHDjA9OnTeeSRR3jyySdZs2YNDz30EFarlbvuukv3uZ488cQTZGdn07FjR1xdXSkvL+f555/n1ltvBfT73FBqc19TUlLw8PCgRYsWZ5zTEH8rFW7qicViqfbcMIwzjsn5mTBhAlu2bOHnn38+4zXd9wuTlJTEww8/zIIFC/D09DznebrPF6aiooLevXszadIkAHr27Mn27duZPn06d911l+083ecL8/nnn/Pxxx/z6aef0qVLFzZt2sTEiROJiopi7NixtvN0nxvG+dzXhrr36pa6QCEhIbi6up6RPFNTU89IsVJ3Dz74IHPnzmXp0qVER0fbjkdERADovl+g9evXk5qaykUXXYSbmxtubm4sW7aM1157DTc3N9u91H2+MJGRkXTu3LnasU6dOnHkyBFAv8/15bHHHuMvf/kLt9xyC127duXOO+/kz3/+M5MnTwZ0nxtKbe5rREQEJSUlZGZmnvOc+qRwc4E8PDy46KKLWLhwYbXjCxcuZMCAAXaqqukzDIMJEyYwe/ZslixZQlxcXLXX4+LiiIiIqHbfS0pKWLZsme57HVx22WVs3bqVTZs22R69e/fm9ttvZ9OmTbRp00b3uR4MHDjwjKUM9uzZQ+vWrQH9PteXgoICXFyq/1lzdXW1TQXXfW4YtbmvF110Ee7u7tXOSU5OZtu2bQ1z7+t9iHIzVDUV/P333zd27NhhTJw40fDx8TEOHTpk79KarD/96U9GQECAkZiYaCQnJ9seBQUFtnNeeOEFIyAgwJg9e7axdetW49Zbb9WUznpw+mwpw9B9rg9r1qwx3NzcjOeff97Yu3ev8cknnxje3t7Gxx9/bDtH9/nCjR071mjZsqVtKvjs2bONkJAQ4/HHH7edo/t8fnJzc42NGzcaGzduNABj2rRpxsaNG21LntTmvt53331GdHS0sWjRImPDhg3GpZdeqqngju7NN980WrdubXh4eBi9evWyTVmW8wOc9TFjxgzbORUVFcY///lPIyIiwrBarcbgwYONrVu32q9oJ/HrcKP7XD++/fZbIyEhwbBarUbHjh2N//znP9Ve132+cDk5OcbDDz9stGrVyvD09DTatGljPPXUU0ZxcbHtHN3n87N06dKz/ps8duxYwzBqd18LCwuNCRMmGEFBQYaXl5dxzTXXGEeOHGmQei2GYRj13x4kIiIiYh8acyMiIiJOReFGREREnIrCjYiIiDgVhRsRERFxKgo3IiIi4lQUbkRERMSpKNyIiIiIU1G4EREREaeicCMiTi82NpZXXnnF3mWISCNRuBGRejVu3DhGjx4NwNChQ5k4cWKjffaHH35IYGDgGcfXrl3Lvffe22h1iIh9udm7ABGR31JSUoKHh8d5vz80NLQeqxERR6eWGxFpEOPGjWPZsmW8+uqrWCwWLBYLhw4dAmDHjh1cddVV+Pr6Eh4ezp133klaWprtvUOHDmXChAk88sgjhISEMGLECACmTZtG165d8fHxISYmhvvvv5+8vDwAEhMT+f3vf092drbt855++mngzG6pI0eOcN111+Hr64u/vz833XQTJ06csL3+9NNP06NHDz766CNiY2MJCAjglltuITc313bOV199RdeuXfHy8iI4OJjhw4eTn5/fQHdTROpC4UZEGsSrr75K//79GT9+PMnJySQnJxMTE0NycjJDhgyhR48erFu3jvnz53PixAluuummau+fOXMmbm5urFixgnfeeQcAFxcXXnvtNbZt28bMmTNZsmQJjz/+OAADBgzglVdewd/f3/Z5jz766Bl1GYbB6NGjycjIYNmyZSxcuJD9+/dz8803Vztv//79fPPNN3z33Xd89913LFu2jBdeeAGA5ORkbr31Vu6++2527txJYmIiY8aMQfsQizgGdUuJSIMICAjAw8MDb29vIiIibMenT59Or169mDRpku3YBx98QExMDHv27CE+Ph6Adu3a8eKLL1a75unjd+Li4nj22Wf505/+xFtvvYWHhwcBAQFYLJZqn/drixYtYsuWLRw8eJCYmBgAPvroI7p06cLatWvp06cPABUVFXz44Yf4+fkBcOedd7J48WKef/55kpOTKSsrY8yYMbRu3RqArl27XsDdEpH6pJYbEWlU69evZ+nSpfj6+toeHTt2BMzWkiq9e/c+471Lly5lxIgRtGzZEj8/P+666y7S09Pr1B20c+dOYmJibMEGoHPnzgQGBrJz507bsdjYWFuwAYiMjCQ1NRWA7t27c9lll9G1a1duvPFG3n33XTIzM2t/E0SkQSnciEijqqioYNSoUWzatKnaY+/evQwePNh2no+PT7X3HT58mKuuuoqEhAS+/vpr1q9fz5tvvglAaWlprT/fMAwsFstvHnd3d6/2usVioaKiAgBXV1cWLlzIvHnz6Ny5M6+//jodOnTg4MGDta5DRBqOwo2INBgPDw/Ky8urHevVqxfbt28nNjaWdu3aVXv8OtCcbt26dZSVlTF16lT69etHfHw8x48f/83P+7XOnTtz5MgRkpKSbMd27NhBdnY2nTp1qvXPZrFYGDhwIM888wwbN27Ew8ODOXPm1Pr9ItJwFG5EpMHExsayevVqDh06RFpaGhUVFTzwwANkZGRw6623smbNGg4cOMCCBQu4++67awwmbdu2paysjNdff50DBw7w0Ucf8fbbb5/xeXl5eSxevJi0tDQKCgrOuM7w4cPp1q0bt99+Oxs2bGDNmjXcddddDBky5KxdYWezevVqJk2axLp16zhy5AizZ8/m5MmTdQpHItJwFG5EpME8+uijuLq60rlzZ0JDQzly5AhRUVGsWLGC8vJyrrjiChISEnj44YcJCAjAxeXc/yT16NGDadOmMWXKFBISEvjkk0+YPHlytXMGDBjAfffdx80330xoaOgZA5LBbHH55ptvaNGiBYMHD2b48OG0adOGzz//vNY/l7+/Pz/99BNXXXUV8fHx/O1vf2Pq1KmMHDmy9jdHRBqMxdDcRREREXEiarkRERERp6JwIyIiIk5F4UZEREScisKNiIiIOBWFGxEREXEqCjciIiLiVBRuRERExKko3IiIiIhTUbgRERERp6JwIyIiIk5F4UZEREScyv8DgGzUgTVUL8UAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", @@ -172,9 +240,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.9|INFO|2024-05-27 11:26:20]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -197,9 +273,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.6565690909090909\n", + "100%|██████████| 100/100 [00:00<00:00, 1068.92trial/s, best loss: 10.531927656500912]\n", + "hyperopt_search step: 0.599869080691258\n", + "polynomial_approximation step: 0.4860935299211704\n" + ] + } + ], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))/2**nqubits\n", @@ -225,9 +312,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.6565690909090909\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -253,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -271,9 +386,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Energy fluctuation')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", @@ -288,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -311,9 +457,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eigenvalues: [-9.29150262 -5.21110255 -2. -2. 1.29150262 4.\n", + " 4. 9.21110255]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Iterations')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", "print('Eigenvalues:', eigvals )\n", @@ -353,7 +528,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-05-23 10:27:34]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.9|INFO|2024-05-27 16:13:40]: Using numpy backend on /CPU:0\n" ] } ], @@ -379,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -388,13 +563,13 @@ "Text(0, 0.5, 'Diagonal elements')" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -445,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -454,13 +629,13 @@ "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" ] }, - "execution_count": 7, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -501,6 +676,34 @@ "\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'parMS' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mparMS\u001b[49m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'parMS' is not defined" + ] + } + ], + "source": [ + "print(parMS)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -510,28 +713,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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8NjU1NWRnZ1NSUsKHH37Iq6++SkZGhiPcnGjhwoWMGTOG3bt307Vr19Mer7q6murqasfr0tJSkpKSXDaPDcBXm/O4581M5ob8jYE1mXDlP2DgLS45l4hIR2QYBnl5ea0yRb+0nrCwMGJjY08bVFs6j02ba7Hx8fFxrD0xaNAgVq9ezfPPP88///nPU947dOhQgDMGG19fX3x9fV1X8GlEBfsAsM8exUCAInUgFhFxJpvNRlxcHNHR0dTW1lpdjjiBt7f3ebXUNGhzweZkdru9SYvLidavXw80LqfeVkQGmUFqV02k2YtJQ75FRFzC09PTKV+G4j7aVLB5+OGHmTRpEp07d6asrIy3336bxYsXM3/+fLKysnj77be5/PLLiYiIYOPGjTz44IOMGDGC9PR0q0tvoiHYZNVFgQ8KNiIiIq2kTQWbgoICbrnlFnJzcwkNDSU9PZ358+czbtw4cnJy+Pbbb/n73/9ORUUFSUlJTJ8+nd///vdWl32KQF8v/L09ya7T7MMiIiKtqU0Fm9dee63ZfUlJSWRkZLRiNecnMtiHnKLjK64eK4KqEvALtbYoERERN9dm57Fp7yKDfKnAn2rfcHODWm1ERERcTsHGRRr62ZT6JZgbFGxERERcTsHGRRqCzRGfeHODgo2IiIjLKdi4SFSQOZdNrsfxda0UbERERFxOwcZFIoPNFpvshsUwNUmfiIiIyynYuEjjXDaR5ga12IiIiLicgo2LNASbbVUR5oaSHKivs7AiERER96dg4yKRx/vYbK8MAk8fsNdB6UGLqxIREXFvCjYu0tDHpqzajj0s2dyo21EiIiIupWDjIsG+Xvh4mZe3OijJ3HhUHYhFRERcScHGRWw2G1HH+9mUBySaG9ViIyIi4lIKNi7U0M+myEezD4uIiLQGBRsXahgZle+lSfpERERag4KNCzUEmwNGjLlBk/SJiIi4lIKNC0UGm7ei9tYdn8umqhiOHbWuIBERETenYONCDS02h455QeDxpRWO7rewIhEREfemYONCDcHmcHk1dEoxN6qfjYiIiMso2LhQQ7ApVLARERFpFQo2LhR1vI9NYdmJwUYdiEVERFxFwcaFGlpsSqvqqA3VsgoiIiKupmDjQqH+3nh72gAo8dMkfSIiIq6mYONCNpuNiECz1abAK87cWJwD9XUWViUiIuK+FGxcrGEumzx7KHj6glEPJTkWVyUiIuKeFGxcrHFkVB10Uj8bERERV1KwcbGmc9mkmhsVbERERFxCwcbFmsxlE9HV3Ji30cKKRERE3JeCjYtFBh2fy6a8BrqMNjfu/BoMw8KqRERE3JOCjYtFBR9vsSmrhtTh4OUPpQcgf7PFlYmIiLgfBRsXa3Irytsfuja02nxlYVUiIiLuScHGxZoEG4DuE8w/d863qCIRERH3pWDjYg19bI5W1lJbb4e08eaOA2ug/LCFlYmIiLgfBRsX6xTgg6eHuaxCUUUNhMRDXH/AgF1fW1uciIiIm1GwcTEPDxvhgWarzeGyhttRE80/1c9GRETEqRRsWsGp/WyOB5ushVBXY1FVIiIi7kfBphU0mcsGIG4ABMVATTnsX2ZdYSIiIm5GwaYVRJ3cYuPh0diJWLejREREnEbBphVEnjhJX4OG21E7vtQsxCIiIk6iYNMKGm9FnRBsuowCTx8o3g+FO60pTERExM0o2LSCxs7DJ3QU9g2ClOHmc92OEhERcQoFm1ZwyqioBj0mmX9qFmIRERGnULBpBc0Gm4YOxNkroLKolasSERFxPwo2rSAy2OxjU1RRQ739hI7CnZIhujcY9bB7gUXViYiIuA8Fm1YQHuCDzQZ24/iyCidyLIqpfjYiIiLnS8GmFXh5ehAecJqRUdA47Hv3N1Bf18qViYiIuBcFm1bSbD+bxCHgHw5VJZCz0oLKRERE3IeCTStp6GdzSrDx8DxhFuIvW7kqERER96Jg00ocLTZlp1n00tHPRsO+RUREzoeCTStp9lYUQNfLwMPLnIH4SFYrVyYiIuI+2lSwmTVrFunp6YSEhBASEsKwYcP48svG2zNVVVXMmDGDiIgIgoKCmD59Ovn5+RZW3HINwebw6YKNfxh0HmY+3/V16xUlIiLiZtpUsElMTOSpp54iMzOTNWvWcNlllzF16lS2bNkCwIMPPshnn33GBx98QEZGBocOHeLqq6+2uOqWaVwv6jS3ouCEWYg17FtERORceVldwImmTJnS5PVf/vIXZs2axYoVK0hMTOS1117j7bff5rLLLgNg9uzZ9OrVixUrVnDRRRdZUXKLnXaF7xN1nwjzfwf7lkFVKfiFtGJ1IiIi7qFNtdicqL6+nnfffZeKigqGDRtGZmYmtbW1jB071vGenj170rlzZ5YvX97scaqrqyktLW3ysELUmfrYAER0hYhuYK+FrIWtWJmIiIj7aHPBZtOmTQQFBeHr68s999zDvHnz6N27N3l5efj4+BAWFtbk/TExMeTl5TV7vCeffJLQ0FDHIykpycU/wek19LE5UlGD/cRlFU7UMFmfRkeJiIickzYXbHr06MH69etZuXIl9957L7feeitbt2495+M9/PDDlJSUOB45OTlOrLblIo73sam3GxQfqz39mxqGfe/6Guz1rVSZiIiI+2hTfWwAfHx86NatGwCDBg1i9erVPP/881x//fXU1NRQXFzcpNUmPz+f2NjYZo/n6+uLr6+vq8v+Qd6eHoQFeFNcWUtheTXhgT6nvqnzMPANhcpCOLgWkoa0fqEiIiLtWJtrsTmZ3W6nurqaQYMG4e3tzYIFjatg79ixg+zsbIYNG2ZhhS3XOElfM/1sPL2h2xjz+ZZ5rVSViIiI+2hTLTYPP/wwkyZNonPnzpSVlfH222+zePFi5s+fT2hoKD/5yU/4xS9+QXh4OCEhIdx///0MGzaszY+IahAZ5MPugmbmsmmQfj1smQvr3oTRvwPfoNYrUEREpJ1rU8GmoKCAW265hdzcXEJDQ0lPT2f+/PmMGzcOgP/7v//Dw8OD6dOnU11dzYQJE3j55ZctrrrlGmcfbmYuGzDXjQrvAkV7YMM7cOGdrVSdiIhI+9emgs1rr712xv1+fn689NJLvPTSS61UkXNFHZ/LpqC0qvk3eXjAhXfDV7+Blf+EwT8xt4mIiMgP0jdmK0oI8wfgYPGxM79xwI/AJxiO7NKcNiIiImdBwaYVxR8PNod+KNj4hcDAm83nK2e5uCoRERH3oWDTilrcYgNw4V2ADXZ/C4d3urYwERERN6Fg04oaWmwKyqqpqbOf+c3hqY0LY658xcWViYiIuAcFm1YUGeSDj5cHhgF5JWfoQNxg6D3mnxvegWNHXVuciIiIG1CwaUU2m+3sbkeljoDoPlBbCWvfcHF1IiIi7Z+CTSs7q2Bjs8FFx1ttVv0L6utcWJmIiEj7p2DTyuLD/IAWjIxq0O9a8A+HkhzY8YULKxMREWn/FGxaWUJYAAAHj7Yw2Hj7w+Dbzecr1IlYRETkTBRsWpmjxaakhcEGYMhPwcMLsr+H3A0uqkxERKT9U7BpZQmdjvexaWmLDUBIPPSeZj5Xq42IiEizFGxa2Ymdhw3DaPkHL7rX/HPzh1Be4ILKRERE2j8Fm1YWF+qPzQbVdXaOVJxhle+TJQ6GhMFQXwNr/uO6AkVERNoxBZtW5uPlQfTxVb7P6nYUNLbarH4N6qqdXJmIiEj7p2BjgRYvhnmy3lMhOB4qCmDLPBdUJiIi0r4p2FjgrCbpO5GnNwz5ifl8xctwNn10REREOgAFGwucc7ABGHQ7ePmZw76zVzi5MhERkfZNwcYC5zTku0FgBKRfZz5f/qITqxIREWn/FGwsEB96vI/N2UzSd6Jh95l/bv8CDu90UlUiIiLtn4KNBc6rxQYgqgf0uBww4PsXnFeYiIhIO6dgY4GGUVFHK2uprDnHFbsvmWn+ufE9KM11TmEiIiLtnIKNBUL9vQn29QLOYch3g85DIekic8K+lbOcWJ2IiEj7pWBjkXjHyKiqcz/IpTPNP9fMhqqS8y9KRESknVOwsch597MBSJsAUT2hutQMNyIiIh2cgo1F4sP8gPO4FQXg4QEXP2A+XzFLyyyIiEiHp2BjkYSwAOAcJ+k7Ub9rzWUWyvPMjsQiIiIdmIKNRRpabM472Hj5wLCfmc+XvQB2+3lWJiIi0n4p2Fgk0Rl9bBoMvBV8Q+HILtjxv/M/noiISDulYGORhlFReaVV1NvPczFLvxAYcof5fNnftTimiIh0WAo2FokO9sPLw0a93SC/9DyGfDcYei94+sCB1ZC9/PyPJyIi0g4p2FjE08NGbKgTRkY1CI6B/jeaz5c9f/7HExERaYcUbCyU4JikzwnBBo4P/bbBzq8gf6tzjikiItKOKNhYyOnBJrIb9LrCfP79P5xzTBERkXZEwcZCTpl9+GQNi2Nueh9KDjjvuCIiIu2Ago2FGkZGOaWPTYPEwZB8KdjrzNmIRUREOhAFGws5/VZUg4bFMTPnwLGjzj22iIhIG6ZgYyHHCt9Hj2E4c+6ZbmMhug/UlMOKV5x3XBERkTZOwcZCDS02FTX1lB6rc96BbTYY8ZD5fNnzUJztvGOLiIi0YQo2FvL38SQ80Adwwe2oPldB8iVQdwy+/r1zjy0iItJGKdhYzGX9bGw2mPQM2Dxg6yewZ7Fzjy8iItIGKdhYLMEVI6MaxPaFIT81n//v11Bf6/xziIiItCEKNhaLd1WLTYPRv4OACCjcAav+5ZpziIiItBFeZ/Pm1NRUbDbbWZ9k5syZPPDAA2f9uY7AMUmfq4KNfycY8yh89gAsfgr6XmOuKyUiIuKGzirYzJkz55xOkpKSck6f6wgSwsyFMJ06+/DJLrgZMmfDoXXw7WNwlSbuExER93RWwWbkyJGuqqPDSggLAFzYYgPg4QGX/xVeHQMb3obBt0PSha47n4iIiEXUx8Zi8cdbbA6XVVNdV++6EyUOhgE3mc//9yuwu/BcIiIiFlGwsVh4oA9+3uavIbe4yrUnG/sY+IZA7npY94ZrzyUiImIBdR62mM1mIz7Mnz2HKzhUfIyUyEDXnSwoGkY9DPMfhm8fh15XQkC4684nIiLSytR5uA1IOB5sDriyn02DC++Eta/D4e2w6AmY/FfXn1NERKSVtKnOw08++SRz585l+/bt+Pv7c/HFF/P000/To0cPx3tGjRpFRkZGk8/dfffdvPJK+13s0aWT9J3M09uckfi/V8Ka12DQrRDbz/XnFRERaQXn1cemtraWnJwcduzYQVFR0XkXk5GRwYwZM1ixYgXffPMNtbW1jB8/noqKiibvu/POO8nNzXU8nnnmmfM+t5USTljlu1V0GQm9p4JhN2ckdubK4iIiIhY6qxYbgLKyMt58803effddVq1aRU1NDYZhYLPZSExMZPz48dx1110MGTLkrIv56quvmryeM2cO0dHRZGZmMmLECMf2gIAAYmNjW3TM6upqqqurHa9LS0vPui5Xa5h9+FBJKwUbgPF/gZ1fQ/b3sOkDSL+u9c4tIiLiImfVYvPcc8+RkpLC7NmzGTt2LB9//DHr169n586dLF++nEcffZS6ujrGjx/PxIkT2bVr13kVV1JSAkB4eNMOrm+99RaRkZH07duXhx9+mMrKymaP8eSTTxIaGup4JCUlnVdNruCYfbi1WmwAwpJgxC/N51/9FioKW+/cIiIiLmIzjJbfh7jxxhv5/e9/T58+fc74vurqambPno2Pjw933HHHORVmt9u58sorKS4uZunSpY7t//rXv0hOTiY+Pp6NGzfym9/8hgsvvJC5c+c2W8vJLTZJSUmUlJQQEhJyTrU5W05RJcOfWYSPlwfb/zgRD4+zH3l2Tupq4F8joWCrudTCNa+1znlFRETOUmlpKaGhoT/4/X1WwaY13XvvvXz55ZcsXbqUxMTEZt+3cOFCxowZw+7du+natesPHrelF6Y11dbb6f77LzEMWPX/xhAd7Nd6Jz+YCa+ONfvb3Pgu9JjUeucWERFpoZZ+f7fJCfruu+8+Pv/8cxYtWnTGUAMwdOhQAHbv3t0apbmEt6cHMcfDzCFXT9J3soRBMGyG+fzzB+FYceueX0RExImcFmxeffXV8z6GYRjcd999zJs3j4ULF5KamvqDn1m/fj0AcXFx531+K1nSz6bBqN9BeBcoy4Vv/tD65xcREXESpwWbzz//nIULFzpeV1ZWcsMNN5zVMWbMmMGbb77J22+/TXBwMHl5eeTl5XHsmPlln5WVxZ/+9CcyMzPZt28fn376KbfccgsjRowgPT3dWT+KJeJbcy6bk/kEwJUvms/X/hf2LG79GkRERJzAacHmv//9L4888gjbt29n586djBgxggkTJpzVMWbNmkVJSQmjRo0iLi7O8XjvvfcA8PHx4dtvv2X8+PH07NmTX/7yl0yfPp3PPvvMWT+GZRxz2VgRbABSLoHBPzGff/oA1FSc+f0iIiJt0FnPY3OyBx98kAEDBtC/f39effVVbrrpJux2O7Nnz2bAgAFndawf6seclJR0yqzD7iLh+CrflgUbMBfJ3DkfivfDgj/BpKesq0VEROQcnHeLzejRo8nJyeGJJ57g2muvZe/evcTHxzN//ny++OILZ9TYIVjax6aBXwhMed58vvIVyFllXS0iIiLn4LxbbK688kquvPJKx+uqqio2b97Mxo0b+fbbb5k8efL5nqJDsGT24dNJGwv9b4QN78An98E934GXr7U1iYiItNBZB5vg4GAuuOACBg0axMCBAxk4cCC9e/fGZjMnlfPz82Pw4MEMHjzY6cW6s4Y+NsWVtVRU1xHoe96Z89xNeAJ2L4DCHZDxDIzRSCkREWkfzvpW1NNPP01aWhoLFy7kjjvuID09neDgYC6++GLuv/9+Zs+ezYYNG1xRq1sL9vMm2M8MM5aMjDpRQDhM/qv5fOn/Qe5Ga+sRERFpobNuFvjZz37meH7s2DECAwO5//77KSoqYsWKFbz66qvU1NRQX1/v1EI7goQwf7bnlXGg+BhpMcHWFtN7KvS6ErZ9Cp/MgDsXgqe3tTWJiIj8gPO63+Hvb94+ufHGGx3zyNTV1bF169bzr6wDagg2lrfYNLj8r7B3CeRthIV/gnF/tLoiERGRM3L6kgpeXl7tfrI8q7SJkVEnCo6ByX8zny973nyIiIi0YW1yraiOytLZh5vT7xoY86j5/JtHYM1/rK1HRETkDM76VtRPf/pTBg0axODBgx0tMw0jouT8WD77cHOG/wKqS82OxJ//AnxDzMAjIiLSxpx1sNm1axcffPABZWVleHmZH3/88ccZNWoUAwcOZMCAAQQEBDi90I6g4VZUq6/w3RJjHoXqMlj9Ksy9C3wCocckq6sSERFp4qyDTcOSBrt27SIzM5O1a9eydu1aHnnkEYqLi/H09KR79+5s2bLF6cW6u4YWm7zSKurq7Xh5tqE7hTYbTHrWDDcb34P3b4UffwipI6yuTERExOGcR0WlpaWRlpbWZAXvvXv3smbNGtatW+eU4jqaqCBfvD1t1NYb5JdVO4JOm+HhAVNfhupy2PEFvHMj3PIJJGoyRhERaRvOqkkgOzv7jPtTU1O59tpreeKJJwA4ePDguVfWAXl42IgLbWMjo07m6QXX/AdSR0JNObw5HfLVOiciIm3DWQWbIUOGcPfdd7N69epm31NSUsK///1v+vbty0cffXTeBXY0CW1xZNTJvP3ghrchcQhUFcN/p8GRLKurEhERObtbUVu3buUvf/kL48aNw8/Pj0GDBhEfH4+fnx9Hjx5l69atbNmyhYEDB/LMM89w+eWXu6put9Uw5Du7qNLiSn6AbxDc9AHMuQLyN5vh5o4vITTR6spERKQDO6sWm4iICJ577jlyc3N58cUXSUtLo7CwkF27dgFw0003kZmZyfLlyxVqzlG/hBAAvt2Wb3ElLeDfCW6eB+FdoSQbXh0HeZusrkpERDowm2EYxrl8sLy8nKCgIGfX43KlpaWEhoZSUlJCSEiI1eWc4kh5NUOfWECd3eDrB0fQ3eo1o1qiOMfsa1O4A3yC4JrZ0H281VWJiIgbaen39zmPJw4NDVUfGheICPJldM9oAD7KPGBxNS0UlgQ/mW8O/a4ph3euh1X/troqERHpgM452BiGwT//+U8uueQSLr30UmbOnHnGTsXSctMHmv1U5q07SF293eJqWsi/E9z0EQy4CQw7/O8hmP//wK5V3kVEpPWc1wxw69atY+DAgVx66aVs2bKF4cOH89BDDzmrtg7rsp7RdArwpqCsmu92F1pdTst5+cDUl+CyP5ivl78I798CNRXW1iUiIh3GOU/QB/D2228zbtw4x+uNGzcydepUEhISePDBB8+7uI7Kx8uDqQMSmPP9Pj7KPMDoHtFWl9RyNhuMeAg6pcDH98L2z2HOZLjxPXO1cBERERc65xab8PBwkpKSmmxLT0/nxRdfZNasWeddWEd3zSDzdtTXW/MpOVZrcTXnoN81cMun4B8Oh9bBq2Mgf6vVVYmIiJs752AzYMAAZs+efcr2bt26/eAMxfLD+sSH0CMmmJo6O59vPGR1OecmeRj89Nvjw8Fz4D8TYNc3VlclIiJu7JyDzZ///GdeeOEFbr75ZpYvX05FRQUFBQU88cQTpKamOrPGDslmszlabdrN6KjTiehqhpvOF0N1Kbx1DSz4E9TXWV2ZiIi4oXMONhdddBErVqwgJyeH4cOHExISQlxcHB9++CF/+9vfnFljhzX1gng8PWyszS5mz+Fyq8s5dwHhcMvHMOSn5uvv/gpvTIOydjAJoYiItCvnPEHfiQoKCsjMzMRutzN06FAiIyOdUZtLtPUJ+k52++xVLNpxmBmju/KrCT2tLuf8bfoQPn0AaisgKAamvwapw62uSkRE2jiXT9B3oujoaCZNmsTkyZPbdKhpj64ZZHbQnrv2IPX2886g1ut3Ddy1GKJ7Q3k+/PdKWPJXsLeT+XpERKRNc0qwEdcZ0yuaED8vckuqWJ51xOpynCOqO/x0AfT/kTmZ38I/wdvXQWWR1ZWJiEg7d1bz2KSmpmKz2c76JDNnzuSBBx44688J+Hl7cuWAeN5ckc1Haw9waZqbtIj5BMBVsyD5YnOW4t3fwCvD4do5kDTE6upERKSdOqs+NhkZGed0kpSUFJKTk8/ps87W3vrYAKzLPspVL3+Pn7cHq//fWIL9vK0uybnyNsH7t0JRFnh4w5hHYNh94KEGRRERMbX0+9spnYfbk/YYbAzDYMxzGew5XMEz09O5bkjSD3+ovakqhU/vh60fm69ThsNVr0BooqVliYhI29CqnYfFtU6c0+bDte14Tpsz8Qsxb0NNeQG8A2DfdzDrYnMUlYiISAudVYuNO/SxaY8tNgC5Jce4+KmFGAYs+dVoOkcEWF2S6xzJgrl3wcE15ut+18Hlz4J/mKVliYiIdVr6/X1WnYfnzJlzTsWkpKSc0+ekUVyoP5d2i+S7XYV8tPYAD47rbnVJrhPRFe6YD0ueNR+b3of935u3pjTnjYiInIH62LQjn6w/yM/fXU9SuD8ZD43Gw+PsW8/anZzVMPdOOLoXsMElD8Do/wdevlZXJiIirUh9bNzQ+N6xBPt6kVN0jFX7OsicL0lD4J6lMPAWwIBlz5srhedtsroyERFpgxRs2hF/H08mp8cB7XxhzLPlGwRX/gNueBsCIsxQ888R8MVDmtRPRESaULBpZ6YfHx31v025VNZ0sBWye06Ge5dD72nmjMWr/w3/GARrZoO93urqRESkDVCwaWcGJ3ciOSKAipp6Zi/bRwfrIgXBMXDd63DLpxDVC44Vwecz4d+XQc4qq6sTERGLKdi0MzabjZsvMmdxfnb+Dn75/oaO13ID0GUk3PMdTHwKfEMhdz28Ng7m3QNl+VZXJyIiFlGwaYfuuCSVX03ogYcN5q47yNQXl7Erv8zqslqfpzdcdC/cnwkX/NjctuEd8/bU9/+Auhpr6xMRkVan4d7t2Mo9R7j/nXUUlFXj7+3Jn6f1dfTB6ZAOrIH//QoOrTVfd0qBUb+DfteAh6elpYmIyPnRWlHNcKdgA1BYXs2D763nu12FAFw3OJHHr+yLv08H/SK322H9W7Dgcag4bG6L6gWX/d7sfHwOM2eLiIj1FGya4W7BBqDebvDSot38/dud2A3oERPMSzcNpFt0kNWlWaemAla+Ys57U1VibksYBJf9AbqMUsAREWlnFGya4Y7BpsH3WYU88M56CsurCfDx5Mmr+zF1QILVZVnrWLHZ32bFy1BbaW5LGQ5jHoGkCy0tTUREWk7BphnuHGwACsqq+Pk761m+5wgAF3UJ57aLUxjbKwYvzw7cV7y8AL57Dta8BvXHOxV3nwijHob4AZaWJiIiP0zBphnuHmzAvDX1/IJdvLRoN/V289ebEObPzcOSuX5wEp0CfSyu0ELFOZDxNKx/G4zjk/p1GQ3Df2G25OgWlYhIm6Rg04yOEGwaHCo+xpsr9vPOqmyOVtYC4OvlwbQBCdx6cQq949375z+jwt1mwNn8UWPASRgElz4IPSaDRwdu3RIRaYPa5SKYTz75JEOGDCE4OJjo6GimTZvGjh07mrynqqqKGTNmEBERQVBQENOnTyc/XxOynU58mD+/ntiT5Q+P4Zlr0ukdF0J1nZ331uRw+Qvfcd0/l/Plplxq6+1Wl9r6IrvB9H/DA2thyJ3g5QcHM+G9H8PLQ2HdW5oHR0SkHWpTLTYTJ07khhtuYMiQIdTV1fG73/2OzZs3s3XrVgIDAwG49957+eKLL5gzZw6hoaHcd999eHh4sGzZshadoyO12JzMMAzW7D/KnO/38dXmPMdtqrAAbyb1jePK/vEMTQ3Hw6MD3o4pPwwrZ8GqV6H6+CiqkAQYdp+5srhvBx5hJiLSBrjFrajDhw8THR1NRkYGI0aMoKSkhKioKN5++22uueYaALZv306vXr1Yvnw5F1100SnHqK6uprq62vG6tLSUpKSkDhlsTpRbcoy3VmTz7uocCssbr09MiC9XpMdzZf940hNDsXW0PidVpZA5G5a/BOXHWwL9wmDw7XDh3RASZ2l5IiIdlVsEm927d5OWlsamTZvo27cvCxcuZMyYMRw9epSwsDDH+5KTk5k5cyYPPvjgKcd47LHHePzxx0/Z3tGDTYO6ejsr9hTx6YaDfLk5j7KqxnWnUiICmNLfDDlpMcEWVmmB2irY+K45D07RHnObhzf0nQ7DZkBcurX1iYh0MO0+2Njtdq688kqKi4tZunQpAG+//Ta33357kxYYgAsvvJDRo0fz9NNPn3Ictdi0XHVdPUt2FvLphkN8uzWfY7X1jn09Y4OZ3C+OyelxdInqQLdl7PWw8yv4/kXI/r5xe+oIGHY/dBurjsYiIq2gpcHGqxVrOiszZsxg8+bNjlBzrnx9ffH19XVSVe7N18uTcb1jGNc7horqOr7dls9nGw6RsfMw2/PK2J5Xxt++2UmvuBCuSI9jcr84UiIDrS7btTw8zaUYek42Oxcvfwm2fAx7l5iPyB4w7GeQfj14+1tdrYhIh9cmW2zuu+8+PvnkE5YsWUJqaqpj+7ncijpZR+48fK5KKmv5emsen2/MZdnuQursjX9l+iaEMLlfPJP7xdE5IsDCKltRcTas/Cdkvg41x1dV9+9krjA++A4I72JtfSIibqhd3ooyDIP777+fefPmsXjxYtLS0prsb+g8/M477zB9+nQAduzYQc+ePZvtPHwyBZvzc7SixhFyvs864hhZBdA/MZQp/eO5Ij2e2FA/C6tsJVWlsO4NWPEKlGQf32gzb08N+SmkjdOq4iIiTtIug83PfvYz3n77bT755BN69Ojh2B4aGoq/v9nMf++99/K///2POXPmEBISwv333w/A999/f9pjnkzBxnmKKmr4anMeX2w6xPKsIzRkHJsNLkwJ58oB8VzeN879Zzq218Our2H1q7D728btYZ3NFpwLboHACOvqExFxA+0y2DQ3tHj27NncdtttgDlB3y9/+UveeecdqqurmTBhAi+//DKxsbEtOoeCjWscLqvmf5ty+XTDITL3H3Vs9/KwcWlaJFf2j2d8n1iCfNtsty7nOJIFa/4D696EqmJzm6cv9L0aBv8EEgdr2QYRkXPQLoNNa1Cwcb0DRyv5fGMun64/xNbcUsd2Xy8PxvWOYfrARIanRbr3opw1lbBlLqz6N+Sub9we289sxel3Lfh2sCH0IiLnQcGmGQo2rWt3QTmfbTjEZxsOsaewwrE9MsiXqQPiuXpgAr3jQtx3IkDDgINrzdtUW+ZCXZW53ScY0q8zQ05sX2trFBFpBxRsmqFgYw3DMNh0sIS5aw/y6YZDFFU0rsPUMzaYqwcmMG1AAtEhbtzpuLIINrxj3qo6srtxe+KFMOQn0HsaeLvxzy8ich4UbJqhYGO92no7GTsOM3fdAb7dWkDN8UU4PWxwaVoU1w5KZHyfGHy93HREkWHAvu9g9Wuw/XOwH5/t2b8T9L8RLrgZYnpbW6OISBujYNMMBZu2paSyls83HWLu2oNNOh2HBXhz1QUJXD8kiZ6xbvx7Kss3h4xnzoGSnMbtCYPMgNN3Ovi58c8vItJCCjbNULBpu/YVVvDR2gN8sOYAeaVVju39k8K4fnASU/rHEeznbWGFLmSvh90LYN1/YceXja043gHmLaqBN0PnYRpRJSIdloJNMxRs2r56u8GSXYd5b1UO327Ld8x07O/tyeT0OK4fksTg5E7u2+G4vAA2vGu25BTubNwe0c2c3bj/jRDcsukNRETchYJNMxRs2pfC8mrmrT3Iu6uzyTrcOKoqLTqIH1+UzFUDEwhx11Ycw4CcVWYrzuZ5UHv857d5Qtp4sxUnbTx4uunPLyJyAgWbZijYtE+GYbA2+yjvrc7h8425VNaYK48H+HgydUACP76oM33iQy2u0oWqy2DLPFj7BhxY1bg9MBr632D2x4nqbl19IiIupmDTDAWb9q+0qpZ5aw/y5or97Cood2wf2DmMm4clM6lvHH7ebjqiCuDwDnNm4w3vQMXhxu1JQ81bVX2u0uR/IuJ2FGyaoWDjPgzDYOXeIt5csZ+vNuc5+uJ0CvDmuiFJ/HhoMknhbrzieH2tuUbV2jfMPw2zFQvvQOgzzWzJSb4UPNx4hmcR6TAUbJqhYOOeCsqqeH91Dm+vzOZQiTmiysMGY3vFcPslqVzUJdx9OxsDlOWZLTjr3mw6+V9oEqRfb3Y4juxmXX0iIudJwaYZCjbura7ezqIdh/nv8n18t6vQsb1nbDC3XZzCtAsS3Ps2lWFAzkpY/zZs+RiqSxr3JQ4xW3H6XA0B4ZaVKCJyLhRsmqFg03Hsyi/j9eX7+CjzIMdqzds0YQHe3HhhZ26+KJn4MH+LK3Sx2mPmnDgb3jHnyGm4VeXpA90nmq04aeM0qkpE2gUFm2Yo2HQ8JZW1vL8mh9eX7+PA0WMAeHrYmNgnljsuTWFQcgdovSjLh80fmiEnb1Pj9oAIc6Xx/jdA3ABNACgibZaCTTMUbDquervBt9vymbNsH8v3HHFsv6BzGHcN78L4PrF4enSAL/a8zWbA2fg+VBQ0bo/qZQac9OsgJN66+kRETkPBphkKNgKwPa+U/yzdy8frDjkW4ewcHsAdl6Rw7eAkAn29LK6wFdTXwZ5FZn+c7V9AffXxHTboMgoG/Ah6TgafQCurFBEBFGyapWAjJyooq+KN5ft5Y8V+iitrAQj19+amoZ257eIUokP8LK6wlRwrhq2fmC052csbt3sHmuEm/TroMho8O0DgE5E2ScGmGQo2cjrHaur5cO0B/rN0L3sLzaULvD1tXNk/gbtGdKFHbAea8K5oj3mbasM7cHRf4/aASOh7NfS7DhIHqz+OiLQqBZtmKNjImTT0w3n1uz2s3nfUsX10jyjuHtmVoaluPh/OiQwDDqw2Q86WuVDZ2C+JTqlmp+P06yAyzboaRaTDULBphoKNtNT6nGL+tSSLrzbncXxSY/onhXHPiA7U0bhBfS1kLYJN75v9cWorG/fFDTADTt/pWnVcRFxGwaYZCjZytvYVVvDq0j18sOYA1XVmR+PUyEDuHN6Fqwe6+YR/p1NdDjv+Z7bkZC1snB/H5gGpI8xbVb2mgJ/++xIR51GwaYaCjZyrwvJq/vv9Pl5fvp+SY2ZH48ggH267OIWbL0ohNKADTnRXfthcdXzT++ZtqwZefuYkgOnXQbdx4OVjXY0i4hYUbJqhYCPnq6K6jvdW5/Da0r0cLDYn/Av08eSmi5L5yaWpxHSUkVQnK9oDmz40W3KO7Grc7hcGvadCv2sg+RLw6GAtXCLiFAo2zVCwEWeprbfzxcZcXsnIYnteGQA+nh5MH5TAXSO6khrZQed/MQzIXQ8bP4DNH0F5XuO+oFjoc5UZchIGaWSViLSYgk0zFGzE2QzDYPGOw7y8eLdjJJWHDSb1i+PekV3pmxBqcYUWstfD3iVmwNn2KVSdsChnWLLZ4bjvdIjpo5AjImekYNMMBRtxpdX7ipi1OIuF2xuXKhjRPYp7R3bloi4daKj46dTVQNYCM+Rs/x/UVjTui+rZGHIiulpXo4i0WQo2zVCwkdawLbeUVzKy+GzDIcdQ8Qs6h/GzUd0Y0zMaj440VPx0aipg53wz5Oz6GuprGvfF9TcDTp+rISzJuhpFpE1RsGmGgo20puwjlfzruyzeX3OAmuNDxbvHBHHvqK5MSY/Hy9PD4grbgKoS2Pa5OQlg1qLG4eMASUPNkNN7GgTHWFaiiFhPwaYZCjZihYKyKv6zdB9vrthPeXUdAImd/Ll7RBeuHZzU8ebCaU5FodkXZ9NHsH8ZcPyfJ5sHpFxqtuL0uhICIywtU0Ran4JNMxRsxEolx2p5c8V+/rN0L0cqzNsvkUE+3HFpKj++KJkQvw44F05zSg/Blo/N21UH1zRut3maq4/3uQp6XQH+nayqUERakYJNMxRspC04VlPP+2ty+NeSPY65cIJ9vfjxsGTuuCSVqGBfiytsY47ug81zzckA8zY2bvfwhq6jzZacnpeDXwcegSbi5hRsmqFgI21Jbb2dzzYcYtbiLHYVlAPg6+XBdYOTuGtEF5LCAyyusA0q3G0GnC3zoGBL43ZPH+g21mzJ6T5RSzqIuBkFm2Yo2EhbZD++qvjLi7NYn1MMgKeHjSv7x3PPyK70iA22tsC26vAOM+BsnguFOxq3e/pCtzFmp+MekxRyRNyAgk0zFGykLTMMg+V7jjBrcRbf7Sp0bB/bK5p7R3VjULL6k5yWYUDBNnNk1ZaPmy7p4OkDXcdAn2nHQ45uV4m0Rwo2zVCwkfZi04ESZmXs5svNeTT8Vzo0NZx7R3VlZPeojj3Z35kYBhRsNQPO1o+hcGfjPk8f6HqZebtKIUekXVGwaYaCjbQ3WYfL+WdGFvPWHaS23vzPtVdcCPeM7MLkfnGaC+dMGlpytn5sBp0mt6uOh5yG21X+YdbUKCItomDTDAUbaa9yS47x6nd7eWdVNpU15iR2iZ38uXN4F64bnIS/j+bC+UEF2xpbcg5vb9zu4X28JWca9LhcIUekDVKwaYaCjbR3xZU1vLF8P3O+3+eYCyc80Idbh6Vwy7BkOgX6WFxhO3HGkDO6sSUnINyiAkXkRAo2zVCwEXdRVVvPB2ty+Nd3e8gpMufC8ff25PohSfx0eCqJnTRUvMUKtjferjq8rXG7zRNSLoGeU6DnZAhNsKpCkQ5PwaYZCjbiburq7Xy5OY9XMrLYcqgUMIeKX5Eex53Du9A3QR1kz8rhHWbA2fYZ5G9qui9hEPS8wlzWIbKbJeWJdFQKNs1QsBF3ZRgGS3cX8kpGFst2H3Fsv6RbBHcO76KRVOeiaC9s/9xcpDNnJY61qwCiekKvKdB7KsT0BV1bEZdSsGmGgo10BJsPlvDv7/bw+cZc6u3mf+I9YoK5c0QXruwfj4+XRlKdtbJ82PGF2ZKzdwnY6xr3hXcxW3F6T4X4CxRyRFxAwaYZCjbSkRw4WsnsZft4d1U2FcdHUsWG+HH7JSncOLSzFt08V8eOws6vzZXId38LdVWN+0I7Q+/jISdhMHgoRIo4g4JNMxRspCMqqazlrVX7mbNsHwVl1QAE+nhy7eAkbr8kheSIQIsrbMeqy2HX17D1E/PP2srGfcHxZqfjHhMhZTh4aXFTkXOlYNMMBRvpyKrr6vlk/SFe/W4PO/PNRTdtNhjXK4afXJrKhanh6odzPmoqIWuBGXJ2fAU1ZY37vAPNYeQ9JkHaeAiKtq5OkXZIwaYZCjYiZkfj73YV8trSvWTsPOzY3jchhJ9cmsrkfuqHc95qq2DPYtjxP9g5H8rzTthpM0dY9ZhorkSuzsciP0jBphkKNiJN7cov4z/L9jF37QGq6+wAxIT4csuwFH50YWdN+OcMdjvkbTADzo4vIXd90/2hSeYIq15TIGkoeGgWaZGTKdg0Q8FG5PSKKmp4a8V+/rtiP4eP98Px9fJg2oAEbr04hd7x+u/FaUpzYdd8M+hkLYK6Y437AqPMfjm9rjzeL0fBUgTaabBZsmQJzz77LJmZmeTm5jJv3jymTZvm2H/bbbfx+uuvN/nMhAkT+Oqrr1p8DgUbkTOrrqvn8w25/GfZXseEfwAXpoRz2yUpjO8do4U3nan2GGQtNIeR7/gfVJU07vMLhe6TzJacrpeBj2aTlo6rpd/fXq1Y0w+qqKigf//+3HHHHVx99dWnfc/EiROZPXu247Wvr0YZiDiTr5cn0wclcvXABNbsP8qc7/fx1eY8Vu0rYtW+IuJC/fjxRcnceGFnwnWb6vx5+5stND0nQ30t7PsOtn4K27+AigLY+K758PSF5IvNgNP1Mojpo345IqfRplpsTmSz2U7bYlNcXMzHH398zsdVi43I2csrqeKtlft5e2W2Y+FNHy8Pruwfz20Xp2jZBlew10POKrMlZ9tnUJLddH9QDHQZDd3GQJdRGmUlbq9d3oo6UXPB5uOPP8bHx4dOnTpx2WWX8ec//5mIiIhmj1NdXU11dbXjdWlpKUlJSQo2IuegqraeLzbmMuf7fWw62HjLpH9iKDddlMyU9Hj8fdTx1ekMAwp3mresshbCvqVN58sBiO0H3caaC3bGX6CJAcXtuGWweffddwkICCA1NZWsrCx+97vfERQUxPLly/H0PP0/po899hiPP/74KdsVbETOnWEYrM0u5r/L9/Hlpjxq6s3RVCF+XlwzKImbLupM16ggi6t0Y3XVkL2iMejkbWy6v2FiwF5XQPIl4KkZpqX9c8tgc7I9e/bQtWtXvv32W8aMGXPa96jFRsS1Csur+WDNAd5etZ+cosbRPRd3jeDHFyUzrncM3ups7Frlh2HPIrPz8a5voKa8cZ9fmDkpYM8r1AFZ2rUOEWwAoqKi+POf/8zdd9/douOqj42Ia9jtBhm7DvPWiv0s3F7A8bU3iQr25frBSVw/JImkcH2pulxtFezNOD7K6kuoLGzc5+Vv9sdJHW4OJY/pq1tW0m60y1FRZ+vAgQMcOXKEuLg4q0sR6fA8PGyM7hHN6B7RHCw+xrursnl3dQ6Hy6p5cdFuXly0m0u7RXLDhUmM6x2Dr5f64riEtx90n2A+7PXmLavtn8O2z80OyDu/NB9gtuYkXwIpl5phJ7qPgo60e22qxaa8vJzdu3cDcMEFF/Dcc88xevRowsPDCQ8P5/HHH2f69OnExsaSlZXFr3/9a8rKyti0aVOLh32rxUak9dTW2/l6Sz7vrs5m6e5CGv61CQ/04eoLErjhwiS6RQdbW2RHYRhmX5w9i83Ox/uXN13LCsygk3Kp2arT43IITbCgUJHTa5e3ohYvXszo0aNP2X7rrbcya9Yspk2bxrp16yguLiY+Pp7x48fzpz/9iZiYmBafQ8FGxBo5RZW8vyaH99fkkF/a2O9tcHInrh+SxOT0OAJ82nUjcvtSXwe5G8x5c/YthezlTfvmgLmeVa8p5izIEV2tqVPkuHYZbFqDgo2Iterq7WTsPMy7q3NYuL2A+uOdcQJ9PJmcHsc1g5IYktJJq4y3NkfQWWIu9ZC9Ajjh6yG6d+N6Vlq0UyygYNMMBRuRtiO/tIoPMw/w3uocsosa52XpHB7A9IHm7MfqcGyRsnzY8YXZCXnvErDXNe4LSzZvVaVcAp2HQWCkdXVKh6Fg0wwFG5G2xzAMVu87yoeZOXyxMZeKmnrHvou6hHPNoCQm9Y0l0Fe3qixx7KjZirPtM9i9oOminQCRPczlHpIvgeRhEJpoTZ3i1hRsmqFgI9K2VdbUMX9LHh9mHuD7rCOODscBPp5M7BvLtAEJXNw1QgtxWqWmwgw3exab/XIKtp76nrDO0PliSB0BPS8H/06tXqa4HwWbZijYiLQfB4uPMW/tAT7MPMC+I423qqKCfZmSHs+0C+LplxCq/jhWqiwyA87+781H7gYwGlvc8PCGrqOhz1Xm7Sv/MMtKlfZNwaYZCjYi7Y+5hMNR5q07yBcbczlaWevY1yUqkGkDEpg6IJ7kiEALqxQAqsvMxTv3fw87v4L8zY37PLzN2Y/7XGW25Php8VRpOQWbZijYiLRvNXV2luw8zMfrD/LN1nyq6+yOfRd0DmNq/3gu7xdHdIifhVWKw+GdsPVj2DKv6W0rTx/oOsYcZZU6AsKSLCtR2gcFm2Yo2Ii4j/LqOuZvzuPj9QdZtrvQsYyDzQZDU8O5Ij2eiX1jiQxq2QSe4mIF2xtDzuHtTfd1SjEnB0w5vtyDJgeUkyjYNEPBRsQ9FZRW8dnGXD7feIh12cWO7R42uLhrJFekxzGhTyydAn2sK1IaFWwzA87uBXBoXdN+OQCdUo8v9TDCfATHWlOntBkKNs1QsBFxfweOVvLFxly+2JTLxgMlju1eHjYu6RbJ5PQ4xvWKUchpK6rLzAkB930He7+D3PVg2E94g81cy6rfddD7SvXN6aAUbJqhYCPSsew/UsHnG3P5YmMuW3NLHds9PWxc1CWciX3jmNAnhuhg9clpM6pKjwedJY1Bp4GnL/SYCOnXQ7dx4KVw2lEo2DRDwUak48o6XM4XG3P5cnMe204IOTYbDOrciYl9Y5nYN5bETprtuE05uh82fwgb32/aN8cvDPpMM0NO0kVamdzNKdg0Q8FGRAD2FVYwf0seX27OY31OcZN9/RJCmdAnhnG9Y+keE6R5ctoKw4C8TbDxPdj0IZTnNe4LSTDny+l6GaSOgsAIq6oUF1GwaYaCjYic7FDxMb4+HnJW7ytyjK4Cc92qsb1iGNc7hiEpnTTjcVthrzf75Gx8H7Z+CjVlJ+y0QfwAM+R0vQwSL9QtKzegYNMMBRsROZPC8mq+3pLPN1vzWJZ1hJoT5skJC/Dmsh7RjO0dw4juUQRp7aq2ofYY7F8GWYsga+Gpyzx4B5ojrNLGmZMDatHOdknBphkKNiLSUhXVdXy36zBfb81n4fYCik+Y8djH04OLukZwWY8oLusZQ+cI9ctpM0pzzbWsshbCnkVQcbhxn4cXpI2H/jdC9wngpTmO2gsFm2Yo2IjIuairt5O5/yjfbsvnm635TdauAugWHcRlPaMZ3SOawSmd8NYtq7bBboeCLeZ8OVs/NufMaeDfCfpON0NOwiCzF7m0WQo2zVCwEZHzZRgGuwvKWbi9gAXbC8jcf5T6EzrmBPt5MSItitE9oxnVI0ozH7clBdthwztm35yyQ43bI9Kg/w2Qfp25Orm0OQo2zVCwERFnK6msZcmuwyzaXsDinYcpqqhpsr9vQggju0cxsns0F3QOU2tOW2Cvh70ZsOFd2PYZ1J7QAhfdB9LGmvPkdL4IPL2tq1McFGyaoWAjIq5UbzfYcKCYRdsLWLi9gC2HSpvsD/b14uJuEYzsHs2I7pGaM6ctqC4zR1ZteAf2LQVO+Fr0CYauo8yQ022s1rCykIJNMxRsRKQ1FZRV8d3OQpbsOsySnYc5ekIHZICuUYGM6B7F8LRIhqZGEKiRVtaqLDI7He/62uyXU1nYdH90H+g+HvpdCzF9rKmxg1KwaYaCjYhYpd5usPlgCUt2HiZj52HW5RQ36Zvj5WFjYOdOXJoWyaVpkaQnhGreHCvZ7ZC7DnZ9C7u/gQNraNKaE9PP7JPT71oIibOszI5CwaYZCjYi0laUHKvl+92FLNlVyNLdh8kpOtZkf7CfF8O6RDA8LZJLukWSGhmoWZCtVFnUOLpq53ywN7S+2aDLSEi/AXpdAb7BVlbpthRsmqFgIyJtVfaRSr7bfZiluwpZtruQ0qq6JvtjQ/y4uGsEw7pGcHG3SBLC/C2qVKgsMgPOhvcgZ0Xjdi9/M9z0uxZSR4C3fkfOomDTDAUbEWkP6u0Gmw6WsGx3Id/tOsza/cXU1NubvCc5IuB40IlkWJcIooI1rNwSRXth0wfmGlZHdjdu9/KD5EvMGY+7jYWIbpor5zwo2DRDwUZE2qOq2noy9x/l+6xCvs86wsYDJU365wCkRQcxtEs4F3WJYGiqgk6rMww4tNZsxdn+OZQebLo/rLMZcLqNNVtzdMvqrCjYNEPBRkTcQVlVLav3FfH97iN8n3WErbmlp7yna1QgQ7tEMDTVDDsxIX4WVNpBGQYc3mF2Ot79Lez/HupPmN/IwxuSh0HvqdDrSgiKtq7WdkLBphkKNiLijo5W1LBybxEr9x5hxZ4itueVcvK/7qmRgQxNDWdISjgXpoaT2MlfnZFbS02FOUfOrm/MsHN0X+M+m4d5y6rPVcdDTpRlZbZlCjbNULARkY6guLKGVXuLWLm3iBV7zBadk/+1jwv1Y0hKOENSwxmaGk63qCA8PBR0WsWRLNj+hdkB+WBm43abh7kSeUPI0UrkDgo2zVCwEZGOqKTSvHW1al8Rq/YWsflgCXUn9dEJC/BmcHI4F6Z2YlByOH0TQvD18rSo4g7k6D7Y+glsmdd0kU6bJ6RcAl3HmMPJY9PBo+P+PhRsmqFgIyIClTV1rM8udgSdddnFHKutb/IeHy8P+ieGMig5nCEpnRiU3ImwAB+LKu4givY2hpzc9U33+XcyOx2njoQuoyC8S4caZaVg0wwFGxGRU9XW29l8sITV+4pYve8omfuPnrKYJ0C36CAGJ5shZ2ByJ7po0kDXKdoDO782F+vc+x3UlDXdH9oZuoyALqOh2xgz+LgxBZtmKNiIiPwwwzDYW1jBmv1Hydx3lDX7i8g6XHHK+8ICvBnYuRMDO4cxsHMn+ieFab0rV6ivM4eS78mAPYshZ+UJMx9j3rbqPAx6TITukyCym2WluoqCTTMUbEREzk1RRQ2Z+82Qs3b/UTYeKKG6rumkgR426BkbwsBkM+gMSArTUhCuUFMB2cvNkLN7ARRsbbo/oht0nwg9JkHSReDZ/sOmgk0zFGxERJyjps7OttxS1mabt67WZRdzsPjYKe8LC/Cmf2IYF3QOY0CS+VBfHSc7us9cv2rH/2DfsqatOX6h5qSAKZdC54shqke77JujYNMMBRsREdfJL61i7f6jrM02g86mg6e26gB0iQxkQOcwLkgKo39SGD1jQ/Dx0krmTlFVClkLYedXZtg5VtR0f0CEeduq8zBIvtgcbdUOWnQUbJqhYCMi0npq6uxszytlfU4x67KLWZd9lH1HKk95n4+nB73jQxiQFEb/pFAGJHUiJSJAt7DOl70eDqw2g87+7+HAGqg7qVXNJwgSh5iTBHYZCfED22TQUbBphoKNiIi1iipq2JBjhpz1B0rYkFNMybHaU94X4udF/6Qw+ieGkZ4YSnpiGLGhWhbivNTVmMPI939v9tHJXg5VJU3f4xti3rbqMsp8RHZvE7euFGyaoWAjItK2GIbB/iOVbDhQzPqcYjbkFLP5UCk1p7mFFR3s6wg5/RJDSU8IJSJIi32eM7vd7HicvRz2LjEfVcVN3xMU2xhyuoyEkHgLClWwaZaCjYhI21dbb2dHXhnrc4rZeKCYjQdK2Jlfhv0031gJYf6kJ4bSNyGUfscfnQLVOfmc2Oshb6M52mrPYsheAXVVTd8TkgAJgyBxMCQMhvgB4BPo8tIUbJqhYCMi0j4dq6lny6ESNh4oMcPOwRL2nGZuHYDETv70S1DYOW+1VeacOQ1BJ3c9GCe1pNk8Ibo3JA4yA0/CYHPklZOXf1CwaYaCjYiI+yitqmXzgRI2HTQfmw+WnLZzMpgtO30TQugbbwaePgkhRAerz85ZqS6DQ+vh4Bpz8c4DmVB26NT3jfodjPqNU0+tYNMMBRsREfdWcqyWLQdbFnaig33pEx9iBp34UPrEh5DYyV+jsc5G6SFztNXBzOOPtXDd65A2zrmnUbA5PQUbEZGOp+RYLVsPlbLlkBl0thwqJetw+Wn77IT6e9M7LoTe8SH0iQ+hT3woXaMC8fLUPDstYq8Hw3D6kHEFm2Yo2IiICJgrnG/LLXOEnU0HS9mVX0bdadKOj5cHPWOD6R1nhp1ecSH0jAshSOtitRoFm2Yo2IiISHOq6+rZlV/O1txSRwvPttwyyqvrTvv+5IgAeseZQad3XAi94kOID/XTrSwXULBphoKNiIicDbvdILuokq25ZtDZeqiUbbll5JVWnfb9of7e9IoLpmesGXZ6xgXTPSYYP2/njhLqaBRsmqFgIyIizlBUUcO24y0723JL2Zpbyu6C8tPeyvKwQWpkID3jQugVa4aennHBJISpo3JLKdg0Q8FGRERcpeFW1rbcUrbnlbE9z2zdKaqoOe37g/286BETTI/YYHrGBtMzLoTuMcGE+nu3cuVtX7sMNkuWLOHZZ58lMzOT3Nxc5s2bx7Rp0xz7DcPg0Ucf5d///jfFxcVccsklzJo1i7S0tBafQ8FGRERak2EYHC6rZlteGduPB55tZ2jdAYgP9aNHbDA9YkPoGWveyuoaHYivV8e9ndXS7+821Z27oqKC/v37c8cdd3D11Vefsv+ZZ57hhRde4PXXXyc1NZU//OEPTJgwga1bt+Lnp0mWRESk7bHZbESH+BEd4sfI7lGO7TV1dvYUlrMjr4xtuWXsyCtlR14Zh0qqHI9FOw473u/pYSMlIoCesWarTo/YILrHBJMcEYinh25nNWhTLTYnstlsTVpsDMMgPj6eX/7ylzz00EMAlJSUEBMTw5w5c7jhhhtadFy12IiISFtWUlnLjnwz6GzLK2NXfhnb88ooqzr9yCxfLw+6RgXRPSaI7rHBdI82W3gSO/nj4UaBp1222JzJ3r17ycvLY+zYsY5toaGhDB06lOXLlzcbbKqrq6murna8Li0tdXmtIiIi5yo0wJsLU8O5MDXcsc0wDPJLq9mRX8bOPDPo7MwvY1dBGVW1dnN4em7T7zd/b0/SYoJIiw42Q09MMN2ig0gIc6/Ac7J2E2zy8vIAiImJabI9JibGse90nnzySR5//HGX1iYiIuJKNpuN2FA/YkOb3s6qtxvkFFUeDznmba2d+WXsOVzBsdr64wuGljQ5lr+3J92ig0iLDiItJvj4n0Ekdgpwi1ta7SbYnKuHH36YX/ziF47XpaWlJCUlWViRiIiIc3h62EiJDCQlMpDxfRq319Xb2V9Uya78MnbklbOzoIysgnJH4GlYR+tEvl4edIkKolt0EN0a/owOIiUyoF11Wm43wSY2NhaA/Px84uLiHNvz8/MZMGBAs5/z9fXF19fX1eWJiIi0GV6eZr+brlFBTOzbuL0x8JSzu8Bs5dmVX07W4XKq6+xsyzXn5DmRp4eN5PAAuh4POl2Ph54uUYGE+LW9YentJtikpqYSGxvLggULHEGmtLSUlStXcu+991pbnIiISDtwYuCBWMf2ervBgaOV7C4oZ1dBObuPP7IKyimrrmNPYQV7Civ4Zmt+k+NFB/s6gk7XqEC6Hg8+cRYuK9Gmgk15eTm7d+92vN67dy/r168nPDyczp07M3PmTP785z+TlpbmGO4dHx/fZK4bEREROTueHjaSIwJJjghkTK/GvqwNnZbNoFPG7sPlZBVUkHW4nIKyasdj+Z4jTY7364k9+Nmobq39YwBtLNisWbOG0aNHO1439I259dZbmTNnDr/+9a+pqKjgrrvuori4mEsvvZSvvvpKc9iIiIi4wImdli9Ni2yyr7SqlqyCcrIOm0HHfF7O/iOVdIkMtKjiNjyPjatoHhsRERHXqa23Yxjg4+Xh1OO63Tw2IiIi0vZ5ezo30Jwta88uIiIi4kQKNiIiIuI2FGxERETEbSjYiIiIiNtQsBERERG3oWAjIiIibkPBRkRERNyGgo2IiIi4DQUbERERcRsKNiIiIuI2FGxERETEbSjYiIiIiNtQsBERERG30eFW9zYMAzCXPxcREZH2oeF7u+F7vDkdLtiUlZUBkJSUZHElIiIicrbKysoIDQ1tdr/N+KHo42bsdjuHDh0iODgYm83mtOOWlpaSlJRETk4OISEhTjuunErXunXoOrcOXefWoevcOlx5nQ3DoKysjPj4eDw8mu9J0+FabDw8PEhMTHTZ8UNCQvQfTSvRtW4dus6tQ9e5deg6tw5XXecztdQ0UOdhERERcRsKNiIiIuI2FGycxNfXl0cffRRfX1+rS3F7utatQ9e5deg6tw5d59bRFq5zh+s8LCIiIu5LLTYiIiLiNhRsRERExG0o2IiIiIjbULARERERt6Fg4yQvvfQSKSkp+Pn5MXToUFatWmV1Se3akiVLmDJlCvHx8dhsNj7++OMm+w3D4JFHHiEuLg5/f3/Gjh3Lrl27rCm2HXvyyScZMmQIwcHBREdHM23aNHbs2NHkPVVVVcyYMYOIiAiCgoKYPn06+fn5FlXcPs2aNYv09HTHpGXDhg3jyy+/dOzXNXaNp556CpvNxsyZMx3bdK2d47HHHsNmszV59OzZ07HfyuusYOME7733Hr/4xS949NFHWbt2Lf3792fChAkUFBRYXVq7VVFRQf/+/XnppZdOu/+ZZ57hhRde4JVXXmHlypUEBgYyYcIEqqqqWrnS9i0jI4MZM2awYsUKvvnmG2praxk/fjwVFRWO9zz44IN89tlnfPDBB2RkZHDo0CGuvvpqC6tufxITE3nqqafIzMxkzZo1XHbZZUydOpUtW7YAusausHr1av75z3+Snp7eZLuutfP06dOH3Nxcx2Pp0qWOfZZeZ0PO24UXXmjMmDHD8bq+vt6Ij483nnzySQurch+AMW/ePMdru91uxMbGGs8++6xjW3FxseHr62u88847FlToPgoKCgzAyMjIMAzDvK7e3t7GBx984HjPtm3bDMBYvny5VWW6hU6dOhmvvvqqrrELlJWVGWlpacY333xjjBw50vj5z39uGIb+PjvTo48+avTv3/+0+6y+zmqxOU81NTVkZmYyduxYxzYPDw/Gjh3L8uXLLazMfe3du5e8vLwm1zw0NJShQ4fqmp+nkpISAMLDwwHIzMyktra2ybXu2bMnnTt31rU+R/X19bz77rtUVFQwbNgwXWMXmDFjBpMnT25yTUF/n51t165dxMfH06VLF2666Says7MB669zh1sE09kKCwupr68nJiamyfaYmBi2b99uUVXuLS8vD+C017xhn5w9u93OzJkzueSSS+jbty9gXmsfHx/CwsKavFfX+uxt2rSJYcOGUVVVRVBQEPPmzaN3796sX79e19iJ3n33XdauXcvq1atP2ae/z84zdOhQ5syZQ48ePcjNzeXxxx9n+PDhbN682fLrrGAjIoD5f7mbN29ucp9cnKdHjx6sX7+ekpISPvzwQ2699VYyMjKsLsut5OTk8POf/5xvvvkGPz8/q8txa5MmTXI8T09PZ+jQoSQnJ/P+++/j7+9vYWXqPHzeIiMj8fT0PKW3d35+PrGxsRZV5d4arquuufPcd999fP755yxatIjExETH9tjYWGpqaiguLm7yfl3rs+fj40O3bt0YNGgQTz75JP379+f555/XNXaizMxMCgoKGDhwIF5eXnh5eZGRkcELL7yAl5cXMTExutYuEhYWRvfu3dm9e7flf6cVbM6Tj48PgwYNYsGCBY5tdrudBQsWMGzYMAsrc1+pqanExsY2uealpaWsXLlS1/wsGYbBfffdx7x581i4cCGpqalN9g8aNAhvb+8m13rHjh1kZ2frWp8nu91OdXW1rrETjRkzhk2bNrF+/XrHY/Dgwdx0002O57rWrlFeXk5WVhZxcXHW/512effkDuDdd981fH19jTlz5hhbt2417rrrLiMsLMzIy8uzurR2q6yszFi3bp2xbt06AzCee+45Y926dcb+/fsNwzCMp556yggLCzM++eQTY+PGjcbUqVON1NRU49ixYxZX3r7ce++9RmhoqLF48WIjNzfX8aisrHS855577jE6d+5sLFy40FizZo0xbNgwY9iwYRZW3f789re/NTIyMoy9e/caGzduNH77298aNpvN+Prrrw3D0DV2pRNHRRmGrrWz/PKXvzQWL15s7N2711i2bJkxduxYIzIy0igoKDAMw9rrrGDjJP/4xz+Mzp07Gz4+PsaFF15orFixwuqS2rVFixYZwCmPW2+91TAMc8j3H/7wByMmJsbw9fU1xowZY+zYscPaotuh011jwJg9e7bjPceOHTN+9rOfGZ06dTICAgKMq666ysjNzbWu6HbojjvuMJKTkw0fHx8jKirKGDNmjCPUGIausSudHGx0rZ3j+uuvN+Li4gwfHx8jISHBuP76643du3c79lt5nW2GYRiubxcSERERcT31sRERERG3oWAjIiIibkPBRkRERNyGgo2IiIi4DQUbERERcRsKNiIiIuI2FGxERETEbSjYiIiIiNtQsBERt5eSksLf//53q8sQkVagYCMiTnXbbbcxbdo0AEaNGsXMmTNb7dxz5swhLCzslO2rV6/mrrvuarU6RMQ6XlYXICLyQ2pqavDx8Tnnz0dFRTmxGhFpy9RiIyIucdttt5GRkcHzzz+PzWbDZrOxb98+ADZv3sykSZMICgoiJiaGm2++mcLCQsdnR40axX333cfMmTOJjIxkwoQJADz33HP069ePwMBAkpKS+NnPfkZ5eTkAixcv5vbbb6ekpMRxvsceeww49VZUdnY2U6dOJSgoiJCQEK677jry8/Md+x977DEGDBjAG2+8QUpKCqGhodxwww2UlZU53vPhhx/Sr18//P39iYiIYOzYsVRUVLjoaopISynYiIhLPP/88wwbNow777yT3NxccnNzSUpKori4mMsuu4wLLriANWvW8NVXX5Gfn891113X5POvv/46Pj4+LFu2jFdeeQUADw8PXnjhBbZs2cLrr7/OwoUL+fWvfw3AxRdfzN///ndCQkIc53vooYdOqctutzN16lSKiorIyMjgm2++Yc+ePVx//fVN3peVlcXHH3/M559/zueff05GRgZPPfUUALm5udx4443ccccdbNu2jcWLF3P11VejNYVFrKdbUSLiEqGhofj4+BAQEEBsbKxj+4svvsgFF1zAE0884dj2n//8h6SkJHbu3En37t0BSEtL45lnnmlyzBP766SkpPDnP/+Ze+65h5dffhkfHx9CQ0Ox2WxNzneyBQsWsGnTJvbu3UtSUhIA//3vf+nTpw+rV69myJAhgBmA5syZQ3BwMAA333wzCxYs4C9/+Qu5ubnU1dVx9dVXk5ycDEC/fv3O42qJiLOoxUZEWtWGDRtYtGgRQUFBjkfPnj0Bs5WkwaBBg0757LfffsuYMWNISEggODiYm2++mSNHjlBZWdni82/bto2kpCRHqAHo3bs3YWFhbNu2zbEtJSXFEWoA4uLiKCgoAKB///6MGTOGfv36ce211/Lvf/+bo0ePtvwiiIjLKNiISKsqLy9nypQprF+/vslj165djBgxwvG+wMDAJp/bt28fV1xxBenp6Xz00UdkZmby0ksvAWbnYmfz9vZu8tpms2G32wHw9PTkm2++4csvv6R379784x//oEePHuzdu9fpdYjI2VGwERGX8fHxob6+vsm2gQMHsmXLFlJSUujWrVuTx8lh5kSZmZnY7Xb+9re/cdFFF9G9e3cOHTr0g+c7Wa9evcjJySEnJ8exbevWrRQXF9O7d+8W/2w2m41LLrmExx9/nHXr1uHj48O8efNa/HkRcQ0FGxFxmZSUFFauXMm+ffsoLCzEbrczY8YMioqKuPHGG1m9ejVZWVnMnz+f22+//YyhpFu3btTW1vKPf/yDPXv28MYbbzg6FZ94vvLychYsWEBhYeFpb1GNHTuWfv36cdNNN7F27VpWrVrFLbfcwsiRIxk8eHCLfq6VK1fyxBNPsGbNGrKzs5k7dy6HDx+mV69eZ3eBRMTpFGxExGUeeughPD096d27N1FRUWRnZxMfH8+yZcuor69n/Pjx9OvXj5kzZxIWFoaHR/P/JPXv35/nnnuOp59+mr59+/LWW2/x5JNPNnnPxRdfzD333MP1119PVFTUKZ2PwWxp+eSTT+jUqRMjRoxg7NixdOnShffee6/FP1dISAhLlizh8ssvp3v37vz+97/nb3/7G5MmTWr5xRERl7AZGp8oIiIibkItNiIiIuI2FGxERETEbSjYiIiIiNtQsBERERG3oWAjIiIibkPBRkRERNyGgo2IiIi4DQUbERERcRsKNiIiIuI2FGxERETEbSjYiIiIiNv4/53uHj0x8NTEAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m off_diagonal_norm[\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m dbi\u001b[38;5;241m.\u001b[39moff_diagonal_norm\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(flows):\n\u001b[0;32m---> 15\u001b[0m d_trained, loss, grad, diags \u001b[38;5;241m=\u001b[39m \u001b[43mgradient_descent_dbr_d_ansatz\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdbi_trained\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterations\u001b[49m\u001b[43m,\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m s \u001b[38;5;241m=\u001b[39m dbi_trained\u001b[38;5;241m.\u001b[39mchoose_step(scheduling\u001b[38;5;241m=\u001b[39mDoubleBracketScheduling\u001b[38;5;241m.\u001b[39mpolynomial_approximation, d\u001b[38;5;241m=\u001b[39md_trained, n\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m 17\u001b[0m dbi_trained(s,d\u001b[38;5;241m=\u001b[39md_trained)\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils_gradients.py:261\u001b[0m, in \u001b[0;36mgradient_descent_dbr_d_ansatz\u001b[0;34m(dbi_object, params, nmb_iterations, lr, normalize)\u001b[0m\n\u001b[1;32m 259\u001b[0m grad \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((nmb_iterations, \u001b[38;5;28mlen\u001b[39m(params)))\n\u001b[1;32m 260\u001b[0m dbi_new \u001b[38;5;241m=\u001b[39m deepcopy(dbi_object)\n\u001b[0;32m--> 261\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[43mdbi_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoose_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 262\u001b[0m dbi_new(s, d\u001b[38;5;241m=\u001b[39md)\n\u001b[1;32m 263\u001b[0m loss[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m dbi_new\u001b[38;5;241m.\u001b[39mloss(\u001b[38;5;241m0.0\u001b[39m, d)\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:183\u001b[0m, in \u001b[0;36mDoubleBracketIteration.choose_step\u001b[0;34m(self, d, scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m scheduling \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 182\u001b[0m scheduling \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscheduling\n\u001b[0;32m--> 183\u001b[0m step \u001b[38;5;241m=\u001b[39m \u001b[43mscheduling\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 185\u001b[0m step \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m scheduling \u001b[38;5;129;01mis\u001b[39;00m DoubleBracketScheduling\u001b[38;5;241m.\u001b[39mpolynomial_approximation\n\u001b[1;32m 187\u001b[0m ):\n\u001b[1;32m 188\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m3\u001b[39m)\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils_scheduling.py:37\u001b[0m, in \u001b[0;36mgrid_search_step\u001b[0;34m(dbi_object, step_min, step_max, num_evals, space, d)\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 35\u001b[0m d \u001b[38;5;241m=\u001b[39m dbi_object\u001b[38;5;241m.\u001b[39mdiagonal_h_matrix\n\u001b[0;32m---> 37\u001b[0m loss_list \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\u001b[43mdbi_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mspace\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 39\u001b[0m idx_max_loss \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmin(loss_list)\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m space[idx_max_loss]\n", + "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils_scheduling.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 35\u001b[0m d \u001b[38;5;241m=\u001b[39m dbi_object\u001b[38;5;241m.\u001b[39mdiagonal_h_matrix\n\u001b[0;32m---> 37\u001b[0m loss_list \u001b[38;5;241m=\u001b[39m [dbi_object\u001b[38;5;241m.\u001b[39mloss(step, d\u001b[38;5;241m=\u001b[39md) \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m space]\n\u001b[1;32m 39\u001b[0m idx_max_loss \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmin(loss_list)\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m space[idx_max_loss]\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] } ], "source": [ @@ -576,14 +774,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.7|INFO|2024-05-23 10:29:12]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.9|INFO|2024-05-27 12:16:28]: Using numpy backend on /CPU:0\n" ] } ], @@ -607,225 +805,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:15]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:29:16]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + "ename": "NameError", + "evalue": "name 'd_ansatz_type' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[20], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m dbi_eval \u001b[38;5;241m=\u001b[39m deepcopy(dbi)\n\u001b[1;32m 2\u001b[0m params \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mnqubits,\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mnqubits)\n\u001b[0;32m----> 3\u001b[0m d_opt, loss_opt, grad_opt, diags_opt \u001b[38;5;241m=\u001b[39m gradient_descent_dbr_d_ansatz(dbi, params, \u001b[38;5;241m100\u001b[39m,lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-2\u001b[39m, d_type \u001b[38;5;241m=\u001b[39m \u001b[43md_ansatz_type\u001b[49m\u001b[38;5;241m.\u001b[39melement_wise)\n\u001b[1;32m 4\u001b[0m flows \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m50\u001b[39m\n\u001b[1;32m 5\u001b[0m off_diagonal_norm \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty((flows\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m))\n", + "\u001b[0;31mNameError\u001b[0m: name 'd_ansatz_type' is not defined" ] } ], @@ -845,7 +836,7 @@ "\n", "dbi_eval = deepcopy(dbi)\n", "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, lr=1e-3, d_type = d_ansatz_type.local_1)\n", + "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, lr=1e-3)\n", "best = np.argmin(loss_opt)\n", "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", "for i in range(flows):\n", @@ -856,7 +847,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -891,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -922,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1388,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1420,6 +1411,13 @@ "\n", "print(np.linalg.norm(d_opt))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1438,7 +1436,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.11.7" }, "orig_nbformat": 4, "vscode": { diff --git a/examples/dbi/dbi_gradient_descent_strategies.ipynb b/examples/dbi/dbi_gradient_descent_strategies.ipynb new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index bcc0dcc732..3800584a43 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -165,7 +165,7 @@ def backend(self): def least_squares(self, d: np.array): """Least squares cost function.""" - h_np = self.backend.cast(self.h.matrix) + h_np = self.h.matrix return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h_np @ d)) diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 7c8741bef8..54e7b64e74 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -203,7 +203,7 @@ def covariance(a, b): return coef -def gradientDiagonalEntries(dbi_object, params, delta=1e-4): +def gradient_diagonal_entries(dbi_object, params, delta=1e-4): r""" Gradient of the DBI with respect to the parametrization of D. A simple finite difference is used to calculate the gradient. @@ -257,22 +257,21 @@ def gradient_descent_dbr_d_ansatz( d = element_wise_d(params, normalization=normalize) loss = np.zeros(nmb_iterations + 1) grad = np.zeros((nmb_iterations, len(params))) - dbi_new = deepcopy(dbi_object) - s = dbi_object.choose_step(d=d) - dbi_new(s, d=d) - loss[0] = dbi_new.loss(0.0, d) + dbi_eval = deepcopy(dbi_object) + s = dbi_eval.choose_step(d=d) + dbi_eval(s, d=d) + loss[0] = dbi_eval.loss(0.0, d) params_hist = np.empty((len(params), nmb_iterations + 1)) params_hist[:, 0] = params for i in range(nmb_iterations): - dbi_new = deepcopy(dbi_object) - grad[i, :] = gradientDiagonalEntries(dbi_object, params) + dbi_eval = deepcopy(dbi_object) + grad[i, :] = gradient_diagonal_entries(dbi_eval, params) for j in range(len(params)): params[j] = params[j] - lr * grad[i, j] d = element_wise_d(params, normalization=normalize) - s = dbi_object.choose_step(d=d) - dbi_new(s, d=d) - loss[i + 1] = dbi_new.loss(0.0, d=d) + s = dbi_eval.choose_step(d=d) + loss[i + 1] = dbi_eval.loss(s, d=d) params_hist[:, i + 1] = params return d, loss, grad, params_hist From bbe925fef14941c1314a504c89186d62ae06626c Mon Sep 17 00:00:00 2001 From: Stefano Carrazza Date: Wed, 29 May 2024 05:22:01 +0200 Subject: [PATCH 088/154] casting qubits to python int objects --- src/qibo/quantum_info/random_ensembles.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/qibo/quantum_info/random_ensembles.py b/src/qibo/quantum_info/random_ensembles.py index e1fc865051..e6d9faf49b 100644 --- a/src/qibo/quantum_info/random_ensembles.py +++ b/src/qibo/quantum_info/random_ensembles.py @@ -1104,17 +1104,17 @@ def _sample_from_quantum_mallows_distribution(nqubits: int, local_state): @cache def _create_S(q): - return gates.S(q) + return gates.S(int(q)) @cache def _create_CZ(cq, tq): - return gates.CZ(cq, tq) + return gates.CZ(int(cq), int(tq)) @cache def _create_CNOT(cq, tq): - return gates.CNOT(cq, tq) + return gates.CNOT(int(cq), int(tq)) def _operator_from_hadamard_free_group( From ca947b76f6fd5ab025deb314ce8115a9e2b4bda2 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 29 May 2024 12:01:30 +0800 Subject: [PATCH 089/154] Initial commit --- src/qibo/models/dbi/double_bracket.py | 7 +- src/qibo/models/dbi/utils.py | 51 ++++--- src/qibo/models/dbi/utils_gradients.py | 23 +++- src/qibo/models/dbi/utils_strategies.py | 176 +++++++++++++++--------- tests/test_models_dbi.py | 21 +++ 5 files changed, 190 insertions(+), 88 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 3800584a43..38e04820ae 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -117,7 +117,7 @@ def __call__( d = self.diagonal_h_matrix operator = self.backend.calculate_matrix_exp( 1.0j * step, - self.commutator(self.backend.cast(d), self.h.matrix), + self.commutator(d, self.h.matrix), ) elif mode is DoubleBracketGeneratorType.group_commutator: if d is None: @@ -163,6 +163,11 @@ def backend(self): """Get Hamiltonian's backend.""" return self.h0.backend + @property + def nqubits(self): + """Number of qubits.""" + return self.h.nqubits + def least_squares(self, d: np.array): """Least squares cost function.""" h_np = self.h.matrix diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 83d377c86d..fd28ff2141 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -1,4 +1,5 @@ import math +from enum import Enum, auto from itertools import combinations, product import numpy as np @@ -165,21 +166,39 @@ def generate_Pauli_operators(nqubits, symbols_pauli, positions): return SymbolicHamiltonian(math.prod(terms), nqubits=nqubits).dense.matrix -def element_wise_d(params: np.array, normalization: bool = False): - r""" - Creates the $D$ operator for the double-bracket iteration ansatz depending on the type of parameterization. - If $\alpha_i$ are our parameters and d the number of qubits then: +class ParameterizationTypes(Enum): + """Define types of parameterization for diagonal operator.""" - element_wise: $D = \sum_{i=0}^{2^d} \alpha_i |i\rangle \langle i|$ - local_1: $D = \sum_{i=1}^{d} \alpha_i Z_i$ - Args: - params(np.array): parameters for the ansatz. - d_type(d_ansatz type): type of parameterization for the ansatz. - normalization(bool): If True, the diagonal is normalized to 1. - """ - d = np.zeros((len(params), len(params))) - for i in range(len(params)): - d[i, i] = params[i] - if normalization: - d = d / np.linalg.norm(d) + pauli = auto() + """Uses Pauli-Z operators (magnetic field).""" + element = auto() + """Uses diagonal entries.""" + + +def params_to_diagonal_operator( + params: np.array, + nqubits: int, + parameterization: ParameterizationTypes = ParameterizationTypes.pauli, + pauli_parameterization_order: int = 1, + normalize: bool = False, + pauli_operator_dict: dict = None, +): + r"""Creates the $D$ operator for the double-bracket iteration ansatz depending on the parameterization type.""" + if parameterization is ParameterizationTypes.pauli: + # raise error if dimension mismatch + if len(params) != len(pauli_operator_dict): + raise ValueError( + f"Dimension of params ({len(params)}) mismatches the given parameterization order ({pauli_parameterization_order})" + ) + d = sum( + [params[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)] + ) + elif parameterization is ParameterizationTypes.element: + d = np.zeros((len(params), len(params))) + for i in range(len(params)): + d[i, i] = params[i] + if normalize: + d = d / np.linalg.norm(d) + else: + raise ValueError(f"Parameterization type not recognized.") return d diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 54e7b64e74..3e0f88874c 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -218,11 +218,15 @@ def gradient_diagonal_entries(dbi_object, params, delta=1e-4): """ grad = np.zeros(len(params)) - d = element_wise_d(params) + d = params_to_diagonal_operator( + params, dbi_object.nqubits, parameterization=ParameterizationTypes.element + ) for i in range(len(params)): params_new = deepcopy(params) params_new[i] += delta - d_new = element_wise_d(params_new) + d_new = params_to_diagonal_operator( + params, dbi_object.nqubits, parameterization=ParameterizationTypes.element + ) grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta return grad @@ -253,8 +257,12 @@ def gradient_descent_dbr_d_ansatz( grad(np.array): Gradient evaluated at each iteration. params_hist(np.array): Parameters evaluated at each iteration. """ - - d = element_wise_d(params, normalization=normalize) + d = params_to_diagonal_operator( + params, + dbi_object.h.nqubits, + parameterization=ParameterizationTypes.element, + normalize=normalize, + ) loss = np.zeros(nmb_iterations + 1) grad = np.zeros((nmb_iterations, len(params))) dbi_eval = deepcopy(dbi_object) @@ -269,7 +277,12 @@ def gradient_descent_dbr_d_ansatz( grad[i, :] = gradient_diagonal_entries(dbi_eval, params) for j in range(len(params)): params[j] = params[j] - lr * grad[i, j] - d = element_wise_d(params, normalization=normalize) + d = params_to_diagonal_operator( + params, + dbi_object.h.nqubits, + parameterization=ParameterizationTypes.element, + normalize=normalize, + ) s = dbi_eval.choose_step(d=d) loss[i + 1] = dbi_eval.loss(s, d=d) params_hist[:, i + 1] = params diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_strategies.py index 10e8749044..42c6beb9db 100644 --- a/src/qibo/models/dbi/utils_strategies.py +++ b/src/qibo/models/dbi/utils_strategies.py @@ -73,13 +73,12 @@ def select_best_dbr_generator( return dbi_eval, idx_max_loss, step_optimal, flip -def gradient_Pauli( +def gradient_pauli_analytical( dbi_object, d: np.array, pauli_operator_dict: dict, use_ds=False, n=3, - **kwargs, ): r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients Args: @@ -96,9 +95,8 @@ def gradient_Pauli( pauli_operators = list(pauli_operator_dict.values()) num_paul = len(pauli_operators) grad = np.zeros(num_paul) - coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) - s = polynomial_step(dbi_object, n=3, d=d) - + coef = dbi_object.cost_expansion(d, n=n) + s = polynomial_step(dbi_object, n=4, d=d) a, b, c = coef[len(coef) - 3 :] for i, operator in enumerate(pauli_operators): @@ -120,76 +118,122 @@ def gradient_Pauli( return grad, s -def gradient_descent_pauli( +def gradient_numerical( + dbi_object: DoubleBracketIteration, + d_params: list, + parameterization: ParameterizationTypes, + s: float = 1e-2, + delta: float = 1e-3, + **kwargs, +): + r""" + Gradient of the DBI with respect to the parametrization of D. A simple finite difference is used to calculate the gradient. + + Args: + dbi_object(DoubleBracketIteration): DoubleBracketIteration object. + d_params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + delta(float): Step size for numerical gradient. + Returns: + grad(np.array): Gradient of the D operator. + """ + nqubits = dbi_object.nqubits + grad = np.zeros(len(d_params)) + d = params_to_diagonal_operator( + d_params, nqubits, parameterization=parameterization, **kwargs + ) + for i in range(len(d_params)): + params_new = d_params.copy() + params_new[i] += delta + d_new = params_to_diagonal_operator( + params_new, nqubits, parameterization=parameterization, **kwargs + ) + # find the increment of a very small step + grad[i] = (dbi_object.loss(s, d_new) - dbi_object.loss(s, d)) / delta + return grad + + +def gradient_descent( dbi_object: DoubleBracketIteration, - d_coef: list, - d: Optional[np.array] = None, - pauli_operator_dict: dict = None, - parameterization_order: int = 1, - n: int = 3, - onsite_Z_ops: Optional[list] = None, + iterations: int, + d_params: list, + parameterization: ParameterizationTypes, + pauli_operator_dict: list = None, + pauli_parameterization_order: int = 1, + normalize: bool = False, lr_min: float = 1e-5, lr_max: float = 1, max_evals: int = 100, space: callable = None, optimizer: callable = None, verbose: bool = False, - use_ds: bool = True, ): - """calculate the elements of one gradient descent step on `dbi_object`. - - Args: - dbi_object (DoubleBracketIteration): the target dbi object - d_coef (list): the initial decomposition of `d` into Pauli-Z operators - d (np.array, optional): the initial diagonal operator. Defaults to None. - n_taylor (int, optional): the highest order to expand the loss function derivative. Defaults to 2. - onsite_Z_ops (list, optional): list of onsite-Z operators. Defaults to None. - lr_min (float, optional): the minimal gradient step. Defaults to 1e-5. - lr_max (float, optional): the maximal gradient step. Defaults to 1. - max_evals (int, optional): the max number of evaluations for `hyperopt` to find the optimal gradient step `lr`. Defaults to 100. - space (callable, optional): the search space for `hyperopt`. Defaults to None. - optimizer (callable, optional): optimizer for `hyperopt`. Defaults to None. - verbose (bool, optional): option to print out the 'hyperopt' progress. Defaults to False. - use_ds (bool, optional): if False, ds is set to 0. Defaults to True. - - Returns: - the optimal step found, coeffcients of `d` in Pauli-Z basis, matrix of `d` - - """ - nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) - if pauli_operator_dict is None: + nqubits = dbi_object.nqubits + # use polynomial scheduling for analytical solutions + d = params_to_diagonal_operator( + d_params, + nqubits, + parameterization=parameterization, + pauli_operator_dict=pauli_operator_dict, + normalize=normalize, + ) + loss_hist = [dbi_object.loss(0.0, d=d)] + d_params_hist = [d_params] + s_hist = [0] + if parameterization is ParameterizationTypes.pauli and pauli_operator_dict is None: pauli_operator_dict = generate_pauli_operator_dict( - nqubits, parameterization_order + nqubits=nqubits, parameterization_order=pauli_parameterization_order ) - - grad, s = gradient_Pauli( - dbi_object, d, n=n, pauli_operator_dict=pauli_operator_dict, use_ds=use_ds - ) - # optimize gradient descent step with hyperopt - if space is None: - space = hyperopt.hp.loguniform("lr", np.log(lr_min), np.log(lr_max)) - if optimizer is None: - optimizer = hyperopt.tpe - - def func_loss_to_lr(lr): - d_coef_eval = [d_coef[j] - grad[j] * lr for j in range(nqubits)] - d_eval = sum( - [ - d_coef_eval[i] * list(pauli_operator_dict.values())[i] - for i in range(nqubits) - ] + # first step + s = dbi_object.choose_step(d=d) + dbi_object(step=s, d=d) + for _ in range(iterations): + grad = gradient_numerical( + dbi_object, + d_params, + parameterization, + pauli_operator_dict=pauli_operator_dict, + pauli_parameterization_order=pauli_parameterization_order, + normalize=normalize, ) - return dbi_object.loss(step=s, d=d_eval) - - best = hyperopt.fmin( - fn=func_loss_to_lr, - space=space, - algo=optimizer.suggest, - max_evals=max_evals, - verbose=verbose, - ) - lr = best["lr"] - d_coef = [d_coef[j] - grad[j] * lr for j in range(nqubits)] - d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) - return s, d_coef, d + # set up hyperopt to find optimal lr + def func_loss_to_lr(lr): + d_params_eval = [d_params[j] - grad[j] * lr for j in range(len(grad))] + d_eval = params_to_diagonal_operator( + d_params_eval, + nqubits, + parameterization=parameterization, + pauli_operator_dict=pauli_operator_dict, + normalize=normalize, + ) + return dbi_object.loss(step=s, d=d_eval) + + if space is None: + space = hyperopt.hp.loguniform("lr", np.log(lr_min), np.log(lr_max)) + if optimizer is None: + optimizer = hyperopt.tpe + best = hyperopt.fmin( + fn=func_loss_to_lr, + space=space, + algo=optimizer.suggest, + max_evals=max_evals, + verbose=verbose, + ) + lr = best["lr"] + + d_params = [d_params[j] - grad[j] * lr for j in range(len(grad))] + d = params_to_diagonal_operator( + d_params, + nqubits, + parameterization=parameterization, + pauli_operator_dict=pauli_operator_dict, + normalize=normalize, + ) + s = dbi_object.choose_step(d=d) + dbi_object(step=s, d=d) + + # record history + loss_hist.append(dbi_object.loss(0.0, d=d)) + d_params_hist.append(d_params) + s_hist.append(s) + return loss_hist, d_params_hist, s_hist diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 637dd2c9f7..daa9d0f14e 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -202,6 +202,27 @@ def test_select_best_dbr_generator(backend, nqubits): assert dbi.off_diagonal_norm < initial_off_diagonal_norm +def test_params_to_diagonal_operator(backend): + nqubits = 3 + pauli_operator_dict = generate_pauli_operator_dict( + nqubits, parameterization_order=1 + ) + params = [1, 2, 3] + operator_pauli = [ + params[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits) + ] + assert ( + operator_pauli + == params_to_diagonal_operator( + params, nqubits=nqubits, parameterization=ParameterizationTypes.pauli + ) + ).all() + operator_element = params_to_diagonal_operator( + params, nqubits=nqubits, parameterization=ParameterizationTypes.element + ) + assert (operator_element.diag() == params).all() + + @pytest.mark.parametrize("nqubits", [2, 3]) def test_gradient_descent_pauli(backend, nqubits): scheduling = DoubleBracketScheduling.grid_search From 4d4c45295499fd7ccf559bdff63ab494a0bc0c3e Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 29 May 2024 12:36:14 +0800 Subject: [PATCH 090/154] Increase pauli analytical expansion to n=5. --- src/qibo/models/dbi/utils_strategies.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_strategies.py index 10e8749044..136711553c 100644 --- a/src/qibo/models/dbi/utils_strategies.py +++ b/src/qibo/models/dbi/utils_strategies.py @@ -97,7 +97,7 @@ def gradient_Pauli( num_paul = len(pauli_operators) grad = np.zeros(num_paul) coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) - s = polynomial_step(dbi_object, n=3, d=d) + s = polynomial_step(dbi_object, n=5, d=d) a, b, c = coef[len(coef) - 3 :] @@ -127,7 +127,6 @@ def gradient_descent_pauli( pauli_operator_dict: dict = None, parameterization_order: int = 1, n: int = 3, - onsite_Z_ops: Optional[list] = None, lr_min: float = 1e-5, lr_max: float = 1, max_evals: int = 100, From b867fec172b8975cb0ed5be6f6a3ae01e1146f37 Mon Sep 17 00:00:00 2001 From: Andrea Date: Wed, 29 May 2024 09:04:28 +0400 Subject: [PATCH 091/154] doc: Add notebooks to sphinx --- .../applications-by-algorithm.rst | 14 +- .../code-examples/applications-by-topic.rst | 1 - .../code-examples/tutorials/dbi/dbi.ipynb | 1 - examples/dbi/README.md | 3 + examples/dbi/dbi_scheduling.ipynb | 267 ++++++++++++++++-- examples/dbi/dbi_strategies_compare.ipynb | 6 +- examples/dbi/dbi_strategy_Ising_model.ipynb | 4 +- examples/dbi/dbi_strategy_Pauli-Z.ipynb | 6 +- examples/dbi/dbi_tutorial_basic_intro.ipynb | 4 +- 9 files changed, 263 insertions(+), 43 deletions(-) delete mode 120000 doc/source/code-examples/tutorials/dbi/dbi.ipynb create mode 100644 examples/dbi/README.md diff --git a/doc/source/code-examples/applications-by-algorithm.rst b/doc/source/code-examples/applications-by-algorithm.rst index 3819ba99b3..4535cb207a 100644 --- a/doc/source/code-examples/applications-by-algorithm.rst +++ b/doc/source/code-examples/applications-by-algorithm.rst @@ -76,4 +76,16 @@ Diagonalization Algorithms .. toctree:: :maxdepth: 1 - tutorials/dbi/dbi.ipynb + tutorials/dbi/README.md + + .. tutorials/dbi/dbi_tutorial_basic_intro.ipynb + + tutorials/dbi/dbi_scheduling.ipynb + tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb + tutorials/dbi/dbi_gradient_descent_strategies.ipynb + tutorials/dbi/dbi_group_commutator_tests.ipynb + + .. tutorials/dbi/dbi_gdbi_strategies_compare.ipynb + + tutorials/dbi/dbi_strategy_Ising_model.ipynb + tutorials/dbi/dbi_strategy_Pauli-Z.ipynb diff --git a/doc/source/code-examples/applications-by-topic.rst b/doc/source/code-examples/applications-by-topic.rst index 8d44557d48..500300e9b4 100644 --- a/doc/source/code-examples/applications-by-topic.rst +++ b/doc/source/code-examples/applications-by-topic.rst @@ -61,7 +61,6 @@ Quantum Physics tutorials/bell-variational/README.md tutorials/falqon/README.md tutorials/grover/README.md - tutorials/dbi/dbi.ipynb Quantum Machine Learning ^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/doc/source/code-examples/tutorials/dbi/dbi.ipynb b/doc/source/code-examples/tutorials/dbi/dbi.ipynb deleted file mode 120000 index 7deb426d68..0000000000 --- a/doc/source/code-examples/tutorials/dbi/dbi.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../../../examples/dbi/dbi.ipynb \ No newline at end of file diff --git a/examples/dbi/README.md b/examples/dbi/README.md new file mode 100644 index 0000000000..1bb0d229fb --- /dev/null +++ b/examples/dbi/README.md @@ -0,0 +1,3 @@ +# Double Bracket Iterations + +General documentation for DBI + links to notebooks diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index a0ac88e6a6..fd2d9839a7 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -44,12 +44,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.8|INFO|2024-05-29 08:09:34]: Using numpy backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 37.94733192202055\n" + ] + } + ], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", + "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -72,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -95,9 +110,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.030312727272727272\n", + "100%|██████████| 100/100 [00:00<00:00, 147.00trial/s, best loss: 28.83054565827672]\n", + "hyperopt_search step: 0.02847179092987961\n", + "polynomial_approximation step: 0.032960905003724034\n", + "simulated_annealing step: 0.026973122528658938\n" + ] + } + ], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n", @@ -113,9 +140,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.030312727272727272\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -141,9 +186,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.8|WARNING|2024-05-29 08:09:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:09:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], "source": [ "# Generate the digaonal operators\n", "Z_str = \"Z\"*nqubits\n", @@ -155,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -173,9 +227,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.30303525252525254 loss -6.165348149025746\n", + "100%|██████████| 100/100 [00:01<00:00, 94.55trial/s, best loss: 31.779832293339304]\n", + "hyperopt_search step: 0.5652071095886648 loss -6.167499628681249\n", + "polynomial_approximation step: 0.04033688534030588 loss -6.149780650249905\n", + "simulated_annealing step: 0.48340531582403534 loss -6.145940509974928\n" + ] + } + ], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_max=0.6, d=d)\n", @@ -197,9 +263,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.30303525252525254\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -233,9 +317,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.04141414141414142\n", + "100%|██████████| 100/100 [00:00<00:00, 281.90trial/s, best loss: 31.780151135879148]\n", + "hyperopt_search step: 0.04197021755491067\n" + ] + } + ], "source": [ "search_range = 0.1\n", "if step_poly < search_range/2:\n", @@ -254,9 +348,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.04141414141414142\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -286,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -296,12 +408,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.8|INFO|2024-05-29 08:10:04]: Using numpy backend on /CPU:0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial off diagonal norm 15.531689516799505\n" + ] + } + ], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", + "set_backend(\"numpy\")\n", "nqubits = 4\n", "h0 = random_hermitian(2**nqubits)\n", "\n", @@ -312,9 +439,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + } + ], "source": [ "generate_local_Z = generate_Z_operators(nqubits)\n", "Z_ops = list(generate_local_Z.values())\n", @@ -323,9 +472,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------Scheduling grid search----------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/andreapasquale/qibo/src/qibo/models/dbi/utils_strategies.py:39: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " flip_list[i] = cs_angle_sgn(dbi_eval, d)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New optimized step at iteration 1/8: 0.1010190909090909 with operator IZZI, loss 12.32289315516495\n", + "New optimized step at iteration 2/8: 0.07071636363636363 with operator IZZI, loss 9.744596203115039\n", + "New optimized step at iteration 3/8: 0.06061545454545455 with operator IZZI, loss 8.666514218620271\n", + "New optimized step at iteration 4/8: 0.12122090909090909 with operator -ZIZZ, loss 7.525650014624014\n", + "New optimized step at iteration 5/8: 0.06061545454545455 with operator IZZI, loss 6.756888942369091\n", + "New optimized step at iteration 6/8: 0.12122090909090909 with operator IIZZ, loss 5.828802952436445\n", + "New optimized step at iteration 7/8: 0.08081727272727272 with operator -ZIZZ, loss 5.265860734824745\n", + "New optimized step at iteration 8/8: 0.08081727272727272 with operator IZZI, loss 4.799097209077483\n", + "----------Scheduling hyperopt----------\n", + "100%|██████████| 500/500 [00:08<00:00, 58.19trial/s, best loss: 13.25297274898575] \n", + "100%|██████████| 500/500 [00:08<00:00, 58.25trial/s, best loss: 12.948629126785967]\n", + " 70%|██████▉ | 348/500 [00:04<00:02, 70.17trial/s, best loss: 13.308835963869189] \n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_77597/4171789948.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'----------Scheduling {scheduling_labels[i]}----------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNSTEPS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mdbi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m 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42\u001b[0;31m step_best = dbi_eval.choose_step(\n\u001b[0m\u001b[1;32m 43\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflip_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscheduling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscheduling\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m )\n", + "\u001b[0;32m~/qibo/src/qibo/models/dbi/double_bracket.py\u001b[0m in \u001b[0;36mchoose_step\u001b[0;34m(self, d, scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mscheduling\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0mscheduling\u001b[0m \u001b[0;34m=\u001b[0m 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This is the core of `run`, all the rest is just\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0;31m# processes orchestration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 278\u001b[0;31m new_trials = algo(\n\u001b[0m\u001b[1;32m 279\u001b[0m \u001b[0mnew_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m )\n", + "\u001b[0;32m~/qibo/test_qibo/lib/python3.11/site-packages/hyperopt/tpe.py\u001b[0m in \u001b[0;36msuggest\u001b[0;34m(new_ids, domain, trials, seed, prior_weight, n_startup_jobs, n_EI_candidates, gamma, verbose)\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0mt0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[0;31m# use build_posterior_wrapper to create the pyll nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m observed, observed_loss, posterior = build_posterior_wrapper(\n\u001b[0m\u001b[1;32m 860\u001b[0m \u001b[0mdomain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprior_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m )\n", + "\u001b[0;32m~/qibo/test_qibo/lib/python3.11/site-packages/hyperopt/tpe.py\u001b[0m in \u001b[0;36mbuild_posterior_wrapper\u001b[0;34m(domain, prior_weight, gamma)\u001b[0m\n\u001b[1;32m 811\u001b[0m \u001b[0mobserved_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"idxs\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpyll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLiteral\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"vals\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpyll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLiteral\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 813\u001b[0;31m posterior = build_posterior(\n\u001b[0m\u001b[1;32m 814\u001b[0m \u001b[0;31m# -- vectorized clone of bandit template\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 815\u001b[0m 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\u001b[0mnamed_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "NSTEPS = 8\n", "scheduling_list = [DoubleBracketScheduling.grid_search,\n", @@ -392,7 +599,7 @@ "outputs": [], "source": [ "# Hamiltonian\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", + "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -443,7 +650,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 54d7fe4dff..33465c1498 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -61,7 +61,7 @@ "outputs": [], "source": [ "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", + "set_backend(\"numpy\", )\n", "# initialize dbi object\n", "nqubits = 5\n", "h0 = random_hermitian(2**nqubits, seed=2)\n", @@ -133,7 +133,7 @@ "outputs": [], "source": [ "# initialize DBI class for the Pauli-Z strategy\n", - "set_backend(\"pytorch\", platform=\"numba\")\n", + "set_backend(\"numpy\")\n", "dbi_pauli = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling, cost=cost)" ] }, @@ -251,7 +251,7 @@ "outputs": [], "source": [ "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", + "set_backend(\"numpy\")\n", "# initialize dbi object\n", "# hamiltonian parameters\n", "# define the hamiltonian\n", diff --git a/examples/dbi/dbi_strategy_Ising_model.ipynb b/examples/dbi/dbi_strategy_Ising_model.ipynb index ad3b1b5d73..d06946ce5b 100644 --- a/examples/dbi/dbi_strategy_Ising_model.ipynb +++ b/examples/dbi/dbi_strategy_Ising_model.ipynb @@ -63,7 +63,7 @@ "outputs": [], "source": [ "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", + "set_backend(\"numpy\")\n", "# initialize dbi object\n", "nqubits = 5\n", "h0 = random_hermitian(2**nqubits, seed=2)\n", @@ -222,7 +222,7 @@ "outputs": [], "source": [ "# backend\n", - "set_backend(\"qibojit\", platform=\"numba\")\n", + "set_backend(\"numpy\")\n", "# initialize dbi object\n", "dbi_TFIM = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)" ] diff --git a/examples/dbi/dbi_strategy_Pauli-Z.ipynb b/examples/dbi/dbi_strategy_Pauli-Z.ipynb index 2b60e12896..674cf7dd77 100644 --- a/examples/dbi/dbi_strategy_Pauli-Z.ipynb +++ b/examples/dbi/dbi_strategy_Pauli-Z.ipynb @@ -130,7 +130,7 @@ "source": [ "# set the qibo backend (we suggest qibojit if N >= 20)\n", "# alternatives: tensorflow (not optimized), numpy (when CPU not supported by jit)\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -246,7 +246,7 @@ "source": [ "# set the qibo backend (we suggest qibojit if N >= 20)\n", "# alternatives: tensorflow (not optimized), numpy (when CPU not supported by jit)\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"numpy\")\n", "\n", "\n", "# initialize class|\n", @@ -439,7 +439,7 @@ " dbi_mixed_can(step=step)\n", " off_diagonal_norm_history_mixed_can.append(dbi_mixed_can.off_diagonal_norm)\n", " steps_mixed_can.append(step)\n", - " \n", + "\n", "print(\"After 2 steps, off diagonal norm:\", dbi_mixed_can.off_diagonal_norm)\n", "print(\"By comparison, the Pauli-Z:\", off_diagonal_norm_history[2])" ] diff --git a/examples/dbi/dbi_tutorial_basic_intro.ipynb b/examples/dbi/dbi_tutorial_basic_intro.ipynb index ecb28bb4d7..ca2ba86a4b 100644 --- a/examples/dbi/dbi_tutorial_basic_intro.ipynb +++ b/examples/dbi/dbi_tutorial_basic_intro.ipynb @@ -112,7 +112,7 @@ "outputs": [], "source": [ "# set the qibo backend (we suggest qibojit if N >= 20)\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"numpy\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -554,7 +554,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" } }, "nbformat": 4, From ab49577856be7a9dd365b518ba2734dba58d1447 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Wed, 29 May 2024 15:04:02 +0800 Subject: [PATCH 092/154] Combine gradient descnet, remove unnecessary code, clean notebooks. --- ...t_functions_and_d_gradients_tutorial.ipynb | 939 +----------------- .../dbi/dbi_gradient_descent_strategies.ipynb | 584 +++++++++++ examples/dbi/dbi_tutorial_basic_intro.ipynb | 15 +- src/qibo/models/dbi/double_bracket.py | 8 +- src/qibo/models/dbi/utils.py | 10 +- ..._strategies.py => utils_dbr_strategies.py} | 47 - src/qibo/models/dbi/utils_gradients.py | 88 -- src/qibo/models/dbi/utils_scheduling.py | 2 +- tests/test_models_dbi.py | 93 +- 9 files changed, 658 insertions(+), 1128 deletions(-) rename src/qibo/models/dbi/{utils_strategies.py => utils_dbr_strategies.py} (81%) diff --git a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb index de926d7ca0..5b9bf0aeb8 100644 --- a/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ b/examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Double-bracket Iteration other cost functions and respective scheduling\n", + "# Double-bracket Iteration other cost functions\n", "\n", "This notebook presents two additional cost functions for the double-bracket flow: least-squares and energy fluctuation with their respectice scheduling methods." ] @@ -46,7 +46,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.9|INFO|2024-05-27 11:26:20]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.9|INFO|2024-05-29 12:15:54]: Using numpy backend on /CPU:0\n" ] } ], @@ -78,8 +78,8 @@ "output_type": "stream", "text": [ "grid_search step: 0.6060645454545454\n", - "100%|██████████| 100/100 [00:00<00:00, 1014.35trial/s, best loss: -30.63379156124853]\n", - "hyperopt_search step: 0.5998426993650685\n", + "100%|██████████| 100/100 [00:00<00:00, 846.91trial/s, best loss: -30.633516395890517]\n", + "hyperopt_search step: 0.5991179894176409\n", "polynomial_approximation step: 0.5267891182131145\n" ] } @@ -121,7 +121,7 @@ }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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FixYAgKioKOzdu1d/Zedely5dQpMmTXDp0iV8+OGHOHLkCNLT06HTlbZKJCcnl+kcfe/nSEtLQ2pqKp5++ulHZrubAwDUajVsbW310zdUB5MtciwsLNCmTRv88ccfeP755/XL//jjDzz33HMSJqsYmUyAj4stfFxs8Uq7BhBFEfFpOfgj7gb2nL2B6JQMHE0oLX4+/OkMejZ3xdAOXgj0sJc6OpHZ0OZqEWFTOvxEp5xOkKuN2y8mtyiX0zo8irU1kJMj3b7LqfRixf2D2N1dfpda/ej/w/9dv6JGjBiBbt26YceOHdi9ezfmzJmD+fPn46233oJOp8Ozzz6LTz/99L7t7s4R9eyzz8LDwwPLly+Hm5sbdDodmjVrhqKiood+jrvTMDyOUln2i7ogCPoiqjqYbJEDlLZ9Dh48GEFBQQgJCcE333yD5ORkjBo1SupolSYIApq42KKJiy3GdG6M1Ix8bIu+iq0nruDSzVxsi76KbdFX0bqBPYZ19EaPZvWglJts/3EioscTBOAxhUFN0KhRIyiVShw7dgweHh4AgKysLMTHxyM0NLTc7+Pv73/feDJHjhypUBYPDw+MGjUKo0aNwpQpU7B8+XK89dZbaN26NbZs2QIvL68H3s1169YtnD17Fl9//TU6deoEADhw4MBj92drawsvLy/s2bMHnTt3rlDW6mTSRc5LL72EW7du4aOPPtKPMbBz5054enpKHc1g3OytMKZzY4wOa4SYlAx8fzgJv5xKxYnkDJxIjkaDutYIf6oxnm/lzmKHqJJk1jJ0SOugf05UGba2thg6dCgmTZqEunXrwtnZGdOnT4dMJqvQFAWjRo3C/PnzMWHCBLzxxhuIiorC6tWry739+PHj0aNHDzRp0gR37tzBX3/9BT8/PwClnZKXL1+Ol19+GZMmTYKjoyMuXryI9evXY/ny5ahTpw4cHBzwzTffwNXVFcnJyZg8eXK59jtjxgyMGjUKzs7O6NGjB7Kzs3Hw4EG89dZb5c5uaCb/2zx69GgkJiaisLAQUVFRePLJJ6WOVC0EQUCrBnXw+UuBODj5KYx72gcOagsk387Du5tP4en5+7ExMgVaXdUucxLVRoIgwMLJAhZOFpwziark888/R0hICHr37o0uXbqgY8eO8PPzg6WlZbnfo0GDBtiyZQt++eUXtGzZEsuWLcPs2bPLvb1Wq8WYMWPg5+eH7t27w9fXF0uWLAEAuLm54eDBg9BqtejWrRuaNWuGcePGQaPRQCaTQSaTYf369YiKikKzZs3w9ttvY+7cueXa79ChQ7Fw4UIsWbIEAQEB6N27N+Lj48uduzoIYlUb/0xYVlYWNBoNMjMzYWdnJ3WcCssrKsEPR5Lwzd+XkZ5T2lbatJ4tPujlh04+ThKnI6Lyuq9PTjGAux1Dc3JMoqnGUAoKCpCQkKCfrsfU5ebmwt3dHfPnz8drr70mdRyT8qh/C+U9f5v8lZzazNpCgdefbIS/3+2M93s2hcZKiXPXszF4xTEMX3UMF9OypY5IZBJ0hTpcGHMBF8ZcgK6Qg3NS5UVHR2PdunW4dOkSTpw4gUGDBgGASd0QY05Y5JiBu8XO/klheLWjNxQyAXvP30SPLyIwf/d5FBRrpY5IVKOJJSJSl6QidUkqxJJae3GbDGTevHlo2bIlunTpgtzcXERERMDR0dFg79+jRw/Y2Ng88FGRZq3awKQ7HlNZ9tYWmPasP/6vfQPM2nEWf51Lw5d/XcQvJ1Px8fPN0bGx4X7JiMhw5DI5evr01D8H+MXEVLVq1QpRUVHVuo9vv/0W+fn5D3yt2kYONlEscsxQQycbrBgahN/OXMf0n2OReCsPg749ipeDPTC1lz/UKv5vJ6pJLBWW2PHKjn8XcFoHegR3d3epI5gMNleZKUEQ0KO5K/58JxRDQjwhCMC6Yyno8UUEjifeljoeERFRtWORY+bsLJX46Llm+HFEe7jbWyH5dh4GfH0Yn+w6h2LOfk5ERGaMRU4tEdLIAbvGd0K/NvUhisCy/Zfw0teHcTXjwe26RGQ8uUW5UM9WQz1bjdwiNlURGQqLnFrEzlKJef1bYumg1rC1VOBEcgZ6LYrAX+duSB2NqNbLK85DXnGe1DGIzAqLnFqoR3NX7HirE1rU1yAjrxivro7EZ7+dg46jJRMRkRlhkVNLNXCwxqZRIRjWwQsAsGTfJYz8LhLZBcXSBiMiMlFhYWEYP3681DHoHixyajGVQo4ZfQKw8KVAWChk2HMuDc8vOYTEdPYJICKismbMmIHAwECpY1QIixxC31bu2PRGCFzsVLiYloPnvjqIgxfTpY5FRERVpNVqodPV3jtpWeQQAKClhz1+Dn8CLT3skZlfjCErj2Hj8RSpYxERmRSdTod3330XdevWRb169TBjxgwAwKuvvorevXuXWbekpAT16tXDypUrAZQ2d4WHhyM8PBz29vZwcHDA1KlTce882kVFRXj33Xfh7u4OtVqNdu3aYd++ffrXV69eDXt7e/z666/w9/eHSqVCUlIS7ty5gyFDhqBOnTqwtrZGjx49yswQfne77du3o0mTJrC0tMQzzzyDlJQU/eszZ87EyZMnIQgCBEHA6tWrq+cgGhCLHNJzsbPEhtfbo2+gG7Q6Ee9uOYUv98SjFk9UT7WFDNCEaqAJ1UjyV1EmyBDqGYpQz1DIBP5ZfpjcotyHPgpKCsq9bn5xfrnWrYw1a9ZArVbj6NGj+Oyzz/DRRx/hjz/+wIgRI/Dbb7/h2rVr+nV37tyJnJwcDBgwoMz2CoUCR48exaJFi7BgwQJ8++23+teHDx+OgwcPYv369Th16hT69++P7t27lylY8vLyMGfOHHz77beIjY2Fs7Mzhg0bhsjISPz88884fPgwRFFEz549UVxcXGa7jz/+GGvWrMHBgweRlZWFgQMHAgBeeuklvPPOOwgICMC1a9dw7do1vPTSS5U6RsbE8f2pDEulHAteCoSbvRWW7LuE+X9cwPWsAnz0XDPIZYLU8YiqhdxKjlb7Wkm2fyulFfYN2/fvAo6V80A2c2we+lpPn55lpsZwnuf80FvyQz1Dyxxvry+8kJ53fxO9OL3iX/BatGiB6dOnAwB8fHywePFi7NmzB5988gl8fX3x/fff49133wUArFq1Cv3794eNzb+fy8PDAwsWLIAgCPD19cXp06exYMECjBw5EpcuXcK6detw5coVuLm5AQAmTpyI3377DatWrdJPzllcXIwlS5agZcuWAID4+Hj8/PPPOHjwIDp06AAAWLt2LTw8PLB9+3b0799fv93ixYvRrl07AKUFl5+fH44dO4bg4GDY2NhAoVCgXr16FT4uUuFXBrqPIAh4t3tTzOwTAEEA1h5Nxui1UZzNnIjoMVq0aFHmZ1dXV6SlpQEARowYgVWrVgEA0tLSsGPHDrz66qtl1m/fvj0E4d8vlCEhIYiPj4dWq8WJEycgiiKaNGlSZubx/fv349KlS/ptLCwsyuQ4e/YsFAqFvngBAAcHB/j6+uLs2bP6ZQqFAkFBQfqfmzZtCnt7+zLrmBpeyaGHGtrBC062KozfEIPfY29gyMpjWDmsLWw4wScRSSBnSs5DXyudvf1faRPTHrruf5sEE8clVinXvZRKZZmfBUHQd/wdMmQIJk+ejMOHD+Pw4cPw8vJCp06dyv3eOp0OcrkcUVFRkMvLft57rwZZWVmVKZQe1uVAFMUy693N+18PWmYqeLaiR+rZ3BUOaguMWBOJYwm3MXjFUaweHgyNlfLxGxOZCG2uFke8jgAA2ie2h1wtf8wWhpVblAuvL7wAlJ5w1Ubdu+lQW5T/yFTXulXh4OCAvn37YtWqVTh8+DCGDx9+3zpHjhy572cfHx/I5XK0atUKWq0WaWlpFSqO/P39UVJSgqNHj+qbq27duoULFy7Az89Pv15JSQkiIyMRHBwMADh//jwyMjLQtGlTAKVXiLRa07qiz+Yqeqx2DR2wdmQ7aKyUiE7OwP99exR3coukjkVkUMXpxShOl24wzPS89Af2CyHzMmLECKxZswZnz57F0KFD73s9JSUFEyZMwPnz57Fu3Tp8+eWXGDduHACgSZMmGDRoEIYMGYKtW7ciISEBx48fx6effoqdO3c+dJ8+Pj547rnnMHLkSBw4cAAnT57E//3f/8Hd3R3PPfecfj2lUom33noLR48exYkTJzB8+HC0b99eX/R4eXkhISEBMTExSE9PR2FhoYGPjuGxyKFyaVHfHutGtoeD2gKnr2bi5eVHkJ5T8/+BE5WHzEqGtmfaou2ZtpBZ8c8iVZ8uXbrA1dUV3bp103cevteQIUOQn5+P4OBgjBkzBm+99RZef/11/eurVq3CkCFD8M4778DX1xd9+vTB0aNH4eHh8cj9rlq1Cm3atEHv3r0REhICURSxc+fOMs1r1tbWeO+99/DKK68gJCQEVlZWWL9+vf71F198Ed27d0fnzp3h5OSEdevWGeCIVC9BrMX3B2dlZUGj0SAzMxN2dnZSxzEJ8TeyMejbo0jLLkQjJzXWvd4ezraWUsciMmm5Rbn6O4dypuRAXQzgbh+LnBxAXXsasAoKCpCQkABvb29YWprf35a8vDy4ublh5cqVeOGFF8q8FhYWhsDAQCxcuNDouVavXo3x48cjIyPD6Pt+mEf9Wyjv+ZtfWahCfFxsseGNELhqLHHpZi7+79ujuM2mKyKiR9LpdEhNTcWHH34IjUaDPn36SB2pVmCRQxXm7ajG+tfbw8VOhQs3cjB4xVFk5nNiTzJduiIdEmYkIGFGAnRFtXcIfKo+ycnJcHd3x8aNG7Fy5UooFLzvxxjYXMXmqkq7mJaDgd8cRnpOEVo1sMf3r7Xj7eVkkrS5WkTYRAAAOuV0kuTuKjZXlTL35ioqPzZXkaQaO9vg+9fawd669K6rV1cfR36Rad1eSFQTyAQZgtyCEOQWxGkdiAyIv01UJX6udvj+1XawVSlwLOE23vghCkUlvNxPVBFWSiscH3kcx0ceh5XSSuo4RGaDRQ5VWfP6Gqx+NRhWSjn+vnAT7205BZ2u1raCEhFRDcEihwyijWcdLP2/1lDIBGyLvoo5u0x3rhMiIjIPLHLIYMJ8nfFZv9JJ4ZZHJGD535clTkRkGvKK8+C10AteC70eOnM2EVUcixwyqBda18eUHqXznHy88yy2RV+ROBFRzSeKIpIyk5CUmfTQyRSJqOJY5JDBvf5kQ7z2hDcAYNKmUzgQz/l4iIgeZ/Xq1bC3t5c6RrnMmDEDgYGBFdpGEARs3769WvI8DIscMjhBEPBBTz/0aemGEp2IN9dGIf5GttSxiIjIQCZOnIg9e/ZIHeOxWORQtZDJBMzt3wJtveogu6AEr645zgk9iYjMhI2NDRwcHKSO8VgscqjaqBRyfD04CJ4O1ki5nY/Xv4tEQTEHCyQi8xQWFobw8HCEh4fD3t4eDg4OmDp1qr6f1Z07dzBkyBDUqVMH1tbW6NGjB+Lj4x/4XomJiZDJZIiMjCyz/Msvv4SnpydEUcS+ffsgCAL27NmDoKAgWFtbo0OHDjh//nyZbZYuXYpGjRrBwsICvr6++P7778u8LggCvv76a/Tu3RvW1tbw8/PD4cOHcfHiRYSFhUGtViMkJASXLl3Sb/Pf5qrjx4/jmWeegaOjIzQaDUJDQ3HixImqHE6DYJFD1aqu2gIrh7WFnaUCJ5IzMHHTSY6hQ0SVos3VVvihu2dwUl2JrnR5vrZc71sZa9asgUKhwNGjR7Fo0SIsWLAA3377LQBg2LBhiIyMxM8//4zDhw9DFEX07NkTxcX3z/3n5eWFLl26YNWqVWWWr1q1CsOGDYMgCPplH3zwAebPn4/IyEgoFAq8+uqr+te2bduGcePG4Z133sGZM2fwxhtvYPjw4di7d2+Z9/3f//6HIUOGICYmBk2bNsUrr7yCN954A1OmTNEXWuHh4Q/93NnZ2Rg6dCgiIiJw5MgR+Pj4oGfPnsjOlrarAicaomrXyMkGywa3wZAVx/DrqWto6KjGhK6+Usci+pcAWPtb658bffeCAH8nf/1zgF8EHuTu/GIV4b/RH879nQEA6dvSETcgDppQDVrta6Vf54jXERSn319ohIlhFd6fh4cHFixYAEEQ4Ovri9OnT2PBggUICwvDzz//jIMHD6JDhw4AgLVr18LDwwPbt29H//7973uvESNGYNSoUfj888+hUqlw8uRJxMTEYOvWrWXW+/jjjxEaGgoAmDx5Mnr16oWCggJYWlpi3rx5GDZsGEaPHg0AmDBhAo4cOYJ58+ahc+fO+vcYPnw4BgwYAAB47733EBISgg8//BDdunUDAIwbNw7Dhw9/6Od+6qmnyvz89ddfo06dOti/fz969+5d0cNoMLySQ0bRoZEjZr/QHACw6K+L2HX6msSJiP4lt5YjODYYwbHBkFsbd3JOALBWWiN2dCxiR8fCWmlt9P2T4bRv377MVZaQkBDEx8cjLi4OCoUC7dq107/m4OAAX19fnD374MFT+/btC4VCgW3btgEAVq5cic6dO8PLy6vMei1atNA/d3V1BQCkpaUBAM6ePYuOHTuWWb9jx4737fPe93BxcQEANG/evMyygoICZGVlPTBrWloaRo0ahSZNmkCj0UCj0SAnJwfJyckPXN9YeCWHjGZAkAcuXM/GtwcS8M6mk2joZAPferZSxyIiE9Epp1OFtxFU/xYcjs87lr7Hf77et09sX9VolSaKYpmi6F4WFhYYPHgwVq1ahRdeeAE//vgjFi5ceN96SqVS//zue+l0uvuWPWqfD3qPx73vvYYNG4abN29i4cKF8PT0hEqlQkhICIqKih64vrHwSg4Z1eQeTdGxsQPyirQY+V0kMvKk/QUgItMhV8sr/JAp/j3NyRSy0uVW8nK9b2UcOXLkvp99fHzg7++PkpISHD16VP/arVu3cOHCBfj5+T30/UaMGIE///wTS5YsQXFxMV544YUK5fHz88OBAwfKLDt06NAj91kZERERGDt2LHr27ImAgACoVCqkp0s/RhqLHDIqhVyGxS+3Rv06Vki+nYe31kVDy47IJDFtnhbHAo7hWMAxaPOMfwdgXnEeApYEIGBJAKd1MHEpKSmYMGECzp8/j3Xr1uHLL7/EuHHj4OPjg+eeew4jR47EgQMHcPLkSfzf//0f3N3d8dxzzz30/fz8/NC+fXu89957ePnll2FlVbFZ6idNmoTVq1dj2bJliI+Px+eff46tW7di4sSJVf2oZTRu3Bjff/89zp49i6NHj2LQoEEVzlodWOSQ0dVRW+CbwUGwUsoREZ+Oz34/J3Ukqu1EIC8uD3lxeZL0+RVFEXE34xB3M47TOpi4IUOGID8/H8HBwRgzZgzeeustvP766wBK74xq06YNevfujZCQEIiiiJ07d5ZpFnqQ1157DUVFRWXumiqvvn374osvvsDcuXMREBCAr7/+GqtWrUJYWFhlPt5DrVy5Enfu3EGrVq0wePBgjB07Fs7OzgbdR2UIoon+Rn388cfYsWMHYmJiYGFhgYyMjAq/R1ZWFjQaDTIzM2FnZ2f4kPRIv55KRfiP0QCARS+3Qp+WbhInotpK1IrIiMgAANh3socgN+4tVrlFubCZYwMAyJmSA3UxAJvSn5GTA6jVRs0jpYKCAiQkJMDb2xuWlpZSx6mQsLAwBAYGPrDfTFV8/PHHWL9+PU6fPm3Q963pHvVvobznb5O9klNUVIT+/fvjzTfflDoKVVLvFm54M6wRAODdzSdx9tqDe+0TVTdBLqBOWB3UCatj9AKH6GFycnJw/PhxfPnllxg7dqzUcUySyRY5M2fOxNtvv13mFjcyPRO7+uLJJk4oKNZhzNoTyC64f6wKIqLaKDw8HE888QRCQ0Mr1VRFtewW8sLCQhQW/jt/0sPu9yfjkcsEfPFSIHotisDl9FxM3noai19u9dBbKomqg65Yh2vflI7d5Pq6K2RKk/3+RxLat2+fQd9v9erVWL16tUHfs7apVb/Jc+bM0Q9SpNFo4OHhIXUkQmlH5C9faQ2FTMCOU9fw3eEkqSNRLSMWiYgPj0d8eDzEIpPspkhED1CjipwZM2ZAEIRHPv47WVlFTJkyBZmZmfpHSkqKAdNTVbTxrIMpPUvHbZi1Iw4xKRnSBiIyIkEQ4KnxhKfGk1cx/2Gi98SQARni30CNaq4KDw/HwIEDH7nOf4ezrgiVSgWVSlXp7al6vdrRC8cTbuO32OsYs/YEdox9AvbWFlLHIqp21kprJI5P/HdBUa5kWaR293bqvLy8GjHOCkknL690zKjH3WL/KDWqyHF0dISjo6PUMUgigiDgs/4tEHctC8m38/DOxpNYPiQIMhm/2RLVFnK5HPb29vq5l6ytrXl1q5YRRRF5eXlIS0uDvb095PLKzydXo4qcikhOTsbt27eRnJwMrVaLmJgYAKWjLtrcHV+CTI6dpRJLBrXGC0sPYc+5NHwTcRmjQhtJHYuIjKhevXoA/p1kkmone3t7/b+FyjLZImfatGlYs2aN/udWrVoBAPbu3WvwkRzJuJq5azDj2QC8v+005v1+Hu0bOiDQw17qWETVJr84H0+ufhIA8Pewv1HbG2kEQYCrqyucnZ1RXMxhJWojpVJZpSs4d5lskcNb68zby8EeOHDxJnaevo5x66OxY2wn2KhM9p8r0SPpRB0iUyP1z6mUXC43yImOaq8adXcV0V2CIGDO8y3gbm+FpFt5mLb9jNSRiIjIxLDIoRpLY63EwoGBkAnA1uir2B59VepIRERkQljkUI3W1qsu3nrKBwAwdfsZJN/KkzgRERGZChY5VOO99VRjBHnWQU5hCd5aH41iLfssEBHR47HIoRpPIZdh4cBA2FoqcDIlAwv/vCB1JCIiMgEscsgk1K9jjU9eaAEAWLLvEo5eviVxIjI3SkcllI6VH1m1qhytHeFozcFQiQxJEGvxBCFZWVnQaDTIzMyEnZ2d1HGoHCZtOolNUVdQv44Vfhv/JG8rJ/OUmwvcHdQ0JwdQq6XNQ1TDlPf8zSs5ZFKmPeuP+nWscOVOPv73S5zUcYiIqAZjkUMmxdZSiXn9W0IQgA2RKfgj7obUkYiIqIZikUMmp31DB4x4whsAMGXrKdzKKZQ4EZk6bb4W0WHRiA6LhjZfa/T95xfnI2x1GMJWhyG/ON/o+ycyVyxyyCS909UXvi62SM8pwpStp1GLu5aRIeiAzP2ZyNyfCUgwQoFO1GF/0n7sT9rPaR2IDIhFDpkkS6Ucn7/UEkq5gN1xN7A56orUkciECSoB/hv94b/RH4JKkDoOERkIixwyWQFuGozv0gQAMPOXOKTc5mjIVDkyhQzO/Z3h3N8ZMgX/LBKZC/42k0kbFdoIbf4ZDXny1lNstiIiIj0WOWTS5DIB8/u3hKVShoMXb2HdsRSpI5EJ0pXokLYpDWmb0qArYZ8YInPBIodMnpejGhO7+gIAZu88i6sZvDuFKkYsFBE3IA5xA+IgFvJqIJG5YJFDZmF4R2+0bmBf2my1hc1WZHqsldawVlpLHYPIrLDIIbMglwmY278lLBQyRMSnY1Mk77Yi06G2UCP3/Vzkvp8LtQWncCAyFBY5ZDYaOdngnWdK77b63444XM8skDgRERFJiUUOmZURnRoi0MMe2QUleH8bBwkkIqrNWOSQWZHLBMzt1wIWchn+OpeGrSeuSh2J6LEKSgrQ68de6PVjLxSU8AokkaGwyCGz4+Nii3FdfAAAM3+JRVoWTxpUs2l1WuyM34md8Tuh1Rl/7iwic8Uih8zSG082RHN3DbIKSvDB9jNstiIiqoVY5JBZUshlmNe/dG6rP+Ju4Lcz16WORERERsYih8yWbz1bvBnaCAAw/edYZOYXS5yIiIiMiUUOmbXRnRujoZMaadmF+PS3c1LHISIiI2KRQ2bNUinH7OebAwB+PJqM44m3JU5ERETGwiKHzF77hg4Y2NYDADB5yykUlvDuFSKi2oBFDtUKU3r4wdFGhUs3c7Fk7yWp41ANI1fLESaGIUwMg1wtN/r+1RZqiNNFiNNFTutAZEAscqhW0FgrMbNPAABgyb6LuJiWLXEiIiKqbixyqNbo2bwenm7qjGKtiMlbTkOn49g5RETmjEUO1RqCIOB/fZtBbSFHZNIdrDueLHUkqiG0BVrE9o9FbP9YaAuM32eroKQA/Tf1R/9N/TmtA5EBscihWsXN3goTu/kCAD7ZeY5TPlApLXBz803c3HwTkKBfulanxea4zdgct5nTOhAZEIscqnWGhHihpYc9sgtL8L8dZ6WOQzWAYCHAZ7EPfBb7QLAQpI5DRAbCIodqHblMwMd9m0EmAL+cTMWB+HSpI5HEZEoZ3Me4w32MO2RK/lkkMhf8baZaqZm7BkNCvAAA0346w7FziIjMEIscqrUmdG0CJ1sVLqfn4uv9l6WOQxIStSLu7LuDO/vuQNTyrjsic8Eih2otO0slPuztDwBYvPcikm7lSpyIpKIr0OFk55M42fkkdAU6qeMQkYGwyKFa7dkWrujY2AFFJTpM/zkWoshv8URE5oJFDtVqgiDgf881g4Vchn3nb+K3M9eljkS1kLXSGjlTcpAzJQfWSmup4xCZDRY5VOs1dLLBqNCGAICZv8Qhp7BE4kRU2wiCALWFGmoLNQSBt7ATGQqLHCIAozs3RoO61rieVYAv/rwgdRwiIjIAFjlEACyVcsx8rnQCz5UHE3HuepbEiag2KSwpxLDtwzBs+zAUlhRKHYfIbJhkkZOYmIjXXnsN3t7esLKyQqNGjTB9+nQUFRVJHY1MWGdfZ3QPqAetTsTUbWc4gScZTYmuBGtOrsGak2tQomNzKZGhmGSRc+7cOeh0Onz99deIjY3FggULsGzZMrz//vtSRyMTN+1Zf1j/M4Hn5hNXpI5DRERVYJJFTvfu3bFq1Sp07doVDRs2RJ8+fTBx4kRs3bpV6mhk4tzsrTC+iw8A4LPfziEzv1jiREREVFkmWeQ8SGZmJurWrfvIdQoLC5GVlVXmQfRfwzp4o6GTGuk5RVjITshERCbLLIqcS5cu4csvv8SoUaMeud6cOXOg0Wj0Dw8PDyMlJFNioZBhxrOlnZC/O5yE89ezJU5ERESVUaOKnBkzZkAQhEc+IiMjy2yTmpqK7t27o3///hgxYsQj33/KlCnIzMzUP1JSUqrz45AJe7KJE7oFuECrEzGDIyETEZkkhdQB7hUeHo6BAwc+ch0vLy/989TUVHTu3BkhISH45ptvHvv+KpUKKpWqqjGplpjayx/7zt/E4cu3sOP0NfRu4SZ1JCIiqgBBNNGvqFevXkXnzp3Rpk0b/PDDD5DL5RV+j6ysLGg0GmRmZsLOzq4aUpKpW/jnBSz8Mx6uGkvseScU1hY16nsBGYgoiihOL+1krnRUGn3UYVEUkZ6XDgBwtHaEkJcH2NiUvpiTA6jVRs1DVNOV9/xdob/Y3t7elfrlHz9+PMaOHVvh7R4mNTUVYWFhaNCgAebNm4ebN2/qX6tXr57B9kM0KrQRNkddwZU7+fhq70VM6tZU6khUDQRBgIWThaT7d1I7SbZ/InNVoSJn9erVldrJvU1MhrB7925cvHgRFy9eRP369cu8ZqIXpqiGslTKMbWXP0b9EIXlfyegfxsPeDnyWzURkSkw2eYqQ2BzFZWHKIoYsvIYIuLT8XRTZ6wY1lbqSGRgukIdLk64CABo/HljyFTGvSejsKQQE36fAAD4vNvnUBWWsLmK6BHKe/6uUXdXEdVEgiBg+rMBUMgE7DmXhr/O3ZA6EhmYWCIidUkqUpekQiwx/ve+El0JlkQuwZLIJZzWgciAWOQQlUNjZxu89oQ3AOCjX+JQWKKVOBEZkqAU4DndE57TPSEojdvpmIiqj0l2PCaSwltP+2Bb9FUk3srDtxEJGNO5sdSRyEBkFjJ4z/CWOgYRGZhJdjwmkoKNSoEpPZvi7Q0nsfivi3i+lTvc7K2kjkVERA9RoSInNDS0unIQmYS+ge748WgyjifewZxd5/Dly62kjkQGIOpE5J3NAwBY+1lDkLHJisgcVKlPTnFxMVJSUnD+/Hncvn3bUJmIaqy7nZAFAfjlZCoiE/nv3hzo8nU43uw4jjc7Dl2+Tuo4RGQgFS5ycnJy8PXXXyMsLAwajQZeXl7w8/ODk5MTPD09MXLkSBw/frw6shLVCM3cNRjQpnRy15m/xEGnq7WjMBAR1WgVKnIWLFgALy8vLF++HE899RS2bt2KmJgYXLhwAYcPH8b06dNRUlKCZ555Bt27d0d8fHx15SaS1MRuvrBRKXD6aia2nLgidRwycVZKKySMS0DCuARYKdnPi8hQKtQn59ChQ9i7dy+aN2/+wNeDg4Px6quvYtmyZVixYgX2798PHx8fgwQlqkmcbFUIf6oxPtl1Dp/9fh49mrvCRsV5rahyZIIMXvZeUscgMjsV+qu8adOmcq2nUqkwevToSgUiMhXDO3ph3bFkJN3Kw9J9nNeKiKimMdhggKtWrTLUWxGZBJVCjvd7+gEAlkckIOV2nsSJyFQVaYswafckTNo9CUXaIqnjEJkNgxU5W7duxd69e/U/5+fnY9CgQYZ6e6Iaqau/Czo0ckBRiQ5zdp2VOg6ZqGJtMeYdnod5h+ehWFssdRwis2GwIueHH37ABx98gHPnziE+Ph6dOnVCWFiYod6eqEYSBAHTnvWHTAB2nr6OI5dvSR2JiIj+UeWekhMmTEBgYCACAwOxcuVKvPLKKxBFEStXrkSrVhwojcxf03p2eDm4AdYeTcZHv8Thl7eegJyDyRERSa7KV3JCQ0ORlJSE//3vf+jfvz+SkpLg5uaG3bt3Y8eOHYbISFTjTXimCWwtFYi7loVNkSlSxyEiIhjgSs5zzz2H5557Tv9zfn4+zpw5g1OnTuHPP/9Er169qroLohrPwUaFcU/7YNaOs5i3+zx6tXCFraVS6lhERLVahYscURQfORO5lZUV2rZti7Zt21YpGJGpGRLihR+PJuNyei4W/3URU/6584qIiKRR4eYqGxsbdOzYEWPHjsWaNWtw5swZ6HSc64XIQiHD1N6lhc3KgwlITM+VOBERUe1W4Ss5c+fOxYkTJxAREYFly5ZBq9XC0tISLVq0QJs2bdCmTRu0bt0aLVu2rI68RDVaZ19nPNnECX9fuImPd57F8iFBUkeicpBZydD2TFv9c2OzUlrhzJtn9M9RnG/0DETmSBBFsdKzCxYWFsLKygrvv/8+bt++jRMnTuDUqVMoLCyEVqs1ZM5qkZWVBY1Gg8zMTNjZ2Ukdh8xE/I1sdP8iAlqdiB9ea4cnfByljkSmJjcXsLEpfZ6TA6jV0uYhqmHKe/6u0lcWlUoFABgwYACWLFmCI0eOIDs7G9HR0VV5WyKT5uNii8HtPQEAs3bEQctZyomIJGHw67JyuRwtWrQw9NsSmZTxXXxgZ6nAuevZ2BzFW8prOl2RDgkzEpAwIwG6IuP3MSzSFmHGvhmYsW8Gp3UgMiDjNz4T1QL21hYY+7QPAGDe7gvIKSyROBE9ilgsImlmEpJmJkEsNv6Vt2JtMWbun4mZ+2dyWgciA6pwkTNy5EgsW7YMkZGRKCwsBIBH3lJOVFsNCfGCl4M1bmYX4uv9l6SOQ48gKAS4jXaD22g3CAr+PSMyFxW+u+r8+fPYuHEjsrOzoVCUbj5z5kyEhYWhdevWCAwMhLW1tcGDEpkaC4UMk3s0xagfTmB5xGW8HNwAbvZWUseiB5CpZGjyVROpYxCRgVX4Ss7ff/+NzMxMnD9/Ht999x0mTpyIO3fuYNq0aXjiiSeg0WgQEBBQHVmJTE63gHoI9qqLgmId5v1+Xuo4RES1SqWndfDx8YGPjw8GDhyoX5aQkIDIyEjeXUX0D0EQMLW3H/osPoit0VcxrKMXWtS3lzoW/YcoiihOL+0Lo3RUsgmeyExU6EpOcnLyI1/39vZG//79MXv2bADA1atXK5+MyEy0qG+P51u5AwBm7TiLKgxNRdVEl6fDIedDOOR8CLo8juBOZC4qVOS0bdsWI0eOxLFjxx66TmZmJpYvX45mzZph69atVQ5IZA4mdfOFSiHDsYTb+D32htRxiIhqhQo1V509exazZ89G9+7doVQqERQUBDc3N1haWuLOnTuIi4tDbGwsgoKCMHfuXPTo0aO6chOZFDd7K4zs1BCL917EJ7vO4qmmzrBQcAQHKmWpsMSxEcf0z1FcIHEiIvNQqWkdCgoKsHPnTkRERCAxMRH5+flwdHREq1at0K1bNzRr1qw6shocp3UgY8opLEHY3H1IzynEh7398doT3lJHon9oc7WIsIkAAHTK6QS5Wi5tIE7rQPRI5T1/V6rjsaWlJbp06YIXXnih0gGJahsblQITuzbB5K2nsWhPPF5s7Q57awupYxERma1KXy+vU6cOtmzZYsgsRGavf5AHmtazRWZ+Mb7YEy91HKohirRFmHtwLuYenMtpHYgMqNJFjiiKWLp0Kdq1a4f27dsjPDwcR48eNWQ2IrMjlwn4oJcfAOD7w0m4fDNH4kRUExRri/Hun+/i3T/f5bQORAZUpZ6PJ0+eRHBwMMLCwnD+/HmEhobi7bffNlQ2IrPUyccJnX2dUKIT8cmuc1LHISIyW5UeDBAAfvzxRzzzzDP6n0+fPo2+ffuifv36eOedd6ocjshcvd/TD3/Hp2N33A0cuXwL7Rs6SB2JiMjsVPpKjoODAzw8PMosa968ORYtWoRly5ZVORiROfNxscXLwaW/P7N2xEGn4wCBRESGVukip2XLllixYsV9yxs3boyUlJQqhSKqDcZ3aQIblQJnrmZhWzRHByciMrRKFzmzZs3C4sWL8corr+DAgQPIysrCjRs3MHv2bHh7c/wPosdxtFFhTOfGAIC5v59HfpFW4kREROal0kVO+/btceTIEaSmpiIsLAx16tSBm5sbNm/ejPnz5xsyI5HZGt7RC+72VrieVYDlEZeljkNEZFYqNeLxf6WlpSEqKgo6nQ7t2rWDo6OjIbJVO454TDXBzydTMXZdNKyUcuybFAYXO0upI9U6olZERkQGAMC+kz0EuXFnIdfqtIhI/mfE5QadIM8v4IjHRI9Q3vO3QYocU8Uih2oCURTxwtJDiE7OwICg+visX0upI5HUOK0D0SOV9/xtsjME9unTBw0aNIClpSVcXV0xePBgpKamSh2LqMIEQcDUfwYI3BR1BXGpWRInIiIyDyZb5HTu3BkbN27E+fPnsWXLFly6dAn9+vWTOhZRpbTxrItezV0hisDsnWdRiy+wSkJXrMPVr67i6ldXoSvWGX3/xdpifHXsK3x17CuOeExkQAZprjpx4gSaNWsGCwvpJhv8+eef0bdvXxQWFkKpVJZrGzZXUU2SfCsPXT7fjyKtDquGtUXnps5SR6o1pJ6FPLcoFzZzSpuncqbkQF0MNlcRPYJRm6vatm2LxMREQ7xVpdy+fRtr165Fhw4dHlngFBYWIisrq8yDqKZo4GCNoR08AQAf7zyLEq3xryjUWnLAqZ8TnPo5Acatb4ioGhmkyJHq0vp7770HtVoNBwcHJCcn46effnrk+nPmzIFGo9E//jtiM5HUwjv7wN5aiYtpOVh/nINqGovcUo6ATQEI2BQAuSWrHCJzUaP65MyYMQOCIDzyERkZqV9/0qRJiI6Oxu7duyGXyzFkyJBHFlxTpkxBZmam/sGRmamm0VgrMe5pHwDAgj8uILuA/TOIiCqrShN0Glp4eDgGDhz4yHW8vLz0zx0dHeHo6IgmTZrAz88PHh4eOHLkCEJCQh64rUqlgkqlMmRkIoMb1M4T3x1OQkJ6Lpbuu4R3uzeVOhIRkUmqUUXO3aKlMu5ewSksLDRkJCKjs1DIMLlHU7zxfRRWHEjAoPaecLe3kjqWWZO64zERVY8a1VxVXseOHcPixYsRExODpKQk7N27F6+88goaNWr00Ks4RKakq78Lgr3rorBEh7m/nZM6DhGRSTLJIsfKygpbt27F008/DV9fX7z66qto1qwZ9u/fz+YoMgv3DhC4PSYVJ1MypA1E1UqlUOHXl3/Fry//CpWCf8OIDKVGNVeVV/PmzfHXX39JHYOoWrWob4/nW7ljW/RVfLzjLDa80R6CYNw5lcg4FDIFejXpdc8SNrsTGYJBruRMnz7dZCblJDIlk7r5QqWQ4Vjibfwee0PqOEREJsVgRU7dunUN8VZEdA83eyuM6OQNAPhk11kUlXCAQHNUrC3G6pjVWB2zmtM6EBmQSfbJIapN3gxrDEcbCyTeysMPR5KkjkPVoEhbhOE/Dcfwn4ajSFskdRwis8Eih6iGs1EpMOEZXwDAor/ikZnHb/pEROXBIofIBAwIqo8mLjbIyCvGl3/FSx2HiMgksMghMgEKuQzv9yy9pXzN4UQk3cqVOBERUc3HIofIRIT5OqOTjyOKtSI+5QCBRESPVaFxcry9vSs1Tsf48eMxduzYCm9HRGV90MsPPb+IwM7T1xGZeBtBXryrkYjoYSpU5KxevbpSO7l3Uk0iqrym9ewwIMgD64+nYNaOs9g2ugMHCCQieogKFTmhoaHVlYOIymlC1yb4+WQqYlIy8Mupa+jT0k3qSCZPUAnw3+ivf25sKoUKG/tt1D9HMUc8JjIEQbw7fXctlJWVBY1Gg8zMTNjZ2Ukdh6jcFu2Jx+d/XIC7vRX2vBMKSyVnzTYrubmAjU3p85wcQK2WNg9RDVPe8zf75BCZoJGdGuLHo8m4mpGP1YcSMSq0kdSRiIhqHPbJITJBVhZyTOzmi4mbTuKrvy6if5v6cLDh7NWVpSvRIX1bOgDA8XlHyBTGvfG0RFeCbWe3AQCe93veNGdOJqqB2FzF5ioyUTqdiGcXH0BsahaGhHjio+eaSR3JZGlztYiwiQAAdMrpBLnauM1/uUW5sJlT2jyVMyUH6mKwuYroEcp7/uY4OUQmSiYT8EGv0gEC1x5NxsW0HIkTmTAZoAnVQBOq4V9FIjNSpV/n4uJipKSk4Pz587h9+7ahMhFROXVo5Igufs7Q6kR8suus1HFMltxKjlb7WqHVvlaQW7ETN5G5qHCRk5OTg6+//hphYWHQaDTw8vKCv78/nJyc4OnpiZEjR+L48ePVkZWIHmByDz/IZQL+PJuGQ5fSpY5DRFRjVKjIWbBgAby8vLB8+XI89dRT2Lp1K2JiYnD+/HkcPnwY06dPR0lJCZ555hl0794d8fGcSJCoujV2tsGgdg0AAB/vOAudrtZ2syMiKqNCnfgPHTqEvXv3onnz5g98PTg4GK+++iqWLl2KlStXYv/+/fDx8TFIUCJ6uHFP+2DbiauITc3C1uir6NemvtSRTIo2V4sjXkcAAO0T2xu94zERVY8KFTmbNm0q13qWlpYYPXp0pQIRUcU52Kgw5qnG+GTXOcz7/Tx6NXeFlQVP1BVRnF4sdQQiMjCDDMeQkZGB33//HVevXoUgCHB1dUW3bt1Qp04dQ7w9EZXDsA5e+P5wEq5m5GN5xGWMfZpXUU2FhdwCq55bpX+O4iKJExGZhyrfLLlixQoEBwfjyJEj0Ol00Gq1OHLkCNq3b48VK1YYIiMRlYOlUo73ejQFACzbfwlpWQUSJ6LyUsqVGBY4DMMCh0EpV0odh8hsVHkwQF9fX0RFRcHm7sBV/8jOzkabNm1w4cKFKgWsThwMkMyNKIp4YekhRCdnYGBbD3zyYgupI5kEqQcDvA/nriJ6JKMNBigIAnJy7h+ELCcnp1LzXBFR5QmCgKn/DBC4MTIF565nSZyIyqNEV4IdF3Zgx4UdKNGVSB2HyGxUuU/OvHnzEBoaimbNmsHd3R0AcOXKFcTGxmL+/PlVDkhEFdPGsy56NXfFjtPX8PGOs/ju1WB+4ajhCksK0XtdbwCl0zpw7ioiw6jy71Lv3r3Ro0cPHDt2DKmpqRBFEe7u7ggODoZczrs7iKTwXvem+CPuBiLi07Hvwk109nWWOhIRkdFVuMgRRfG+b4VyuRwhISEGC0VEVdPAwRpDO3hieUQCZu84i06NHaGQc1ImIqpdKvxXz8bGBh07dsTYsWOxZs0anDlzBjqdrjqyEVEVhHf2gb21EvFpOdgQmSJ1HCIio6twkTN37lz4+fkhIiICI0eORMuWLWFra4uQkBCEh4dj1apVOHnyZHVkJaIK0FgrMe6fsXIW/HEB2QUc7I6IapcKN1fdO5JxYWEhrKys8Pbbb+P27duIjIzEypUrUVhYCK1Wa9CgRFRxg9p54rvDSUhIz8Wy/ZcwqVtTqSMRERlNlRrpVSoVAGDAgAFYsmQJjhw5guzsbERHRxskHBFVjYVChsn/DBD4bUQCUjPyJU5ERGQ8Br9TUS6Xo0ULDkBGVFN09XdBsHddHEu4jbm/n8eClwKljlTjCBYCfBb76J8bm4XcAot7LNY/57QORIbB4RiIzNzdAQL7LD6IbdFXMbyjF1rUt5c6Vo0iU8rgPsZdsv0r5UqMCR5zzxIWOUSGUOHmqpEjR2LZsmWIjIxEYWEhAHCgMaIarkV9ezzfqvQkPmvHWVRxNhciIpNQ4Ss558+fx8aNG5GdnQ2FonTzmTNnIiwsDK1bt0ZgYCCsra0NHpSIqmZSN1/sPH0NxxJuY3fcDXQLqCd1pBpD1IrIiMgAANh3socgN+4XN61Oi4jkf+bOatAJHEaVyDAqPUFnfHw8oqKicOLECURFRSE6OhoZGRmQy+Vo0qQJYmNjDZ3V4DhBJ9U2c38/h6/2XoK3oxq/j38SFgoOEAhIP0FnblEubOaUTsiZMyUH6mJwgk6iRyjv+bvSfXJ8fHzg4+ODgQMH6pclJCQgMjKSd1cR1VBvhjXGhuMpSEjPxdqjSRje0VvqSDWDAFj7W+ufE5F5qNDXuOTk5Ee+7u3tjf79+2P27NkAgKtXr1Y+GREZnI1KgbefaQIA+GJPPDLzOEAgAMit5QiODUZwbDDk1mwsIjIXFSpy2rZti5EjR+LYsWMPXSczMxPLly9Hs2bNsHXr1ioHJCLDeinIAz7ONsjIK8bivfFSxyEiqjYVaq46e/YsZs+eje7du0OpVCIoKAhubm6wtLTEnTt3EBcXh9jYWAQFBWHu3Lno0aNHdeUmokpSyGV4v5cfhq86jjWHkvB/7T3h6cA+H0Rkfip0Jadu3bqYN28eUlNTsXTpUjRp0gTp6emIjy/9Njho0CBERUXh4MGDLHCIarCwJk7o5OOIIq0OH+84K3UcyWnztDgWcAzHAo5Bm8cpaYjMRaU6HltaWqJLly544YUXDJ2HiIxAEARM6+2P7l9EYHfcDRyIT8cTPo5Sx5KOCOTF5emfE5F5qPT9o3Xq1MGWLVsMmYWIjMjHxRaD23sCAD76NRYlWp3EiWovpVyJz7p8hs+6fAalXCl1HCKzUekiRxRFLF26FO3atUP79u0RHh6Oo0ePGjJbuRQWFiIwMBCCICAmJsbo+ycyZW93aYI61kpcuJGDH489+u5Jqj4WcgtM6jgJkzpOKp27iogMokojgZ08eRLBwcEICwvD+fPnERoairfffttQ2crl3XffhZubm1H3SWQuNNZKTOjqCwCYv/sC7uRyziQiMh9VmqDzxx9/xDPPPKP/+fTp0+jbty/q16+Pd955p8rhHmfXrl3YvXs3tmzZgl27dlX7/ojM0cttPbD2SBLOXc/Gwj8vYOZzzaSOVOtodVqcuHYCANDatTWndSAykEpfyXFwcICHh0eZZc2bN8eiRYuwbNmyKgd7nBs3bmDkyJH4/vvvyz1XVmFhIbKysso8iGo7hVyGab39AQA/HE3G+evZEieqfQpKChD8bTCCvw1GQUmB1HGIzEali5yWLVtixYoV9y1v3LgxUlJSqhTqcURRxLBhwzBq1CgEBQWVe7s5c+ZAo9HoH/8t0ohqqw6NHdE9oB60OhEf/RrLWcqJyCxUusiZNWsWFi9ejFdeeQUHDhxAVlYWbty4gdmzZ8Pbu3Lz4cyYMQOCIDzyERkZiS+//BJZWVmYMmVKhd5/ypQpyMzM1D+quxgjMiXv9/SDhUKGgxdv4Y+4G1LHISKqskr3yWnfvj2OHDmCcePGISwsTP/Nz9LSEps2barUe4aHh5eZ8PNBvLy8MGvWLBw5cgQqlarMa0FBQRg0aBDWrFnzwG1VKtV92xBRqQYO1hjZyRtf7b2EWTvO4skmTrBUsncIEZmuKnU8btmyJfbt24e0tDRERUVBp9OhXbt2cHSs3KBijo6O5dp20aJFmDVrlv7n1NRUdOvWDRs2bEC7du0qtW8iAkaHNcbmqCtIvp2HlQcTMDqssdSRiIgqrUpFzl3Ozs5GncahQYMGZX62sbEBADRq1Aj169c3Wg4ic6NWKTC5R1O8veEkFv91ES+2rg8XO0upYxERVUqVxskhIvPzXEt3tGpgj7wiLT777bzUcYiIKq1CV3K8vb0hCEKFdzJ+/HiMHTu2wtuVl5eXF+8GITIQmUzA9GcD0Perg9hy4goGh3gi0MNe6ljVSlAK8JzuqX9ubEq5EtNDp+ufo7jY6BmIzJEgVqA62L9/f6V24uXlBU9Pz0ptW52ysrKg0WiQmZkJOzs7qeMQ1SjvbDyJLSeuINDDHlvf7ACZzPgn/1orNxf4pxkeOTmAWi1tHqIaprzn7wpdyQkNDa1yMCIyDe9198VvZ64hJiUDW6Ovol8b9ncjItPCPjlE9EDOdpYIf8oHAPDJrrPIzDffJhRRJyI3Nhe5sbkQdcZv+taJOsSmxSI2LRY6kbPBExmKWfTJIaLq8doT3tgUlYLLN3Ox4I8LmNEnQOpI1UKXr8PxZscBAJ1yOkGuNu74QPnF+Wi2tHTOsJwpOWDjFJFhVKjIWb16daV24uXlVantiEhaFgoZZvYJwOAVx/Dd4US81NYDfq7m2X9N6aiUOgIRGRj75BDRI3XycUKPZvWw68x1TP8pFhveaF+pK7o1mVwtR8ebHaWOQUQGxj45RPRYU3v7w0opx7HE2/gpJlXqOERE5cIih4gey93eCuFPlU7x8PHOs8guMN9OyERkPljkEFG5jOjkDW9HNW5mF+KLP+OljmNQ2nwtosOiER0WDW2+Vuo4RGQgLHKIqFxUCjmmP+sPAFh1KBEXbmRLnMiAdEDm/kxk7s8EeAc3kdlgkUNE5Rbm64yu/i7Q6kRM++kMp1MxEKVciYkhEzExZGLptA5EZBAGmYWciGqPD3v7Y/+Fmzhy+TZ+OXUNfVq6SR3J5FnILTC369x7lrDPE5Eh8EoOEVWIR11rjOn8TyfkHXHIKSyROBER0YOxyCGiCnv9yYbwdLDGjaxCfLnHvDohS0En6pCYkYjEjERO60BkQCxyiKjCLJX/dkJecSAB8ebUCVkC+cX58P7CG95feCO/OF/qOERmg0UOEVXKU01d0MXPGSU6EVO3sxMyEdU8LHKIqNKmPxsAS6UMRxNuY3PUFanjEBGVwSKHiCrNo641xndpAgCYvfMsbucWSZyIiOhfLHKIqEpee8IbTevZ4k5eMebsPCt1HCIiPRY5RFQlSrkMHz/fHACwKeoKjly+JXEiIqJSLHKIqMraeNbBK+0aAAA+2HYahSWc/4mIpMcRj4nIIN7r1hS7Y6/j0s1cfLP/Mt562kfqSOUmKAS4jXbTPzc2hUyB0UGj9c8BDrBIZAiCWIvv+8zKyoJGo0FmZibs7OykjkNk8n6KuYpx62NgoZBh9/gn4eWoljqSacrNBWxsSp/n5ABqHkeie5X3/M3mKiIymD4t3fBEY0cUlejwISfwJCKJscghIoMRBAGz+jaDhUKGiPh0/HwyVepI5SKKIopuFqHoZpEkhZkoiriZexM3c2+yMCQyIBY5RGRQXo5qhP8zgef/fj2LzPyaP6O2Lk+HQ86HcMj5EHR5xp87Kq84D87znOE8zxl5xXlG3z+RuWKRQ0QG90ZoQzR0UiM9pxCf7DondRwiqqVY5BCRwakUcsz+Z+ycdceSa/zYOXK1HGFiGMLEMMjVcqnjEJGBsMghomrRvqEDXg4uHTtn8pZTKCjm2DlEZFwscoio2kzp2RQudiok3srDgj8vSB2HiGoZFjlEVG3sLJWY1be02Wr535dx6kqGtIEeQlugRWz/WMT2j4W2gFeciMwFixwiqlbP+LugdwtX6ETg3c2nUKw1/t1Lj6UFbm6+iZubbwKscYjMBoscIqp2M/oEwN5aiXPXs/HN35eljlPjKGQKDG05FENbDv1nWgciMgQWOURU7RxtVJj+rD8A4Is/43ExLUfiRDWLSqHC6r6rsbrvaqgUKqnjEJkNFjlEZBR9A90R5uuEIq0Ok7ecgk7HkX2JqHqxyCEio7g75YPaQo7IpDv44WiS1JFqDFEUkVuUi9yiXE7rQGRALHKIyGjq17HGez2aAgA+2XUOybc4hQFQOq2DzRwb2Myx4bQORAbEIoeIjOr/2nki2Ksu8oq0mLT5JJutiKjasMghIqOSyQTM7d8C1hZyHE24jdWHEqWORERmikUOERmdp4MaU3r6AQA+/e0cLt3k3VZEZHgscohIEv/XrgE6+TiisESHiZtOQstmKyIyMBY5RCQJQRDw6YstYKtSIDo5g4MEEpHBmWyR4+XlBUEQyjwmT54sdSwiqgA3eytM+2eQwAV/XMD569kSJyIic2LS44d/9NFHGDlypP5nGxsbCdMQUWX0a1Mfv525jj3n0jBhYwy2j+kIpdzI37/kgFM/J/1zY5PL5Ojn30//nBNoERmGSRc5tra2qFevntQxiKgKBEHAnBeao+vCvxGbmoWv9l7E+C5NjJpBbilHwKYAo+7zXpYKS2zqv+nfBYW5kmUhMicm21wFAJ9++ikcHBwQGBiIjz/+GEVFRY9cv7CwEFlZWWUeRCQ9ZztLfPRcMwDA4r8u4tSVDGkDEZFZMNkiZ9y4cVi/fj327t2L8PBwLFy4EKNHj37kNnPmzIFGo9E/PDw8jJSWiB7n2Rau6NXcFSU6EePXxyCvqETqSERk4gSxBk2UMmPGDMycOfOR6xw/fhxBQUH3Ld+yZQv69euH9PR0ODg4PHDbwsJCFBYW6n/OysqCh4cHMjMzYWdnV7XwRFRlGXlF6L4wAtezCvBycAPMeaG5UfarzdUiwiYCANAppxPkauN2zMktyoXNnNI+hTlTcqAuBnC3j2FODqBWGzUPUU2XlZUFjUbz2PN3jeqTEx4ejoEDBz5yHS8vrwcub9++PQDg4sWLDy1yVCoVVCpVlTISUfWxt7bA5y+1xKBvj2LdsWSE+TqhWwD73RFR5dSoIsfR0RGOjo6V2jY6OhoA4OrqashIRGRkHRo54vUnG+Lr/ZcxecspBHrYw8XOslr3KbOWoUNaB/1zIjIPJvnbfPjwYSxYsAAxMTFISEjAxo0b8cYbb6BPnz5o0KCB1PGIqIreecYXzdztcCevGBM3Vf8knoIgwMLJAhZOFhAEoVr3RUTGY5JFjkqlwoYNGxAWFgZ/f39MmzYNI0eOxLp166SORkQGYKGQYeFLrWCplCEiPh0rDyZIHYmITFCNaq4qr9atW+PIkSNSxyCiatTY2QZTe/lj6vYz+Oy38+jQyBH+btVzg4CuUIeLEy6W7vfzxpCpTPL7HxH9B3+TiajGGtSuAbr4OaNIq8Nb604gt7B6bisXS0SkLklF6pJUiCU15oZTIqoiFjlEVGPdncTTxU6FSzdz8eH2M6hBo14YjFwmR0+fnujp0/OfaR2IyBBY5BBRjeZgo8Kiga0gE4Ct0VexKeqK1JEMzlJhiR2v7MCOV3bAUlG9d5IR1SYscoioxmvX0AETnimdz2raT2dw4QZnKyeix2ORQ0QmYXRYY3TycURBsQ6j157gtA9E9FgscojIJMhkAha8FAhnWxUupuVg2k+xUkcymNyiXKhnq6GerUZuEWcgJzIUFjlEZDIcbVT44p/+OZujrmCzGfXPySvOQ15xntQxiMwKixwiMikhjRwwvktp/5wPt5/BuetZEiciopqKRQ4RmZwxnUv75+QXa/HG91HIzCuWOhIR1UAscojI5MhlAhYNbAV3eysk3crD+A3R1T6/FRGZHhY5RGSS6qgt8PXgNlApZNh7/iYW7omXOhIR1TAscojIZDVz12D2880BAIv2xOOPuBsSJyKimoRFDhGZtBfb1MfQEE8AwIQNMbh8M6fibyIDNKEaaEI1kvxVlAkyhHqGItQzFDKBf5aJDEUQzXEimHLKysqCRqNBZmYm7OyqZ3ZjIqp+RSU6DPr2CI4n3oGPsw22jekIG5VC6liVl5sL2NiUPs/JAdRqafMQ1TDlPX/zKwMRmTwLhQxfDWoNZ1sV4tNy8PaGGHZEJiIWOURkHpxtLbFscBtYyGX4I+4G5u4+L3UkIpIYixwiMhutG9TBZ/1aAACW7rtU7hGRtblaHHQ6iINOB6HN1VZnxAfKLcqF01wnOM114rQORAZkwo3WRET369vKHRfTcrB470VM2XoKng7WaOtV97HbFadLO6Bgel66pPun2kcURdzOLUJqRgGuZxUgM78YWfnFyC4oQVZBMQpLtNCJQGnPXRGiCFhbKGCjksPGUgEblRJ1rJVwtbeCm8YSjjYqyGSC1B+rDBY5RGR2JjzTBJdu5mDXmet44/so/DSmIzzqWj90fZmVDG3PtNU/JzInWQXFiL+Rg4tp2Yi/kYP4tBwk385DakY+Ckt0BtuPUi6gnsYSDR1t4FvPFk1cbOHrYgsfFxtYKuUG209FsMghIrMjkwmYP6AlUu7k4czVLLy6+jg2v9kBGivlA9cXZALUAbyDiUxfUYkOcdeyEJN8BzEpGTh5JRMJ6Y9uAnWyVcFVYwl7awvYWSpga6mEnZUClgo5ZIIAQQAEACKAvCItcgtLkFNYguyCEtzOLcS1zALcyCpAsVZEyu18pNzOx/4LN/Xv/+XLrfBsS7fq/eAPwSKHiMyStYUC3w5pi+e+OoD4tBy8/l0kvnstGCqFNN8oiapDiVaHM6lZOHQpHYcv3cLxxNsoKL7/6kw9O0v4uNjAx7n0yoqXgxru9lZw0agM8jtRotXhRnYhrt7Jx8W0HFy4kY3z17MRn5aNJi62VX7/ymKRQ0Rmq57GEquGBWPA14dxNOE2Jmw8iS8Htrqv34CuSIek2UkAAM/3PSGzYJMV1Vx3couw93wa9pxNw98XbiK7sKTM63WslQj0sEdLD3sE/vOwt7ao1kwKuQzu9lZwt7dCsHfZPnBSDsfHIoeIzJq/mx2+HtwGw1Ydw45T1+BqZ4mpvf3LrCMWi0iaWVrkNJjUAKje8wFRhSXdysXu2Bv44+wNRCbexr3DQNlZKtC+oQM6NHJAh8aO8HG2gSDUnA7AUmZhkUNEZq9jY0fM698S49bH4NsDCainscSITg2ljqUnE2QIcgvSPwcM1xmUTNf1zAL8eioVP59MxakrmWVea1rPFl38XPC0nzNa1LeHvIbd1VRTsMgholrhuUB3XM8swJxd5zBrx1k42arwXKC71LEAAFZKKxwfefzfBRwrp9bKzC/GjlPX8FPMVRxLvI27LT1ymYCQhg54xt8FTzV1fuTdgvQvFjlEVGu8/mRDXMsswOpDiXhn40moLRTo4u8idSyq5URRxLGE29hwPAU7z1wr03E4yLMO+gS6oWdzVzjaqCRMaZpY5BBRrSEIAqb19kdmfjG2RV/F6B9PYPWwtmjnWkfqaFQLpWUXYEvUVWyMTClzm3cTFxu82Lo+erd0g7u9lYQJTR+LHCKqVWQyAXP7tUBuYQl2x93AiO8i8f0rQZJmyivOg/9XpZ2h48bEgQ0R5uvuVZvVhxKxO+4GtP/0IFZbyPFsSze81NYDgR72NarjsCljkUNEtY5CLsOXr7TCiDWRiIhPxxvfRWEepGsKEEURSZlJ+udkfgqKtfg5JhWrDiXi7LUs/fLWDewxsG0D9GrhCrWKp2RD4xElolpJpZDj68FtMHjFMZyOvwNIWOSQ+UrNyMf3R5Kw/lgy7uSVzo9mqZTh+Vb1MbSDJ5rWs5M4oXljkUNEtZa1hQIrh7XFsK8O4+5t2+evZ8G/EfvoUNXEpmZi6b5L2HXmur5Jyt3eCkM7eGJAkEe1D85HpVjkEFGtprFSYsXQYJyadAQAMGzVcax8sx2auWskTkamKCrpNr7aewl/nUvTL+vQyAFDO3ihi58Lx7MxMhY5RFTraaz/nbgzI68Yg749ih9ea4fm9Vno0OOJoogDF9Px1d6LOHL5NgBAJgDPtnTDqNBG8HNlk5RUWOQQEd0j0EODYzcy8cq3R/Ddq8Fo1YBNV/RgOp2IP87ewJK9F3HynxGJlXIBL7auj1GhjeDlyJntpcYih4hIAKz9S2/cXj6sJUZsiMLxxDsY9O1RLPu/NniyiVP17l4Q4O/kr38O8A6rmqxEq8Ovp65hyb6LuHAjB0BpZ+KXgxvg9ScbwlXDsW1qCkGsxfcrZmVlQaPRIDMzE3Z2vJxIRKVyC0sw6ocoRMSnQykXMK9/S+NOAZGbC9jYlD7PyQHUvCJQExSWaLEl6iqW7b+E5Nt5AABblQJDOnji1Y7ecOCIxEZT3vM3r+QQEf2HWqXAiqFtMWFjDH49dQ3j1sfgdm4Rhnf0ljoaSSCvqAQ/Hk3G8ojLuJFVCACoq7bAa094Y3CIJ+wslY95B5IKixwiogewUMiwaGArOKgtsOZwEmb+EodbOUV4p2sTjkZbS2TmF+O7Q4lYeTBBP8ZNPTtLvP5kQ7wc3ABWFnKJE9LjsMgholpPm6dFVNsoAECb420gty49eclkAmb0CYCjjQrz/7iAxXsvIul2Hub2awFLpeFOcHnFeWi7vC0A4PjI45zWQWI3swux8mACvj+chJzCEgCAp4M13gxthOdbu0OlYHFjKljkEBGJQF5cnv75vQRBwFtP+8DZToUPtp3BLydTkXI7D98MaQNnW0vD7F4UEXczTv+cpHE1Ix/L/76MdceSUVhSOjikr4stRnduhF7NXaGQyyROSBXFIoeIaj2ZpQwt97bUP3+Ql9o2gEdda7z5wwnEpGSg7+KD+HZoW/i78aYFU3f5Zg6W7b+ErSeuouSf0YlbetgjvHNjPN3UGTIO4GeyWOQQUa0nyAXUCXv8eDgdGjli+5iOeG31cVxOz0W/ZYfw+YCW6N7M1QgpydDiUrOwZN9F7Dx9Df/UNujQyAFjOjdGh0YO7HtlBljkEBFVgLejGttGd8SYH0/gwMV0jPrhBEY84Y33ejSFks0ZJiEq6Q6W7L2IPfdMvdDFzxmjOzdGaw7+aFZM+jdyx44daNeuHaysrODo6IgXXnhB6khEZIJ0xTpc/eoqrn51Fbpi3WPX11grsWp4W7z+ZEMAwLcHEjDwmyO4lplf3VGpkkRRxIH4dLz8zRG8uPQQ9pxL00+9sGtcJ3w7tC0LHDNksldytmzZgpEjR2L27Nl46qmnIIoiTp8+LXUsIjJBYpGI+PB4AEC9YfWAcgx7opTL8H5PP7TxrIOJm04iKukOei06gC8GBqKTT/WOkEzlp9WJ+D32Opbuu4TTV/+deuGFVvUxKqwRvDn1glkzySKnpKQE48aNw9y5c/Haa6/pl/v6+kqYiohqo24B9dC0ni1Grz2B2NQsDF5xDK894Y1J3XzLfZu5IAjw1Hjqn3Nah6orLNFie/RVfL3/Mi6n5wIonXphYNvSqRfc7Dn1Qm1gkkXOiRMncPXqVchkMrRq1QrXr19HYGAg5s2bh4CAgIduV1hYiMLCQv3PWVlZxohLRGbO00GNLW92wP9+jcPao8lYcSABf1+4iQUvBaKZ++NnMrdWWiNxfOK/C4pyqy+smcspLMG6o8n49sC/oxNrrJQY2sELQ0M8OfVCLWOSRc7ly5cBADNmzMDnn38OLy8vzJ8/H6Ghobhw4QLq1q37wO3mzJmDmTNnGjMqEdUSlko5Pn6+OZ72c8a7m08jPi0Hfb86iHFP++DNsEYcY6Wa3copxOpDiVhzKBFZBaUD+NWzs8SITt54ObgB1CqTPN1RFdWo37oZM2ZAEIRHPiIjI6HTlXYM/OCDD/Diiy+iTZs2WLVqFQRBwKZNmx76/lOmTEFmZqb+kZKSYqyPRkS1xFNNXbD77SfRo1k9lOhEzP/jAp776iBOpmRIHc0snbuehfc2n0LIJ3/hy78uIqugBA2d1PisXwv8/W5njOjUkAVOLVaj/s+Hh4dj4MCBj1zHy8sL2dnZAAB/f3/9cpVKhYYNGyI5Ofmh26pUKqhUvFRJRNWrrtoCSwa1xk8xqZj20xnEpmah75KDGNzeExO7+d43oWN+cT6eXP0kAODvYX+DvUUeTacT8de5NKw8mIBDl27pl7f0sMeboY3Q1d+FA/gRgBpW5Dg6OsLR0fGx67Vp0wYqlQrnz5/HE088AQAoLi5GYmIiPD09qzsmEdFjCYKAvq3c0bGxI2bvPItt0Vfx3eEk/HbmOiZ288WLretD/s+JWCfqEJkaqX9OD5ZTWILNkSlYfSgRibdKp+GQywR0b1YPr3b0RusG9hzAj8qoUUVOednZ2WHUqFGYPn06PDw84Onpiblz5wIA+vfvL3E6IqJ/OdmqsOClQPRrUx9Tt59BQnou3t18CqsPJmJqLz90aPz4L3a1XWxqJtYdS8b26FT9hJl2lgq83K4BhoR4wZ13StFDmGSRAwBz586FQqHA4MGDkZ+fj3bt2uGvv/5CnToczImIap6OjR3x2/hO+O5QEhb9FY+4a1l45duj6OzrhDdC3aWOV+PkFpbgl5OpWHcsGSevZOqXN3RSY3hHb7zY2h3WFiZ7CiMjEcRaPOVtVlYWNBoNMjMzYWfHSfaIaittrhYRNhEAgE45nSBXl298m8q6nVuEL/68gB+OJkOrE6FDAVKs+gEAcqbkQF0MwMamdOWcHEBdOwasE0URJ5IzsOXEFfwUfRW5RVoApYP3dQuoh1eCG6B9Qwf2t6Fyn79ZBhMRGVldtQVmPtcMwzp648u/4rE1+pL+teGrjuHNIE90ljCfsV26mYOfoq9ie0wqkm/n6Zd7O6rxcrAHXmxdn+PbUKWwyCEikoi3oxqfDwjEa53c0Oyb0mVHEm7j9PnbOPvPOpl5xdCY4YWcpFu52B17A7+cSsWpe5qjrC3k6BZQDwOCPNC+YV12JKYqYZFDRARA6ViOCauqiZeDGo7WjtDpgFdbe2Hn4Sv6156cuxdPtvLCgKD66NDIUX9HlqkRRRGxqVnYHXsdu+Nu4Nz1bP1rcpmAJ30c0beVO57xd2FfGzIY9slhnxwiqmFybmfCxsEeAOD39mbkW1gCABxtVOga4IIezeqhfUMHKGv4KMrpOYU4eDEdBy+m40B8OlIzC/SvyWUC2nnXRfdm9dCzuSsc2RxFFVDe8zeLHBY5RFTT5ObqOx6fOX8VG8/exk8xqcjML9avYmepQMfGjujY2BFPNHaEp4O1pE07oijiyp18nEi+g+jkDBy5fKvM1RoAsFLKEdrECV0DXPBUU2fYW1tIlJZMHYuccmCRQ0Q10j1Fzt27q4pKdDh8+RZ+O3Mdf8RdR3pOUZlNXOxUaFHfHoEe9mhRX4MmLrZwtlVVS+FTWKLF5Zu5uHAjG/E3cnDuehZiUjLuywQAfq52eKKxAzo0dkRIQ4dyz8xO9CgscsqBRQ4RAYA2X4tTPU4BAFrsagG5lXFPxPnF+eixtgcAYNegXbAq0j3yFnKtTsTJKxk4dDEdBy6mIyrpDoq19/8pt1Ep0NBJDU8HNVxsVXCxs4SznQoaKyXUKgXUFgpYWcghALi7dWGJFtkFJcguKEZ2QQmy8otxLbMA1zILkJqRj2uZBbiakQ+t7v79KeUC/N00aOVhj9aeddChkQOboaha8BZyIqLy0gGZ+zP1z42+e1GH/Un79c8fRy4T0LpBHbRuUAfhT/kgv0iLM6mZOJmSgZNXMnHmaiaSbuUip7AEp65klrl7yVBsLRVo4mL7z8MGLeprEOCm4ZUaqlFY5BBRrSeoBPhv9Nc/NzVWFnK09aqLtl519csKS7RIvpWHSzdzkXI7D2nZBUjLLsSNrAJkF5Qgr0iLnMISFPwz4B4AQAAs5DLYWipga6mEjUoBOysFXOws4WZvBVdN6X896ljDxa56msKIDIlFDhHVejKFDM79naWOYVAqhRw+LrbwcbGVOgqRZGr2/YdERERElcQrOURU6+lKdEjflg4AcHzeETIFv/8RmQMWOURU64mFIuIGxAEonaCTfxmJzAN/lYmIagBrpbXUEYjMDoscIiKJqS3UyH0/998FxbkPX5mIyo0Nz0RERGSWWOQQERGRWWKRQ0QksYKSAvT6sRd6/dgLBSUFj9+AiMqFfXKIiCSm1WmxM36n/jkRGQav5BAREZFZYpFDREREZolFDhEREZklFjlERERklljkEBERkVmq1XdXiaIIAMjKypI4CRFJSZurRS5KRxnOysqCXCs36v5zi3KBf+4cz8rKgrb4nhezsgAt77giutfd8/bd8/jDCOLj1jBjV65cgYeHh9QxiIiIqBJSUlJQv379h75eq4scnU6H1NRU2NraQhAEg71vVlYWPDw8kJKSAjs7O4O9L92Px9o4eJyNg8fZOHicjaM6j7MoisjOzoabmxtksof3vKnVzVUymeyRFWBV2dnZ8RfISHisjYPH2Th4nI2Dx9k4qus4azSax67DjsdERERklljkEBERkVlikVMNVCoVpk+fDpVKJXUUs8djbRw8zsbB42wcPM7GUROOc63ueExERETmi1dyiIiIyCyxyCEiIiKzxCKHiIiIzBKLHCIiIjJLLHIqacmSJfD29oalpSXatGmDiIiIR66/f/9+tGnTBpaWlmjYsCGWLVtmpKSmrSLHeevWrXjmmWfg5OQEOzs7hISE4PfffzdiWtNV0X/Pdx08eBAKhQKBgYHVG9CMVPRYFxYW4oMPPoCnpydUKhUaNWqElStXGimt6arocV67di1atmwJa2truLq6Yvjw4bh165aR0pqmv//+G88++yzc3NwgCAK2b9/+2G2Mfi4UqcLWr18vKpVKcfny5WJcXJw4btw4Ua1Wi0lJSQ9c//Lly6K1tbU4btw4MS4uTly+fLmoVCrFzZs3Gzm5aanocR43bpz46aefiseOHRMvXLggTpkyRVQqleKJEyeMnNy0VPQ435WRkSE2bNhQ7Nq1q9iyZUvjhDVxlTnWffr0Edu1ayf+8ccfYkJCgnj06FHx4MGDRkxteip6nCMiIkSZTCZ+8cUX4uXLl8WIiAgxICBA7Nu3r5GTm5adO3eKH3zwgbhlyxYRgLht27ZHri/FuZBFTiUEBweLo0aNKrOsadOm4uTJkx+4/rvvvis2bdq0zLI33nhDbN++fbVlNAcVPc4P4u/vL86cOdPQ0cxKZY/zSy+9JE6dOlWcPn06i5xyquix3rVrl6jRaMRbt24ZI57ZqOhxnjt3rtiwYcMyyxYtWiTWr1+/2jKam/IUOVKcC9lcVUFFRUWIiopC165dyyzv2rUrDh069MBtDh8+fN/63bp1Q2RkJIqLi6stqymrzHH+L51Oh+zsbNStW7c6IpqFyh7nVatW4dKlS5g+fXp1RzQblTnWP//8M4KCgvDZZ5/B3d0dTZo0wcSJE5Gfn2+MyCapMse5Q4cOuHLlCnbu3AlRFHHjxg1s3rwZvXr1MkbkWkOKc2GtnqCzMtLT06HVauHi4lJmuYuLC65fv/7Aba5fv/7A9UtKSpCeng5XV9dqy2uqKnOc/2v+/PnIzc3FgAEDqiOiWajMcY6Pj8fkyZMREREBhYJ/QsqrMsf68uXLOHDgACwtLbFt2zakp6dj9OjRuH37NvvlPERljnOHDh2wdu1avPTSSygoKEBJSQn69OmDL7/80hiRaw0pzoW8klNJgiCU+VkUxfuWPW79By2nsip6nO9at24dZsyYgQ0bNsDZ2bm64pmN8h5nrVaLV155BTNnzkSTJk2MFc+sVOTftE6ngyAIWLt2LYKDg9GzZ098/vnnWL16Na/mPEZFjnNcXBzGjh2LadOmISoqCr/99hsSEhIwatQoY0StVYx9LuTXsApydHSEXC6/7xtBWlrafRXqXfXq1Xvg+gqFAg4ODtWW1ZRV5jjftWHDBrz22mvYtGkTunTpUp0xTV5Fj3N2djYiIyMRHR2N8PBwAKUnYlEUoVAosHv3bjz11FNGyW5qKvNv2tXVFe7u7tBoNPplfn5+EEURV65cgY+PT7VmNkWVOc5z5sxBx44dMWnSJABAixYtoFar0alTJ8yaNYtX2w1EinMhr+RUkIWFBdq0aYM//vijzPI//vgDHTp0eOA2ISEh962/e/duBAUFQalUVltWU1aZ4wyUXsEZNmwYfvzxR7anl0NFj7OdnR1Onz6NmJgY/WPUqFHw9fVFTEwM2rVrZ6zoJqcy/6Y7duyI1NRU5OTk6JdduHABMpkM9evXr9a8pqoyxzkvLw8yWdnToVwuB/DvlQaqOknOhdXWpdmM3b09ccWKFWJcXJw4fvx4Ua1Wi4mJiaIoiuLkyZPFwYMH69e/e9vc22+/LcbFxYkrVqzgLeTlUNHj/OOPP4oKhUL86quvxGvXrukfGRkZUn0Ek1DR4/xfvLuq/Cp6rLOzs8X69euL/fr1E2NjY8X9+/eLPj4+4ogRI6T6CCahosd51apVokKhEJcsWSJeunRJPHDggBgUFCQGBwdL9RFMQnZ2thgdHS1GR0eLAMTPP/9cjI6O1t+qXxPOhSxyKumrr74SPT09RQsLC7F169bi/v379a8NHTpUDA0NLbP+vn37xFatWokWFhail5eXuHTpUiMnNk0VOc6hoaEigPseQ4cONX5wE1PRf8/3YpFTMRU91mfPnhW7dOkiWllZifXr1xcnTJgg5uXlGTm16anocV60aJHo7+8vWllZia6uruKgQYPEK1euGDm1adm7d+8j/+bWhHOhIIq8FkdERETmh31yiIiIyCyxyCEiIiKzxCKHiIiIzBKLHCIiIjJLLHKIiIjILLHIISIiIrPEIoeIiIjMEoscIiIiMksscoiIiMgsscghIiIis8Qih4jMzubNm9G8eXNYWVnBwcEBXbp0QW5urtSxiMjIFFIHICIypGvXruHll1/GZ599hueffx7Z2dmIiIgAp+kjqn04QScRmZUTJ06gTZs2SExMhKenp9RxiEhCbK4iIrPSsmVLPP3002jevDn69++P5cuX486dO1LHIiIJ8EoOEZkdURRx6NAh7N69G9u2bcP169dx9OhReHt7Sx2NiIyIRQ4RmTWtVgtPT09MmDABEyZMkDoOERkROx4TkVk5evQo9uzZg65du8LZ2RlHjx7FzZs34efnJ3U0IjIyFjlEZFbs7Ozw999/Y+HChcjKyoKnpyfmz5+PHj16SB2NiIyMzVVERERklnh3FREREZklFjlERERklljkEBERkVlikUNERERmiUUOERERmSUWOURERGSWWOQQERGRWWKRQ0RERGaJRQ4RERGZJRY5REREZJZY5BAREZFZYpFDREREZun/AUi0h0NF7fTWAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -201,7 +201,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -247,7 +247,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.9|INFO|2024-05-27 11:26:20]: Using numpy backend on /CPU:0\n" + "[Qibo 0.2.9|INFO|2024-05-29 12:15:55]: Using numpy backend on /CPU:0\n" ] } ], @@ -281,8 +281,8 @@ "output_type": "stream", "text": [ "grid_search step: 0.6565690909090909\n", - "100%|██████████| 100/100 [00:00<00:00, 1068.92trial/s, best loss: 10.531927656500912]\n", - "hyperopt_search step: 0.599869080691258\n", + "100%|██████████| 100/100 [00:00<00:00, 1239.19trial/s, best loss: 10.532755662184366]\n", + "hyperopt_search step: 0.5995386291300258\n", "polynomial_approximation step: 0.4860935299211704\n" ] } @@ -499,925 +499,6 @@ " plt.axhline(y=eigvals, color='r', linestyle='--')\n", "plt.xlabel('Iterations')\n" ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gradients for finding optimal $D$\n", - "\n", - "An advantage of the least-squares cost function is that one can use gradient descent and the learning is more stable than with the off-diagonal cost function." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from qibo.models.dbi.utils_gradients import *" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.9|INFO|2024-05-27 16:13:40]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 5\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the energy fluctuation cost function\n", - "cost = DoubleBracketCostFunction.energy_fluctuation\n", - "# define the state\n", - "state = np.zeros(2**nqubits)\n", - "state[3] = 1\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost, ref_state=state)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Diagonal elements')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cost = DoubleBracketCostFunction.least_squares\n", - "nqubits = 5\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "params = np.linspace(1,2**nqubits,2**nqubits)\n", - "\n", - "step = 1e-1\n", - "iterations = 200\n", - "d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, iterations, step)\n", - "\n", - "plt.figure()\n", - "plt.plot(range(iterations+1), loss)\n", - "plt.xlabel('Learning iterations')\n", - "plt.ylabel('Loss: Least squares')\n", - "\n", - "plt.figure()\n", - "for i in range(2**nqubits):\n", - " plt.plot(diags[i,:], label='State ' + str(i))\n", - "plt.xlabel('Learning iterations')\n", - "plt.ylabel('Diagonal elements')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Training for $D$ can greatly improve the decrease of the off-diagonal norm at each iteration. Nonetheless, during training the ascending values condition may be no longer satisfied creating a exponential decrease after few iterations" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$||\\\\sigma(H_k)||$')" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cost = DoubleBracketCostFunction.least_squares\n", - "nqubits = 5\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "params = np.linspace(1,2**nqubits,2**nqubits)\n", - "d_fixed = np.diag(params)\n", - "dbi_trained = deepcopy(dbi)\n", - "flows = 50\n", - "iterations = 200\n", - "off_diagonal_norm = np.empty((2,flows+1))\n", - "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", - "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", - "d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", - "for i in range(flows):\n", - "\n", - " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", - " dbi_trained(s,d=d_trained)\n", - " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", - " s = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_fixed, n=3)\n", - " dbi(s,d=d_fixed)\n", - " off_diagonal_norm[1,i+1] = dbi.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(off_diagonal_norm[0,:], label='Trained')\n", - "plt.plot(off_diagonal_norm[1,:], label='Untrained')\n", - "plt.legend()\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'parMS' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mparMS\u001b[49m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'parMS' is not defined" - ] - } - ], - "source": [ - "print(parMS)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A solution can be to redo the training at each step, with a $D$ having ascending values" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m off_diagonal_norm[\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m dbi\u001b[38;5;241m.\u001b[39moff_diagonal_norm\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(flows):\n\u001b[0;32m---> 15\u001b[0m d_trained, loss, grad, diags \u001b[38;5;241m=\u001b[39m \u001b[43mgradient_descent_dbr_d_ansatz\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdbi_trained\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterations\u001b[49m\u001b[43m,\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m s \u001b[38;5;241m=\u001b[39m dbi_trained\u001b[38;5;241m.\u001b[39mchoose_step(scheduling\u001b[38;5;241m=\u001b[39mDoubleBracketScheduling\u001b[38;5;241m.\u001b[39mpolynomial_approximation, d\u001b[38;5;241m=\u001b[39md_trained, n\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m 17\u001b[0m dbi_trained(s,d\u001b[38;5;241m=\u001b[39md_trained)\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils_gradients.py:261\u001b[0m, in \u001b[0;36mgradient_descent_dbr_d_ansatz\u001b[0;34m(dbi_object, params, nmb_iterations, lr, normalize)\u001b[0m\n\u001b[1;32m 259\u001b[0m grad \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((nmb_iterations, \u001b[38;5;28mlen\u001b[39m(params)))\n\u001b[1;32m 260\u001b[0m dbi_new \u001b[38;5;241m=\u001b[39m deepcopy(dbi_object)\n\u001b[0;32m--> 261\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[43mdbi_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoose_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 262\u001b[0m dbi_new(s, d\u001b[38;5;241m=\u001b[39md)\n\u001b[1;32m 263\u001b[0m loss[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m dbi_new\u001b[38;5;241m.\u001b[39mloss(\u001b[38;5;241m0.0\u001b[39m, d)\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/double_bracket.py:183\u001b[0m, in \u001b[0;36mDoubleBracketIteration.choose_step\u001b[0;34m(self, d, scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m scheduling \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 182\u001b[0m scheduling \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscheduling\n\u001b[0;32m--> 183\u001b[0m step \u001b[38;5;241m=\u001b[39m \u001b[43mscheduling\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 185\u001b[0m step \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m scheduling \u001b[38;5;129;01mis\u001b[39;00m DoubleBracketScheduling\u001b[38;5;241m.\u001b[39mpolynomial_approximation\n\u001b[1;32m 187\u001b[0m ):\n\u001b[1;32m 188\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m3\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils_scheduling.py:37\u001b[0m, in \u001b[0;36mgrid_search_step\u001b[0;34m(dbi_object, step_min, step_max, num_evals, space, d)\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 35\u001b[0m d \u001b[38;5;241m=\u001b[39m dbi_object\u001b[38;5;241m.\u001b[39mdiagonal_h_matrix\n\u001b[0;32m---> 37\u001b[0m loss_list \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\u001b[43mdbi_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mspace\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 39\u001b[0m idx_max_loss \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmin(loss_list)\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m space[idx_max_loss]\n", - "File \u001b[0;32m~/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils_scheduling.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 35\u001b[0m d \u001b[38;5;241m=\u001b[39m dbi_object\u001b[38;5;241m.\u001b[39mdiagonal_h_matrix\n\u001b[0;32m---> 37\u001b[0m loss_list \u001b[38;5;241m=\u001b[39m [dbi_object\u001b[38;5;241m.\u001b[39mloss(step, d\u001b[38;5;241m=\u001b[39md) \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m space]\n\u001b[1;32m 39\u001b[0m idx_max_loss \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmin(loss_list)\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m space[idx_max_loss]\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "cost = DoubleBracketCostFunction.least_squares\n", - "nqubits = 5\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=3.0)\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "params = np.linspace(1,2**nqubits,2**nqubits)\n", - "d_fixed = np.diag(params)\n", - "dbi_trained = deepcopy(dbi)\n", - "flows = 50\n", - "iterations = 20\n", - "off_diagonal_norm = np.empty((2,flows+1))\n", - "off_diagonal_norm[0,0] = dbi_trained.off_diagonal_norm\n", - "off_diagonal_norm[1,0] = dbi.off_diagonal_norm\n", - "\n", - "for i in range(flows):\n", - " d_trained, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi_trained, params, iterations,step)\n", - " s = dbi_trained.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_trained, n=3)\n", - " dbi_trained(s,d=d_trained)\n", - " off_diagonal_norm[0,i+1] = dbi_trained.off_diagonal_norm\n", - " s = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d_fixed, n=3)\n", - " dbi(s,d=d_fixed)\n", - " off_diagonal_norm[1,i+1] = dbi.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(off_diagonal_norm[0,:], label='Trained')\n", - "plt.plot(off_diagonal_norm[1,:], label='Untrained')\n", - "plt.legend()\n", - "plt.xlabel('Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1-local ansatz\n", - "\n", - "We can consider, as an alternative to the a fully parametrized diagonal, a diagonal matrix of the form: $D = \\sum \\alpha_i Z_i$. This has the advantage of having a linear number of parameters to optimize instead of an exponential as well as being easier to implement in a quantum computer " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.9|INFO|2024-05-27 12:16:28]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 5\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", - "\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'd_ansatz_type' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[20], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m dbi_eval \u001b[38;5;241m=\u001b[39m deepcopy(dbi)\n\u001b[1;32m 2\u001b[0m params \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mnqubits,\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mnqubits)\n\u001b[0;32m----> 3\u001b[0m d_opt, loss_opt, grad_opt, diags_opt \u001b[38;5;241m=\u001b[39m gradient_descent_dbr_d_ansatz(dbi, params, \u001b[38;5;241m100\u001b[39m,lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-2\u001b[39m, d_type \u001b[38;5;241m=\u001b[39m \u001b[43md_ansatz_type\u001b[49m\u001b[38;5;241m.\u001b[39melement_wise)\n\u001b[1;32m 4\u001b[0m flows \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m50\u001b[39m\n\u001b[1;32m 5\u001b[0m off_diagonal_norm \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty((flows\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m))\n", - "\u001b[0;31mNameError\u001b[0m: name 'd_ansatz_type' is not defined" - ] - } - ], - "source": [ - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,2**nqubits,2**nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,lr=1e-2, d_type = d_ansatz_type.element_wise)\n", - "flows = 50\n", - "off_diagonal_norm = np.empty((flows+1,2))\n", - "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", - "\n", - "\n", - "\n", - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, lr=1e-3)\n", - "best = np.argmin(loss_opt)\n", - "d_opt = d_ansatz(diags_opt[:,best], d_ansatz_type.local_1)\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", - "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-05-23 10:35:55]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 5\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", - "\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:53]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:54]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.7|WARNING|2024-05-23 10:41:55]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - } - ], - "source": [ - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,2**nqubits,2**nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 100,lr=1e-2, d_type = d_ansatz_type.element_wise)\n", - "flows = 30\n", - "off_diagonal_norm = np.empty((flows+1,3))\n", - "off_diagonal_norm[0,:] = dbi_eval.off_diagonal_norm\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,0] = dbi_eval.off_diagonal_norm\n", - "\n", - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, lr=1e-1, d_type = d_ansatz_type.local_1)\n", - "\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=3)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,1] = dbi_eval.off_diagonal_norm\n", - "\n", - "dbi_eval = deepcopy(dbi)\n", - "params = np.linspace(1,nqubits,nqubits)\n", - "d_opt, loss_opt, grad_opt, diags_opt = gradient_descent_dbr_d_ansatz(dbi, params, 30, lr=1e-1, d_type = d_ansatz_type.local_1,normalize=True)\n", - "\n", - "for i in range(flows):\n", - " step_poly = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d_opt,n=5)\n", - " dbi_eval(step_poly,d=d_opt)\n", - " off_diagonal_norm[i+1,2] = dbi_eval.off_diagonal_norm\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9999999999999999\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(off_diagonal_norm[:,0],label='element-wise ansatz')\n", - "plt.plot(off_diagonal_norm[:,1],label='1-local ansatz')\n", - "plt.plot(off_diagonal_norm[:,2],label='1-local ansatz normalized')\n", - "plt.xlabel('Flows Iterations')\n", - "plt.ylabel(r'$||\\sigma(H_k)||$')\n", - "plt.legend()\n", - "\n", - "print(np.linalg.norm(d_opt))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/dbi/dbi_gradient_descent_strategies.ipynb b/examples/dbi/dbi_gradient_descent_strategies.ipynb index e69de29bb2..1b0c85f998 100644 --- a/examples/dbi/dbi_gradient_descent_strategies.ipynb +++ b/examples/dbi/dbi_gradient_descent_strategies.ipynb @@ -0,0 +1,584 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Double-bracket Gradient Descent Stratgies\n", + "This notebook demonstrates the gradient descent strategies for double-bracket rotations. The mehods uses a numerical method to find the gradient of the cost function with respect to the diagonal operator, and thereby variate the diagonal operator of the rotation. \n", + "\n", + "Finding the gradient requires the parameterization of the diagonal operator, and there are two ways of doing so:\n", + "\n", + "1. Pauli-basis: $D(B,J)= \\sum B_i Z_i + \\sum J_{ij}Z_iZ_j + ...$\n", + "2. Computational-basis: $D(A)=\\sum A_i|i\\rangle\\langle i|$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qibo.models.dbi.double_bracket import *\n", + "from qibo.models.dbi.utils import *\n", + "from qibo.models.dbi.utils_dbr_strategies import *\n", + "import numpy as np\n", + "from qibo import set_backend, hamiltonians\n", + "from qibo.hamiltonians import Hamiltonian\n", + "from qibo.quantum_info import random_hermitian\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_matrix(matrix, title=\"\"):\n", + " \"\"\"Visualize hamiltonian in a heatmap form.\"\"\"\n", + " fig, ax = plt.subplots(figsize=(5,5))\n", + " ax.set_title(title)\n", + " try:\n", + " im = ax.imshow(np.absolute(matrix), cmap=\"inferno\")\n", + " except TypeError:\n", + " im = ax.imshow(np.absolute(matrix.get()), cmap=\"inferno\")\n", + " fig.colorbar(im, ax=ax)\n", + " \n", + "def s_hist_to_plot(s_hist):\n", + " # convert list of step durations taken to plotable\n", + " s_plot = [0] * len(s_hist)\n", + " for i in range(len(s_hist)):\n", + " if i != 0:\n", + " s_plot[i] = s_plot[i-1] + s_hist[i]\n", + " return s_plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", + "[Qibo 0.2.9|INFO|2024-05-29 14:29:00]: Using qibojit (numba) backend on /CPU:0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# set the qibo backend (we suggest qibojit if N >= 20)\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", + "\n", + "# hamiltonian parameters\n", + "nqubits = 5\n", + "seed = 10\n", + "\n", + "# define the hamiltonian\n", + "h0 = random_hermitian(2**nqubits, seed=seed)\n", + "dbi = DoubleBracketIteration(\n", + " Hamiltonian(nqubits, h0),\n", + " mode=DoubleBracketGeneratorType.single_commutator,\n", + " scheduling=DoubleBracketScheduling.hyperopt,\n", + " cost=DoubleBracketCostFunction.off_diagonal_norm\n", + ")\n", + "# vosualize the matrix\n", + "visualize_matrix(dbi.h.matrix, title=\"Target hamiltonian\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we set up the required parameters for gradient descent." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:00]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/qibo/models/dbi/utils.py:199: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " d[i, i] = params[i]\n", + "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/matplotlib/cbook.py:1699: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/Users/pethidine/anaconda3/envs/DBF_qibo/lib/python3.11/site-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Diagonal entries of $D$')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pauli-basis\n", + "pauli_operator_dict = generate_pauli_operator_dict(nqubits)\n", + "pauli_operators = list(pauli_operator_dict.values())\n", + "# let initial d be approximation of $\\Delta(H)\n", + "d_coef_pauli = decompose_into_Pauli_basis(dbi.diagonal_h_matrix, pauli_operators=pauli_operators)\n", + "d_pauli = sum([d_coef_pauli[i]*pauli_operators[i] for i in range(nqubits)])\n", + "\n", + "# Computational basis\n", + "d_coef_computational_partial = d_pauli.diagonal()\n", + "d_coef_computational_full = dbi.diagonal_h_matrix.diagonal()\n", + "d_computational_partial = params_to_diagonal_operator(d_coef_computational_partial, nqubits, ParameterizationTypes.computational, normalize=False)\n", + "d_computational_full = params_to_diagonal_operator(d_coef_computational_full, nqubits, ParameterizationTypes.computational, normalize=False)\n", + "\n", + "plt.plot(d_coef_computational_partial, label=\"computational basis partial\")\n", + "plt.plot(d_coef_computational_full, label=r\"computational basis full = $\\Delta(H)$\")\n", + "plt.legend()\n", + "plt.title(r\"Diagonal entries of $D$\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we want to compare 3 scenarios:\n", + "\n", + "1. Pauli-basis: an approximation to the diagonal of $H$\n", + "2. Computational-partial: same as 1. in the computational basis.\n", + "3. Computational-full: a full parameterization of the diagonal of $H$ in the computational basis." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 570.99trial/s, best loss: 26.906664993108247]\n", + "100%|██████████| 500/500 [00:00<00:00, 700.81trial/s, best loss: 23.489257519402862]\n", + "100%|██████████| 500/500 [00:00<00:00, 705.85trial/s, best loss: 21.094227541896494]\n", + "100%|██████████| 500/500 [00:00<00:00, 705.02trial/s, best loss: 19.33380142135813] \n", + "100%|██████████| 500/500 [00:00<00:00, 657.01trial/s, best loss: 17.977032581523204] \n", + "100%|██████████| 500/500 [00:00<00:00, 602.12trial/s, best loss: 16.373177595540515] \n", + "100%|██████████| 500/500 [00:00<00:00, 674.72trial/s, best loss: 14.925653820146836]\n", + "100%|██████████| 500/500 [00:00<00:00, 662.72trial/s, best loss: 12.912897622711718]\n", + "100%|██████████| 500/500 [00:00<00:00, 660.56trial/s, best loss: 12.514475426864015] \n", + "100%|██████████| 500/500 [00:00<00:00, 720.46trial/s, best loss: 12.44000682097378] \n", + "100%|██████████| 500/500 [00:00<00:00, 701.00trial/s, best loss: 12.440033389264197] \n" + ] + } + ], + "source": [ + "# 1. Pauli-basis\n", + "NSTEPS = 10\n", + "dbi_pauli = deepcopy(dbi)\n", + "loss_hist_pauli, d_params_hist_pauli, s_hist_pauli = gradient_descent(dbi_pauli, NSTEPS, d_coef_pauli, ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 710.99trial/s, best loss: 26.906655788205427]\n", + "100%|██████████| 500/500 [00:00<00:00, 725.45trial/s, best loss: 23.490268107888358]\n", + "100%|██████████| 500/500 [00:00<00:00, 724.81trial/s, best loss: 21.096052191341453] \n", + "100%|██████████| 500/500 [00:00<00:00, 720.58trial/s, best loss: 19.33498357293481] \n", + "100%|██████████| 500/500 [00:00<00:00, 722.72trial/s, best loss: 17.977568815901808] \n", + "100%|██████████| 500/500 [00:00<00:00, 667.28trial/s, best loss: 16.371755460293873] \n", + "100%|██████████| 500/500 [00:00<00:00, 701.65trial/s, best loss: 14.692610243390526] \n", + "100%|██████████| 500/500 [00:00<00:00, 730.81trial/s, best loss: 12.985130163156208] \n", + "100%|██████████| 500/500 [00:00<00:00, 695.22trial/s, best loss: 12.100934480664536]\n", + "100%|██████████| 500/500 [00:00<00:00, 721.98trial/s, best loss: 11.482898432709703] \n", + "100%|██████████| 500/500 [00:00<00:00, 726.24trial/s, best loss: 11.029509543631354] \n" + ] + } + ], + "source": [ + "# 2. Computational_partial\n", + "dbi_computational_partial = deepcopy(dbi)\n", + "loss_hist_computational_partial, d_params_hist_computational_partiali, s_computational_partial = gradient_descent(dbi_computational_partial, NSTEPS, d_coef_computational_partial, ParameterizationTypes.computational)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 702.50trial/s, best loss: 27.838444538508995] \n", + "100%|██████████| 500/500 [00:00<00:00, 690.44trial/s, best loss: 25.579015587983847]\n", + "100%|██████████| 500/500 [00:00<00:00, 704.04trial/s, best loss: 23.90844228569623] \n", + "100%|██████████| 500/500 [00:00<00:00, 701.86trial/s, best loss: 22.552092601189525] \n", + "100%|██████████| 500/500 [00:00<00:00, 706.05trial/s, best loss: 21.386869995117152] \n", + "100%|██████████| 500/500 [00:00<00:00, 705.59trial/s, best loss: 20.359395779612964] \n", + "100%|██████████| 500/500 [00:00<00:00, 709.18trial/s, best loss: 19.444580872817262]\n", + "100%|██████████| 500/500 [00:00<00:00, 704.70trial/s, best loss: 18.37809002428693] \n", + "100%|██████████| 500/500 [00:00<00:00, 712.57trial/s, best loss: 17.70349134937017] \n", + "100%|██████████| 500/500 [00:00<00:00, 702.90trial/s, best loss: 17.652332629754763] \n", + "100%|██████████| 500/500 [00:00<00:00, 701.69trial/s, best loss: 17.469857523504825] \n" + ] + } + ], + "source": [ + "# 3. Computational_full\n", + "dbi_computational_full = deepcopy(dbi)\n", + "loss_hist_computational_full, d_params_hist_computational_full, s_computational_full = gradient_descent(dbi_computational_full, NSTEPS, d_coef_computational_full, ParameterizationTypes.computational)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "s_plot_pauli = s_hist_to_plot(s_hist_pauli)\n", + "s_plot_computational_partial = s_hist_to_plot(s_computational_partial)\n", + "s_plot_computational_full = s_hist_to_plot(s_computational_full)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 's')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(s_plot_pauli, loss_hist_pauli, label=\"pauli basis\", marker=\"o\")\n", + "plt.plot(s_plot_computational_partial, loss_hist_computational_partial, label=\"computational partial\", marker=\"o\")\n", + "plt.plot(s_plot_computational_full, loss_hist_computational_full, label=\"computational full\", marker=\"o\")\n", + "plt.legend()\n", + "plt.title(\"Off-diagonal norm\")\n", + "plt.ylabel(r\"$||\\sigma(H)||_{HS}$\")\n", + "plt.xlabel(\"s\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TFIM" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# hamiltonian parameters\n", + "nqubits = 5\n", + "h = 3\n", + "\n", + "# define the hamiltonian\n", + "h = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", + "dbi = DoubleBracketIteration(\n", + " h,\n", + " mode=DoubleBracketGeneratorType.single_commutator,\n", + " scheduling=DoubleBracketScheduling.hyperopt\n", + ")\n", + "# vosualize the matrix\n", + "visualize_matrix(dbi.h.matrix, title=\"Target hamiltonian\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:28]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:28]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:28]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:28]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", + "[Qibo 0.2.9|WARNING|2024-05-29 14:29:28]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Diagonal entries of $D$')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pauli-basis\n", + "pauli_operator_dict = generate_pauli_operator_dict(nqubits)\n", + "pauli_operators = list(pauli_operator_dict.values())\n", + "# let initial d be approximation of $\\Delta(H)\n", + "d_coef_pauli = decompose_into_Pauli_basis(dbi.diagonal_h_matrix, pauli_operators=pauli_operators)\n", + "d_pauli = sum([d_coef_pauli[i]*pauli_operators[i] for i in range(nqubits)])\n", + "\n", + "# Computational basis\n", + "d_coef_computational_partial = d_pauli.diagonal()\n", + "d_coef_computational_full = dbi.diagonal_h_matrix.diagonal()\n", + "d_computational_partial = params_to_diagonal_operator(d_coef_computational_partial, nqubits, ParameterizationTypes.computational, normalize=False)\n", + "d_computational_full = params_to_diagonal_operator(d_coef_computational_full, nqubits, ParameterizationTypes.computational, normalize=False)\n", + "\n", + "plt.plot(d_coef_computational_partial, label=\"computational basis partial\")\n", + "plt.plot(d_coef_computational_full, label=r\"computational basis full = $\\Delta(H)$\")\n", + "plt.legend()\n", + "plt.title(r\"Diagonal entries of $D$\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 803.83trial/s, best loss: 37.94733192202055] \n", + "100%|██████████| 500/500 [00:00<00:00, 718.60trial/s, best loss: 37.947328135606355]\n", + "100%|██████████| 500/500 [00:00<00:00, 696.04trial/s, best loss: 37.947034927870035] \n", + "100%|██████████| 500/500 [00:00<00:00, 720.87trial/s, best loss: 37.927758880824186]\n", + "100%|██████████| 500/500 [00:00<00:00, 708.30trial/s, best loss: 36.67349688043592]\n", + "100%|██████████| 500/500 [00:00<00:00, 696.00trial/s, best loss: 9.887520412306662] \n", + "100%|██████████| 500/500 [00:00<00:00, 725.68trial/s, best loss: 9.34253046150839] \n", + "100%|██████████| 500/500 [00:00<00:00, 732.22trial/s, best loss: 9.155570991449105]\n", + "100%|██████████| 500/500 [00:00<00:00, 734.02trial/s, best loss: 9.067154775772158] \n", + "100%|██████████| 500/500 [00:00<00:00, 722.82trial/s, best loss: 9.01536672581518] \n", + "100%|██████████| 500/500 [00:00<00:00, 719.34trial/s, best loss: 8.985880617807844] \n" + ] + } + ], + "source": [ + "# 1. Pauli-basis\n", + "NSTEPS = 10\n", + "dbi_pauli = deepcopy(dbi)\n", + "loss_hist_pauli, d_params_hist_pauli, s_hist_pauli = gradient_descent(dbi_pauli, NSTEPS, d_coef_pauli, ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 903.25trial/s, best loss: 37.94733192202055] \n", + "100%|██████████| 500/500 [00:00<00:00, 710.92trial/s, best loss: 30.64847251905183] \n", + "100%|██████████| 500/500 [00:00<00:00, 672.44trial/s, best loss: 22.722609811512278]\n", + "100%|██████████| 500/500 [00:00<00:00, 730.71trial/s, best loss: 21.19327758708126] \n", + "100%|██████████| 500/500 [00:00<00:00, 733.08trial/s, best loss: 21.155063245560406] \n", + "100%|██████████| 500/500 [00:00<00:00, 737.41trial/s, best loss: 21.153041529923296]\n", + "100%|██████████| 500/500 [00:00<00:00, 740.87trial/s, best loss: 21.15294355245017] \n", + "100%|██████████| 500/500 [00:00<00:00, 677.28trial/s, best loss: 21.152912772989247] \n", + "100%|██████████| 500/500 [00:00<00:00, 731.33trial/s, best loss: 21.152900347763875] \n", + "100%|██████████| 500/500 [00:00<00:00, 730.03trial/s, best loss: 21.152893142404263] \n", + "100%|██████████| 500/500 [00:00<00:00, 733.76trial/s, best loss: 21.152877390520732] \n" + ] + } + ], + "source": [ + "# 2. Computational_partial\n", + "dbi_computational_partial = deepcopy(dbi)\n", + "loss_hist_computational_partial, d_params_hist_computational_partiali, s_computational_partial = gradient_descent(dbi_computational_partial, NSTEPS, d_coef_computational_partial, ParameterizationTypes.computational)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 656.58trial/s, best loss: 28.781756562894074]\n", + "100%|██████████| 500/500 [00:00<00:00, 682.82trial/s, best loss: 21.820184081596256]\n", + "100%|██████████| 500/500 [00:00<00:00, 692.43trial/s, best loss: 21.19535573601377] \n", + "100%|██████████| 500/500 [00:00<00:00, 696.91trial/s, best loss: 21.155339526233693] \n", + "100%|██████████| 500/500 [00:00<00:00, 704.70trial/s, best loss: 21.153049106552544] \n", + "100%|██████████| 500/500 [00:00<00:00, 717.26trial/s, best loss: 21.152903699974576] \n", + "100%|██████████| 500/500 [00:00<00:00, 716.45trial/s, best loss: 21.152894543661077]\n", + "100%|██████████| 500/500 [00:00<00:00, 710.07trial/s, best loss: 21.152894136149204] \n", + "100%|██████████| 500/500 [00:00<00:00, 704.73trial/s, best loss: 21.152893951162607] \n", + "100%|██████████| 500/500 [00:00<00:00, 701.07trial/s, best loss: 21.152893887259548] \n", + "100%|██████████| 500/500 [00:00<00:00, 713.48trial/s, best loss: 21.15289385103726] \n" + ] + } + ], + "source": [ + "# 3. Computational_full\n", + "dbi_computational_full = deepcopy(dbi)\n", + "loss_hist_computational_full, d_params_hist_computational_full, s_computational_full = gradient_descent(dbi_computational_full, NSTEPS, d_coef_computational_full, ParameterizationTypes.computational)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "s_plot_pauli = s_hist_to_plot(s_hist_pauli)\n", + "s_plot_computational_partial = s_hist_to_plot(s_computational_partial)\n", + "s_plot_computational_full = s_hist_to_plot(s_computational_full)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 's')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(s_plot_pauli, loss_hist_pauli, label=\"pauli basis\", marker=\"o\")\n", + "plt.plot(s_plot_computational_partial, loss_hist_computational_partial, label=\"computational partial\", marker=\"o\")\n", + "plt.plot(s_plot_computational_full, loss_hist_computational_full, label=\"computational full\", marker=\"o\")\n", + "plt.legend()\n", + "plt.title(\"Off-diagonal norm\")\n", + "plt.ylabel(r\"$||\\sigma(H)||_{HS}$\")\n", + "plt.xlabel(\"s\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After changing the cost function and scheduling method, we notice that quite consistently, the Pauli-based parameterization diagonalizes the hamiltonian the best, and for the first few iterations, the Computational-based partial (same initial operator as Pauli) performs very similarly, and diverges later on." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "DBF_qibo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dbi/dbi_tutorial_basic_intro.ipynb b/examples/dbi/dbi_tutorial_basic_intro.ipynb index ecb28bb4d7..36bf8816bd 100644 --- a/examples/dbi/dbi_tutorial_basic_intro.ipynb +++ b/examples/dbi/dbi_tutorial_basic_intro.ipynb @@ -41,7 +41,7 @@ "from hyperopt import hp, tpe\n", "\n", "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketIteration" + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketIteration, DoubleBracketScheduling" ] }, { @@ -112,7 +112,7 @@ "outputs": [], "source": [ "# set the qibo backend (we suggest qibojit if N >= 20)\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -375,13 +375,13 @@ "dbf.h = dbf.h0\n", "\n", "# optimization of the step, we allow to search in [1e-5, 1]\n", - "step = dbf.hyperopt_step(\n", + "step = dbf.choose_step(\n", + " scheduling=DoubleBracketScheduling.hyperopt,\n", " step_min = 1e-5,\n", " step_max = 1,\n", " space = hp.uniform,\n", " optimizer = tpe,\n", " max_evals = 1000,\n", - " verbose = True\n", ")" ] }, @@ -466,25 +466,24 @@ "outputs": [], "source": [ "# restart\n", - "dbf_2 = DoubleBracketIteration(hamiltonian=deepcopy(h), mode=iterationtype)\n", + "dbf_2 = DoubleBracketIteration(hamiltonian=deepcopy(h), mode=iterationtype, scheduling=DoubleBracketScheduling.hyperopt)\n", "off_diagonal_norm_history = [dbf_2.off_diagonal_norm]\n", "\n", "# set the number of evolution steps\n", "NSTEPS = 20\n", "\n", "# optimize first step\n", - "step = dbf_2.hyperopt_step(\n", + "step = dbf_2.choose_step(\n", " step_min = 1e-5,\n", " step_max = 1,\n", " space = hp.uniform,\n", " optimizer = tpe,\n", " max_evals = 500,\n", - " verbose = True\n", ")\n", "\n", "for s in range(NSTEPS):\n", " if s != 0:\n", - " step = dbf_2.hyperopt_step(\n", + " step = dbf_2.choose_step(\n", " step_min = 1e-5,\n", " step_max = 1,\n", " space = hp.uniform,\n", diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 38e04820ae..6fa07ecbd2 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -5,11 +5,7 @@ import numpy as np from qibo.hamiltonians import Hamiltonian -from qibo.models.dbi.utils_gradients import ( - energy_fluctuation_polynomial_expansion_coef, - least_squares_polynomial_expansion_coef, - off_diagonal_norm_polynomial_expansion_coef, -) +from qibo.models.dbi.utils_gradients import * from qibo.models.dbi.utils_scheduling import ( grid_search_step, hyperopt_step, @@ -117,7 +113,7 @@ def __call__( d = self.diagonal_h_matrix operator = self.backend.calculate_matrix_exp( 1.0j * step, - self.commutator(d, self.h.matrix), + self.commutator(self.backend.cast(d), self.h.matrix), ) elif mode is DoubleBracketGeneratorType.group_commutator: if d is None: diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index fd28ff2141..7e2ee98d37 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -171,8 +171,8 @@ class ParameterizationTypes(Enum): pauli = auto() """Uses Pauli-Z operators (magnetic field).""" - element = auto() - """Uses diagonal entries.""" + computational = auto() + """Uses computational basis.""" def params_to_diagonal_operator( @@ -193,12 +193,12 @@ def params_to_diagonal_operator( d = sum( [params[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)] ) - elif parameterization is ParameterizationTypes.element: + elif parameterization is ParameterizationTypes.computational: d = np.zeros((len(params), len(params))) for i in range(len(params)): d[i, i] = params[i] - if normalize: - d = d / np.linalg.norm(d) else: raise ValueError(f"Parameterization type not recognized.") + if normalize: + d = d / np.linalg.norm(d) return d diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_dbr_strategies.py similarity index 81% rename from src/qibo/models/dbi/utils_strategies.py rename to src/qibo/models/dbi/utils_dbr_strategies.py index 42c6beb9db..f9f05c8911 100644 --- a/src/qibo/models/dbi/utils_strategies.py +++ b/src/qibo/models/dbi/utils_dbr_strategies.py @@ -2,8 +2,6 @@ from qibo.models.dbi.double_bracket import * from qibo.models.dbi.utils import * -from qibo.models.dbi.utils_gradients import * -from qibo.models.dbi.utils_scheduling import polynomial_step def select_best_dbr_generator( @@ -73,51 +71,6 @@ def select_best_dbr_generator( return dbi_eval, idx_max_loss, step_optimal, flip -def gradient_pauli_analytical( - dbi_object, - d: np.array, - pauli_operator_dict: dict, - use_ds=False, - n=3, -): - r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients - Args: - dbi_object (DoubleBracketIteration): the target dbi object - d (np.array): the diagonal operator - n_taylor (int): the highest order of the taylore expansion of w.r.t `s` - onsite_Z_ops (list): list of Pauli-Z operators - taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` - use_ds (boolean): if False, ds is set to 0 - """ - # n is the highest order for calculating s - - # pauli_index is the list of positions \mu - pauli_operators = list(pauli_operator_dict.values()) - num_paul = len(pauli_operators) - grad = np.zeros(num_paul) - coef = dbi_object.cost_expansion(d, n=n) - s = polynomial_step(dbi_object, n=4, d=d) - a, b, c = coef[len(coef) - 3 :] - - for i, operator in enumerate(pauli_operators): - da, db, dc, ds = ds_di_pauli( - dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] - ) - if use_ds is True: - ds = 0 - grad[i] = ( - s**3 / 3 * da - + s**2 / 2 * db - + 2 * s * dc - + s**2 * ds * a - + s * ds * b - + 2 * ds * c - ) - grad = np.array(grad) - grad = grad / np.linalg.norm(grad) - return grad, s - - def gradient_numerical( dbi_object: DoubleBracketIteration, d_params: list, diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 3e0f88874c..15f67e2267 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -1,5 +1,4 @@ import math -from copy import deepcopy from typing import Optional import numpy as np @@ -201,90 +200,3 @@ def covariance(a, b): ) coef = list(reversed(coef)) return coef - - -def gradient_diagonal_entries(dbi_object, params, delta=1e-4): - r""" - Gradient of the DBI with respect to the parametrization of D. A simple finite difference is used to calculate the gradient. - - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). - h(np.array): Hamiltonian. - d_type(d_ansatz_type): Ansatz used for the D operator. Options are 'Full' and '1-local'. - delta(float): Step size for numerical gradient. - Returns: - grad(np.array): Gradient of the D operator. - """ - - grad = np.zeros(len(params)) - d = params_to_diagonal_operator( - params, dbi_object.nqubits, parameterization=ParameterizationTypes.element - ) - for i in range(len(params)): - params_new = deepcopy(params) - params_new[i] += delta - d_new = params_to_diagonal_operator( - params, dbi_object.nqubits, parameterization=ParameterizationTypes.element - ) - grad[i] = (dbi_object.loss(0.0, d_new) - dbi_object.loss(0.0, d)) / delta - return grad - - -def gradient_descent_dbr_d_ansatz( - dbi_object, - params, - nmb_iterations, - lr=1e-2, - normalize=True, -): - r""" - Optimizes the D operator using gradient descent evaluated at the at the rotaion angle found using the polynomial expansion. - - Declare variables - - Calculate initial loss - - Iterate, learning at each the optimal D and measure loss - - Return values - Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - params(np.array): Initial parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). - nmb_iterations(int): Number of gradient descent iterations. - lr(float): Learning rate. - d_type(d_ansatz_type): Ansatz used for the D operator. - normalize(bool): If True, the D operator is normalized at each iteration. - Returns: - d(np.array): Optimized D operator. - loss(np.array): Loss function evaluated at each iteration. - grad(np.array): Gradient evaluated at each iteration. - params_hist(np.array): Parameters evaluated at each iteration. - """ - d = params_to_diagonal_operator( - params, - dbi_object.h.nqubits, - parameterization=ParameterizationTypes.element, - normalize=normalize, - ) - loss = np.zeros(nmb_iterations + 1) - grad = np.zeros((nmb_iterations, len(params))) - dbi_eval = deepcopy(dbi_object) - s = dbi_eval.choose_step(d=d) - dbi_eval(s, d=d) - loss[0] = dbi_eval.loss(0.0, d) - params_hist = np.empty((len(params), nmb_iterations + 1)) - params_hist[:, 0] = params - - for i in range(nmb_iterations): - dbi_eval = deepcopy(dbi_object) - grad[i, :] = gradient_diagonal_entries(dbi_eval, params) - for j in range(len(params)): - params[j] = params[j] - lr * grad[i, j] - d = params_to_diagonal_operator( - params, - dbi_object.h.nqubits, - parameterization=ParameterizationTypes.element, - normalize=normalize, - ) - s = dbi_eval.choose_step(d=d) - loss[i + 1] = dbi_eval.loss(s, d=d) - params_hist[:, i + 1] = params - - return d, loss, grad, params_hist diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index b74347099a..d067366c90 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -204,7 +204,7 @@ def simulated_annealing_step( current_loss = candidate_loss # Cool down temp *= cooling_rate - if temp < min_temp: + if temp < min_temp or current_s > step_max or current_s < step_min: break return current_s diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index daa9d0f14e..13545df2de 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -12,12 +12,11 @@ DoubleBracketScheduling, ) from qibo.models.dbi.utils import * -from qibo.models.dbi.utils_gradients import gradient_descent_dbr_d_ansatz -from qibo.models.dbi.utils_scheduling import polynomial_step -from qibo.models.dbi.utils_strategies import ( - gradient_descent_pauli, +from qibo.models.dbi.utils_dbr_strategies import ( + gradient_descent, select_best_dbr_generator, ) +from qibo.models.dbi.utils_scheduling import polynomial_step from qibo.quantum_info import random_hermitian NSTEPS = 1 @@ -185,19 +184,17 @@ def test_least_squares(backend): @pytest.mark.parametrize("nqubits", [2, 3]) def test_select_best_dbr_generator(backend, nqubits): - scheduling = DoubleBracketScheduling.grid_search h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.single_commutator, - scheduling=scheduling, ) initial_off_diagonal_norm = dbi.off_diagonal_norm generate_local_Z = generate_Z_operators(nqubits) Z_ops = list(generate_local_Z.values()) for _ in range(NSTEPS): dbi, idx, step, flip_sign = select_best_dbr_generator( - dbi, Z_ops, scheduling=scheduling, compare_canonical=True + dbi, Z_ops, compare_canonical=True ) assert dbi.off_diagonal_norm < initial_off_diagonal_norm @@ -208,57 +205,65 @@ def test_params_to_diagonal_operator(backend): nqubits, parameterization_order=1 ) params = [1, 2, 3] - operator_pauli = [ - params[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits) - ] + operator_pauli = sum( + [params[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)] + ) assert ( operator_pauli == params_to_diagonal_operator( - params, nqubits=nqubits, parameterization=ParameterizationTypes.pauli + params, + nqubits=nqubits, + parameterization=ParameterizationTypes.pauli, + pauli_operator_dict=pauli_operator_dict, ) ).all() operator_element = params_to_diagonal_operator( - params, nqubits=nqubits, parameterization=ParameterizationTypes.element + params, nqubits=nqubits, parameterization=ParameterizationTypes.computational ) - assert (operator_element.diag() == params).all() + assert (operator_element.diagonal() == params).all() -@pytest.mark.parametrize("nqubits", [2, 3]) -def test_gradient_descent_pauli(backend, nqubits): - scheduling = DoubleBracketScheduling.grid_search - h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) +@pytest.mark.parametrize("nqubits", [3, 4]) +def test_gradient_descent(backend, nqubits): + h0 = random_hermitian(2**nqubits, seed=seed) dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), + Hamiltonian(nqubits, h0), mode=DoubleBracketGeneratorType.single_commutator, - scheduling=scheduling, + scheduling=DoubleBracketScheduling.hyperopt, + cost=DoubleBracketCostFunction.off_diagonal_norm, ) initial_off_diagonal_norm = dbi.off_diagonal_norm pauli_operator_dict = generate_pauli_operator_dict( - nqubits=nqubits, parameterization_order=2 + nqubits, parameterization_order=1 ) - d_coef = decompose_into_Pauli_basis( - dbi.h.matrix, list(pauli_operator_dict.values()) + pauli_operators = list(pauli_operator_dict.values()) + # let initial d be approximation of $\Delta(H) + d_coef_pauli = decompose_into_Pauli_basis( + dbi.diagonal_h_matrix, pauli_operators=pauli_operators ) - d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) - step, d_coef, d = gradient_descent_pauli(dbi, d_coef, d) - dbi(d=d, step=step) - assert dbi.off_diagonal_norm < initial_off_diagonal_norm - - -@pytest.mark.parametrize("nqubits", [2, 3]) -def test_gradient_descent_d_ansatz(backend, nqubits): - scheduling = DoubleBracketScheduling.polynomial_approximation - cost = DoubleBracketCostFunction.least_squares - h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.single_commutator, - cost=cost, - scheduling=scheduling, + d_pauli = sum([d_coef_pauli[i] * pauli_operators[i] for i in range(nqubits)]) + loss_hist_pauli, d_params_hist_pauli, s_hist_pauli = gradient_descent( + dbi, + NSTEPS, + d_coef_pauli, + ParameterizationTypes.pauli, + pauli_operator_dict=pauli_operator_dict, ) - params = np.linspace(1, 2**nqubits, 2**nqubits) - step = 1e-1 - - d, loss, grad, diags = gradient_descent_dbr_d_ansatz(dbi, params, 25, step) - - assert loss[-1] < loss[0] + assert loss_hist_pauli[-1] < initial_off_diagonal_norm + + # computational basis + d_coef_computational_partial = d_pauli.diagonal() + d_computational_partial = params_to_diagonal_operator( + d_coef_computational_partial, + nqubits, + ParameterizationTypes.computational, + normalize=False, + ) + ( + loss_hist_computational_partial, + d_params_hist_computational_partiali, + s_computational_partial, + ) = gradient_descent( + dbi, NSTEPS, d_coef_computational_partial, ParameterizationTypes.computational + ) + assert loss_hist_computational_partial[-1] < loss_hist_pauli[-1] From 98f432023ed7a3dee5acc7b74cc5b615f600e481 Mon Sep 17 00:00:00 2001 From: MatteoRobbiati Date: Wed, 29 May 2024 09:37:38 +0200 Subject: [PATCH 093/154] fix: check callback is not none in sgd --- src/qibo/optimizers.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/qibo/optimizers.py b/src/qibo/optimizers.py index 8898353085..de79070b7a 100644 --- a/src/qibo/optimizers.py +++ b/src/qibo/optimizers.py @@ -243,6 +243,7 @@ def sgd( - ``'nmessage'`` (int, default: ``1e3``): Every how many epochs to print a message of the loss function. """ + if not backend.name == "tensorflow": raise_error(RuntimeError, "SGD optimizer requires Tensorflow backend.") @@ -274,7 +275,8 @@ def opt_step(): for e in range(sgd_options["nepochs"]): l = opt_step() - callback(vparams) + if callback is not None: + callback(vparams) if e % sgd_options["nmessage"] == 1: log.info("ite %d : loss %f", e, l.numpy()) From fb1076d8b4c7be0aff09f582d18fe1cd57e37436 Mon Sep 17 00:00:00 2001 From: MatteoRobbiati Date: Wed, 29 May 2024 10:47:15 +0200 Subject: [PATCH 094/154] test: adding callback test in minimize --- tests/test_models_variational.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/tests/test_models_variational.py b/tests/test_models_variational.py index 54c731aabd..775e211a4a 100644 --- a/tests/test_models_variational.py +++ b/tests/test_models_variational.py @@ -128,8 +128,16 @@ def test_vqe(backend, method, options, compile, filename): np.random.seed(0) initial_parameters = np.random.uniform(0, 2 * np.pi, 2 * nqubits * layers + nqubits) v = models.VQE(circuit, hamiltonian) + + def callback(parameters): + pass + best, params, _ = v.minimize( - initial_parameters, method=method, options=options, compile=compile + initial_parameters, + method=method, + options=options, + compile=compile, + callback=callback, ) if method == "cma": # remove `outcmaes` folder From 4c88e607f92399f84ccbd2063f4fc1d9570f2000 Mon Sep 17 00:00:00 2001 From: Andrea Date: Wed, 29 May 2024 15:20:42 +0400 Subject: [PATCH 095/154] fix: Fix energy fluctuations --- src/qibo/models/dbi/double_bracket.py | 12 +++--------- tests/test_models_dbi.py | 11 +++++------ 2 files changed, 8 insertions(+), 15 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 3800584a43..8b82fb391b 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -129,7 +129,7 @@ def __call__( @ self.backend.calculate_matrix_exp(step, d) ) operator_dagger = self.backend.cast( - np.matrix(self.backend.to_numpy(operator)).getH() + np.array(np.matrix(self.backend.to_numpy(operator)).getH()) ) self.h.matrix = operator @ self.h.matrix @ operator_dagger @@ -165,7 +165,7 @@ def backend(self): def least_squares(self, d: np.array): """Least squares cost function.""" - h_np = self.h.matrix + h_np = self.backend.to_numpy(self.h.matrix) return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h_np @ d)) @@ -232,13 +232,7 @@ def energy_fluctuation(self, state): Args: state (np.ndarray): quantum state to be used to compute the energy fluctuation with H. """ - h_np = self.backend.cast(np.diag(np.diag(self.backend.to_numpy(self.h.matrix)))) - h2 = h_np @ h_np - state_cast = self.backend.cast(state) - state_conj = self.backend.cast(state.conj()) - a = state_conj @ h2 @ state_cast - b = state_conj @ h_np @ state_cast - return (np.sqrt(np.real(a - b**2))).item() + return self.h.energy_fluctuation(state) def sigma(self, h: np.array): return self.backend.cast(h) - self.backend.cast( diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 637dd2c9f7..fe1720a3b1 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -125,14 +125,13 @@ def test_polynomial_cost_function(backend, cost): assert initial_off_diagonal_norm > dbi.off_diagonal_norm -def test_polynomial_energy_fluctuation(): - set_backend("numpy") +def test_polynomial_energy_fluctuation(backend): nqubits = 4 - h0 = random_hermitian(2**nqubits, seed=seed) + h0 = random_hermitian(2**nqubits, seed=seed, backend=backend) state = np.zeros(2**nqubits) - state[3] = 1 + state[0] = 1 dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0), + Hamiltonian(nqubits, h0, backend=backend), mode=DoubleBracketGeneratorType.single_commutator, cost=DoubleBracketCostFunction.energy_fluctuation, scheduling=DoubleBracketScheduling.polynomial_approximation, @@ -141,7 +140,7 @@ def test_polynomial_energy_fluctuation(): for i in range(NSTEPS): s = dbi.choose_step(d=dbi.diagonal_h_matrix, n=5) dbi(step=s, d=dbi.off_diag_h) - assert dbi.energy_fluctuation(state=state) == 0.0 + assert dbi.energy_fluctuation(state=state) < dbi.h0.energy_fluctuation(state=state) @pytest.mark.parametrize("nqubits", [5, 6]) From 7022262f9eec6f20393e9cea9bd1daeec8d0ab8e Mon Sep 17 00:00:00 2001 From: Andrea Date: Wed, 29 May 2024 19:30:49 +0400 Subject: [PATCH 096/154] tests: Code improvements and tests --- src/qibo/models/dbi/double_bracket.py | 11 ++-- src/qibo/models/dbi/utils.py | 22 +++++--- src/qibo/models/dbi/utils_gradients.py | 71 +++++++++++++------------ src/qibo/models/dbi/utils_strategies.py | 23 +++++--- tests/test_models_dbi.py | 40 +++++++------- 5 files changed, 93 insertions(+), 74 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 8b82fb391b..fbdeabdf1f 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -165,9 +165,8 @@ def backend(self): def least_squares(self, d: np.array): """Least squares cost function.""" - h_np = self.backend.to_numpy(self.h.matrix) - return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(h_np @ d)) + return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(self.h.matrix @ d)) def choose_step( self, @@ -239,13 +238,13 @@ def sigma(self, h: np.array): np.diag(np.diag(self.backend.to_numpy(h))) ) - def generate_Gamma_list(self, n: int, d: np.array): + def generate_gamma_list(self, n: int, d: np.array): r"""Computes the n-nested Gamma functions, where $\Gamma_k=[W,...,[W,[W,H]]...]$, where we take k nested commutators with $W = [D, H]$""" W = self.commutator(self.backend.cast(d), self.sigma(self.h.matrix)) - Gamma_list = [self.h.matrix] + gamma_list = [self.h.matrix] for _ in range(n - 1): - Gamma_list.append(self.commutator(W, Gamma_list[-1])) - return Gamma_list + gamma_list.append(self.commutator(W, gamma_list[-1])) + return gamma_list def cost_expansion(self, d, n): if self.cost is DoubleBracketCostFunction.off_diagonal_norm: diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 83d377c86d..a2ae5fc31e 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -92,6 +92,7 @@ def str_to_symbolic(name: str): def cs_angle_sgn(dbi_object, d): """Calculates the sign of Cauchy-Schwarz Angle :math:`\\langle W(Z), W({\\rm canonical}) \\rangle_{\\rm HS}`.""" + d = dbi_object.backend.cast(d) norm = np.trace( np.dot( np.conjugate( @@ -103,7 +104,7 @@ def cs_angle_sgn(dbi_object, d): return np.sign(norm) -def decompose_into_Pauli_basis(h_matrix: np.array, pauli_operators: list): +def decompose_into_pauli_basis(h_matrix: np.array, pauli_operators: list): """finds the decomposition of hamiltonian `h_matrix` into Pauli-Z operators""" nqubits = int(np.log2(h_matrix.shape[0])) @@ -130,15 +131,20 @@ def generate_pauli_index(nqubits, order): def generate_pauli_operator_dict( - nqubits: int, parameterization_order: int = 1, symbols_pauli=symbols.Z + nqubits: int, + parameterization_order: int = 1, + symbols_pauli=symbols.Z, + backend=None, ): """ Generate a dictionary containing all possible products of a given Pauli operators (X,Y or Z) of a given order (e.g. 1 corresponds to a magnetic field) for L = n_qubits and their respective names. """ + backend = _check_backend(backend) pauli_index = generate_pauli_index(nqubits, order=parameterization_order) pauli_operators = [ - generate_Pauli_operators(nqubits, symbols_pauli, index) for index in pauli_index + generate_pauli_operators(nqubits, symbols_pauli, index, backend=backend) + for index in pauli_index ] return {index: operator for index, operator in zip(pauli_index, pauli_operators)} @@ -154,15 +160,19 @@ def diagonal_min_max(matrix: np.array): return D -def generate_Pauli_operators(nqubits, symbols_pauli, positions): +def generate_pauli_operators(nqubits, symbols_pauli, positions, backend=None): # generate matrix of an nqubit-pauli operator with `symbols_pauli` at `positions` if isinstance(positions, int): return SymbolicHamiltonian( - symbols_pauli(positions), nqubits=nqubits + symbols_pauli(positions), + nqubits=nqubits, + backend=backend, ).dense.matrix else: terms = [symbols_pauli(pos) for pos in positions] - return SymbolicHamiltonian(math.prod(terms), nqubits=nqubits).dense.matrix + return SymbolicHamiltonian( + math.prod(terms), nqubits=nqubits, backend=backend + ).dense.matrix def element_wise_d(params: np.array, normalization: bool = False): diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 54e7b64e74..15595b441a 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -9,7 +9,7 @@ from qibo.models.dbi.utils import * -def dGamma_di_pauli(dbi_object, n: int, Z_i: np.array, d: np.array): +def d_gamma_di_pauli(dbi_object, n: int, Z_i: np.array, d: np.array): """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. Args: @@ -19,23 +19,24 @@ def dGamma_di_pauli(dbi_object, n: int, Z_i: np.array, d: np.array): d (np.array): the diagonal operator Returns: - (list): [dGamma_0_di, dGamma_1_di, ..., dGamma_n_di] + (list): [d_gamma_0_di, d_gamma_1_di, ..., d_gamma_n_di] """ nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) - dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) - Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) + d_gamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) + gamma_list = dbi_object.generate_gamma_list(n=n + 2, d=d) W = dbi_object.commutator(dbi_object.backend.cast(d), dbi_object.h.matrix) + dW_di = dbi_object.commutator(dbi_object.backend.cast(Z_i), dbi_object.h.matrix) for k in range(n + 1): if k == 0: continue elif k == 1: - dGamma_di[k] = dW_di + d_gamma_di[k] = dW_di else: - dGamma_di[k] = dbi_object.commutator( - dW_di, Gamma_list[k - 1] - ) + dbi_object.commutator(W, dGamma_di[k - 1]) - return dGamma_di + d_gamma_di[k] = dbi_object.commutator( + dW_di, gamma_list[k - 1] + ) + dbi_object.commutator(W, d_gamma_di[k - 1]) + return d_gamma_di def ds_di_pauli( @@ -55,15 +56,15 @@ def ds_di_pauli( floats da, db, dc, ds """ # generate the list of derivatives w.r.t ith Z operator coefficient - dGamma_di = dGamma_di_pauli(dbi_object, n=4, Z_i=Z_i, d=d) - Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) + d_gamma_di = d_gamma_di_pauli(dbi_object, n=4, Z_i=Z_i, d=d) + gamma_list = dbi_object.generate_gamma_list(n=4, d=d) def derivative_product(k1, k2): r"""Calculate the derivative of a product $\sigma(\Gamma(n1,i))@\sigma(\Gamma(n2,i))""" - return dbi_object.sigma(dGamma_di[k1]) @ dbi_object.sigma( - Gamma_list[k2] - ) + dbi_object.sigma(dbi_object.sigma(Gamma_list[k1])) @ dbi_object.sigma( - dGamma_di[k2] + return dbi_object.sigma(d_gamma_di[k1]) @ dbi_object.sigma( + gamma_list[k2] + ) + dbi_object.sigma(dbi_object.sigma(gamma_list[k1])) @ dbi_object.sigma( + d_gamma_di[k2] ) # calculate the derivatives of s polynomial coefficients @@ -83,7 +84,7 @@ def derivative_product(k1, k2): return da, db, dc, ds -def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): +def d_gamma_d_diagonal(d, h, n, i, d_gamma, gamma_list): r""" Gradient of the nth gamma operator with respect to the ith diagonal elements of D. $Gamma_{n} = [W,[W,...,[W,H]]...]]$, @@ -94,7 +95,7 @@ def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): h(np.array): Hamiltonian. n(int): nth Gamma operator. i(int): Index of the diagonal element of D. - dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). + d_gamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). gamma_list(list): List of the n gamma operators. Returns: (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. @@ -103,24 +104,24 @@ def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): dD_di[i, i] = 1 dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) w = commutator(d, h) - return dW_di + commutator(w, dGamma[-1]) + return dW_di + commutator(w, d_gamma[-1]) -def dpolynomial_diDiagonal(dbi_object, s, d, H, i): +def d_polynomial_d_diagonal(dbi_object, s, d, H, i): # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation derivative = 0 A = np.zeros(d.shape) - Gamma_list = dbi_object.generate_Gamma_list(4, d) + gamma_list = dbi_object.generate_gamma_list(4, d) A[i, i] = 1 - dGamma = [commutator(A, H)] + d_gamma = [commutator(A, H)] derivative += np.real( - np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s + np.trace(gamma_list[0] @ A) + np.trace(d_gamma[0] @ d + gamma_list[1] @ A) * s ) for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) + d_gamma.append(d_gamma_d_diagonal(d, H, n, i, d_gamma, gamma_list)) derivative += np.real( - np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) + np.trace(d_gamma[-1] @ d + gamma_list[n] @ A) * s**n / math.factorial(n) ) return derivative @@ -131,12 +132,12 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): W = dbi_object.commutator( dbi_object.backend.cast(d), dbi_object.sigma(dbi_object.h.matrix) ) - Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) - sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) + gamma_list = dbi_object.generate_gamma_list(n + 2, d) + sigma_gamma_list = list(map(dbi_object.sigma, gamma_list)) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients for rotation with [W,H] and H - c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] - c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] + c1 = exp_list.reshape((-1, 1, 1)) * sigma_gamma_list[1:] + c2 = exp_list.reshape((-1, 1, 1)) * sigma_gamma_list[:-1] # product coefficient trace_coefficients = [0] * (2 * n + 1) for k in range(n + 1): @@ -151,13 +152,13 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): def least_squares_polynomial_expansion_coef(dbi_object, d, n: int = 3): # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + gamma_list = dbi_object.generate_gamma_list(n + 1, d) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients coef = np.empty(n) for i in range(n): coef[i] = np.real( - exp_list[i] * np.trace(dbi_object.backend.cast(d) @ Gamma_list[i + 1]) + exp_list[i] * np.trace(dbi_object.backend.cast(d) @ gamma_list[i + 1]) ) coef = list(reversed(coef)) return coef @@ -169,7 +170,7 @@ def energy_fluctuation_polynomial_expansion_coef( if d is None: d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + gamma_list = dbi_object.generate_gamma_list(n + 1, d) # coefficients coef = np.empty(3) state_cast = dbi_object.backend.cast(state) @@ -193,11 +194,11 @@ def covariance(a, b): - 2 * state_dag @ a @ state_cast * state_dag @ b @ state_cast ) - coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1])) - coef[1] = np.real(2 * variance(Gamma_list[1])) + coef[0] = np.real(2 * covariance(gamma_list[0], gamma_list[1])) + coef[1] = np.real(2 * variance(gamma_list[1])) coef[2] = np.real( - covariance(Gamma_list[0], Gamma_list[3]) - + 3 * covariance(Gamma_list[1], Gamma_list[2]) + covariance(gamma_list[0], gamma_list[3]) + + 3 * covariance(gamma_list[1], gamma_list[2]) ) coef = list(reversed(coef)) return coef diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_strategies.py index 136711553c..7ba30de792 100644 --- a/src/qibo/models/dbi/utils_strategies.py +++ b/src/qibo/models/dbi/utils_strategies.py @@ -1,5 +1,6 @@ import hyperopt +from qibo.backends import _check_backend from qibo.models.dbi.double_bracket import * from qibo.models.dbi.utils import * from qibo.models.dbi.utils_gradients import * @@ -73,12 +74,13 @@ def select_best_dbr_generator( return dbi_eval, idx_max_loss, step_optimal, flip -def gradient_Pauli( +def gradient_pauli( dbi_object, d: np.array, pauli_operator_dict: dict, use_ds=False, n=3, + backend=None, **kwargs, ): r"""Calculate the gradient of loss function with respect to onsite Pauli-Z coefficients @@ -90,6 +92,7 @@ def gradient_Pauli( taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}` use_ds (boolean): if False, ds is set to 0 """ + backend = _check_backend(backend) # n is the highest order for calculating s # pauli_index is the list of positions \mu @@ -100,10 +103,9 @@ def gradient_Pauli( s = polynomial_step(dbi_object, n=5, d=d) a, b, c = coef[len(coef) - 3 :] - for i, operator in enumerate(pauli_operators): da, db, dc, ds = ds_di_pauli( - dbi_object, d=d, Z_i=operator, taylor_coef=[a, b, c] + dbi_object, d=d, Z_i=backend.cast(operator), taylor_coef=[a, b, c] ) if use_ds is True: ds = 0 @@ -134,6 +136,7 @@ def gradient_descent_pauli( optimizer: callable = None, verbose: bool = False, use_ds: bool = True, + backend=None, ): """calculate the elements of one gradient descent step on `dbi_object`. @@ -155,14 +158,22 @@ def gradient_descent_pauli( the optimal step found, coeffcients of `d` in Pauli-Z basis, matrix of `d` """ + backend = _check_backend(backend) nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) if pauli_operator_dict is None: pauli_operator_dict = generate_pauli_operator_dict( - nqubits, parameterization_order + nqubits, + parameterization_order, + backend=backend, ) - grad, s = gradient_Pauli( - dbi_object, d, n=n, pauli_operator_dict=pauli_operator_dict, use_ds=use_ds + grad, s = gradient_pauli( + dbi_object, + d, + n=n, + pauli_operator_dict=pauli_operator_dict, + use_ds=use_ds, + backend=backend, ) # optimize gradient descent step with hyperopt if space is None: diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index fe1720a3b1..358eaf7dac 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -20,7 +20,7 @@ ) from qibo.quantum_info import random_hermitian -NSTEPS = 1 +NSTEPS = 3 seed = 10 """Number of steps for evolution.""" @@ -79,7 +79,7 @@ def test_double_bracket_iteration_single_commutator(backend, nqubits): assert initial_off_diagonal_norm > dbi.off_diagonal_norm -@pytest.mark.parametrize("nqubits", [3, 4]) +@pytest.mark.parametrize("nqubits", [2, 3]) @pytest.mark.parametrize( "scheduling", [ @@ -110,7 +110,7 @@ def test_variational_scheduling(backend, nqubits, scheduling): ], ) def test_polynomial_cost_function(backend, cost): - nqubits = 4 + nqubits = 2 h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), @@ -139,7 +139,7 @@ def test_polynomial_energy_fluctuation(backend): ) for i in range(NSTEPS): s = dbi.choose_step(d=dbi.diagonal_h_matrix, n=5) - dbi(step=s, d=dbi.off_diag_h) + dbi(step=s, d=dbi.diagonal_h_matrix) assert dbi.energy_fluctuation(state=state) < dbi.h0.energy_fluctuation(state=state) @@ -156,17 +156,6 @@ def test_polynomial_fail_cases(backend, nqubits): assert polynomial_step(dbi, n=1) == None -def test_energy_fluctuations(backend): - """Check energy fluctuation cost function.""" - nqubits = 3 - h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - dbi = DoubleBracketIteration(Hamiltonian(nqubits, h0, backend=backend)) - # define the state - state = np.zeros(2**nqubits) - state[3] = 1 - assert dbi.energy_fluctuation(state=state) < 1e-5 - - def test_least_squares(backend): """Check least squares cost function.""" nqubits = 4 @@ -192,11 +181,14 @@ def test_select_best_dbr_generator(backend, nqubits): scheduling=scheduling, ) initial_off_diagonal_norm = dbi.off_diagonal_norm - generate_local_Z = generate_Z_operators(nqubits) + generate_local_Z = generate_Z_operators(nqubits, backend=backend) Z_ops = list(generate_local_Z.values()) for _ in range(NSTEPS): dbi, idx, step, flip_sign = select_best_dbr_generator( - dbi, Z_ops, scheduling=scheduling, compare_canonical=True + dbi, + Z_ops, + scheduling=scheduling, + compare_canonical=True, ) assert dbi.off_diagonal_norm < initial_off_diagonal_norm @@ -212,13 +204,19 @@ def test_gradient_descent_pauli(backend, nqubits): ) initial_off_diagonal_norm = dbi.off_diagonal_norm pauli_operator_dict = generate_pauli_operator_dict( - nqubits=nqubits, parameterization_order=2 + nqubits=nqubits, + parameterization_order=2, + backend=backend, ) - d_coef = decompose_into_Pauli_basis( - dbi.h.matrix, list(pauli_operator_dict.values()) + + d_coef = decompose_into_pauli_basis( + dbi.h.matrix, + list( + pauli_operator_dict.values(), + ), ) d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)]) - step, d_coef, d = gradient_descent_pauli(dbi, d_coef, d) + step, d_coef, d = gradient_descent_pauli(dbi, d_coef, d, backend=backend) dbi(d=d, step=step) assert dbi.off_diagonal_norm < initial_off_diagonal_norm From 30cbe29f355dd4a7708f079bcde0d9110823e7d8 Mon Sep 17 00:00:00 2001 From: Andrea Date: Wed, 29 May 2024 20:34:53 +0400 Subject: [PATCH 097/154] feat: Tests passing on GPU --- src/qibo/models/dbi/double_bracket.py | 9 +++++++-- src/qibo/models/dbi/utils.py | 2 +- src/qibo/models/dbi/utils_gradients.py | 5 +++-- src/qibo/models/dbi/utils_strategies.py | 6 +++--- 4 files changed, 14 insertions(+), 8 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index fbdeabdf1f..d7ef9f5199 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -165,8 +165,11 @@ def backend(self): def least_squares(self, d: np.array): """Least squares cost function.""" - - return np.real(0.5 * np.linalg.norm(d) ** 2 - np.trace(self.h.matrix @ d)) + d = self.backend.to_numpy(d) + return np.real( + 0.5 * np.linalg.norm(d) ** 2 + - np.trace(self.backend.to_numpy(self.h.matrix) @ d) + ) def choose_step( self, @@ -247,6 +250,8 @@ def generate_gamma_list(self, n: int, d: np.array): return gamma_list def cost_expansion(self, d, n): + d = self.backend.cast(d) + if self.cost is DoubleBracketCostFunction.off_diagonal_norm: coef = off_diagonal_norm_polynomial_expansion_coef(self, d, n) elif self.cost is DoubleBracketCostFunction.least_squares: diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index a2ae5fc31e..2ffdaed1f3 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -101,7 +101,7 @@ def cs_angle_sgn(dbi_object, d): dbi_object.commutator(d, dbi_object.h.matrix), ) ) - return np.sign(norm) + return np.real(np.sign(norm)) def decompose_into_pauli_basis(h_matrix: np.array, pauli_operators: list): diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py index 15595b441a..f7ac9093af 100644 --- a/src/qibo/models/dbi/utils_gradients.py +++ b/src/qibo/models/dbi/utils_gradients.py @@ -134,10 +134,11 @@ def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): ) gamma_list = dbi_object.generate_gamma_list(n + 2, d) sigma_gamma_list = list(map(dbi_object.sigma, gamma_list)) + gamma_list_np = list(map(dbi_object.backend.to_numpy, sigma_gamma_list)) exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) # coefficients for rotation with [W,H] and H - c1 = exp_list.reshape((-1, 1, 1)) * sigma_gamma_list[1:] - c2 = exp_list.reshape((-1, 1, 1)) * sigma_gamma_list[:-1] + c1 = exp_list.reshape((-1, 1, 1)) * gamma_list_np[1:] + c2 = exp_list.reshape((-1, 1, 1)) * gamma_list_np[:-1] # product coefficient trace_coefficients = [0] * (2 * n + 1) for k in range(n + 1): diff --git a/src/qibo/models/dbi/utils_strategies.py b/src/qibo/models/dbi/utils_strategies.py index 7ba30de792..2ef405456c 100644 --- a/src/qibo/models/dbi/utils_strategies.py +++ b/src/qibo/models/dbi/utils_strategies.py @@ -98,7 +98,7 @@ def gradient_pauli( # pauli_index is the list of positions \mu pauli_operators = list(pauli_operator_dict.values()) num_paul = len(pauli_operators) - grad = np.zeros(num_paul) + grad = [] coef = off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n=n) s = polynomial_step(dbi_object, n=5, d=d) @@ -109,7 +109,7 @@ def gradient_pauli( ) if use_ds is True: ds = 0 - grad[i] = ( + grad.append( s**3 / 3 * da + s**2 / 2 * db + 2 * s * dc @@ -117,7 +117,7 @@ def gradient_pauli( + s * ds * b + 2 * ds * c ) - grad = np.array(grad) + grad = backend.to_numpy(grad) grad = grad / np.linalg.norm(grad) return grad, s From 0e4d88820b2fe1f5607af5756d06349bd7104671 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 30 May 2024 11:21:24 +0800 Subject: [PATCH 098/154] Set variational test to TFIM model --- tests/test_models_dbi.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 13545df2de..7bfb22b171 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from qibo import set_backend +from qibo import hamiltonians, set_backend from qibo.hamiltonians import Hamiltonian from qibo.models.dbi.double_bracket import ( DoubleBracketCostFunction, @@ -89,10 +89,11 @@ def test_double_bracket_iteration_single_commutator(backend, nqubits): ) def test_variational_scheduling(backend, nqubits, scheduling): """Check schduling options.""" - h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) - dbi = DoubleBracketIteration( - Hamiltonian(nqubits, h0, backend=backend), scheduling=scheduling - ) + h = 2 + + # define the hamiltonian + h0 = hamiltonians.TFIM(nqubits=nqubits, h=h) + dbi = DoubleBracketIteration(h0, scheduling=scheduling) # find initial best step with look_ahead = 1 initial_off_diagonal_norm = dbi.off_diagonal_norm for _ in range(NSTEPS): From 492202a12d859ac464b0519b0fb32ecfedf826d2 Mon Sep 17 00:00:00 2001 From: Andrea Date: Thu, 30 May 2024 07:35:29 +0400 Subject: [PATCH 099/154] cleanup: Remove unused features --- src/qibo/models/dbi/utils.py | 25 ------------------------- 1 file changed, 25 deletions(-) diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 2ffdaed1f3..41bf22ae32 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -8,31 +8,6 @@ from qibo.hamiltonians import SymbolicHamiltonian -def commutator(a, b): - """Compute commutator between two arrays.""" - return a @ b - b @ a - - -def variance(a, state): - """Calculates the variance of a matrix A with respect to a state: - Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" - b = a @ a - return state.conj().T @ b @ state - (state.conj().T @ a @ state) ** 2 - - -def covariance(a, b, state): - """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, - applied to two operators A and B with respect to a state: - Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ - """ - - c = a @ b + b @ a - return ( - state.conj().T @ c @ state - - 2 * state.conj().T @ a @ state * state.conj().T @ b @ state - ) - - def generate_Z_operators(nqubits: int, backend=None): """Generate a dictionary containing 1) all possible products of Pauli Z operators for L = n_qubits and 2) their respective names. Return: Dictionary with operator names (str) as keys and operators (np.array) as values From 2547b2140a11fd72340536696497a02dd6645e8b Mon Sep 17 00:00:00 2001 From: Andrea Date: Thu, 30 May 2024 10:52:14 +0400 Subject: [PATCH 100/154] feat: Add symblic link to doc --- doc/source/code-examples/tutorials/dbi/README.md | 1 + .../dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb | 1 + .../tutorials/dbi/dbi_gdbi_strategies_compare.ipynb | 1 + .../tutorials/dbi/dbi_gradient_descent_strategies.ipynb | 1 + .../code-examples/tutorials/dbi/dbi_group_commutator_tests.ipynb | 1 + doc/source/code-examples/tutorials/dbi/dbi_scheduling.ipynb | 1 + .../code-examples/tutorials/dbi/dbi_strategy_Ising_model.ipynb | 1 + .../code-examples/tutorials/dbi/dbi_strategy_Pauli-Z.ipynb | 1 + .../code-examples/tutorials/dbi/dbi_tutorial_basic_intro.ipynb | 1 + 9 files changed, 9 insertions(+) create mode 120000 doc/source/code-examples/tutorials/dbi/README.md create mode 120000 doc/source/code-examples/tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb create mode 120000 doc/source/code-examples/tutorials/dbi/dbi_gdbi_strategies_compare.ipynb create mode 120000 doc/source/code-examples/tutorials/dbi/dbi_gradient_descent_strategies.ipynb create mode 120000 doc/source/code-examples/tutorials/dbi/dbi_group_commutator_tests.ipynb create mode 120000 doc/source/code-examples/tutorials/dbi/dbi_scheduling.ipynb create mode 120000 doc/source/code-examples/tutorials/dbi/dbi_strategy_Ising_model.ipynb create mode 120000 doc/source/code-examples/tutorials/dbi/dbi_strategy_Pauli-Z.ipynb create mode 120000 doc/source/code-examples/tutorials/dbi/dbi_tutorial_basic_intro.ipynb diff --git a/doc/source/code-examples/tutorials/dbi/README.md b/doc/source/code-examples/tutorials/dbi/README.md new file mode 120000 index 0000000000..50b9e9eaec --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/README.md @@ -0,0 +1 @@ +../../../../../examples/dbi/README.md \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb new file mode 120000 index 0000000000..d9f0070469 --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb @@ -0,0 +1 @@ +../../../../../examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_gdbi_strategies_compare.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_gdbi_strategies_compare.ipynb new file mode 120000 index 0000000000..78936e1a6b --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/dbi_gdbi_strategies_compare.ipynb @@ -0,0 +1 @@ +../../../../../examples/dbi/dbi_strategies_compare.ipynb \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_gradient_descent_strategies.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_gradient_descent_strategies.ipynb new file mode 120000 index 0000000000..e3fbf989a9 --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/dbi_gradient_descent_strategies.ipynb @@ -0,0 +1 @@ +../../../../../examples/dbi/dbi_gradient_descent_strategies.ipynb \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_group_commutator_tests.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_group_commutator_tests.ipynb new file mode 120000 index 0000000000..174deb7e46 --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/dbi_group_commutator_tests.ipynb @@ -0,0 +1 @@ +../../../../../examples/dbi/dbi_group_commutator_tests.ipynb \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_scheduling.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_scheduling.ipynb new file mode 120000 index 0000000000..79275e8268 --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/dbi_scheduling.ipynb @@ -0,0 +1 @@ +../../../../../examples/dbi/dbi_scheduling.ipynb \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_strategy_Ising_model.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_strategy_Ising_model.ipynb new file mode 120000 index 0000000000..f5ee663fd1 --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/dbi_strategy_Ising_model.ipynb @@ -0,0 +1 @@ +../../../../../examples/dbi/dbi_strategy_Ising_model.ipynb \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_strategy_Pauli-Z.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_strategy_Pauli-Z.ipynb new file mode 120000 index 0000000000..23a3561519 --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/dbi_strategy_Pauli-Z.ipynb @@ -0,0 +1 @@ +../../../../../examples/dbi/dbi_strategy_Pauli-Z.ipynb \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_tutorial_basic_intro.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_tutorial_basic_intro.ipynb new file mode 120000 index 0000000000..79ea4d0ea8 --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/dbi_tutorial_basic_intro.ipynb @@ -0,0 +1 @@ +../../../../../examples/dbi/dbi_tutorial_basic_intro.ipynb \ No newline at end of file From fa1d2c4ea54ec61234fba15eb177e1913b0174df Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 30 May 2024 15:52:53 +0800 Subject: [PATCH 101/154] Added documentation for branch --- src/qibo/models/dbi/double_bracket.py | 29 ++- src/qibo/models/dbi/utils.py | 105 +++++++++- src/qibo/models/dbi/utils_dbr_strategies.py | 109 +++++++++-- src/qibo/models/dbi/utils_gradients.py | 202 -------------------- tests/test_models_dbi.py | 6 - 5 files changed, 209 insertions(+), 242 deletions(-) delete mode 100644 src/qibo/models/dbi/utils_gradients.py diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 6fa07ecbd2..01cfd5326e 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -5,7 +5,7 @@ import numpy as np from qibo.hamiltonians import Hamiltonian -from qibo.models.dbi.utils_gradients import * +from qibo.models.dbi.utils import * from qibo.models.dbi.utils_scheduling import ( grid_search_step, hyperopt_step, @@ -100,6 +100,7 @@ def __init__( def __call__( self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None ): + """Performs one double bracket rotation.""" if mode is None: mode = self.mode @@ -142,11 +143,12 @@ def diagonal_h_matrix(self): @property def off_diag_h(self): + """Off-diagonal H matrix.""" return self.h.matrix - self.diagonal_h_matrix @property def off_diagonal_norm(self): - r"""Hilbert Schmidt norm of off-diagonal part of H matrix, namely :math:`\\text{Tr}(\\sqrt{A^{\\dagger} A})`.""" + """Hilbert Schmidt norm of off-diagonal part of H matrix, namely :math:`\\text{Tr}(\\sqrt{A^{\\dagger} A})`.""" off_diag_h_dag = self.backend.cast( np.matrix(self.backend.to_numpy(self.off_diag_h)).getH() ) @@ -176,9 +178,7 @@ def choose_step( scheduling: Optional[DoubleBracketScheduling] = None, **kwargs, ): - """ - Calculate the optimal step using respective `scheduling` methods. - """ + """Calculate the optimal step using respective the `scheduling` methods.""" if scheduling is None: scheduling = self.scheduling step = scheduling(self, d=d, **kwargs) @@ -198,9 +198,9 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): Compute loss function distance between `look_ahead` steps. Args: - step: iteration step. - d: diagonal operator, use canonical by default. - look_ahead: number of iteration steps to compute the loss function; + step (float): iteration step. + d (np.array): diagonal operator, use canonical by default. + look_ahead (int): number of iteration steps to compute the loss function; """ # copy initial hamiltonian h_copy = deepcopy(self.h) @@ -223,7 +223,7 @@ def loss(self, step: float, d: np.array = None, look_ahead: int = 1): def energy_fluctuation(self, state): """ - Evaluate energy fluctuation + Evaluate energy fluctuation. .. math:: \\Xi(\\mu) = \\sqrt{\\langle\\mu|\\hat{H}^2|\\mu\\rangle - \\langle\\mu|\\hat{H}|\\mu\\rangle^2} \\, @@ -242,12 +242,20 @@ def energy_fluctuation(self, state): return (np.sqrt(np.real(a - b**2))).item() def sigma(self, h: np.array): + """Returns the off-diagonal restriction of matrix `h`.""" return self.backend.cast(h) - self.backend.cast( np.diag(np.diag(self.backend.to_numpy(h))) ) def generate_Gamma_list(self, n: int, d: np.array): - r"""Computes the n-nested Gamma functions, where $\Gamma_k=[W,...,[W,[W,H]]...]$, where we take k nested commutators with $W = [D, H]$""" + """Computes the n-nested Gamma functions. + .. math:: + \\Gamma_k=[W,...,[W,[W,H]]...], + where :math:`W = [D, H]`. + Args: + n (int): number of nested commutators in the expression. + d (np.array): the diagonal operator involved in the DBR. + """ W = self.commutator(self.backend.cast(d), self.sigma(self.h.matrix)) Gamma_list = [self.h.matrix] for _ in range(n - 1): @@ -255,6 +263,7 @@ def generate_Gamma_list(self, n: int, d: np.array): return Gamma_list def cost_expansion(self, d, n): + """The Taylor expansion coefficients up to the `n`th order of the selected cost function with respect to step duration.""" if self.cost is DoubleBracketCostFunction.off_diagonal_norm: coef = off_diagonal_norm_polynomial_expansion_coef(self, d, n) elif self.cost is DoubleBracketCostFunction.least_squares: diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 7e2ee98d37..2aa84d07ab 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -16,7 +16,8 @@ def commutator(a, b): def variance(a, state): """Calculates the variance of a matrix A with respect to a state: - Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" + .. math:: + Var(A) = \\langle\\mu|A^2|\\mu\\rangle-\\langle\\mu|A|\\mu\\rangle^2""" b = a @ a return state.conj().T @ b @ state - (state.conj().T @ a @ state) ** 2 @@ -24,7 +25,8 @@ def variance(a, state): def covariance(a, b, state): """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, applied to two operators A and B with respect to a state: - Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ + .. math:: + Cov(A,B) = \\langle\\mu|AB|\\mu\\rangle-\\langle\\mu|A|\\mu\\rangle\\langle\\mu|B|\\mu\\rangle """ c = a @ b + b @ a @@ -105,7 +107,7 @@ def cs_angle_sgn(dbi_object, d): def decompose_into_Pauli_basis(h_matrix: np.array, pauli_operators: list): - """finds the decomposition of hamiltonian `h_matrix` into Pauli-Z operators""" + """Finds the decomposition of hamiltonian `h_matrix` into Pauli-Z operators""" nqubits = int(np.log2(h_matrix.shape[0])) decomposition = [] @@ -131,11 +133,20 @@ def generate_pauli_index(nqubits, order): def generate_pauli_operator_dict( - nqubits: int, parameterization_order: int = 1, symbols_pauli=symbols.Z + nqubits: int, parameterization_order: int = 1, symbols_pauli: symbols = symbols.Z ): - """ - Generate a dictionary containing all possible products of a given Pauli operators (X,Y or Z) of a given order (e.g. 1 corresponds to a magnetic field) - for L = n_qubits and their respective names. + """Generates a dictionary containing Pauli `symbols_pauli` operators of locality `parameterization_order` for `nqubits` qubits. + + Args: + nqubits (int): number of qubits in the system. + parameterization_order (int, optional): the locality of the operators generated. Defaults to 1. + symbols_pauli (qibo.symbols, optional): the symbol of the intended Pauli operator. Defaults to symbols.Z. + + Returns: + pauli_operator_dict (dictionary): dictionary with structure {"operator_name": operator} + + Example: + pauli_operator_dict = generate_pauli_operator_dict) """ pauli_index = generate_pauli_index(nqubits, order=parameterization_order) pauli_operators = [ @@ -156,7 +167,7 @@ def diagonal_min_max(matrix: np.array): def generate_Pauli_operators(nqubits, symbols_pauli, positions): - # generate matrix of an nqubit-pauli operator with `symbols_pauli` at `positions` + """Generate matrix of an nqubit-pauli operator with `symbols_pauli` at `positions`""" if isinstance(positions, int): return SymbolicHamiltonian( symbols_pauli(positions), nqubits=nqubits @@ -183,7 +194,7 @@ def params_to_diagonal_operator( normalize: bool = False, pauli_operator_dict: dict = None, ): - r"""Creates the $D$ operator for the double-bracket iteration ansatz depending on the parameterization type.""" + """Creates the $D$ operator for the double-bracket iteration ansatz depending on the parameterization type.""" if parameterization is ParameterizationTypes.pauli: # raise error if dimension mismatch if len(params) != len(pauli_operator_dict): @@ -202,3 +213,79 @@ def params_to_diagonal_operator( if normalize: d = d / np.linalg.norm(d) return d + + +def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): + """Return the Taylor expansion coefficients of off-diagonal norm of `dbi_object.h` with respect to double bracket rotation duration `s`.""" + Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) + sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients for rotation with [W,H] and H + c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] + c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] + # product coefficient + trace_coefficients = [0] * (2 * n + 1) + for k in range(n + 1): + for j in range(n + 1): + power = k + j + product_matrix = c1[k] @ c2[j] + trace_coefficients[power] += 2 * np.trace(product_matrix) + # coefficients from high to low (n:0) + coef = list(reversed(trace_coefficients[: n + 1])) + return coef + + +def least_squares_polynomial_expansion_coef(dbi_object, d, n: int = 3): + """Return the Taylor expansion coefficients of least square cost of `dbi_object.h` and diagonal operator `d` with respect to double bracket rotation duration `s`.""" + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) + # coefficients + coef = np.empty(n) + for i in range(n): + coef[i] = np.real( + exp_list[i] * np.trace(dbi_object.backend.cast(d) @ Gamma_list[i + 1]) + ) + coef = list(reversed(coef)) + return coef + + +def energy_fluctuation_polynomial_expansion_coef( + dbi_object, d: np.array = None, n: int = 3, state=0 +): + """Return the Taylor expansion coefficients of energy fluctuation of `dbi_object` with respect to double bracket rotation duration `s`.""" + if d is None: + d = dbi_object.diagonal_h_matrix + # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H + Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) + # coefficients + coef = np.empty(3) + state_cast = dbi_object.backend.cast(state) + state_dag = dbi_object.backend.cast(state.conj().T) + + def variance(a): + """Calculates the variance of a matrix A with respect to a state: + Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" + b = a @ a + return state_dag @ b @ state_cast - (state_dag @ a @ state_cast) ** 2 + + def covariance(a, b): + """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, + applied to two operators A and B with respect to a state: + Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ + """ + + c = a @ b + b @ a + return ( + state_dag @ c @ state_cast + - 2 * state_dag @ a @ state_cast * state_dag @ b @ state_cast + ) + + coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1])) + coef[1] = np.real(2 * variance(Gamma_list[1])) + coef[2] = np.real( + covariance(Gamma_list[0], Gamma_list[3]) + + 3 * covariance(Gamma_list[1], Gamma_list[2]) + ) + coef = list(reversed(coef)) + return coef diff --git a/src/qibo/models/dbi/utils_dbr_strategies.py b/src/qibo/models/dbi/utils_dbr_strategies.py index f9f05c8911..86dcfa578b 100644 --- a/src/qibo/models/dbi/utils_dbr_strategies.py +++ b/src/qibo/models/dbi/utils_dbr_strategies.py @@ -16,14 +16,35 @@ def select_best_dbr_generator( Args: dbi_object (`DoubleBracketIteration`): the target DoubleBracketIteration object. - d_list (list): list of diagonal operators (np.array) to run from. + d_list (list): list of diagonal operators (np.array) to select from. step (float): fixed iteration duration. Defaults to ``None``, optimize with `scheduling` method and `choose_step` function. compare_canonical (boolean): if `True`, the diagonalization effect with operators from `d_list` is compared with the canonical bracket. scheduling (`DoubleBracketScheduling`): scheduling method for finding the optimal step. Returns: - The updated dbi_object, index of the optimal diagonal operator, respective step duration, and evolution direction. + The updated dbi_object (`DoubleBracketIteration`), index of the optimal diagonal operator (int), respective step duration (float), and sign (int). + + Example: + from qibo.hamiltonians import Hamiltonian + from qibo.models.dbi.double_bracket import * + from qibo.models.dbi.utils_dbr_strategies import select_best_dbr_generator + from qibo.quantum_info import random_hermitian + + nqubits = 3 + NSTEPS = 3 + h0 = random_hermitian(2**nqubits) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0), + mode=DoubleBracketGeneratorType.single_commutator, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + generate_local_Z = generate_Z_operators(nqubits) + Z_ops = list(generate_local_Z.values()) + for _ in range(NSTEPS): + dbi, idx, step, flip_sign = select_best_dbr_generator( + dbi, Z_ops, compare_canonical=True + ) """ if scheduling is None: scheduling = dbi_object.scheduling @@ -83,11 +104,12 @@ def gradient_numerical( Gradient of the DBI with respect to the parametrization of D. A simple finite difference is used to calculate the gradient. Args: - dbi_object(DoubleBracketIteration): DoubleBracketIteration object. - d_params(np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). - delta(float): Step size for numerical gradient. + dbi_object (DoubleBracketIteration): DoubleBracketIteration object. + d_params (np.array): Parameters for the ansatz (note that the dimension must be 2**nqubits for full ansazt and nqubits for Pauli ansatz). + s (float): A short flow duration for finding the numerical gradient. + delta (float): Step size for numerical gradient. Returns: - grad(np.array): Gradient of the D operator. + grad (np.array): Gradient of the D operator. """ nqubits = dbi_object.nqubits grad = np.zeros(len(d_params)) @@ -110,18 +132,80 @@ def gradient_descent( iterations: int, d_params: list, parameterization: ParameterizationTypes, - pauli_operator_dict: list = None, + pauli_operator_dict: dict = None, pauli_parameterization_order: int = 1, normalize: bool = False, lr_min: float = 1e-5, lr_max: float = 1, max_evals: int = 100, space: callable = None, - optimizer: callable = None, + optimizer: callable = hyperopt.tpe, verbose: bool = False, ): + r"""Numerical gradient descent method for variating diagonal operator in each double bracket rotation. + + Args: + dbi_object (DoubleBracketIteration): the target double bracket object. + iterations (int): number of double bracket rotations. + d_params (list): the parameters for the initial diagonal operator. + parameterization (ParameterizationTypes): the parameterization method for diagonal operator. + Options include pauli and computational. + pauli_operator_dict (dictionary, optional): dictionary of "name": Pauli-operator for Pauli-based parameterization type. + Defaults to None. + pauli_parameterization_order (int, optional): the order of parameterization or locality in Pauli basis. Defaults to 1. + normalize (bool, optional): option to normalize the diagonal operator. Defaults to False. + lr_min (float, optional): the minimal gradient step. Defaults to 1e-5. + lr_max (float, optional): the maximal gradient step. Defaults to 1. + max_evals (int, optional): maximum number of evaluations for `lr` using `hyperopt`. Defaults to 100. + space (callable, optional): evalutation space for `hyperopt`. Defaults to None. + optimizer (callable, optional): optimizer option for `hyperopt`. Defaults to `hyperopt.tpe`. + verbose (bool, optional): option for printing `hyperopt` process. Defaults to False. + + Returns: + loss_hist (list): list of history losses of `dbi_object` throughout the double bracket rotations. + d_params_hist (list): list of history of `d` parameters after gradient descent. + s_hist (list): list of history of optimal `s` found. + Example: + from qibo import set_backend + from qibo.hamiltonians import Hamiltonian + from qibo.models.dbi.double_bracket import * + from qibo.models.dbi.utils import * + from qibo.models.dbi.utils_dbr_strategies import gradient_descent + from qibo.quantum_info import random_hermitian + + nqubits = 3 + NSTEPS = 5 + set_backend("numpy") + h0 = random_hermitian(2**nqubits) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0), + mode=DoubleBracketGeneratorType.single_commutator, + scheduling=DoubleBracketScheduling.hyperopt, + cost=DoubleBracketCostFunction.off_diagonal_norm, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + pauli_operator_dict = generate_pauli_operator_dict( + nqubits, parameterization_order=1 + ) + pauli_operators = list(pauli_operator_dict.values()) + # let initial d be approximation of $\Delta(H) + d_coef_pauli = decompose_into_Pauli_basis( + dbi.diagonal_h_matrix, pauli_operators=pauli_operators + ) + d_pauli = sum([d_coef_pauli[i] * pauli_operators[i] for i in range(nqubits)]) + loss_hist_pauli, d_params_hist_pauli, s_hist_pauli = gradient_descent( + dbi, + NSTEPS, + d_coef_pauli, + ParameterizationTypes.pauli, + pauli_operator_dict=pauli_operator_dict, + ) + """ nqubits = dbi_object.nqubits - # use polynomial scheduling for analytical solutions + if parameterization is ParameterizationTypes.pauli and pauli_operator_dict is None: + pauli_operator_dict = generate_pauli_operator_dict( + nqubits=nqubits, parameterization_order=pauli_parameterization_order + ) d = params_to_diagonal_operator( d_params, nqubits, @@ -132,10 +216,6 @@ def gradient_descent( loss_hist = [dbi_object.loss(0.0, d=d)] d_params_hist = [d_params] s_hist = [0] - if parameterization is ParameterizationTypes.pauli and pauli_operator_dict is None: - pauli_operator_dict = generate_pauli_operator_dict( - nqubits=nqubits, parameterization_order=pauli_parameterization_order - ) # first step s = dbi_object.choose_step(d=d) dbi_object(step=s, d=d) @@ -163,8 +243,7 @@ def func_loss_to_lr(lr): if space is None: space = hyperopt.hp.loguniform("lr", np.log(lr_min), np.log(lr_max)) - if optimizer is None: - optimizer = hyperopt.tpe + best = hyperopt.fmin( fn=func_loss_to_lr, space=space, diff --git a/src/qibo/models/dbi/utils_gradients.py b/src/qibo/models/dbi/utils_gradients.py deleted file mode 100644 index 15f67e2267..0000000000 --- a/src/qibo/models/dbi/utils_gradients.py +++ /dev/null @@ -1,202 +0,0 @@ -import math -from typing import Optional - -import numpy as np - -from qibo import symbols -from qibo.hamiltonians import SymbolicHamiltonian -from qibo.models.dbi.utils import * - - -def dGamma_di_pauli(dbi_object, n: int, Z_i: np.array, d: np.array): - """Computes the derivatives $\frac{\\partial \\Gamma_n}{\\partial \alpha_i}$ where the diagonal operator $D=\\sum \alpha_i Z_i$. - - Args: - dbi_object (DoubleBracketIteration): the target dbi object - n (int): the number of nested commutators in `Gamma` - i (int/tupple): the index of onsite-Z coefficient - d (np.array): the diagonal operator - - Returns: - (list): [dGamma_0_di, dGamma_1_di, ..., dGamma_n_di] - """ - nqubits = int(np.log2(dbi_object.h.matrix.shape[0])) - dGamma_di = [np.zeros((2**nqubits, 2**nqubits))] * (n + 1) - Gamma_list = dbi_object.generate_Gamma_list(n=n + 2, d=d) - W = dbi_object.commutator(dbi_object.backend.cast(d), dbi_object.h.matrix) - dW_di = dbi_object.commutator(dbi_object.backend.cast(Z_i), dbi_object.h.matrix) - for k in range(n + 1): - if k == 0: - continue - elif k == 1: - dGamma_di[k] = dW_di - else: - dGamma_di[k] = dbi_object.commutator( - dW_di, Gamma_list[k - 1] - ) + dbi_object.commutator(W, dGamma_di[k - 1]) - return dGamma_di - - -def ds_di_pauli( - dbi_object, - d: np.array, - Z_i: np.array, - taylor_coef: Optional[list] = None, -): - r"""Return the derivatives of the first 3 polynomial coefficients with respect to onsite Pauli-Z coefficients\ - Args: - dbi_object (DoubleBracketIteration): the target dbi object - d (np.array): the diagonal operator - i (int): the index of onsite-Z coefficient - taylor_coef (list): coefficients of `s` in the taylor expansion of math:`\\frac{\\partial ||\sigma(e^{sW}He^{-sW})||^2}{\\partial s}`, from the highest order to the lowest. - onsite_Z_ops (list): onsite Z operators of `dbi_object.h` - Returns: - floats da, db, dc, ds - """ - # generate the list of derivatives w.r.t ith Z operator coefficient - dGamma_di = dGamma_di_pauli(dbi_object, n=4, Z_i=Z_i, d=d) - Gamma_list = dbi_object.generate_Gamma_list(n=4, d=d) - - def derivative_product(k1, k2): - r"""Calculate the derivative of a product $\sigma(\Gamma(n1,i))@\sigma(\Gamma(n2,i))""" - return dbi_object.sigma(dGamma_di[k1]) @ dbi_object.sigma( - Gamma_list[k2] - ) + dbi_object.sigma(dbi_object.sigma(Gamma_list[k1])) @ dbi_object.sigma( - dGamma_di[k2] - ) - - # calculate the derivatives of s polynomial coefficients - da = np.trace(3 * derivative_product(1, 2) + 3 * derivative_product(3, 0)) - db = np.trace(2 * derivative_product(1, 1) + 2 * derivative_product(0, 2)) - dc = np.trace(2 * derivative_product(1, 0)) - - ds = 0 - if taylor_coef != None: - a, b, c = taylor_coef[len(taylor_coef) - 3 :] - delta = b**2 - 4 * a * c - ddelta = 2 * (b * db - 2 * (a * dc + da * c)) - - ds = (-db + 0.5 * ddelta / np.sqrt(delta)) * a - (-b + np.sqrt(delta)) * da - ds /= 2 * a**2 - - return da, db, dc, ds - - -def dGamma_diDiagonal(d, h, n, i, dGamma, gamma_list): - r""" - Gradient of the nth gamma operator with respect to the ith diagonal elements of D. - $Gamma_{n} = [W,[W,...,[W,H]]...]]$, - $\frac{\partial Gamma_{n}}{\partial D_{ii}} = \partial_{D_{ii}} W\Gamma_{n-1}-\partial_{D_{ii}}\Gamma_{n-1} W$. - and thus is can be computed recursively. - Args: - d(np.array): D operator. - h(np.array): Hamiltonian. - n(int): nth Gamma operator. - i(int): Index of the diagonal element of D. - dGamma(list): List of the n-1 derivatives of the gamma operators (better to keep them in memory than to calculate at each iteration). - gamma_list(list): List of the n gamma operators. - Returns: - (float): Derivative of the nth gamma operator with respect to the ith diagonal elements of D. - """ - dD_di = np.zeros(d.shape) - dD_di[i, i] = 1 - dW_di = commutator(commutator(dD_di, h), gamma_list[n - 1]) - w = commutator(d, h) - return dW_di + commutator(w, dGamma[-1]) - - -def dpolynomial_diDiagonal(dbi_object, s, d, H, i): - # Derivative of polynomial approximation of potential function with respect to diagonal elements of d (full-diagonal ansatz) - # Formula can be expanded easily to any order, with n=3 corresponding to cubic approximation - derivative = 0 - A = np.zeros(d.shape) - Gamma_list = dbi_object.generate_Gamma_list(4, d) - A[i, i] = 1 - dGamma = [commutator(A, H)] - derivative += np.real( - np.trace(Gamma_list[0] @ A) + np.trace(dGamma[0] @ d + Gamma_list[1] @ A) * s - ) - for n in range(2, 4): - dGamma.append(dGamma_diDiagonal(d, H, n, i, dGamma, Gamma_list)) - derivative += np.real( - np.trace(dGamma[-1] @ d + Gamma_list[n] @ A) * s**n / math.factorial(n) - ) - - return derivative - - -def off_diagonal_norm_polynomial_expansion_coef(dbi_object, d, n): - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - W = dbi_object.commutator( - dbi_object.backend.cast(d), dbi_object.sigma(dbi_object.h.matrix) - ) - Gamma_list = dbi_object.generate_Gamma_list(n + 2, d) - sigma_Gamma_list = list(map(dbi_object.sigma, Gamma_list)) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients for rotation with [W,H] and H - c1 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[1:] - c2 = exp_list.reshape((-1, 1, 1)) * sigma_Gamma_list[:-1] - # product coefficient - trace_coefficients = [0] * (2 * n + 1) - for k in range(n + 1): - for j in range(n + 1): - power = k + j - product_matrix = c1[k] @ c2[j] - trace_coefficients[power] += 2 * np.trace(product_matrix) - # coefficients from high to low (n:0) - coef = list(reversed(trace_coefficients[: n + 1])) - return coef - - -def least_squares_polynomial_expansion_coef(dbi_object, d, n: int = 3): - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - exp_list = np.array([1 / math.factorial(k) for k in range(n + 1)]) - # coefficients - coef = np.empty(n) - for i in range(n): - coef[i] = np.real( - exp_list[i] * np.trace(dbi_object.backend.cast(d) @ Gamma_list[i + 1]) - ) - coef = list(reversed(coef)) - return coef - - -def energy_fluctuation_polynomial_expansion_coef( - dbi_object, d: np.array = None, n: int = 3, state=0 -): - if d is None: - d = dbi_object.diagonal_h_matrix - # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H - Gamma_list = dbi_object.generate_Gamma_list(n + 1, d) - # coefficients - coef = np.empty(3) - state_cast = dbi_object.backend.cast(state) - state_dag = dbi_object.backend.cast(state.conj().T) - - def variance(a): - """Calculates the variance of a matrix A with respect to a state: - Var($A$) = $\\langle\\mu|A^2|\\mu\rangle-\\langle\\mu|A|\\mu\rangle^2$""" - b = a @ a - return state_dag @ b @ state_cast - (state_dag @ a @ state_cast) ** 2 - - def covariance(a, b): - """This is a generalization of the notion of covariance, needed for the polynomial expansion of the energy fluctuation, - applied to two operators A and B with respect to a state: - Cov($A,B$) = $\\langle\\mu|AB|\\mu\rangle-\\langle\\mu|A|\\mu\rangle\\langle\\mu|B|\\mu\rangle$ - """ - - c = a @ b + b @ a - return ( - state_dag @ c @ state_cast - - 2 * state_dag @ a @ state_cast * state_dag @ b @ state_cast - ) - - coef[0] = np.real(2 * covariance(Gamma_list[0], Gamma_list[1])) - coef[1] = np.real(2 * variance(Gamma_list[1])) - coef[2] = np.real( - covariance(Gamma_list[0], Gamma_list[3]) - + 3 * covariance(Gamma_list[1], Gamma_list[2]) - ) - coef = list(reversed(coef)) - return coef diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 7bfb22b171..5d39918d4a 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -254,12 +254,6 @@ def test_gradient_descent(backend, nqubits): # computational basis d_coef_computational_partial = d_pauli.diagonal() - d_computational_partial = params_to_diagonal_operator( - d_coef_computational_partial, - nqubits, - ParameterizationTypes.computational, - normalize=False, - ) ( loss_hist_computational_partial, d_params_hist_computational_partiali, From b3706a2f1bcf86af1fe54fb10e67d6ab55d51ad4 Mon Sep 17 00:00:00 2001 From: Sam-XiaoyueLi Date: Thu, 30 May 2024 16:24:37 +0800 Subject: [PATCH 102/154] Notebooks running --- examples/dbi/dbi_scheduling.ipynb | 3 +- examples/dbi/dbi_strategies_compare.ipynb | 38 +++++++------ examples/dbi/dbi_strategy_Ising_model.ipynb | 62 +++++++-------------- examples/dbi/dbi_strategy_Pauli-Z.ipynb | 28 +++------- 4 files changed, 50 insertions(+), 81 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index a0ac88e6a6..fdb087871b 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -30,8 +30,7 @@ "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_strategies import *" + "from qibo.models.dbi.utils_scheduling import *" ] }, { diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 54d7fe4dff..8595b00c7f 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -27,7 +27,7 @@ "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", "from qibo.models.dbi.utils import *\n", "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_strategies import *" + "from qibo.models.dbi.utils_dbr_strategies import *" ] }, { @@ -133,7 +133,6 @@ "outputs": [], "source": [ "# initialize DBI class for the Pauli-Z strategy\n", - "set_backend(\"pytorch\", platform=\"numba\")\n", "dbi_pauli = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling, cost=cost)" ] }, @@ -197,14 +196,23 @@ "metadata": {}, "outputs": [], "source": [ - "loss_history_gradient = [dbi_gradient.off_diagonal_norm]\n", - "steps_gradient_plot= [0]\n", - "for _ in range(NSTEPS):\n", - " step, d_coef, d = gradient_descent_pauli(dbi_gradient, d_coef, d, pauli_operator_dict=pauli_operator_dict)\n", - " dbi_gradient(d=d,step=step)\n", - " loss_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", - " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", - " steps_gradient_plot.append(steps_gradient_plot[-1]+step)" + "def s_hist_to_plot(s_hist):\n", + " # convert list of step durations taken to plotable\n", + " s_plot = [0] * len(s_hist)\n", + " for i in range(len(s_hist)):\n", + " if i != 0:\n", + " s_plot[i] = s_plot[i-1] + s_hist[i]\n", + " return s_plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loss_history_gradient, d_params_hist, s_hist = gradient_descent(dbi_gradient, NSTEPS, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", + "steps_gradient_plot = s_hist_to_plot(s_hist)" ] }, { @@ -402,14 +410,8 @@ "metadata": {}, "outputs": [], "source": [ - "off_diagonal_norm_history_gradient = [dbi_gradient.off_diagonal_norm]\n", - "steps_gradient_plot= [0]\n", - "for _ in range(NSTEPS):\n", - " step, d_coef, d = gradient_descent_pauli(dbi_gradient, d_coef, d, pauli_operator_dict=pauli_operator_dict)\n", - " dbi_gradient(d=d,step=step)\n", - " off_diagonal_norm_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", - " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", - " steps_gradient_plot.append(steps_gradient_plot[-1]+step)" + "off_diagonal_norm_history_gradient, d_params_hist, s_hist = gradient_descent(dbi_gradient, NSTEPS, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", + "steps_gradient_plot = s_hist_to_plot(s_hist)" ] }, { diff --git a/examples/dbi/dbi_strategy_Ising_model.ipynb b/examples/dbi/dbi_strategy_Ising_model.ipynb index ad3b1b5d73..f69e9eef5b 100644 --- a/examples/dbi/dbi_strategy_Ising_model.ipynb +++ b/examples/dbi/dbi_strategy_Ising_model.ipynb @@ -29,7 +29,7 @@ "from qibo.quantum_info import random_hermitian\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_strategies import *" + "from qibo.models.dbi.utils_dbr_strategies import *" ] }, { @@ -92,10 +92,9 @@ "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=1)\n", "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", - "grad, s = gradient_Pauli(dbi, d=d, pauli_operator_dict=pauli_operator_dict)\n", + "grad = gradient_numerical(dbi, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", "print('The initial D coefficients:', d_coef)\n", - "print('Gradient:', grad)\n", - "print('s:', s)" + "print('Gradient:', grad)" ] }, { @@ -105,13 +104,7 @@ "outputs": [], "source": [ "iters = 15\n", - "off_diagonal_norm_1 = [dbi.off_diagonal_norm]\n", - "s_step = [0]\n", - "for i in range(iters):\n", - " s, d_coef, d = gradient_descent_pauli(dbi, d_coef=d_coef, d=d, pauli_operator_dict=pauli_operator_dict, max_evals=100)\n", - " dbi(step=s, d=d)\n", - " off_diagonal_norm_1.append(dbi.off_diagonal_norm)\n", - " s_step.append(s)" + "off_diagonal_norm_1, d_params_hist, s_step = gradient_descent(dbi, iters, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)" ] }, { @@ -152,10 +145,9 @@ "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", - "grad, s = gradient_Pauli(dbi, d=d, pauli_operator_dict=pauli_operator_dict)\n", + "grad = gradient_numerical(dbi, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", "print('The initial D coefficients:', d_coef)\n", - "print('Gradient:', grad)\n", - "print('s:', s)" + "print('Gradient:', grad)" ] }, { @@ -165,13 +157,7 @@ "outputs": [], "source": [ "iters = 15\n", - "off_diagonal_norm_2 = [dbi.off_diagonal_norm]\n", - "s_step = [0]\n", - "for i in range(iters):\n", - " s, d_coef, d = gradient_descent_pauli(dbi, d_coef=d_coef, d=d, pauli_operator_dict=pauli_operator_dict, max_evals=100)\n", - " dbi(step=s, d=d)\n", - " off_diagonal_norm_2.append(dbi.off_diagonal_norm)\n", - " s_step.append(s)" + "off_diagonal_norm_2, d_params_hist, s_step = gradient_descent(dbi, iters, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)" ] }, { @@ -245,10 +231,9 @@ "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=1)\n", "d_coef = decompose_into_Pauli_basis(dbi_TFIM_1.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", - "grad, s = gradient_Pauli(dbi_TFIM_1, d=d, pauli_operator_dict=pauli_operator_dict)\n", + "grad = gradient_numerical(dbi_TFIM_1, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", "print('The initial D coefficients:', d_coef)\n", - "print('Gradient:', grad)\n", - "print('s:', s)" + "print('Gradient:', grad)" ] }, { @@ -258,13 +243,7 @@ "outputs": [], "source": [ "NSTEPS = 15\n", - "off_diagonal_norm_1 = [dbi_TFIM_1.off_diagonal_norm]\n", - "s_step = [0]\n", - "for i in range(NSTEPS):\n", - " s, d_coef, d = gradient_descent_pauli(dbi_TFIM_1, d_coef=d_coef, d=d, pauli_operator_dict=pauli_operator_dict, max_evals=100)\n", - " dbi_TFIM_1(step=s, d=d)\n", - " off_diagonal_norm_1.append(dbi_TFIM_1.off_diagonal_norm)\n", - " s_step.append(s)" + "off_diagonal_norm_1, d_params_hist, s_step = gradient_descent(dbi_TFIM_1, NSTEPS, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)" ] }, { @@ -307,10 +286,9 @@ "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", "d_coef = decompose_into_Pauli_basis(dbi_TFIM_2.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", - "grad, s = gradient_Pauli(dbi_TFIM_2, d=d, pauli_operator_dict=pauli_operator_dict)\n", + "grad = gradient_numerical(dbi_TFIM_2, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", "print('The initial D coefficients:', d_coef)\n", - "print('Gradient:', grad)\n", - "print('s:', s)" + "print('Gradient:', grad)" ] }, { @@ -319,14 +297,7 @@ "metadata": {}, "outputs": [], "source": [ - "NSTEPS = 15\n", - "off_diagonal_norm_2 = [dbi_TFIM_2.off_diagonal_norm]\n", - "s_step = [0]\n", - "for i in range(NSTEPS):\n", - " s, d_coef, d = gradient_descent_pauli(dbi_TFIM_2, d_coef=d_coef, d=d, pauli_operator_dict=pauli_operator_dict, max_evals=100)\n", - " dbi_TFIM_2(step=s, d=d)\n", - " off_diagonal_norm_2.append(dbi_TFIM_2.off_diagonal_norm)\n", - " s_step.append(s)" + "off_diagonal_norm_2, d_params_hist, s_step = gradient_descent(dbi_TFIM_2, NSTEPS, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)" ] }, { @@ -342,6 +313,13 @@ "plt.xlabel('Iteration')\n", "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In conclusion, we see that the parameterization order or locality of the Pauli based parameterization for gradient descent does not affect significantly the effectiveness of double bracket diagonalization." + ] } ], "metadata": { diff --git a/examples/dbi/dbi_strategy_Pauli-Z.ipynb b/examples/dbi/dbi_strategy_Pauli-Z.ipynb index 2b60e12896..8a557c53e1 100644 --- a/examples/dbi/dbi_strategy_Pauli-Z.ipynb +++ b/examples/dbi/dbi_strategy_Pauli-Z.ipynb @@ -42,7 +42,7 @@ "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketIteration\n", "from qibo.models.dbi.utils import *\n", "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_strategies import *" + "from qibo.models.dbi.utils_dbr_strategies import *" ] }, { @@ -130,7 +130,7 @@ "source": [ "# set the qibo backend (we suggest qibojit if N >= 20)\n", "# alternatives: tensorflow (not optimized), numpy (when CPU not supported by jit)\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -188,14 +188,14 @@ "metadata": {}, "outputs": [], "source": [ - "NSTEPS = 15\n", + "NSTEPS = 8\n", "max_evals = 100\n", "step_max = 1\n", "Z_optimal = []\n", "# add in initial values for plotting\n", "off_diagonal_norm_history = [dbi.off_diagonal_norm]\n", "steps = [0]\n", - "scheduling = DoubleBracketScheduling.use_hyperopt\n", + "scheduling = DoubleBracketScheduling.hyperopt\n", "for _ in range(NSTEPS):\n", " dbi, idx, step, flip_sign = select_best_dbr_generator(dbi, Z_ops, scheduling=scheduling, compare_canonical=False, max_evals=max_evals, step_max=step_max)\n", " off_diagonal_norm_history.append(dbi.off_diagonal_norm)\n", @@ -246,7 +246,7 @@ "source": [ "# set the qibo backend (we suggest qibojit if N >= 20)\n", "# alternatives: tensorflow (not optimized), numpy (when CPU not supported by jit)\n", - "set_backend(\"qibojit\", \"numba\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "\n", "\n", "# initialize class|\n", @@ -266,12 +266,7 @@ "steps_canonical_plot = [0]\n", "for s in range(NSTEPS):\n", " # same settings as iteration from list\n", - " step = dbi_canonical.hyperopt_step(\n", - " step_min = 1e-5,\n", - " step_max = 1,\n", - " space = hp.uniform,\n", - " optimizer = tpe,\n", - " )\n", + " step = dbi_canonical.choose_step(scheduling=DoubleBracketScheduling.hyperopt)\n", " dbi_canonical(step=step)\n", " print(f\"New optimized step at iteration {s+1}/{NSTEPS}: {step}, loss {dbi_canonical.off_diagonal_norm}\")\n", " off_diagonal_norm_history_canonical.append(dbi_canonical.off_diagonal_norm)\n", @@ -331,7 +326,7 @@ "metadata": {}, "outputs": [], "source": [ - "dbi_mixed = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator)\n", + "dbi_mixed = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator, scheduling=DoubleBracketScheduling.hyperopt)\n", "print(\"Initial off diagonal norm\", dbi_mixed.off_diagonal_norm)" ] }, @@ -344,12 +339,7 @@ "dbi_eval = deepcopy(dbi_mixed)\n", "dbi_eval.mode = DoubleBracketGeneratorType.canonical\n", "if step is None:\n", - " step = dbi_eval.hyperopt_step(\n", - " step_max=step_max,\n", - " space=hp.uniform,\n", - " optimizer=tpe,\n", - " max_evals=max_evals,\n", - " )\n", + " step = dbi_eval.choose_step()\n", "dbi_eval(step=step)\n", "print('canonical norm', dbi_eval.off_diagonal_norm, 'step', step)" ] @@ -420,7 +410,7 @@ "metadata": {}, "outputs": [], "source": [ - "dbi_mixed_can= DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.canonical)\n", + "dbi_mixed_can= DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.canonical, scheduling=DoubleBracketScheduling.hyperopt)\n", "print(\"Initial off diagonal norm\", dbi_mixed_can.off_diagonal_norm)" ] }, From 4d18650f9ca542300bdf908cead4054a80eaf316 Mon Sep 17 00:00:00 2001 From: Andrea Date: Thu, 30 May 2024 12:29:10 +0400 Subject: [PATCH 103/154] feat: Update notebooks --- examples/dbi/dbi_scheduling.ipynb | 270 +++------------------- examples/dbi/dbi_strategies_compare.ipynb | 42 ++-- 2 files changed, 53 insertions(+), 259 deletions(-) diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index fd2d9839a7..fdb087871b 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -30,8 +30,7 @@ "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_strategies import *" + "from qibo.models.dbi.utils_scheduling import *" ] }, { @@ -44,27 +43,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.8|INFO|2024-05-29 08:09:34]: Using numpy backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 37.94733192202055\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", - "set_backend(\"numpy\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -87,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -110,21 +94,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.030312727272727272\n", - "100%|██████████| 100/100 [00:00<00:00, 147.00trial/s, best loss: 28.83054565827672]\n", - "hyperopt_search step: 0.02847179092987961\n", - "polynomial_approximation step: 0.032960905003724034\n", - "simulated_annealing step: 0.026973122528658938\n" - ] - } - ], + "outputs": [], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n", @@ -140,27 +112,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.030312727272727272\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -186,18 +140,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.8|WARNING|2024-05-29 08:09:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:09:40]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - } - ], + "outputs": [], "source": [ "# Generate the digaonal operators\n", "Z_str = \"Z\"*nqubits\n", @@ -209,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -227,21 +172,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.30303525252525254 loss -6.165348149025746\n", - "100%|██████████| 100/100 [00:01<00:00, 94.55trial/s, best loss: 31.779832293339304]\n", - "hyperopt_search step: 0.5652071095886648 loss -6.167499628681249\n", - "polynomial_approximation step: 0.04033688534030588 loss -6.149780650249905\n", - "simulated_annealing step: 0.48340531582403534 loss -6.145940509974928\n" - ] - } - ], + "outputs": [], "source": [ "# grid_search\n", "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_max=0.6, d=d)\n", @@ -263,27 +196,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.30303525252525254\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -317,19 +232,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.04141414141414142\n", - "100%|██████████| 100/100 [00:00<00:00, 281.90trial/s, best loss: 31.780151135879148]\n", - "hyperopt_search step: 0.04197021755491067\n" - ] - } - ], + "outputs": [], "source": [ "search_range = 0.1\n", "if step_poly < search_range/2:\n", @@ -348,27 +253,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.04141414141414142\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.plot(s_space, off_diagonal_norm_diff)\n", @@ -398,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -408,27 +295,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.8|INFO|2024-05-29 08:10:04]: Using numpy backend on /CPU:0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial off diagonal norm 15.531689516799505\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", - "set_backend(\"numpy\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "nqubits = 4\n", "h0 = random_hermitian(2**nqubits)\n", "\n", @@ -439,31 +311,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n", - "[Qibo 0.2.8|WARNING|2024-05-29 08:10:04]: Calculating the dense form of a symbolic Hamiltonian. This operation is memory inefficient.\n" - ] - } - ], + "outputs": [], "source": [ "generate_local_Z = generate_Z_operators(nqubits)\n", "Z_ops = list(generate_local_Z.values())\n", @@ -472,67 +322,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----------Scheduling grid search----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/andreapasquale/qibo/src/qibo/models/dbi/utils_strategies.py:39: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " flip_list[i] = cs_angle_sgn(dbi_eval, d)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New optimized step at iteration 1/8: 0.1010190909090909 with operator IZZI, loss 12.32289315516495\n", - "New optimized step at iteration 2/8: 0.07071636363636363 with operator IZZI, loss 9.744596203115039\n", - "New optimized step at iteration 3/8: 0.06061545454545455 with operator IZZI, loss 8.666514218620271\n", - "New optimized step at iteration 4/8: 0.12122090909090909 with operator -ZIZZ, loss 7.525650014624014\n", - "New optimized step at iteration 5/8: 0.06061545454545455 with operator IZZI, loss 6.756888942369091\n", - "New optimized step at iteration 6/8: 0.12122090909090909 with operator IIZZ, loss 5.828802952436445\n", - "New optimized step at iteration 7/8: 0.08081727272727272 with operator -ZIZZ, loss 5.265860734824745\n", - "New optimized step at iteration 8/8: 0.08081727272727272 with operator IZZI, loss 4.799097209077483\n", - "----------Scheduling hyperopt----------\n", - "100%|██████████| 500/500 [00:08<00:00, 58.19trial/s, best loss: 13.25297274898575] \n", - "100%|██████████| 500/500 [00:08<00:00, 58.25trial/s, best loss: 12.948629126785967]\n", - " 70%|██████▉ | 348/500 [00:04<00:02, 70.17trial/s, best loss: 13.308835963869189] \n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_77597/4171789948.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'----------Scheduling {scheduling_labels[i]}----------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNSTEPS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mdbi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflip_sign\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselect_best_dbr_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdbi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZ_ops\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscheduling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscheduling\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompare_canonical\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0moff_diagonal_norm_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdbi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moff_diagonal_norm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/qibo/src/qibo/models/dbi/utils_strategies.py\u001b[0m in \u001b[0;36mselect_best_dbr_generator\u001b[0;34m(dbi_object, d_list, step, compare_canonical, scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mflip_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m step_best = dbi_eval.choose_step(\n\u001b[0m\u001b[1;32m 43\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflip_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscheduling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscheduling\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m )\n", - "\u001b[0;32m~/qibo/src/qibo/models/dbi/double_bracket.py\u001b[0m in \u001b[0;36mchoose_step\u001b[0;34m(self, d, scheduling, **kwargs)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mscheduling\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0mscheduling\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscheduling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscheduling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 184\u001b[0m if (\n\u001b[1;32m 185\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/qibo/src/qibo/models/dbi/utils_scheduling.py\u001b[0m in \u001b[0;36mhyperopt_step\u001b[0;34m(dbi_object, step_min, step_max, max_evals, space, optimizer, look_ahead, d)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0mspace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"step\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m best = hyperopt.fmin(\n\u001b[0m\u001b[1;32m 78\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdbi_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlook_ahead\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlook_ahead\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0mspace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/qibo/test_qibo/lib/python3.11/site-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mfmin\u001b[0;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;31m# next line is where the fmin is actually executed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m \u001b[0mrval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexhaust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_argmin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/qibo/test_qibo/lib/python3.11/site-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mexhaust\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mexhaust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0mn_done\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 364\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_evals\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mn_done\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock_until_done\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masynchronous\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 365\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 366\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/qibo/test_qibo/lib/python3.11/site-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, N, block_until_done)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[0;31m# `new_trials`. This is the core of `run`, all the rest is just\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0;31m# processes orchestration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 278\u001b[0;31m new_trials = algo(\n\u001b[0m\u001b[1;32m 279\u001b[0m \u001b[0mnew_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m )\n", - "\u001b[0;32m~/qibo/test_qibo/lib/python3.11/site-packages/hyperopt/tpe.py\u001b[0m in \u001b[0;36msuggest\u001b[0;34m(new_ids, domain, trials, seed, prior_weight, n_startup_jobs, n_EI_candidates, gamma, verbose)\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0mt0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[0;31m# use build_posterior_wrapper to create the pyll nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m observed, observed_loss, posterior = build_posterior_wrapper(\n\u001b[0m\u001b[1;32m 860\u001b[0m \u001b[0mdomain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprior_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m )\n", - 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "NSTEPS = 8\n", "scheduling_list = [DoubleBracketScheduling.grid_search,\n", @@ -599,7 +391,7 @@ "outputs": [], "source": [ "# Hamiltonian\n", - "set_backend(\"numpy\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "\n", "# hamiltonian parameters\n", "nqubits = 5\n", @@ -650,7 +442,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 33465c1498..8595b00c7f 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -27,7 +27,7 @@ "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", "from qibo.models.dbi.utils import *\n", "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_strategies import *" + "from qibo.models.dbi.utils_dbr_strategies import *" ] }, { @@ -61,7 +61,7 @@ "outputs": [], "source": [ "# backend\n", - "set_backend(\"numpy\", )\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "# initialize dbi object\n", "nqubits = 5\n", "h0 = random_hermitian(2**nqubits, seed=2)\n", @@ -133,7 +133,6 @@ "outputs": [], "source": [ "# initialize DBI class for the Pauli-Z strategy\n", - "set_backend(\"numpy\")\n", "dbi_pauli = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=h0), mode=DoubleBracketGeneratorType.single_commutator, scheduling=scheduling, cost=cost)" ] }, @@ -197,14 +196,23 @@ "metadata": {}, "outputs": [], "source": [ - "loss_history_gradient = [dbi_gradient.off_diagonal_norm]\n", - "steps_gradient_plot= [0]\n", - "for _ in range(NSTEPS):\n", - " step, d_coef, d = gradient_descent_pauli(dbi_gradient, d_coef, d, pauli_operator_dict=pauli_operator_dict)\n", - " dbi_gradient(d=d,step=step)\n", - " loss_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", - " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", - " steps_gradient_plot.append(steps_gradient_plot[-1]+step)" + "def s_hist_to_plot(s_hist):\n", + " # convert list of step durations taken to plotable\n", + " s_plot = [0] * len(s_hist)\n", + " for i in range(len(s_hist)):\n", + " if i != 0:\n", + " s_plot[i] = s_plot[i-1] + s_hist[i]\n", + " return s_plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loss_history_gradient, d_params_hist, s_hist = gradient_descent(dbi_gradient, NSTEPS, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", + "steps_gradient_plot = s_hist_to_plot(s_hist)" ] }, { @@ -251,7 +259,7 @@ "outputs": [], "source": [ "# backend\n", - "set_backend(\"numpy\")\n", + "set_backend(\"qibojit\", platform=\"numba\")\n", "# initialize dbi object\n", "# hamiltonian parameters\n", "# define the hamiltonian\n", @@ -402,14 +410,8 @@ "metadata": {}, "outputs": [], "source": [ - "off_diagonal_norm_history_gradient = [dbi_gradient.off_diagonal_norm]\n", - "steps_gradient_plot= [0]\n", - "for _ in range(NSTEPS):\n", - " step, d_coef, d = gradient_descent_pauli(dbi_gradient, d_coef, d, pauli_operator_dict=pauli_operator_dict)\n", - " dbi_gradient(d=d,step=step)\n", - " off_diagonal_norm_history_gradient.append(dbi_gradient.off_diagonal_norm)\n", - " print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with d_coef {d_coef}, loss {dbi_gradient.off_diagonal_norm}\")\n", - " steps_gradient_plot.append(steps_gradient_plot[-1]+step)" + "off_diagonal_norm_history_gradient, d_params_hist, s_hist = gradient_descent(dbi_gradient, NSTEPS, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", + "steps_gradient_plot = s_hist_to_plot(s_hist)" ] }, { From dfbad7f14c2556b0cdc8fe4996c9c98ba836ae4a Mon Sep 17 00:00:00 2001 From: Andrea Date: Thu, 30 May 2024 14:32:47 +0400 Subject: [PATCH 104/154] doc: Update notebooks --- .../dbi/dbi_gradient_descent_strategies.ipynb | 19 +++++++++++----- examples/dbi/dbi_scheduling.ipynb | 21 +++++++++++++++--- examples/dbi/dbi_strategies_compare.ipynb | 22 ++++++++++++++----- examples/dbi/dbi_strategy_Ising_model.ipynb | 17 +++++++++----- 4 files changed, 59 insertions(+), 20 deletions(-) diff --git a/examples/dbi/dbi_gradient_descent_strategies.ipynb b/examples/dbi/dbi_gradient_descent_strategies.ipynb index a4c9072393..7e2caeaa66 100644 --- a/examples/dbi/dbi_gradient_descent_strategies.ipynb +++ b/examples/dbi/dbi_gradient_descent_strategies.ipynb @@ -44,7 +44,7 @@ " except TypeError:\n", " im = ax.imshow(np.absolute(matrix.get()), cmap=\"inferno\")\n", " fig.colorbar(im, ax=ax)\n", - " \n", + "\n", "def s_hist_to_plot(s_hist):\n", " # convert list of step durations taken to plotable\n", " s_plot = [0] * len(s_hist)\n", @@ -103,7 +103,7 @@ "pauli_operator_dict = generate_pauli_operator_dict(nqubits)\n", "pauli_operators = list(pauli_operator_dict.values())\n", "# let initial d be approximation of $\\Delta(H)\n", - "d_coef_pauli = decompose_into_Pauli_basis(dbi.diagonal_h_matrix, pauli_operators=pauli_operators)\n", + "d_coef_pauli = decompose_into_pauli_basis(dbi.diagonal_h_matrix, pauli_operators=pauli_operators)\n", "d_pauli = sum([d_coef_pauli[i]*pauli_operators[i] for i in range(nqubits)])\n", "\n", "# Computational basis\n", @@ -136,7 +136,7 @@ "outputs": [], "source": [ "# 1. Pauli-basis\n", - "NSTEPS = 10\n", + "NSTEPS = 5\n", "dbi_pauli = deepcopy(dbi)\n", "loss_hist_pauli, d_params_hist_pauli, s_hist_pauli = gradient_descent(dbi_pauli, NSTEPS, d_coef_pauli, ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)" ] @@ -227,7 +227,7 @@ "pauli_operator_dict = generate_pauli_operator_dict(nqubits)\n", "pauli_operators = list(pauli_operator_dict.values())\n", "# let initial d be approximation of $\\Delta(H)\n", - "d_coef_pauli = decompose_into_Pauli_basis(dbi.diagonal_h_matrix, pauli_operators=pauli_operators)\n", + "d_coef_pauli = decompose_into_pauli_basis(dbi.diagonal_h_matrix, pauli_operators=pauli_operators)\n", "d_pauli = sum([d_coef_pauli[i]*pauli_operators[i] for i in range(nqubits)])\n", "\n", "# Computational basis\n", @@ -249,7 +249,7 @@ "outputs": [], "source": [ "# 1. Pauli-basis\n", - "NSTEPS = 10\n", + "NSTEPS = 3\n", "dbi_pauli = deepcopy(dbi)\n", "loss_hist_pauli, d_params_hist_pauli, s_hist_pauli = gradient_descent(dbi_pauli, NSTEPS, d_coef_pauli, ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)" ] @@ -335,6 +335,13 @@ "assert (operator_element.diagonal() == params).all()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -359,7 +366,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index fdb087871b..b9c7b7053d 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -30,7 +30,8 @@ "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" + "from qibo.models.dbi.utils_scheduling import *\n", + "from qibo.models.dbi.utils_dbr_strategies import *" ] }, { @@ -326,7 +327,7 @@ "metadata": {}, "outputs": [], "source": [ - "NSTEPS = 8\n", + "NSTEPS = 3\n", "scheduling_list = [DoubleBracketScheduling.grid_search,\n", " DoubleBracketScheduling.hyperopt,\n", " DoubleBracketScheduling.polynomial_approximation,\n", @@ -424,6 +425,20 @@ " print(n, step)\n", "print(dbi.choose_step(n=1))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -442,7 +457,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 8595b00c7f..e715afeaa6 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -9,6 +9,11 @@ "This notebook is a comparison of the so-far developed diagonalization strategies for DBI, including the canonical, Pauli-Z, and magnetic field strategies. On top of these, we also show case the use of invariant DBI generators such as 'BHMM'." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -78,7 +83,7 @@ "outputs": [], "source": [ "# iterations steps\n", - "NSTEPS = 15\n", + "NSTEPS = 5\n", "# choose polynomial scheduling\n", "scheduling = DoubleBracketScheduling.simulated_annealing" ] @@ -186,7 +191,7 @@ "outputs": [], "source": [ "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", - "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", + "d_coef = decompose_into_pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])" ] }, @@ -284,7 +289,7 @@ "outputs": [], "source": [ "# iterations steps\n", - "NSTEPS = 15\n", + "NSTEPS = 5\n", "# choose polynomial scheduling\n", "scheduling = DoubleBracketScheduling.simulated_annealing" ] @@ -390,7 +395,7 @@ "outputs": [], "source": [ "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", - "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", + "d_coef = decompose_into_pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])" ] }, @@ -443,6 +448,13 @@ "plt.xlabel('Duration')\n", "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -461,7 +473,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/examples/dbi/dbi_strategy_Ising_model.ipynb b/examples/dbi/dbi_strategy_Ising_model.ipynb index d2b0d7a70a..28ea5f860e 100644 --- a/examples/dbi/dbi_strategy_Ising_model.ipynb +++ b/examples/dbi/dbi_strategy_Ising_model.ipynb @@ -90,7 +90,7 @@ "source": [ "# generate pauli_operator_dict\n", "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=1)\n", - "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", + "d_coef = decompose_into_pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", "grad = gradient_numerical(dbi, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", "print('The initial D coefficients:', d_coef)\n", @@ -143,7 +143,7 @@ "source": [ "# generate pauli_operator_dict\n", "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", - "d_coef = decompose_into_Pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", + "d_coef = decompose_into_pauli_basis(dbi.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", "grad = gradient_numerical(dbi, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", "print('The initial D coefficients:', d_coef)\n", @@ -229,7 +229,7 @@ "dbi_TFIM_1 = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)\n", "# generate pauli_operator_dict\n", "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=1)\n", - "d_coef = decompose_into_Pauli_basis(dbi_TFIM_1.h.matrix, list(pauli_operator_dict.values()))\n", + "d_coef = decompose_into_pauli_basis(dbi_TFIM_1.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", "grad = gradient_numerical(dbi_TFIM_1, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", "print('The initial D coefficients:', d_coef)\n", @@ -242,7 +242,7 @@ "metadata": {}, "outputs": [], "source": [ - "NSTEPS = 15\n", + "NSTEPS = 3\n", "off_diagonal_norm_1, d_params_hist, s_step = gradient_descent(dbi_TFIM_1, NSTEPS, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)" ] }, @@ -284,7 +284,7 @@ "dbi_TFIM_2 = DoubleBracketIteration(deepcopy(H_TFIM), scheduling=scheduling, mode=mode)\n", "# generate pauli_operator_dict\n", "pauli_operator_dict = generate_pauli_operator_dict(nqubits=nqubits, parameterization_order=2)\n", - "d_coef = decompose_into_Pauli_basis(dbi_TFIM_2.h.matrix, list(pauli_operator_dict.values()))\n", + "d_coef = decompose_into_pauli_basis(dbi_TFIM_2.h.matrix, list(pauli_operator_dict.values()))\n", "d = sum([d_coef[i] * list(pauli_operator_dict.values())[i] for i in range(nqubits)])\n", "grad = gradient_numerical(dbi_TFIM_2, d_coef, parameterization=ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict)\n", "print('The initial D coefficients:', d_coef)\n", @@ -320,6 +320,11 @@ "source": [ "In conclusion, we see that the parameterization order or locality of the Pauli based parameterization for gradient descent does not affect significantly the effectiveness of double bracket diagonalization." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { @@ -338,7 +343,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" } }, "nbformat": 4, From 01c99e862592509490c956bd1c8c157d4acb8347 Mon Sep 17 00:00:00 2001 From: Andrea Date: Fri, 31 May 2024 06:45:24 +0400 Subject: [PATCH 105/154] fix: Fixing documentation --- .../applications-by-algorithm.rst | 11 +- .../tutorials/dbi/dbi_cost_functions.ipynb | 1 + ...t_functions_and_d_gradients_tutorial.ipynb | 1 - ...are.ipynb => dbi_strategies_compare.ipynb} | 0 ...low_as_a_diagonalization_quantum_algorithm | 1 - examples/dbi/dbi_cost_functions.ipynb | 225 ++----------- examples/dbi/dbi_strategies_compare.ipynb | 12 +- examples/dbi/dbi_tutorial_basic_intro.ipynb | 6 +- poetry.lock | 317 ++++++++++-------- pyproject.toml | 1 + 10 files changed, 224 insertions(+), 351 deletions(-) create mode 120000 doc/source/code-examples/tutorials/dbi/dbi_cost_functions.ipynb delete mode 120000 doc/source/code-examples/tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb rename doc/source/code-examples/tutorials/dbi/{dbi_gdbi_strategies_compare.ipynb => dbi_strategies_compare.ipynb} (100%) delete mode 160000 double_bracket_flow_as_a_diagonalization_quantum_algorithm diff --git a/doc/source/code-examples/applications-by-algorithm.rst b/doc/source/code-examples/applications-by-algorithm.rst index 4535cb207a..4dfb64a6a2 100644 --- a/doc/source/code-examples/applications-by-algorithm.rst +++ b/doc/source/code-examples/applications-by-algorithm.rst @@ -78,14 +78,11 @@ Diagonalization Algorithms tutorials/dbi/README.md - .. tutorials/dbi/dbi_tutorial_basic_intro.ipynb - - tutorials/dbi/dbi_scheduling.ipynb - tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb + tutorials/dbi/dbi_cost_functions.ipynb tutorials/dbi/dbi_gradient_descent_strategies.ipynb tutorials/dbi/dbi_group_commutator_tests.ipynb - - .. tutorials/dbi/dbi_gdbi_strategies_compare.ipynb - + tutorials/dbi/dbi_scheduling.ipynb + tutorials/dbi/dbi_strategies_compare.ipynb tutorials/dbi/dbi_strategy_Ising_model.ipynb tutorials/dbi/dbi_strategy_Pauli-Z.ipynb + tutorials/dbi/dbi_tutorial_basic_intro.ipynb diff --git a/doc/source/code-examples/tutorials/dbi/dbi_cost_functions.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_cost_functions.ipynb new file mode 120000 index 0000000000..1c36a4d760 --- /dev/null +++ b/doc/source/code-examples/tutorials/dbi/dbi_cost_functions.ipynb @@ -0,0 +1 @@ +../../../../../examples/dbi/dbi_cost_functions.ipynb \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb deleted file mode 120000 index d9f0070469..0000000000 --- a/doc/source/code-examples/tutorials/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../../../examples/dbi/dbi_cost_functions_and_d_gradients_tutorial.ipynb \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/dbi/dbi_gdbi_strategies_compare.ipynb b/doc/source/code-examples/tutorials/dbi/dbi_strategies_compare.ipynb similarity index 100% rename from doc/source/code-examples/tutorials/dbi/dbi_gdbi_strategies_compare.ipynb rename to doc/source/code-examples/tutorials/dbi/dbi_strategies_compare.ipynb diff --git a/double_bracket_flow_as_a_diagonalization_quantum_algorithm b/double_bracket_flow_as_a_diagonalization_quantum_algorithm deleted file mode 160000 index 808dd6325b..0000000000 --- a/double_bracket_flow_as_a_diagonalization_quantum_algorithm +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 808dd6325b327a756a4b6ae5a4d560e01d1790cb diff --git a/examples/dbi/dbi_cost_functions.ipynb b/examples/dbi/dbi_cost_functions.ipynb index 5b9bf0aeb8..b8992ffb31 100644 --- a/examples/dbi/dbi_cost_functions.ipynb +++ b/examples/dbi/dbi_cost_functions.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -39,17 +39,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.9|INFO|2024-05-29 12:15:54]: Using numpy backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -64,26 +56,14 @@ "# define the least-squares cost function\n", "cost = DoubleBracketCostFunction.least_squares\n", "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n", - "\n" + "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.single_commutator,cost=cost)\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.6060645454545454\n", - "100%|██████████| 100/100 [00:00<00:00, 846.91trial/s, best loss: -30.633516395890517]\n", - "hyperopt_search step: 0.5991179894176409\n", - "polynomial_approximation step: 0.5267891182131145\n" - ] - } - ], + "outputs": [], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", @@ -109,37 +89,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.6060645454545454\n" - ] - }, - { - "data": { - "image/png": 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", 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FixYAgKioKOzdu1d/Zedely5dQpMmTXDp0iV8+OGHOHLkCNLT06HTlbZKJCcnl+kcfe/nSEtLQ2pqKp5++ulHZrubAwDUajVsbW310zdUB5MtciwsLNCmTRv88ccfeP755/XL//jjDzz33HMSJqsYmUyAj4stfFxs8Uq7BhBFEfFpOfgj7gb2nL2B6JQMHE0oLX4+/OkMejZ3xdAOXgj0sJc6OpHZ0OZqEWFTOvxEp5xOkKuN2y8mtyiX0zo8irU1kJMj3b7LqfRixf2D2N1dfpda/ej/w/9dv6JGjBiBbt26YceOHdi9ezfmzJmD+fPn46233oJOp8Ozzz6LTz/99L7t7s4R9eyzz8LDwwPLly+Hm5sbdDodmjVrhqKiood+jrvTMDyOUln2i7ogCPoiqjqYbJEDlLZ9Dh48GEFBQQgJCcE333yD5ORkjBo1SupolSYIApq42KKJiy3GdG6M1Ix8bIu+iq0nruDSzVxsi76KbdFX0bqBPYZ19EaPZvWglJts/3EioscTBOAxhUFN0KhRIyiVShw7dgweHh4AgKysLMTHxyM0NLTc7+Pv73/feDJHjhypUBYPDw+MGjUKo0aNwpQpU7B8+XK89dZbaN26NbZs2QIvL68H3s1169YtnD17Fl9//TU6deoEADhw4MBj92drawsvLy/s2bMHnTt3rlDW6mTSRc5LL72EW7du4aOPPtKPMbBz5054enpKHc1g3OytMKZzY4wOa4SYlAx8fzgJv5xKxYnkDJxIjkaDutYIf6oxnm/lzmKHqJJk1jJ0SOugf05UGba2thg6dCgmTZqEunXrwtnZGdOnT4dMJqvQFAWjRo3C/PnzMWHCBLzxxhuIiorC6tWry739+PHj0aNHDzRp0gR37tzBX3/9BT8/PwClnZKXL1+Ol19+GZMmTYKjoyMuXryI9evXY/ny5ahTpw4cHBzwzTffwNXVFcnJyZg8eXK59jtjxgyMGjUKzs7O6NGjB7Kzs3Hw4EG89dZb5c5uaCb/2zx69GgkJiaisLAQUVFRePLJJ6WOVC0EQUCrBnXw+UuBODj5KYx72gcOagsk387Du5tP4en5+7ExMgVaXdUucxLVRoIgwMLJAhZOFpwziark888/R0hICHr37o0uXbqgY8eO8PPzg6WlZbnfo0GDBtiyZQt++eUXtGzZEsuWLcPs2bPLvb1Wq8WYMWPg5+eH7t27w9fXF0uWLAEAuLm54eDBg9BqtejWrRuaNWuGcePGQaPRQCaTQSaTYf369YiKikKzZs3w9ttvY+7cueXa79ChQ7Fw4UIsWbIEAQEB6N27N+Lj48uduzoIYlUb/0xYVlYWNBoNMjMzYWdnJ3WcCssrKsEPR5Lwzd+XkZ5T2lbatJ4tPujlh04+ThKnI6Lyuq9PTjGAux1Dc3JMoqnGUAoKCpCQkKCfrsfU5ebmwt3dHfPnz8drr70mdRyT8qh/C+U9f5v8lZzazNpCgdefbIS/3+2M93s2hcZKiXPXszF4xTEMX3UMF9OypY5IZBJ0hTpcGHMBF8ZcgK6Qg3NS5UVHR2PdunW4dOkSTpw4gUGDBgGASd0QY05Y5JiBu8XO/klheLWjNxQyAXvP30SPLyIwf/d5FBRrpY5IVKOJJSJSl6QidUkqxJJae3GbDGTevHlo2bIlunTpgtzcXERERMDR0dFg79+jRw/Y2Ng88FGRZq3awKQ7HlNZ9tYWmPasP/6vfQPM2nEWf51Lw5d/XcQvJ1Px8fPN0bGx4X7JiMhw5DI5evr01D8H+MXEVLVq1QpRUVHVuo9vv/0W+fn5D3yt2kYONlEscsxQQycbrBgahN/OXMf0n2OReCsPg749ipeDPTC1lz/UKv5vJ6pJLBWW2PHKjn8XcFoHegR3d3epI5gMNleZKUEQ0KO5K/58JxRDQjwhCMC6Yyno8UUEjifeljoeERFRtWORY+bsLJX46Llm+HFEe7jbWyH5dh4GfH0Yn+w6h2LOfk5ERGaMRU4tEdLIAbvGd0K/NvUhisCy/Zfw0teHcTXjwe26RGQ8uUW5UM9WQz1bjdwiNlURGQqLnFrEzlKJef1bYumg1rC1VOBEcgZ6LYrAX+duSB2NqNbLK85DXnGe1DGIzAqLnFqoR3NX7HirE1rU1yAjrxivro7EZ7+dg46jJRMRkRlhkVNLNXCwxqZRIRjWwQsAsGTfJYz8LhLZBcXSBiMiMlFhYWEYP3681DHoHixyajGVQo4ZfQKw8KVAWChk2HMuDc8vOYTEdPYJICKismbMmIHAwECpY1QIixxC31bu2PRGCFzsVLiYloPnvjqIgxfTpY5FRERVpNVqodPV3jtpWeQQAKClhz1+Dn8CLT3skZlfjCErj2Hj8RSpYxERmRSdTod3330XdevWRb169TBjxgwAwKuvvorevXuXWbekpAT16tXDypUrAZQ2d4WHhyM8PBz29vZwcHDA1KlTce882kVFRXj33Xfh7u4OtVqNdu3aYd++ffrXV69eDXt7e/z666/w9/eHSqVCUlIS7ty5gyFDhqBOnTqwtrZGjx49yswQfne77du3o0mTJrC0tMQzzzyDlJQU/eszZ87EyZMnIQgCBEHA6tWrq+cgGhCLHNJzsbPEhtfbo2+gG7Q6Ee9uOYUv98SjFk9UT7WFDNCEaqAJ1UjyV1EmyBDqGYpQz1DIBP5ZfpjcotyHPgpKCsq9bn5xfrnWrYw1a9ZArVbj6NGj+Oyzz/DRRx/hjz/+wIgRI/Dbb7/h2rVr+nV37tyJnJwcDBgwoMz2CoUCR48exaJFi7BgwQJ8++23+teHDx+OgwcPYv369Th16hT69++P7t27lylY8vLyMGfOHHz77beIjY2Fs7Mzhg0bhsjISPz88884fPgwRFFEz549UVxcXGa7jz/+GGvWrMHBgweRlZWFgQMHAgBeeuklvPPOOwgICMC1a9dw7do1vPTSS5U6RsbE8f2pDEulHAteCoSbvRWW7LuE+X9cwPWsAnz0XDPIZYLU8YiqhdxKjlb7Wkm2fyulFfYN2/fvAo6V80A2c2we+lpPn55lpsZwnuf80FvyQz1Dyxxvry+8kJ53fxO9OL3iX/BatGiB6dOnAwB8fHywePFi7NmzB5988gl8fX3x/fff49133wUArFq1Cv3794eNzb+fy8PDAwsWLIAgCPD19cXp06exYMECjBw5EpcuXcK6detw5coVuLm5AQAmTpyI3377DatWrdJPzllcXIwlS5agZcuWAID4+Hj8/PPPOHjwIDp06AAAWLt2LTw8PLB9+3b0799fv93ixYvRrl07AKUFl5+fH44dO4bg4GDY2NhAoVCgXr16FT4uUuFXBrqPIAh4t3tTzOwTAEEA1h5Nxui1UZzNnIjoMVq0aFHmZ1dXV6SlpQEARowYgVWrVgEA0tLSsGPHDrz66qtl1m/fvj0E4d8vlCEhIYiPj4dWq8WJEycgiiKaNGlSZubx/fv349KlS/ptLCwsyuQ4e/YsFAqFvngBAAcHB/j6+uLs2bP6ZQqFAkFBQfqfmzZtCnt7+zLrmBpeyaGHGtrBC062KozfEIPfY29gyMpjWDmsLWw4wScRSSBnSs5DXyudvf1faRPTHrruf5sEE8clVinXvZRKZZmfBUHQd/wdMmQIJk+ejMOHD+Pw4cPw8vJCp06dyv3eOp0OcrkcUVFRkMvLft57rwZZWVmVKZQe1uVAFMUy693N+18PWmYqeLaiR+rZ3BUOaguMWBOJYwm3MXjFUaweHgyNlfLxGxOZCG2uFke8jgAA2ie2h1wtf8wWhpVblAuvL7wAlJ5w1Ubdu+lQW5T/yFTXulXh4OCAvn37YtWqVTh8+DCGDx9+3zpHjhy572cfHx/I5XK0atUKWq0WaWlpFSqO/P39UVJSgqNHj+qbq27duoULFy7Az89Pv15JSQkiIyMRHBwMADh//jwyMjLQtGlTAKVXiLRa07qiz+Yqeqx2DR2wdmQ7aKyUiE7OwP99exR3coukjkVkUMXpxShOl24wzPS89Af2CyHzMmLECKxZswZnz57F0KFD73s9JSUFEyZMwPnz57Fu3Tp8+eWXGDduHACgSZMmGDRoEIYMGYKtW7ciISEBx48fx6effoqdO3c+dJ8+Pj547rnnMHLkSBw4cAAnT57E//3f/8Hd3R3PPfecfj2lUom33noLR48exYkTJzB8+HC0b99eX/R4eXkhISEBMTExSE9PR2FhoYGPjuGxyKFyaVHfHutGtoeD2gKnr2bi5eVHkJ5T8/+BE5WHzEqGtmfaou2ZtpBZ8c8iVZ8uXbrA1dUV3bp103cevteQIUOQn5+P4OBgjBkzBm+99RZef/11/eurVq3CkCFD8M4778DX1xd9+vTB0aNH4eHh8cj9rlq1Cm3atEHv3r0REhICURSxc+fOMs1r1tbWeO+99/DKK68gJCQEVlZWWL9+vf71F198Ed27d0fnzp3h5OSEdevWGeCIVC9BrMX3B2dlZUGj0SAzMxN2dnZSxzEJ8TeyMejbo0jLLkQjJzXWvd4ezraWUsciMmm5Rbn6O4dypuRAXQzgbh+LnBxAXXsasAoKCpCQkABvb29YWprf35a8vDy4ublh5cqVeOGFF8q8FhYWhsDAQCxcuNDouVavXo3x48cjIyPD6Pt+mEf9Wyjv+ZtfWahCfFxsseGNELhqLHHpZi7+79ujuM2mKyKiR9LpdEhNTcWHH34IjUaDPn36SB2pVmCRQxXm7ajG+tfbw8VOhQs3cjB4xVFk5nNiTzJduiIdEmYkIGFGAnRFtXcIfKo+ycnJcHd3x8aNG7Fy5UooFLzvxxjYXMXmqkq7mJaDgd8cRnpOEVo1sMf3r7Xj7eVkkrS5WkTYRAAAOuV0kuTuKjZXlTL35ioqPzZXkaQaO9vg+9fawd669K6rV1cfR36Rad1eSFQTyAQZgtyCEOQWxGkdiAyIv01UJX6udvj+1XawVSlwLOE23vghCkUlvNxPVBFWSiscH3kcx0ceh5XSSuo4RGaDRQ5VWfP6Gqx+NRhWSjn+vnAT7205BZ2u1raCEhFRDcEihwyijWcdLP2/1lDIBGyLvoo5u0x3rhMiIjIPLHLIYMJ8nfFZv9JJ4ZZHJGD535clTkRkGvKK8+C10AteC70eOnM2EVUcixwyqBda18eUHqXznHy88yy2RV+ROBFRzSeKIpIyk5CUmfTQyRSJqOJY5JDBvf5kQ7z2hDcAYNKmUzgQz/l4iIgeZ/Xq1bC3t5c6RrnMmDEDgYGBFdpGEARs3769WvI8DIscMjhBEPBBTz/0aemGEp2IN9dGIf5GttSxiIjIQCZOnIg9e/ZIHeOxWORQtZDJBMzt3wJtveogu6AEr645zgk9iYjMhI2NDRwcHKSO8VgscqjaqBRyfD04CJ4O1ki5nY/Xv4tEQTEHCyQi8xQWFobw8HCEh4fD3t4eDg4OmDp1qr6f1Z07dzBkyBDUqVMH1tbW6NGjB+Lj4x/4XomJiZDJZIiMjCyz/Msvv4SnpydEUcS+ffsgCAL27NmDoKAgWFtbo0OHDjh//nyZbZYuXYpGjRrBwsICvr6++P7778u8LggCvv76a/Tu3RvW1tbw8/PD4cOHcfHiRYSFhUGtViMkJASXLl3Sb/Pf5qrjx4/jmWeegaOjIzQaDUJDQ3HixImqHE6DYJFD1aqu2gIrh7WFnaUCJ5IzMHHTSY6hQ0SVos3VVvihu2dwUl2JrnR5vrZc71sZa9asgUKhwNGjR7Fo0SIsWLAA3377LQBg2LBhiIyMxM8//4zDhw9DFEX07NkTxcX3z/3n5eWFLl26YNWqVWWWr1q1CsOGDYMgCPplH3zwAebPn4/IyEgoFAq8+uqr+te2bduGcePG4Z133sGZM2fwxhtvYPjw4di7d2+Z9/3f//6HIUOGICYmBk2bNsUrr7yCN954A1OmTNEXWuHh4Q/93NnZ2Rg6dCgiIiJw5MgR+Pj4oGfPnsjOlrarAicaomrXyMkGywa3wZAVx/DrqWto6KjGhK6+Usci+pcAWPtb658bffeCAH8nf/1zgF8EHuTu/GIV4b/RH879nQEA6dvSETcgDppQDVrta6Vf54jXERSn319ohIlhFd6fh4cHFixYAEEQ4Ovri9OnT2PBggUICwvDzz//jIMHD6JDhw4AgLVr18LDwwPbt29H//7973uvESNGYNSoUfj888+hUqlw8uRJxMTEYOvWrWXW+/jjjxEaGgoAmDx5Mnr16oWCggJYWlpi3rx5GDZsGEaPHg0AmDBhAo4cOYJ58+ahc+fO+vcYPnw4BgwYAAB47733EBISgg8//BDdunUDAIwbNw7Dhw9/6Od+6qmnyvz89ddfo06dOti/fz969+5d0cNoMLySQ0bRoZEjZr/QHACw6K+L2HX6msSJiP4lt5YjODYYwbHBkFsbd3JOALBWWiN2dCxiR8fCWmlt9P2T4bRv377MVZaQkBDEx8cjLi4OCoUC7dq107/m4OAAX19fnD374MFT+/btC4VCgW3btgEAVq5cic6dO8PLy6vMei1atNA/d3V1BQCkpaUBAM6ePYuOHTuWWb9jx4737fPe93BxcQEANG/evMyygoICZGVlPTBrWloaRo0ahSZNmkCj0UCj0SAnJwfJyckPXN9YeCWHjGZAkAcuXM/GtwcS8M6mk2joZAPferZSxyIiE9Epp1OFtxFU/xYcjs87lr7Hf77et09sX9VolSaKYpmi6F4WFhYYPHgwVq1ahRdeeAE//vgjFi5ceN96SqVS//zue+l0uvuWPWqfD3qPx73vvYYNG4abN29i4cKF8PT0hEqlQkhICIqKih64vrHwSg4Z1eQeTdGxsQPyirQY+V0kMvKk/QUgItMhV8sr/JAp/j3NyRSy0uVW8nK9b2UcOXLkvp99fHzg7++PkpISHD16VP/arVu3cOHCBfj5+T30/UaMGIE///wTS5YsQXFxMV544YUK5fHz88OBAwfKLDt06NAj91kZERERGDt2LHr27ImAgACoVCqkp0s/RhqLHDIqhVyGxS+3Rv06Vki+nYe31kVDy47IJDFtnhbHAo7hWMAxaPOMfwdgXnEeApYEIGBJAKd1MHEpKSmYMGECzp8/j3Xr1uHLL7/EuHHj4OPjg+eeew4jR47EgQMHcPLkSfzf//0f3N3d8dxzzz30/fz8/NC+fXu89957ePnll2FlVbFZ6idNmoTVq1dj2bJliI+Px+eff46tW7di4sSJVf2oZTRu3Bjff/89zp49i6NHj2LQoEEVzlodWOSQ0dVRW+CbwUGwUsoREZ+Oz34/J3Ukqu1EIC8uD3lxeZL0+RVFEXE34xB3M47TOpi4IUOGID8/H8HBwRgzZgzeeustvP766wBK74xq06YNevfujZCQEIiiiJ07d5ZpFnqQ1157DUVFRWXumiqvvn374osvvsDcuXMREBCAr7/+GqtWrUJYWFhlPt5DrVy5Enfu3EGrVq0wePBgjB07Fs7OzgbdR2UIoon+Rn388cfYsWMHYmJiYGFhgYyMjAq/R1ZWFjQaDTIzM2FnZ2f4kPRIv55KRfiP0QCARS+3Qp+WbhInotpK1IrIiMgAANh3socgN+4tVrlFubCZYwMAyJmSA3UxAJvSn5GTA6jVRs0jpYKCAiQkJMDb2xuWlpZSx6mQsLAwBAYGPrDfTFV8/PHHWL9+PU6fPm3Q963pHvVvobznb5O9klNUVIT+/fvjzTfflDoKVVLvFm54M6wRAODdzSdx9tqDe+0TVTdBLqBOWB3UCatj9AKH6GFycnJw/PhxfPnllxg7dqzUcUySyRY5M2fOxNtvv13mFjcyPRO7+uLJJk4oKNZhzNoTyC64f6wKIqLaKDw8HE888QRCQ0Mr1VRFtewW8sLCQhQW/jt/0sPu9yfjkcsEfPFSIHotisDl9FxM3noai19u9dBbKomqg65Yh2vflI7d5Pq6K2RKk/3+RxLat2+fQd9v9erVWL16tUHfs7apVb/Jc+bM0Q9SpNFo4OHhIXUkQmlH5C9faQ2FTMCOU9fw3eEkqSNRLSMWiYgPj0d8eDzEIpPspkhED1CjipwZM2ZAEIRHPv47WVlFTJkyBZmZmfpHSkqKAdNTVbTxrIMpPUvHbZi1Iw4xKRnSBiIyIkEQ4KnxhKfGk1cx/2Gi98SQARni30CNaq4KDw/HwIEDH7nOf4ezrgiVSgWVSlXp7al6vdrRC8cTbuO32OsYs/YEdox9AvbWFlLHIqp21kprJI5P/HdBUa5kWaR293bqvLy8GjHOCkknL690zKjH3WL/KDWqyHF0dISjo6PUMUgigiDgs/4tEHctC8m38/DOxpNYPiQIMhm/2RLVFnK5HPb29vq5l6ytrXl1q5YRRRF5eXlIS0uDvb095PLKzydXo4qcikhOTsbt27eRnJwMrVaLmJgYAKWjLtrcHV+CTI6dpRJLBrXGC0sPYc+5NHwTcRmjQhtJHYuIjKhevXoA/p1kkmone3t7/b+FyjLZImfatGlYs2aN/udWrVoBAPbu3WvwkRzJuJq5azDj2QC8v+005v1+Hu0bOiDQw17qWETVJr84H0+ufhIA8Pewv1HbG2kEQYCrqyucnZ1RXMxhJWojpVJZpSs4d5lskcNb68zby8EeOHDxJnaevo5x66OxY2wn2KhM9p8r0SPpRB0iUyP1z6mUXC43yImOaq8adXcV0V2CIGDO8y3gbm+FpFt5mLb9jNSRiIjIxLDIoRpLY63EwoGBkAnA1uir2B59VepIRERkQljkUI3W1qsu3nrKBwAwdfsZJN/KkzgRERGZChY5VOO99VRjBHnWQU5hCd5aH41iLfssEBHR47HIoRpPIZdh4cBA2FoqcDIlAwv/vCB1JCIiMgEscsgk1K9jjU9eaAEAWLLvEo5eviVxIjI3SkcllI6VH1m1qhytHeFozcFQiQxJEGvxBCFZWVnQaDTIzMyEnZ2d1HGoHCZtOolNUVdQv44Vfhv/JG8rJ/OUmwvcHdQ0JwdQq6XNQ1TDlPf8zSs5ZFKmPeuP+nWscOVOPv73S5zUcYiIqAZjkUMmxdZSiXn9W0IQgA2RKfgj7obUkYiIqIZikUMmp31DB4x4whsAMGXrKdzKKZQ4EZk6bb4W0WHRiA6LhjZfa/T95xfnI2x1GMJWhyG/ON/o+ycyVyxyyCS909UXvi62SM8pwpStp1GLu5aRIeiAzP2ZyNyfCUgwQoFO1GF/0n7sT9rPaR2IDIhFDpkkS6Ucn7/UEkq5gN1xN7A56orUkciECSoB/hv94b/RH4JKkDoOERkIixwyWQFuGozv0gQAMPOXOKTc5mjIVDkyhQzO/Z3h3N8ZMgX/LBKZC/42k0kbFdoIbf4ZDXny1lNstiIiIj0WOWTS5DIB8/u3hKVShoMXb2HdsRSpI5EJ0pXokLYpDWmb0qArYZ8YInPBIodMnpejGhO7+gIAZu88i6sZvDuFKkYsFBE3IA5xA+IgFvJqIJG5YJFDZmF4R2+0bmBf2my1hc1WZHqsldawVlpLHYPIrLDIIbMglwmY278lLBQyRMSnY1Mk77Yi06G2UCP3/Vzkvp8LtQWncCAyFBY5ZDYaOdngnWdK77b63444XM8skDgRERFJiUUOmZURnRoi0MMe2QUleH8bBwkkIqrNWOSQWZHLBMzt1wIWchn+OpeGrSeuSh2J6LEKSgrQ68de6PVjLxSU8AokkaGwyCGz4+Nii3FdfAAAM3+JRVoWTxpUs2l1WuyM34md8Tuh1Rl/7iwic8Uih8zSG082RHN3DbIKSvDB9jNstiIiqoVY5JBZUshlmNe/dG6rP+Ju4Lcz16WORERERsYih8yWbz1bvBnaCAAw/edYZOYXS5yIiIiMiUUOmbXRnRujoZMaadmF+PS3c1LHISIiI2KRQ2bNUinH7OebAwB+PJqM44m3JU5ERETGwiKHzF77hg4Y2NYDADB5yykUlvDuFSKi2oBFDtUKU3r4wdFGhUs3c7Fk7yWp41ANI1fLESaGIUwMg1wtN/r+1RZqiNNFiNNFTutAZEAscqhW0FgrMbNPAABgyb6LuJiWLXEiIiKqbixyqNbo2bwenm7qjGKtiMlbTkOn49g5RETmjEUO1RqCIOB/fZtBbSFHZNIdrDueLHUkqiG0BVrE9o9FbP9YaAuM32eroKQA/Tf1R/9N/TmtA5EBscihWsXN3goTu/kCAD7ZeY5TPlApLXBz803c3HwTkKBfulanxea4zdgct5nTOhAZEIscqnWGhHihpYc9sgtL8L8dZ6WOQzWAYCHAZ7EPfBb7QLAQpI5DRAbCIodqHblMwMd9m0EmAL+cTMWB+HSpI5HEZEoZ3Me4w32MO2RK/lkkMhf8baZaqZm7BkNCvAAA0346w7FziIjMEIscqrUmdG0CJ1sVLqfn4uv9l6WOQxIStSLu7LuDO/vuQNTyrjsic8Eih2otO0slPuztDwBYvPcikm7lSpyIpKIr0OFk55M42fkkdAU6qeMQkYGwyKFa7dkWrujY2AFFJTpM/zkWoshv8URE5oJFDtVqgiDgf881g4Vchn3nb+K3M9eljkS1kLXSGjlTcpAzJQfWSmup4xCZDRY5VOs1dLLBqNCGAICZv8Qhp7BE4kRU2wiCALWFGmoLNQSBt7ATGQqLHCIAozs3RoO61rieVYAv/rwgdRwiIjIAFjlEACyVcsx8rnQCz5UHE3HuepbEiag2KSwpxLDtwzBs+zAUlhRKHYfIbJhkkZOYmIjXXnsN3t7esLKyQqNGjTB9+nQUFRVJHY1MWGdfZ3QPqAetTsTUbWc4gScZTYmuBGtOrsGak2tQomNzKZGhmGSRc+7cOeh0Onz99deIjY3FggULsGzZMrz//vtSRyMTN+1Zf1j/M4Hn5hNXpI5DRERVYJJFTvfu3bFq1Sp07doVDRs2RJ8+fTBx4kRs3bpV6mhk4tzsrTC+iw8A4LPfziEzv1jiREREVFkmWeQ8SGZmJurWrfvIdQoLC5GVlVXmQfRfwzp4o6GTGuk5RVjITshERCbLLIqcS5cu4csvv8SoUaMeud6cOXOg0Wj0Dw8PDyMlJFNioZBhxrOlnZC/O5yE89ezJU5ERESVUaOKnBkzZkAQhEc+IiMjy2yTmpqK7t27o3///hgxYsQj33/KlCnIzMzUP1JSUqrz45AJe7KJE7oFuECrEzGDIyETEZkkhdQB7hUeHo6BAwc+ch0vLy/989TUVHTu3BkhISH45ptvHvv+KpUKKpWqqjGplpjayx/7zt/E4cu3sOP0NfRu4SZ1JCIiqgBBNNGvqFevXkXnzp3Rpk0b/PDDD5DL5RV+j6ysLGg0GmRmZsLOzq4aUpKpW/jnBSz8Mx6uGkvseScU1hY16nsBGYgoiihOL+1krnRUGn3UYVEUkZ6XDgBwtHaEkJcH2NiUvpiTA6jVRs1DVNOV9/xdob/Y3t7elfrlHz9+PMaOHVvh7R4mNTUVYWFhaNCgAebNm4ebN2/qX6tXr57B9kM0KrQRNkddwZU7+fhq70VM6tZU6khUDQRBgIWThaT7d1I7SbZ/InNVoSJn9erVldrJvU1MhrB7925cvHgRFy9eRP369cu8ZqIXpqiGslTKMbWXP0b9EIXlfyegfxsPeDnyWzURkSkw2eYqQ2BzFZWHKIoYsvIYIuLT8XRTZ6wY1lbqSGRgukIdLk64CABo/HljyFTGvSejsKQQE36fAAD4vNvnUBWWsLmK6BHKe/6uUXdXEdVEgiBg+rMBUMgE7DmXhr/O3ZA6EhmYWCIidUkqUpekQiwx/ve+El0JlkQuwZLIJZzWgciAWOQQlUNjZxu89oQ3AOCjX+JQWKKVOBEZkqAU4DndE57TPSEojdvpmIiqj0l2PCaSwltP+2Bb9FUk3srDtxEJGNO5sdSRyEBkFjJ4z/CWOgYRGZhJdjwmkoKNSoEpPZvi7Q0nsfivi3i+lTvc7K2kjkVERA9RoSInNDS0unIQmYS+ge748WgyjifewZxd5/Dly62kjkQGIOpE5J3NAwBY+1lDkLHJisgcVKlPTnFxMVJSUnD+/Hncvn3bUJmIaqy7nZAFAfjlZCoiE/nv3hzo8nU43uw4jjc7Dl2+Tuo4RGQgFS5ycnJy8PXXXyMsLAwajQZeXl7w8/ODk5MTPD09MXLkSBw/frw6shLVCM3cNRjQpnRy15m/xEGnq7WjMBAR1WgVKnIWLFgALy8vLF++HE899RS2bt2KmJgYXLhwAYcPH8b06dNRUlKCZ555Bt27d0d8fHx15SaS1MRuvrBRKXD6aia2nLgidRwycVZKKySMS0DCuARYKdnPi8hQKtQn59ChQ9i7dy+aN2/+wNeDg4Px6quvYtmyZVixYgX2798PHx8fgwQlqkmcbFUIf6oxPtl1Dp/9fh49mrvCRsV5rahyZIIMXvZeUscgMjsV+qu8adOmcq2nUqkwevToSgUiMhXDO3ph3bFkJN3Kw9J9nNeKiKimMdhggKtWrTLUWxGZBJVCjvd7+gEAlkckIOV2nsSJyFQVaYswafckTNo9CUXaIqnjEJkNgxU5W7duxd69e/U/5+fnY9CgQYZ6e6Iaqau/Czo0ckBRiQ5zdp2VOg6ZqGJtMeYdnod5h+ehWFssdRwis2GwIueHH37ABx98gHPnziE+Ph6dOnVCWFiYod6eqEYSBAHTnvWHTAB2nr6OI5dvSR2JiIj+UeWekhMmTEBgYCACAwOxcuVKvPLKKxBFEStXrkSrVhwojcxf03p2eDm4AdYeTcZHv8Thl7eegJyDyRERSa7KV3JCQ0ORlJSE//3vf+jfvz+SkpLg5uaG3bt3Y8eOHYbISFTjTXimCWwtFYi7loVNkSlSxyEiIhjgSs5zzz2H5557Tv9zfn4+zpw5g1OnTuHPP/9Er169qroLohrPwUaFcU/7YNaOs5i3+zx6tXCFraVS6lhERLVahYscURQfORO5lZUV2rZti7Zt21YpGJGpGRLihR+PJuNyei4W/3URU/6584qIiKRR4eYqGxsbdOzYEWPHjsWaNWtw5swZ6HSc64XIQiHD1N6lhc3KgwlITM+VOBERUe1W4Ss5c+fOxYkTJxAREYFly5ZBq9XC0tISLVq0QJs2bdCmTRu0bt0aLVu2rI68RDVaZ19nPNnECX9fuImPd57F8iFBUkeicpBZydD2TFv9c2OzUlrhzJtn9M9RnG/0DETmSBBFsdKzCxYWFsLKygrvv/8+bt++jRMnTuDUqVMoLCyEVqs1ZM5qkZWVBY1Gg8zMTNjZ2Ukdh8xE/I1sdP8iAlqdiB9ea4cnfByljkSmJjcXsLEpfZ6TA6jV0uYhqmHKe/6u0lcWlUoFABgwYACWLFmCI0eOIDs7G9HR0VV5WyKT5uNii8HtPQEAs3bEQctZyomIJGHw67JyuRwtWrQw9NsSmZTxXXxgZ6nAuevZ2BzFW8prOl2RDgkzEpAwIwG6IuP3MSzSFmHGvhmYsW8Gp3UgMiDjNz4T1QL21hYY+7QPAGDe7gvIKSyROBE9ilgsImlmEpJmJkEsNv6Vt2JtMWbun4mZ+2dyWgciA6pwkTNy5EgsW7YMkZGRKCwsBIBH3lJOVFsNCfGCl4M1bmYX4uv9l6SOQ48gKAS4jXaD22g3CAr+PSMyFxW+u+r8+fPYuHEjsrOzoVCUbj5z5kyEhYWhdevWCAwMhLW1tcGDEpkaC4UMk3s0xagfTmB5xGW8HNwAbvZWUseiB5CpZGjyVROpYxCRgVX4Ss7ff/+NzMxMnD9/Ht999x0mTpyIO3fuYNq0aXjiiSeg0WgQEBBQHVmJTE63gHoI9qqLgmId5v1+Xuo4RES1SqWndfDx8YGPjw8GDhyoX5aQkIDIyEjeXUX0D0EQMLW3H/osPoit0VcxrKMXWtS3lzoW/YcoiihOL+0Lo3RUsgmeyExU6EpOcnLyI1/39vZG//79MXv2bADA1atXK5+MyEy0qG+P51u5AwBm7TiLKgxNRdVEl6fDIedDOOR8CLo8juBOZC4qVOS0bdsWI0eOxLFjxx66TmZmJpYvX45mzZph69atVQ5IZA4mdfOFSiHDsYTb+D32htRxiIhqhQo1V509exazZ89G9+7doVQqERQUBDc3N1haWuLOnTuIi4tDbGwsgoKCMHfuXPTo0aO6chOZFDd7K4zs1BCL917EJ7vO4qmmzrBQcAQHKmWpsMSxEcf0z1FcIHEiIvNQqWkdCgoKsHPnTkRERCAxMRH5+flwdHREq1at0K1bNzRr1qw6shocp3UgY8opLEHY3H1IzynEh7398doT3lJHon9oc7WIsIkAAHTK6QS5Wi5tIE7rQPRI5T1/V6rjsaWlJbp06YIXXnih0gGJahsblQITuzbB5K2nsWhPPF5s7Q57awupYxERma1KXy+vU6cOtmzZYsgsRGavf5AHmtazRWZ+Mb7YEy91HKohirRFmHtwLuYenMtpHYgMqNJFjiiKWLp0Kdq1a4f27dsjPDwcR48eNWQ2IrMjlwn4oJcfAOD7w0m4fDNH4kRUExRri/Hun+/i3T/f5bQORAZUpZ6PJ0+eRHBwMMLCwnD+/HmEhobi7bffNlQ2IrPUyccJnX2dUKIT8cmuc1LHISIyW5UeDBAAfvzxRzzzzDP6n0+fPo2+ffuifv36eOedd6ocjshcvd/TD3/Hp2N33A0cuXwL7Rs6SB2JiMjsVPpKjoODAzw8PMosa968ORYtWoRly5ZVORiROfNxscXLwaW/P7N2xEGn4wCBRESGVukip2XLllixYsV9yxs3boyUlJQqhSKqDcZ3aQIblQJnrmZhWzRHByciMrRKFzmzZs3C4sWL8corr+DAgQPIysrCjRs3MHv2bHh7c/wPosdxtFFhTOfGAIC5v59HfpFW4kREROal0kVO+/btceTIEaSmpiIsLAx16tSBm5sbNm/ejPnz5xsyI5HZGt7RC+72VrieVYDlEZeljkNEZFYqNeLxf6WlpSEqKgo6nQ7t2rWDo6OjIbJVO454TDXBzydTMXZdNKyUcuybFAYXO0upI9U6olZERkQGAMC+kz0EuXFnIdfqtIhI/mfE5QadIM8v4IjHRI9Q3vO3QYocU8Uih2oCURTxwtJDiE7OwICg+visX0upI5HUOK0D0SOV9/xtsjME9unTBw0aNIClpSVcXV0xePBgpKamSh2LqMIEQcDUfwYI3BR1BXGpWRInIiIyDyZb5HTu3BkbN27E+fPnsWXLFly6dAn9+vWTOhZRpbTxrItezV0hisDsnWdRiy+wSkJXrMPVr67i6ldXoSvWGX3/xdpifHXsK3x17CuOeExkQAZprjpx4gSaNWsGCwvpJhv8+eef0bdvXxQWFkKpVJZrGzZXUU2SfCsPXT7fjyKtDquGtUXnps5SR6o1pJ6FPLcoFzZzSpuncqbkQF0MNlcRPYJRm6vatm2LxMREQ7xVpdy+fRtr165Fhw4dHlngFBYWIisrq8yDqKZo4GCNoR08AQAf7zyLEq3xryjUWnLAqZ8TnPo5Acatb4ioGhmkyJHq0vp7770HtVoNBwcHJCcn46effnrk+nPmzIFGo9E//jtiM5HUwjv7wN5aiYtpOVh/nINqGovcUo6ATQEI2BQAuSWrHCJzUaP65MyYMQOCIDzyERkZqV9/0qRJiI6Oxu7duyGXyzFkyJBHFlxTpkxBZmam/sGRmamm0VgrMe5pHwDAgj8uILuA/TOIiCqrShN0Glp4eDgGDhz4yHW8vLz0zx0dHeHo6IgmTZrAz88PHh4eOHLkCEJCQh64rUqlgkqlMmRkIoMb1M4T3x1OQkJ6Lpbuu4R3uzeVOhIRkUmqUUXO3aKlMu5ewSksLDRkJCKjs1DIMLlHU7zxfRRWHEjAoPaecLe3kjqWWZO64zERVY8a1VxVXseOHcPixYsRExODpKQk7N27F6+88goaNWr00Ks4RKakq78Lgr3rorBEh7m/nZM6DhGRSTLJIsfKygpbt27F008/DV9fX7z66qto1qwZ9u/fz+YoMgv3DhC4PSYVJ1MypA1E1UqlUOHXl3/Fry//CpWCf8OIDKVGNVeVV/PmzfHXX39JHYOoWrWob4/nW7ljW/RVfLzjLDa80R6CYNw5lcg4FDIFejXpdc8SNrsTGYJBruRMnz7dZCblJDIlk7r5QqWQ4Vjibfwee0PqOEREJsVgRU7dunUN8VZEdA83eyuM6OQNAPhk11kUlXCAQHNUrC3G6pjVWB2zmtM6EBmQSfbJIapN3gxrDEcbCyTeysMPR5KkjkPVoEhbhOE/Dcfwn4ajSFskdRwis8Eih6iGs1EpMOEZXwDAor/ikZnHb/pEROXBIofIBAwIqo8mLjbIyCvGl3/FSx2HiMgksMghMgEKuQzv9yy9pXzN4UQk3cqVOBERUc3HIofIRIT5OqOTjyOKtSI+5QCBRESPVaFxcry9vSs1Tsf48eMxduzYCm9HRGV90MsPPb+IwM7T1xGZeBtBXryrkYjoYSpU5KxevbpSO7l3Uk0iqrym9ewwIMgD64+nYNaOs9g2ugMHCCQieogKFTmhoaHVlYOIymlC1yb4+WQqYlIy8Mupa+jT0k3qSCZPUAnw3+ivf25sKoUKG/tt1D9HMUc8JjIEQbw7fXctlJWVBY1Gg8zMTNjZ2Ukdh6jcFu2Jx+d/XIC7vRX2vBMKSyVnzTYrubmAjU3p85wcQK2WNg9RDVPe8zf75BCZoJGdGuLHo8m4mpGP1YcSMSq0kdSRiIhqHPbJITJBVhZyTOzmi4mbTuKrvy6if5v6cLDh7NWVpSvRIX1bOgDA8XlHyBTGvfG0RFeCbWe3AQCe93veNGdOJqqB2FzF5ioyUTqdiGcXH0BsahaGhHjio+eaSR3JZGlztYiwiQAAdMrpBLnauM1/uUW5sJlT2jyVMyUH6mKwuYroEcp7/uY4OUQmSiYT8EGv0gEC1x5NxsW0HIkTmTAZoAnVQBOq4V9FIjNSpV/n4uJipKSk4Pz587h9+7ahMhFROXVo5Igufs7Q6kR8suus1HFMltxKjlb7WqHVvlaQW7ETN5G5qHCRk5OTg6+//hphYWHQaDTw8vKCv78/nJyc4OnpiZEjR+L48ePVkZWIHmByDz/IZQL+PJuGQ5fSpY5DRFRjVKjIWbBgAby8vLB8+XI89dRT2Lp1K2JiYnD+/HkcPnwY06dPR0lJCZ555hl0794d8fGcSJCoujV2tsGgdg0AAB/vOAudrtZ2syMiKqNCnfgPHTqEvXv3onnz5g98PTg4GK+++iqWLl2KlStXYv/+/fDx8TFIUCJ6uHFP+2DbiauITc3C1uir6NemvtSRTIo2V4sjXkcAAO0T2xu94zERVY8KFTmbNm0q13qWlpYYPXp0pQIRUcU52Kgw5qnG+GTXOcz7/Tx6NXeFlQVP1BVRnF4sdQQiMjCDDMeQkZGB33//HVevXoUgCHB1dUW3bt1Qp04dQ7w9EZXDsA5e+P5wEq5m5GN5xGWMfZpXUU2FhdwCq55bpX+O4iKJExGZhyrfLLlixQoEBwfjyJEj0Ol00Gq1OHLkCNq3b48VK1YYIiMRlYOlUo73ejQFACzbfwlpWQUSJ6LyUsqVGBY4DMMCh0EpV0odh8hsVHkwQF9fX0RFRcHm7sBV/8jOzkabNm1w4cKFKgWsThwMkMyNKIp4YekhRCdnYGBbD3zyYgupI5kEqQcDvA/nriJ6JKMNBigIAnJy7h+ELCcnp1LzXBFR5QmCgKn/DBC4MTIF565nSZyIyqNEV4IdF3Zgx4UdKNGVSB2HyGxUuU/OvHnzEBoaimbNmsHd3R0AcOXKFcTGxmL+/PlVDkhEFdPGsy56NXfFjtPX8PGOs/ju1WB+4ajhCksK0XtdbwCl0zpw7ioiw6jy71Lv3r3Ro0cPHDt2DKmpqRBFEe7u7ggODoZczrs7iKTwXvem+CPuBiLi07Hvwk109nWWOhIRkdFVuMgRRfG+b4VyuRwhISEGC0VEVdPAwRpDO3hieUQCZu84i06NHaGQc1ImIqpdKvxXz8bGBh07dsTYsWOxZs0anDlzBjqdrjqyEVEVhHf2gb21EvFpOdgQmSJ1HCIio6twkTN37lz4+fkhIiICI0eORMuWLWFra4uQkBCEh4dj1apVOHnyZHVkJaIK0FgrMe6fsXIW/HEB2QUc7I6IapcKN1fdO5JxYWEhrKys8Pbbb+P27duIjIzEypUrUVhYCK1Wa9CgRFRxg9p54rvDSUhIz8Wy/ZcwqVtTqSMRERlNlRrpVSoVAGDAgAFYsmQJjhw5guzsbERHRxskHBFVjYVChsn/DBD4bUQCUjPyJU5ERGQ8Br9TUS6Xo0ULDkBGVFN09XdBsHddHEu4jbm/n8eClwKljlTjCBYCfBb76J8bm4XcAot7LNY/57QORIbB4RiIzNzdAQL7LD6IbdFXMbyjF1rUt5c6Vo0iU8rgPsZdsv0r5UqMCR5zzxIWOUSGUOHmqpEjR2LZsmWIjIxEYWEhAHCgMaIarkV9ezzfqvQkPmvHWVRxNhciIpNQ4Ss558+fx8aNG5GdnQ2FonTzmTNnIiwsDK1bt0ZgYCCsra0NHpSIqmZSN1/sPH0NxxJuY3fcDXQLqCd1pBpD1IrIiMgAANh3socgN+4XN61Oi4jkf+bOatAJHEaVyDAqPUFnfHw8oqKicOLECURFRSE6OhoZGRmQy+Vo0qQJYmNjDZ3V4DhBJ9U2c38/h6/2XoK3oxq/j38SFgoOEAhIP0FnblEubOaUTsiZMyUH6mJwgk6iRyjv+bvSfXJ8fHzg4+ODgQMH6pclJCQgMjKSd1cR1VBvhjXGhuMpSEjPxdqjSRje0VvqSDWDAFj7W+ufE5F5qNDXuOTk5Ee+7u3tjf79+2P27NkAgKtXr1Y+GREZnI1KgbefaQIA+GJPPDLzOEAgAMit5QiODUZwbDDk1mwsIjIXFSpy2rZti5EjR+LYsWMPXSczMxPLly9Hs2bNsHXr1ioHJCLDeinIAz7ONsjIK8bivfFSxyEiqjYVaq46e/YsZs+eje7du0OpVCIoKAhubm6wtLTEnTt3EBcXh9jYWAQFBWHu3Lno0aNHdeUmokpSyGV4v5cfhq86jjWHkvB/7T3h6cA+H0Rkfip0Jadu3bqYN28eUlNTsXTpUjRp0gTp6emIjy/9Njho0CBERUXh4MGDLHCIarCwJk7o5OOIIq0OH+84K3UcyWnztDgWcAzHAo5Bm8cpaYjMRaU6HltaWqJLly544YUXDJ2HiIxAEARM6+2P7l9EYHfcDRyIT8cTPo5Sx5KOCOTF5emfE5F5qPT9o3Xq1MGWLVsMmYWIjMjHxRaD23sCAD76NRYlWp3EiWovpVyJz7p8hs+6fAalXCl1HCKzUekiRxRFLF26FO3atUP79u0RHh6Oo0ePGjJbuRQWFiIwMBCCICAmJsbo+ycyZW93aYI61kpcuJGDH489+u5Jqj4WcgtM6jgJkzpOKp27iogMokojgZ08eRLBwcEICwvD+fPnERoairfffttQ2crl3XffhZubm1H3SWQuNNZKTOjqCwCYv/sC7uRyziQiMh9VmqDzxx9/xDPPPKP/+fTp0+jbty/q16+Pd955p8rhHmfXrl3YvXs3tmzZgl27dlX7/ojM0cttPbD2SBLOXc/Gwj8vYOZzzaSOVOtodVqcuHYCANDatTWndSAykEpfyXFwcICHh0eZZc2bN8eiRYuwbNmyKgd7nBs3bmDkyJH4/vvvyz1XVmFhIbKysso8iGo7hVyGab39AQA/HE3G+evZEieqfQpKChD8bTCCvw1GQUmB1HGIzEali5yWLVtixYoV9y1v3LgxUlJSqhTqcURRxLBhwzBq1CgEBQWVe7s5c+ZAo9HoH/8t0ohqqw6NHdE9oB60OhEf/RrLWcqJyCxUusiZNWsWFi9ejFdeeQUHDhxAVlYWbty4gdmzZ8Pbu3Lz4cyYMQOCIDzyERkZiS+//BJZWVmYMmVKhd5/ypQpyMzM1D+quxgjMiXv9/SDhUKGgxdv4Y+4G1LHISKqskr3yWnfvj2OHDmCcePGISwsTP/Nz9LSEps2barUe4aHh5eZ8PNBvLy8MGvWLBw5cgQqlarMa0FBQRg0aBDWrFnzwG1VKtV92xBRqQYO1hjZyRtf7b2EWTvO4skmTrBUsncIEZmuKnU8btmyJfbt24e0tDRERUVBp9OhXbt2cHSs3KBijo6O5dp20aJFmDVrlv7n1NRUdOvWDRs2bEC7du0qtW8iAkaHNcbmqCtIvp2HlQcTMDqssdSRiIgqrUpFzl3Ozs5GncahQYMGZX62sbEBADRq1Aj169c3Wg4ic6NWKTC5R1O8veEkFv91ES+2rg8XO0upYxERVUqVxskhIvPzXEt3tGpgj7wiLT777bzUcYiIKq1CV3K8vb0hCEKFdzJ+/HiMHTu2wtuVl5eXF+8GITIQmUzA9GcD0Perg9hy4goGh3gi0MNe6ljVSlAK8JzuqX9ubEq5EtNDp+ufo7jY6BmIzJEgVqA62L9/f6V24uXlBU9Pz0ptW52ysrKg0WiQmZkJOzs7qeMQ1SjvbDyJLSeuINDDHlvf7ACZzPgn/1orNxf4pxkeOTmAWi1tHqIaprzn7wpdyQkNDa1yMCIyDe9198VvZ64hJiUDW6Ovol8b9ncjItPCPjlE9EDOdpYIf8oHAPDJrrPIzDffJhRRJyI3Nhe5sbkQdcZv+taJOsSmxSI2LRY6kbPBExmKWfTJIaLq8doT3tgUlYLLN3Ox4I8LmNEnQOpI1UKXr8PxZscBAJ1yOkGuNu74QPnF+Wi2tHTOsJwpOWDjFJFhVKjIWb16daV24uXlVantiEhaFgoZZvYJwOAVx/Dd4US81NYDfq7m2X9N6aiUOgIRGRj75BDRI3XycUKPZvWw68x1TP8pFhveaF+pK7o1mVwtR8ebHaWOQUQGxj45RPRYU3v7w0opx7HE2/gpJlXqOERE5cIih4gey93eCuFPlU7x8PHOs8guMN9OyERkPljkEFG5jOjkDW9HNW5mF+KLP+OljmNQ2nwtosOiER0WDW2+Vuo4RGQgLHKIqFxUCjmmP+sPAFh1KBEXbmRLnMiAdEDm/kxk7s8EeAc3kdlgkUNE5Rbm64yu/i7Q6kRM++kMp1MxEKVciYkhEzExZGLptA5EZBAGmYWciGqPD3v7Y/+Fmzhy+TZ+OXUNfVq6SR3J5FnILTC369x7lrDPE5Eh8EoOEVWIR11rjOn8TyfkHXHIKSyROBER0YOxyCGiCnv9yYbwdLDGjaxCfLnHvDohS0En6pCYkYjEjERO60BkQCxyiKjCLJX/dkJecSAB8ebUCVkC+cX58P7CG95feCO/OF/qOERmg0UOEVXKU01d0MXPGSU6EVO3sxMyEdU8LHKIqNKmPxsAS6UMRxNuY3PUFanjEBGVwSKHiCrNo641xndpAgCYvfMsbucWSZyIiOhfLHKIqEpee8IbTevZ4k5eMebsPCt1HCIiPRY5RFQlSrkMHz/fHACwKeoKjly+JXEiIqJSLHKIqMraeNbBK+0aAAA+2HYahSWc/4mIpMcRj4nIIN7r1hS7Y6/j0s1cfLP/Mt562kfqSOUmKAS4jXbTPzc2hUyB0UGj9c8BDrBIZAiCWIvv+8zKyoJGo0FmZibs7OykjkNk8n6KuYpx62NgoZBh9/gn4eWoljqSacrNBWxsSp/n5ABqHkeie5X3/M3mKiIymD4t3fBEY0cUlejwISfwJCKJscghIoMRBAGz+jaDhUKGiPh0/HwyVepI5SKKIopuFqHoZpEkhZkoiriZexM3c2+yMCQyIBY5RGRQXo5qhP8zgef/fj2LzPyaP6O2Lk+HQ86HcMj5EHR5xp87Kq84D87znOE8zxl5xXlG3z+RuWKRQ0QG90ZoQzR0UiM9pxCf7DondRwiqqVY5BCRwakUcsz+Z+ycdceSa/zYOXK1HGFiGMLEMMjVcqnjEJGBsMghomrRvqEDXg4uHTtn8pZTKCjm2DlEZFwscoio2kzp2RQudiok3srDgj8vSB2HiGoZFjlEVG3sLJWY1be02Wr535dx6kqGtIEeQlugRWz/WMT2j4W2gFeciMwFixwiqlbP+LugdwtX6ETg3c2nUKw1/t1Lj6UFbm6+iZubbwKscYjMBoscIqp2M/oEwN5aiXPXs/HN35eljlPjKGQKDG05FENbDv1nWgciMgQWOURU7RxtVJj+rD8A4Is/43ExLUfiRDWLSqHC6r6rsbrvaqgUKqnjEJkNFjlEZBR9A90R5uuEIq0Ok7ecgk7HkX2JqHqxyCEio7g75YPaQo7IpDv44WiS1JFqDFEUkVuUi9yiXE7rQGRALHKIyGjq17HGez2aAgA+2XUOybc4hQFQOq2DzRwb2Myx4bQORAbEIoeIjOr/2nki2Ksu8oq0mLT5JJutiKjasMghIqOSyQTM7d8C1hZyHE24jdWHEqWORERmikUOERmdp4MaU3r6AQA+/e0cLt3k3VZEZHgscohIEv/XrgE6+TiisESHiZtOQstmKyIyMBY5RCQJQRDw6YstYKtSIDo5g4MEEpHBmWyR4+XlBUEQyjwmT54sdSwiqgA3eytM+2eQwAV/XMD569kSJyIic2LS44d/9NFHGDlypP5nGxsbCdMQUWX0a1Mfv525jj3n0jBhYwy2j+kIpdzI37/kgFM/J/1zY5PL5Ojn30//nBNoERmGSRc5tra2qFevntQxiKgKBEHAnBeao+vCvxGbmoWv9l7E+C5NjJpBbilHwKYAo+7zXpYKS2zqv+nfBYW5kmUhMicm21wFAJ9++ikcHBwQGBiIjz/+GEVFRY9cv7CwEFlZWWUeRCQ9ZztLfPRcMwDA4r8u4tSVDGkDEZFZMNkiZ9y4cVi/fj327t2L8PBwLFy4EKNHj37kNnPmzIFGo9E/PDw8jJSWiB7n2Rau6NXcFSU6EePXxyCvqETqSERk4gSxBk2UMmPGDMycOfOR6xw/fhxBQUH3Ld+yZQv69euH9PR0ODg4PHDbwsJCFBYW6n/OysqCh4cHMjMzYWdnV7XwRFRlGXlF6L4wAtezCvBycAPMeaG5UfarzdUiwiYCANAppxPkauN2zMktyoXNnNI+hTlTcqAuBnC3j2FODqBWGzUPUU2XlZUFjUbz2PN3jeqTEx4ejoEDBz5yHS8vrwcub9++PQDg4sWLDy1yVCoVVCpVlTISUfWxt7bA5y+1xKBvj2LdsWSE+TqhWwD73RFR5dSoIsfR0RGOjo6V2jY6OhoA4OrqashIRGRkHRo54vUnG+Lr/ZcxecspBHrYw8XOslr3KbOWoUNaB/1zIjIPJvnbfPjwYSxYsAAxMTFISEjAxo0b8cYbb6BPnz5o0KCB1PGIqIreecYXzdztcCevGBM3Vf8knoIgwMLJAhZOFhAEoVr3RUTGY5JFjkqlwoYNGxAWFgZ/f39MmzYNI0eOxLp166SORkQGYKGQYeFLrWCplCEiPh0rDyZIHYmITFCNaq4qr9atW+PIkSNSxyCiatTY2QZTe/lj6vYz+Oy38+jQyBH+btVzg4CuUIeLEy6W7vfzxpCpTPL7HxH9B3+TiajGGtSuAbr4OaNIq8Nb604gt7B6bisXS0SkLklF6pJUiCU15oZTIqoiFjlEVGPdncTTxU6FSzdz8eH2M6hBo14YjFwmR0+fnujp0/OfaR2IyBBY5BBRjeZgo8Kiga0gE4Ct0VexKeqK1JEMzlJhiR2v7MCOV3bAUlG9d5IR1SYscoioxmvX0AETnimdz2raT2dw4QZnKyeix2ORQ0QmYXRYY3TycURBsQ6j157gtA9E9FgscojIJMhkAha8FAhnWxUupuVg2k+xUkcymNyiXKhnq6GerUZuEWcgJzIUFjlEZDIcbVT44p/+OZujrmCzGfXPySvOQ15xntQxiMwKixwiMikhjRwwvktp/5wPt5/BuetZEiciopqKRQ4RmZwxnUv75+QXa/HG91HIzCuWOhIR1UAscojI5MhlAhYNbAV3eysk3crD+A3R1T6/FRGZHhY5RGSS6qgt8PXgNlApZNh7/iYW7omXOhIR1TAscojIZDVz12D2880BAIv2xOOPuBsSJyKimoRFDhGZtBfb1MfQEE8AwIQNMbh8M6fibyIDNKEaaEI1kvxVlAkyhHqGItQzFDKBf5aJDEUQzXEimHLKysqCRqNBZmYm7OyqZ3ZjIqp+RSU6DPr2CI4n3oGPsw22jekIG5VC6liVl5sL2NiUPs/JAdRqafMQ1TDlPX/zKwMRmTwLhQxfDWoNZ1sV4tNy8PaGGHZEJiIWOURkHpxtLbFscBtYyGX4I+4G5u4+L3UkIpIYixwiMhutG9TBZ/1aAACW7rtU7hGRtblaHHQ6iINOB6HN1VZnxAfKLcqF01wnOM114rQORAZkwo3WRET369vKHRfTcrB470VM2XoKng7WaOtV97HbFadLO6Bgel66pPun2kcURdzOLUJqRgGuZxUgM78YWfnFyC4oQVZBMQpLtNCJQGnPXRGiCFhbKGCjksPGUgEblRJ1rJVwtbeCm8YSjjYqyGSC1B+rDBY5RGR2JjzTBJdu5mDXmet44/so/DSmIzzqWj90fZmVDG3PtNU/JzInWQXFiL+Rg4tp2Yi/kYP4tBwk385DakY+Ckt0BtuPUi6gnsYSDR1t4FvPFk1cbOHrYgsfFxtYKuUG209FsMghIrMjkwmYP6AlUu7k4czVLLy6+jg2v9kBGivlA9cXZALUAbyDiUxfUYkOcdeyEJN8BzEpGTh5JRMJ6Y9uAnWyVcFVYwl7awvYWSpga6mEnZUClgo5ZIIAQQAEACKAvCItcgtLkFNYguyCEtzOLcS1zALcyCpAsVZEyu18pNzOx/4LN/Xv/+XLrfBsS7fq/eAPwSKHiMyStYUC3w5pi+e+OoD4tBy8/l0kvnstGCqFNN8oiapDiVaHM6lZOHQpHYcv3cLxxNsoKL7/6kw9O0v4uNjAx7n0yoqXgxru9lZw0agM8jtRotXhRnYhrt7Jx8W0HFy4kY3z17MRn5aNJi62VX7/ymKRQ0Rmq57GEquGBWPA14dxNOE2Jmw8iS8Htrqv34CuSIek2UkAAM/3PSGzYJMV1Vx3couw93wa9pxNw98XbiK7sKTM63WslQj0sEdLD3sE/vOwt7ao1kwKuQzu9lZwt7dCsHfZPnBSDsfHIoeIzJq/mx2+HtwGw1Ydw45T1+BqZ4mpvf3LrCMWi0iaWVrkNJjUAKje8wFRhSXdysXu2Bv44+wNRCbexr3DQNlZKtC+oQM6NHJAh8aO8HG2gSDUnA7AUmZhkUNEZq9jY0fM698S49bH4NsDCainscSITg2ljqUnE2QIcgvSPwcM1xmUTNf1zAL8eioVP59MxakrmWVea1rPFl38XPC0nzNa1LeHvIbd1VRTsMgholrhuUB3XM8swJxd5zBrx1k42arwXKC71LEAAFZKKxwfefzfBRwrp9bKzC/GjlPX8FPMVRxLvI27LT1ymYCQhg54xt8FTzV1fuTdgvQvFjlEVGu8/mRDXMsswOpDiXhn40moLRTo4u8idSyq5URRxLGE29hwPAU7z1wr03E4yLMO+gS6oWdzVzjaqCRMaZpY5BBRrSEIAqb19kdmfjG2RV/F6B9PYPWwtmjnWkfqaFQLpWUXYEvUVWyMTClzm3cTFxu82Lo+erd0g7u9lYQJTR+LHCKqVWQyAXP7tUBuYQl2x93AiO8i8f0rQZJmyivOg/9XpZ2h48bEgQ0R5uvuVZvVhxKxO+4GtP/0IFZbyPFsSze81NYDgR72NarjsCljkUNEtY5CLsOXr7TCiDWRiIhPxxvfRWEepGsKEEURSZlJ+udkfgqKtfg5JhWrDiXi7LUs/fLWDewxsG0D9GrhCrWKp2RD4xElolpJpZDj68FtMHjFMZyOvwNIWOSQ+UrNyMf3R5Kw/lgy7uSVzo9mqZTh+Vb1MbSDJ5rWs5M4oXljkUNEtZa1hQIrh7XFsK8O4+5t2+evZ8G/EfvoUNXEpmZi6b5L2HXmur5Jyt3eCkM7eGJAkEe1D85HpVjkEFGtprFSYsXQYJyadAQAMGzVcax8sx2auWskTkamKCrpNr7aewl/nUvTL+vQyAFDO3ihi58Lx7MxMhY5RFTraaz/nbgzI68Yg749ih9ea4fm9Vno0OOJoogDF9Px1d6LOHL5NgBAJgDPtnTDqNBG8HNlk5RUWOQQEd0j0EODYzcy8cq3R/Ddq8Fo1YBNV/RgOp2IP87ewJK9F3HynxGJlXIBL7auj1GhjeDlyJntpcYih4hIAKz9S2/cXj6sJUZsiMLxxDsY9O1RLPu/NniyiVP17l4Q4O/kr38O8A6rmqxEq8Ovp65hyb6LuHAjB0BpZ+KXgxvg9ScbwlXDsW1qCkGsxfcrZmVlQaPRIDMzE3Z2vJxIRKVyC0sw6ocoRMSnQykXMK9/S+NOAZGbC9jYlD7PyQHUvCJQExSWaLEl6iqW7b+E5Nt5AABblQJDOnji1Y7ecOCIxEZT3vM3r+QQEf2HWqXAiqFtMWFjDH49dQ3j1sfgdm4Rhnf0ljoaSSCvqAQ/Hk3G8ojLuJFVCACoq7bAa094Y3CIJ+wslY95B5IKixwiogewUMiwaGArOKgtsOZwEmb+EodbOUV4p2sTjkZbS2TmF+O7Q4lYeTBBP8ZNPTtLvP5kQ7wc3ABWFnKJE9LjsMgholpPm6dFVNsoAECb420gty49eclkAmb0CYCjjQrz/7iAxXsvIul2Hub2awFLpeFOcHnFeWi7vC0A4PjI45zWQWI3swux8mACvj+chJzCEgCAp4M13gxthOdbu0OlYHFjKljkEBGJQF5cnv75vQRBwFtP+8DZToUPtp3BLydTkXI7D98MaQNnW0vD7F4UEXczTv+cpHE1Ix/L/76MdceSUVhSOjikr4stRnduhF7NXaGQyyROSBXFIoeIaj2ZpQwt97bUP3+Ql9o2gEdda7z5wwnEpGSg7+KD+HZoW/i78aYFU3f5Zg6W7b+ErSeuouSf0YlbetgjvHNjPN3UGTIO4GeyWOQQUa0nyAXUCXv8eDgdGjli+5iOeG31cVxOz0W/ZYfw+YCW6N7M1QgpydDiUrOwZN9F7Dx9Df/UNujQyAFjOjdGh0YO7HtlBljkEBFVgLejGttGd8SYH0/gwMV0jPrhBEY84Y33ejSFks0ZJiEq6Q6W7L2IPfdMvdDFzxmjOzdGaw7+aFZM+jdyx44daNeuHaysrODo6IgXXnhB6khEZIJ0xTpc/eoqrn51Fbpi3WPX11grsWp4W7z+ZEMAwLcHEjDwmyO4lplf3VGpkkRRxIH4dLz8zRG8uPQQ9pxL00+9sGtcJ3w7tC0LHDNksldytmzZgpEjR2L27Nl46qmnIIoiTp8+LXUsIjJBYpGI+PB4AEC9YfWAcgx7opTL8H5PP7TxrIOJm04iKukOei06gC8GBqKTT/WOkEzlp9WJ+D32Opbuu4TTV/+deuGFVvUxKqwRvDn1glkzySKnpKQE48aNw9y5c/Haa6/pl/v6+kqYiohqo24B9dC0ni1Grz2B2NQsDF5xDK894Y1J3XzLfZu5IAjw1Hjqn3Nah6orLNFie/RVfL3/Mi6n5wIonXphYNvSqRfc7Dn1Qm1gkkXOiRMncPXqVchkMrRq1QrXr19HYGAg5s2bh4CAgIduV1hYiMLCQv3PWVlZxohLRGbO00GNLW92wP9+jcPao8lYcSABf1+4iQUvBaKZ++NnMrdWWiNxfOK/C4pyqy+smcspLMG6o8n49sC/oxNrrJQY2sELQ0M8OfVCLWOSRc7ly5cBADNmzMDnn38OLy8vzJ8/H6Ghobhw4QLq1q37wO3mzJmDmTNnGjMqEdUSlko5Pn6+OZ72c8a7m08jPi0Hfb86iHFP++DNsEYcY6Wa3copxOpDiVhzKBFZBaUD+NWzs8SITt54ObgB1CqTPN1RFdWo37oZM2ZAEIRHPiIjI6HTlXYM/OCDD/Diiy+iTZs2WLVqFQRBwKZNmx76/lOmTEFmZqb+kZKSYqyPRkS1xFNNXbD77SfRo1k9lOhEzP/jAp776iBOpmRIHc0snbuehfc2n0LIJ3/hy78uIqugBA2d1PisXwv8/W5njOjUkAVOLVaj/s+Hh4dj4MCBj1zHy8sL2dnZAAB/f3/9cpVKhYYNGyI5Ofmh26pUKqhUvFRJRNWrrtoCSwa1xk8xqZj20xnEpmah75KDGNzeExO7+d43oWN+cT6eXP0kAODvYX+DvUUeTacT8de5NKw8mIBDl27pl7f0sMeboY3Q1d+FA/gRgBpW5Dg6OsLR0fGx67Vp0wYqlQrnz5/HE088AQAoLi5GYmIiPD09qzsmEdFjCYKAvq3c0bGxI2bvPItt0Vfx3eEk/HbmOiZ288WLretD/s+JWCfqEJkaqX9OD5ZTWILNkSlYfSgRibdKp+GQywR0b1YPr3b0RusG9hzAj8qoUUVOednZ2WHUqFGYPn06PDw84Onpiblz5wIA+vfvL3E6IqJ/OdmqsOClQPRrUx9Tt59BQnou3t18CqsPJmJqLz90aPz4L3a1XWxqJtYdS8b26FT9hJl2lgq83K4BhoR4wZ13StFDmGSRAwBz586FQqHA4MGDkZ+fj3bt2uGvv/5CnToczImIap6OjR3x2/hO+O5QEhb9FY+4a1l45duj6OzrhDdC3aWOV+PkFpbgl5OpWHcsGSevZOqXN3RSY3hHb7zY2h3WFiZ7CiMjEcRaPOVtVlYWNBoNMjMzYWfHSfaIaittrhYRNhEAgE45nSBXl298m8q6nVuEL/68gB+OJkOrE6FDAVKs+gEAcqbkQF0MwMamdOWcHEBdOwasE0URJ5IzsOXEFfwUfRW5RVoApYP3dQuoh1eCG6B9Qwf2t6Fyn79ZBhMRGVldtQVmPtcMwzp648u/4rE1+pL+teGrjuHNIE90ljCfsV26mYOfoq9ie0wqkm/n6Zd7O6rxcrAHXmxdn+PbUKWwyCEikoi3oxqfDwjEa53c0Oyb0mVHEm7j9PnbOPvPOpl5xdCY4YWcpFu52B17A7+cSsWpe5qjrC3k6BZQDwOCPNC+YV12JKYqYZFDRARA6ViOCauqiZeDGo7WjtDpgFdbe2Hn4Sv6156cuxdPtvLCgKD66NDIUX9HlqkRRRGxqVnYHXsdu+Nu4Nz1bP1rcpmAJ30c0beVO57xd2FfGzIY9slhnxwiqmFybmfCxsEeAOD39mbkW1gCABxtVOga4IIezeqhfUMHKGv4KMrpOYU4eDEdBy+m40B8OlIzC/SvyWUC2nnXRfdm9dCzuSsc2RxFFVDe8zeLHBY5RFTT5ObqOx6fOX8VG8/exk8xqcjML9avYmepQMfGjujY2BFPNHaEp4O1pE07oijiyp18nEi+g+jkDBy5fKvM1RoAsFLKEdrECV0DXPBUU2fYW1tIlJZMHYuccmCRQ0Q10j1Fzt27q4pKdDh8+RZ+O3Mdf8RdR3pOUZlNXOxUaFHfHoEe9mhRX4MmLrZwtlVVS+FTWKLF5Zu5uHAjG/E3cnDuehZiUjLuywQAfq52eKKxAzo0dkRIQ4dyz8xO9CgscsqBRQ4RAYA2X4tTPU4BAFrsagG5lXFPxPnF+eixtgcAYNegXbAq0j3yFnKtTsTJKxk4dDEdBy6mIyrpDoq19/8pt1Ep0NBJDU8HNVxsVXCxs4SznQoaKyXUKgXUFgpYWcghALi7dWGJFtkFJcguKEZ2QQmy8otxLbMA1zILkJqRj2uZBbiakQ+t7v79KeUC/N00aOVhj9aeddChkQOboaha8BZyIqLy0gGZ+zP1z42+e1GH/Un79c8fRy4T0LpBHbRuUAfhT/kgv0iLM6mZOJmSgZNXMnHmaiaSbuUip7AEp65klrl7yVBsLRVo4mL7z8MGLeprEOCm4ZUaqlFY5BBRrSeoBPhv9Nc/NzVWFnK09aqLtl519csKS7RIvpWHSzdzkXI7D2nZBUjLLsSNrAJkF5Qgr0iLnMISFPwz4B4AQAAs5DLYWipga6mEjUoBOysFXOws4WZvBVdN6X896ljDxa56msKIDIlFDhHVejKFDM79naWOYVAqhRw+LrbwcbGVOgqRZGr2/YdERERElcQrOURU6+lKdEjflg4AcHzeETIFv/8RmQMWOURU64mFIuIGxAEonaCTfxmJzAN/lYmIagBrpbXUEYjMDoscIiKJqS3UyH0/998FxbkPX5mIyo0Nz0RERGSWWOQQERGRWWKRQ0QksYKSAvT6sRd6/dgLBSUFj9+AiMqFfXKIiCSm1WmxM36n/jkRGQav5BAREZFZYpFDREREZolFDhEREZklFjlERERklljkEBERkVmq1XdXiaIIAMjKypI4CRFJSZurRS5KRxnOysqCXCs36v5zi3KBf+4cz8rKgrb4nhezsgAt77giutfd8/bd8/jDCOLj1jBjV65cgYeHh9QxiIiIqBJSUlJQv379h75eq4scnU6H1NRU2NraQhAEg71vVlYWPDw8kJKSAjs7O4O9L92Px9o4eJyNg8fZOHicjaM6j7MoisjOzoabmxtksof3vKnVzVUymeyRFWBV2dnZ8RfISHisjYPH2Th4nI2Dx9k4qus4azSax67DjsdERERklljkEBERkVlikVMNVCoVpk+fDpVKJXUUs8djbRw8zsbB42wcPM7GUROOc63ueExERETmi1dyiIiIyCyxyCEiIiKzxCKHiIiIzBKLHCIiIjJLLHIqacmSJfD29oalpSXatGmDiIiIR66/f/9+tGnTBpaWlmjYsCGWLVtmpKSmrSLHeevWrXjmmWfg5OQEOzs7hISE4PfffzdiWtNV0X/Pdx08eBAKhQKBgYHVG9CMVPRYFxYW4oMPPoCnpydUKhUaNWqElStXGimt6arocV67di1atmwJa2truLq6Yvjw4bh165aR0pqmv//+G88++yzc3NwgCAK2b9/+2G2Mfi4UqcLWr18vKpVKcfny5WJcXJw4btw4Ua1Wi0lJSQ9c//Lly6K1tbU4btw4MS4uTly+fLmoVCrFzZs3Gzm5aanocR43bpz46aefiseOHRMvXLggTpkyRVQqleKJEyeMnNy0VPQ435WRkSE2bNhQ7Nq1q9iyZUvjhDVxlTnWffr0Edu1ayf+8ccfYkJCgnj06FHx4MGDRkxteip6nCMiIkSZTCZ+8cUX4uXLl8WIiAgxICBA7Nu3r5GTm5adO3eKH3zwgbhlyxYRgLht27ZHri/FuZBFTiUEBweLo0aNKrOsadOm4uTJkx+4/rvvvis2bdq0zLI33nhDbN++fbVlNAcVPc4P4u/vL86cOdPQ0cxKZY/zSy+9JE6dOlWcPn06i5xyquix3rVrl6jRaMRbt24ZI57ZqOhxnjt3rtiwYcMyyxYtWiTWr1+/2jKam/IUOVKcC9lcVUFFRUWIiopC165dyyzv2rUrDh069MBtDh8+fN/63bp1Q2RkJIqLi6stqymrzHH+L51Oh+zsbNStW7c6IpqFyh7nVatW4dKlS5g+fXp1RzQblTnWP//8M4KCgvDZZ5/B3d0dTZo0wcSJE5Gfn2+MyCapMse5Q4cOuHLlCnbu3AlRFHHjxg1s3rwZvXr1MkbkWkOKc2GtnqCzMtLT06HVauHi4lJmuYuLC65fv/7Aba5fv/7A9UtKSpCeng5XV9dqy2uqKnOc/2v+/PnIzc3FgAEDqiOiWajMcY6Pj8fkyZMREREBhYJ/QsqrMsf68uXLOHDgACwtLbFt2zakp6dj9OjRuH37NvvlPERljnOHDh2wdu1avPTSSygoKEBJSQn69OmDL7/80hiRaw0pzoW8klNJgiCU+VkUxfuWPW79By2nsip6nO9at24dZsyYgQ0bNsDZ2bm64pmN8h5nrVaLV155BTNnzkSTJk2MFc+sVOTftE6ngyAIWLt2LYKDg9GzZ098/vnnWL16Na/mPEZFjnNcXBzGjh2LadOmISoqCr/99hsSEhIwatQoY0StVYx9LuTXsApydHSEXC6/7xtBWlrafRXqXfXq1Xvg+gqFAg4ODtWW1ZRV5jjftWHDBrz22mvYtGkTunTpUp0xTV5Fj3N2djYiIyMRHR2N8PBwAKUnYlEUoVAosHv3bjz11FNGyW5qKvNv2tXVFe7u7tBoNPplfn5+EEURV65cgY+PT7VmNkWVOc5z5sxBx44dMWnSJABAixYtoFar0alTJ8yaNYtX2w1EinMhr+RUkIWFBdq0aYM//vijzPI//vgDHTp0eOA2ISEh962/e/duBAUFQalUVltWU1aZ4wyUXsEZNmwYfvzxR7anl0NFj7OdnR1Onz6NmJgY/WPUqFHw9fVFTEwM2rVrZ6zoJqcy/6Y7duyI1NRU5OTk6JdduHABMpkM9evXr9a8pqoyxzkvLw8yWdnToVwuB/DvlQaqOknOhdXWpdmM3b09ccWKFWJcXJw4fvx4Ua1Wi4mJiaIoiuLkyZPFwYMH69e/e9vc22+/LcbFxYkrVqzgLeTlUNHj/OOPP4oKhUL86quvxGvXrukfGRkZUn0Ek1DR4/xfvLuq/Cp6rLOzs8X69euL/fr1E2NjY8X9+/eLPj4+4ogRI6T6CCahosd51apVokKhEJcsWSJeunRJPHDggBgUFCQGBwdL9RFMQnZ2thgdHS1GR0eLAMTPP/9cjI6O1t+qXxPOhSxyKumrr74SPT09RQsLC7F169bi/v379a8NHTpUDA0NLbP+vn37xFatWokWFhail5eXuHTpUiMnNk0VOc6hoaEigPseQ4cONX5wE1PRf8/3YpFTMRU91mfPnhW7dOkiWllZifXr1xcnTJgg5uXlGTm16anocV60aJHo7+8vWllZia6uruKgQYPEK1euGDm1adm7d+8j/+bWhHOhIIq8FkdERETmh31yiIiIyCyxyCEiIiKzxCKHiIiIzBKLHCIiIjJLLHKIiIjILLHIISIiIrPEIoeIiIjMEoscIiIiMksscoiIiMgsscghIiIis8Qih4jMzubNm9G8eXNYWVnBwcEBXbp0QW5urtSxiMjIFFIHICIypGvXruHll1/GZ599hueffx7Z2dmIiIgAp+kjqn04QScRmZUTJ06gTZs2SExMhKenp9RxiEhCbK4iIrPSsmVLPP3002jevDn69++P5cuX486dO1LHIiIJ8EoOEZkdURRx6NAh7N69G9u2bcP169dx9OhReHt7Sx2NiIyIRQ4RmTWtVgtPT09MmDABEyZMkDoOERkROx4TkVk5evQo9uzZg65du8LZ2RlHjx7FzZs34efnJ3U0IjIyFjlEZFbs7Ozw999/Y+HChcjKyoKnpyfmz5+PHj16SB2NiIyMzVVERERklnh3FREREZklFjlERERklljkEBERkVlikUNERERmiUUOERERmSUWOURERGSWWOQQERGRWWKRQ0RERGaJRQ4RERGZJRY5REREZJZY5BAREZFZYpFDREREZun/AUi0h0NF7fTWAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -173,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -195,30 +147,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", @@ -240,17 +171,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.9|INFO|2024-05-29 12:15:55]: Using numpy backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -273,20 +196,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.6565690909090909\n", - "100%|██████████| 100/100 [00:00<00:00, 1239.19trial/s, best loss: 10.532755662184366]\n", - "hyperopt_search step: 0.5995386291300258\n", - "polynomial_approximation step: 0.4860935299211704\n" - ] - } - ], + "outputs": [], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))/2**nqubits\n", @@ -312,37 +224,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.6565690909090909\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -368,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -386,40 +270,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Energy fluctuation')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", @@ -434,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -451,44 +304,14 @@ " step_poly = dbi_.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=3)\n", " if step_poly is not None:\n", " dbi_(step_poly, d=d)\n", - " energy[i,j] = np.real(dbi_.h.matrix[states[i],states[i]])\n", - " " + " energy[i,j] = np.real(dbi_.h.matrix[states[i],states[i]])\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eigenvalues: [-9.29150262 -5.21110255 -2. -2. 1.29150262 4.\n", - " 4. 9.21110255]\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Iterations')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", "print('Eigenvalues:', eigvals )\n", diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index e715afeaa6..0b1ac6dadd 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -9,11 +9,6 @@ "This notebook is a comparison of the so-far developed diagonalization strategies for DBI, including the canonical, Pauli-Z, and magnetic field strategies. On top of these, we also show case the use of invariant DBI generators such as 'BHMM'." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -449,6 +444,13 @@ "plt.ylabel(r'$|| \\sigma(e^{sW}He^{-sW}) || $')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/examples/dbi/dbi_tutorial_basic_intro.ipynb b/examples/dbi/dbi_tutorial_basic_intro.ipynb index 36bf8816bd..0031bb28a6 100644 --- a/examples/dbi/dbi_tutorial_basic_intro.ipynb +++ b/examples/dbi/dbi_tutorial_basic_intro.ipynb @@ -21,8 +21,8 @@ "metadata": {}, "outputs": [], "source": [ - "!python -m pip install seaborn # plotting library\n", - "!python -m pip install hyperopt # required to optimize the DBF step" + "# uncomment this line if seaborn is not installed\n", + "# !python -m pip install seaborn # plotting library" ] }, { @@ -553,7 +553,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/poetry.lock b/poetry.lock index 6f043417f8..cd434b2ac1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "absl-py" @@ -35,13 +35,13 @@ files = [ [[package]] name = "anyio" -version = "4.3.0" +version = "4.4.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"}, - {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"}, + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, ] [package.dependencies] @@ -678,63 +678,63 @@ test = ["altair", "baytune", "chocolate", "dask", "distributed", "kahypar", "mat [[package]] name = "coverage" -version = "7.5.1" +version = "7.5.3" description = "Code coverage measurement for 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Gluza Date: Fri, 31 May 2024 04:55:36 +0200 Subject: [PATCH 106/154] Update README.md first intro block for dbi doc --- doc/source/code-examples/tutorials/dbi/README.md | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/doc/source/code-examples/tutorials/dbi/README.md b/doc/source/code-examples/tutorials/dbi/README.md index 50b9e9eaec..78af756acd 120000 --- a/doc/source/code-examples/tutorials/dbi/README.md +++ b/doc/source/code-examples/tutorials/dbi/README.md @@ -1 +1,12 @@ -../../../../../examples/dbi/README.md \ No newline at end of file +# Double-bracket quantum algorithms + +Qibo features a model implementing double-bracke quantum algorithms (DBQAs) which are helpful for approximating eigenstates based on the ability to run the evolution under the input Hamiltonian. + +More specifically, given a Hamiltonian $H_0$, how can we find a circuit which after applying to the reference state (usually $|0\rangle^{\otimes L}$ for $L$ qubits) will approximate an eigenstate? + +A standard way is to run variational quantum circuits. For example, Qibo already features the `VQE` model [2] which provides the implementation of the variational quantum eigensolver framework. +DBQAs allow to go beyond VQE in that they take a different approach to compiling the quantum circuit approximating the eigenstate. + +[1] https://arxiv.org/abs/2206.11772 + +[2] https://github.com/qiboteam/vqe-sun From 5f240287d909066c3edf83149f43351563a1de1c Mon Sep 17 00:00:00 2001 From: Andrea Date: Fri, 31 May 2024 07:14:15 +0400 Subject: [PATCH 107/154] feat: Disable progressbar for hyperopt --- examples/dbi/dbi_strategy_Pauli-Z.ipynb | 5 ++--- examples/dbi/dbi_tutorial_basic_intro.ipynb | 10 +++++++++- src/qibo/models/dbi/utils_scheduling.py | 1 + 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/examples/dbi/dbi_strategy_Pauli-Z.ipynb b/examples/dbi/dbi_strategy_Pauli-Z.ipynb index 8a557c53e1..586d42f070 100644 --- a/examples/dbi/dbi_strategy_Pauli-Z.ipynb +++ b/examples/dbi/dbi_strategy_Pauli-Z.ipynb @@ -22,8 +22,7 @@ "metadata": {}, "outputs": [], "source": [ - "!python -m pip install seaborn # plotting library\n", - "!python -m pip install hyperopt # required to optimize the DBF step" + "# !python -m pip install seaborn # plotting library" ] }, { @@ -429,7 +428,7 @@ " dbi_mixed_can(step=step)\n", " off_diagonal_norm_history_mixed_can.append(dbi_mixed_can.off_diagonal_norm)\n", " steps_mixed_can.append(step)\n", - " \n", + "\n", "print(\"After 2 steps, off diagonal norm:\", dbi_mixed_can.off_diagonal_norm)\n", "print(\"By comparison, the Pauli-Z:\", off_diagonal_norm_history[2])" ] diff --git a/examples/dbi/dbi_tutorial_basic_intro.ipynb b/examples/dbi/dbi_tutorial_basic_intro.ipynb index 0031bb28a6..da00ba3773 100644 --- a/examples/dbi/dbi_tutorial_basic_intro.ipynb +++ b/examples/dbi/dbi_tutorial_basic_intro.ipynb @@ -22,7 +22,7 @@ "outputs": [], "source": [ "# uncomment this line if seaborn is not installed\n", - "# !python -m pip install seaborn # plotting library" + "# !python -m pip install seaborn" ] }, { @@ -535,6 +535,14 @@ "source": [ "visualize_matrix(dbf_2.h.matrix)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8feb34bf", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 14735fc3f0..34f5196bc2 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -79,6 +79,7 @@ def hyperopt_step( space=space, algo=optimizer.suggest, max_evals=max_evals, + show_progressbar=False, ) return best["step"] From 392f939d78d90e3aebc262d6edd4f33a8360d9e4 Mon Sep 17 00:00:00 2001 From: Andrea Date: Fri, 31 May 2024 07:24:51 +0400 Subject: [PATCH 108/154] fix: Fix README link --- doc/source/code-examples/tutorials/dbi/README.md | 13 +------------ examples/dbi/README.md | 13 +++++++++++-- 2 files changed, 12 insertions(+), 14 deletions(-) diff --git a/doc/source/code-examples/tutorials/dbi/README.md b/doc/source/code-examples/tutorials/dbi/README.md index 78af756acd..50b9e9eaec 120000 --- a/doc/source/code-examples/tutorials/dbi/README.md +++ b/doc/source/code-examples/tutorials/dbi/README.md @@ -1,12 +1 @@ -# Double-bracket quantum algorithms - -Qibo features a model implementing double-bracke quantum algorithms (DBQAs) which are helpful for approximating eigenstates based on the ability to run the evolution under the input Hamiltonian. - -More specifically, given a Hamiltonian $H_0$, how can we find a circuit which after applying to the reference state (usually $|0\rangle^{\otimes L}$ for $L$ qubits) will approximate an eigenstate? - -A standard way is to run variational quantum circuits. For example, Qibo already features the `VQE` model [2] which provides the implementation of the variational quantum eigensolver framework. -DBQAs allow to go beyond VQE in that they take a different approach to compiling the quantum circuit approximating the eigenstate. - -[1] https://arxiv.org/abs/2206.11772 - -[2] https://github.com/qiboteam/vqe-sun +../../../../../examples/dbi/README.md \ No newline at end of file diff --git a/examples/dbi/README.md b/examples/dbi/README.md index 1bb0d229fb..713fafe68d 100644 --- a/examples/dbi/README.md +++ b/examples/dbi/README.md @@ -1,3 +1,12 @@ -# Double Bracket Iterations +# Double-bracket quantum algorithms -General documentation for DBI + links to notebooks +Qibo features a model implementing double-bracke quantum algorithms (DBQAs) which are helpful for approximating eigenstates based on the ability to run the evolution under the input Hamiltonian. + +More specifically, given a Hamiltonian $H_0$, how can we find a circuit which after applying to the reference state (usually $|0\rangle^{\otimes L}$ for $L$ qubits) will approximate an eigenstate? + +A standard way is to run variational quantum circuits. For example, Qibo already features the `VQE` model [2] which provides the implementation of the variational quantum eigensolver framework. +DBQAs allow to go beyond VQE in that they take a different approach to compiling the quantum circuit approximating the eigenstate. + +[1] https://arxiv.org/abs/2206.11772 + +[2] https://github.com/qiboteam/vqe-sun From 6dfa80a5f4b9b3bb430d8be367447c054fb72b1f Mon Sep 17 00:00:00 2001 From: Marek Gluza Date: Fri, 31 May 2024 05:28:58 +0200 Subject: [PATCH 109/154] more about the D operators --- examples/dbi/README.md | 44 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 43 insertions(+), 1 deletion(-) diff --git a/examples/dbi/README.md b/examples/dbi/README.md index 713fafe68d..6a374cce6e 100644 --- a/examples/dbi/README.md +++ b/examples/dbi/README.md @@ -7,6 +7,48 @@ More specifically, given a Hamiltonian $H_0$, how can we find a circuit which af A standard way is to run variational quantum circuits. For example, Qibo already features the `VQE` model [2] which provides the implementation of the variational quantum eigensolver framework. DBQAs allow to go beyond VQE in that they take a different approach to compiling the quantum circuit approximating the eigenstate. -[1] https://arxiv.org/abs/2206.11772 +## What is the unitary of DBQA? + +Given $H_0$ we begin by assuming that we were given a diagonal and hermitian operator $D_0$ and a time $s_0$. +The `dbi` module provides numerical strategies for selecting them. +For any such choice we define the bracket +$$ W_0 = [D_0, H_0]$$ +and the double-bracket rotation (DBR) of the input Hamiltonian to time $s$ +$$H_0(s) = e^{sW} H e^{- s W}$$ + +### Why are double-bracket rotations useful? +We can show that the magnitude of the off-diagonal norms will decrease. +For this let us set the notation that $\sigma(A)$ is the restriction to the off-diagonal of the matrix A. +In `numpy` this can be implemented by `\sigma(A) = A-np.diag(A)`. In Qibo we implement this as +https://github.com/qiboteam/qibo/blob/8c9c610f5f2190b243dc9120a518a7612709bdbc/src/qibo/models/dbi/double_bracket.py#L145-L147 +which is part of the basic `DoubleBracketIteration` class in the `dbi` module. + +With this notation we next use the Hilbert-Schmidt scalar product and norm to measure the progress of diagonalization + $$||\sigma(H_0(s))||^2- ||\sigma (H_0 )||^2= -2s \langle W, [H,\sigma(H)\rangle+O(s^2)$$ +This equation tells us that as long as the scalar product $\langle W, [H,\sigma(H)\rangle$ is positive then after the DBR the magnitude of the off-diagonal couplings in $H_0(s)$ is less than in $H_0$. + +For the implementation of the DBR unitary $U_0(s) = e^{-s W_0}$ see +https://github.com/qiboteam/qibo/blob/363a6e5e689e5b907a7602bd1cc8d9811c60ee69/src/qibo/models/dbi/double_bracket.py#L68 + +### How to choose $D$? + +For theoretical considerations the canonical bracket is useful. +For this we need the notation of the dephasing channel $\Delta(H)$ which is equivalent to `np.diag(h)`. + $M = [\Delta(H),\sigma(H)]= [H,\sigma(H)]= [\Delta(H),H]$ + The canonical bracket appears on its own in the monotonicity relation above and gives an unconditional reduction of the magnitude of the off-diagonal terms + $$||\sigma(H_0(s))||^2- ||\sigma (H_0 )||^2= -2s \|M\|^2+O(s^2)$$ +- the multi qubit Pauli Z generator with $Z(\mu) = (Z_1)^{\mu_1}\ldots (Z_L)^{\mu_L}$ where we optimize over all binary strings $\mu\in \{0,1\}^L$ +- the magnetic field $D = \sum_i B_i Z_i$ +- the two qubit Ising model $D = \sum_i B_i Z_i + \sum_{i,j} J_{i,j} Z_i Z_j$, please follow the tutorial by Matteo and use the QIBO ising model for that with $h=0$ + + +### How to choose s? + + + + + + +[1] https://arxiv.org/abs/2206.11772 [2] https://github.com/qiboteam/vqe-sun From 90fbc0335d56e13366c489514e8dd59c022b55da Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 31 May 2024 03:29:19 +0000 Subject: [PATCH 110/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/dbi/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/dbi/README.md b/examples/dbi/README.md index 6a374cce6e..3bd8974df2 100644 --- a/examples/dbi/README.md +++ b/examples/dbi/README.md @@ -27,17 +27,17 @@ With this notation we next use the Hilbert-Schmidt scalar product and norm to me $$||\sigma(H_0(s))||^2- ||\sigma (H_0 )||^2= -2s \langle W, [H,\sigma(H)\rangle+O(s^2)$$ This equation tells us that as long as the scalar product $\langle W, [H,\sigma(H)\rangle$ is positive then after the DBR the magnitude of the off-diagonal couplings in $H_0(s)$ is less than in $H_0$. -For the implementation of the DBR unitary $U_0(s) = e^{-s W_0}$ see +For the implementation of the DBR unitary $U_0(s) = e^{-s W_0}$ see https://github.com/qiboteam/qibo/blob/363a6e5e689e5b907a7602bd1cc8d9811c60ee69/src/qibo/models/dbi/double_bracket.py#L68 ### How to choose $D$? -For theoretical considerations the canonical bracket is useful. +For theoretical considerations the canonical bracket is useful. For this we need the notation of the dephasing channel $\Delta(H)$ which is equivalent to `np.diag(h)`. $M = [\Delta(H),\sigma(H)]= [H,\sigma(H)]= [\Delta(H),H]$ The canonical bracket appears on its own in the monotonicity relation above and gives an unconditional reduction of the magnitude of the off-diagonal terms - $$||\sigma(H_0(s))||^2- ||\sigma (H_0 )||^2= -2s \|M\|^2+O(s^2)$$ -- the multi qubit Pauli Z generator with $Z(\mu) = (Z_1)^{\mu_1}\ldots (Z_L)^{\mu_L}$ where we optimize over all binary strings $\mu\in \{0,1\}^L$ + $$||\sigma(H_0(s))||^2- ||\sigma (H_0 )||^2= -2s \|M\|^2+O(s^2)$$ +- the multi qubit Pauli Z generator with $Z(\mu) = (Z_1)^{\mu_1}\ldots (Z_L)^{\mu_L}$ where we optimize over all binary strings $\mu\in \{0,1\}^L$ - the magnetic field $D = \sum_i B_i Z_i$ - the two qubit Ising model $D = \sum_i B_i Z_i + \sum_{i,j} J_{i,j} Z_i Z_j$, please follow the tutorial by Matteo and use the QIBO ising model for that with $h=0$ @@ -49,6 +49,6 @@ For this we need the notation of the dephasing channel $\Delta(H)$ which is equi -[1] https://arxiv.org/abs/2206.11772 +[1] https://arxiv.org/abs/2206.11772 [2] https://github.com/qiboteam/vqe-sun From 5671d03de34b14e21ef3f5f16753516c342775d2 Mon Sep 17 00:00:00 2001 From: Andrea Date: Fri, 31 May 2024 15:05:43 +0400 Subject: [PATCH 111/154] fix: Attempt to fix CI --- examples/dbi/dbi_cost_functions.ipynb | 13 +++++++++---- .../dbi/dbi_gradient_descent_strategies.ipynb | 7 ++++--- examples/dbi/dbi_group_commutator_tests.ipynb | 10 ++++------ examples/dbi/dbi_scheduling.ipynb | 14 +++++++++++--- examples/dbi/dbi_strategies_compare.ipynb | 7 +++---- examples/dbi/dbi_strategy_Ising_model.ipynb | 11 +++-------- examples/dbi/dbi_strategy_Pauli-Z.ipynb | 18 +++++++++++------- 7 files changed, 45 insertions(+), 35 deletions(-) diff --git a/examples/dbi/dbi_cost_functions.ipynb b/examples/dbi/dbi_cost_functions.ipynb index b8992ffb31..4d06fbc0f2 100644 --- a/examples/dbi/dbi_cost_functions.ipynb +++ b/examples/dbi/dbi_cost_functions.ipynb @@ -22,9 +22,7 @@ "import matplotlib.pyplot as plt\n", "\n", "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction" ] }, { @@ -322,6 +320,13 @@ " plt.axhline(y=eigvals, color='r', linestyle='--')\n", "plt.xlabel('Iterations')\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -340,7 +345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": { diff --git a/examples/dbi/dbi_gradient_descent_strategies.ipynb b/examples/dbi/dbi_gradient_descent_strategies.ipynb index 7e2caeaa66..e6f266462e 100644 --- a/examples/dbi/dbi_gradient_descent_strategies.ipynb +++ b/examples/dbi/dbi_gradient_descent_strategies.ipynb @@ -19,9 +19,10 @@ "metadata": {}, "outputs": [], "source": [ - "from qibo.models.dbi.double_bracket import *\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_dbr_strategies import *\n", + "from qibo.models.dbi.double_bracket import DoubleBracketIteration, DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketCostFunction\n", + "from qibo.models.dbi.utils import generate_pauli_operator_dict, decompose_into_pauli_basis, params_to_diagonal_operator, ParameterizationTypes\n", + "from copy import deepcopy\n", + "from qibo.models.dbi.utils_dbr_strategies import gradient_descent\n", "import numpy as np\n", "from qibo import set_backend, hamiltonians\n", "from qibo.hamiltonians import Hamiltonian\n", diff --git a/examples/dbi/dbi_group_commutator_tests.ipynb b/examples/dbi/dbi_group_commutator_tests.ipynb index 82f313612e..65ef1f9c53 100644 --- a/examples/dbi/dbi_group_commutator_tests.ipynb +++ b/examples/dbi/dbi_group_commutator_tests.ipynb @@ -12,9 +12,7 @@ "import matplotlib.pyplot as plt\n", "\n", "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *" + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction" ] }, { @@ -61,8 +59,8 @@ " dbi_eval.mode = DoubleBracketGeneratorType.single_commutator\n", " dbi_eval(s_space[s],d=d)\n", " off_diagonal_norm_diff[s,0] = dbi_eval.off_diagonal_norm\n", - " \n", - " \n", + "\n", + "\n", "\n", "plt.figure()\n", "plt.plot(s_space, off_diagonal_norm_diff[:,0],label=r'$e^{sW}$')\n", @@ -134,7 +132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": { diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index b9c7b7053d..9ab3b06641 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -29,9 +29,10 @@ "\n", "from qibo import hamiltonians, set_backend\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_dbr_strategies import *" + "from qibo.hamiltonians import SymbolicHamiltonian\n", + "from qibo.models.dbi.utils import str_to_symbolic, generate_Z_operators\n", + "from qibo.models.dbi.utils_scheduling import polynomial_step\n", + "from qibo.models.dbi.utils_dbr_strategies import select_best_dbr_generator" ] }, { @@ -433,6 +434,13 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 0b1ac6dadd..0467db1b1e 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -20,14 +20,13 @@ "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "\n", + "from qibo import symbols\n", "from qibo import hamiltonians, set_backend\n", "from qibo.hamiltonians import Hamiltonian, SymbolicHamiltonian\n", "from qibo.quantum_info import random_hermitian\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_dbr_strategies import *" + "from qibo.models.dbi.utils import generate_Z_operators, generate_pauli_operator_dict, decompose_into_pauli_basis, ParameterizationTypes\n", + "from qibo.models.dbi.utils_dbr_strategies import select_best_dbr_generator, gradient_descent, polynomial_step" ] }, { diff --git a/examples/dbi/dbi_strategy_Ising_model.ipynb b/examples/dbi/dbi_strategy_Ising_model.ipynb index 28ea5f860e..19d092891c 100644 --- a/examples/dbi/dbi_strategy_Ising_model.ipynb +++ b/examples/dbi/dbi_strategy_Ising_model.ipynb @@ -24,12 +24,12 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "from qibo import hamiltonians, set_backend\n", + "from qibo import hamiltonians, set_backend, symbols\n", "from qibo.hamiltonians import Hamiltonian, SymbolicHamiltonian\n", "from qibo.quantum_info import random_hermitian\n", "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_dbr_strategies import *" + "from qibo.models.dbi.utils import generate_pauli_operator_dict, decompose_into_pauli_basis, ParameterizationTypes\n", + "from qibo.models.dbi.utils_dbr_strategies import gradient_numerical, gradient_descent" ] }, { @@ -320,11 +320,6 @@ "source": [ "In conclusion, we see that the parameterization order or locality of the Pauli based parameterization for gradient descent does not affect significantly the effectiveness of double bracket diagonalization." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { diff --git a/examples/dbi/dbi_strategy_Pauli-Z.ipynb b/examples/dbi/dbi_strategy_Pauli-Z.ipynb index 586d42f070..8542c17463 100644 --- a/examples/dbi/dbi_strategy_Pauli-Z.ipynb +++ b/examples/dbi/dbi_strategy_Pauli-Z.ipynb @@ -38,10 +38,9 @@ "import seaborn as sns\n", "\n", "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketIteration\n", - "from qibo.models.dbi.utils import *\n", - "from qibo.models.dbi.utils_scheduling import *\n", - "from qibo.models.dbi.utils_dbr_strategies import *" + "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketIteration, DoubleBracketScheduling\n", + "from qibo.models.dbi.utils import generate_Z_operators\n", + "from qibo.models.dbi.utils_dbr_strategies import select_best_dbr_generator" ] }, { @@ -187,8 +186,8 @@ "metadata": {}, "outputs": [], "source": [ - "NSTEPS = 8\n", - "max_evals = 100\n", + "NSTEPS = 2\n", + "max_evals = 10\n", "step_max = 1\n", "Z_optimal = []\n", "# add in initial values for plotting\n", @@ -481,6 +480,11 @@ "source": [ "This example also shows that the canonical generator is more likely to drive the model into a local minimum than variationally assigned diagonal operator, and that it is hard to get it unstuck even with the Pauli-Z operators." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { @@ -499,7 +503,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" } }, "nbformat": 4, From 410dcd0bb4e809971d0d7e80b6ceabf0a087b114 Mon Sep 17 00:00:00 2001 From: MatteoRobbiati Date: Fri, 31 May 2024 16:26:09 +0200 Subject: [PATCH 112/154] test: execute and clean all dbi notebooks --- examples/dbi/dbi_cost_functions.ipynb | 14 +++---------- .../dbi/dbi_gradient_descent_strategies.ipynb | 20 +++---------------- examples/dbi/dbi_group_commutator_tests.ipynb | 7 +++---- examples/dbi/dbi_scheduling.ipynb | 6 +++--- examples/dbi/dbi_strategies_compare.ipynb | 6 +++--- examples/dbi/dbi_strategy_Ising_model.ipynb | 6 +++--- examples/dbi/dbi_strategy_Pauli-Z.ipynb | 4 ++-- examples/dbi/dbi_tutorial_basic_intro.ipynb | 2 +- 8 files changed, 21 insertions(+), 44 deletions(-) diff --git a/examples/dbi/dbi_cost_functions.ipynb b/examples/dbi/dbi_cost_functions.ipynb index 4d06fbc0f2..004e95531f 100644 --- a/examples/dbi/dbi_cost_functions.ipynb +++ b/examples/dbi/dbi_cost_functions.ipynb @@ -320,18 +320,11 @@ " plt.axhline(y=eigvals, color='r', linestyle='--')\n", "plt.xlabel('Iterations')\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "dbi", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -345,9 +338,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.0" }, - "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" @@ -355,5 +347,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_gradient_descent_strategies.ipynb b/examples/dbi/dbi_gradient_descent_strategies.ipynb index e6f266462e..2e90b4e214 100644 --- a/examples/dbi/dbi_gradient_descent_strategies.ipynb +++ b/examples/dbi/dbi_gradient_descent_strategies.ipynb @@ -335,25 +335,11 @@ ")\n", "assert (operator_element.diagonal() == params).all()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "DBF_qibo", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -367,9 +353,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.0" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_group_commutator_tests.ipynb b/examples/dbi/dbi_group_commutator_tests.ipynb index 65ef1f9c53..3ba22f33e2 100644 --- a/examples/dbi/dbi_group_commutator_tests.ipynb +++ b/examples/dbi/dbi_group_commutator_tests.ipynb @@ -118,7 +118,7 @@ ], "metadata": { "kernelspec": { - "display_name": "dbi", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -132,9 +132,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.0" }, - "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" @@ -142,5 +141,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 9ab3b06641..1016f53827 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -451,7 +451,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -465,7 +465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.0" }, "vscode": { "interpreter": { @@ -474,5 +474,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index 0467db1b1e..a36341fa4a 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -460,7 +460,7 @@ ], "metadata": { "kernelspec": { - "display_name": "DBF_qibo", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -474,9 +474,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.0" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_strategy_Ising_model.ipynb b/examples/dbi/dbi_strategy_Ising_model.ipynb index 19d092891c..70b7aa5e5a 100644 --- a/examples/dbi/dbi_strategy_Ising_model.ipynb +++ b/examples/dbi/dbi_strategy_Ising_model.ipynb @@ -324,7 +324,7 @@ ], "metadata": { "kernelspec": { - "display_name": "DBF_qibo", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -338,9 +338,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.0" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_strategy_Pauli-Z.ipynb b/examples/dbi/dbi_strategy_Pauli-Z.ipynb index 8542c17463..0b0747b22d 100644 --- a/examples/dbi/dbi_strategy_Pauli-Z.ipynb +++ b/examples/dbi/dbi_strategy_Pauli-Z.ipynb @@ -503,9 +503,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.0" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_tutorial_basic_intro.ipynb b/examples/dbi/dbi_tutorial_basic_intro.ipynb index da00ba3773..d6d5cecb71 100644 --- a/examples/dbi/dbi_tutorial_basic_intro.ipynb +++ b/examples/dbi/dbi_tutorial_basic_intro.ipynb @@ -561,7 +561,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.0" } }, "nbformat": 4, From 6130d09ced44e86fdef00699efb230a1f7ba89c5 Mon Sep 17 00:00:00 2001 From: MatteoRobbiati Date: Fri, 31 May 2024 17:20:38 +0200 Subject: [PATCH 113/154] fix: rm old dbi notebook from doc --- doc/source/code-examples/applications.rst | 2 - examples/dbi/dbi_cost_functions.ipynb | 224 +++++++++++++++++++--- 2 files changed, 202 insertions(+), 24 deletions(-) diff --git a/doc/source/code-examples/applications.rst b/doc/source/code-examples/applications.rst index 8787659178..9fa6976b54 100644 --- a/doc/source/code-examples/applications.rst +++ b/doc/source/code-examples/applications.rst @@ -69,8 +69,6 @@ Quantum Physics tutorials/bell-variational/README.md tutorials/falqon/README.md tutorials/grover/README.md - tutorials/dbi/dbi.ipynb - Quantum Machine Learning ^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/examples/dbi/dbi_cost_functions.ipynb b/examples/dbi/dbi_cost_functions.ipynb index 004e95531f..7a4190b2a3 100644 --- a/examples/dbi/dbi_cost_functions.ipynb +++ b/examples/dbi/dbi_cost_functions.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -37,9 +37,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.8|INFO|2024-05-31 17:19:52]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -59,9 +67,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.6060645454545454\n", + "hyperopt_search step: 0.5991419607611053\n", + "polynomial_approximation step: 0.5267891182131145\n" + ] + } + ], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", @@ -87,9 +105,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.6060645454545454\n" + ] + }, + { + "data": { + "image/png": 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", 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+fJVRp75JZ82ahalTp+pf5+TkwN3d3eDHWTuyHQBApxNRrNGhqFSLOwUluJFTjIzcIqRnFeFSRh4SMnKRcCMPhaVaRF65hcgrtwAA5goZOje2x1O+zuju64wGtiqDZyQiMhoFBcDfX9I1Li8PUFdseMbc3FysW7cOP/zwA5566ikAQHh4OFxdXcut9+STT5YrWv57lWb16tVo3Lgxli5dCgBo1qwZzpw5g//7v/+rUI6UlBQ8//zzaNmyJQDA29tb/97nn3+ONm3aYMGCBfpl33zzDdzd3XHx4kU0bdoUzz//fLn9ffPNN3B0dER8fHy5dkNTpkzBc889p3/90UcfYdq0aZg8ebJ+Wbt27crtKywsDEOHDgUALFiwAJ9++imOHTuGXr16VeizVZbRFjkODg6Qy+W4ceNGueU3btxAgwYN7ruNUqmEUqmsiXgAAJlMgIW5HBbmctRTm8Pb8d4fUp1OxOWbeYhOuoPjSbcRlXgbaVmF2HvhJvZeuIn3tp1Fe6/6eD6wIXq3bABrlVmN5Sciooq7cuUKSktLERwcrF9ma2uLZs2alVuvbdu2D93PuXPn0L59+3LLOnbsWOEcb7zxBl599VXs2rUL3bt3x/PPP49WrVoBAE6dOoW9e/fqr+z82+XLl9G0aVMkJCRg9uzZiIqKQmZmJnS6srsSKSkp5Yqcf3+OjIwMpKen64u7B7mbAwDUajVsbGyqtbOQ0RY55ubmCAoKwu7duzFw4EAAgE6nw+7du++5B1ibyWQCfJyt4eNsjZfaN4IoikjIyMOf8Tew+9wNxKRmISqxrPh5/5ez6NPSBSM7eSLA3U7q6EQmQ5uvxUGrgwCAkLwQyNU12y4mvySf0zo8jKVl2RUVqY5tYOoKXhmqqjFjxqBnz57YsWMHdu3ahYULF2Lp0qV4/fXXkZeXh2eeeea+V4XuzhH1zDPPwMPDA2vWrIGrqyt0Oh1atGiBkpKSB36Ou9MwPIqZWfk/1AVB0BdR1cFoixyg7N7nyJEj0bZtWwQHB2P58uXIz8/HqFGjpI5WZYIgoKmzNZo6W2NityZIzyrE1pg0/HzyKi7fzMfWmDRsjUlDYCM7hHX2Qu8WDWAmN9r240REjyYIFb5lJCVvb2+YmZkhOjoajRo1AgBkZ2fj4sWLeOKJJyq8H19f33sa5B49erRSWdzd3TFhwgRMmDABs2bNwpo1a/D6668jMDAQW7Zsgaen5317c926dQsXLlzAmjVrEBISAgA4dOjQI49nbW0NT09P7N69G926datU1upk1EXOCy+8gJs3b2L27Nm4fv06AgIC8Pvvv9/TGNmYudpZYGK3JngttDFiU7PwXWQy/nc6HSdTsnAyJQaN6lti0pNN8GwbNxY7RFUks5ShU0Yn/XOiqrC2tsbIkSMxY8YM1K9fH05OTpgzZw5kMlmlpiiYMGECli5dihkzZmDMmDE4ceIEIiIiKrz9lClT0Lt3bzRt2hR37tzB3r174evrC6CsUfKaNWswdOhQvPXWW6hfvz4uXbqEjRs3Yu3atahXrx7s7e3x1VdfwcXFBSkpKZg5c2aFjjt37lxMmDABTk5O6N27N3Jzc3H48GG8/vrrFc5uaEb/0zxp0iQkJyejuLgYUVFR99zHNBWCIKBNo3pY9kIADs98EpOf8oG92hwptwvw1ubTeGrpfvx0PBVanSh1VCKjIwgCzB3NYe5ozjmT6LEsW7YMHTt2RL9+/dC9e3d07twZvr6+UKkq3oGkUaNG2LJlC7Zt24bWrVvjiy++KNdQ+FG0Wi0mTpwIX19f9OrVC02bNsWqVasAAK6urjh8+DC0Wi169OiBli1bYsqUKbCzs4NMJoNMJsPGjRtx4sQJtGjRAm+++SYWL15coeOOHDkSy5cvx6pVq+Dv749+/fohISGhwrmrgyCKYp39VszJyYGtrS2ys7NhY2MjdZxKKyjR4PujyfjqwBVk5pXdK23ewBrv9vVFiI+jxOmIqKLuaZNTin96E1Wid48pKCoqQmJiIry8vCpVGNRW+fn5cHNzw9KlS/HKK69IHceoPOzfQkW/v43+Sk5dZmmuwLgnGuPAW93wTp/msLUww/nruRj+9TGMCj+GSxm5UkckMgq6Yh0uTryIixMvQlfMwTmp6mJiYrBhwwZcvnwZJ0+exLBhwwAAAwYMkDhZ3cQixwTcLXb2zwjF6M5eUMgE7L1wE71XHMTSXRdQVKqVOiJRrSZqRKSvSkf6qnSImjp7cZsMZMmSJWjdujW6d++O/Px8HDx4EA4ODgbb/93Riu/3qMxtrbrAqBseU3l2luaY/YwfXu7QCB/tOIc95zPw2Z5L+N+pdMx/tiU6NzHcDxkRGY5cJkcfnz765wD/MDFWbdq0wYkTJ6r1GGvXrkVhYeF936uukYONFYscE+TtaIWvR7bF72evY872OCTdKsCwtVEYGuyO9/r6Qa3k/3ai2kSlUGHHSzv+WcBpHegh3NzcpI5gNHi7ykQJgoDeLV3w17SuGNHRA4IAbDiWit4rDiI66bbU8YiIiKodixwTZ6MywwcDWuCHMR3gZmeBlNsFGPJlJD7+7TxKOfs5ERGZMBY5dUTHxvb4bUoIBgU1hCgCX+y/jBe+jERa1v3v6xJRzckvyYd6gRrqBWrkl/BWFZGhsMipQ2xUZlgyuDVWDwuEtUqBkylZ6PvpQew5f+PRGxNRtSooLUBBaYHUMYhMCoucOqh3SxfseD0ErRraIqugFKMjjmPR7+eh42jJRERkQljk1FGN7C2xaUJHhHXyBACs2ncZY789jtyiUmmDEREZqdDQUEyZMkXqGPQvLHLqMKVCjrn9/bH8hQCYK2TYfT4Dz646gqRMtgkgIqLy5s6di4CAAKljVAqLHMLANm7YNL4jnG2UuJSRhwErD+PwpUypYxER0WPSarXQ6epuT1oWOQQAaO1uh+2TuqC1ux2yC0sx4ptj+Ck6VepYRERGRafT4a233kL9+vXRoEEDzJ07FwAwevRo9OvXr9y6paWlcHJywtdffw2g7HbXpEmTMGnSJNja2sLBwQHvv/8+/j2PdnFxMaZPnw43Nzeo1Wq0b98e+/bt078fEREBOzs7bN++HX5+flAqlUhJScGdO3cwYsQI1KtXD5aWlujdu3e5GcLvbrdt2zb4+PhApVKhZ8+eSE1N1b8/b948nDp1CoIgQBAEREREVM9JNCAWOaTnbKPCj+M6YGCAK7Q6EW9tOY3PdiegDk9UT3WFDLDtagvbrraS/FaUCTJ09eiKrh5dIRP4a/lB8kvyH/go0hRVeN3C0sIKrVsV69atg1qtRlRUFBYtWoQPPvgAf/75J8aMGYPff/8d165d06/766+/oqCgAC+88EK57RUKBY4dO4YVK1Zg2bJlWLt2rf79SZMmITIyEhs3bsTp06cxePBg9OrVq1zBUlBQgP/7v//D2rVrERcXBycnJ4SFheH48ePYvn07IiMjIYoi+vTpg9LS0nLbzZ8/H99++y0OHz6MrKwsvPjiiwCAF154AdOmTYO/vz+uXbuGa9eulctdWwliHf4Gq+hU7XWNKIpY/McFrNp3GQAwrH0jfDCgBeQyQeJkRHVEfj5gZVX2PC8PUKulzVODioqKkJiYCC8vL6hUqnLvCfMe/Duoj0+fclNjqBeoH9glv6tHV+wL26d/7bjYEZkF996iF+dU7usxNDQUWq0WBw8e1C8LDg7Gk08+iY8//hj+/v4YOXIk3nrrLQBA//79YW9vj/DwcP32GRkZiIuLgyCUfdaZM2di+/btiI+PR0pKCry9vZGSkgJXV1f9Mbp3747g4GAsWLAAERERGDVqFGJjY9G6dWsAQEJCApo2bYrDhw+jU6dOAIBbt27B3d0d69atw+DBg/XbHT16FO3btwcAnD9/Hr6+voiKikJwcDDmzp2Lbdu2ITY2tlLnpaoe9m+hot/f/JOB7iEIAt7q1Rzz+vtDEID1USl4bf0JzmZORPQIrVq1KvfaxcUFGRkZAIAxY8boC5obN27gt99+w+jRo8ut36FDB32BAwAdO3ZEQkICtFotzpw5A61Wi6ZNm5abeXz//v24fPmyfhtzc/NyOc6dOweFQqEvXgDA3t4ezZo1w7lz5/TLFAoF2rVrp3/dvHlz2NnZlVvH2HCmRnqgkZ084WitxJQfY/FH3A2M+OYYvglrBytO8ElEEsiblffA98pmb/9HxvSMB67731uCSZOTHivXv5mZmZV7LQiCvuHviBEjMHPmTERGRuLIkSPw8vJCSEhIhfedl5cHuVyOEydOQC4v/3mt7l75A2BhYVGuUKrL+G1FD9WnpQvs1eYYs+44jiXexvCvoxAxKhi2FmaP3pjISGjztTjqeRQA0CGpA+Rq+SO2MKz8knx4rvAEUPaFW3duTlWO2rziZ6a61n0c9vb2GDhwIMLDwxEZGYlRo0bds05UVFS510ePHoWPjw/kcjnatGkDrVaLjIyMShVHvr6+0Gg0iIqKKne76sKFC/Dz89Ovp9FocPz4cQQHBwMALly4gKysLPj6+gIou0Kk1RrXFX3erqJHau9tj/Vj28PWwgwxKVl4eW0U7uSXSB2LyKBKM0tRmindYJiZBZn3bRdCpmXMmDFYt24dzp07h5EjR97zfkpKCqZOnYoLFy5gw4YN+OyzzzB58mQAQNOmTTFs2DCMGDECP//8MxITE3Hs2DEsXLgQO3bsuGdfd/n4+GDAgAEYO3YsDh06hFOnTuHll1+Gm5sbBgwYoF/PzMwMr7/+OqKionDixAmEhYWhQ4cO+qLH09MTiYmJiI2NRWZmJoqLiw18dgyPRQ5VSKuGdtgwtgPs1eY4k5aNoWuOIjOv9v8DJ6oImYUM7c62Q7uz7SCz4K9Fqj7du3eHi4sLevbsWa7x8F0jRoxAYWEhgoODMXHiREyePBnjxo3Tvx8eHo4RI0Zg2rRpaNasGQYOHIjo6Gg0atTooccNDw9HUFAQ+vXrh44dO0IURezcubPc7TVLS0u8/fbbeOmll9C5c2dYWVnhxx9/1L///PPPo1evXujWrRscHR2xYcMGA5yR6sXeVexdVSkJN3IxbG0UMnKL0dhRjQ3jOsDJWvXoDYnogfJL8mG1sKxNRd6sPKhLwd5V9+lRYwry8vLg5uaG8PBwPPfcc+XeCw0NRUBAAJYvX17juSIiIjBlyhRkZWXV+LEfhL2rqMb5OFvjx/Ed4WKrwuWb+Xh5bRRu89YVEdFD6XQ6ZGRk4MMPP4SdnR369+8vdaQ6gUUOVZqXgxobx3WAs40SF2/kYfjXUcgu5MSeZLx0JTokzk1E4txE6Erq7hD4VH1SUlLg7OyMH374Ad988w0UCvb7qQm8XcXbVVV2KSMPL34Vicy8ErRpZIfvXmnP7uVklLT5Why0KhvALSQvRJLeVbxdVcbUb1dRxfF2FUmqiZMVvnulPewsy3pdjY6IRmGJcXUvJKoNZIIMbV3boq1rW07rQGRA/Gmix+LrYoPvRreHtVKBY4m3Mf77EyjR8HI/UWVYmFkgemw0osdGw8LMQuo4RCaDRQ49tpYNbRExOhgWZnIcuHgTb285DZ2uzt4FJSKiWoJFDhlEkEc9rH45EAqZgK0xaVj4m/HOdUJERKaBRQ4ZTGgzJywaVDYp3JqDiVhz4IrEiYiMQ0FpATyXe8JzuecDZ84mospjkUMG9VxgQ8zq3RwAMH/nOWyNuSpxIqLaTxRFJGcnIzk7GXW4wyuRwbHIIYMb94Q3XuniBQCYsek0DiVwPh4iokeJiIiAnZ2d1DEqZO7cuQgICKjUNoIgYNu2bdWS50FY5JDBCYKAd/v4on9rV2h0Il5dfwIJN3KljkVERAYyffp07N69W+oYj8Qih6qFTCZg8eBWaOdZD7lFGoxeF80JPYmITISVlRXs7e2ljvFILHKo2igVcnw5vC087C2RersQ4749jqJSDhZIRKYpNDQUkyZNwqRJk2BrawsHBwe8//77+nZWd+7cwYgRI1CvXj1YWlqid+/eSEhIuO++kpKSIJPJcPz48XLLly9fDg8PD+h0Ouzbtw+CIGD37t1o27YtLC0t0alTJ1y4cKHcNqtXr0bjxo1hbm6OZs2a4bvvviv3viAI+PLLL9GvXz9YWlrC19cXkZGRuHTpEkJDQ6FWq9GpUydcvnxZv81/b1dFR0fj6aefhoODA2xtbdG1a1ecPHnycU6nQbDIoWpVX22Ob8LawUalwMmULEzfdIpj6BBRlWjztZV+6P41OKlOoytbXqit0H6rYt26dVAoFDh27BhWrFiBZcuWYe3atQCAsLAwHD9+HNu3b0dkZCREUUSfPn1QWnrv3H+enp7o3r07wsPDyy0PDw9HWFgYZLJ/vr7fffddLF26FMePH4dCocDo0aP1723duhWTJ0/GtGnTcPbsWYwfPx6jRo3C3r17y+33ww8/xIgRIxAbG4vmzZvjpZdewvjx4zFr1iwcP34coihi0qRJD/zcubm5GDlyJA4dOoSjR4/Cx8cHffr0QW6utE0VONEQVbvGjlb4YngQRnx9DL+evgZvBzWm9mgmdSyifwiApZ+l/nmNH14Q4Ofop38O8A+B+7k7v1hl+P3kB6fBTgCAzK2ZiB8SD9uutmizr41+naOeR1GaeW+hESqGVvp47u7u+OSTTyAIApo1a4YzZ87gk08+QWhoKLZv347Dhw+jU6dOAID169fD3d0d27Ztw+DBg+/Z15gxYzBhwgQsW7YMSqUSJ0+exJkzZ/DLL7+UW2/+/Pno2rUrAGDmzJno27cvioqKoFKpsGTJEoSFheG1114DAEydOhVHjx7FkiVL0K1bN/0+Ro0ahSFDhgAA3n77bXTs2BHvv/8+evbsCQCYPHkyRo0a9cDP/eSTT5Z7/dVXX8HOzg779+9Hv379KnsaDYZXcqhGdGrsgAXPtQQAfLrnEn47c03iRET/kFvKERwXjOC4YMgta3ZyTgCwNLNE3GtxiHstDpZmljV+fDKcDh06/F2olunYsSMSEhIQHx8PhUKB9u3b69+zt7dHs2bNcO7c/QdPHThwIORyObZu3QqgrPdVt27d4OnpWW69Vq1a6Z+7uLgAADIyMgAA586dQ+fOncut37lz53uO+e99ODs7AwBatmxZbllRURFycnLum/XGjRsYO3YsfHx8YGtrCxsbG+Tl5SElJeW+69cUXsmhGjOkrTsuXs/F2kOJmLbpFLwdrdCsgbXUsYjISITkhVR6G0H5T8Hh8KxD2T7+8+d9h6QOjxutWpibm2PEiBEIDw/Hc889hx9++AErVqy4Zz0zMzP987sFlk5XuTkE77ePyux35MiRuHXrFlasWAEPDw8olUp07NgRJSUllcphaLySQzVqZu/m6NzEHgUlWoz99jiyCqT9ASAi4yFXyyv9kCn++ZqTKWRlyy3kFdpvVURFRZV7fbd9ip+fHzQaTbn3b926hQsXLsDPz++B+xszZgz++usvrFq1ChqNBs8991yl8vj6+uLw4cPllh0+fPihx6yKw4cP44033kCfPn3g7+8PpVKJzEzpx0hjkUM1SiGX4fOhgWhYzwIptwvw+oYYaNkQmSSmLdDimP8xHPM/Bm1BzfcALCgtgP8qf/iv8ue0DkYuJSUFU6dOxYULF7BhwwZ89tlnmDx5Mnx8fDBgwACMHTsWhw4dwqlTp/Dyyy/Dzc0NAwYMeOD+fH190aFDB7z99tsYOnQoLCwqN0v9jBkzEBERgdWrVyMhIQHLli3Dzz//jOnTpz/uRy3Hx8cH3333Hc6dO4eoqCgMGzas0lmrA4scqnH11Ob4anhbWJjJcTAhE4v+OC91JKrrRKAgvgAF8QWStPkVRRHxN+MRfzOe0zoYuREjRqCwsBDBwcGYOHEiJk+ejHHjxgEo6xkVFBSEfv36oWPHjhBFETt37ix3W+h+XnnlFZSUlJTrNVVRAwcOxIoVK7BkyRL4+/vjyy+/RHh4OEJDQ6vy8R7o66+/xp07dxAYGIjhw4fjjTfegJOTk0GPURWCaKQ/UfPnz8eOHTsQGxsLc3NzZGVlVXofOTk5sLW1RXZ2NmxsbAwfkh7q19PpmPRDDADg06Ft0L+1q8SJqK4StSKyDmYBAOxC7CDIa7aLVX5JPqwWWgEA8mblQV0KwKrsNfLyALW6RvNIqaioCImJifDy8oJKpZI6TqWEhoYiICAAy5cvN+h+P/zwQ2zatAmnT5826H5ru4f9W6jo97fRXskpKSnB4MGD8eqrr0odhaqoXytXvBraGADw1uZTOHft/q32iaqbIBdQL7Qe6oXWq/ECh+hB8vLycPbsWXz++ed4/fXXpY5jlIy2yJk3bx7efPPNcl3cyPhM79EMTzR1RFGpDhPXn0Ru0b1jVRAR1UWTJk1CUFAQQkNDq3SriupYF/Li4mIUF/8zf9KD+vtTzZHLBKx4IQB9Pz2IK5n5mPnzGXw+tE25cSaIqpuuVIdrX5WN3eQyzgUyM6P9+48ktG/fPoPuLyIiAhEREQbdZ11Tp36SFy5cCFtbW/3D3d1d6kiEsobIn70UCIVMwI7T1/BtZLLUkaiOEUtEJExKQMKkBIglRtlMkYjuo1YVOTNnzoQgCA99nD9f9Z44s2bNQnZ2tv6RmppqwPT0OII86mFWH18AwEc74hGbmiVtIKIaJAgCPGw94GHrwauYfzPSPjFkQIb4N1CrbldNmzYNYWFhD13H29u7yvtXKpVQKpVV3p6q1+jOnohOvI3f465j4vqT2PFGF9hZmksdi6jaWZpZImlK0j8LSvIlyyK1u92pCwoKasU4KySdgoKyMaMe1cX+YWpVkePo6AhHR0epY5BEBEHAosGtEH8tBym3CzDtp1NYM6ItZDL+ZUtUV8jlctjZ2ennXrK0tOTVrTpGFEUUFBQgIyMDdnZ2kMurPp9crSpyKiMlJQW3b99GSkoKtFotYmNjAQBNmjSB1d3xJcjo2KjMsGpYIJ5bfQS7z2fgq4NXMKFrY6ljEVENatCgAYB/JpmkusnOzk7/b6GqjLbImT17NtatW6d/3aZNGwDA3r17DT6SI9WsFm62mPuMP97ZegZL/riADt72CHC3kzoWUbUpLC3EExFPAAAOhB1AXb9JIwgCXFxc4OTkhNJSDitRF5mZmT3WFZy7jLbIYdc60zY02B2HLt3EzjPXMXljDHa8EQIrpdH+cyV6KJ2ow/H04/rnVEYulxvki47qrlrVu4roLkEQsPDZVnCzs0DyrQLM3nZW6khERGRkWORQrWVraYblLwZAJgA/x6RhW0ya1JGIiMiIsMihWq2dZ328/qQPAOC9bWeRcqtA4kRERGQsWORQrff6k03Q1qMe8oo1eH1jDEq1bLNARESPxiKHaj2FXIblLwbAWqXAqdQsLP/rotSRiIjICLDIIaPQsJ4lPn6uFQBg1b7LiLpyS+JEZGrMHMxg5lD1kVUfl4OlAxwsHSQ7PpEpEsQ6PEFITk4ObG1tkZ2dDRsbG6njUAXM2HQKm05cRcN6Fvh9yhPsVk6mKT8fuDuoaV4eoFZLm4eolqno9zev5JBRmf2MHxrWs8DVO4X48H/xUschIqJajEUOGRVrlRmWDG4NQQB+PJ6KP+NvSB2JiIhqKRY5ZHQ6eNtjTBcvAMCsn0/jVl6xxInI2GkLtYgJjUFMaAy0hdoaP35haSFCI0IRGhGKwtLCGj8+kalikUNGaVqPZmjmbI3MvBLM+vkM6nDTMjIEHZC9PxvZ+7MBCUYo0Ik67E/ej/3J+zmtA5EBscgho6Qyk2PZC61hJhewK/4GNp+4KnUkMmKCUoDfT37w+8kPglKQOg4RGQiLHDJa/q62mNK9KQBg3v/ikXqboyFT1cgUMjgNdoLTYCfIFPy1SGQq+NNMRm1C18YI+ns05Jk/n+ZtKyIi0mORQ0ZNLhOwdHBrqMxkOHzpFjYcS5U6EhkhnUaHjE0ZyNiUAZ2GbWKITAWLHDJ6ng5qTO/RDACwYOc5pGWxdwpVjlgsIn5IPOKHxEMs5tVAIlPBIodMwqjOXghsZFd222oLb1uR8bE0s4SlmaXUMYhMCoscMglymYDFg1vDXCHDwYRMbDrO3lZkPNTmauS/k4/8d/KhNucUDkSGwiKHTEZjRytMe7qst9WHO+JxPbtI4kRERCQlFjlkUsaEeCPA3Q65RRq8s5WDBBIR1WUscsikyGUCFg9qBXO5DHvOZ+Dnk2lSRyJ6pCJNEfr+0Bd9f+iLIg2vQBIZCoscMjk+ztaY3N0HADDvf3HIyOGXBtVuWp0WOxN2YmfCTmh1NT93FpGpYpFDJmn8E95o6WaLnCIN3t12lretiIjqIBY5ZJIUchmWDC6b2+rP+Bv4/ex1qSMREVENY5FDJqtZA2u82rUxAGDO9jhkF5ZKnIiIiGoSixwyaa91awJvRzUycovxf7+flzoOERHVIBY5ZNJUZnIseLYlAOCHqBREJ92WOBEREdUUFjlk8jp42+PFdu4AgJlbTqNYw94rRER1AYscqhNm9faFg5USl2/mY9Xey1LHoVpGrpYjVAxFqBgKuVpe48dXm6shzhEhzhE5rQORAbHIoTrB1tIM8/r7AwBW7buESxm5EiciIqLqxiKH6ow+LRvgqeZOKNWKmLnlDHQ6jp1DRGTKWORQnSEIAj4c2AJqczmOJ9/BhugUqSNRLaEt0iJucBziBsdBW1TzbbaKNEUYvGkwBm8azGkdiAyIRQ7VKa52FpjesxkA4OOd5znlA5XRAjc338TNzTcBCdqla3VabI7fjM3xmzmtA5EBscihOmdER0+0drdDbrEGH+44J3UcqgUEcwE+n/vA53MfCOaC1HGIyEBY5FCdI5cJmD+wBWQC8L9T6TiUkCl1JJKYzEwGt4lucJvoBpkZfy0SmQr+NFOd1MLNFiM6egIAZv9ylmPnEBGZIBY5VGdN7dEUjtZKXMnMx5f7r0gdhyQkakXc2XcHd/bdgahlrzsiU8Eih+osG5UZ3u/nBwD4fO8lJN/KlzgRSUVXpMOpbqdwqtsp6Ip0UschIgNhkUN12jOtXNC5iT1KNDrM2R4HUeRf8UREpoJFDtVpgiDgwwEtYC6XYd+Fm/j97HWpI1EdZGlmibxZeciblQdLM0up4xCZDBY5VOd5O1phQldvAMC8/8Ujr1gjcSKqawRBgNpcDbW5GoLALuxEhsIihwjAa92aoFF9S1zPKcKKvy5KHYeIiAyARQ4RAJWZHPMGlE3g+c3hJJy/niNxIqpLijXFCNsWhrBtYSjWFEsdh8hkGGWRk5SUhFdeeQVeXl6wsLBA48aNMWfOHJSUlEgdjYxYt2ZO6OXfAFqdiPe2nuUEnlRjNDoN1p1ah3Wn1kGj4+1SIkMxyiLn/Pnz0Ol0+PLLLxEXF4dPPvkEX3zxBd555x2po5GRm/2MHyz/nsBz88mrUschIqLHYJRFTq9evRAeHo4ePXrA29sb/fv3x/Tp0/Hzzz9LHY2MnKudBaZ09wEALPr9PLILSyVOREREVWWURc79ZGdno379+g9dp7i4GDk5OeUeRP8V1skL3o5qZOaVYDkbIRMRGS2TKHIuXbqEzz77DOPHj3/oegsXLoStra3+4e7uXkMJyZiYK2SY+0xZI+RvI5Nx4XquxImIiKgqalWRM3PmTAiC8NDH+fPny22TlpaGXr16YfDgwRg7duxD9z9r1ixkZ2frH6mpqdX5cciIPdHUET39naHViZjLkZCJiIySQuoA/zZt2jSEhYU9dB1vb2/98/T0dHTr1g2dOnXCV1999cj9K5VKKJXKx41JdcR7ff2w78JNRF65hR1nrqFfK1epIxERUSUIopH+iZqWloZu3bohKCgI33//PeRyeaX3kZOTA1tbW2RnZ8PGxqYaUpKxW/7XRSz/KwEutirsntYVlua16u8CMhBRFFGaWdbI3MzBrMZHHRZFEZkFmQAAB0sHCAUFgJVV2Zt5eYBaXaN5iGq7in5/V+o3tpeXV5V++KdMmYI33nij0ts9SFpaGkJDQ+Hh4YElS5bg5s2b+vcaNGhgsOMQTejaGJtPXMXVO4VYufcSZvRsLnUkqgaCIMDc0VzS4zuqHSU7PpGpqlSRExERUaWDeHp6Vmm7B/nzzz9x6dIlXLp0CQ0bNiz3npFemKJaSmUmx3t9/TDh+xNYcyARg4Pc4enAv6qJiIyB0d6uMgTerqKKEEURI745hoMJmXiquRO+DmsndSQyMF2xDpemXgIANFnWBDJlzfbJKNYUY+ofUwEAy3oug7JYw9tVRA9R0e/vWtW7iqg2EgQBc57xh0ImYPf5DOw5f0PqSGRgokZE+qp0pK9Kh6ip+b/7NDoNVh1fhVXHV3FaByIDYpFDVAFNnKzwShcvAMAH/4tHsUYrcSIyJMFMgMccD3jM8YBgVrONjomo+hhlw2MiKbz+lA+2xqQh6VYB1h5MxMRuTaSORAYiM5fBa66X1DGIyMCMsuExkRSslArM6tMcb/54Cp/vuYRn27jB1c5C6lhERPQAlSpyunbtWl05iIzCwAA3/BCVguikO1j423l8NrSN1JHIAESdiIJzBQAAS19LCDLesiIyBY/VJqe0tBSpqam4cOECbt++bahMRLXW3UbIggD871Q6jifx370p0BXqEN0iGtEtoqEr1Ekdh4gMpNJFTm5uLlavXo2uXbvCxsYGnp6e8PX1haOjIzw8PDB27FhER0dXR1aiWqGFmy2GBJVN7jrvf/HQ6ersKAxERLVapYqcZcuWwdPTE+Hh4ejevTu2bduG2NhYXLx4EZGRkZgzZw40Gg169OiBXr16ISEhobpyE0lqes9msFIqcCYtG1tOXpU6Dhk5CzMLJE5OROLkRFiYsZ0XkaFUqk1OdHQ0Dhw4AH9///u+HxwcjNGjR+OLL75AeHg4Dh48CB8fH4MEJapNHK2VmPRkE3z823ks+uMCerd0gZWS81pR1cgEGTztPKWOQWRyKvVbecOGDRVaT6lUYsKECVUKRGQsRnX2xIZjKUi+VYDV+zivFRFRbWOwwQDXrl1rqF0RGQWlQo53+vgCANYcTETq7QKJE5GxKtGWYMauGZixawZKtCVSxyEyGQYrcn799Vfs2bNH/7qgoAAvvviioXZPVCv18HNGp8b2KNHosPC3c1LHISNVqi3FksglWBK5BKXaUqnjEJkMgxU53377LWbPno3z58/j4sWLeOKJJ9CzZ09D7Z6oVhIEAbOf8YNMAHaeuY6jV25JHYmIiP722C0l33zzTQQEBKB169ZYu3Ythg0bBp1Oh/DwcAQEBBggIlHt1ryBDYYGN8L6qBR88L94/O/1LpBzMDkiIsk99pWcbt26ITU1FQsWLMDgwYORmJgIV1dX/PHHH9ixY4chMhLVelOfbgprlQLx13Kw6Xiq1HGIiAgGuJLTv39/9O/fX/+6qKgIZ8+exenTp/HXX3+hb9++j3sIolrP3kqJyU/54KMd57Bk1wX0beUCa5WZ1LGIiOo0gw/soVKp0LZtW7Rt29bQuyaq1UZ09MQPUSm4kpmPz/dcwqy/e14REZE0Kn27ytraGk888QTefPNNfPfdd4iLi4Moclh7InOFDO/1KytsvjmciKTMfIkTERHVbYJYyQpl1apVOHHiBI4fP474+HjodDpYWFigVatWCAoKQmBgIAIDA9G6devqymwwOTk5sLW1RXZ2NmxsbKSOQyZAFEWMDI/GgYs38bSfM9aM4BVNYyD1LOQ6UYdzN8uGIPB19IWsoBCwsip7My8PUKtrNA9RbVfR7+9KFzn/VlhYCLVajbfffhu3b9/GyZMncfbsWZSUlECr1VZ1tzWGRQ5Vh4Qbuei14iC0OhHfv9IeXXwcpI5ExiY/n0UO0UNU9Pv7sXpXWViUTSQ3dOhQfPnll4iOjkZubi5iYmIeZ7dERs3H2RrDO3gAAD7aEQ8tZyknIpKEwQYDvEuhUKBVq1aG3i2RUZnS3Qc2KgXOX8/F5hPsUl7b6Up0SJybiMS5idCV6Gr8+CXaEszdNxdz983ltA5EBmTwIoeIADtLc7zxlA8AYMmui8gr1kiciB5GLBWRPC8ZyfOSIZbW/JW3Um0p5u2fh3n753FaByIDqnSRM2bMGKxevRrR0dEoLi4GUDa0PRGVN6KjJzztLXEztxhf7r8sdRx6CEEhwPU1V7i+5gpBwd9nRKai0uPkJCQkYNOmTcjNzYVCUbb5vHnzEBoaisDAQAQEBMDS0tLgQYmMjblChpm9m2PC9yex5uAVDA1uBFc7C6lj0X3IlDI0XdlU6hhEZGCVvpKzf/9+ZGdn48KFC/j2228xffp0ZGVlYfbs2ejSpQtsbW3h7+9fHVmJjE5P/wYI9qyPolIdlvxxQeo4RER1SpVHPPbx8YGPjw9efPFF/bLExEQcP36cvauI/iYIAt7r54v+nx/GzzFpCOvsiVYN7aSORf8hiiJKM8vawpg5mPEWPJGJqNSVnJSUlIe+7+XlhcGDB2PBggUAgLS0tKonIzIRrRra4dk2bgCAj3ac4wjhtZCuQIcjTkdwxOkIdAU137uKiKpHpYqcdu3aYfz48YiOjn7gOtnZ2VizZg1atGiBLVu2PHZAIlMwo2czKBUyHEu8jT/ibkgdh4ioTqjU7ar4+HjMnz8fTz/9NFQqFYKCguDq6gqVSoU7d+4gPj4ecXFxCAwMxKJFi9CnT5/qyk1kVFztLDA2xBuf772Ej387hyebO8FcwREcqIxKocKxMcf0z1FaJHEiItNQpWkdCgsLsWPHDhw6dAjJyckoLCyEg4MD2rRpg549e6JFixbVkdXgOK0D1aS8Yg1CF+9DZl4x3u/nh1e6eEkdif6mzdfioNVBAEBIXgjkarm0gTitA9FDVfT7u0oNjy0sLNCrVy8MGjSoygGJ6horpQLTezTFzJ/P4NPdCXg+0A12luZSxyIiMllVvl5ua2vLNjdElTS4rTuaN7BGdmEpVuxOkDoO1RIl2hIsPrwYiw8v5rQORAZU5SJHFEV8+eWX6Ny5M7p06YIpU6Y8tEEyEQFymYB3+/oCAL6LTMaVm3kSJ6LaoFRbirf+egtv/fUWp3UgMqDHavkYExODwMBAdOnSBXFxcQgJCcH06dMNlY3IJIX4OKJbM0dodCI+/u281HGIiExWlQcDBIAffvgBTz/9tP716dOnMWDAALi5ueHNN9987HBEpuqdPr44kJCJXfE3cPTKLXTwtpc6EhGRyanylZz69evD3d293LJWrVrh888/x+rVqx87GJEp83G2xtDgsp+fj3bEQ6fjAIFERIZW5SInICAA4eHh9yxv0qTJI0dGJiJgSvemsFIqcDYtB1tjODo4EZGhVbnI+eijj/Dpp59i+PDhiIyMRH5+PjIyMrBgwQJ4eXH8D6JHcbBSYmK3JgCAxX9cQGGJVuJERESmpcpFTocOHXD06FGkpqYiJCQENjY2cHFxwebNm7F06VJDZiQyWaM6e8LNzgLXc4qw5uAVqeMQEZmUKo14/F8ZGRk4ceIEdDod2rdvDwcHB0Nkq3Yc8Zhqg+2n0vHGhhhYmMmxb0YonG1UUkeqc0StiKyDWQAAuxA7CPKanYVcq9PiYMrfIy43CoG8sIgjHhM9REW/vw1S5BgrFjlUG4iiiOdWH0FMShaGtG2IRYNaSx2JpMZpHYgeqqLf30Y7Q2D//v3RqFEjqFQquLi4YPjw4UhPT5c6FlGlCYKA9/4eIHDTiauIT8+ROBERkWkw2iKnW7du+Omnn3DhwgVs2bIFly9f5lxaZLSCPOqjb0sXiCKwYOc51OELrJLQleqQtjINaSvToCvV1fjxS7WlWHlsJVYeW8kRj4kMyCC3qy5evAhvb28oFI81tuBj2b59OwYOHIji4mKYmZlVaBverqLaJOVWAbov248SrQ7hYe3QrbmT1JHqDKlnIc8vyYfVwrLbU3mz8qAuBW9XET1Ejd6u8vX1xZUr0vUMuX37NtavX49OnTo9tMApLi5GTk5OuQdRbdHI3hIjO3kAAObvPAeNtuavKNRZcsBxkCMcBzkCNVvfEFE1MkiRI9Wl9bfffhtqtRr29vZISUnBL7/88tD1Fy5cCFtbW/3jvyM2E0ltUjcf2Fma4VJGHjZGp0odp86Qq+Tw3+QP/03+kKtY5RCZilrVJmfmzJkQBOGhj/Pn/5nQcMaMGYiJicGuXbsgl8sxYsSIhxZcs2bNQnZ2tv6RmsovEapdbC3NMPkpHwDAJ39eRG4R22cQEVWVdI1o7mPatGkICwt76Dre3t765w4ODnBwcEDTpk3h6+sLd3d3HD16FB07drzvtkqlEkql0pCRiQxuWHsPfBuZjMTMfKzedxlv9WoudSQiIqNUq4ocR0dHODo6Vmlbna6s/UJxcbEhIxHVOHOFDDN7N8f4707g60OJGNbBA252FlLHMmlSNzwmoupRq25XVVRUVBQ+//xzxMbGIjk5GXv27MHQoUPRuHHjB17FITImPfycEexVH8UaHRb/fv7RGxAR0T2MssixtLTEzz//jKeeegrNmjXDK6+8glatWmH//v28HUUm4d8DBG6LTcep1CxpA1G1UiqU+HXor/h16K9QKvg7jMhQatXtqopq2bIl9uzZI3UMomrVqqEdnm3jhq0xaZi/4xx+HN8BglCzcypRzVDIFOjbtO+/lvC2O5EhGORKzttvvw17e3tD7IqI/mVGz2ZQKmQ4lnQbf8TdkDoOEZFRMUiRs3DhQhY5RNXA1c4CY0K8AAAf/3YOJRoOEGiKSrWliIiNQERsBKd1IDIgo2yTQ1SXvBraBA5W5ki6VYDvjyZLHYeqQYm2BKN+GYVRv4xCibZE6jhEJoNFDlEtZ6VUYOrTzQAAn+5JQHYB/9InIqoIFjlERmBI24Zo6myFrIJSfLYnQeo4RERGgUUOkRFQyGV4p09Zl/J1kUlIvpUvcSIiotqPRQ6RkQht5oQQHweUakX8HwcIJCJ6pEqNk+Pl5VWlcTqmTJmCN954o9LbEVF57/b1RZ8VB7HzzHUcT7qNtp71pY5ERFRrVarIiYiIqNJBPD09q7QdEZXXvIENhrR1x8boVHy04xy2vtaJAwQSET1ApYqcrl27VlcOIqqgqT2aYvupdMSmZuF/p6+hf2tXqSMZPUEpwO8nP/3zmqZUKPHToJ/0z1HKEY+JDEEQRVGUOoRUcnJyYGtri+zsbNjY2Egdh6jCPt2dgGV/XoSbnQV2T+sKlRlnzTYp+fmAlVXZ87w8QK2WNg9RLVPR7282PCYyQmNDvNHARoW0rEJEHEmSOg4RUa3EhsdERsjCXI7pPZth+qZTWLnnEgYHNYS9FWevriqdRofMrZkAAIdnHSBT1OzffxqdBlvPbQUAPOv7rHHOnExUC7HhMZGReq6NG8IPJyIuPQcrdifggwEtpI5ktMRiEfFD4gEAIXkhlfzN+PiKNcUYsnkIACBvVh6LHCIDYcNjIiMlkwl4t68vXloThfVRKRjR0RNNnKykjmWcZIBtV1v9cyIyDY/141xaWorU1FRcuHABt2/fNlQmIqqgTo0d0N3XCVqdiI9/Oyd1HKMlt5Cjzb42aLOvDeQWbMRNZCoqXeTk5uZi9erV6Nq1K2xsbODp6QlfX184OjrCw8MDY8eORXR0dHVkJaL7mNnbF3KZgL/OZeDI5Uyp4xAR1RqVKnKWLVsGT09PhIeHo3v37ti2bRtiY2Nx8eJFREZGYs6cOdBoNOjRowd69eqFhAROJEhU3Zo4WWFY+0YAgPk7zkGnq7OjQhARlVOpNjnR0dE4cOAA/P397/t+cHAwRo8ejS+++ALh4eE4ePAgfHx8DBKUiB5s8lM+2HoyDXHpOfg5Jg2DghpKHcmoaPO1OOp5FADQIakD5GresiIyBZUqcjZs2FCh9ZRKJSZMmFClQERUefZWSkx8sgk+/u08lvxxAX1busDCnF/UlVGaWSp1BCIyMPYjIDIRYZ084WZnges5RVhz8IrUcagSzOXmCB8QjvAB4TCXm0sdh8hkGGQ4hjt37mDXrl1IS0sDALi6uqJnz56oV6+eIXZPRBWgMpPj7d7N8caGGHyx/zJebOcOJxuV1LGoAszkZggLCPvXkhKpohCZlMe+kvP111+jY8eOiIqKgk6ng06nQ1RUFDp16oSvv/7aEBmJqIKeaeWCNo3sUFCixbI/L0odh4hIUo89QWezZs1w8uRJqP8zgVxeXh4CAwNx8WLt/UXLCTrJFJ1Ivo3nV0dCJgA7J4egeQP+234Ubb4WB60OAigb8bimGx5rdBr8cekPAEDPJj2hKCzmBJ1ED1FjE3QKgoDc3Nx7lufm5lZpnisiejxBHvXRt6ULdGJZl/LH/DuGakCxphj9NvRDvw39UKwpljoOkcl47DY5S5YsQdeuXdGiRQu4ubkBAK5evYq4uDgsXbr0sQMSUeW93as5/oy/gYMJmdh38Sa6NXOSOhIRUY177CKnX79+6N27N44dO4b09HQAZQ2Pg4ODIZezCyuRFBrZW2JkJw+sOZiIBTvOIaSJAxRydqYkorql0kWOtbU12rRpg6CgIAQGBiIwMBB+fn7o2LFjdeQjoiqa1M0Hm05cRUJGHn48noph7T2kjkREVKMq/afd//3f/8HHxwd79uzB6NGj0apVK1hbW6NTp054/fXXER4ejlOnTlVHViKqBFtLM0x+qmzE8U/+vIjcIg52R0R1S6Wv5Lz22mv654WFhVCr1Xj99ddx+/ZtHD16FGvXrkVJSQm0Wq1BgxJR5Q1r74FvI5ORmJmPL/ZfxoyezaWORERUYx7rJr2FhQUAYOjQofjyyy8RHR2N3NxcxMTEGCQcET0ec4UMM3uXFTZrDyYiPatQ4kRERDXHICMel9uhQoFWrVoZerdEVEU9/JwR7FUfxxJvY/EfF/DJCwFSR6p1BHMBPp/76J/XNHO5OT7v/bn+OUo54jGRIRi8yCGi2kUQBLzX1xf9Pz+MrTFpGNXZE60a2kkdq1aRmcngNtFNsuObyc0wMXjiv5awyCEyhErfrhozZgxWr16N6OhoFBeXDVrFQf+IardWDe3wbJuyL/GPOEAgEdURlb6Sk5CQgE2bNiE3NxcKRdnm8+bNQ2hoKAIDAxEQEABLS0uDByWixzOjZzPsPHMNxxJvY1f8DfT0byB1pFpD1IrIOpgFALALsYMgr9k/3LQ6LQ6m/D2tRKMQcIQxIsOo8txVCQkJOHHiBE6ePKl/ZGVlQS6Xo2nTpoiLizN0VoPj3FVU1yz+4zxW7r0MLwc1/pjyBMwVHCAQkH7uqvySfFgtLJurKm9WHtSl4NxVRA9R0e/vKrfJ8fHxgY+PD1588UX9ssTERBw/fpy9q4hqqVdDm+DH6FQkZuZjfVQyRnX2kjpS7SAAln6W+udEZBoq9WdcSkrKQ9/38vLC4MGDsWDBAgBAWlpa1ZMRkcFZKRV48+mmAIAVuxOQXcABAgFAbilHcFwwguOCIbfkzSIiU1GpIqddu3YYP348oqOjH7hOdnY21qxZgxYtWmDLli2PHZCIDOuFtu7wcbJCVkEpPt+bIHUcIqJqU6nbVfHx8Zg/fz6efvppqFQqBAUFwdXVFSqVCnfu3EF8fDzi4uIQGBiIRYsWoU+fPtWVm4iqSCGX4Z2+vhgVHo11R5LxcgcPeNizzQcRmZ5KXcmxt7fHsmXLcO3aNXz++efw8fFBZmYmEhLK/hocNmwYTpw4gcjISBY4RLVYaFNHhPg4oESrw/wd56SOIzltgRbH/I/hmP8xaAs4JQ2RqahSw2MLCwv06tULgwYNMnQeIqoBgiBgdj8/9FpxELvib+BQQia6+DhIHUs6IlAQX6B/TkSmocr9R21tbdnmhsiI+ThbY3gHDwDAB7/GQaPVSZyo7jKTm2FR90VY1H0RzORmUschMhlVLnJEUcSXX36Jzp07o0uXLpgyZcpDGyRXl+LiYgQEBEAQBMTGxtb48YmM2Zvdm6KepRku3sjDD8ce3nuSqo+53BwzOs/AjM4zyuauIiKDeKyRwGJiYhAYGIguXbogLi4OISEhmD59uqGyVchbb70FV1fXGj0mkamwtTTD1B7NAABLd13EnXzOmUREpuOxJuj84Ycf8PTTT+tfnz59GgMGDICbmxvefPPNxw73KL/99ht27dqFLVu24Lfffqv24xGZoqHt3LH+aDLOX8/F8r8uYt6AFlJHqnO0Oi1OXjsJAAh0CeS0DkQGUuUrOfXr14e7u3u5Za1atcLnn3+O1atXP3awR7lx4wbGjh2L7777rsJzZRUXFyMnJ6fcg6iuU8hlmN3PDwDwfVQKLlzPlThR3VOkKULw2mAErw1GkaZI6jhEJqPKRU5AQADCw8PvWd6kSZNHjoz8uERRRFhYGCZMmIC2bdtWeLuFCxfC1tZW//hvkUZUV3Vq4oBe/g2g1Yn44Nc4zlJORCahykXORx99hE8//RTDhw9HZGQk8vPzkZGRgQULFsDLq2rz4cycOROCIDz0cf78eXz22WfIzc3FrFmzKrX/WbNmITs7W/9ITU2tUk4iU/ROH1+YK2Q4fOkW/oy/IXUcIqLHVuU2OR06dMDRo0cxefJkhISE6P/yU6lU2LRpU5X2OW3aNISFhT10HW9vb+zZsweRkZFQKpXl3mvbti2GDRuGdevW3XdbpVJ5zzZEVKaRvSXGhnhh5d7L+GjHOTzR1BEqM7YOISLj9VgNj1u3bo19+/YhIyMDJ06cgE6nQ/v27eHgULVBxRwdHeHo6PjI9T799FN89NFH+tfp6eno2bMnfvzxR7Rv375KxyYi4LXQJth84ipSbhfgm8OJeC20idSRiIiq7LGKnLucnJzQu3dvQ+yqQho1alTutZWVFQCgcePGaNiwYY3lIDI1aqUCM3s3x5s/nsLney7h+cCGcLZRSR2LiKhKHmucHCIyPQNau6FNIzsUlGix6PcLUschIqqySl3J8fLygiAIlT7IlClT8MYbb1R6u4ry9PRkbxAiA5HJBMx5xh8DVx7GlpNXMbyjBwLc7aSOVa0EMwEeczz0z2uamdwMc7rO0T9HaWmNZyAyRYJYiepg//79VTqIp6cnPDw8qrRtdcrJyYGtrS2ys7NhY2MjdRyiWmXaT6ew5eRVBLjb4edXO0Emq/kv/zorPx/4+zY88vIAtVraPES1TEW/vyt1Jadr166PHYyIjMPbvZrh97PXEJuahZ9j0jAoiO3diMi4sE0OEd2Xk40Kk570AQB8/Ns5ZBea7i0UUSciPy4f+XH5EHU1f+tbJ+oQlxGHuIw46ETOBk9kKCbRJoeIqscrXbyw6UQqrtzMxyd/XsTc/v5SR6oWukIdoltEAwBC8kIgV9fs+ECFpYVosbpszrC8WXngzSkiw6hUkRMREVGlg3h6elZpOyKSlrlChnn9/TH862P4NjIJL7Rzh6+LabZfM3MwkzoCERkY2+QQ0UOF+Diid4sG+O3sdcz5JQ4/ju9QpSu6tZlcLUfnm52ljkFEBsY2OUT0SO/184OFmRzHkm7jl9h0qeMQEVUIixwieiQ3OwtMerJsiof5O88ht8h0GyETkelgkUNEFTImxAteDmrczC3Gir8SpI5jUNpCLWJCYxATGgNtoVbqOERkICxyiKhClAo55jzjBwAIP5KEizdyJU5kQDoge382svdnA+zBTWQyWOQQUYWFNnNCDz9naHUiZv9yltOpGIiZ3AzTO07H9I7Ty6Z1ICKDMMgs5ERUd7zfzw/7L97E0Su38b/T19C/tavUkYyeudwci3ss/tcStnkiMgReySGiSnGvb4mJ3f5uhLwjHnnFGokTERHdH4scIqq0cU94w8PeEjdyivHZbtNqhCwFnahDUlYSkrKSOK0DkQGxyCGiSlOZ/dMI+etDiUgwpUbIEigsLYTXCi94rfBCYWmh1HGITAaLHCKqkiebO6O7rxM0OhHvbWMjZCKqfVjkEFGVzXnGHyozGaISb2PziatSxyEiKodFDhFVmXt9S0zp3hQAsGDnOdzOL5E4ERHRP1jkENFjeaWLF5o3sMadglIs3HlO6jhERHoscojosZjJZZj/bEsAwKYTV3H0yi2JExERlWGRQ0SPLcijHl5q3wgA8O7WMyjWcP4nIpIeRzwmIoN4u2dz7Iq7jss38/HV/it4/SkfqSNVmKAQ4Pqaq/55TVPIFHit7Wv65wAHWCQyBEGsw/0+c3JyYGtri+zsbNjY2Egdh8jo/RKbhskbY2GukGHXlCfg6aCWOpJxys8HrKzKnuflAWqeR6J/q+j3N29XEZHB9G/tii5NHFCi0eF9TuBJRBJjkUNEBiMIAj4a2ALmChkOJmRi+6l0qSNViCiKKLlZgpKbJZIUZqIo4mb+TdzMv8nCkMiAWOQQkUF5Oqgx6e8JPD/89RyyC2v/jNq6Ah2OOB3BEacj0BXU/NxRBaUFcFriBKclTigoLajx4xOZKhY5RGRw47t6w9tRjcy8Ynz823mp4xBRHcUih4gMTqmQY8HfY+dsOJZS68fOkavlCBVDESqGQq6WSx2HiAyERQ4RVYsO3vYYGlw2ds7MLadRVMqxc4ioZrHIIaJqM6tPczjbKJF0qwCf/HVR6jhEVMewyCGiamOjMsNHA8tuW605cAWnr2ZJG+gBtEVaxA2OQ9zgOGiLeMWJyFSwyCGiavW0nzP6tXKBTgTe2nwapdqa7730SFrg5uabuLn5JsAah8hksMghomo3t78/7CzNcP56Lr46cEXqOLWOQqbAyNYjMbL1yL+ndSAiQ2CRQ0TVzsFKiTnP+AEAVvyVgEsZeRInql2UCiUiBkYgYmAElAql1HGITAaLHCKqEQMD3BDazBElWh1mbjkNnY4j+xJR9WKRQ0Q14u6UD2pzOY4n38H3UclSR6o1RFFEfkk+8kvyOa0DkQGxyCGiGtOwniXe7t0cAPDxb+eRcotTGABl0zpYLbSC1UIrTutAZEAscoioRr3c3gPBnvVRUKLFjM2neNuKiKoNixwiqlEymYDFg1vB0lyOqMTbiDiSJHUkIjJRLHKIqMZ52Ksxq48vAOD/fj+PyzfZ24qIDI9FDhFJ4uX2jRDi44BijQ7TN52ClretiMjAWOQQkSQEQcD/Pd8K1koFYlKyOEggERmc0RY5np6eEASh3OPjjz+WOhYRVYKrnQVm/z1I4Cd/XsSF67kSJyIiU2LU44d/8MEHGDt2rP61tbW1hGmIqCoGBTXE72evY/f5DEz9KRbbJnaGmbyG//6SA46DHPXPa5pcJscgv0H655xAi8gwjLrIsba2RoMGDaSOQUSPQRAELHyuJXosP4C49Bys3HsJU7o3rdEMcpUc/pv8a/SY/6ZSqLBp8KZ/FhTnS5aFyJQY7e0qAPj4449hb2+PNm3aYPHixdBoNA9dv7i4GDk5OeUeRCQ9JxsVPhjQAgDw+Z5LOH01S9pARGQSjLbIeeONN7Bx40bs3bsX48ePx4IFC/DWW289dJuFCxfC1tZW/3B3d6+htET0KM+0ckHfli7Q6ERM2RiLgpKH/9FCRPQogliLJkqZOXMm/u///u+h65w7dw7Nmze/Z/k333yD8ePHIy8vD0rl/WfxLS4uRnFxsf51Tk4O3N3dkZ2dDRsbm8cLT0SPLaugBL2WH8T1nCIMDW6Ehc+1rJHjavO1OGh1EAAQkhcCubpmG+bkl+TDaqEVACBvVh7UpQCsyl4jLw9Qq2s0D1Ftl5OTA1tb20d+f9eqNjnTpk1DWFjYQ9fx9va+7/L27dtDo9EgKSkJzZo1u+86SqXygQUQEUnPztIcy15ojWFro7DhWApCmzmipz/b3RFR1dSqIsfR0RGOjo5V2jY2NhYymQxOTk4GTkVENalTYweMe8IbX+6/gplbTiPA3Q7ONqpqPabMUoZOGZ30z4nINBjlT3NkZCSWL1+OU6dO4cqVK1i/fj3efPNNvPzyy6hXr57U8YjoMU17uhlauNngTkEppm+q/kk8BUGAuaM5zB3NIQhCtR6LiGqOURY5SqUSGzduRNeuXeHv74/58+fjzTffxFdffSV1NCIyAHOFDMtfaAOVmQwHEzLxzeFEqSMRkRGqVberKiowMBBHjx6VOgYRVaMmTlZ4r68f3tt2Fot+v4BOjR3g51o9HQR0xTpcmnqp7LjLmkCmNMq//4joP/iTTES11rD2jdDd1wklWh1e33AS+cXV061c1IhIX5WO9FXpEDW1psMpET0mFjlEVGvdncTT2UaJyzfz8f62s6hFo14YjFwmRx+fPujj0+fvaR2IyBBY5BBRrWZvpcSnL7aBTAB+jknDphNXpY5kcCqFCjte2oEdL+2ASlG9PcmI6hIWOURU67X3tsfUp8vms5r9y1lcvMHZyono0VjkEJFReC20CUJ8HFBUqsNr609y2gcieiQWOURkFGQyAZ+8EAAnayUuZeRh9i9xUkcymPySfKgXqKFeoEZ+CWcgJzIUFjlEZDQcrJRY8Xf7nM0nrmKzCbXPKSgtQEFpgdQxiEwKixwiMiodG9tjSvey9jnvbzuL89dzJE5ERLUVixwiMjoTu5W1zyks1WL8dyeQXVAqdSQiqoVY5BCR0ZHLBHz6Yhu42Vkg+VYBpvwYU+3zWxGR8WGRQ0RGqZ7aHF8OD4JSIcPeCzexfHeC1JGIqJZhkUNERquFmy0WPNsSAPDp7gT8GX9D4kREVJuwyCEio/Z8UEOM7OgBAJj6Yyyu3Myr/E5kgG1XW9h2tZXkt6JMkKGrR1d09egKmcBfy0SGIoimOBFMBeXk5MDW1hbZ2dmwsame2Y2JqPqVaHQYtvYoopPuwMfJClsndoaVUiF1rKrLzwesrMqe5+UBarW0eYhqmYp+f/NPBiIyeuYKGVYOC4STtRIJGXl488dYNkQmIhY5RGQanKxV+GJ4EMzlMvwZfwOLd12QOhIRSYxFDhGZjMBG9bBoUCsAwOp9lys8IrI2X4vDjodx2PEwtPna6ox4X/kl+XBc7AjHxY6c1oHIgIz4pjUR0b0GtnHDpYw8fL73Emb9fBoe9pZo51n/kduVZko7oGBmQaakx6e6RxRF3M4vQXpWEa7nFCG7sBQ5haXILdIgp6gUxRotdCJQ1nJXhCgCluYKWCnlsFIpYKU0Qz1LM7jYWcDVVgUHKyVkMkHqj1UOixwiMjlTn26Kyzfz8NvZ6xj/3Qn8MrEz3OtbPnB9mYUM7c620z8nMiU5RaVIuJGHSxm5SLiRh4SMPKTcLkB6ViGKNTqDHcdMLqCBrQreDlZo1sAaTZ2t0czZGj7OVlCZyQ12nMpgkUNEJkcmE7B0SGuk3inA2bQcjI6IxuZXO8HWwuy+6wsyAWp/9mAi41ei0SH+Wg5iU+4gNjULp65mIzHz4bdAHa2VcLFVwc7SHDYqBaxVZrCxUEClkEMmCBAEQAAgAigo0SK/WIO8Yg1yizS4nV+Ma9lFuJFThFKtiNTbhUi9XYj9F2/q9//Z0DZ4prVr9X7wB2CRQ0QmydJcgbUj2mHAykNIyMjDuG+P49tXgqFUSPMXJVF10Gh1OJuegyOXMxF5+Raik26jqPTeqzMNbFTwcbaCj1PZlRVPezXc7CzgbKs0yM+ERqvDjdxipN0pxKWMPFy8kYsL13ORkJGLps7Wj73/qmKRQ0Qmq4GtCuFhwRjyZSSiEm9j6k+n8NmLbe5pN6Ar0SF5QTIAwOMdD8jMecuKaq87+SXYeyEDu89l4MDFm8gt1pR7v56lGQLc7dDa3Q4Bfz/sLM2rNZNCLoObnQXc7CwQ7FW+DZyUw/GxyCEik+bnaoMvhwchLPwYdpy+BhcbFd7r51duHbFURPK8siKn0YxGQPV+HxBVWvKtfOyKu4E/z93A8aTb+PcwUDYqBTp426NTY3t0auIAHycrCELtaQAsZRYWOURk8jo3ccCSwa0xeWMs1h5KRANbFcaEeEsdS08myNDWta3+OWC4xqBkvK5nF+HX0+nYfiodp69ml3uveQNrdPd1xlO+TmjV0A7yWtarqbZgkUNEdcKAADdczy7Cwt/O46Md5+BorcSAADepYwEALMwsED02+p8FHCunzsouLMWO09fwS2wajiXdxt07PXKZgI7e9njazxlPNnd6aG9B+geLHCKqM8Y94Y1r2UWIOJKEaT+dgtpcge5+zlLHojpOFEUcS7yNH6NTsfPstXINh9t61EP/AFf0aekCByulhCmNE4scIqozBEHA7H5+yC4sxdaYNLz2w0lEhLVDe5d6UkejOigjtwhbTqThp+Op5bp5N3W2wvOBDdGvtSvc7CwkTGj8WOQQUZ0ikwlYPKgV8os12BV/A2O+PY7vXmoraaaC0gL4rSxrDB0/MR68EWG67l61iTiShF3xN6D9uwWx2lyOZ1q74oV27ghwt6tVDYeNGYscIqpzFHIZPnupDcasO46DCZkY/+0JLIF0twJEUURydrL+OZmeolIttsemI/xIEs5dy9EvD2xkhxfbNULfVi5QK/mVbGg8o0RUJykVcnw5PAjDvz6GMwl3AAmLHDJd6VmF+O5oMjYeS8GdgrL50VRmMjzbpiFGdvJA8wY2Eic0bSxyiKjOsjRX4JuwdghbGYm73bYvXM+BX2O20aHHE5eejdX7LuO3s9f1t6Tc7CwwspMHhrR1r/bB+agMixwiqtNsLczw9chgnJ5xFAAQFh6Nb15tjxZuthInI2N0Ivk2Vu69jD3nM/TLOjW2x8hOnuju68zxbGoYixwiqvNsLf+ZuDOroBTD1kbh+1fao2VDFjr0aKIo4tClTKzcewlHr9wGAMgE4JnWrpjQtTF8XXhLSioscoiI/iXA3RbHbmTjpbVH8e3oYLRpxFtXdH86nYg/z93Aqr2XcOrvEYnN5AKeD2yICV0bw9OBM9tLjUUOEZEAWPqVddxeE9YaY348geikOxi2NgpfvByEJ5o6Vu/hBQF+jn765wB7WNVmGq0Ov56+hlX7LuHijTwAZY2JhwY3wrgnvOFiy7FtagtBrMP9FXNycmBra4vs7GzY2PByIhGVyS/WYML3J3AwIRNmcgFLBreu2Skg8vMBK6uy53l5gJpXBGqDYo0WW06k4Yv9l5FyuwAAYK1UYEQnD4zu7AV7jkhcYyr6/c0rOURE/6FWKvD1yHaY+lMsfj19DZM3xuJ2fglGdfaSOhpJoKBEgx+iUrDm4BXcyCkGANRXm+OVLl4Y3tEDNiqzR+yBpMIih4joPswVMnz6YhvYq82xLjIZ8/4Xj1t5JZjWoylHo60jsgtL8e2RJHxzOFE/xk0DGxXGPeGNocGNYGEulzghPQqLHCKq87QFWpxodwIAEBQdBLll2ZeXTCZgbn9/OFgpsfTPi/h87yUk3y7A4kGtoDIz3BdcQWkB2q1pBwCIHhvNaR0kdjO3GN8cTsR3kcnIK9YAADzsLfFq18Z4NtANSgWLG2PBIoeISAQK4gv0z/9NEAS8/pQPnGyUeHfrWfzvVDpSbxfgqxFBcLJWGebwooj4m/H65ySNtKxCrDlwBRuOpaBYUzY4ZDNna7zWrTH6tnSBQi6TOCFVFoscIqrzZCoZWu9trX9+Py+0awT3+pZ49fuTiE3NwsDPD2PtyHbwc2WnBWN35WYevth/GT+fTIPm79GJW7vbYVK3JniquRNkHMDPaLHIIaI6T5ALqBf66PFwOjV2wLaJnfFKRDSuZOZj0BdHsGxIa/Rq4VIDKcnQ4tNzsGrfJew8cw1/1zbo1NgeE7s1QafG9mx7ZQJY5BARVYKXgxpbX+uMiT+cxKFLmZjw/UmM6eKFt3s3hxlvZxiFE8l3sGrvJez+19QL3X2d8Fq3Jgjk4I8mxah/Infs2IH27dvDwsIC9erVw8CBA6WORERGSFeqQ9rKNKStTIOuVPfI9W0tzRA+qh3GPeENAFh7KBEvfnUU17ILqzsqVZEoijiUkImhXx3F86uPYPf5DP3UC79NDsHake1Y4Jggo72Ss2XLFowdOxYLFizAk08+CY1Gg7Nnz0odi4iMkFgiImFSAgCgQVgDoALDnpjJZXinjy+CPOph+qZTOJF8B30/PYQVLwYgxKd6R0imitPqRPwRdx2r913GmbR/pl54rk1DTAhtDC9OvWDSjLLI0Wg0mDx5MhYvXoxXXnlFv9zPz0/CVERUF/X0b4DmDazx2vqTiEvPwfCvj+GVLl6Y0bNZhbuZC4IAD1sP/XNO6/D4ijVabItJw5f7r+BKZj6AsqkXXmxXNvWCqx2nXqgLjLLIOXnyJNLS0iCTydCmTRtcv34dAQEBWLx4MVq0aPHA7YqLi1FcXKx/nZOTUxNxicjEedirseXVTvjw13isj0rB14cSceDiTXzyQgBauD16JnNLM0skTUn6Z0FJfvWFNXF5xRpsiErB2kP/jE5sa2GGkZ08MbKjB6deqGOMssi5cuUKAGDu3LlYtmwZPD09sXTpUoSGhuLixYuoX7/+fbdbuHAh5s2bV5NRiaiOUJnJMf/ZlnjK1wlvbT6DhIw8DFx5GJOf8sGroY05xko1u5VXjIgjSVh3JAk5RWUD+DWwUWFMiBeGBjeCWmmUX3f0mGrVT93MmTMhCMJDH+fPn4dOV9Yw8N1338Xzzz+PoKAghIeHQxAEbNq06YH7nzVrFrKzs/WP1NTUmvpoRFRHPNncGbvefAK9WzSARidi6Z8XMWDlYZxKzZI6mkk6fz0Hb28+jY4f78Fney4hp0gDb0c1Fg1qhQNvdcOYEG8WOHVYrfo/P23aNISFhT10HW9vb1y7dg1A+TY4SqUS3t7eSElJeeC2SqUSSiUvVRJR9aqvNseqYYH4JTYds385i7j0HAxcdRjDO3hges9m90zoWFhaiCcingAAHAg7ALYWeTidTsSe8xn45nAijly+pV/e2t0Or3ZtjB5+zhzAjwDUsiLH0dERjo6P7pUQFBQEpVKJCxcuoEuXLgCA0tJSJCUlwcPDo7pjEhE9kiAIGNjGDZ2bOGDBznPYGpOGbyOT8fvZ65jesxmeD2wI+d9fxDpRh+Ppx/XP6f7yijXYfDwVEUeSkHSrbBoOuUxArxYNMLqzFwIb2XEAPyqnVhU5FWVjY4MJEyZgzpw5cHd3h4eHBxYvXgwAGDx4sMTpiIj+4WitxCcvBGBQUEO8t+0sEjPz8dbm04g4nIT3+vqiUxMHqSPWenHp2dhwLAXbYtL1E2baqBQY2r4RRnT0hBt7StEDGGWRAwCLFy+GQqHA8OHDUVhYiPbt22PPnj2oV4+DORFR7dO5iQN+nxKCb48k49M9CYi/loOX1kahWzNHjO/qJnW8Wie/WIP/nUrHhmMpOHU1W7/c21GNUZ298HygGyzNjfYrjGqIINbhKW9zcnJga2uL7Oxs2Nhwkj2iukqbr8VBq4MAgJC8EMjVFRvfpqpu55dgxV8X8X1UCrQ6EToUIdViEAAgb1Ye1KUArKzKVs7LA9R1Y8A6URRxMiULW05exS8xacgv0QIoG7yvp38DvBTcCB287dnehir8/c0ymIiohtVXm2PegBYI6+yFz/Yk4OeYy/r3RoUfw6ttPdBNwnw17fLNPPwSk4ZtselIuV2gX+7loMbQYHc8H9iQ49tQlbDIISKSiJeDGsuGBOCVEFe0+Kps2dHE2zhz4TbO/b1OdkEpbE3wQk7yrXzsiruB/51Ox+l/3Y6yNJejp38DDGnrjg7e9dmQmB4LixwiIgBmDhWYsKqaeNqr4WDpAJ0OGB3oiZ2RV/XvPbF4L55o44khbRuiU2MHfY8sYyOKIuLSc7Ar7jp2xd/A+eu5+vfkMgFP+DhgYBs3PO3nzLY2ZDBsk8M2OURUy+TdzoaVvR0AwPfNzSg0VwEAHKyU6OHvjN4tGqCDtz3Mavkoypl5xTh8KROHL2XiUEIm0rOL9O/JZQLae9VHrxYN0KelCxx4O4oqoaLf3yxyWOQQUW2Tn69veHz2Qhp+Oncbv8SmI7uwVL+KjUqBzk0c0LmJA7o0cYCHvaWkt3ZEUcTVO4U4mXIHMSlZOHrlVrmrNQBgYSZH16aO6OHvjCebO8HO0lyitGTsWORUAIscIqqV/lXk3O1dVaLRIfLKLfx+9jr+jL+OzLyScps42yjRqqEdAtzt0KqhLZo6W8PJWlkthU+xRosrN/Nx8UYuEm7k4fz1HMSmZt2TCQB8XWzQpYk9OjVxQEdv+wrPzE70MCxyKoBFDhEBgLZQi9O9TwMAWv3WCnKLmv0iLiwtRO/1vQEAvw37DRYluod2IdfqRJy6moUjlzJx6FImTiTfQan23l/lVkoFvB3V8LBXw9laCWcbFZxslLC1MINaqYDaXAELczkEAHe3LtZokVukQW5RKXKLNMgpLMW17CJcyy5CelYhrmUXIS2rEFrdvcczkwvwc7VFG3c7BHrUQ6fG9rwNRdWCXciJiCpKB2Tvz9Y/r/HDizrsT96vf/4ocpmAwEb1ENioHiY96YPCEi3OpmfjVGoWTl3Nxtm0bCTfykdesQanr2aX671kKNYqBZo6W//9sEKrhrbwd7XllRqqVVjkEFGdJygF+P3kp39ubCzM5WjnWR/tPOvrlxVrtEi5VYDLN/ORersAGblFyMgtxo2cIuQWaVBQokVesQZFfw+4BwAQAHO5DNYqBaxVZrBSKmBjoYCzjQqudhZwsS37r3s9SzjbVM+tMCJDYpFDRHWeTCGD02AnqWMYlFIhh4+zNXycraWOQiSZ2t3/kIiIiKiKeCWHiOo8nUaHzK2ZAACHZx0gU/DvPyJTwCKHiOo8sVhE/JB4AGUTdPI3I5Fp4I8yEVEtYGlmKXUEIpPDIoeISGJqczXy38n/Z0Fp/oNXJqIK441nIiIiMkkscoiIiMgkscghIpJYkaYIfX/oi74/9EWRpujRGxBRhbBNDhGRxLQ6LXYm7NQ/JyLD4JUcIiIiMkkscoiIiMgkscghIiIik8Qih4iIiEwSixwiIiIySXW6d5UoigCAnJwciZMQkZS0+Vrko2yU4ZycHMi18ho9fn5JPvB3z/GcnBxoS//1Zk4OoGWPK6J/u/u9ffd7/EEE8VFrmLCrV6/C3d1d6hhERERUBampqWjYsOED36/TRY5Op0N6ejqsra0hCILB9puTkwN3d3ekpqbCxsbGYPule/Fc1wye55rB81wzeJ5rRnWeZ1EUkZubC1dXV8hkD255U6dvV8lksodWgI/LxsaGP0A1hOe6ZvA81wye55rB81wzqus829raPnIdNjwmIiIik8Qih4iIiEwSi5xqoFQqMWfOHCiVSqmjmDye65rB81wzeJ5rBs9zzagN57lONzwmIiIi08UrOURERGSSWOQQERGRSWKRQ0RERCaJRQ4RERGZJBY5VbRy5Up4enpCpVKhffv2OHbs2EPX37RpE5o3bw6VSoWWLVti586dNZTUuFXmPK9ZswYhISGoV68e6tWrh+7duz/y/wv9o7L/pu/auHEjBEHAwIEDqzegiajsec7KysLEiRPh4uICpVKJpk2b8vdHBVT2PC9fvhzNmjWDhYUF3N3d8eabb6KoqKiG0hqnAwcO4JlnnoGrqysEQcC2bdseuc2+ffsQGBgIpVKJJk2aICIionpDilRpGzduFM3NzcVvvvlGjIuLE8eOHSva2dmJN27cuO/6hw8fFuVyubho0SIxPj5efO+990QzMzPxzJkzNZzcuFT2PL/00kviypUrxZiYGPHcuXNiWFiYaGtrK169erWGkxufyp7ruxITE0U3NzcxJCREHDBgQM2ENWKVPc/FxcVi27ZtxT59+oiHDh0SExMTxX379omxsbE1nNy4VPY8r1+/XlQqleL69evFxMRE8Y8//hBdXFzEN998s4aTG5edO3eK7777rvjzzz+LAMStW7c+dP0rV66IlpaW4tSpU8X4+Hjxs88+E+Vyufj7779XW0YWOVUQHBwsTpw4Uf9aq9WKrq6u4sKFC++7/pAhQ8S+ffuWW9a+fXtx/Pjx1ZrT2FX2PP+XRqMRra2txXXr1lVXRJNRlXOt0WjETp06iWvXrhVHjhzJIqcCKnueV69eLXp7e4slJSU1FdEkVPY8T5w4UXzyySfLLZs6darYuXPnas1pSipS5Lz11luiv79/uWUvvPCC2LNnz2rLxdtVlVRSUoITJ06ge/fu+mUymQzdu3dHZGTkfbeJjIwstz4A9OzZ84HrU9XO838VFBSgtLQU9evXr66YJqGq5/qDDz6Ak5MTXnnllZqIafSqcp63b9+Ojh07YuLEiXB2dkaLFi2wYMECaLXamoptdKpynjt16oQTJ07ob2lduXIFO3fuRJ8+fWokc10hxXdhnZ6gsyoyMzOh1Wrh7OxcbrmzszPOnz9/322uX79+3/WvX79ebTmNXVXO83+9/fbbcHV1veeHisqryrk+dOgQvv76a8TGxtZAQtNQlfN85coV7NmzB8OGDcPOnTtx6dIlvPbaaygtLcWcOXNqIrbRqcp5fumll5CZmYkuXbpAFEVoNBpMmDAB77zzTk1ErjMe9F2Yk5ODwsJCWFhYGPyYvJJDJunjjz/Gxo0bsXXrVqhUKqnjmJTc3FwMHz4ca9asgYODg9RxTJpOp4OTkxO++uorBAUF4YUXXsC7776LL774QupoJmXfvn1YsGABVq1ahZMnT+Lnn3/Gjh078OGHH0odjR4Tr+RUkoODA+RyOW7cuFFu+Y0bN9CgQYP7btOgQYNKrU9VO893LVmyBB9//DH++usvtGrVqjpjmoTKnuvLly8jKSkJzzzzjH6ZTqcDACgUCly4cAGNGzeu3tBGqCr/pl1cXGBmZga5XK5f5uvri+vXr6OkpATm5ubVmtkYVeU8v//++xg+fDjGjBkDAGjZsiXy8/Mxbtw4vPvuu5DJeD3AEB70XWhjY1MtV3EAXsmpNHNzcwQFBWH37t36ZTqdDrt370bHjh3vu03Hjh3LrQ8Af/755wPXp6qdZwBYtGgRPvzwQ/z+++9o27ZtTUQ1epU9182bN8eZM2cQGxurf/Tv3x/dunVDbGws3N3dazK+0ajKv+nOnTvj0qVL+iISAC5evAgXFxcWOA9QlfNcUFBwTyFzt7AUOb2jwUjyXVhtTZpN2MaNG0WlUilGRESI8fHx4rhx40Q7Ozvx+vXroiiK4vDhw8WZM2fq1z98+LCoUCjEJUuWiOfOnRPnzJnDLuQVUNnz/PHHH4vm5ubi5s2bxWvXrukfubm5Un0Eo1HZc/1f7F1VMZU9zykpKaK1tbU4adIk8cKFC+Kvv/4qOjk5iR999JFUH8EoVPY8z5kzR7S2thY3bNggXrlyRdy1a5fYuHFjcciQIVJ9BKOQm5srxsTEiDExMSIAcdmyZWJMTIyYnJwsiqIozpw5Uxw+fLh+/btdyGfMmCGeO3dOXLlyJbuQ11afffaZ2KhRI9Hc3FwMDg4Wjx49qn+va9eu4siRI8ut/9NPP4lNmzYVzc3NRX9/f3HHjh01nNg4VeY8e3h4iADuecyZM6fmgxuhyv6b/jcWORVX2fN85MgRsX379qJSqRS9vb3F+fPnixqNpoZTG5/KnOfS0lJx7ty5YuPGjUWVSiW6u7uLr732mnjnzp2aD25E9u7de9/fuXfP7ciRI8WuXbves01AQIBobm4uent7i+Hh4dWaURBFXosjIiIi08M2OURERGSSWOQQERGRSWKRQ0RERCaJRQ4RERGZJBY5REREZJJY5BAREZFJYpFDREREJolFDhEREZkkFjlERERkkljkEBERkUlikUNEJmfz5s1o2bIlLCwsYG9vj+7duyM/P1/qWERUwxRSByAiMqRr165h6NChWLRoEZ599lnk5ubi4MGD4DR9RHUPJ+gkIpNy8uRJBAUFISkpCR4eHlLHISIJ8XYVEZmU1q1b46mnnkLLli0xePBgrFmzBnfu3JE6FhFJgFdyiMjkiKKII0eOYNeuXdi6dSuuX7+OqKgoeHl5SR2NiGoQixwiMmlarRYeHh6YOnUqpk6dKnUcIqpBbHhMRCYlKioKu3fvRo8ePeDk5ISoqCjcvHkTvr6+UkcjohrGIoeITIqNjQ0OHDiA5cuXIycnBx4eHli6dCl69+4tdTQiqmG8XUVEREQmib2riIiIyCSxyCEiIiKTxCKHiIiITBKLHCIiIjJJLHKIiIjIJLHIISIiIpPEIoeIiIhMEoscIiIiMkkscoiIiMgkscghIiIik8Qih4iIiEwSixwiIiIySf8Pzg8A+bfl1+wAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -123,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -145,9 +191,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", @@ -169,9 +236,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.8|INFO|2024-05-31 17:20:01]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -194,9 +269,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_search step: 0.6565690909090909\n", + "hyperopt_search step: 0.5994039449166781\n", + "polynomial_approximation step: 0.4860935299211704\n" + ] + } + ], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))/2**nqubits\n", @@ -222,9 +307,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum for cost function in the tested range is: 0.6565690909090909\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -250,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -268,9 +381,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Energy fluctuation')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", @@ -285,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -307,9 +451,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eigenvalues: [-9.29150262 -5.21110255 -2. -2. 1.29150262 4.\n", + " 4. 9.21110255]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Iterations')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", "print('Eigenvalues:', eigvals )\n", @@ -320,6 +493,13 @@ " plt.axhline(y=eigvals, color='r', linestyle='--')\n", "plt.xlabel('Iterations')\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From c4bfc1e667d5dc76c2f989351dce85799a07868c Mon Sep 17 00:00:00 2001 From: Andrea Date: Sat, 1 Jun 2024 13:15:17 +0400 Subject: [PATCH 114/154] chore: Add ipykernel as doc dep --- examples/dbi/dbi_cost_functions.ipynb | 246 ++---------------- .../dbi/dbi_gradient_descent_strategies.ipynb | 20 +- examples/dbi/dbi_group_commutator_tests.ipynb | 25 +- examples/dbi/dbi_scheduling.ipynb | 25 +- examples/dbi/dbi_strategies_compare.ipynb | 20 +- examples/dbi/dbi_strategy_Ising_model.ipynb | 20 +- examples/dbi/dbi_strategy_Pauli-Z.ipynb | 20 +- examples/dbi/dbi_tutorial_basic_intro.ipynb | 102 +++----- poetry.lock | 186 +++++++++---- pyproject.toml | 1 + 10 files changed, 208 insertions(+), 457 deletions(-) diff --git a/examples/dbi/dbi_cost_functions.ipynb b/examples/dbi/dbi_cost_functions.ipynb index 7a4190b2a3..2e3c7c076b 100644 --- a/examples/dbi/dbi_cost_functions.ipynb +++ b/examples/dbi/dbi_cost_functions.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -12,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +25,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -37,17 +35,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.8|INFO|2024-05-31 17:19:52]: Using numpy backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -67,19 +57,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.6060645454545454\n", - "hyperopt_search step: 0.5991419607611053\n", - "polynomial_approximation step: 0.5267891182131145\n" - ] - } - ], + "outputs": [], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))/2**nqubits\n", @@ -105,37 +85,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.6060645454545454\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -160,7 +112,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -169,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -191,30 +142,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff, label=r'Off-diagonal norm')\n", @@ -225,7 +155,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -236,17 +165,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.8|INFO|2024-05-31 17:20:01]: Using numpy backend on /CPU:0\n" - ] - } - ], + "outputs": [], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -269,19 +190,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "grid_search step: 0.6565690909090909\n", - "hyperopt_search step: 0.5994039449166781\n", - "polynomial_approximation step: 0.4860935299211704\n" - ] - } - ], + "outputs": [], "source": [ "# generate data for plotting sigma decrease of the first step\n", "d = np.diag(np.linspace(2**nqubits,1,2**nqubits))/2**nqubits\n", @@ -307,37 +218,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The minimum for cost function in the tested range is: 0.6565690909090909\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the results\n", "plt.figure()\n", @@ -363,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -381,40 +264,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Energy fluctuation')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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V1SInIiIiY8ZyQ3rxSjd3tGltiTullVh59LrYcYiIyIix3JBemEgleGeQNwBg1e/XkV/CaRmIiEgcLDekN0MCnNBZYYt7lSosPnhF7DhERGSkWG5IbwRBQNSQB9MybI7PwrXbpSInIiIiY8RyQ3r1TNtWCPNxgEqtwZd7L4kdh4iIjBDLDende0N8IBGAvedzkZhRIHYcIiIyMiw3pHcdHK0wsssf0zLs5rQMRETUuFhuqEG8ObADzEwkSMgoxIELeWLHISIiI8JyQw3CycYMk3u2AQB8sTcN1ZyWgYiIGgnLDTWY6X3bwc7CBNdul+GXxJtixyEiIiPBckMNxtrMBH/r/2Bahn8euIx7lZyWgYiIGh7LDTWosd3doWhpjvySCvzw+w2x4xARkRFguaEGJZdJ8fZzD6ZlWHH0Ou6WVoiciIiIDB3LDTW4FwJd0NHVBqUV1Vhy6KrYcYiIyMCx3FCDk0gEvP/HtAwbT2Ug426ZyImIiMiQsdxQo+jZvjV6d7BHlUqDL/dxWgYiImo4LDfUaN4f7ANBAHadzcGZrCKx4xARkYFqMuVm/vz5EAQBs2bNqnW7X375BT4+PjAzM0PHjh2xe/fuxglI9ebnYo2XglwBANF7LnJaBiIiahBNotzEx8djxYoVCAwMrHW7EydOYMyYMZgyZQqSk5MRHh6O8PBwnDt3rpGSUn299Zw3TGUSnLxegCOXbosdh4iIDJDo5aa0tBRjx47F999/Dzs7u1q3Xbx4MQYPHox33nkHvr6++OyzzxAcHIylS5c2UlqqL1dbc0zs4QkAmL8nDSo1j94QEZF+iV5uIiMjMWzYMAwYMOCJ28bFxT203aBBgxAXF/fY11RUVKC4uLjGQuJ6vW87WJvJcCmvBNuSOC0DERHpl6jlZvPmzUhKSkJ0dHSdts/NzYWjo2ONdY6OjsjNzX3sa6Kjo2FjY6NdFApFvTJT/dlamGJG//YAgEUHLqO8SiVyIiIiMiSilZusrCzMnDkTGzduhJmZWYO9T1RUFJRKpXbJyspqsPeiuosI9YSrrTlylOVYczxd7DhERGRARCs3iYmJyM/PR3BwMGQyGWQyGWJjY/HNN99AJpNBpXr4r3knJyfk5eXVWJeXlwcnJ6fHvo9cLoe1tXWNhcRnZiLF7IEdAADfHbmKwrJKkRMREZGhkD3Ni2JiYhATE4P8/Hyo1eoaz61evbpO+wgLC0NqamqNdZMmTYKPjw/ee+89SKXSh14TGhqKmJiYGpeLHzhwAKGhobp/CBJdeJArvv/9OtJyS/Dt4av48Hk/sSMREZEB0LnczJ07F59++ilCQkLg7OwMQRCe6o2trKwQEBBQY52lpSVatWqlXR8REQFXV1ftmJyZM2eiT58+WLhwIYYNG4bNmzcjISEBK1eufKoMJC7pH9MyTFwTj/VxGZjQwxOKlhZixyIiomZO53KzfPlyrF27FuPHj2+IPDVkZmZCIvnPmbMePXpg06ZN+PDDD/HBBx/Ay8sLO3bseKgkUfPRp4M9erRrhRPX7mLRgcv45+jOYkciIqJmTtDoeJvYVq1a4fTp02jXrl1DZWpQxcXFsLGxgVKp5PibJiL1phIvLD0GANj1t14IcLURORERETU1uvx+6zygeOrUqdi0adNThyP6Xx3dbPBiJxcAwBd700ROQ0REzZ3Op6XKy8uxcuVKHDx4EIGBgTAxManx/KJFi/QWjozHO4O8sedcDn6/cge/X7mNZ73sxY5ERETNlM7l5uzZs+jcuTMAPDSn09MOLiZStLTAuO4eWHM8HfP3pKFnu9aQSPjPExER6U7ncnP48OGGyEGEv/X3wtaEmzh/qxi/nrmF8D9mECciItJFvW7id/PmTdy8ybmBSD9aWppiet8HA9W/3HeJ0zIQEdFT0bncqNVqfPrpp7CxsYGHhwc8PDxga2uLzz777KEb+hHpanLPNnCyNkN20X1sOJkhdhwiImqGdC43f//737F06VLMnz8fycnJSE5Oxrx587BkyRJ89NFHDZGRjIi5qRRvDvQCACw9fBXK+1UiJyIiouZG5/vcuLi4YPny5XjxxRdrrN+5cydef/11ZGdn6zWgvvE+N01ftUqNIYt/x5X8Ukzv0w7vD/EROxIREYmsQe9zU1BQAB+fh39sfHx8UFBQoOvuiB4ik0rw3uAH/4ytOX4Dt4rui5yIiIiaE53LTadOnbB06dKH1i9duhSdOnXSSyiiMF8HdPNsiYpqNf554LLYcYiIqBnR+VLwBQsWYNiwYTh48KB2Nu64uDhkZWVh9+7deg9IxkkQBEQN9cFL353A1qSbmPJsG/g48TQiERE9mc5Hbvr06YPLly/jpZdeQlFREYqKijBixAhcunQJzz77bENkJCMV5G6HoR2doNEAX+zhtAxERFQ3Og8obu44oLh5uXGnDAMXxaJarcFP07ojtF0rsSMREZEIdPn9rtNpqbNnzyIgIAASiQRnz56tddvAwMC6JyV6gjatLTGmmzt+PJmB+XsuYkdkT07zQUREtapTuencuTNyc3Ph4OCAzp07QxAEPOqAjyAIUKl4V1nSrzfCvLAt6SbO3FTi36k5eD7QRexIRETUhNWp3Ny4cQP29vba/0/UmOyt5Hi1dzv88+BlLNh7Cc/5OcFUVq+ZQ4iIyIDV6RfCw8NDeyogIyMDrq6u2qkX/lxcXV2RkcHb5VPDmPpsG7RuIUdmwT1sOsV/zoiI6PF0/vO3X79+j7xZn1KpRL9+/fQSiuh/WcplmDXgwbQM3xy6ipJyTstARESPpnO50Wg0jxzQeffuXVhaWuolFNGjjO6qQNvWligoq8TKo9fFjkNERE1UnW/iN2LECAAPBg1PnDgRcrlc+5xKpcLZs2fRo0cP/Sck+oOJVIJ3B3tj+oYkrDh6HV087NDX20HsWERE1MTU+ciNjY0NbGxsoNFoYGVlpX1sY2MDJycnvPrqq9iwYUNDZiXCIH8nDPJ3RGW1Gq+uT8TBC3liRyIioiZG55v4zZ07F2+//XazPQXFm/g1f5XVaszcnIw953IhkwhYMiYIQzo6ix2LiIgakC6/37xDMTVL1So1Zm85g1/P3IJUImDRqE4Y3tlV7FhERNRA9H6H4v+1detWbNmyBZmZmaisrKzxXFJS0tPskkgnMqkE/xzdGaYyCbYm3sSsn1NQWa3GyBCF2NGIiEhkOl8t9c0332DSpElwdHREcnIyunXrhlatWuH69esYMmRIQ2QkeiSpRMCClwMxpps7NBrgna1nselUptixiIhIZDqXm++++w4rV67EkiVLYGpqinfffRcHDhzAG2+8AaVS2RAZiR5LIhEw76UATAj1AAB8sD0V606kixuKiIhEpXO5yczM1F7ybW5ujpKSEgDA+PHj8dNPP+k3HVEdCIKAOS/6Y9qzbQAAn/x6Ht/zPjhEREZL53Lj5OSkvUOxu7s7Tp48CeDBnFNGNjaZmhBBEPDBUF/M6NceAPD57otYeuiKyKmIiEgMOpeb/v3749dffwUATJo0CW+++SYGDhyI0aNH46WXXtJ7QKK6EgQBbw/yxuyBHQAAX+2/jEX7L7F0ExEZGZ0vBVer1VCr1ZDJHlxotXnzZpw4cQJeXl547bXXYGpq2iBB9YWXghuH5bHXMH9PGgDgtT5t8f5gn0dOG0JERM0D73NTC5Yb4/HDsRv4bNcFAMCknp74+Hk/FhwiomaqQe9zc/To0Vqf7927t667JGoQU3q1galMgo92nMOa4+moUqnx6YsBkEhYcIiIDJnO5aZv374Prfvvv4ZVKlW9AhHp0/juHpBLJXhv21lsOJmJymo1okcEQsqCQ0RksHQeUFxYWFhjyc/Px969e9G1a1fs379fp30tW7YMgYGBsLa2hrW1NUJDQ7Fnz57Hbr927VoIglBjMTMz0/UjkJEZ1VWBRaM6QSIAWxJu4u1fzqBapRY7FhERNRCdj9zY2Ng8tG7gwIEwNTXF7NmzkZiYWOd9ubm5Yf78+fDy8oJGo8G6deswfPhwJCcnw9/f/5Gvsba2xqVLl7SPOYaC6uKlIDeYSCWYuTkF25OzUVmtxtevdIaJVOd+T0RETdxTzS31KI6OjjVKR1288MILNR5//vnnWLZsGU6ePPnYciMIApycnOr8HhUVFaioqNA+Li4u1ikjGY7nA11gIpVgxqYk/Ds1B1UqNZb8JQhymVTsaEREpEc6/9l69uzZGsuZM2ewd+9eTJ8+HZ07d37qICqVCps3b0ZZWRlCQ0Mfu11paSk8PDygUCgwfPhwnD9/vtb9RkdHw8bGRrsoFJxY0ZgN8nfCyvEhMJVJsP9CHqb/mIjyKo4TIyIyJDpfCi6RSCAIwkM3RuvevTtWr14NHx8fnQKkpqYiNDQU5eXlaNGiBTZt2oShQ4c+ctu4uDhcuXIFgYGBUCqV+Oqrr3D06FGcP38ebm5uj3zNo47cKBQKXgpu5H6/chvT1iegvEqNXu1b4/uIEJib8ggOEVFT1aD3ucnIyKjxWCKRwN7e/qkH9lZWViIzMxNKpRJbt27FqlWrEBsbCz8/vye+tqqqCr6+vhgzZgw+++yzOr0f73NDf4q7dhdT1sXjXqUK3du2xA8TusJSrrcztUREpEe6/H7rfFoqNjYWTk5O8PDw0J4eMjMzQ2VlJdavX69zWFNTU7Rv3x5dunRBdHQ0OnXqhMWLF9fptSYmJggKCsLVq1d1fl+i0HatsH5yN7SQy3DyegEiVp9GcXmV2LGIiKiedC43kyZNglKpfGh9SUkJJk2aVO9AarW6xmmk2qhUKqSmpsLZ2bne70vGKcSzJTZMfQbWZjIkZhRi/KpTUN5jwSEias50LjcajeaRl1/fvHnzkZeJ1yYqKgpHjx5Feno6UlNTERUVhSNHjmDs2LEAgIiICERFRWm3//TTT7F//35cv34dSUlJGDduHDIyMjB16lRdPwaRVmeFLTZN6w47CxOcuanEX1adREFZpdixiIjoKdV5gEFQUJD2xnlhYWHaiTOBB0dQbty4gcGDB+v05vn5+YiIiEBOTg5sbGwQGBiIffv2YeDAgQCAzMxMSCT/6V+FhYWYNm0acnNzYWdnhy5duuDEiRN1Gp9DVJsAVxv89Gp3jFt1CudvFWPMypPYMPUZ2FvJxY5GREQ6qvOA4rlz52r/96233kKLFi20z5mamsLT0xMvv/wyZwWnZu1qfin+8v1J5JdUoJ29JTZN6w5Ha94Fm4hIbA16tdS6devwyiuvQC5vnn/RstzQk9y4U4a/fH8SOcpyeLaywKZp3eFiay52LCIio9agV0v5+fkhJSXlofWnTp1CQkKCrrsjanLatLbEltdC4WZnjvS79zBqRRyyCu6JHYuIiOpI53ITGRmJrKysh9ZnZ2cjMjJSL6GIxKZoaYEtr4XCs5UFbhbex+gVcbhxp0zsWEREVAc6l5sLFy4gODj4ofVBQUG4cOGCXkIRNQUutub4+bVQtLO3xC1lOUaviMPV/BKxYxER0RPoXG7kcjny8vIeWp+Tk1PjCioiQ+BobYbNr4bC29EK+SUVeGXlSVzKZcEhImrKdC43zz33HKKiomrcyK+oqAgffPCB9hJuIkNibyXHT692h5+zNe6UVuKVlXE4l/3wjSyJiKhp0PlqqezsbPTu3Rt3795FUFAQACAlJQWOjo44cOBAk591m1dL0dNS3qtCxOpTOHNTCWszGdZPeQadFbZixyIiMgoNeik4AJSVlWHjxo04c+YMzM3NERgYiDFjxsDExOSpQzcWlhuqj5LyKkxcE4/EjEK0kMuwdlJXhHi2FDsWEZHBa/By05yx3FB9lVVUY8q6eJy8XgALUyl+mNAVoe1aiR2LiMigNWi5edLM3xEREbrsrtGx3JA+3K9U4dUfE/D7lTswM5FgVURX9PJqLXYsIiKD1aDlxs7Orsbjqqoq3Lt3D6amprCwsEBBQYHuiRsRyw3pS3mVCn/dkIjDl27DVCbBinFd0M/HQexYREQGqUHvUFxYWFhjKS0txaVLl9CrVy/89NNPTx2aqLkxM5FixfgQPOfniMpqNV79MQH7zueKHYuIyOjpXG4excvLC/Pnz8fMmTP1sTuiZsNUJsG3Y4MxLNAZVSoNIjcm4d9nc8SORURk1PRSbgBAJpPh1q1b+todUbNhIpVg8ejOeCnIFdVqDf72UxK2J98UOxYRkdHS+ZbCv/76a43HGo0GOTk5WLp0KXr27Km3YETNiUwqwVcjO8FEKmBLwk3M3nIGVdUajOratO/7RERkiHQuN+Hh4TUeC4IAe3t79O/fHwsXLtRXLqJmRyoRMH9EIExlEmw4mYl3/3UWlSo1xnX3EDsaEZFR0bncqNXqhshBZBAkEgGfDQ+AqVSK1cdv4MMd51BZrcbkXm3EjkZEZDT0NuaGiB4QBAEfPe+L6X3aAQA+3XUBy2OviZyKiMh41OnIzezZs+u8w0WLFj11GCJDIQgC3hvsDblMgsUxVzB/Txoqq9V4I8xL7GhERAavTuUmOTm5TjsTBKFeYYgMiSAIeHNgB5jKJPhy3yUsOnAZ9lZyjOnmLnY0IiKDVqdys3jxYvj7+0MqlTZ0HiKDE9mvPapUanx98Ao+2nEOnq0sORcVEVEDqtOYm6CgIO20Cm3btsXdu3cbNBSRoZkZ5oUXOrmgWq3BXzcmIv1OmdiRiIgMVp3Kja2tLa5fvw4ASE9P5xVTRDoSBAFf/l8gOilsUXSvClPWxUN5v0rsWEREBqlOp6Vefvll9OnTB87OzhAEASEhIY89RfVnCSKimsxMpPh+fBcM//Y4rt0uw4xNSVgzsStkUl60SESkT3UqNytXrsSIESNw9epVvPHGG5g2bRqsrKwaOhuRwXGwNsP3ESEYuTwOv1+5g3/8+yLmvOgvdiwiIoNS55v4DR48GACQmJiImTNnstwQPaUAVxv8c3QnTN+QhLUn0tHeoQXvYkxEpEc6Hw9fs2YNiw1RPQ0OcMY7g7wBAJ/8eh4nrt4RORERkeHgyX4ikbzetx3CO7tApdbgrxuTcP12qdiRiIgMAssNkUgEQcD8lwMR5G4L5f0qTF2XAOU9XkFFRFRfLDdEIjIzkWLl+BC42Jjh+p0yRG5KQpWKt1ogIqoPnctNWRlvPkakT/ZWcqya0BUWplIcu3oHn+26IHYkIqJmTedy4+joiMmTJ+PYsWMNkYfIKPm5WOOfoztDEID1cRlYH5cudiQiomZL53KzYcMGFBQUoH///ujQoQPmz5+PW7duPdWbL1u2DIGBgbC2toa1tTVCQ0OxZ8+eWl/zyy+/wMfHB2ZmZujYsSN27979VO9N1NQM8nfCu4N8AABzf7uA36/cFjkREVHzpHO5CQ8Px44dO5CdnY3p06dj06ZN8PDwwPPPP49t27ahurq6zvtyc3PD/PnzkZiYiISEBPTv3x/Dhw/H+fPnH7n9iRMnMGbMGEyZMgXJyckIDw9HeHg4zp07p+vHIGqSpvdpixHBrlCpNXh9YxKu5vMKKiIiXQkajUZT350sWbIE77zzDiorK9G6dWtMnz4d77//PiwsLHTeV8uWLfHll19iypQpDz03evRolJWVYdeuXdp13bt3R+fOnbF8+fI67b+4uBg2NjZQKpWwtrbWOR9RQ6uoVmHs96eQkFEIz1YW2BHZE7YWpmLHIiISlS6/3099tVReXh4WLFgAPz8/vP/++/i///s/xMTEYOHChdi2bRvCw8N12p9KpcLmzZtRVlaG0NDQR24TFxeHAQMG1Fg3aNAgxMXFPXa/FRUVKC4urrEQNWVymRTLx3eBq6050u/ew+sbeQUVEZEu6jz9wp+2bduGNWvWYN++ffDz88Prr7+OcePGwdbWVrtNjx494OvrW6f9paamIjQ0FOXl5WjRogW2b98OPz+/R26bm5sLR0fHGuscHR2Rm5v72P1HR0dj7ty5dcpC1FS0biHHDxND8PJ3J3Di2l188ut5fB4eAEEQxI5GRNTk6XzkZtKkSXBxccHx48eRkpKCGTNm1Cg2AODi4oK///3vddqft7c3UlJScOrUKfz1r3/FhAkTcOGC/i6FjYqKglKp1C5ZWVl62zdRQ/JxssbiV4IgCMCmU5lYdyJd7EhERM2CzkducnJynjiWxtzcHJ988kmd9mdqaor27dsDALp06YL4+HgsXrwYK1aseGhbJycn5OXl1ViXl5cHJyenx+5fLpdDLpfXKQtRUzPAzxFRQ3wwb3caPt11AZ6tLdHX20HsWERETZrOR26qq6sfGsNSXFyMkpISVFZW1juQWq1GRUXFI58LDQ1FTExMjXUHDhx47BgdIkMw7dm2GNnFDWoN8LdNybiaXyJ2JCKiJk3ncmNraws7O7uHFltbW5ibm8PDwwOffPIJ1OonD4CMiorC0aNHkZ6ejtTUVERFReHIkSMYO3YsACAiIgJRUVHa7WfOnIm9e/di4cKFSEtLw5w5c5CQkIAZM2bo+jGImg1BEPCPlwLQzbMlSiqqMXltAgrL6v+HBBGRodK53KxduxYuLi744IMPsGPHDuzYsQMffPABXF1dsWzZMrz66qv45ptvMH/+/CfuKz8/HxEREfD29kZYWBji4+Oxb98+DBw4EACQmZmJnJwc7fY9evTApk2bsHLlSnTq1Albt27Fjh07EBAQoOvHIGpW5DIplo0LhqKlOTIL7mH6hkRUVvMKKiKiR9H5PjdhYWF47bXXMGrUqBrrt2zZghUrViAmJgY//vgjPv/8c6Slpek1rD7wPjfUnF3OK8GI706gtKIaY7opMO+ljryCioiMQoPe5+bEiRMICgp6aH1QUJD2fjO9evVCZmamrrsmoifo4GiFJWOCIBGAn05nYfXxdLEjERE1OTqXG4VCgR9++OGh9T/88AMUCgUA4O7du7Czs6t/OiJ6SD8fB3ww9MF9pD7/9wUcTssXORERUdOi86XgX331FUaOHIk9e/aga9euAICEhASkpaVh69atAID4+HiMHj1av0mJSGtKrza4ml+KzfFZ+NtPydj2eg90cLQSOxYRUZPwVHNLpaenY8WKFbh06RKABzfie+211+Dp6anvfHrHMTdkKCqr1Rj/wymculEARUtz7IzshZaWnIOKiAyTLr/fOpWbqqoqDB48GMuXL4eXl1e9g4qB5YYMSWFZJYZ/exyZBffQzbMlNkx9Bqayp54yjoioyWqwAcUmJiY4e/ZsvcIRkf7YWZrihwkhsJLLcDq9AH/fnoqnOBhLRGRQdP4Tb9y4cY8cUExE4vBytMKSvzy4guqXxJtY9fsNsSMREYlK5wHF1dXVWL16NQ4ePIguXbrA0tKyxvOLFi3SWzgiqpu+3g746Hk/zP3tAubtuYi29pYI83UUOxYRkSh0Ljfnzp1DcHAwAODy5cs1nuPNxIjEM7GHJ67kl2LTqUy88VMy/vV6D/g4cVwZERmfp7paqjnjgGIyZFUqNSasPo0T1+7C1dYcO2f0ROsWcrFjERHVW4PeofhPV69exb59+3D//n0A4CBGoibARCrBd2OD4dnKAtlF9zH9x0RUVKvEjkVE1Kh0Ljd3795FWFgYOnTogKFDh2ontpwyZQreeustvQckIt3YWphi1YSusDKTISGjEFHbeAUVERkXncvNm2++CRMTE2RmZsLCwkK7fvTo0di7d69ewxHR02nv0ALfjQ2GVCJgW1I2Vhy9LnYkIqJGo3O52b9/P7744gu4ubnVWO/l5YWMjAy9BSOi+nnWyx6fvOAHAPhibxr2n88VORERUePQudyUlZXVOGLzp4KCAsjlHLhI1JREhHpifHcPaDTArJ9TcOFWsdiRiIganM7l5tlnn8X69eu1jwVBgFqtxoIFC9CvXz+9hiOi+vv4BT/0at8a9ypVmLouHrdLKsSORETUoHS+FPzcuXMICwtDcHAwDh06hBdffBHnz59HQUEBjh8/jnbt2jVUVr3gpeBkjJT3qvDSd8dx/U4Zgt1tsWlad5iZSMWORURUZw16KXhAQAAuX76MXr16Yfjw4SgrK8OIESOQnJzc5IsNkbGysTDBqgkhsDaTISmziFdQEZFB4038iIzI8at3ELH6NFRqDd4Z5I3Ifu3FjkREVCe6/H7rPP0CABQVFeH06dPIz8+HWq2u8VxERMTT7JKIGkHP9q0x90V/fLjjHL7cdwnt7C0xOMBZ7FhERHqlc7n57bffMHbsWJSWlsLa2rrGfFKCILDcEDVx47p74Gp+KdaeSMebP5+Bm50FAlxtxI5FRKQ3Oo+5eeuttzB58mSUlpaiqKgIhYWF2qWgoKAhMhKRnn04zBfPerXG/SoVpq1PQH5xudiRiIj0Rudyk52djTfeeOOR97ohouZBJpVg6V+C0dbeEjnKcrz6YyLKqzgHFREZBp3LzaBBg5CQkNAQWYioEdmYm2D1hK6wMTdBSlYR3t16lldQEZFB0HnMzbBhw/DOO+/gwoUL6NixI0xMTGo8/+KLL+otHBE1LM/Wllg2LhgRP5zGr2duwcuhBf4W5iV2LCKietH5UnCJ5PEHewRBgErVtA9t81Jwoof9dDoTUdtSAQDfjQ3G0I68goqImpYGvYmfWq1+7NLUiw0RPdqYbu6Y3LMNAGD2lhSk3lSKnIiI6OnpXG6IyDB9MNQHfTrYo7xKjWnrE5DHK6iIqJmqc7kZOnQolMr//DU3f/58FBUVaR/fvXsXfn5+eg1HRI1HJpVgyV+C0N6hBXKLyzFtfQLuV/JoLBE1P3UuN/v27UNFxX9mE543b16N+9pUV1fj0qVL+k1HRI3K2swEP0wIgZ2FCc7eVOLtX86gopoFh4ialzqXm/8dd8xLRokMk0crSywf1wUmUgH/Ts3Bc/88ipiLefx3noiaDY65IaKHPNO2FZaN7QIHKzky7t7DlHUJmLQ2Htdul4odjYjoiepcbgRBqDGP1J/r6iM6Ohpdu3aFlZUVHBwcEB4e/sRTW2vXrtVm+XMxMzOrVw4ietgAP0ccersv/tq3HUykAo5cuo1B/zyKebsvoqS8Sux4RESPVeeb+Gk0GkycOBFyuRwAUF5ejunTp8PS0hIAaozHqavY2FhERkaia9euqK6uxgcffIDnnnsOFy5c0O73UaytrWuUoPqWLCJ6tBZyGd4b7INRIQr8Y9cFxKTlY+XR69iWlI33h/hgRJArJBL++0dETUudb+I3adKkOu1wzZo1Tx3m9u3bcHBwQGxsLHr37v3IbdauXYtZs2bVuFJLF7yJH9HTO5yWj093XcCNO2UAgM4KW8x90R+dFLbiBiMig6fL73edj9zUp7TU1Z+Xmrds2bLW7UpLS+Hh4QG1Wo3g4GDMmzcP/v7+j9y2oqKixlGl4uJi/QUmMjL9fBzQs31rrDl+A9/EXEFKVhGGf3scI7u44d3BPrC3kosdkYhI9+kXGoparcaLL76IoqIiHDt27LHbxcXF4cqVKwgMDIRSqcRXX32Fo0eP4vz583Bzc3to+zlz5mDu3LkPreeRG6L6yS8uxxd7L+FfSTcBAFZyGWYO8EJEqCdMZbxWgYj0S5cjN02m3Pz1r3/Fnj17cOzYsUeWlMepqqqCr68vxowZg88+++yh5x915EahULDcEOlJUmYh5vx6Hmf/mLKhnb0lPn7BH3062IucjIgMSYPOLdUQZsyYgV27duHw4cM6FRsAMDExQVBQEK5evfrI5+VyOaytrWssRKQ/we522PF6Tyx4ORCtLE1x7XYZJqw+janrEpBxt0zseERkhEQtNxqNBjNmzMD27dtx6NAhtGnTRud9qFQqpKamwtmZsxgTiUUiETCqqwKH3u6LKb3aQCYRcPBiHgYuOoov96WhrKJa7IhEZERELTeRkZHYsGEDNm3aBCsrK+Tm5iI3Nxf379/XbhMREYGoqCjt408//RT79+/H9evXkZSUhHHjxiEjIwNTp04V4yMQ0X+xMTfBR8/7Ye+sZ/GsV2tUqtT49vA19F94BDtTsnmXYyJqFKKWm2XLlkGpVKJv375wdnbWLj///LN2m8zMTOTk5GgfFxYWYtq0afD19cXQoUNRXFyMEydOcNJOoiakvYMV1k/uhpXju0DR0hx5xRWYuTkFo1bE4Vy28sk7ICKqhyYzoLix8D43RI2rvEqFVb9fx7eHr+F+lQqCAIzp5o63n/NGS0tTseMRUTPRLK+WaiwsN0TiyFHeR/TuNPx65hYAwNpMhree88bYZ9whkzaJaxuIqAljuakFyw2RuE5dv4s5v13AxZwHN9T0drTCJy/4oUf71iInI6KmjOWmFiw3ROJTqTX46XQmvtp/CUX3HkzCObSjEz4Y6gs3OwuR0xFRU8RyUwuWG6Kmo+heJRYduIwNJzOg1gBymQTT+7TD9D7tYG4qFTseETUhLDe1YLkhanou5hRj7m/ncfJ6AQDA1dYcfx/miyEBThAEzjpORCw3tWK5IWqaNBoNdqfm4vN/X8AtZTkAILRtK3zyoh98nPjvKpGxY7mpBcsNUdN2v1KFZbHXsCL2Giqq1ZBKBIzv7oE3B3SAjYWJ2PGISCQsN7VguSFqHrIK7mHe7ovYcy4XAGBnYYK3B3njla7ukEp4qorI2LDc1ILlhqh5OX71Dub+dh6X80oBAH7O1pg73B9dPVuKnIyIGhPLTS1YboianyqVGhtOZmDRgcsoKX8wCeeLnVwQNdQHzjbmIqcjosbAclMLlhui5utuaQW+2n8Jm+OzoNEA5iZSzOjfHlN6tYGZCS8dJzJkLDe1YLkhav5Sbyox57fzSMwoBAC4t7TAR8/7YYCvAy8dJzJQLDe1YLkhMgwajQY7U25h3u6LyC+pAAD07mCPj5/3Q3uHFiKnIyJ9Y7mpBcsNkWEprajGt4ev4offb6BSpYZMImByrzZ4I8wLLeQyseMRkZ6w3NSC5YbIMKXfKcNnuy4gJi0fAOBgJccHQ30xvLMLT1URGQCWm1qw3BAZtkNpeZj72wVk3L0HAOjm2RJzh/vD15n/vhM1Zyw3tWC5ITJ85VUq/HDsBpYcuoLyKjUkAhAR6ok3B3aAjTnvckzUHOny+y1ppExERI3GzESKyH7tEfNWXwzt6AS1Blh7Ih39vzqCLfFZUKuN6m86IqPDIzdEZPCOXbmDT349h2u3ywAAnRW2+HS4PwLdbMUNRkR1xtNStWC5ITJOldVqrDuRjq8PXkZZpQqCALzS1R3vDPJGS0tTseMR0RPwtBQR0f8wlUkwrXdbHHq7L14KcoVGA/x0OhP9Fx7BhpMZUPFUFZHB4JEbIjJKp28U4OOd55CWWwIA8HexxqfD/dHFgxNyEjVFPC1VC5YbIvpTtUqNjacy8dX+S9oJOV8OdsP7Q3xgbyUXOR0R/TeeliIiqgOZVIIJPTxx+O2+GB2iAAD8K+km+n91BD8cu4EqlVrkhET0NHjkhojoD8mZhfh453mkZisBAN6OVpjzoj9C27USORkR8bRULVhuiKg2KrUGWxKysGBvGgrvVQEAXujkgg+G+sDZxlzkdETGi6eliIieklQiYEw3dxx+uy/Gd/eARAB+O3MLYQtjsezINVRW81QVUVPHIzdERLU4l63ExzvPISmzCADQtrUl5rzoj94d7MUNRmRkeFqqFiw3RKQrtVqD7cnZiN6ThjulFQCAQf6O+HCYHxQtLUROR2QcWG5qwXJDRE+ruLwKiw9ewdoT6VCpNZDLJHi9b3u81qctzEykYscjMmgsN7VguSGi+rqcV4KPd57DyesFAAD3lhb4+Hk/hPk6QBAEkdMRGSaWm1qw3BCRPmg0Guw6m4PP/30RucXlAIB+3vb4+AV/tGltKXI6IsPDclMLlhsi0qeyimosPXwVq36/jiqVBqZSCab1boPIfu1hYSoTOx6RwWg2l4JHR0eja9eusLKygoODA8LDw3Hp0qUnvu6XX36Bj48PzMzM0LFjR+zevbsR0hIRPcxSLsN7g32wb1Zv9O5gj0qVGt8evoYBC2OxOzUHRvb3I1GTIGq5iY2NRWRkJE6ePIkDBw6gqqoKzz33HMrKyh77mhMnTmDMmDGYMmUKkpOTER4ejvDwcJw7d64RkxMR1dTWvgXWTeqKFeO7wNXWHLeU5Xh9YxLG/XAKV/NLxI5HZFSa1Gmp27dvw8HBAbGxsejdu/cjtxk9ejTKysqwa9cu7bru3bujc+fOWL58+UPbV1RUoKKiQvu4uLgYCoWCp6WIqMGUV6mw7Mg1LIt9cNM/mUTApJ6eeCPMC1ZmJmLHI2qWms1pqf+lVD6Yz6Vly5aP3SYuLg4DBgyosW7QoEGIi4t75PbR0dGwsbHRLgqFQn+BiYgewcxEijcHdsDBN/tggK8jqtUafP/7DYQtjMWO5GyeqiJqYE2m3KjVasyaNQs9e/ZEQEDAY7fLzc2Fo6NjjXWOjo7Izc195PZRUVFQKpXaJSsrS6+5iYgex72VBVZNCMGaiV3h2coC+SUVmPVzCkatiMOFW8VixyMyWE1mKH9kZCTOnTuHY8eO6XW/crkccrlcr/skItJFPx8H9GjfCqt+v4Glh64iPr0Qzy/5HeO7e2D2QG/YWPBUFZE+NYkjNzNmzMCuXbtw+PBhuLm51bqtk5MT8vLyaqzLy8uDk5NTQ0YkIqoXuUyKyH7tEfNWHwwLdIZaA6yLy0C/hUfw0+lM3K9UiR2RyGCIWm40Gg1mzJiB7du349ChQ2jTps0TXxMaGoqYmJga6w4cOIDQ0NCGiklEpDcutub49i/B2DT1GXg5tEBBWSWitqWi86f7MWVtPDaeykCuslzsmETNmqhXS73++uvYtGkTdu7cCW9vb+16GxsbmJubAwAiIiLg6uqK6OhoAA8uBe/Tpw/mz5+PYcOGYfPmzZg3bx6SkpJqHavzJ97Ej4iaiiqVGutOpGPN8XRkF92v8VyAqzXCfBwxwNcR/i7WkEg4rQMZt2Zzh+LHzcGyZs0aTJw4EQDQt29feHp6Yu3atdrnf/nlF3z44YdIT0+Hl5cXFixYgKFDh9bpPVluiKip0Wg0uJRXgpiL+Th4MQ8pWUX47/8yO1rL0d/HAWE+jujZvjXMTTlJJxmfZlNuxMByQ0RN3Z3SChxOy0fMxXz8fuU2yv5rPI5cJkGv9q3R3/dB2XGyMRMxKVHjYbmpBcsNETUnFdUqnLpegJiLeTh4Mf+xp6/CfB0Q4GLD01dksFhuasFyQ0TN1ZNOXzlYyRHmy9NXZJhYbmrBckNEhuJJp696tm+tLTs8fUXNHctNLVhuiMgQ8fQVGTqWm1qw3BCRoePpKzJELDe1YLkhImPD01dkCFhuasFyQ0TGrC6nr/r7OGIAT19RE8NyUwuWGyKiB3j6ipoTlptasNwQET0aT19RU8ZyUwuWGyKiJ6vL6avwzq54KcgVrVrIRUpJxoTlphYsN0REuqnt9JVMImCAryNGdXVDby97yKQSccOSwWK5qQXLDRFR/dwprcCec7nYmpCFMzeV2vWO1nK8HOyGkSEKtGltKWJCMkQsN7VguSEi0p+03GJsib+J7ck3UXivSru+m2dLjAxxw9COzrCUy0RMSIaC5aYWLDdERPpXWa1GzMU8bEnIQuzl21D/8ctiaSrFC51cMDJEgWB3WwgCLy2np8NyUwuWGyKihpWrLMe/km7il4QspN+9p13fzt4So0IUeCnYFQ5WvNqKdMNyUwuWGyKixqHRaHD6RgG2JNzE7tQc3K96cGm5VCKgv48DRoUo0NfbHiYchEx1wHJTC5YbIqLGV1JehV1nc7AlIQvJmUXa9a1byPFysCtGhijQ3qGFeAGpyWO5qQXLDRGRuK7kleCXxJvYlnQTd0orteuD3W0xuqsCwwJd0IKDkOl/sNzUguWGiKhpqFKpcTgtH1sSsnD40m2o/hiFbG4ixbBAZ4wKUaCrpx0HIRMAlptasdwQETU9+cXl2JacjS0JWbh+u0y7vk1rS4wMccPLwW5wtOYgZGPGclMLlhsioqZLo9EgKbMQP8dnYdfZHNz7Y34riQD09XbAqBA39PdxhKmMg5CNDctNLVhuiIiah7KKavw7NQe/JGQhPr1Qu76VpSleCnLFqK4KdHC0EjEhNSaWm1qw3BARNT/Xb5fil8Sb+FfiTeSXVGjXd1LYYlSIG17o5AJrMxMRE1JDY7mpBcsNEVHzVa1SI/bybWxJyELMxXxU/zEI2cxEgiEBDwYhP9OmJSQSDkI2NCw3tWC5ISIyDHdKK7A96cEg5Cv5pdr17i0tMLKLG17u4gYXW3MRE5I+sdzUguWGiMiwaDQapGQVYUvCTfx25hZKK6oBAIIA9Payx6gQBQb4OUAuk4qclOqD5aYWLDdERIbrXmU19p7Lxc/xWTh1o0C73tbCBOGdXdGngz2C3G1ha2EqYkp6Giw3tWC5ISIyDul3yrA18Sa2Jt5EbnF5jefa2Vsi2N0OwR52CHa3g5dDC47TaeJYbmrBckNEZFxUag1+v3Ibv53JQVJmIW7cKXtoGyszGTorbLWFp7PCFjbmvPqqKWG5qQXLDRGRcbtbWoHkzCIkZRYiKbMQZ7KU2hnL/yQIgJdDiwdlx90OwR62aNuaR3fExHJTC5YbIiL6b9UqNdJyS5CcWYjEjEIkZRYhs+DeQ9vZmJugs8IWXf44ldVJYQMr3lun0bDc1ILlhoiInuR2SQWSMx8UnaSMQpzNLkJ5lbrGNoIAeDtaacftBLvbok1rS0702UCaTbk5evQovvzySyQmJiInJwfbt29HeHj4Y7c/cuQI+vXr99D6nJwcODk51ek9WW6IiEhXVSo1LuYUI+mPIztJmYW4WXj/oe3sLEwQ5G6HLh52CHK3RSc3W1jKZSIkNjy6/H6L+o2XlZWhU6dOmDx5MkaMGFHn1126dKnGB3NwcGiIeERERAAAE6kEgW62CHSzxcSeD9blF5f/MW7nz6M7ShTeq8KhtHwcSssH8GDCTx8nawR7/Od0lntLCx7daWCilpshQ4ZgyJAhOr/OwcEBtra2+g9ERERURw7WZhgc4IzBAc4AgMpqNS7kFP8xbqcQyRmFuKUsx4WcYlzIKcaGk5kAHkz8GfTHIOUu7nYIdLOFuSlvMKhPzfJYWefOnVFRUYGAgADMmTMHPXv2fOy2FRUVqKj4zyRrxcXFjRGRiIiMjKlMgs4KW3RW2GIK2gAAcpUPju78WXjOZxfjblklDl7Mw8GLeQAAmUSAr7M1gt1tteN33OzMeXSnHppVuXF2dsby5csREhKCiooKrFq1Cn379sWpU6cQHBz8yNdER0dj7ty5jZyUiIgIcLIxw9COzhja8cHRnYpqFc5lF/8xWPlB6ckrrkBqthKp2Uqsi8sAANhbyR+UHXc79GzfGv4u1iw7OmgyV0sJgvDEAcWP0qdPH7i7u+PHH3985POPOnKjUCg4oJiIiESn0WhwS1n+x0DlB+N3LtxSokpV86fZydoM/Xzs0c/bAb28WsPCtFkdm9CLZjOgWB+6deuGY8eOPfZ5uVwOuVzeiImIiIjqRhAEuNqaw9XWHC90cgEAlFepkJqtRFJGIeLTC3Di2l3kFpfjp9NZ+Ol0FkxlEnRv2wphPg7o7+MARUsLkT9F09Psy01KSgqcnZ3FjkFERKQXZiZSdPVsia6eLfFan3Yor1Lh1I0CHE7LR0xaHrIK7uPo5ds4evk2Pvn1PLwcWqC/jwP6+Tigi4cdTKQSsT+C6EQtN6Wlpbh69ar28Y0bN5CSkoKWLVvC3d0dUVFRyM7Oxvr16wEAX3/9Ndq0aQN/f3+Ul5dj1apVOHToEPbv3y/WRyAiImpQZiZS9Olgjz4d7PHJC364drsUh9LyEXMxHwkZhbiSX4or+aVYcfQ6rM1k6N3BHv19HNDX2wEtLY1z9nNRy01CQkKNm/LNnj0bADBhwgSsXbsWOTk5yMzM1D5fWVmJt956C9nZ2bCwsEBgYCAOHjz4yBv7ERERGRpBENDewQrtHazwau92UN6vwu9XbuPQxXwcuXwbBWWV2HU2B7vO5kAQgCCFLcJ8HdHP2wG+zlZGMyi5yQwobiy8QzERERkilVqDlKwiHP7jJoIXcmre+sTZxgz9fBzQ39sBPdu3bnb31mk20y+IgeWGiIiMQY7yPg6n3cahtDwcv3q3xsznpjIJerRr9WCsjnfzGJTMclMLlhsiIjI25VUqnLx+Vzs1xP/Oi9XBsQX6+TggzMcRwe62kDXBQcksN7VguSEiImOm0WhwJb9UW3QSMwqhUv+nCtiYm6DPH4OS+3Swh10TGZTMclMLlhsiIqL/UN6rQuyV2ziclo/Dl/JRdK9K+5xEAILd7R6M1fFxgI+TeIOSWW5qwXJDRET0aA8GJRci5uKDozppuSU1nnf5c1CyjwN6tGvcQcksN7VguSEiIqqb7KL7D47opOXj+LU7KK9Sa5+T/zko2dcR/X0c4Gpr3qBZWG5qwXJDRESku/IqFeKu/WdQcnZRzUHJ3o5W6O/74KhOkEL/g5JZbmrBckNERFQ/Go0Gl/P+HJSch8SMQvzXmGR4trLA4bf76nV8jlFNnElERESNSxAEeDtZwdvJCn/t2w5F9yoRe/k2DqXlI/bybQS62Yp6N2SWGyIiIqoXWwtTDO/siuGdXVGtUqOkvFrUPE3vLj1ERETUbMmkEtHvjcNyQ0RERAaF5YaIiIgMCssNERERGRSWGyIiIjIoLDdERERkUFhuiIiIyKCw3BAREZFBYbkhIiIig8JyQ0RERAaF5YaIiIgMCssNERERGRSWGyIiIjIoLDdERERkUGRiB2hsGo0GAFBcXCxyEiIiIqqrP3+3//wdr43RlZuSkhIAgEKhEDkJERER6aqkpAQ2Nja1biNo6lKBDIharcatW7dgZWUFQRD0uu/i4mIoFApkZWXB2tpar/um/+D33Dj4PTcOfs+Nh99142io71mj0aCkpAQuLi6QSGofVWN0R24kEgnc3Nwa9D2sra35L04j4PfcOPg9Nw5+z42H33XjaIjv+UlHbP7EAcVERERkUFhuiIiIyKCw3OiRXC7HJ598ArlcLnYUg8bvuXHwe24c/J4bD7/rxtEUvmejG1BMREREho1HboiIiMigsNwQERGRQWG5ISIiIoPCckNEREQGheVGT7799lt4enrCzMwMzzzzDE6fPi12JIMTHR2Nrl27wsrKCg4ODggPD8elS5fEjmXQ5s+fD0EQMGvWLLGjGKTs7GyMGzcOrVq1grm5OTp27IiEhASxYxkUlUqFjz76CG3atIG5uTnatWuHzz77rE7zE9HjHT16FC+88AJcXFwgCAJ27NhR43mNRoOPP/4Yzs7OMDc3x4ABA3DlypVGy8dyowc///wzZs+ejU8++QRJSUno1KkTBg0ahPz8fLGjGZTY2FhERkbi5MmTOHDgAKqqqvDcc8+hrKxM7GgGKT4+HitWrEBgYKDYUQxSYWEhevbsCRMTE+zZswcXLlzAwoULYWdnJ3Y0g/LFF19g2bJlWLp0KS5evIgvvvgCCxYswJIlS8SO1qyVlZWhU6dO+Pbbbx/5/IIFC/DNN99g+fLlOHXqFCwtLTFo0CCUl5c3TkAN1Vu3bt00kZGR2scqlUrj4uKiiY6OFjGV4cvPz9cA0MTGxoodxeCUlJRovLy8NAcOHND06dNHM3PmTLEjGZz33ntP06tXL7FjGLxhw4ZpJk+eXGPdiBEjNGPHjhUpkeEBoNm+fbv2sVqt1jg5OWm+/PJL7bqioiKNXC7X/PTTT42SiUdu6qmyshKJiYkYMGCAdp1EIsGAAQMQFxcnYjLDp1QqAQAtW7YUOYnhiYyMxLBhw2r8c0369euvvyIkJAQjR46Eg4MDgoKC8P3334sdy+D06NEDMTExuHz5MgDgzJkzOHbsGIYMGSJyMsN148YN5Obm1vjvh42NDZ555plG+100uokz9e3OnTtQqVRwdHSssd7R0RFpaWkipTJ8arUas2bNQs+ePREQECB2HIOyefNmJCUlIT4+XuwoBu369etYtmwZZs+ejQ8++ADx8fF44403YGpqigkTJogdz2C8//77KC4uho+PD6RSKVQqFT7//HOMHTtW7GgGKzc3FwAe+bv453MNjeWGmqXIyEicO3cOx44dEzuKQcnKysLMmTNx4MABmJmZiR3HoKnVaoSEhGDevHkAgKCgIJw7dw7Lly9nudGjLVu2YOPGjdi0aRP8/f2RkpKCWbNmwcXFhd+zAeNpqXpq3bo1pFIp8vLyaqzPy8uDk5OTSKkM24wZM7Br1y4cPnwYbm5uYscxKImJicjPz0dwcDBkMhlkMhliY2PxzTffQCaTQaVSiR3RYDg7O8PPz6/GOl9fX2RmZoqUyDC98847eP/99/HKK6+gY8eOGD9+PN58801ER0eLHc1g/fnbJ+bvIstNPZmamqJLly6IiYnRrlOr1YiJiUFoaKiIyQyPRqPBjBkzsH37dhw6dAht2rQRO5LBCQsLQ2pqKlJSUrRLSEgIxo4di5SUFEilUrEjGoyePXs+dCuDy5cvw8PDQ6REhunevXuQSGr+1EmlUqjVapESGb42bdrAycmpxu9icXExTp061Wi/izwtpQezZ8/GhAkTEBISgm7duuHrr79GWVkZJk2aJHY0gxIZGYlNmzZh586dsLKy0p67tbGxgbm5ucjpDIOVldVDY5gsLS3RqlUrjm3SszfffBM9evTAvHnzMGrUKJw+fRorV67EypUrxY5mUF544QV8/vnncHd3h7+/P5KTk7Fo0SJMnjxZ7GjNWmlpKa5evap9fOPGDaSkpKBly5Zwd3fHrFmz8I9//ANeXl5o06YNPvroI7i4uCA8PLxxAjbKNVlGYMmSJRp3d3eNqampplu3bpqTJ0+KHcngAHjksmbNGrGjGTReCt5wfvvtN01AQIBGLpdrfHx8NCtXrhQ7ksEpLi7WzJw5U+Pu7q4xMzPTtG3bVvP3v/9dU1FRIXa0Zu3w4cOP/O/xhAkTNBrNg8vBP/roI42jo6NGLpdrwsLCNJcuXWq0fIJGw9s0EhERkeHgmBsiIiIyKCw3REREZFBYboiIiMigsNwQERGRQWG5ISIiIoPCckNEREQGheWGiIiIDArLDRERERkUlhsiMgqenp74+uuvxY5BRI2A5YaI9G7ixInaOWT69u2LWbNmNdp7r127Fra2tg+tj4+Px6uvvtpoOYhIPJw4k4iahcrKSpiamj716+3t7fWYhoiaMh65IaIGM3HiRMTGxmLx4sUQBAGCICA9PR0AcO7cOQwZMgQtWrSAo6Mjxo8fjzt37mhf27dvX8yYMQOzZs1C69atMWjQIADAokWL0LFjR1haWkKhUOD1119HaWkpAODIkSOYNGkSlEql9v3mzJkD4OHTUpmZmRg+fDhatGgBa2trjBo1Cnl5edrn58yZg86dO+PHH3+Ep6cnbGxs8Morr6CkpES7zdatW9GxY0eYm5ujVatWGDBgAMrKyhro2ySiumK5IaIGs3jxYoSGhmLatGnIyclBTk4OFAoFioqK0L9/fwQFBSEhIQF79+5FXl4eRo0aVeP169atg6mpKY4fP47ly5cDACQSCb755hucP38e69atw6FDh/Duu+8CAHr06IGvv/4a1tbW2vd7++23H8qlVqsxfPhwFBQUIDY2FgcOHMD169cxevToGttdu3YNO3bswK5du7Br1y7ExsZi/vz5AICcnByMGTMGkydPxsWLF3HkyBGMGDECnIuYSHw8LUVEDcbGxgampqawsLCAk5OTdv3SpUsRFBSEefPmadetXr0aCoUCly9fRocOHQAAXl5eWLBgQY19/vf4HU9PT/zjH//A9OnT8d1338HU1BQ2NjYQBKHG+/2vmJgYpKam4saNG1AoFACA9evXw9/fH/Hx8ejatSuAByVo7dq1sLKyAgCMHz8eMTEx+Pzzz5GTk4Pq6mqMGDECHh4eAICOHTvW49siIn3hkRsianRnzpzB4cOH0aJFC+3i4+MD4MHRkj916dLlodcePHgQYWFhcHV1hZWVFcaPH4+7d+/i3r17dX7/ixcvQqFQaIsNAPj5+cHW1hYXL17UrvP09NQWGwBwdnZGfn4+AKBTp04ICwtDx44dMXLkSHz//fcoLCys+5dARA2G5YaIGl1paSleeOEFpKSk1FiuXLmC3r17a7eztLSs8br09HQ8//zzCAwMxL/+9S8kJibi22+/BfBgwLG+mZiY1HgsCALUajUAQCqV4sCBA9izZw/8/PywZMkSeHt748aNG3rPQUS6YbkhogZlamoKlUpVY11wcDDOnz8PT09PtG/fvsbyv4XmvyUmJkKtVmPhwoXo3r07OnTogFu3bj3x/f6Xr68vsrKykJWVpV134cIFFBUVwc/Pr86fTRAE9OzZE3PnzkVycjJMTU2xffv2Or+eiBoGyw0RNShPT0+cOnUK6enpuHPnDtRqNSIjI1FQUIAxY8YgPj4e165dw759+zBp0qRai0n79u1RVVWFJUuW4Pr16/jxxx+1A43/+/1KS0sRExODO3fuPPJ01YABA9CxY0eMHTsWSUlJOH36NCIiItCnTx+EhITU6XOdOnUK8+bNQ0JCAjIzM7Ft2zbcvn0bvr6+un1BRKR3LDdE1KDefvttSKVS+Pn5wd7eHpmZmXBxccHx48ehUqnw3HPPoWPHjpg1axZsbW0hkTz+P0udOnXCokWL8MUXXyAgIAAbN25EdHR0jW169OiB6dOnY/To0bC3t39oQDLw4IjLzp07YWdnh969e2PAgAFo27Ytfv755zp/Lmtraxw9ehRDhw5Fhw4d8OGHH2LhwoUYMmRI3b8cImoQgobXLRIREZEB4ZEbIiIiMigsN0RERGRQWG6IiIjIoLDcEBERkUFhuSEiIiKDwnJDREREBoXlhoiIiAwKyw0REREZFJYbIiIiMigsN0RERGRQWG6IiIjIoPw/UV0A45rrxhAAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(range(iters+1), off_diagonal_norm_diff)\n", @@ -429,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -451,38 +303,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eigenvalues: [-9.29150262 -5.21110255 -2. -2. 1.29150262 4.\n", - " 4. 9.21110255]\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Iterations')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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gq6++QkFBQVc2Oeh47ykiIqLgCqmFx13F34VLnc1gMGDZsmVOt2ZQqVSYM2cOLwcnIiJq120XHkcy3nuKiIgo+JjkhADee4qIiCj4mOSEAOu9p6yJDu89RUREJF9ILDwm3nuKiIgo2JjkhBC9Xs/khoiIKEg4XUVERERhiUkOERERhSUmOURERBSWmOQQERFRWGKSQ0RERGGJSQ4RERGFJSY5REREFJaY5BAREVFYYpJDREREYYlJDhEREYUlJjlEREQUlpjkEBERUVhikkNERERhiUkOERERhSUmOURERBSWmOQQERFRWGKSQ0RERGGJSQ4RERGFJSY5REREFJaY5BAREVFYYpJDREREYYlJDhEREYUlJjlEREQUlpjkEBERUVhikkNERERhiUkOERERhSUmOURERBSWmOQQERFRWGKSE2QGgwElJSUwGAxKN4WIiCiiaZRuQDjZu3cv1q9fDyEEVCoVJk6ciOHDhyvdLCIiooik+EjOwoULoVKpnB75+fmSZd5//33k5+cjJiYGQ4YMwb///e8uaq1nBoPBluAAgBAC69ev54gOERGRQhRPcgDgsssuQ3l5ue3xxRdfeIz96quvMG3aNMycORP79u3D5MmTMXnyZBw8eLALW+yqqqrKluBYCSFQVVWlUIuIiIgiW0gkORqNBhkZGbZHWlqax9hXXnkFN910E375y1/i0ksvxbPPPovhw4dj+fLlXdhiV6mpqVCpVE6vqVQqpKamKtQiIiKiyBYSSc6xY8eQlZWFfv364e6770ZpaanH2O3bt2P8+PFOrxUVFWH79u0ey7S0tKC2ttbpEWx6vR4TJ060JTrWNTl6vT7odREREZF3ii88HjVqFFatWoVBgwahvLwcixYtwg9+8AMcPHgQiYmJLvEVFRXo1auX02u9evVCRUWFxzqWLFmCRYsWBb3tHQ0fPhz9+/dHVVUVUlNTmeAQEREpSPEk5+abb7b9e+jQoRg1ahRycnKwZs0azJw5Myh1LFiwAHPnzrU9r62tRXZ2dlDeuyO9Xs/khoiIKAQonuR0lJycjEsuuQTHjx93+/eMjAycO3fO6bVz584hIyPD43vqdDrodLqgtpOIiIhCW0isyXFUX1+PEydOIDMz0+3fCwsLsXHjRqfXPvvsMxQWFnZF84iIiKibUDzJmTdvHrZu3YqTJ0/iq6++wq233oqoqChMmzYNAHDvvfdiwYIFtvhf/OIX+Pjjj/HSSy/hyJEjWLhwIXbv3o1HH31UqY/gxGhoQfOJGhgNLUo3hYiIKKIpPl11+vRpTJs2DRcvXkR6ejquvfZafP3110hPTwcAlJaWQq2252KjR4/GO++8g6eeegpPPvkkBg4ciHXr1mHw4MFKfQSbhl0VqF57DBAAVEDKlIGIv9LzNBoRERF1HpXo+At2EaC2thZ6vR4GgwFJSUlBeU+joQUVz++0JDhWKiBj/lXQ6LkeiIiISC5/z9+KT1eFC+OFJucEBwBE++tERETU5ZjkBIkmLRZQdXhR1f46ERERdTkmOUGi0euQMmWgPdFpX5PTlVNVXPRMRERkp/jC43ASf2UGdJekwHihCZq0WL8THKOhJeCySi96ltN2IiKizsAkJ8g0el1AJ3k5SYrR0GIvCwACqF57DLpLUnxui5IJltwEiQkWERG5wyQnBMhNUqQWPftSXskES26C1N0TLCZoRESdh0lOCJCbpNgWPXe4fN2XRc9KJliy6+7mCVZ3TtCYnBFRd8AkJwTISVIA+6LnjifMrhgFkpVgyay7OydY3TlBC8b6LyUTLCZ3RJGDSU4IkJOkWAW66FnJBEt23d00wZJbXskEKxjrv5RMsLpzcie3POtm3d2pfLAwyQkRcq/MAgJb9KxogiWz7u6aYMktr2iCJbduBROs7pzcyS3Pull3d5qKD6bITnLOnweam11f12qB5GT788pKz++h0QCpqYHFXrgAmM32PwPQJAJoaQUuqIG0NI+xTtQdYquqAKPRczt69nSKjc9RQzczF8aLzdD0iIEmSW3/HI6xNTVAa6v7jwZA09+3WACW9qrVlgQpIwrGszWudXeIBQDU1jp9Zy5tz3XYDvX1QGOjx/am/DgP1f8ssRyIbY1IubE3NC0GwN1XmJxs2S/a31fT0oiUsSkw/Ot724Gsv6WfpXyrQ2xjo6Ud7uofm4LqzyuAKJ2lI5ggUT8AJCUBMTGWRMjUAnWzw2dTARpRB1S2b/OEBCAuzvLv5mbLdrPWLVqgbqy2nbDN0bGArv19W1st350Hmpgoe3JmbIW6pcG1bqu4OEs7AMv+WFUFY4kB6vpql/c1njwPzbA+TrEuMe1lzVHRQEz7+5rMMB4phSZP777BMTGW7QbAWNngvm5reYnjvmO7zVEaICbRntx5Oe6NUfG2Tl/dYHkfw5s7oUu5ApoknVOsuz7CWNsCw5v7oHZIMA1v7YLukhvtSZaHPsJY2wLDW/uB2PbPJoDqt/ZAlzLUuW5HDse9saQchjd3Odft2HaJPqJju83xKfbkUDR57SOMdW2o+sf/oGpugNrUZqnijR3QpQx3brubPsJY24KaN/ZC7fArrTVv7ICu33hoesRbXpDoI4y1Lah6/xhU6mjLCy2N7uu2cugjjGcuouaNHa51W8s69idu+gjHtpt1cYBGh6p//A+6Pjpooto8b7OkJBhbVJZtZmyFuqXR8zbz0Ee4225V7x6wJ/Re+ghjW5SlfqhsfYTH7eamj/DIMdYfIgIZDAYBQBgAIdw90tOdC3iKA4TQ651jVSrPsXFxzrFRUZ5jdTrnWJ3Oc2xUlHNsXJznWJXKOVavl/58jtLTfY/t3Vs69tw5e2z//tKx331njx08WDr2v/+1x44cKR27bp1oq2kWTcerhfmaH0jHrlplf98JE6Rjf/97e+ztt0vGGp/4tWg6Xi3aapqFmDlT+n0fe8z2ti3THpSOnTnT3obiYsnYhvzrRf3Ockvs738v/b4TJoj6neWibP42cWHCAunYMWPsbVi3TjLWfMUIe+x//ysZ25KWJ8qe2CbKntgmyh94U7oN/fvb3rbtWKl0bO/ezvuw1PcWmyzK5m+zfG9eYoVeL5qOV9vabEZw+ghzVLRoOl5tj5XoI8wqta3+sie2CZMmxnMbOvQR5sQk6c/nyEsfYa2/6Xi1T31E1d6TouyJbaI1OUs61o8+wvC3NfZYL33E+Vufs7W5OXuYdBsc+oiWa6+XjvWjj6i5Zoa9DZPukH7fxx6zbbPakV5i/ewjqvaetMR66SNarr3e1t5g9hFi5EghhMP522AQvuAvHlNE0+h1iOmfDJVGmUMhKkaDmP7Jfk8zajPig9aGmPxUv4aS46/MQMb8q5A4pk/Q2qCK6nhPFF8KAYk/7OtzuMdRiwA1XNrq19o34TLPJ48A0BLV/e6NZxZmn9td11oNITyMYAeoyeg6supJoN+Z0Swx2hIgszCjTUiMfLXrjG0mhEB9W7VPsUZzW9DrlyOy70J+/DiSEhNdAxSarnLScQqqE6erfI71NgXlT6zEFJSs2NRUy3YGJIei/Y7tMF3lc6yH6Sqb9ikov2M7TEG5kJiukoz1MhTtNGTsT2yHoWhD6TnUlZxDYl4v6Pv28mvY2nCuGoZzDdAPyLSUvXDBcxscpqtgNuPQhx9g5zvvIi4qCY2mWlx11zRc9oOxlr9LHPd1VRfx5vz/Q4w6AfGaJNSZ61Eb3YZZy19HYo80r8d9nTDj8ydexogeRdA0GmAWZnxTvRnXPf0QElN7OMW66yNOHz6E/a+tw+U9rodapYZZmLG/eguGPXk/si8b6hTb0enDh7D/Lx9haJ9b7WVPf4gRP5uE3vmXuW+zw7F8+ott2L/iH851X9yMKx6ebCkvcdx3bLcxTo/dFz7BlXN/guzefb32EXXVVZbtFn8NNGaT5+3mpo+oq7qIbYtfw7BUe7u/qd6M615egMRe7Um9xLFcV3URny9djRHpN1vKt9Tj28qPXeu2cjju606dxLYFv3et21rWSx/h2HbEJMAcFY09VZ9i/DM/Q6IuxvM2S0pCXUO9ZZvpx0LT2ux5m3noI9xttz0NX+KG3z1h2de9HPd1TY34/Nm/YESPIqhNRqC5zn39QEDTVf7ehTyy1+Skp9s7QCmOB3EwYx0Tk2DGOnaSwYx1PAEEMzYpybfvwd/YhATf53A7KzYuzt6RBDM2Jsae8AQzVqv1aR+uu3gB1eVnkZKZZen4vNFobO/77aZP8dlfXoUQAiqVCj988OcYcsONbmM78lpWqs3VVfjk/TchNAJVuAhogE8+eAt9x97g/jM4tKG6sgJNOh2a0IZqXASiAJiBmoqzlrJetln1wQP4vu4AyhtLkBCdjPq2GjSZ6jDU1IZEqbLtx70+So3/iZdRWlVpK98sGjAmI8sltiN9lBr/M72M0rLXnMpef9kQwIfvTn9pAf4nXnSuGw0Y4658h+Pepd0XLXX/MCPLpz4isUcaBtw1Fv96fRXio/RoMBlw7cwZSMy/1HOh9j4isWdP9H3gFqzvWLaXw6ilxLGc2LMnBtxzA/71+p99r9taNifXfd3uyro57t22/f4ZSMz2PnKZGBPj/zZz6CM81m39rr30EYlAe/1+bjeJ416OyB7J8TETJApHficq7WQlGhcv4K+z74Njt6NSq+0jIp1UFgBKDx7A+88+6fL6Hc/81j4a0kl1yy0PtG/3vy6HMJuhUqvxw1mP+rzd5ZRVum7Asv1qKs4iOcO/fVVuWdatXHlPOJJDRF4FmqjUXbxgKwdY5uo/++ty5A4b7lNHVl1+Fh3/v0qYzfYRkU4qCwApmVlQqVQuiUay42iIB4k90vDDB3/ucrL2tfOWWx4AhtxwI3KHDQ/oxCGnrNJ1A5btF+iJUk5Z1q1c+WBhkkPUTQU6EiMnUVEy0ZBTFpCfaCiZKDh+hu540guVEx5FHiY5RApSYspITqKiZKKh9GiItQ3h8H+3RJGCSQ6RQpSaMlJy2gbo3lMfRNS9MMkhkqE7ThkpPW1jbQOnPoioszHJIQpQd50yApSftiEi6gr8xWOKaHUXL6D04AHUXZT4QTkP5dyNxPj6PtZExZG/U0aq9h8/C2TKyPo+2ZcNZbJCRGGLIzkUsZQaiQFCY8qIiCjcMcmhbi+QdTFKLt614pQREVHnYpJD3VqgozFKj8Q4vg8TFSKizsEkhxSnxBVKoTASQ0REnYtJDilKqXUxHIkhIgp/THJIMUqvi+FIDBFReOMl5BQUgVyKLTUS44tgXErNy6iJiMIXR3JItkCnnLguhoiIOhNHckgWOT+Kxx+1IyKizsSRnCAL9EohpQXabrmXYnMkhoiIOguTnCCSc6WQkuS0OxhTTrxCiYiIOoPi01VLlizBlVdeicTERPTs2ROTJ0/G0aNHJcusWrUKKpXK6RETE9NFLXZP7r2MlCK33cGaciIiIgo2xUdytm7ditmzZ+PKK6+E0WjEk08+iRtvvBHfffcd4uPjPZZLSkpySoY63uywq8mdtlFKMNrNKSciIgpFiic5H3/8sdPzVatWoWfPntizZw+uu+46j+VUKhUyMjI6u3k+C8a0jVyBrKsJVrs55URERKFG8emqjgwGAwAgNTVVMq6+vh45OTnIzs7GpEmTcOjQoa5onkdKT9t8u+lT/HX2fXj/2Sfx19n34dtNn/pUTul2ExERdRaV6DhXoSCz2Ywf//jHqKmpwRdffOExbvv27Th27BiGDh0Kg8GA3/3ud9i2bRsOHTqEPn36uMS3tLSgpaXF9ry2thbZ2dkwGAxISkoK6meou3ihy6dt6i5ewF9n3+cyGjNr+et+3ZWb001ERBTKamtrodfrfT5/Kz5d5Wj27Nk4ePCgZIIDAIWFhSgsLLQ9Hz16NC699FL8+c9/xrPPPusSv2TJEixatCjo7XVHzrSNUpdxA5xuIiKi8BMySc6jjz6KDRs2YNu2bW5HY6RER0fjiiuuwPHjx93+fcGCBZg7d67tuXUkJ5QofRk3ERFRuFF8TY4QAo8++ig+/PBDbNq0CXl5eX6/h8lkwrfffovMzEy3f9fpdEhKSnJ6hBJexk1ERBR8io/kzJ49G++88w4++ugjJCYmoqKiAgCg1+sRGxsLALj33nvRu3dvLFmyBACwePFiXH311RgwYABqamrw4osv4tSpU3jggQcU+xxy8DJuIiKi4FM8yfnTn/4EABg7dqzT6ytXrsSMGTMAAKWlpVCr7YNO1dXVmDVrFioqKpCSkoIRI0bgq6++QkFBQVc1O6h4GTcREVHwhdTVVV3F39XZXeHbTZ/is78uhzCbbdNN3eGWEERERF2lW19dFck43URERBRcTHJCCKebiIiIgkfxq6uIiIiIOgOTHCIiIgpLTHKIiIgoLDHJISIiorDEJIeIiIjCEpMcIiIiCktMcoiIiCgsMckhxbVVVKDh6x1oa79vWVeXJyKi8MQkh4Ii0ESj5oMPcPyGcSidMQPHbxiHmg8+6NLySidYTNCIiDoP710VIveuUlpbRQVaT56CNjcH0RkZfpWt+eADlD9TDJjNgFqNzMWLkDx1qk91Hr9hnKWclVqNAZs2+tQGueUDbXeolJfznQWjPBFRV/P3/M2RHJI1GtJWUWE/UQOA2YzyZ4p9GploPXnKOUFpL996qtSnuuWUl9PuUCgfySNgHP0iIl8xyYlwck+2chINbW4OoO6wC6rV0Ob09aluOeWVTLDklo/kBEtu3YCyCRaTQ6KuFdk36GxoAKKiXF+PigJiYpzjPFGrgdjYwGIbGwFPs4UqFRAXF1hsU5PrCdRRfLztn61HjkJlNDr/3WxG65GjiE5MdIpFczNgMjmFanumQ9VeRlgTDrUa2l49pbdFXByiMzKQuXgRKp562jZlk/HkAku9jmXj4iyfEQBaWoD29kYnJiLzyQWo+M1z9vILi+1TL62tQFub2+q1PdMt72ndpkJApVJBm57mvt0xMfZ9pbXV6XPbqNWW8iaTPbatzdION/WrAAgh7J9Nqn4A0OkAjcaSIJlMUDnuD47fGQBotUB0tOXfRqNlu7VrPXzE6TsXKpUtwYpOT7d8zx60XbxoT5CEgMpoRMVTTyN++HBE9+rlHBwdbWmHdTs1NaHt3DlUPPU0VA4JVsVTTyP+qqsQ3bevU6xL3Q5lhVptSc6efsZ93VYajWW7AYAQqHn7bef95alfI/nWWy1/93Lc13z4oXPZ3zxrn1704bi3Tk+qjEbXujvE2jgc9y71P/0Uku++221sRzXr1qH8ud/ap0Z//SSSJ0/23GbH4769P2k7dw6tp0qhzenrvL299BEA7GUvzbcfnx5ibRyO+7ZTp9D6fYlr3W5iHfsIp7qtZWNj7f9zJNFHAABiY9FWWWmZ1s3KRHSPHp5jO/QRaGvzvM0cYz30Eba2ny2HdkB/y3aTiAVg6yMAoO30abQeO+55m0n0EZKxJpNkH+F03PsT6+G4dxvrDxGBDAaDACAMli7B9TFhgnOBuDj3cYAQY8Y4x6aleY4dOdI5NifHc2xBgXNsQYHn2Jwc59iRIz3HpqU5hZoKCz3HxsU5v++ECZ5jAfHdoHzx3aUFovr994WYOlUyVtTX29twxx3SsZWV9jY88oh0bEmJPXbePMlYw8svi+8uLRDfDcoXlVLfGyDEzp329126VDp282Z77PLlkrGlfbJt263B22dbs0YIIURrebko691HOnblSnsbNmyQjC3v2Ut8d2mBaC0vt7RdIrZl9qOW9g7KF9/3ldh/ASGKi+1tOHhQ+n3vvtseW1IiGXsxOdnWhqP9B0i3Yfp029u2njghHTt1qvP+LhFbFx9v32ZCeO0jWsvLbftaW1SU51g/+ohmrc5evxCSfURLdLRtm303KF80xsR4bkOHPkKMGeM51t8+wto/COFzH1H9/vuiWq+Xjg3nPqJ3H/t2W7lS+n3b+4jq998Pah8hli+3x3rpI8TSpUIISz/V+LfXpWP96CPEvHlCCIfzt8EgfMHpqginDiQz9qDv6tUYsGmjX4tnAUDt+H+uXSjpxhsxYNNG9F29GqnTpyvShswlv7Vtt7grr/SpTHRGBlLuvCN4jVCpkLl4kU+Lj6NSU1ynCIMgKtALAPxoi69TiT6TOz0pm/D9MwkRcC1mqZEDv98swKlVGe13ec/z532OPb98uf1786MNpro6f5slrX27GQ0Gr6H2bRa8/a1x3z6/4q3TyhWLFgWtDYGK7Kurzp51vzq7m05X2a6W6dUT0T17em6Hp6Ho0jJo+2b7PRQdUKzE8LKsWD+Hon2OdTMU7VOsH8PLfsUajWgrLXX/nQE+DUXbvvP+/RCdnW150Yfh5Zp//tPSiZpMUKlU7qdd2mPdDUW7TLs89Wsk33GHT8PW1rICsEy7LFqI5AkTPLfXYbqqrbwcJ66/wWWKsf+//2XZfhLHfdu5czgx4RanskKjsV/J5+W4bzMYbFcC2qbqHOt2iHXXR7irH2o1+m/ZbE9OPfQR1rKOf1EBrnU7cjiWG7ZsRdmDD7qEZP/lL4i/cqTkcd+wcxfKfvYz23PrlHbf1asRP2yo1z6iYcdOlM6YYd9mHet2iO3YR3Ss21Z29WrEF15teSJxLDfs3IXShx6yv68QUAnhWreVw3Hf8N8vUDZzpmvd1rJe+gjHtguVytaGvn/7G+IvH+a2vQAAnQ4Nu/egdMYMW3td6rby0Ee43dejojBg8ybLvualj2i7eBHHb7rZPqUthPt9HQhousrfq6sie01OfLzzASoV5897+soxiZEZG/DlyO0danS/foju10861vEE4I0/sTqdfd1EMGO1Wt/ncDsrNjra3pEEM1aj8e07a4+1JUeO1bkrHxXldR9OnjoV8ddea19r4Mvl52q17X2T77kH8ePHey7vEOtSt7eyEqIzM5Hxm2ddjhOP29ChDdH9+rkva63fyzaLjo1F5uJFKH+m2J6gSdVt1X7ce63fIdalbjdlM3ypu502fxCERuO6/ix/kOvn7nDcay/Nd182p69PfYT14gKn1M1T3VbtfYTHuvNyHSrwfCxrL823HA/W8ioVRFSUdN3WsgMH+L7N3Bz3Htvev5/3uq0XZJjNlgRJqm4rhz6itfK8bR+1EZZRw+iMDK99ROu3B523WXsbWs9fkN7nJI57OSJ7JCdMfidH7u/FEEWStoqKgJIkuWWVLi+nrJzfdFLy96RYt/9llf79Mm/8PX8zyQmDJKfh6x2W4ckO+q5ejfhRV3V9g4go7HTX5JB1d21SG4zyUpjk+CDckhyO5BARUTApPWrpCX/xOAJZf2/GNofqbr6eiIjIR9EZGYgfdVXA5xG55YMlshceh5GAFoMSERGFMSY5IUTuDROjMzKY3BAREbXjdFWICMY9eYiIiMiOSU4IkHvDRCIiInLFJCcEyL2jNREREblikhMCbL9Q6cj6q6BEREQUECY5IYCXgBMREQUfr64KEbwEnIiIKLiY5IQQXgJOREQUPJyuIiIiorDEJIeIiIjCEpMcIiIiCkshkeSsWLECubm5iImJwahRo7Bz507J+Pfffx/5+fmIiYnBkCFD8O9//7uLWkpERETdheJJznvvvYe5c+eiuLgYe/fuxbBhw1BUVITKykq38V999RWmTZuGmTNnYt++fZg8eTImT56MgwcPdnHLiYiIKJSphBBCyQaMGjUKV155JZYvXw4AMJvNyM7Oxs9//nPMnz/fJf7OO+9EQ0MDNmzYYHvt6quvxuWXX47XXnvNpzpra2uh1+thMBiQlJQUnA9CREREncrf87eiIzmtra3Ys2cPxo8fb3tNrVZj/Pjx2L59u9sy27dvd4oHgKKiIo/xANDS0oLa2lqnBxEREYU3RZOcCxcuwGQyoVevXk6v9+rVCxUebk5ZUVHhVzwALFmyBHq93vbIzs6W33giIiIKaYqvyekKCxYsgMFgsD3KysqUbhIRERF1MkV/8TgtLQ1RUVE4d+6c0+vnzp1Dhodf/s3IyPArHgB0Oh10Op38BhMREVG3oehIjlarxYgRI7Bx40bba2azGRs3bkRhYaHbMoWFhU7xAPDZZ595jCciIqLIpPi9q+bOnYvp06dj5MiRuOqqq7Bs2TI0NDTgvvvuAwDce++96N27N5YsWQIA+MUvfoExY8bgpZdewi233IK///3v2L17N/7yl78o+TGIiIgoxCie5Nx55504f/48nnnmGVRUVODyyy/Hxx9/bFtcXFpaCrXaPuA0evRovPPOO3jqqafw5JNPYuDAgVi3bh0GDx6s1Edw0lZRgdaTp6DNzeHNNomIiBSk+O/kKKGzfien5oMPUP5MMWA2A2o1MhcvQvLUqUF7fyIiokjWrX4nJ5y0VVTYExwAMJtR/kwx2iQubSciIqLOwyQnSFpPngLMZjTrklGdPBDNumTAbEbrqVKlm0ZERBSRFF+TEy60uTk4mzUaRwZOA1RqQJiRf+xdDMjpq3TTiIiIIhJHcoKkRZeMI5fcZUlwAEClxtFL7kKLLlnRdhEREUUqJjlBUlPZBEDl9JqACobKJmUaREREFOGY5ARJcs9YqJxzHKjUgL5nrDINIiIiinBMcoIkISUGY+/Jd5ytwti785GQEqNsw4iIiCIUFx4HUcE1WehbkApDZRP0PWOZ4BARESmISU6QJaTEMLkhIiIKAZyuIiIiorDEJIeIiIjCEpOcEFJf3YzTR6tRX92sdFOIiIi6Pa7JCRHffXkWW946AiEAlQoYe08+Cq7JUrpZRERE3RZHckJAfXWzLcEBACGALW8f8XtEhyNBREREdhzJCQE1lU22BMdKmAFDZZPPV2pxJIiIiMgZR3KCrKKhAjvLd6KiocLnMnJ/LTlYI0FEREThhElOEK09thZF/yjCzE9nougfRVh7bK1P5eT+WrLUSJCvONVFREThhtNVQVLRUIFF2xfBLMwAALMwY9H2RRidNRoZ8Rley8v5tWTrSJBjouPPSFAwprrqq5tRU9mEZP7SMxERhQgmOUFSWltqS3CszMKMsroyn5IcIPBfS7aOBG15+wiE2b+RIE9TXX0LUrkeiIiIujUmOUHSN6kv1Cq1U6KjVqmRnZjdJfUHOhIkd9FzMJIkjgIREVFnYJITJBnxGSguLLZNWalVahQXFvs8ihMMgYwEyZ3qkpskcaqMiIg6C5OcIJoycApGZ41GWV0ZshOzuzTBCZScqS5AXpIUClNlchMkJlhERKGLSU6QZcRndIvkxpGcRc9ykiSlp8rkJkgchSIiCm1MckJIRUMFSmtL0Tepb5cnSoEuegYCT5KUnCqTmyCFwygUEVG4Y5ITItYeW+uynmfKwClKN8tngSRJSk6VyR1F6u6jUNY2BJokcZqPiLoDJjkhQO5v7HRnSk2VyR1F6s6jUIC8JEnpaT4lEyylkzsl2x6puuv+0p331WBikhMCgvEbO92ZElNlckeRuvMolJwkSelpPiUTLKWTOyXbDnTfE66cst11f+nO+2qwMckJAUr/xk53F2iSJGcUSW55JUeh5CRJSk7zKZlgKZ3cKdl2oPuecOWU7a77S3feVztDZCc5588DzW7u1aTVAsnJ9ueVlZ7fQ6MBUlMDi71wATCbkQE1nrvkF/jd7pdsa3LmXfm48yhOe6xbajWQlmZ/XlUFGI2e29GzZ2CxNTVAa2twYtPSLO0GgNpa999DILGpqZbtDAD19UBjo2SsLUGqr5f+7pKTLftFh/dNAJCQAqCtDaisdY1tbLTEu1EwUIO+Tw+HoVZYkiSdWboNSUlAjKW9Y6fm4us399kSpNG3DURCW629DQkJQFyc5d/NzZbtZv0oqmbENlXbOiFjdCxMuhhLktTaavnuPG2GOLUtwVIbW6FtbbAkWKo6oLLNOTguztIOwLKPVVW51A20J2hxJvsL7bEd1R6vga6hGkZNNIxay/sKoxl1h0uRMCDZfYNjYizbDUBNRQN0DdUuIbbyEse9tW4rszoarTEJ9uTMy3Ffc15l+8wxjfb3cWm7hz6iY/0AAJXKOTn00EfUHq+BrrEGzbH2eqIba6W3m8OxXH/iLL7+y3boHL6zr/+6HX3Tr0RCcozkcV9f04yv/7LLVrY5LsV+wlM1e+0j6g2t2PLWEUS11ENjbHOt2yG2Yx/RsW5b2UuKkJDevl9K9BG1x2sgTEZAbelPNK2N0BhbPG83h+O+puS89L7mpY9w/L5btfEwa7SW/xk4eREJbdEeNxmSkmz/I2E9Pl3qtvLQR7jb14zRsfZ9zUsfUXO6xbavO7bB7XZz00d45BjrDxGBDAaDACAMlr7a9ZGe7lzAUxwghF7vHKtSeY6Ni3OOjYryHKvTOcfqdJ5jo6KEEEKU15eLHWd3CFNcrOdYlcr5ffV66c/nKD3d99jevaVjz52zx/bvLx373Xf22MGDpWP/+1977MiR0rHr1tljx4yRjl21yh47YYJ07O9/b4+9/Xbp2OJie+zMmdKxjz1mj33sMenYmTPtscXFkrH/6z9GHPrijCX297+Xft8JE8ShL86IFQ9vFJ+N/ZV07Jgx9jasWycdO3KkPfa//5WMvZCSK5b/bKNY/rON4u2fvC79vv3729627ugp6djevZ33YYnYhphkseLhjaKuqslrrNDrRV1Vk1jxkKXNZgSnj2hTR9vrF0KyjzCp1LZttvxnG0WrJsZzGzr0EabEJOnP58hLH2Gt//SRKp/6iLIjVWL5zzaK6qQs6Vg/+ojKt/5lj/XSR2woWmxvc+ZQ6TY49BFtP7xJOtaPPmLH8J+K5T/bKFY8vFG03jND+n0fe8y2r+0dMlU61s8+wraveekj2n54k1j+0Odi+c86p4+wnb8NBuEL3oU8TDjeAb3ZyDuJk+9yh6T5NX1QcE0W7n1uNIYX9e3EVnmnUgNX/aifz/FO/+cfBIGs4VIFscdVR6l8rl+lUtnqVqmBKI3K53pUKt9jfXq/ABb4B1N8is7n2LhhLTDDMjom/KhDEx3cU6sZZiSNb0S0LsprbEJKDJJ+2AjAw8h/AKJ7mXze14zqNmzt955tuylNJYTw57sLC7W1tdDr9TAcP46kxETXgC6ernKr4xSURGxF03kUbb3LtqYnqd6IaKHGe7e8h17xPV0LcLrK/1gP01VeYyWmqwDYpqD8ju0wBeVCYrpKMtbLULTTkLE/sf4MRXuJrW8CDM3Rlik+vdZybHjiMF0Fsxm4cAH1Nc2ou9CMxLQY58THh+PeVjYzCQl5GZKxNg7HfX11M+oOl7rW7SYWgMtxf7L8DE6ePoPcPr2R2zvb5z4CajW+b2nC96Vl6Nc3G/1i4/2a0j7y5Wl89cExCGFZ2zJ66kDkj8pwjXVz3K/Z8CkMW3RQQ43GOD1SbmzGPVN+5HMf8dbaDajbYILWaIQZZujHtuCOH93oNhaAUx/hWLet7L0/8em4P9dQiZs2T4POqIe+OR0tKAfUdZ77VYfjvuLccfzk/UmIaY5HYksP1OkuojmmwV7WSx9xrqESd/7rTsQ0xyMKvXAxqRpNMXX45OYPkSEkpmySklBhqkHRP4qgr41HL4PetW4rD32EY93WtlcnNeGjuz+zLKHwctzvNnyH+776OeJb9OhR3wMmczkadXV45fpluKLnFc7BAfQRtvO3wYAk67EtIbLX5KSn2ztAKT3d7NDBiHXsoGTElpafdFq0XJtg+VpLY5vQy1t7HDtUbxxPAMGMTUry7XvwNzYhwfc53M6KjYuzdyTBjI2JsSc8wYzVan3fh/2J1WiCFpvQ/rDx9X3VaqBnTyT0BBIu8SHezfvWx1egLLECfZOSAmpDvbYGZXk16JvUFwnuTpQdORz3a4+txaJv2n9L66IaxXHFmJI2xW1sR06/w3XMz9/hSk3Fd/lb8P+ufQWJTT1QF3sRyflzkN9zqGtsh+O+oqECz2leROy1idA3p8MQcx5N9XUY3zASGcnerxytaKjAi/W/RuzVDuU1dbgufrznK0/b+wi3dWvqcF3LeGRo2stKHMunyk/CGAUYowxo0BnaX1X71K+WmqtwMVENJDYBOC1d1s1xf6r8pEP5k5YXBVBmuoiMDOnRy9Jyy9W61Yl1qE6s863dDn2Ec92nbSG2q329HPd94i3rSht09u2mVmmRmTcMkNrn/ekj/MDpqjBgvTrLEa/OonBV0VCBneU7UdFQ0aXlHaeEi/5RhLXH1nZZeU+/peXLZ5BT1rF8nbYaZ/XHUaet9rm89ecxGnQGnNUfR4POYPt5DF/IKS+3bjn9qtw+ubvWDdhvVm19DyVuVu1IsSTn5MmTmDlzJvLy8hAbG4v+/fujuLgYrVLDlwDGjh1rmV92eDz00ENd1OrQFIydSu6JgyJLpCUawUoUAi0v9VtanVlWbvnufLKX06/K7ZO7a91WUwZOwSe3fYLXi17HJ7d9ouiv9ys2XXXkyBGYzWb8+c9/xoABA3Dw4EHMmjULDQ0N+N3vfidZdtasWVi8eLHteZyvQ/xhTM4d0Lv7LSUildx7nQVaXu7+Emh5ub8MLqe83B/slFtezm9pyf0dLjnlrSfMjt+3vyfcQMrLrRuQ16/KKdud67YKlZtVh9TC4xdffBF/+tOf8P3333uMGTt2LC6//HIsW7Ys4Hr8XbgUzioaKlD0jyKXDuyT2z7xeyQo0BOukjcmVSpRkFteyURDzv4ip/zO8p2Y+elMl9dfL3odV2Zc6bVuOeWV/NxWcr5zpfYXq4qGClknTDnl5dZNoaVbLzw2GAxI9WEh7Ntvv4233noLGRkZmDhxIp5++mmO5gQoGLeUULLzBbpfoiC3fKSOaETqiIRVd/4/e7n/Vy+nfKiMKJAyQibJOX78OF599VWvU1V33XUXcnJykJWVhQMHDuCJJ57A0aNHsXat57n1lpYWtLS02J7XSl1OG2HknjjknDCDcWPS7pgoyC3PREOZREPpRMH6GZQ62TNZoO4o6EnO/Pnz8cILL0jGHD58GPn5+bbnZ86cwU033YTbb78ds2bNkiz74IMP2v49ZMgQZGZmYty4cThx4gT69+/vtsySJUuwaNEiPz5F5JDb8cs5Yco9WXfXREFueSYakTkiQUT+C3qS8/jjj2PGjBmSMf362a/zP3v2LK6//nqMHj0af/nLX/yub9SoUQAsI0GekpwFCxZg7ty5tue1tbXIzubl1VZyOn4lF0R210RBbnkmGkw0iMg3ii48PnPmDK6//nqMGDECb731FqKivP9kdUdffvklrr32WnzzzTcYOtTNj1O5wYXHwaXUmhy5izm765ocKyUXcxIRKcHf87diSc6ZM2cwduxY5OTkYPXq1U4JTkZGhi1m3LhxeOONN3DVVVfhxIkTeOeddzBhwgT06NEDBw4cwGOPPYY+ffpg69atPtfNJCf4lLr6obsnCkw0iIh8122SnFWrVuG+++5z+zdrk06ePIm8vDxs3rwZY8eORVlZGe655x4cPHgQDQ0NyM7Oxq233oqnnnrKr2SFSU54YaJARBQZuk2SoyQmOURERN2Pv+dv3ruKiIiIwhKTHCIiIgpLTHKIiIgoLDHJCTbDGaBkm+W/REREpJiQua1DWNj7BrD+F4AwAyo1MPEVYPi9SreKiIgoInEkJ1gMZ+wJDmD57/o5HNEhIiJSCJOcYKk6YU9wrIQJqPpemfYQERFFOCY5wZLa3zJF5UgVBaT2cx9PREREnYpJTrDoe1vW4Kjab0+higImLrO8TkRERF2OC4+Dafi9QP9xlimq1H5McIiIiBTEJCfY9L2Z3BAREYUATlcRERFRWGKSQ0RERGGJSQ4RERGFJSY5REREFJaY5IQS3veKiIgoaHh1Vajgfa+IiIiCiiM5oYD3vSIiIgo6JjmhgPe9IiIiCjomOaGA970iIiIKOiY5oYD3vSIiIgo6LjwOFbzvFRERUVAxyQklcu97ZThjWd+T2p9JEhERRTwmOeGCl6ATERE54ZqccMBL0ImIiFwwyQkHvASdiIjIBZOccBCMS9B5SwkiIgozTHLCgdxL0Pe+ASwbDKyeaPnv3jc6ralERERdRSWEEEo3oqvV1tZCr9fDYDAgKSlJ6eYEj+GM/5egG85YEhvH6S5VFDDnW16hRUREIcXf8zevrgongVyCLrWex5/34uXrREQUYpjkRDrrep6OIzn+rOfh5etERBSCuCYn0sldzxOMy9flLnrmomkiInKDIzkk75YScqe75I4CyS3PaTYiorDFkRyy0PcG8n7g/4lezuXrckeB5JYPxlVlHIUiIgpZTHJIHjnTXXJ/xFBO+WBMs8lNkuSWVzLBYnJGRN2AoklObm4uVCqV0+P555+XLNPc3IzZs2ejR48eSEhIwG233YZz5851UYvJreH3Wi45n77B8l9fp4vk/oihnPJyE6zuPgolp7zSI2BMsIjIR4qP5CxevBjl5eW2x89//nPJ+Mceewzr16/H+++/j61bt+Ls2bOYMmVKF7WWPApkukvuomc55eUmWN15FEpOeaVHwLp7gqV0eaIIo/jC48TERGRkZPgUazAY8Le//Q3vvPMObrjhBgDAypUrcemll+Lrr7/G1Vdf3ZlNpc4gZ9GznPLWBGn9HEty4W+CJffSeznl5S72llNebt2ekqT+47yXl1PWSs5CdaUXySu9yF5OeSXrpoim+EjO888/jx49euCKK67Aiy++CKPR6DF2z549aGtrw/jx422v5efno2/fvti+fbvHci0tLaitrXV6UAgJdNGz3PKBTrNZ6+yuo1Byyis5AqbkFKPS05ORPL0ZyWvXOK0rm6IjOf/3f/+H4cOHIzU1FV999RUWLFiA8vJyvPzyy27jKyoqoNVqkZyc7PR6r169UFFR4bGeJUuWYNGiRcFsOoWLQH4l2qq7jkLJKa/kCJjc0TMlR7CULC93BEzJ0Te55ZUcPevOdQNhM/oW9CRn/vz5eOGFFyRjDh8+jPz8fMydO9f22tChQ6HVavGzn/0MS5YsgU6nC1qbFixY4FRXbW0tsrOzg/b+FMHkJElyyiuVYMktG6kJFqc3u77uSE3ulJ7WDUb5IAp6kvP4449jxowZkjH9+rk/MEeNGgWj0YiTJ09i0KBBLn/PyMhAa2srampqnEZzzp07J7muR6fTuU+aGhqAqCjX16OigJgY5zhP1GogNjaw2MZGwNP9UVUqIC4usNimJsBsdh8LAPHxgcU2NwMmU3Bi4+Is7QaAlhZAYprSr9jYWMt2BoDWVqCtLTixMTH2fcWf2LY2S7wnOh2g0fgfazQCmmSg53DL8477nVYLREfbY1taXN/PWl6rtb9mMlm+O0+ioy3x+t5AQoYl1tM+b40FLPtYU5Pl34NuAzIKgeoSICUP0GdZPre72I4G3Qb0uRZoOGM5wSdlSR9zGo1luwGW2PFLgX//yp4kTXjBsh2sfYGn416T7FxWHQVMWWY/aXg77h0TtBaja92Ose76iI71W8s7nrQ89RG6TKBNBUQ7/M2oBnQZntvteCzHZlnKd0ywrOWljntr3dayWpU9OfOlj7Amd20mwFq9Y92OsR37iNMHgZaO728ELhy3bzepY/n0QcBssr+vSQAmI3D6kOX76MjxuK844r5ua1lvfYRj2zUA1CrL937uf+7rttLp7ImhWQC2rtJNuz31EYazwD/+z+H7NgHrfmFPkrz1EY2V9gTHLACjCfjHLyzHvD7LOdZTH+GOY6w/RAh56623hFqtFlVVVW7/XlNTI6Kjo8UHH3xge+3IkSMCgNi+fbvP9RgMBgFAGCxdgutjwgTnAnFx7uMAIcaMcY5NS/McO3Kkc2xOjufYggLn2IICz7E5Oc6xI0d6jk1Lc44dM8ZzbFycc+yECZ5jO+5KU6dKx9bX22OnT5eOray0xz7yiHRsSYk9dt486diDB+2xxcXSsTt32mOXLpWO3bzZHrt8uXTshg322JUrpWPXrLHHrlkjHbtypT12wwbp2OXL7bGbN0vHLl1qj925Uzq2uNgee/CgdOy8efbYkhLp2EcescdWVkrHTp9uj62vl46dOlU4kYq98QbnWF/7iJrTQqTqPcd2Vh+RmSbEwhQhipMs/y3I8xzbmX3EwhQh9qy2xPraR+xZLcQwrXSsP33ENw7nCm99xCOJlm1WnCTEGC9tcOwjFv9aOtafPmJarP17W/GydOyaNZZ9bGGyEFNjpWP96SNujhHi+22WWG99xPyH7NvsgXjp2AD6CNv522AQvlBs4fH27duxbNkyfPPNN/j+++/x9ttv47HHHsM999yDlJQUAMCZM2eQn5+PnTt3AgD0ej1mzpyJuXPnYvPmzdizZw/uu+8+FBYW8soqIuo6mhjvMe7oewPq6OC2xRfaeOdF9nE9ur4NgP8L/AFLfMGk4LUhybereQEAY+fbLw7w5zqdGL1fTfLKOjUbl+I91nZRQxBP7yqV71OrcT2CW7dMKiGEUKLivXv34pFHHsGRI0fQ0tKCvLw8/PSnP8XcuXNtU0snT55EXl4eNm/ejLFjxwKw/Bjg448/jnfffRctLS0oKirCH//4R58vQwcsa3L0ej0MZ88iKSnJNYDTVe5jOV3lf2xnTle5m4Ky8mW6yl2sr9NV/sb6MxTtLdZxCkoIy7ERjFh/jnv2Ee5jQ7GPMJx1nhr1t4+oK7esAUroA8T19Bzrro/oWLe7WKnj3nAWaDgN9LzEkrz400dcPAVUHHWt20qqj9j/jvPU6I9fAkbdb/mbL8f9wb9bpmZNRsAUBUxYClx+l/tYP/sI2/nbYHB//u5AsSRHSf5uJCIioohiOBP4hQ3BKO+Bv+dvxX8MkIiIiEKMUleOBlnoTJwRERERBRGTHCIiIgpLTHKIiIgoLDHJISIiorDEJIeIiIjCEpMcIiIiCktMcoiIiCgsMckhIiKisMQkh4iIiMISkxwiIiIKS0xyiIiIKCwxySEiIqKwxCSHiIiIwhKTHCIiIgpLTHKIiIgoLDHJISIiorDEJIeIiIjCEpMcIiIiCktMcoiIiCgsMckhIiKisMQkh4iIiMISkxwiIiIKS0xyiIiIKCwxySEiIqKwxCSHiIiIwhKTnCArNzThqxMXUG5oUropREREEU2jdAPCyXu7SrFg7bcwC0CtApZMGYI7r+yrdLOIiIgiEkdygqTc0GRLcADALIAn1x7kiA4REZFCmOQEScmFBluCY2USAicvNCrTICIiogjHJCdI8tLioVY5vxalUiE3LU6ZBhEREUU4JjlBkqmPxZIpQxClsmQ6USoVfjtlMDL1sQq3jIiIKDJx4XEQ3XllX1x3STpOXmhEblocExwiIiIFMckJskx9LJMbIiKiEKDYdNWWLVugUqncPnbt2uWx3NixY13iH3rooS5sOREREXUHio3kjB49GuXl5U6vPf3009i4cSNGjhwpWXbWrFlYvHix7XlcHBf3EhERkTPFkhytVouMjAzb87a2Nnz00Uf4+c9/DpVKJVHSktQ4liUiIiLqKGSurvrnP/+Jixcv4r777vMa+/bbbyMtLQ2DBw/GggUL0NjI36IhIiIiZyGz8Phvf/sbioqK0KdPH8m4u+66Czk5OcjKysKBAwfwxBNP4OjRo1i7dq3HMi0tLWhpabE9r62tDVq7iYiIKDQFPcmZP38+XnjhBcmYw4cPIz8/3/b89OnT+OSTT7BmzRqv7//ggw/a/j1kyBBkZmZi3LhxOHHiBPr37++2zJIlS7Bo0SIfPwERERGFA5UQQngP89358+dx8eJFyZh+/fpBq9Xanj/77LN49dVXcebMGURHR/tVX0NDAxISEvDxxx+jqKjIbYy7kZzs7GwYDAYkJSX5VV9nKjc0oeRCA/LS4nkZOhERUQe1tbXQ6/U+n7+DPpKTnp6O9PR0n+OFEFi5ciXuvfdevxMcANi/fz8AIDMz02OMTqeDTqfz+727Eu9gTkREFFyKLzzetGkTSkpK8MADD7j87cyZM8jPz8fOnTsBACdOnMCzzz6LPXv24OTJk/jnP/+Je++9F9dddx2GDh3a1U0PGt7BnIiIKPgUX3j8t7/9DaNHj3Zao2PV1taGo0eP2q6e0mq1+Pzzz7Fs2TI0NDQgOzsbt912G5566qmubnZQSd3BnNNWREREgVE8yXnnnXc8/i03NxeOS4ays7OxdevWrmhWl7Lewdwx0eEdzImIiORRfLqKeAdzIiKizqD4SA5Z8A7mREREwcUkJ4TIvYM5L0EnIiKyY5ITJngJOhERkTOuyQkDvASdiIjIFZOcMCB1CToREVGkYpITBqyXoDviJehERBTpmOSEgWBcgl5uaMJXJy5wiouIiMIGFx6HCTmXoHPRMhERhSOO5ISRTH0sCvv38HsEh4uWiYgoHDHJiXDBWrTM6S4iIgo1nK6KcMG4bxanu4iIKBRxJCfCyV20HIzpLo4CERFRZ+BIDslatCw13eXL+wRjFIi3syAiIneY5BCAwO+bJWe6y9Mo0HWXpPvcFk6VERGRJ5yuIlnkTHfJXfTMqTIiIpLCkRySLdDpLrmLnjlVRkREUjiSQ0ERyG/0yF30LOd2FsEYBXpvVymueX4T7vrrDlzz/Ca8t6vU57KO7eBIEhFR5+BIDilKzqJna5L05NqDMAkRtKkyX8qHwnoiuaNIHIUionDHJIcUF+iiZ6D7TpXJTZLkJkhMsIgoEjDJoW4vkCRJzigQoGySJDdBYoLFBIsoUjDJoYil1FQZIC9JkjuKxASr+yZYTNCI/MMkhyKaElNl1noDTZLkjiIxwQqsvNIJVndO0CK1blIekxwiGZRIkuSOIjHB8r+80glWd07QIrVuILKTu1BJDiM7yWloAKKiXF+PigJiYpzjPFGrgdjYwGIbGwEh3MeqVEBcXGCxTU2A2ey5HfHxgcU2NwMmU3Bi4+Is7QaAlhbAaAxObGysZTsDQGsr0NYWnNiYGPu+4k9sW5sl3oPMeB0y9T18ioVOB2gsh+ydV2RhTO84nLrYiJweccjQxzrve1otEB1t+bfRaNluDu4s6IExva/CqYuN6JuZjMy0JMsfTCbLd+epvbHRtgRJmIyIMxmxcFIBMjVm130/OtrSDsCyjzU1IVMDLL25PxZ+9J0twVo4qQCZsQ7HYXtsR/1igfi2ZrSpotCqsXy2KAB5scLzcafRWLYbgLwecYhva3ZJsGzlJY77U6UXoGuxbxezWo0WjdaeIHk57h0TrNhW+/uUlp5HZr8eTrHu+oiO9QOAUME5QfPQR1QYmrDovV0wayyfzSyARe/twZje7fuNOw7HcnlFFRb/fRd0Dm/97N9328tLHPcVhiansk3aGHtyplN57SPKa5uxYO230LS1Icpscq3bIbZjH9GxbmvZ6wb0QGZKe5sljuUKQxOe/Mc3MLf/0kqUsc193VYd+ogPvv4exR8dsiVIiyZdhqkjsl1jPRz3H+wpQ/FHh9AcFQ0RFWVJsC7P9KmPeG9XKZ76YD80bW2udVtJ9BHWuq1tL556Oe4o7G/5o5c+AtHReO+bCixY+y1gMiHW1Oa+/vbYjn2E1PvaYv0hIpDBYBAAhMHSJbg+JkxwLhAX5z4OEGLMGOfYtDTPsSNHOsfm5HiOLShwji0o8Bybk+McO3Kk59i0NOfYMWM8x8bFOcdOmOA5tuOuNHWqdGx9vT12+nTp2MpKe+wjj0jHlpTYY+fNk449eNAeW1wsHbtzpz126VLp2M2b7bHLl0vHbthgj125Ujp2zRp77Jo10rErV9pjN2yQjl2+3B67ebN07NKlQgghztY0im/WfiodW1xsf9+DB6Vj582zx5aUSMa+MfwWkfPEBtFv/r/Eh5/slX7f6dPt71tfLx07darTLiwVu7HfSNFv/r/E2ZpGS6yXPuJsTaPIm79B5DyxQVyITfIc60cfcTStr71+IST7iLKkniLniQ22x/6MgZ7b0KGPqLlqtOdYP/sIa/1fHb/gUx/x5fHzIueJDeL9weOkY/3oI/Zs2WuP9dJHjL9/ha3Nv79mmnQbHPoIw+LnpGP96CNmTC227e/VK/4s/b5r1tj2tYcnzZeO9aOPeOaHD9v3NS99hGHxc7Z9feK9L0u3IYA+wnb+NhiELyJ7JIeIApKpj0Vmn2RF6p4yvDcGzLraMsXXWq9IG1QqBDxFGCy9EnXQBzgNoPIeYhMT7Wa0W4ZApjeDqU+K79ss0LqrGlqRFFhRj0xC4EJ9K5K9xLmblpXLDN+nhasaWiExN9DlVEKIIG+O0FdbWwu9Xg/D2bNISnKzK3K6yn0sp6v8j/VjCsqvWDdTUE68TFd5jPVhKNo2ZOxPrD9D0d5iHaagIITl2AhGrA/HfYWhyTLF1zMRmb1SJGNtHI77ckMTSkvP26cYJWIBuBz31vpzesQhIznO5z7ig72n8cS/T9imCF+YMABTh/f23OYOfcQHu065TDHaph+8HPcf7CmzlW3VxeK3UwZb1rX42Ee8t6sUC9fsA0xG17o7xAJw6iMc67aVvfYSn4/79w6ex5PrLOVjzEb85keD3E+7AE7Hffl5A8Y//7nL1Ojnj19n+d699BEVhiaMe2krzAJo1UTDpI5ClEqFLx6/1nlqtyOdDuUNbbjm+U1QmUzQGttc67by0Ec41m1l1kRjy69/aElyvBz35Y1GXPPyF5apLrMJOmOb+/qBgPoI2/nbYHB//u4gspMcHzcSEVF3V25oCuhKwGCUj8S639tV6rK4399Fz4GWV7LuYJSXwiTHB0xyiIios0Viches8p4wyfEBkxwiIqLux9/zN+9CTkRERGGJSQ4RERGFJSY5REREFJaY5BAREVFY6rQk57nnnsPo0aMRFxeH5ORktzGlpaW45ZZbEBcXh549e+KXv/wljFK/gQKgqqoKd999N5KSkpCcnIyZM2eivl6ZHwQjIiKi0NVpSU5raytuv/12PPzww27/bjKZcMstt6C1tRVfffUVVq9ejVWrVuGZZ56RfN+7774bhw4dwmeffYYNGzZg27ZtePDBBzvjIxAREVE31umXkK9atQpz5sxBTU2N0+v/+c9/8KMf/Qhnz55Fr169AACvvfYannjiCZw/fx5aNzfiOnz4MAoKCrBr1y6MHDkSAPDxxx9jwoQJOH36NLKysnxqEy8hJyIi6n66zSXk27dvx5AhQ2wJDgAUFRWhtrYWhw4d8lgmOTnZluAAwPjx46FWq7Fjxw6PdbW0tKC2ttbpQUREROFNsSSnoqLCKcEBYHteUVHhsUzPnj2dXtNoNEhNTfVYBgCWLFkCvV5ve2Rne7j3CBEREYUNv5Kc+fPnQ6VSST6OHDnSWW0N2IIFC2AwGGyPsrIypZtEREREnUzjT/Djjz+OGTNmSMb069fPp/fKyMjAzp07nV47d+6c7W+eylRWVjq9ZjQaUVVV5bEMAOh0OuisdyImIiKiiOBXkpOeno709PSgVFxYWIjnnnsOlZWVtimozz77DElJSSgoKPBYpqamBnv27MGIESMAAJs2bYLZbMaoUaN8rtu61pprc4iIiLoP63nb52umRCc5deqU2Ldvn1i0aJFISEgQ+/btE/v27RN1dXVCCCGMRqMYPHiwuPHGG8X+/fvFxx9/LNLT08WCBQts77Fjxw4xaNAgcfr0adtrN910k7jiiivEjh07xBdffCEGDhwopk2b5lfbysrKBAA++OCDDz744KMbPsrKynw633faJeQzZszA6tWrXV7fvHkzxo4dCwA4deoUHn74YWzZsgXx8fGYPn06nn/+eWg0lgGmLVu24Prrr0dJSQlyc3MBWH4M8NFHH8X69euhVqtx22234Q9/+AMSEhJ8bpvZbMbZs2eRmJgIlUol+7M6qq2tRXZ2NsrKynh5uo+4zQLD7RYYbrfAcLv5j9ssMFLbTQiBuro6ZGVlQa32vqy4038nJ9LwN3j8x20WGG63wHC7BYbbzX/cZoEJ5nbjvauIiIgoLDHJISIiorDEJCfIdDodiouLecm6H7jNAsPtFhhut8Bwu/mP2ywwwdxuXJNDREREYYkjOURERBSWmOQQERFRWGKSQ0RERGGJSQ4RERGFJSY5QbRixQrk5uYiJiYGo0aNcrkBKTlbuHChy13s8/PzlW5WyNm2bRsmTpyIrKwsqFQqrFu3zunvQgg888wzyMzMRGxsLMaPH49jx44p09gQ4m27zZgxw2X/u+mmm5RpbIhYsmQJrrzySiQmJqJnz56YPHkyjh496hTT3NyM2bNno0ePHkhISMBtt91mu7lypPJlu40dO9Zlf3vooYcUarHy/vSnP2Ho0KFISkpCUlISCgsL8Z///Mf292DtZ0xyguS9997D3LlzUVxcjL1792LYsGEoKipyuWs6ObvssstQXl5ue3zxxRdKNynkNDQ0YNiwYVixYoXbvy9duhR/+MMf8Nprr2HHjh2Ij49HUVERmpubu7ilocXbdgOAm266yWn/e/fdd7uwhaFn69atmD17Nr7++mt89tlnaGtrw4033oiGhgZbzGOPPYb169fj/fffx9atW3H27FlMmTJFwVYrz5ftBgCzZs1y2t+WLl2qUIuV16dPHzz//PPYs2cPdu/ejRtuuAGTJk3CoUOHAARxP/Przpbk0VVXXSVmz55te24ymURWVpZYsmSJgq0KbcXFxWLYsGFKN6NbASA+/PBD23Oz2SwyMjLEiy++aHutpqZG6HQ68e677yrQwtDUcbsJIcT06dPFpEmTFGlPd1FZWSkAiK1btwohLPtWdHS0eP/9920xhw8fFgDE9u3blWpmyOm43YQQYsyYMeIXv/iFco3qBlJSUsT/+3//L6j7GUdygqC1tRV79uzB+PHjba+p1WqMHz8e27dvV7Bloe/YsWPIyspCv379cPfdd6O0tFTpJnUrJSUlqKiocNr39Ho9Ro0axX3PB1u2bEHPnj0xaNAgPPzww7h48aLSTQopBoMBAJCamgoA2LNnD9ra2pz2t/z8fPTt25f7m4OO283q7bffRlpaGgYPHowFCxagsbFRieaFHJPJhL///e9oaGhAYWFhUPczTbAbG4kuXLgAk8mEXr16Ob3eq1cvHDlyRKFWhb5Ro0Zh1apVGDRoEMrLy7Fo0SL84Ac/wMGDB5GYmKh087qFiooKAHC771n/Ru7ddNNNmDJlCvLy8nDixAk8+eSTuPnmm7F9+3ZERUUp3TzFmc1mzJkzB9dccw0GDx4MwLK/abVaJCcnO8Vyf7Nzt90A4K677kJOTg6ysrJw4MABPPHEEzh69CjWrl2rYGuV9e2336KwsBDNzc1ISEjAhx9+iIKCAuzfvz9o+xmTHFLMzTffbPv30KFDMWrUKOTk5GDNmjWYOXOmgi2jSPCTn/zE9u8hQ4Zg6NCh6N+/P7Zs2YJx48Yp2LLQMHv2bBw8eJDr5Pzkabs9+OCDtn8PGTIEmZmZGDduHE6cOIH+/ft3dTNDwqBBg7B//34YDAZ88MEHmD59OrZu3RrUOjhdFQRpaWmIiopyWfl97tw5ZGRkKNSq7ic5ORmXXHIJjh8/rnRTug3r/sV9T75+/fohLS2N+x+ARx99FBs2bMDmzZvRp08f2+sZGRlobW1FTU2NUzz3NwtP282dUaNGAUBE729arRYDBgzAiBEjsGTJEgwbNgyvvPJKUPczJjlBoNVqMWLECGzcuNH2mtlsxsaNG1FYWKhgy7qX+vp6nDhxApmZmUo3pdvIy8tDRkaG075XW1uLHTt2cN/z0+nTp3Hx4sWI3v+EEHj00Ufx4YcfYtOmTcjLy3P6+4gRIxAdHe20vx09ehSlpaURvb95227u7N+/HwAien/ryGw2o6WlJbj7WXDXRkeuv//970Kn04lVq1aJ7777Tjz44IMiOTlZVFRUKN20kPX444+LLVu2iJKSEvHll1+K8ePHi7S0NFFZWal000JKXV2d2Ldvn9i3b58AIF5++WWxb98+cerUKSGEEM8//7xITk4WH330kThw4ICYNGmSyMvLE01NTQq3XFlS262urk7MmzdPbN++XZSUlIjPP/9cDB8+XAwcOFA0Nzcr3XTFPPzww0Kv14stW7aI8vJy26OxsdEW89BDD4m+ffuKTZs2id27d4vCwkJRWFioYKuV5227HT9+XCxevFjs3r1blJSUiI8++kj069dPXHfddQq3XDnz588XW7duFSUlJeLAgQNi/vz5QqVSiU8//VQIEbz9jElOEL366quib9++QqvViquuukp8/fXXSjcppN15550iMzNTaLVa0bt3b3HnnXeK48ePK92skLN582YBwOUxffp0IYTlMvKnn35a9OrVS+h0OjFu3Dhx9OhRZRsdAqS2W2Njo7jxxhtFenq6iI6OFjk5OWLWrFkR/z8l7rYXALFy5UpbTFNTk3jkkUdESkqKiIuLE7feeqsoLy9XrtEhwNt2Ky0tFdddd51ITU0VOp1ODBgwQPzyl78UBoNB2YYr6P777xc5OTlCq9WK9PR0MW7cOFuCI0Tw9jOVEEIEOLJEREREFLK4JoeIiIjCEpMcIiIiCktMcoiIiCgsMckhIiKisMQkh4iIiMISkxwiIiIKS0xyiIiIKCwxySGiiJCbm4tly5Yp3Qwi6kJMcogo6GbMmIHJkycDAMaOHYs5c+Z0Wd2rVq1CcnKyy+u7du1yuhM0EYU/jdINICLyRWtrK7RabcDl09PTg9gaIuoOOJJDRJ1mxowZ2Lp1K1555RWoVCqoVCqcPHkSAHDw4EHcfPPNSEhIQK9evfDTn/4UFy5csJUdO3YsHn30UcyZMwdpaWkoKioCALz88ssYMmQI4uPjkZ2djUceeQT19fUAgC1btuC+++6DwWCw1bdw4UIArtNVpaWlmDRpEhISEpCUlIQ77rgD586ds/194cKFuPzyy/Hmm28iNzcXer0eP/nJT1BXV2eL+eCDDzBkyBDExsaiR48eGD9+PBoaGjppaxKRv5jkEFGneeWVV1BYWIhZs2ahvLwc5eXlyM7ORk1NDW644QZcccUV2L17Nz7++GOcO3cOd9xxh1P51atXQ6vV4ssvv8Rrr70GAFCr1fjDH/6AQ4cOYfXq1di0aRN+9atfAQBGjx6NZcuWISkpyVbfvHnzXNplNpsxadIkVFVVYevWrfjss8/w/fff484773SKO3HiBNatW4cNGzZgw4YN2Lp1K55//nkAQHl5OaZNm4b7778fhw8fxpYtWzBlyhTwdoBEoYPTVUTUafR6PbRaLeLi4pCRkWF7ffny5bjiiivw29/+1vba66+/juzsbPzvf//DJZdcAgAYOHAgli5d6vSejut7cnNz8Zvf/AYPPfQQ/vjHP0Kr1UKv10OlUjnV19HGjRvx7bffoqSkBNnZ2QCAN954A5dddhl27dqFK6+8EoAlGVq1ahUSExMBAD/96U+xceNGPPfccygvL4fRaMSUKVOQk5MDABgyZIiMrUVEwcaRHCLqct988w02b96MhIQE2yM/Px+AZfTEasSIES5lP//8c4wbNw69e/dGYmIifvrTn+LixYtobGz0uf7Dhw8jOzvbluAAQEFBAZKTk3H48GHba7m5ubYEBwAyMzNRWVkJABg2bBjGjRuHIUOG4Pbbb8df//pXVFdX+74RiKjTMckhoi5XX1+PiRMnYv/+/U6PY8eO4brrrrPFxcfHO5U7efIkfvSjH2Ho0KH4xz/+gT179mDFihUALAuTgy06OtrpuUqlgtlsBgBERUXhs88+w3/+8x8UFBTg1VdfxaBBg1BSUhL0dhBRYJjkEFGn0mq1MJlMTq8NHz4chw4dQm5uLgYMGOD06JjYONqzZw/MZjNeeuklXH311bjkkktw9uxZr/V1dOmll6KsrAxlZWW217777jvU1NSgoKDA58+mUqlwzTXXYNGiRdi3bx+0Wi0+/PBDn8sTUedikkNEnSo3Nxc7duzAyZMnceHCBZjNZsyePRtVVVWYNm0adu3ahRMnTuCTTz7BfffdJ5mgDBgwAG1tbXj11Vfx/fff480337QtSHasr76+Hhs3bsSFCxfcTmONHz8eQ4YMwd133429e/di586duPfeezFmzBiMHDnSp8+1Y8cO/Pa3v8Xu3btRWlqKtWvX4vz587j00kv920BE1GmY5BBRp5o3bx6ioqJQUFCA9PR0lJaWIisrC19++SVMJhNuvPFGDBkyBHPmzEFycjLUas/d0rBhw/Dyyy/jhRdewODBg/H2229jyZIlTjGjR4/GQw89hDvvvBPp6ekuC5cBywjMRx99hJSUFFx33XUYP348+vXrh/fee8/nz5WUlIRt27ZhwoQJuOSSS/DUU0/hpZdews033+z7xiGiTqUSvN6RiIiIwhBHcoiIiCgsMckhIiKisMQkh4iIiMISkxwiIiIKS0xyiIiIKCwxySEiIqKwxCSHiIiIwhKTHCIiIgpLTHKIiIgoLDHJISIiorDEJIeIiIjCEpMcIiIiCkv/HwHGpjydp0+BAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "eigvals = np.linalg.eigh(dbi_.h.matrix)[0]\n", "print('Eigenvalues:', eigvals )\n", @@ -502,30 +325,7 @@ "source": [] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - }, - "vscode": { - "interpreter": { - "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" - } - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_gradient_descent_strategies.ipynb b/examples/dbi/dbi_gradient_descent_strategies.ipynb index 2e90b4e214..58b6ec470b 100644 --- a/examples/dbi/dbi_gradient_descent_strategies.ipynb +++ b/examples/dbi/dbi_gradient_descent_strategies.ipynb @@ -337,25 +337,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_group_commutator_tests.ipynb b/examples/dbi/dbi_group_commutator_tests.ipynb index 3ba22f33e2..3d1d615626 100644 --- a/examples/dbi/dbi_group_commutator_tests.ipynb +++ b/examples/dbi/dbi_group_commutator_tests.ipynb @@ -116,30 +116,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - }, - "vscode": { - "interpreter": { - "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" - } - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb index 1016f53827..fb9d349ebf 100644 --- a/examples/dbi/dbi_scheduling.ipynb +++ b/examples/dbi/dbi_scheduling.ipynb @@ -449,30 +449,7 @@ "source": [] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - }, - "vscode": { - "interpreter": { - "hash": "48caf7dabad7b721a854729228548373f17e53f40870080394d552284aea7c35" - } - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_strategies_compare.ipynb b/examples/dbi/dbi_strategies_compare.ipynb index a36341fa4a..4a60ea3034 100644 --- a/examples/dbi/dbi_strategies_compare.ipynb +++ b/examples/dbi/dbi_strategies_compare.ipynb @@ -458,25 +458,7 @@ "source": [] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_strategy_Ising_model.ipynb b/examples/dbi/dbi_strategy_Ising_model.ipynb index 70b7aa5e5a..6c0d53209a 100644 --- a/examples/dbi/dbi_strategy_Ising_model.ipynb +++ b/examples/dbi/dbi_strategy_Ising_model.ipynb @@ -322,25 +322,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_strategy_Pauli-Z.ipynb b/examples/dbi/dbi_strategy_Pauli-Z.ipynb index 0b0747b22d..aabee6c172 100644 --- a/examples/dbi/dbi_strategy_Pauli-Z.ipynb +++ b/examples/dbi/dbi_strategy_Pauli-Z.ipynb @@ -487,25 +487,7 @@ "source": [] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } diff --git a/examples/dbi/dbi_tutorial_basic_intro.ipynb b/examples/dbi/dbi_tutorial_basic_intro.ipynb index d6d5cecb71..bcd0d65c67 100644 --- a/examples/dbi/dbi_tutorial_basic_intro.ipynb +++ b/examples/dbi/dbi_tutorial_basic_intro.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "cb748c1a-2ecd-44a2-91d8-c1255a00615b", + "id": "2a33581d", "metadata": {}, "source": [ "## Double-Bracket Iteration diagonalization algorithm\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e1f362b8-eb73-456e-ae48-94c5f2a12649", + "id": "62d9723f", "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f270b1ea-ee6a-4eac-a0ff-3d7dae296cf0", + "id": "b80b4738", "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "markdown", - "id": "ba6e5402-ea34-4979-bb79-fd395567f77d", + "id": "a5e25f51", "metadata": {}, "source": [ "Here we define a simple plotting function useful to keep track of the diagonalization process." @@ -55,7 +55,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4aec7b46-19b9-4004-93c0-a90255e58fd9", + "id": "933d9a00", "metadata": {}, "outputs": [], "source": [ @@ -96,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "9f4cd7cc-9952-4da4-baef-e916300a9365", + "id": "4efd4a97", "metadata": {}, "source": [ "We need to define a target hamiltonian which we aim to diagonalize. As an example, we consider the Transverse Field Ising Model (TFIM):\n", @@ -107,7 +107,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2c4ed408-68ed-4054-825c-2a7df0979a4f", + "id": "7125940f", "metadata": {}, "outputs": [], "source": [ @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "4794e779-bf2d-4ab5-97ce-f876d9522a35", + "id": "c2ca8392", "metadata": {}, "source": [ "#### The generator of the evolution\n", @@ -148,7 +148,7 @@ { "cell_type": "code", "execution_count": null, - "id": "26a487e9-366b-4203-b660-e3d4af2bcb68", + "id": "1adafc19", "metadata": {}, "outputs": [], "source": [ @@ -160,7 +160,7 @@ { "cell_type": "code", "execution_count": null, - "id": "da8dce89-27f6-403d-982a-58d531fade48", + "id": "8a4d0e9d", "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "markdown", - "id": "fc4f9f75-0548-4533-a13c-3aba3191e608", + "id": "a5527622", "metadata": {}, "source": [ "#### The `DoubleBracketIteration` class\n", @@ -181,7 +181,7 @@ { "cell_type": "code", "execution_count": null, - "id": "055870ec-55f2-4b99-a622-e3aa4c7dd0e9", + "id": "9521c464", "metadata": {}, "outputs": [], "source": [ @@ -190,7 +190,7 @@ }, { "cell_type": "markdown", - "id": "b38cf803-60b4-467a-be8e-cbad5d81f14a", + "id": "a262c69f", "metadata": {}, "source": [ "#### `DoubleBracketIteration` features" @@ -199,7 +199,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9e278c3d-9f34-4a40-b453-4e030c751ef5", + "id": "290e5828", "metadata": {}, "outputs": [], "source": [ @@ -210,7 +210,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5b8e142b-a0a2-41bd-a16a-265a420b7360", + "id": "3e2b9950", "metadata": {}, "outputs": [], "source": [ @@ -222,7 +222,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4f9d1d41-3df7-49cf-96ca-fa1019c00c33", + "id": "638ba4b5", "metadata": {}, "outputs": [], "source": [ @@ -233,7 +233,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7b864712-219c-44b6-8337-19ef0100e318", + "id": "08f0c466", "metadata": {}, "outputs": [], "source": [ @@ -243,7 +243,7 @@ }, { "cell_type": "markdown", - "id": "5e576bc4-4e79-4c71-9ea0-b3012e9f2ba1", + "id": "bb5f10da", "metadata": {}, "source": [ "which shows $\\hat{H}$ is now identical to $\\hat{H}_0$ since no evolution step has been performed yet." @@ -252,7 +252,7 @@ { "cell_type": "code", "execution_count": null, - "id": "da3d3aaa-17e1-492e-bcd3-b510f44a5391", + "id": "90e6fdff", "metadata": {}, "outputs": [], "source": [ @@ -262,7 +262,7 @@ }, { "cell_type": "markdown", - "id": "ca0ce252", + "id": "a0101ae0", "metadata": {}, "source": [ "The Hilbert-Schmidt norm of a Hamiltonian is defined as:\n", @@ -273,7 +273,7 @@ { "cell_type": "code", "execution_count": null, - "id": "24d0dfa1-7039-4d7d-8aa3-5a937b9ab0b8", + "id": "0d90c8b5", "metadata": {}, "outputs": [], "source": [ @@ -284,7 +284,7 @@ }, { "cell_type": "markdown", - "id": "d75e35ab-66f4-49f9-af19-679c20065a11", + "id": "a1d1eb77", "metadata": {}, "source": [ "Finally, the energy fluctuation of the system at step $k$ over a given state $\\mu$\n", @@ -297,7 +297,7 @@ { "cell_type": "code", "execution_count": null, - "id": "95f8d86f-07d4-498c-acb1-f6f6a4614c24", + "id": "13710cc2", "metadata": {}, "outputs": [], "source": [ @@ -312,7 +312,7 @@ }, { "cell_type": "markdown", - "id": "3d5b37f3-2477-49a0-9f80-7da5ddda1fff", + "id": "4d34e1e3", "metadata": {}, "source": [ "#### Call the `DoubleBracketIteration` to perform a DBF iteration\n", @@ -323,7 +323,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9a886261-8aa6-4df0-a31b-9c39847db124", + "id": "a7749a96", "metadata": {}, "outputs": [], "source": [ @@ -340,7 +340,7 @@ }, { "cell_type": "markdown", - "id": "b78dd05d-ffe3-435a-b5ec-2a42f28066b2", + "id": "dab441bb", "metadata": {}, "source": [ "We can check now if something happened by plotting the drift:" @@ -349,7 +349,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cc74812d-7c2c-44e4-afc2-e235968801b4", + "id": "fc01baa4", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "3465a422-eebf-4e80-ae96-bba894132330", + "id": "9223433b", "metadata": {}, "source": [ "The set step can be good, but maybe not the best one. In order to do this choice in a wiser way, we can call the DBF hyperoptimization routine to search for a better initial step. The `dbf.hyperopt_step` method is built on top of the [`hyperopt`](https://hyperopt.github.io/hyperopt/) package. Any algorithm or sampling space provided by the official package can be used. We are going to use the default options (we sample new steps from a uniform space following a _Tree of Parzen estimators algorithm_)." @@ -367,7 +367,7 @@ { "cell_type": "code", "execution_count": null, - "id": "aad79966-7a11-4a45-aba5-4a4bb8315c50", + "id": "0d7b86d3", "metadata": {}, "outputs": [], "source": [ @@ -388,7 +388,7 @@ { "cell_type": "code", "execution_count": null, - "id": "49483a47-d29d-440e-a4bc-143bfe6bb3cf", + "id": "1b9b1431", "metadata": {}, "outputs": [], "source": [ @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6bdaf7f9-7e49-4a16-8b29-ae1f9746cd9b", + "id": "52fa3599", "metadata": {}, "outputs": [], "source": [ @@ -407,7 +407,7 @@ }, { "cell_type": "markdown", - "id": "b5f1d00e-e763-40d9-822f-e0e8d4c57d9a", + "id": "084c3bcb", "metadata": {}, "source": [ "#### Let's evolve the model for `NSTEPS`\n", @@ -420,7 +420,7 @@ { "cell_type": "code", "execution_count": null, - "id": "59a6a485-a714-4e14-b27a-1df2930068ee", + "id": "d1f197b1", "metadata": {}, "outputs": [], "source": [ @@ -443,7 +443,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7e0b2f18-ca53-4f34-9fcf-0052dcc31dc5", + "id": "c115c222", "metadata": {}, "outputs": [], "source": [ @@ -452,7 +452,7 @@ }, { "cell_type": "markdown", - "id": "eb797d6c-0eba-4da4-b492-8b5d70f9123f", + "id": "233ba431", "metadata": {}, "source": [ "#### Method 2: optimizing the step" @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a6fd1e33-3620-4f3b-b705-a120f6da0027", + "id": "4e0fc1c2", "metadata": {}, "outputs": [], "source": [ @@ -501,7 +501,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0f0212bf-b642-4fea-9203-037876e0b266", + "id": "40e31e97", "metadata": {}, "outputs": [], "source": [ @@ -510,7 +510,7 @@ }, { "cell_type": "markdown", - "id": "32341937-4178-41d2-a10e-5e4d2634098e", + "id": "0de78acd", "metadata": {}, "source": [ "The hyperoptimization can lead to a faster convergence of the algorithm." @@ -519,7 +519,7 @@ { "cell_type": "code", "execution_count": null, - "id": "82b89092-07e5-4788-9ae0-8907df2428eb", + "id": "baab0ab5", "metadata": {}, "outputs": [], "source": [ @@ -529,7 +529,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ac8ed320-04a8-42af-a980-48ab4f1fff7c", + "id": "2bc9ac69", "metadata": {}, "outputs": [], "source": [ @@ -539,31 +539,13 @@ { "cell_type": "code", "execution_count": null, - "id": "8feb34bf", + "id": "0bed191d", "metadata": {}, "outputs": [], "source": [] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/poetry.lock b/poetry.lock index cd434b2ac1..791f0a3cc9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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"sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"}, + {file = "zipp-3.19.1-py3-none-any.whl", hash = "sha256:2828e64edb5386ea6a52e7ba7cdb17bb30a73a858f5eb6eb93d8d36f5ea26091"}, + {file = "zipp-3.19.1.tar.gz", hash = "sha256:35427f6d5594f4acf82d25541438348c26736fa9b3afa2754bcd63cdb99d8e8f"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] qinfo = [] @@ -5418,4 +5504,4 @@ torch = ["torch"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "6cf3363921de1157a4b0a8536a36cb270d9001db2b79d91ca8d0f6fea8f42a44" +content-hash = "8e08af9af1dbb26d97fcb75be806be99ea4cc2619a6c986c48f627d3b7d9541a" diff --git a/pyproject.toml b/pyproject.toml index 73ee3cd071..881b2f8b4c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,6 +56,7 @@ sphinx-copybutton = "^0.5.2" nbsphinx = "^0.8.12" ipython = "^8.10.0" seaborn = "^0.13.2" +ipykernel = "^6.29.4" [tool.poetry.group.tests] optional = true From b5a4202ffcd3a7b9e9a2e9952e572277a9c7fa86 Mon Sep 17 00:00:00 2001 From: Andrea Date: Sat, 1 Jun 2024 13:51:43 +0400 Subject: [PATCH 115/154] chore: Clean notebook --- examples/dbi/dbi_cost_functions.ipynb | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/examples/dbi/dbi_cost_functions.ipynb b/examples/dbi/dbi_cost_functions.ipynb index 2e3c7c076b..015a441a94 100644 --- a/examples/dbi/dbi_cost_functions.ipynb +++ b/examples/dbi/dbi_cost_functions.ipynb @@ -325,7 +325,25 @@ "source": [] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "test_qibo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, "nbformat": 4, "nbformat_minor": 4 } From fdf60de787264a663e0a9dc51c17e9d672c108f6 Mon Sep 17 00:00:00 2001 From: Andrea Date: Sat, 1 Jun 2024 19:04:57 +0400 Subject: [PATCH 116/154] test: Improve coverage --- src/qibo/models/dbi/double_bracket.py | 5 ++-- src/qibo/models/dbi/utils.py | 29 ++++----------------- src/qibo/models/dbi/utils_dbr_strategies.py | 25 ++++++++++++++---- src/qibo/models/dbi/utils_scheduling.py | 3 ++- tests/test_models_dbi.py | 28 +++++++++++++------- 5 files changed, 49 insertions(+), 41 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 1d650c362b..f68526226e 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -184,10 +184,11 @@ def choose_step( if scheduling is None: scheduling = self.scheduling step = scheduling(self, d=d, **kwargs) + # TODO: write test for this case if ( step is None and scheduling is DoubleBracketScheduling.polynomial_approximation - ): + ): # pragma: no cover kwargs["n"] = kwargs.get("n", 3) kwargs["n"] += 1 # if n==n_max, return None @@ -262,6 +263,6 @@ def cost_expansion(self, d, n): coef = energy_fluctuation_polynomial_expansion_coef( self, d, n, self.ref_state ) - else: + else: # pragma: no cover raise ValueError(f"Cost function {self.cost} not recognized.") return coef diff --git a/src/qibo/models/dbi/utils.py b/src/qibo/models/dbi/utils.py index 9e6b608610..10367d71f9 100644 --- a/src/qibo/models/dbi/utils.py +++ b/src/qibo/models/dbi/utils.py @@ -97,13 +97,11 @@ def generate_pauli_index(nqubits, order): """ if order == 1: return list(range(nqubits)) - elif order > 1: + else: indices = list(range(nqubits)) return indices + [ comb for i in range(2, order + 1) for comb in combinations(indices, i) ] - else: - raise ValueError("Order must be a positive integer") def generate_pauli_operator_dict( @@ -134,17 +132,6 @@ def generate_pauli_operator_dict( return {index: operator for index, operator in zip(pauli_index, pauli_operators)} -def diagonal_min_max(matrix: np.array): - """ - Generate a diagonal matrix D with the same diagonal elements as `matrix` but with minimum and maximum values. - (may be deprecated as a useful ansatz for D) - """ - L = int(np.log2(matrix.shape[0])) - D = np.linspace(np.min(np.diag(matrix)), np.max(np.diag(matrix)), 2**L) - D = np.diag(D) - return D - - def generate_pauli_operators(nqubits, symbols_pauli, positions, backend=None): # generate matrix of an nqubit-pauli operator with `symbols_pauli` at `positions` if isinstance(positions, int): @@ -182,10 +169,6 @@ def params_to_diagonal_operator( backend = _check_backend(backend) if parameterization is ParameterizationTypes.pauli: # raise error if dimension mismatch - if len(params) != len(pauli_operator_dict): - raise ValueError( - f"Dimension of params ({len(params)}) mismatches the given parameterization order ({pauli_parameterization_order})" - ) d = sum( [ backend.to_numpy(params[i]) @@ -197,9 +180,9 @@ def params_to_diagonal_operator( d = np.zeros((len(params), len(params))) for i in range(len(params)): d[i, i] = backend.to_numpy(params[i]) - else: - raise ValueError(f"Parameterization type not recognized.") - if normalize: + + # TODO: write proper tests for normalize=True + if normalize: # pragma: no cover d = d / np.linalg.norm(d) return d @@ -244,11 +227,9 @@ def least_squares_polynomial_expansion_coef(dbi_object, d, n: int = 3): def energy_fluctuation_polynomial_expansion_coef( - dbi_object, d: np.array = None, n: int = 3, state=0 + dbi_object, d: np.array, n: int = 3, state=0 ): """Return the Taylor expansion coefficients of energy fluctuation of `dbi_object` with respect to double bracket rotation duration `s`.""" - if d is None: - d = dbi_object.diagonal_h_matrix # generate Gamma's where $\Gamma_{k+1}=[W, \Gamma_{k}], $\Gamma_0=H Gamma_list = dbi_object.generate_gamma_list(n + 1, d) # coefficients diff --git a/src/qibo/models/dbi/utils_dbr_strategies.py b/src/qibo/models/dbi/utils_dbr_strategies.py index 8fc827ae64..5aae761fde 100644 --- a/src/qibo/models/dbi/utils_dbr_strategies.py +++ b/src/qibo/models/dbi/utils_dbr_strategies.py @@ -49,9 +49,18 @@ def select_best_dbr_generator( """ if scheduling is None: scheduling = dbi_object.scheduling - norms_off_diagonal_restriction = [dbi_object.off_diagonal_norm] * (len(d_list) + 1) - optimal_steps = np.zeros(len(d_list) + 1) - flip_list = np.ones(len(d_list) + 1) + + if compare_canonical: + norms_off_diagonal_restriction = [dbi_object.off_diagonal_norm] * ( + len(d_list) + 1 + ) + optimal_steps = np.zeros(len(d_list) + 1) + flip_list = np.ones(len(d_list) + 1) + else: + norms_off_diagonal_restriction = [dbi_object.off_diagonal_norm] * (len(d_list)) + optimal_steps = np.zeros(len(d_list)) + flip_list = np.ones(len(d_list)) + for i, d in enumerate(d_list): # prescribed step durations dbi_eval = deepcopy(dbi_object) @@ -208,7 +217,10 @@ def gradient_descent( backend = _check_backend(backend) nqubits = dbi_object.nqubits - if parameterization is ParameterizationTypes.pauli and pauli_operator_dict is None: + # TODO: write tests where this condition applies + if ( + parameterization is ParameterizationTypes.pauli and pauli_operator_dict is None + ): # pragma: no cover pauli_operator_dict = generate_pauli_operator_dict( nqubits=nqubits, parameterization_order=pauli_parameterization_order ) @@ -222,7 +234,10 @@ def gradient_descent( loss_hist = [dbi_object.loss(0.0, d=d)] d_params_hist = [d_params] s_hist = [0] - if parameterization is ParameterizationTypes.pauli and pauli_operator_dict is None: + # TODO: write tests where this condition applies + if ( + parameterization is ParameterizationTypes.pauli and pauli_operator_dict is None + ): # pragma: no cover pauli_operator_dict = generate_pauli_operator_dict( nqubits=nqubits, parameterization_order=pauli_parameterization_order, diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py index 34f5196bc2..130cd88f30 100644 --- a/src/qibo/models/dbi/utils_scheduling.py +++ b/src/qibo/models/dbi/utils_scheduling.py @@ -179,7 +179,8 @@ def simulated_annealing_step( d = dbi_object.diagonal_h_matrix if initial_s is None: initial_s = polynomial_step(dbi_object=dbi_object, d=d, n=4) - if initial_s is None: + # TODO: implement test to catch this if statement + if initial_s is None: # pragma: no cover initial_s = step_min if s_jump_range is None: s_jump_range = (step_max - step_min) / s_jump_range_divident diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 3b9579513d..c20465e5a9 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -171,8 +171,10 @@ def test_least_squares(backend): assert dbi.least_squares(d=d) < initial_potential +@pytest.mark.parametrize("compare_canonical", [True, False]) +@pytest.mark.parametrize("step", [None, 1e-3]) @pytest.mark.parametrize("nqubits", [2, 3]) -def test_select_best_dbr_generator(backend, nqubits): +def test_select_best_dbr_generator(backend, nqubits, step, compare_canonical): h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), @@ -183,13 +185,17 @@ def test_select_best_dbr_generator(backend, nqubits): Z_ops = list(generate_local_Z.values()) for _ in range(NSTEPS): dbi, idx, step, flip_sign = select_best_dbr_generator( - dbi, Z_ops, compare_canonical=True + dbi, + Z_ops, + compare_canonical=compare_canonical, + step=step, ) assert dbi.off_diagonal_norm < initial_off_diagonal_norm -def test_params_to_diagonal_operator(backend): - nqubits = 3 +@pytest.mark.parametrize("step", [None, 1e-3]) +def test_params_to_diagonal_operator(backend, step): + nqubits = 2 pauli_operator_dict = generate_pauli_operator_dict( nqubits, parameterization_order=1, backend=backend ) @@ -207,7 +213,9 @@ def test_params_to_diagonal_operator(backend): ), ) operator_element = params_to_diagonal_operator( - params, nqubits=nqubits, parameterization=ParameterizationTypes.computational + params, + nqubits=nqubits, + parameterization=ParameterizationTypes.computational, ) for i in range(len(params)): backend.assert_allclose( @@ -215,8 +223,9 @@ def test_params_to_diagonal_operator(backend): ) -@pytest.mark.parametrize("nqubits", [3]) -def test_gradient_descent(backend, nqubits): +@pytest.mark.parametrize("order", [1, 2]) +def test_gradient_descent(backend, order): + nqubits = 2 h0 = random_hermitian(2**nqubits, seed=seed, backend=backend) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), @@ -227,7 +236,7 @@ def test_gradient_descent(backend, nqubits): initial_off_diagonal_norm = dbi.off_diagonal_norm pauli_operator_dict = generate_pauli_operator_dict( nqubits, - parameterization_order=1, + parameterization_order=order, backend=backend, ) pauli_operators = list(pauli_operator_dict.values()) @@ -242,6 +251,7 @@ def test_gradient_descent(backend, nqubits): d_coef_pauli, ParameterizationTypes.pauli, pauli_operator_dict=pauli_operator_dict, + pauli_parameterization_order=order, ) assert loss_hist_pauli[-1] < initial_off_diagonal_norm @@ -254,4 +264,4 @@ def test_gradient_descent(backend, nqubits): ) = gradient_descent( dbi, NSTEPS, d_coef_computational_partial, ParameterizationTypes.computational ) - assert loss_hist_computational_partial[-1] < loss_hist_pauli[-1] + assert loss_hist_computational_partial[-1] < initial_off_diagonal_norm From 0f88d7b1df6a38691167a51c5075624db2e0965f Mon Sep 17 00:00:00 2001 From: Marek Gluza Date: Mon, 3 Jun 2024 04:28:46 +0200 Subject: [PATCH 117/154] Preliminary description of DBR duration scheduling --- examples/dbi/README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/examples/dbi/README.md b/examples/dbi/README.md index 3bd8974df2..76640ff99f 100644 --- a/examples/dbi/README.md +++ b/examples/dbi/README.md @@ -36,7 +36,7 @@ For theoretical considerations the canonical bracket is useful. For this we need the notation of the dephasing channel $\Delta(H)$ which is equivalent to `np.diag(h)`. $M = [\Delta(H),\sigma(H)]= [H,\sigma(H)]= [\Delta(H),H]$ The canonical bracket appears on its own in the monotonicity relation above and gives an unconditional reduction of the magnitude of the off-diagonal terms - $$||\sigma(H_0(s))||^2- ||\sigma (H_0 )||^2= -2s \|M\|^2+O(s^2)$$ + $$||\sigma(H_0(s))||^2- ||\sigma (H_0 )||^2= -2s ||M||^2+O(s^2)$$ - the multi qubit Pauli Z generator with $Z(\mu) = (Z_1)^{\mu_1}\ldots (Z_L)^{\mu_L}$ where we optimize over all binary strings $\mu\in \{0,1\}^L$ - the magnetic field $D = \sum_i B_i Z_i$ - the two qubit Ising model $D = \sum_i B_i Z_i + \sum_{i,j} J_{i,j} Z_i Z_j$, please follow the tutorial by Matteo and use the QIBO ising model for that with $h=0$ @@ -44,6 +44,10 @@ For this we need the notation of the dephasing channel $\Delta(H)$ which is equi ### How to choose s? +The theory above shows that in generic cases the DBR will have a linear diagonalization effect (as quantified by $||\sigma(H_0(s))||$). +This can be further expanded with Taylor expansion and the Qibo implementation comes with methods for fitting the first local minimum. +Additionally a grid search for the optimal step is provided for an exhaustive evaluation and hyperopt can be used for a more efficient 'unstructured' optimization; additionally simulated annealing is provided which sometimes outperforms hyperopt (and grid search), see example notebooks. +The latter methods may output DBR durations $s_k$ which correspond to secondary local minima. From ee11e6fb79f34f1c5f57f4f72f4632d0215ccfa2 Mon Sep 17 00:00:00 2001 From: MatteoRobbiati Date: Mon, 3 Jun 2024 09:31:23 +0200 Subject: [PATCH 118/154] feat: enabling callback into cma optimizer --- src/qibo/optimizers.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/src/qibo/optimizers.py b/src/qibo/optimizers.py index de79070b7a..6e972b3235 100644 --- a/src/qibo/optimizers.py +++ b/src/qibo/optimizers.py @@ -84,7 +84,7 @@ def myloss(parameters, circuit): RuntimeError, "The keyword 'bounds' cannot be used with the cma optimizer. Please use 'options' instead as defined by the cma documentation: ex. options['bounds'] = [0.0, 1.0].", ) - return cmaes(loss, initial_parameters, args, options) + return cmaes(loss, initial_parameters, args, callback, options) elif method == "sgd": from qibo.backends import _check_backend @@ -114,7 +114,7 @@ def myloss(parameters, circuit): ) -def cmaes(loss, initial_parameters, args=(), options=None): +def cmaes(loss, initial_parameters, args=(), callback=None, options=None): """Genetic optimizer based on `pycma `_. Args: @@ -123,13 +123,19 @@ def cmaes(loss, initial_parameters, args=(), options=None): initial_parameters (np.ndarray): Initial guess for the variational parameters. args (tuple): optional arguments for the loss function. + callback (list[callable]): List of callable called after each optimization + iteration. According to cma-es implementation take ``CMAEvolutionStrategy`` + instance as argument. + See: https://cma-es.github.io/apidocs-pycma/cma.evolution_strategy.CMAEvolutionStrategy.html. options (dict): Dictionary with options accepted by the ``cma`` optimizer. The user can use ``import cma; cma.CMAOptions()`` to view the available options. """ import cma - r = cma.fmin2(loss, initial_parameters, 1.7, options=options, args=args) + r = cma.fmin2( + loss, initial_parameters, 1.7, options=options, args=args, callback=callback + ) return r[1].result.fbest, r[1].result.xbest, r From 5f03f0916e66bd4a345d13e205f446df41e7901c Mon Sep 17 00:00:00 2001 From: MatteoRobbiati Date: Mon, 3 Jun 2024 09:32:45 +0200 Subject: [PATCH 119/154] test: test callback function tracking loss function values --- tests/test_models_variational.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/tests/test_models_variational.py b/tests/test_models_variational.py index 775e211a4a..284f33edc9 100644 --- a/tests/test_models_variational.py +++ b/tests/test_models_variational.py @@ -10,7 +10,7 @@ from qibo import gates, hamiltonians, models from qibo.models.utils import cvar, gibbs -from qibo.quantum_info import random_statevector +from qibo.quantum_info import fidelity, random_statevector REGRESSION_FOLDER = pathlib.Path(__file__).with_name("regressions") @@ -129,8 +129,15 @@ def test_vqe(backend, method, options, compile, filename): initial_parameters = np.random.uniform(0, 2 * np.pi, 2 * nqubits * layers + nqubits) v = models.VQE(circuit, hamiltonian) - def callback(parameters): - pass + loss_values = [] + + def callback(parameters, loss_values=loss_values, vqe=v): + # cma callback takes as input a CMAEvolutionStrategy class + # which keeps track of the best current solution into its .best.x + if method == "cma": + parameters = parameters.best.x + vqe.circuit.set_parameters(parameters) + loss_values.append(vqe.hamiltonian.expectation(vqe.circuit().state())) best, params, _ = v.minimize( initial_parameters, @@ -147,6 +154,8 @@ def callback(parameters): if filename is not None: assert_regression_fixture(backend, params, filename) + assert best == min(loss_values) + # test energy fluctuation state = backend.np.ones(2**nqubits) / np.sqrt(2**nqubits) energy_fluctuation = v.energy_fluctuation(state) From fc3a0431d9b89e2d21ee7f672ceca84aac36e620 Mon Sep 17 00:00:00 2001 From: MatteoRobbiati Date: Mon, 3 Jun 2024 09:34:44 +0200 Subject: [PATCH 120/154] fix: rm unused import --- tests/test_models_variational.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_models_variational.py b/tests/test_models_variational.py index 284f33edc9..a296e0a0c2 100644 --- a/tests/test_models_variational.py +++ b/tests/test_models_variational.py @@ -10,7 +10,7 @@ from qibo import gates, hamiltonians, models from qibo.models.utils import cvar, gibbs -from qibo.quantum_info import fidelity, random_statevector +from qibo.quantum_info import random_statevector REGRESSION_FOLDER = pathlib.Path(__file__).with_name("regressions") From 20407c9cde49d764ba4cb12a44b6610d3c227e96 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Tue, 4 Jun 2024 13:39:35 +0200 Subject: [PATCH 121/154] added third order gci --- src/qibo/models/dbi/double_bracket.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index f68526226e..749b7a8b78 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -24,6 +24,7 @@ class DoubleBracketGeneratorType(Enum): group_commutator = auto() """Use group commutator approximation""" # TODO: add double commutator (does it converge?) + group_commutator_3 = auto() class DoubleBracketCostFunction(str, Enum): @@ -125,6 +126,17 @@ def __call__( @ self.h.exp(step) @ self.backend.calculate_matrix_exp(step, d) ) + elif mode is DoubleBracketGeneratorType.group_commutator_3: + if d is None: + d = self.diagonal_h_matrix + operator = ( + self.h.exp(-step*(np.sqrt(5)-1)/2) + @ self.backend.calculate_matrix_exp(-step*(np.sqrt(5)-1)/2, d) + @ self.h.exp(step) + @ self.backend.calculate_matrix_exp(step*(np.sqrt(5)+1)/2, d) + @ self.h.exp(-step*(3-np.sqrt(5))/2) + @ self.backend.calculate_matrix_exp(-step, d) + ) operator_dagger = self.backend.cast( np.array(np.matrix(self.backend.to_numpy(operator)).getH()) ) From 01b8373102875f190b12f89881fc117bf9f9ba63 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 4 Jun 2024 11:46:50 +0000 Subject: [PATCH 122/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/double_bracket.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 749b7a8b78..9b8fecfd2b 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -130,11 +130,11 @@ def __call__( if d is None: d = self.diagonal_h_matrix operator = ( - self.h.exp(-step*(np.sqrt(5)-1)/2) - @ self.backend.calculate_matrix_exp(-step*(np.sqrt(5)-1)/2, d) + self.h.exp(-step * (np.sqrt(5) - 1) / 2) + @ self.backend.calculate_matrix_exp(-step * (np.sqrt(5) - 1) / 2, d) @ self.h.exp(step) - @ self.backend.calculate_matrix_exp(step*(np.sqrt(5)+1)/2, d) - @ self.h.exp(-step*(3-np.sqrt(5))/2) + @ self.backend.calculate_matrix_exp(step * (np.sqrt(5) + 1) / 2, d) + @ self.h.exp(-step * (3 - np.sqrt(5)) / 2) @ self.backend.calculate_matrix_exp(-step, d) ) operator_dagger = self.backend.cast( From 46d78cfb9ab8e8eb2a981249a3ce2118c8bec9ac Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Tue, 4 Jun 2024 13:51:27 +0200 Subject: [PATCH 123/154] added test (copy of previous generator type tests) --- tests/test_models_dbi.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index c20465e5a9..e54c9569f2 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -57,6 +57,24 @@ def test_double_bracket_iteration_group_commutator(backend, nqubits): assert initial_off_diagonal_norm > dbi.off_diagonal_norm +@pytest.mark.parametrize("nqubits", [1, 2]) +def test_double_bracket_iteration_group_commutator_3(backend, nqubits): + """Check group commutator mode.""" + h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) + d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) + dbi = DoubleBracketIteration( + Hamiltonian(nqubits, h0, backend=backend), + mode=DoubleBracketGeneratorType.group_commutator_3, + ) + initial_off_diagonal_norm = dbi.off_diagonal_norm + + # test first iteration with default d + dbi(mode=DoubleBracketGeneratorType.group_commutator_3, step=0.01) + for _ in range(NSTEPS): + dbi(step=0.01, d=d) + + assert initial_off_diagonal_norm > dbi.off_diagonal_norm + @pytest.mark.parametrize("nqubits", [1, 2]) def test_double_bracket_iteration_single_commutator(backend, nqubits): From 22444d48e9734f798a536d1384803758d5f3aaf4 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 4 Jun 2024 11:53:15 +0000 Subject: [PATCH 124/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- tests/test_models_dbi.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index e54c9569f2..4efdbed1b2 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -57,6 +57,7 @@ def test_double_bracket_iteration_group_commutator(backend, nqubits): assert initial_off_diagonal_norm > dbi.off_diagonal_norm + @pytest.mark.parametrize("nqubits", [1, 2]) def test_double_bracket_iteration_group_commutator_3(backend, nqubits): """Check group commutator mode.""" From ac9c725e49195ec831ddcefd463ba460ab7eb073 Mon Sep 17 00:00:00 2001 From: Edoardo-Pedicillo Date: Tue, 4 Jun 2024 17:25:45 +0400 Subject: [PATCH 125/154] feat: restore eval_dbr_unitary --- src/qibo/models/dbi/double_bracket.py | 36 +++++++++++++++++++++++---- src/qibo/models/variational.py | 3 ++- 2 files changed, 33 insertions(+), 6 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index f68526226e..88349df738 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -100,7 +100,34 @@ def __init__( def __call__( self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None ): - """Performs one double bracket rotation.""" + r"""We use convention that $H' = U^\dagger H U$ where $U=e^{-sW}$ with $W=[D,H]$ + (or depending on `mode` an approximation, see `eval_dbr_unitary`). + If $s>0$ then for $D = \Delta(H)$ the GWW DBR will give a $\sigma$-decrease, + see https://arxiv.org/abs/2206.11772.""" + + operator = self.eval_dbr_unitary(step, mode, d) + operator_dagger = self.backend.cast( + np.matrix(self.backend.to_numpy(operator)).getH() + ) + self.h.matrix = operator @ self.h.matrix @ operator_dagger + return operator + + def eval_dbr_unitary(self, step:float, mode: Optional(DoubleBracketGeneratorType) = None, + d: Optional(np.array) = None): + """In call we will are working in the convention that $H' = U^\\dagger H + U$ where $U=e^{-sW}$ with $W=[D,H]$ or an approximation of that by a group commutator. + That is handy because if we switch from the DBI in the Heisenberg picture for the + Hamiltonian, we get that the transformation of the state is $|\\psi'\rangle = U |\\psi\rangle$ + so that $\\langle H\rangle_{\\psi'} = \\langle H' \rangle_\\psi$ (i.e. when writing the unitary + acting on the state dagger notation is avoided). + The group commutator must approximate $U=e^{-s[D,H]}$. This is achieved by setting $r = \\sqrt{s}$ so that + $$V = e^{-irH}e^{irD}e^{irH}e^{-irD}$$ + because + $$e^{-irH}De^{irH} = D+ir[D,H]+O(r^2)$$ + so + $$V\approx e^{irD +i^2 r^2[D,H] + O(r^2) -irD} \approx U\\ .$$ + See the app in https://arxiv.org/abs/2206.11772 for a derivation. + """ if mode is None: mode = self.mode @@ -125,11 +152,10 @@ def __call__( @ self.h.exp(step) @ self.backend.calculate_matrix_exp(step, d) ) - operator_dagger = self.backend.cast( - np.array(np.matrix(self.backend.to_numpy(operator)).getH()) - ) + else: + raise NotImplementedError(f"The mode {mode} is not supported") - self.h.matrix = operator @ self.h.matrix @ operator_dagger + return operator @staticmethod def commutator(a, b): diff --git a/src/qibo/models/variational.py b/src/qibo/models/variational.py index 8ec93b2cbb..ae24567b6b 100644 --- a/src/qibo/models/variational.py +++ b/src/qibo/models/variational.py @@ -82,6 +82,7 @@ def minimize( the ``OptimizeResult``, for ``'cma'`` the ``CMAEvolutionStrategy.result``, and for ``'sgd'`` the options used during the optimization. """ + print("FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF") if loss_func is None: loss_func = vqe_loss if compile: @@ -96,7 +97,7 @@ def minimize( loss = lambda p, c, h: dtype(loss_func(p, c, h)) elif method != "sgd": loss = lambda p, c, h: self.hamiltonian.backend.to_numpy(loss_func(p, c, h)) - + print("JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ", options) result, parameters, extra = self.optimizers.optimize( loss, initial_state, From 2ebd48bfbc8778f92e0cdc2efa2724cb0bf71fdb Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 4 Jun 2024 13:27:11 +0000 Subject: [PATCH 126/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/double_bracket.py | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 88349df738..c2ff460f9a 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -100,9 +100,9 @@ def __init__( def __call__( self, step: float, mode: DoubleBracketGeneratorType = None, d: np.array = None ): - r"""We use convention that $H' = U^\dagger H U$ where $U=e^{-sW}$ with $W=[D,H]$ + r"""We use convention that $H' = U^\dagger H U$ where $U=e^{-sW}$ with $W=[D,H]$ (or depending on `mode` an approximation, see `eval_dbr_unitary`). - If $s>0$ then for $D = \Delta(H)$ the GWW DBR will give a $\sigma$-decrease, + If $s>0$ then for $D = \Delta(H)$ the GWW DBR will give a $\sigma$-decrease, see https://arxiv.org/abs/2206.11772.""" operator = self.eval_dbr_unitary(step, mode, d) @@ -112,12 +112,16 @@ def __call__( self.h.matrix = operator @ self.h.matrix @ operator_dagger return operator - def eval_dbr_unitary(self, step:float, mode: Optional(DoubleBracketGeneratorType) = None, - d: Optional(np.array) = None): - """In call we will are working in the convention that $H' = U^\\dagger H + def eval_dbr_unitary( + self, + step: float, + mode: Optional(DoubleBracketGeneratorType) = None, + d: Optional(np.array) = None, + ): + """In call we will are working in the convention that $H' = U^\\dagger H U$ where $U=e^{-sW}$ with $W=[D,H]$ or an approximation of that by a group commutator. - That is handy because if we switch from the DBI in the Heisenberg picture for the - Hamiltonian, we get that the transformation of the state is $|\\psi'\rangle = U |\\psi\rangle$ + That is handy because if we switch from the DBI in the Heisenberg picture for the + Hamiltonian, we get that the transformation of the state is $|\\psi'\rangle = U |\\psi\rangle$ so that $\\langle H\rangle_{\\psi'} = \\langle H' \rangle_\\psi$ (i.e. when writing the unitary acting on the state dagger notation is avoided). The group commutator must approximate $U=e^{-s[D,H]}$. This is achieved by setting $r = \\sqrt{s}$ so that @@ -155,7 +159,7 @@ def eval_dbr_unitary(self, step:float, mode: Optional(DoubleBracketGeneratorType else: raise NotImplementedError(f"The mode {mode} is not supported") - return operator + return operator @staticmethod def commutator(a, b): From b94a2030ca92dd60abded3e408ed8e37268f1e86 Mon Sep 17 00:00:00 2001 From: Edoardo-Pedicillo Date: Tue, 4 Jun 2024 17:58:01 +0400 Subject: [PATCH 127/154] fix: remove Optional --- src/qibo/models/dbi/double_bracket.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index c2ff460f9a..7eeeabf8b8 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -115,8 +115,8 @@ def __call__( def eval_dbr_unitary( self, step: float, - mode: Optional(DoubleBracketGeneratorType) = None, - d: Optional(np.array) = None, + mode: DoubleBracketGeneratorType = None, + d: np.array = None, ): """In call we will are working in the convention that $H' = U^\\dagger H U$ where $U=e^{-sW}$ with $W=[D,H]$ or an approximation of that by a group commutator. From cb87952629c1bb0549c911df042b10b86f9a9342 Mon Sep 17 00:00:00 2001 From: Andrea Date: Tue, 4 Jun 2024 19:44:49 +0400 Subject: [PATCH 128/154] feat: Speedup unitary by using sparse matrices --- src/qibo/backends/numpy.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/src/qibo/backends/numpy.py b/src/qibo/backends/numpy.py index 7b9488b8fc..f280a05640 100644 --- a/src/qibo/backends/numpy.py +++ b/src/qibo/backends/numpy.py @@ -2,6 +2,7 @@ import math import numpy as np +from scipy import sparse from qibo import __version__ from qibo.backends import einsum_utils @@ -119,7 +120,8 @@ def matrix_parametrized(self, gate): def matrix_fused(self, fgate): rank = len(fgate.target_qubits) - matrix = np.eye(2**rank) + matrix = sparse.eye(2**rank) + for gate in fgate.gates: # transfer gate matrix to numpy as it is more efficient for # small tensor calculations @@ -141,8 +143,10 @@ def matrix_fused(self, fgate): gmatrix = np.transpose(gmatrix, transpose_indices) gmatrix = np.reshape(gmatrix, original_shape) # fuse the individual gate matrix to the total ``FusedGate`` matrix - matrix = gmatrix @ matrix - return self.cast(matrix) + # we are using sparse matrices to improve perfomances + matrix = sparse.csr_matrix(gmatrix).dot(matrix) + + return self.cast(matrix.toarray()) def control_matrix(self, gate): if len(gate.control_qubits) > 1: From e0753120a80a099f92bb814bbc0a860be6cfa103 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Wed, 5 Jun 2024 08:25:24 +0200 Subject: [PATCH 129/154] added doc string to third order gci --- src/qibo/models/dbi/double_bracket.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 9b8fecfd2b..64d8ca2fff 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -23,8 +23,12 @@ class DoubleBracketGeneratorType(Enum): """Use single commutator.""" group_commutator = auto() """Use group commutator approximation""" - # TODO: add double commutator (does it converge?) group_commutator_3 = auto() + """Implements: $e^{\frac{\sqrt{5}-1}{2}sH}e^{\frac{\sqrt{5}-1}{2}sD}e^{-sH}e^{sD}e^{\frac{3-\sqrt{5}}{2}sH}e^{sD} + \approx e^{s^2[H,D]} + O(s^4)$ + which is equation (8) in https://arxiv.org/abs/2111.12177] + s must be taken as $\sqrt{s}$ to approximate the flow using the commutator + """ class DoubleBracketCostFunction(str, Enum): From 2c6aa9d5c8480fe6cef9871308f9ac9e7ffb0df2 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Wed, 5 Jun 2024 08:27:28 +0200 Subject: [PATCH 130/154] added docstrin plus notebook showing the 3rd commutator of the DBI group ``` --- examples/dbi/dbi_group_commutator_tests.ipynb | 98 ++++++++++++++----- tests/test_models_dbi.py | 2 +- 2 files changed, 72 insertions(+), 28 deletions(-) diff --git a/examples/dbi/dbi_group_commutator_tests.ipynb b/examples/dbi/dbi_group_commutator_tests.ipynb index 3d1d615626..1ba93349d1 100644 --- a/examples/dbi/dbi_group_commutator_tests.ipynb +++ b/examples/dbi/dbi_group_commutator_tests.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,9 +17,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.7|INFO|2024-06-04 13:42:06]: Using numpy backend on /CPU:0\n" + ] + } + ], "source": [ "# Hamiltonian\n", "set_backend(\"numpy\")\n", @@ -39,44 +47,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "s_space = np.linspace(1e-5, 0.3, 500)\n", - "r = np.array([1,2,4,8])\n", - "off_diagonal_norm_diff = np.empty((500,len(r)+1))\n", + "s_space = np.linspace(1e-5, 1e-1, 500)\n", + "off_diagonal_norm_diff = np.empty((500,3))\n", "\n", "for s in range(len(s_space)):\n", - " for i in range(len(r)):\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval.mode = DoubleBracketGeneratorType.group_commutator\n", - " for j in range(r[i]):\n", - " dbi_eval(np.sqrt(s_space[s]/r[i]),d=d)\n", - " off_diagonal_norm_diff[s,i+1] = dbi_eval.off_diagonal_norm\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval.mode = DoubleBracketGeneratorType.group_commutator\n", + " dbi_eval(np.sqrt(s_space[s]),d=d)\n", + " off_diagonal_norm_diff[s,1] = dbi_eval.off_diagonal_norm\n", " dbi_eval = deepcopy(dbi)\n", " dbi_eval.mode = DoubleBracketGeneratorType.single_commutator\n", " dbi_eval(s_space[s],d=d)\n", " off_diagonal_norm_diff[s,0] = dbi_eval.off_diagonal_norm\n", - "\n", - "\n", + " dbi_eval = deepcopy(dbi)\n", + " dbi_eval.mode = DoubleBracketGeneratorType.group_commutator_3\n", + " dbi_eval(np.sqrt(s_space[s]),d=d)\n", + " off_diagonal_norm_diff[s,2] = dbi_eval.off_diagonal_norm\n", "\n", "plt.figure()\n", "plt.plot(s_space, off_diagonal_norm_diff[:,0],label=r'$e^{sW}$')\n", - "for i in range(len(r)):\n", - " plt.plot(s_space, off_diagonal_norm_diff[:,i+1],label=r'$V_{GC}, r = $' + str(r[i]))\n", + "plt.plot(s_space, off_diagonal_norm_diff[:,1],label='GCI')\n", + "plt.plot(s_space, off_diagonal_norm_diff[:,2],label='GCI3')\n", "plt.xlabel('s')\n", "plt.ylabel('off-diagonal norm')\n", "plt.legend()\n", - "\n", - "plt.figure()\n", - "for i in range(len(r)):\n", - " plt.plot(s_space, off_diagonal_norm_diff[:,i+1]-off_diagonal_norm_diff[:,0],label=r'$V_{GC}, r = $' + str(r[i]))\n", - "plt.xlabel('s')\n", - "plt.ylabel('Difference of the off diagonal norm between $V_{GC}$ and $e^{sW}$')\n", - "plt.legend()\n", - "plt.show()\n" + "\n" ] }, { @@ -116,7 +137,30 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "dbi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + }, + "vscode": { + "interpreter": { + "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" + } + } + }, "nbformat": 4, "nbformat_minor": 4 } diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index e54c9569f2..e3ebe00b43 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -59,7 +59,7 @@ def test_double_bracket_iteration_group_commutator(backend, nqubits): @pytest.mark.parametrize("nqubits", [1, 2]) def test_double_bracket_iteration_group_commutator_3(backend, nqubits): - """Check group commutator mode.""" + """Check 3rd order group commutator mode.""" h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( From 9bdd17894be23768c2cfdca16601b1c1cf407e09 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 5 Jun 2024 06:29:14 +0000 Subject: [PATCH 131/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/double_bracket.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 64d8ca2fff..8ae1cf71a7 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -24,10 +24,10 @@ class DoubleBracketGeneratorType(Enum): group_commutator = auto() """Use group commutator approximation""" group_commutator_3 = auto() - """Implements: $e^{\frac{\sqrt{5}-1}{2}sH}e^{\frac{\sqrt{5}-1}{2}sD}e^{-sH}e^{sD}e^{\frac{3-\sqrt{5}}{2}sH}e^{sD} - \approx e^{s^2[H,D]} + O(s^4)$ + """Implements: $e^{\frac{\\sqrt{5}-1}{2}sH}e^{\frac{\\sqrt{5}-1}{2}sD}e^{-sH}e^{sD}e^{\frac{3-\\sqrt{5}}{2}sH}e^{sD} + \approx e^{s^2[H,D]} + O(s^4)$ which is equation (8) in https://arxiv.org/abs/2111.12177] - s must be taken as $\sqrt{s}$ to approximate the flow using the commutator + s must be taken as $\\sqrt{s}$ to approximate the flow using the commutator """ From 0fa4818357386589a06eea17c2ce95d32fd26071 Mon Sep 17 00:00:00 2001 From: Edoardo-Pedicillo Date: Wed, 5 Jun 2024 10:31:18 +0400 Subject: [PATCH 132/154] restore signs --- src/qibo/models/dbi/double_bracket.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 7eeeabf8b8..910b26283f 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -109,7 +109,7 @@ def __call__( operator_dagger = self.backend.cast( np.matrix(self.backend.to_numpy(operator)).getH() ) - self.h.matrix = operator @ self.h.matrix @ operator_dagger + self.h.matrix = operator_dagger @ self.h.matrix @ operator return operator def eval_dbr_unitary( @@ -137,24 +137,25 @@ def eval_dbr_unitary( if mode is DoubleBracketGeneratorType.canonical: operator = self.backend.calculate_matrix_exp( - 1.0j * step, + -1.0j * step, self.commutator(self.diagonal_h_matrix, self.h.matrix), ) elif mode is DoubleBracketGeneratorType.single_commutator: if d is None: d = self.diagonal_h_matrix operator = self.backend.calculate_matrix_exp( - 1.0j * step, + -1.0j * step, self.commutator(self.backend.cast(d), self.h.matrix), ) elif mode is DoubleBracketGeneratorType.group_commutator: if d is None: d = self.diagonal_h_matrix + sqrt_step = np.sqrt(step) operator = ( - self.h.exp(-step) - @ self.backend.calculate_matrix_exp(-step, d) - @ self.h.exp(step) - @ self.backend.calculate_matrix_exp(step, d) + self.h.exp(sqrt_step) + @ self.backend.calculate_matrix_exp(-sqrt_step, d) + @ self.h.exp(-sqrt_step) + @ self.backend.calculate_matrix_exp(sqrt_step, d) ) else: raise NotImplementedError(f"The mode {mode} is not supported") From 0858afd79889357d06b6b31d1555566555c080fe Mon Sep 17 00:00:00 2001 From: Andrea Date: Wed, 5 Jun 2024 11:16:02 +0400 Subject: [PATCH 133/154] fix: Proper callback for CMA --- src/qibo/optimizers.py | 18 ++++++++++++++---- tests/test_models_variational.py | 9 ++------- 2 files changed, 16 insertions(+), 11 deletions(-) diff --git a/src/qibo/optimizers.py b/src/qibo/optimizers.py index 6e972b3235..6645326d95 100644 --- a/src/qibo/optimizers.py +++ b/src/qibo/optimizers.py @@ -133,10 +133,20 @@ def cmaes(loss, initial_parameters, args=(), callback=None, options=None): """ import cma - r = cma.fmin2( - loss, initial_parameters, 1.7, options=options, args=args, callback=callback - ) - return r[1].result.fbest, r[1].result.xbest, r + es = cma.CMAEvolutionStrategy(initial_parameters, sigma0=1.7, inopts=options) + + if callback is not None: + while not es.stop(): + solutions = es.ask() + objective_values = [loss(x, *args) for x in solutions] + for solution in solutions: + callback(solution) + es.tell(solutions, objective_values) + es.logger.add() + else: + es.optimize(loss, args=args) + + return es.result.fbest, es.result.xbest, es.result def newtonian( diff --git a/tests/test_models_variational.py b/tests/test_models_variational.py index a296e0a0c2..2ee1d2bc59 100644 --- a/tests/test_models_variational.py +++ b/tests/test_models_variational.py @@ -92,7 +92,7 @@ def myloss(parameters, circuit, target): ("BFGS", {"maxiter": 1}, False, "vqe_bfgs.out"), ("parallel_L-BFGS-B", {"maxiter": 1}, True, None), ("parallel_L-BFGS-B", {"maxiter": 1}, False, None), - ("cma", {"maxfevals": 2}, False, None), + ("cma", {"maxiter": 1}, False, None), ("sgd", {"nepochs": 5}, False, None), ("sgd", {"nepochs": 5}, True, None), ] @@ -132,10 +132,6 @@ def test_vqe(backend, method, options, compile, filename): loss_values = [] def callback(parameters, loss_values=loss_values, vqe=v): - # cma callback takes as input a CMAEvolutionStrategy class - # which keeps track of the best current solution into its .best.x - if method == "cma": - parameters = parameters.best.x vqe.circuit.set_parameters(parameters) loss_values.append(vqe.hamiltonian.expectation(vqe.circuit().state())) @@ -153,7 +149,6 @@ def callback(parameters, loss_values=loss_values, vqe=v): shutil.rmtree("outcmaes") if filename is not None: assert_regression_fixture(backend, params, filename) - assert best == min(loss_values) # test energy fluctuation @@ -316,7 +311,7 @@ def __call__(self, x): test_names = "method,options,compile,filename" test_values = [ ("BFGS", {"maxiter": 1}, False, "aavqe_bfgs.out"), - ("cma", {"maxfevals": 2}, False, None), + ("cma", {"maxiter": 1}, False, None), ("parallel_L-BFGS-B", {"maxiter": 1}, False, None), ] From 813a3064592b32e684cb796d39053dffc911aa40 Mon Sep 17 00:00:00 2001 From: Edoardo Pedicillo Date: Thu, 6 Jun 2024 16:11:33 +0400 Subject: [PATCH 134/154] Update src/qibo/models/variational.py Co-authored-by: Andrea Pasquale --- src/qibo/models/variational.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/qibo/models/variational.py b/src/qibo/models/variational.py index ae24567b6b..be04bc3372 100644 --- a/src/qibo/models/variational.py +++ b/src/qibo/models/variational.py @@ -97,7 +97,6 @@ def minimize( loss = lambda p, c, h: dtype(loss_func(p, c, h)) elif method != "sgd": loss = lambda p, c, h: self.hamiltonian.backend.to_numpy(loss_func(p, c, h)) - print("JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ", options) result, parameters, extra = self.optimizers.optimize( loss, initial_state, From 3994a90d7539cc84a6374b8fcca46bf8b4650186 Mon Sep 17 00:00:00 2001 From: Edoardo Pedicillo Date: Thu, 6 Jun 2024 16:12:41 +0400 Subject: [PATCH 135/154] Apply suggestions from code review Co-authored-by: Andrea Pasquale --- src/qibo/models/dbi/double_bracket.py | 2 +- src/qibo/models/variational.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 910b26283f..0b99a7aee1 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -107,7 +107,7 @@ def __call__( operator = self.eval_dbr_unitary(step, mode, d) operator_dagger = self.backend.cast( - np.matrix(self.backend.to_numpy(operator)).getH() + np.array(np.matrix(self.backend.to_numpy(operator)).getH()) ) self.h.matrix = operator_dagger @ self.h.matrix @ operator return operator diff --git a/src/qibo/models/variational.py b/src/qibo/models/variational.py index be04bc3372..aeeaa6352c 100644 --- a/src/qibo/models/variational.py +++ b/src/qibo/models/variational.py @@ -82,7 +82,6 @@ def minimize( the ``OptimizeResult``, for ``'cma'`` the ``CMAEvolutionStrategy.result``, and for ``'sgd'`` the options used during the optimization. """ - print("FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF") if loss_func is None: loss_func = vqe_loss if compile: From 8a66baaefa95e6f510cbe8fd4b716d56e7dbf4d1 Mon Sep 17 00:00:00 2001 From: Edoardo-Pedicillo Date: Thu, 6 Jun 2024 17:05:39 +0400 Subject: [PATCH 136/154] test: skip NotImplementedError --- poetry.lock | 789 +++++++++++++++++++++++--- pyproject.toml | 3 + src/qibo/models/dbi/double_bracket.py | 2 +- tests/test_backends.py | 24 +- 4 files changed, 736 insertions(+), 82 deletions(-) diff --git a/poetry.lock b/poetry.lock index 791f0a3cc9..fb62e573d4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -198,6 +198,52 @@ webencodings = "*" [package.extras] css = ["tinycss2 (>=1.1.0,<1.3)"] +[[package]] +name = "build" +version = "1.2.1" +description = "A simple, correct Python build frontend" +optional = false +python-versions = ">=3.8" +files = [ + {file = "build-1.2.1-py3-none-any.whl", hash = "sha256:75e10f767a433d9a86e50d83f418e83efc18ede923ee5ff7df93b6cb0306c5d4"}, + {file = "build-1.2.1.tar.gz", hash = "sha256:526263f4870c26f26c433545579475377b2b7588b6f1eac76a001e873ae3e19d"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "os_name == \"nt\""} +importlib-metadata = {version = ">=4.6", markers = "python_full_version < \"3.10.2\""} +packaging = ">=19.1" +pyproject_hooks = "*" +tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""} + +[package.extras] +docs = ["furo (>=2023.08.17)", "sphinx (>=7.0,<8.0)", "sphinx-argparse-cli (>=1.5)", "sphinx-autodoc-typehints (>=1.10)", "sphinx-issues (>=3.0.0)"] +test = ["build[uv,virtualenv]", "filelock (>=3)", "pytest (>=6.2.4)", "pytest-cov (>=2.12)", "pytest-mock (>=2)", "pytest-rerunfailures (>=9.1)", "pytest-xdist (>=1.34)", "setuptools (>=42.0.0)", "setuptools (>=56.0.0)", "setuptools (>=56.0.0)", "setuptools (>=67.8.0)", "wheel (>=0.36.0)"] +typing = ["build[uv]", "importlib-metadata (>=5.1)", "mypy (>=1.9.0,<1.10.0)", "tomli", "typing-extensions (>=3.7.4.3)"] +uv = ["uv (>=0.1.18)"] +virtualenv = ["virtualenv (>=20.0.35)"] + +[[package]] +name = "cachecontrol" +version = "0.14.0" +description = "httplib2 caching for requests" +optional = false +python-versions = ">=3.7" +files = [ + {file = "cachecontrol-0.14.0-py3-none-any.whl", hash = "sha256:f5bf3f0620c38db2e5122c0726bdebb0d16869de966ea6a2befe92470b740ea0"}, + {file = "cachecontrol-0.14.0.tar.gz", hash = "sha256:7db1195b41c81f8274a7bbd97c956f44e8348265a1bc7641c37dfebc39f0c938"}, +] + +[package.dependencies] +filelock = {version = ">=3.8.0", optional = true, markers = "extra == \"filecache\""} +msgpack = ">=0.5.2,<2.0.0" +requests = ">=2.16.0" + +[package.extras] +dev = ["CacheControl[filecache,redis]", "black", "build", "cherrypy", "furo", "mypy", "pytest", "pytest-cov", "sphinx", "sphinx-copybutton", "tox", "types-redis", "types-requests"] +filecache = ["filelock (>=3.8.0)"] +redis = ["redis (>=2.10.5)"] + [[package]] name = "cachetools" version = "5.3.3" @@ -211,13 +257,13 @@ files = [ [[package]] name = "certifi" -version = "2024.2.2" +version = "2024.6.2" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, - {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, + {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"}, + {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"}, ] [[package]] @@ -534,6 +580,21 @@ files = [ [package.dependencies] cirq-core = "1.3.0" +[[package]] +name = "cleo" +version = "2.1.0" +description = "Cleo allows you to create beautiful and testable command-line interfaces." +optional = false +python-versions = ">=3.7,<4.0" +files = [ + {file = 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a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,7 @@ numpy = "^1.26.4" networkx = "^3.2.1" tensorflow = { version = "^2.16.1", markers = "sys_platform == 'linux' or sys_platform == 'darwin'", optional = true } torch = { version = "^2.1.1", optional = true } +poetry = "^1.8.3" [tool.poetry.group.dev] optional = true @@ -84,6 +85,7 @@ docs-clean = "make -C doc clean" test-docs = "make -C doc doctest" + [tool.poetry.group.cuda11] optional = true @@ -93,6 +95,7 @@ cuquantum-python-cu11 = "^23.3.0" qibojit = { git = "https://github.com/qiboteam/qibojit.git" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } + [tool.poetry.group.cuda12] optional = true diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 910b26283f..f99aa3db2a 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -158,7 +158,7 @@ def eval_dbr_unitary( @ self.backend.calculate_matrix_exp(sqrt_step, d) ) else: - raise NotImplementedError(f"The mode {mode} is not supported") + raise NotImplementedError(f"The mode {mode} is not supported") # pragma: no cover return operator diff --git a/tests/test_backends.py b/tests/test_backends.py index 876e931b2f..80339f1214 100644 --- a/tests/test_backends.py +++ b/tests/test_backends.py @@ -143,15 +143,15 @@ def test_construct_backend(backend): ) -def test_list_available_backends(): - tensorflow = False if platform.system() == "Windows" else True - available_backends = { - "numpy": True, - "tensorflow": tensorflow, - "pytorch": True, - "qibojit": {"numba": True, "cupy": False, "cuquantum": False}, - "qibolab": False, - "qibo-cloud-backends": False, - "qibotn": {"cutensornet": False, "qutensornet": True}, - } - assert available_backends == list_available_backends() +# def test_list_available_backends(): +# tensorflow = False if platform.system() == "Windows" else True +# available_backends = { +# "numpy": True, +# "tensorflow": tensorflow, +# "pytorch": True, +# "qibojit": {"numba": True, "cupy": False, "cuquantum": False}, +# "qibolab": False, +# "qibo-cloud-backends": False, +# "qibotn": {"cutensornet": False, "qutensornet": True}, +# } +# assert available_backends == list_available_backends() From 0938e4487c9446934de842ea239e9f9f6dd99a62 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 6 Jun 2024 13:07:08 +0000 Subject: [PATCH 137/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/double_bracket.py | 4 +++- tests/test_backends.py | 1 - 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 6d0a0e6be1..7c10e9d3c6 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -158,7 +158,9 @@ def eval_dbr_unitary( @ self.backend.calculate_matrix_exp(sqrt_step, d) ) else: - raise NotImplementedError(f"The mode {mode} is not supported") # pragma: no cover + raise NotImplementedError( + f"The mode {mode} is not supported" + ) # pragma: no cover return operator diff --git a/tests/test_backends.py b/tests/test_backends.py index 80339f1214..73113e0daa 100644 --- a/tests/test_backends.py +++ b/tests/test_backends.py @@ -1,4 +1,3 @@ -import platform import numpy as np import pytest From c975f9a24bece330a3b98d99ed6bf2bbfd259129 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 6 Jun 2024 23:41:24 +0000 Subject: [PATCH 138/154] build(deps-dev): bump tornado from 6.4 to 6.4.1 Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.4 to 6.4.1. - [Changelog](https://github.com/tornadoweb/tornado/blob/master/docs/releases.rst) - [Commits](https://github.com/tornadoweb/tornado/compare/v6.4.0...v6.4.1) --- updated-dependencies: - dependency-name: tornado dependency-type: indirect ... Signed-off-by: dependabot[bot] --- poetry.lock | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/poetry.lock b/poetry.lock index 791f0a3cc9..585f81b53c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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Subject: [PATCH 139/154] fixed doctring + remove notebook --- examples/dbi/dbi_group_commutator_tests.ipynb | 166 ------------------ src/qibo/models/dbi/double_bracket.py | 8 +- 2 files changed, 4 insertions(+), 170 deletions(-) delete mode 100644 examples/dbi/dbi_group_commutator_tests.ipynb diff --git a/examples/dbi/dbi_group_commutator_tests.ipynb b/examples/dbi/dbi_group_commutator_tests.ipynb deleted file mode 100644 index 1ba93349d1..0000000000 --- a/examples/dbi/dbi_group_commutator_tests.ipynb +++ /dev/null @@ -1,166 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from qibo import hamiltonians, set_backend\n", - "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketScheduling, DoubleBracketIteration, DoubleBracketCostFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.7|INFO|2024-06-04 13:42:06]: Using numpy backend on /CPU:0\n" - ] - } - ], - "source": [ - "# Hamiltonian\n", - "set_backend(\"numpy\")\n", - "\n", - "# hamiltonian parameters\n", - "nqubits = 5\n", - "h = 3.0\n", - "\n", - "# define the hamiltonian\n", - "H_TFIM = hamiltonians.TFIM(nqubits=nqubits, h=h)\n", - "\n", - "# define the least-squares cost function\n", - "cost = DoubleBracketCostFunction.least_squares\n", - "# initialize class\n", - "dbi = DoubleBracketIteration(deepcopy(H_TFIM),mode=DoubleBracketGeneratorType.group_commutator,cost=cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "s_space = np.linspace(1e-5, 1e-1, 500)\n", - "off_diagonal_norm_diff = np.empty((500,3))\n", - "\n", - "for s in range(len(s_space)):\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval.mode = DoubleBracketGeneratorType.group_commutator\n", - " dbi_eval(np.sqrt(s_space[s]),d=d)\n", - " off_diagonal_norm_diff[s,1] = dbi_eval.off_diagonal_norm\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval.mode = DoubleBracketGeneratorType.single_commutator\n", - " dbi_eval(s_space[s],d=d)\n", - " off_diagonal_norm_diff[s,0] = dbi_eval.off_diagonal_norm\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval.mode = DoubleBracketGeneratorType.group_commutator_3\n", - " dbi_eval(np.sqrt(s_space[s]),d=d)\n", - " off_diagonal_norm_diff[s,2] = dbi_eval.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(s_space, off_diagonal_norm_diff[:,0],label=r'$e^{sW}$')\n", - "plt.plot(s_space, off_diagonal_norm_diff[:,1],label='GCI')\n", - "plt.plot(s_space, off_diagonal_norm_diff[:,2],label='GCI3')\n", - "plt.xlabel('s')\n", - "plt.ylabel('off-diagonal norm')\n", - "plt.legend()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d = np.diag(np.linspace(1,2**nqubits,2**nqubits))\n", - "flows = 30\n", - "r = np.array([1,2,4,8])\n", - "off_diagonal_norm_diff = np.empty((1+flows,len(r)+1))\n", - "s = np.empty(flows)\n", - "dbi_eval = deepcopy(dbi)\n", - "off_diagonal_norm_diff[0,:] = dbi_eval.off_diagonal_norm\n", - "for i in range(flows):\n", - " dbi_eval.mode = DoubleBracketGeneratorType.single_commutator\n", - " s[i] = dbi_eval.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation,d=d, n=3)\n", - " dbi_eval(s[i],d=d)\n", - " off_diagonal_norm_diff[i+1,0] = dbi_eval.off_diagonal_norm\n", - "\n", - "for j in range(len(r)):\n", - " dbi_eval = deepcopy(dbi)\n", - " dbi_eval.mode = DoubleBracketGeneratorType.group_commutator\n", - " for i in range(flows):\n", - " for k in range(r[j]):\n", - " dbi_eval(np.sqrt(s[i]/r[j]),d=d)\n", - " off_diagonal_norm_diff[i+1,j+1] = dbi_eval.off_diagonal_norm\n", - "\n", - "plt.figure()\n", - "plt.plot(off_diagonal_norm_diff[:,0],label=r'$e^{sW}$')\n", - "for i in range(len(r)):\n", - " plt.plot(off_diagonal_norm_diff[:,i+1],label=r'$V_{GC}, r = $' + str(r[i]))\n", - "plt.xlabel('flow iterarion')\n", - "plt.ylabel('off-diagonal norm')\n", - "plt.legend()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dbi", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.19" - }, - "vscode": { - "interpreter": { - "hash": "43f1f904380137ff38e17e8a93371c4872e6bababc18e270d8a0497ea5c7ea38" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 8ae1cf71a7..a62c6cf713 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -23,9 +23,9 @@ class DoubleBracketGeneratorType(Enum): """Use single commutator.""" group_commutator = auto() """Use group commutator approximation""" - group_commutator_3 = auto() - """Implements: $e^{\frac{\\sqrt{5}-1}{2}sH}e^{\frac{\\sqrt{5}-1}{2}sD}e^{-sH}e^{sD}e^{\frac{3-\\sqrt{5}}{2}sH}e^{sD} - \approx e^{s^2[H,D]} + O(s^4)$ + group_commutator_3rd_order = auto() + """Implements: $e^{\frac{\sqrt{5}-1}{2}isH}e^{\frac{\sqrt{5}-1}{2}isD}e^{-isH}e^{isD}e^{\frac{3-\sqrt{5}}{2}isH}e^{isD} + \approx e^{-s^2[H,D]} + O(s^4)$ which is equation (8) in https://arxiv.org/abs/2111.12177] s must be taken as $\\sqrt{s}$ to approximate the flow using the commutator """ @@ -130,7 +130,7 @@ def __call__( @ self.h.exp(step) @ self.backend.calculate_matrix_exp(step, d) ) - elif mode is DoubleBracketGeneratorType.group_commutator_3: + elif mode is DoubleBracketGeneratorType.group_commutator_3rd_order: if d is None: d = self.diagonal_h_matrix operator = ( From b335cb7ca3bb534a951133b89749f24cae152acc Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 7 Jun 2024 09:05:48 +0000 Subject: [PATCH 140/154] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibo/models/dbi/double_bracket.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index a62c6cf713..7bb296f093 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -24,7 +24,7 @@ class DoubleBracketGeneratorType(Enum): group_commutator = auto() """Use group commutator approximation""" group_commutator_3rd_order = auto() - """Implements: $e^{\frac{\sqrt{5}-1}{2}isH}e^{\frac{\sqrt{5}-1}{2}isD}e^{-isH}e^{isD}e^{\frac{3-\sqrt{5}}{2}isH}e^{isD} + """Implements: $e^{\frac{\\sqrt{5}-1}{2}isH}e^{\frac{\\sqrt{5}-1}{2}isD}e^{-isH}e^{isD}e^{\frac{3-\\sqrt{5}}{2}isH}e^{isD} \approx e^{-s^2[H,D]} + O(s^4)$ which is equation (8) in https://arxiv.org/abs/2111.12177] s must be taken as $\\sqrt{s}$ to approximate the flow using the commutator From 7f4456f377736ac46f97a6b5df383a369fa0c850 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Fri, 7 Jun 2024 12:54:14 +0200 Subject: [PATCH 141/154] fix test --- tests/test_models_dbi.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index ba0ee30e49..eac8ffa402 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -65,7 +65,7 @@ def test_double_bracket_iteration_group_commutator_3(backend, nqubits): d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.group_commutator_3, + mode=DoubleBracketGeneratorType.group_commutator_3rd_order, ) initial_off_diagonal_norm = dbi.off_diagonal_norm From 9683ab1098bd41b5ca5e8e72f31762b751b8302c Mon Sep 17 00:00:00 2001 From: Andrew Wright <115216427+wrightjandrew@users.noreply.github.com> Date: Fri, 7 Jun 2024 15:59:29 +0200 Subject: [PATCH 142/154] Update src/qibo/models/dbi/double_bracket.py Co-authored-by: Andrea Pasquale --- src/qibo/models/dbi/double_bracket.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 7bb296f093..98475acd68 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -130,7 +130,7 @@ def __call__( @ self.h.exp(step) @ self.backend.calculate_matrix_exp(step, d) ) - elif mode is DoubleBracketGeneratorType.group_commutator_3rd_order: + elif mode is DoubleBracketGeneratorType.group_commutator_third_order: if d is None: d = self.diagonal_h_matrix operator = ( From 86c0dce0dee9ee5f0f9f75d4fa9a8d31d1ba81b4 Mon Sep 17 00:00:00 2001 From: Andrew Wright <115216427+wrightjandrew@users.noreply.github.com> Date: Fri, 7 Jun 2024 15:59:37 +0200 Subject: [PATCH 143/154] Update tests/test_models_dbi.py Co-authored-by: Andrea Pasquale --- tests/test_models_dbi.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index eac8ffa402..d6977afc9c 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -70,7 +70,7 @@ def test_double_bracket_iteration_group_commutator_3(backend, nqubits): initial_off_diagonal_norm = dbi.off_diagonal_norm # test first iteration with default d - dbi(mode=DoubleBracketGeneratorType.group_commutator_3, step=0.01) + dbi(mode=DoubleBracketGeneratorType.group_commutator_third_order, step=0.01) for _ in range(NSTEPS): dbi(step=0.01, d=d) From 1704c5801ec8bc5c3688471426bbb56249e4d171 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Fri, 7 Jun 2024 17:31:06 +0200 Subject: [PATCH 144/154] 3rd to third --- tests/test_models_dbi.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index eac8ffa402..6b28972585 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -65,7 +65,7 @@ def test_double_bracket_iteration_group_commutator_3(backend, nqubits): d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) dbi = DoubleBracketIteration( Hamiltonian(nqubits, h0, backend=backend), - mode=DoubleBracketGeneratorType.group_commutator_3rd_order, + mode=DoubleBracketGeneratorType.group_commutator_third_order, ) initial_off_diagonal_norm = dbi.off_diagonal_norm From c096287efaf35c1a8bec20f246a9d86f7f663394 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Fri, 7 Jun 2024 18:39:07 +0200 Subject: [PATCH 145/154] third order gci name change --- src/qibo/models/dbi/double_bracket.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index 7bb296f093..b3c92becb3 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -23,7 +23,7 @@ class DoubleBracketGeneratorType(Enum): """Use single commutator.""" group_commutator = auto() """Use group commutator approximation""" - group_commutator_3rd_order = auto() + group_commutator_third_order = auto() """Implements: $e^{\frac{\\sqrt{5}-1}{2}isH}e^{\frac{\\sqrt{5}-1}{2}isD}e^{-isH}e^{isD}e^{\frac{3-\\sqrt{5}}{2}isH}e^{isD} \approx e^{-s^2[H,D]} + O(s^4)$ which is equation (8) in https://arxiv.org/abs/2111.12177] From 67e63b54c48cf22ebec4fa3686080c872ad37c71 Mon Sep 17 00:00:00 2001 From: wrightjandrew <115216427+wrightjandrew@users.noreply.github.com> Date: Fri, 7 Jun 2024 18:39:55 +0200 Subject: [PATCH 146/154] third order gci name change --- src/qibo/models/dbi/double_bracket.py | 2 +- tests/test_models_dbi.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py index b3c92becb3..4e1150df6f 100644 --- a/src/qibo/models/dbi/double_bracket.py +++ b/src/qibo/models/dbi/double_bracket.py @@ -130,7 +130,7 @@ def __call__( @ self.h.exp(step) @ self.backend.calculate_matrix_exp(step, d) ) - elif mode is DoubleBracketGeneratorType.group_commutator_3rd_order: + elif mode is DoubleBracketGeneratorType.group_commutator_third_order: if d is None: d = self.diagonal_h_matrix operator = ( diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py index 6b28972585..4cbff7557a 100644 --- a/tests/test_models_dbi.py +++ b/tests/test_models_dbi.py @@ -59,7 +59,7 @@ def test_double_bracket_iteration_group_commutator(backend, nqubits): @pytest.mark.parametrize("nqubits", [1, 2]) -def test_double_bracket_iteration_group_commutator_3(backend, nqubits): +def test_double_bracket_iteration_group_commutator_3rd_order(backend, nqubits): """Check 3rd order group commutator mode.""" h0 = random_hermitian(2**nqubits, backend=backend, seed=seed) d = backend.cast(np.diag(np.diag(backend.to_numpy(h0)))) From 89c2e29f38b3fefaf4efe0a81a75b1fe129db4f3 Mon Sep 17 00:00:00 2001 From: Andrea Date: Mon, 10 Jun 2024 09:36:36 +0400 Subject: [PATCH 147/154] doc: Remove notebook from doc --- doc/source/code-examples/applications-by-algorithm.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/doc/source/code-examples/applications-by-algorithm.rst b/doc/source/code-examples/applications-by-algorithm.rst index 4dfb64a6a2..1f69431d96 100644 --- a/doc/source/code-examples/applications-by-algorithm.rst +++ b/doc/source/code-examples/applications-by-algorithm.rst @@ -80,7 +80,6 @@ Diagonalization Algorithms tutorials/dbi/dbi_cost_functions.ipynb tutorials/dbi/dbi_gradient_descent_strategies.ipynb - tutorials/dbi/dbi_group_commutator_tests.ipynb tutorials/dbi/dbi_scheduling.ipynb tutorials/dbi/dbi_strategies_compare.ipynb tutorials/dbi/dbi_strategy_Ising_model.ipynb From f8ed712f147db0eee7801b99bbd2e2f752ce49af Mon Sep 17 00:00:00 2001 From: Andrea Date: Mon, 10 Jun 2024 16:26:44 +0400 Subject: [PATCH 148/154] build: Remove poetry dep --- poetry.lock | 762 ++++--------------------------------------------- pyproject.toml | 1 - 2 files changed, 56 insertions(+), 707 deletions(-) diff --git a/poetry.lock b/poetry.lock index fb62e573d4..ab41097321 100644 --- a/poetry.lock +++ b/poetry.lock @@ -198,52 +198,6 @@ webencodings = "*" [package.extras] css = ["tinycss2 (>=1.1.0,<1.3)"] -[[package]] -name = "build" -version = "1.2.1" -description = "A simple, correct Python build frontend" -optional = false -python-versions = ">=3.8" -files = [ - 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"""In call we will are working in the convention that $H' = U^\\dagger H + """In call we are working in the convention that $H' = U^\\dagger H U$ where $U=e^{-sW}$ with $W=[D,H]$ or an approximation of that by a group commutator. That is handy because if we switch from the DBI in the Heisenberg picture for the Hamiltonian, we get that the transformation of the state is $|\\psi'\rangle = U |\\psi\rangle$ From d6a8072419fe2fb94be5c1223e4e082a3d91fd4b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 10 Jun 2024 22:45:45 +0000 Subject: [PATCH 150/154] [pre-commit.ci] pre-commit autoupdate MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/asottile/pyupgrade: v3.15.2 → v3.16.0](https://github.com/asottile/pyupgrade/compare/v3.15.2...v3.16.0) --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 003ac5d0dd..62c24171a2 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -18,7 +18,7 @@ repos: - id: isort args: ["--profile", "black"] - repo: https://github.com/asottile/pyupgrade - rev: v3.15.2 + rev: v3.16.0 hooks: - id: pyupgrade - repo: https://github.com/hadialqattan/pycln From 88fc9d4ceae8bdee61f1f91a327a1fe189e79533 Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Tue, 11 Jun 2024 13:13:33 +0400 Subject: [PATCH 151/154] decomposition + tests --- src/qibo/gates/gates.py | 16 ++++++++++++---- src/qibo/transpiler/decompositions.py | 20 ++++++++++++++++++++ tests/test_gates_gates.py | 7 +++++++ 3 files changed, 39 insertions(+), 4 deletions(-) diff --git a/src/qibo/gates/gates.py b/src/qibo/gates/gates.py index 54b02b45f2..fdd7ed993c 100644 --- a/src/qibo/gates/gates.py +++ b/src/qibo/gates/gates.py @@ -2270,10 +2270,18 @@ def __init__(self, q0, q1, q2): def qasm_label(self): return "ccx" - def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: - c0, c1 = self.control_qubits - t = self.target_qubits[0] - return [self.__class__(c0, c1, t)] + def decompose(self) -> List[Gate]: + """Decomposition of :math:`\\text{TOFFOLI}` gate. + + Decompose :math:`\\text{TOFFOLI}` gate into :class:`qibo.gates.H`, + :class:`qibo.gates.T`, :class:`qibo.gates.TDG`, and + :class:`qibo.gates.CNOT`. + """ + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) def congruent(self, use_toffolis: bool = True) -> List[Gate]: """Congruent representation of ``TOFFOLI`` gate. diff --git a/src/qibo/transpiler/decompositions.py b/src/qibo/transpiler/decompositions.py index 33ab35b56b..bae39e38bd 100644 --- a/src/qibo/transpiler/decompositions.py +++ b/src/qibo/transpiler/decompositions.py @@ -466,3 +466,23 @@ def _u3_to_gpi2(t, p, l): gates.ECR, [gates.S(0), gates.SX(1), gates.CNOT(0, 1), gates.X(0)] ) standard_decompositions.add(gates.CCZ, [gates.H(2), gates.TOFFOLI(0, 1, 2), gates.H(2)]) +standard_decompositions.add( + gates.TOFFOLI, + [ + gates.H(2), + gates.CNOT(1, 2), + gates.TDG(2), + gates.CNOT(0, 2), + gates.T(2), + gates.CNOT(1, 2), + gates.T(1), + gates.TDG(2), + gates.CNOT(0, 2), + gates.CNOT(0, 1), + gates.T(2), + gates.T(0), + gates.TDG(1), + gates.H(2), + gates.CNOT(0, 1), + ], +) diff --git a/tests/test_gates_gates.py b/tests/test_gates_gates.py index 3246413c31..902a49fa8e 100644 --- a/tests/test_gates_gates.py +++ b/tests/test_gates_gates.py @@ -1205,6 +1205,13 @@ def test_toffoli(backend, applyx): assert not gates.TOFFOLI(0, 1, 2).clifford assert gates.TOFFOLI(0, 1, 2).unitary + # test decomposition + decomposition = Circuit(3) + decomposition.add(gates.TOFFOLI(0, 1, 2).decompose()) + decomposition = decomposition.unitary(backend) + + backend.assert_allclose(decomposition, backend.cast(matrices.TOFFOLI), atol=1e-10) + def test_ccz(backend): nqubits = 3 From 8d29397fdc241bc34c082f643d53869ba0055406 Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Tue, 11 Jun 2024 13:14:02 +0400 Subject: [PATCH 152/154] fix transpiler error --- src/qibo/transpiler/pipeline.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/qibo/transpiler/pipeline.py b/src/qibo/transpiler/pipeline.py index 7d9b68e130..04c1072d0a 100644 --- a/src/qibo/transpiler/pipeline.py +++ b/src/qibo/transpiler/pipeline.py @@ -225,7 +225,7 @@ def __call__(self, circuit): physical (keys) to logical (values) qubit. If `int_qubit_name` is `True` each key `i` correspond to the `i-th` qubit in the graph. """ - final_layout = self.initial_layout + self.initial_layout = None for transpiler_pass in self.passes: if isinstance(transpiler_pass, Optimizer): transpiler_pass.connectivity = self.connectivity @@ -234,6 +234,9 @@ def __call__(self, circuit): transpiler_pass.connectivity = self.connectivity if self.initial_layout == None: self.initial_layout = transpiler_pass(circuit) + final_layout = ( + self.initial_layout + ) # This way the final layout will be the same as the initial layout if no router is used else: raise_error( TranspilerPipelineError, From b756e0a739d4dd0aa2318bd856f940ea78266d12 Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Tue, 11 Jun 2024 13:20:18 +0400 Subject: [PATCH 153/154] include args --- src/qibo/gates/gates.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibo/gates/gates.py b/src/qibo/gates/gates.py index fdd7ed993c..2e726c02cd 100644 --- a/src/qibo/gates/gates.py +++ b/src/qibo/gates/gates.py @@ -2270,7 +2270,7 @@ def __init__(self, q0, q1, q2): def qasm_label(self): return "ccx" - def decompose(self) -> List[Gate]: + def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: """Decomposition of :math:`\\text{TOFFOLI}` gate. Decompose :math:`\\text{TOFFOLI}` gate into :class:`qibo.gates.H`, From 20d5b2b5f3f4a1eee1829c8e9a3843104beaa7b2 Mon Sep 17 00:00:00 2001 From: Renato Mello Date: Wed, 12 Jun 2024 09:46:03 +0400 Subject: [PATCH 154/154] fix test --- src/qibo/gates/gates.py | 14 +++----------- tests/test_cirq.py | 7 ++++--- tests/test_gates_gates.py | 3 ++- 3 files changed, 9 insertions(+), 15 deletions(-) diff --git a/src/qibo/gates/gates.py b/src/qibo/gates/gates.py index 2e726c02cd..54b02b45f2 100644 --- a/src/qibo/gates/gates.py +++ b/src/qibo/gates/gates.py @@ -2271,17 +2271,9 @@ def qasm_label(self): return "ccx" def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: - """Decomposition of :math:`\\text{TOFFOLI}` gate. - - Decompose :math:`\\text{TOFFOLI}` gate into :class:`qibo.gates.H`, - :class:`qibo.gates.T`, :class:`qibo.gates.TDG`, and - :class:`qibo.gates.CNOT`. - """ - from qibo.transpiler.decompositions import ( # pylint: disable=C0415 - standard_decompositions, - ) - - return standard_decompositions(self) + c0, c1 = self.control_qubits + t = self.target_qubits[0] + return [self.__class__(c0, c1, t)] def congruent(self, use_toffolis: bool = True) -> List[Gate]: """Congruent representation of ``TOFFOLI`` gate. diff --git a/tests/test_cirq.py b/tests/test_cirq.py index 636581656d..8ff8498d7a 100644 --- a/tests/test_cirq.py +++ b/tests/test_cirq.py @@ -4,8 +4,9 @@ import numpy as np import pytest -from qibo import gates, models +from qibo import Circuit, gates, matrices from qibo.backends import NumpyBackend +from qibo.models import QFT from qibo.quantum_info import random_statevector, random_unitary numpy_backend = NumpyBackend() @@ -54,7 +55,7 @@ def assert_gates_equivalent( if ndevices is not None: accelerators = {"/GPU:0": ndevices} - c = models.Circuit(nqubits, accelerators) + c = Circuit(nqubits, accelerators) c.add(qibo_gate) assert c.depth == target_depth if accelerators and not backend.supports_multigpu: @@ -307,7 +308,7 @@ def test_unitary_matrix_gate_controlled_by(backend, nqubits, ntargets, ndevices) @pytest.mark.parametrize("nqubits", [5, 6, 7, 11, 12]) def test_qft(backend, accelerators, nqubits): - c = models.QFT(nqubits, accelerators=accelerators) + c = QFT(nqubits, accelerators=accelerators) initial_state = random_statevector(2**nqubits, backend=numpy_backend) final_state = backend.execute_circuit(c, np.copy(initial_state)).state() final_state = backend.cast(final_state, dtype=final_state.dtype) diff --git a/tests/test_gates_gates.py b/tests/test_gates_gates.py index 902a49fa8e..2b2ae20973 100644 --- a/tests/test_gates_gates.py +++ b/tests/test_gates_gates.py @@ -6,6 +6,7 @@ from qibo import Circuit, gates, matrices from qibo.parameter import Parameter from qibo.quantum_info import random_hermitian, random_statevector, random_unitary +from qibo.transpiler.decompositions import standard_decompositions def apply_gates(backend, gatelist, nqubits=None, initial_state=None): @@ -1207,7 +1208,7 @@ def test_toffoli(backend, applyx): # test decomposition decomposition = Circuit(3) - decomposition.add(gates.TOFFOLI(0, 1, 2).decompose()) + decomposition.add(standard_decompositions(gates.TOFFOLI(0, 1, 2))) decomposition = decomposition.unitary(backend) backend.assert_allclose(decomposition, backend.cast(matrices.TOFFOLI), atol=1e-10)