Fig 4 Eigenstates L = 3 variational flow TFIM J = 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simple_exact_diagonalization_routines.local_matrix_class import *\n",
    "from scipy.linalg import expm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# System size\n",
    "L = 3\n",
    "\n",
    "# Create objects of Pauli operators\n",
    "X = X_class(L)\n",
    "Y = Y_class(L)\n",
    "Z = Z_class(L)\n",
    "S_plus = S_plus_class(L)\n",
    "S_minus = S_minus_class(L)\n",
    "\n",
    "# Trivial initialization of the right size matrix\n",
    "H_TFIM = 0*np.eye( 2**L ) \n",
    "\n",
    "for x in range(L-1):\n",
    "    H_TFIM = H_TFIM + 2*X.at(x+1).dot(X.at(x+2)) + Z.at(x+1)\n",
    "H_TFIM = H_TFIM + Z.at(L)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from class_double_bracket_flow import *\n",
    "\n",
    "\n",
    "flow = double_bracket_flow( H_TFIM )\n",
    "\n",
    "flow.flow_step_max =1\n",
    "flow.flow_step_min = 0\n",
    "flow.nmb_search_points_minimizing_s_search = 200\n",
    "flow.please_use_binary_search  =False\n",
    "\n",
    "flow.nmb_flow_steps = 15\n",
    "flow.flow_forwards( states = np.eye(2**L).tolist() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marek/Projects_git/double_bracket_flow_as_a_diagonalization_quantum_algorithm/class_double_bracket_flow.py:1143: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/home/marek/Projects_git/double_bracket_flow_as_a_diagonalization_quantum_algorithm/class_double_bracket_flow.py:1170: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow.show_flow_forwards_results_fancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flow step  0\n",
      "    Found minimum norm =  8.0  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  8.0\n",
      "    Found minimum norm =  6.9282521150469005  for s =  0.5882352941176471\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.5882352941176471   minimal norm  6.9282521150469005\n",
      "    Found minimum norm =  6.693552712355328  for s =  0.4789915966386554\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.4789915966386554   minimal norm  6.693552712355328\n",
      "    Found minimum norm =  6.173673008351243  for s =  0.12605042016806722\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.12605042016806722   minimal norm  6.173673008351243\n",
      "    Found minimum norm =  6.928252115046901  for s =  0.5882352941176471\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.5882352941176471   minimal norm  6.928252115046901\n",
      "    Found minimum norm =  5.022300865727476  for s =  0.47058823529411764\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.47058823529411764   minimal norm  5.022300865727476\n",
      "    Found minimum norm =  6.173673008351243  for s =  0.12605042016806722\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.12605042016806722   minimal norm  6.173673008351243\n",
      "    Found minimum norm =  8.0  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  8.0\n",
      "    Found minimum norm =  5.84332918001646  for s =  0.04201680672268907\n",
      "Found that canonical is worse because  5.84332918001646 5.022300865727476\n",
      "    Found minimum norm =  5.022300865727476  for s =  0.47058823529411764\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  1\n",
      "    Found minimum norm =  5.022300865727476  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  5.022300865727476\n",
      "    Found minimum norm =  4.791567013161571  for s =  0.11764705882352941\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.11764705882352941   minimal norm  4.791567013161571\n",
      "    Found minimum norm =  5.022300865727476  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  5.022300865727476\n",
      "    Found minimum norm =  5.022300865727476  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.0   minimal norm  5.022300865727476\n",
      "    Found minimum norm =  4.791567013161571  for s =  0.11764705882352941\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.11764705882352941   minimal norm  4.791567013161571\n",
      "    Found minimum norm =  4.468407809308276  for s =  0.10084033613445378\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.10084033613445378   minimal norm  4.468407809308276\n",
      "    Found minimum norm =  5.022300865727476  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  5.022300865727476\n",
      "    Found minimum norm =  5.022300865727476  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  5.022300865727476\n",
      "    Found minimum norm =  2.8065508108034263  for s =  0.6134453781512604\n",
      "Found that canonical is better because  2.8065508108034263 4.468407809308276\n",
      "    Found minimum norm =  2.8065508108034263  for s =  0.6134453781512604\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  2\n",
      "    Found minimum norm =  2.8065508108034263  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  2.8065508108034263\n",
      "    Found minimum norm =  2.8065508108034263  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  2.8065508108034263\n",
      "    Found minimum norm =  2.8065508108034263  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  2.8065508108034263\n",
      "    Found minimum norm =  2.2572926651266103  for s =  0.1092436974789916\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.1092436974789916   minimal norm  2.2572926651266103\n",
      "    Found minimum norm =  2.8065508108034263  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  2.8065508108034263\n",
      "    Found minimum norm =  2.7840190839140893  for s =  0.01680672268907563\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.01680672268907563   minimal norm  2.7840190839140893\n",
      "    Found minimum norm =  2.2572926651266116  for s =  0.1092436974789916\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.1092436974789916   minimal norm  2.2572926651266116\n",
      "    Found minimum norm =  2.8065508108034263  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  2.8065508108034263\n",
      "    Found minimum norm =  1.7988673490185259  for s =  0.04201680672268907\n",
      "Found that canonical is better because  1.7988673490185259 2.2572926651266103\n",
      "    Found minimum norm =  1.7988673490185259  for s =  0.04201680672268907\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  3\n",
      "    Found minimum norm =  1.7988673490185259  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  1.7988673490185259\n",
      "    Found minimum norm =  1.7988673490185259  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  1.7988673490185259\n",
      "    Found minimum norm =  1.7988673490185259  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  1.7988673490185259\n",
      "    Found minimum norm =  1.6089132176127452  for s =  0.06722689075630252\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.06722689075630252   minimal norm  1.6089132176127452\n",
      "    Found minimum norm =  1.7988673490185259  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  1.7988673490185259\n",
      "    Found minimum norm =  1.786246854161759  for s =  0.06722689075630252\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.06722689075630252   minimal norm  1.786246854161759\n",
      "    Found minimum norm =  1.608913217612746  for s =  0.06722689075630252\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.06722689075630252   minimal norm  1.608913217612746\n",
      "    Found minimum norm =  1.7988673490185259  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  1.7988673490185259\n",
      "    Found minimum norm =  1.652936044504573  for s =  0.008403361344537815\n",
      "Found that canonical is worse because  1.652936044504573 1.6089132176127452\n",
      "    Found minimum norm =  1.6089132176127452  for s =  0.06722689075630252\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  4\n",
      "    Found minimum norm =  1.6089132176127452  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  1.6089132176127452\n",
      "    Found minimum norm =  1.5396693426282788  for s =  0.23529411764705882\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.23529411764705882   minimal norm  1.5396693426282788\n",
      "    Found minimum norm =  1.6089132176127452  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  1.6089132176127452\n",
      "    Found minimum norm =  1.0475810081602202  for s =  0.31092436974789917\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.31092436974789917   minimal norm  1.0475810081602202\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Found minimum norm =  1.3978048549509927  for s =  0.3277310924369748\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.3277310924369748   minimal norm  1.3978048549509927\n",
      "    Found minimum norm =  1.5995702271854497  for s =  0.06722689075630252\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.06722689075630252   minimal norm  1.5995702271854497\n",
      "    Found minimum norm =  1.5420044481684132  for s =  0.29411764705882354\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.29411764705882354   minimal norm  1.5420044481684132\n",
      "    Found minimum norm =  1.6089132176127452  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  1.6089132176127452\n",
      "    Found minimum norm =  1.5833368921147766  for s =  0.01680672268907563\n",
      "Found that canonical is worse because  1.5833368921147766 1.0475810081602202\n",
      "    Found minimum norm =  1.0475810081602202  for s =  0.31092436974789917\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  5\n",
      "    Found minimum norm =  1.0475810081602202  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  1.0475810081602202\n",
      "    Found minimum norm =  1.0475810081602202  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  1.0475810081602202\n",
      "    Found minimum norm =  1.0475810081602202  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  1.0475810081602202\n",
      "    Found minimum norm =  0.5440166334852545  for s =  0.058823529411764705\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.058823529411764705   minimal norm  0.5440166334852545\n",
      "    Found minimum norm =  1.0475810081602202  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  1.0475810081602202\n",
      "    Found minimum norm =  1.0280187651513057  for s =  0.058823529411764705\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.058823529411764705   minimal norm  1.0280187651513057\n",
      "    Found minimum norm =  0.7283604942360872  for s =  0.05042016806722689\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.05042016806722689   minimal norm  0.7283604942360872\n",
      "    Found minimum norm =  1.0475810081602202  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  1.0475810081602202\n",
      "    Found minimum norm =  0.6744251982630624  for s =  0.008403361344537815\n",
      "Found that canonical is worse because  0.6744251982630624 0.5440166334852545\n",
      "    Found minimum norm =  0.5440166334852545  for s =  0.058823529411764705\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  6\n",
      "    Found minimum norm =  0.5440166334852545  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.5440166334852545\n",
      "    Found minimum norm =  0.5440166334852545  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.5440166334852545\n",
      "    Found minimum norm =  0.5440166334852545  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.5440166334852545\n",
      "    Found minimum norm =  0.35059595169742724  for s =  0.19327731092436973\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.19327731092436973   minimal norm  0.35059595169742724\n",
      "    Found minimum norm =  0.5440166334852545  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.5440166334852545\n",
      "    Found minimum norm =  0.5173082082302115  for s =  0.07563025210084033\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.07563025210084033   minimal norm  0.5173082082302115\n",
      "    Found minimum norm =  0.5440166334852545  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.5440166334852545\n",
      "    Found minimum norm =  0.5440166334852545  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.5440166334852545\n",
      "    Found minimum norm =  0.4734424630857055  for s =  0.03361344537815126\n",
      "Found that canonical is worse because  0.4734424630857055 0.35059595169742724\n",
      "    Found minimum norm =  0.35059595169742724  for s =  0.19327731092436973\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  7\n",
      "    Found minimum norm =  0.35059595169742724  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.35059595169742724\n",
      "    Found minimum norm =  0.35059595169742724  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.35059595169742724\n",
      "    Found minimum norm =  0.35059595169742724  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.35059595169742724\n",
      "    Found minimum norm =  0.2714502687228996  for s =  0.06722689075630252\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.06722689075630252   minimal norm  0.2714502687228996\n",
      "    Found minimum norm =  0.35059595169742724  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.35059595169742724\n",
      "    Found minimum norm =  0.3097613797861103  for s =  0.08403361344537814\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.08403361344537814   minimal norm  0.3097613797861103\n",
      "    Found minimum norm =  0.2700067628088399  for s =  0.058823529411764705\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.058823529411764705   minimal norm  0.2700067628088399\n",
      "    Found minimum norm =  0.35059595169742724  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.35059595169742724\n",
      "    Found minimum norm =  0.25713482102074653  for s =  0.01680672268907563\n",
      "Found that canonical is better because  0.25713482102074653 0.2700067628088399\n",
      "    Found minimum norm =  0.25713482102074653  for s =  0.01680672268907563\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  8\n",
      "    Found minimum norm =  0.25713482102074653  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.25713482102074653\n",
      "    Found minimum norm =  0.25713482102074653  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.25713482102074653\n",
      "    Found minimum norm =  0.25713482102074653  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.25713482102074653\n",
      "    Found minimum norm =  0.2120092962513258  for s =  0.09243697478991596\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.09243697478991596   minimal norm  0.2120092962513258\n",
      "    Found minimum norm =  0.25713482102074653  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.25713482102074653\n",
      "    Found minimum norm =  0.23704101397229513  for s =  0.07563025210084033\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.07563025210084033   minimal norm  0.23704101397229513\n",
      "    Found minimum norm =  0.2357511264656566  for s =  0.05042016806722689\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.05042016806722689   minimal norm  0.2357511264656566\n",
      "    Found minimum norm =  0.25713482102074653  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.25713482102074653\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Found minimum norm =  0.21277977700679657  for s =  0.025210084033613446\n",
      "Found that canonical is worse because  0.21277977700679657 0.2120092962513258\n",
      "    Found minimum norm =  0.2120092962513258  for s =  0.09243697478991596\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  9\n",
      "    Found minimum norm =  0.2120092962513258  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.2120092962513258\n",
      "    Found minimum norm =  0.2120092962513258  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.2120092962513258\n",
      "    Found minimum norm =  0.2120092962513258  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.2120092962513258\n",
      "    Found minimum norm =  0.1877678933437769  for s =  0.09243697478991596\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.09243697478991596   minimal norm  0.1877678933437769\n",
      "    Found minimum norm =  0.2120092962513258  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.2120092962513258\n",
      "    Found minimum norm =  0.18611166359009448  for s =  0.07563025210084033\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.07563025210084033   minimal norm  0.18611166359009448\n",
      "    Found minimum norm =  0.17223394760809216  for s =  0.07563025210084033\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.07563025210084033   minimal norm  0.17223394760809216\n",
      "    Found minimum norm =  0.2120092962513258  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.2120092962513258\n",
      "    Found minimum norm =  0.16405412109858558  for s =  0.025210084033613446\n",
      "Found that canonical is better because  0.16405412109858558 0.17223394760809216\n",
      "    Found minimum norm =  0.16405412109858558  for s =  0.025210084033613446\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  10\n",
      "    Found minimum norm =  0.16405412109858558  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.16405412109858558\n",
      "    Found minimum norm =  0.16405412109858558  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.16405412109858558\n",
      "    Found minimum norm =  0.16405412109858558  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.16405412109858558\n",
      "    Found minimum norm =  0.13314066686355472  for s =  0.08403361344537814\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.08403361344537814   minimal norm  0.13314066686355472\n",
      "    Found minimum norm =  0.16405412109858558  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.16405412109858558\n",
      "    Found minimum norm =  0.15789456339333263  for s =  0.058823529411764705\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.058823529411764705   minimal norm  0.15789456339333263\n",
      "    Found minimum norm =  0.15021738707096016  for s =  0.05042016806722689\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.05042016806722689   minimal norm  0.15021738707096016\n",
      "    Found minimum norm =  0.16405412109858558  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.16405412109858558\n",
      "    Found minimum norm =  0.13645740422860958  for s =  0.01680672268907563\n",
      "Found that canonical is worse because  0.13645740422860958 0.13314066686355472\n",
      "    Found minimum norm =  0.13314066686355472  for s =  0.08403361344537814\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  11\n",
      "    Found minimum norm =  0.13314066686355472  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.13314066686355472\n",
      "    Found minimum norm =  0.13314066686355472  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.13314066686355472\n",
      "    Found minimum norm =  0.13314066686355472  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.13314066686355472\n",
      "    Found minimum norm =  0.11535660621831823  for s =  0.10084033613445378\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.10084033613445378   minimal norm  0.11535660621831823\n",
      "    Found minimum norm =  0.13314066686355472  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.13314066686355472\n",
      "    Found minimum norm =  0.1254587445319005  for s =  0.058823529411764705\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.058823529411764705   minimal norm  0.1254587445319005\n",
      "    Found minimum norm =  0.11533625584236899  for s =  0.06722689075630252\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.06722689075630252   minimal norm  0.11533625584236899\n",
      "    Found minimum norm =  0.13314066686355472  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.13314066686355472\n",
      "    Found minimum norm =  0.10669808538040178  for s =  0.025210084033613446\n",
      "Found that canonical is better because  0.10669808538040178 0.11533625584236899\n",
      "    Found minimum norm =  0.10669808538040178  for s =  0.025210084033613446\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  12\n",
      "    Found minimum norm =  0.10669808538040178  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.10669808538040178\n",
      "    Found minimum norm =  0.10669808538040178  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.10669808538040178\n",
      "    Found minimum norm =  0.10669808538040178  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.10669808538040178\n",
      "    Found minimum norm =  0.08532474059117799  for s =  0.08403361344537814\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.08403361344537814   minimal norm  0.08532474059117799\n",
      "    Found minimum norm =  0.10669808538040178  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.10669808538040178\n",
      "    Found minimum norm =  0.10559321069801277  for s =  0.03361344537815126\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.03361344537815126   minimal norm  0.10559321069801277\n",
      "    Found minimum norm =  0.1006896126024765  for s =  0.04201680672268907\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.04201680672268907   minimal norm  0.1006896126024765\n",
      "    Found minimum norm =  0.10669808538040178  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.10669808538040178\n",
      "    Found minimum norm =  0.09047936376913962  for s =  0.01680672268907563\n",
      "Found that canonical is worse because  0.09047936376913962 0.08532474059117799\n",
      "    Found minimum norm =  0.08532474059117799  for s =  0.08403361344537814\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  13\n",
      "    Found minimum norm =  0.08532474059117799  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.08532474059117799\n",
      "    Found minimum norm =  0.08532474059117799  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.08532474059117799\n",
      "    Found minimum norm =  0.08532474059117799  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.08532474059117799\n",
      "    Found minimum norm =  0.07242627446741404  for s =  0.1092436974789916\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.1092436974789916   minimal norm  0.07242627446741404\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Found minimum norm =  0.08532474059117799  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.08532474059117799\n",
      "    Found minimum norm =  0.08393811870104892  for s =  0.03361344537815126\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.03361344537815126   minimal norm  0.08393811870104892\n",
      "    Found minimum norm =  0.07737655676736957  for s =  0.058823529411764705\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.058823529411764705   minimal norm  0.07737655676736957\n",
      "    Found minimum norm =  0.08532474059117799  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.08532474059117799\n",
      "    Found minimum norm =  0.07026305853186009  for s =  0.025210084033613446\n",
      "Found that canonical is better because  0.07026305853186009 0.07242627446741404\n",
      "    Found minimum norm =  0.07026305853186009  for s =  0.025210084033613446\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  14\n",
      "    Found minimum norm =  0.07026305853186009  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.07026305853186009\n",
      "    Found minimum norm =  0.07026305853186009  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.07026305853186009\n",
      "    Found minimum norm =  0.07026305853186009  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.07026305853186009\n",
      "    Found minimum norm =  0.05557001806773144  for s =  0.09243697478991596\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.09243697478991596   minimal norm  0.05557001806773144\n",
      "    Found minimum norm =  0.07026305853186009  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.07026305853186009\n",
      "    Found minimum norm =  0.07025757124018128  for s =  0.008403361344537815\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.008403361344537815   minimal norm  0.07025757124018128\n",
      "    Found minimum norm =  0.0677103275702197  for s =  0.03361344537815126\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.03361344537815126   minimal norm  0.0677103275702197\n",
      "    Found minimum norm =  0.07026305853186009  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.07026305853186009\n",
      "    Found minimum norm =  0.06053949500608896  for s =  0.01680672268907563\n",
      "Found that canonical is worse because  0.06053949500608896 0.05557001806773144\n",
      "    Found minimum norm =  0.05557001806773144  for s =  0.09243697478991596\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "flow2 = double_bracket_flow( H_TFIM )\n",
    "flow2.please_be_exhaustively_verbose = False\n",
    "flow2.please_be_verbose = True\n",
    "ini_state = np.kron( np.kron([0,1],[1,0]), [0,1])\n",
    "flow2.compute_observables_during_flow( initial_state=ini_state )\n",
    "flow2.flow_step_max =1\n",
    "flow2.flow_step_min = 0\n",
    "flow2.nmb_search_points_minimizing_s_search = 120\n",
    "flow2.please_use_binary_search  = False\n",
    "flow2.nmb_flow_steps = 15\n",
    "flow2.flow_via_best_Z_search( states = np.eye(2**L).tolist(), please_also_check_canonical_bracket=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marek/Projects_git/double_bracket_flow_as_a_diagonalization_quantum_algorithm/class_double_bracket_flow.py:1143: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/home/marek/Projects_git/double_bracket_flow_as_a_diagonalization_quantum_algorithm/class_double_bracket_flow.py:1170: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow2.show_flow_forwards_results()\n",
    "flow2.show_flow_forwards_results_fancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1Z1',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " 'Z11',\n",
       " 'Z11',\n",
       " 'Z11',\n",
       " 'Z11',\n",
       " 'Canonical',\n",
       " 'Z11',\n",
       " 'Canonical',\n",
       " 'Z11',\n",
       " 'Canonical',\n",
       " 'Z11',\n",
       " 'Canonical',\n",
       " 'Z11']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow2.flow_outputs['strs_of_minimizer_Z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f = plt.figure(figsize = (12,5))\n",
    "\n",
    "f.add_subplot(121)\n",
    "for i, k in enumerate([0,1,2,3,4,5,6,7]):\n",
    "    e = flow.observable_over_steps( k, flow.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow.observable_over_steps( k, flow.flow_outputs['expected_energy_fluctuation'] ))\n",
    "    plt.errorbar( np.linspace( i * 0.02, flow.nmb_flow_steps + i * 0.02, \n",
    "                               flow.nmb_flow_steps), e, yerr = ef ) \n",
    "plt.ylim( [-7.5,7.5])\n",
    "\n",
    "#plt.yticks(np.round([max(last_e),min(last_e),0],1))\n",
    "plt.xlabel(r'Flow step $n$')\n",
    "plt.ylabel(r'Average energy $\\langle \\hat H_n \\rangle$')\n",
    "plt.title(r'Wegner flow')\n",
    "    \n",
    "\n",
    " \n",
    "f.add_subplot(122)\n",
    "for i, k in enumerate([0,1,2,3,4,5,6,7]):\n",
    "    e = flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy_fluctuation'] ))\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "\n",
    "    plt.errorbar( np.linspace( i * 0.05, flow.nmb_flow_steps + i * 0.02, \n",
    "                              flow.nmb_flow_steps), e, yerr = ef , \n",
    "                 label= label) \n",
    "plt.ylim( [-7.5,7.5])\n",
    "#plt.yticks(np.round([max(last_e),min(last_e),0],1), labels=[])\n",
    "plt.xlabel(r'Flow step $n$')\n",
    "plt.title(r'Wegner and $Z$-product flow')\n",
    "plt.grid()\n",
    "plt.legend(bbox_to_anchor=(1, 1.05))\n",
    "plt.savefig( 'figs/eigenstates_L_%d.pdf'%L)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1Z1', 'Canonical', 'Canonical', 'Z11', 'Z11', 'Z11', 'Z11', 'Canonical', 'Z11', 'Canonical', 'Z11', 'Canonical', 'Z11', 'Canonical', 'Z11']\n"
     ]
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "import plotly.express as px\n",
    "color_discrete_sequence=px.colors.qualitative.G10\n",
    "\n",
    "fig = make_subplots(rows=1, cols=3, subplot_titles=(\"Plot 1\", \"Plot 2\", \"plot 3\"))\n",
    "\n",
    "fig.layout.annotations[0].update(font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),x=0.15, y=1.01, text=r'$\\text{Norm off-diagonal } \\|\\sigma(\\hat H_k)\\|_{\\text{HS}}$')\n",
    "fig.layout.annotations[1].update(font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),x=0.50, y=1.01,text=r'$\\text{Expected energy value } E^{(k)}_\\mu$')\n",
    "fig.layout.annotations[2].update(font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),x=0.855, y=1.01,text=r'$\\text{Expected energy value } E^{(k)}_\\mu$')\n",
    "\n",
    "x = [ sum(flow.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow.flow_outputs['minimizing_flow_step']))]\n",
    "\n",
    "x2 = [ sum(flow2.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow2.flow_outputs['minimizing_flow_step']))]\n",
    "norms = flow.flow_outputs['minimal_norm_sigma_H_s']\n",
    "norms2 = flow2.flow_outputs['minimal_norm_sigma_H_s']\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=x,\n",
    "    y=norms,\n",
    "    mode = 'lines+markers',\n",
    "        name = \"GWW\",\n",
    "        showlegend=False\n",
    "),  row=1, col=1)\n",
    " \n",
    "fig.add_trace( go.Scatter(\n",
    "        name = \"Variational\",\n",
    "    x=x2,\n",
    "    y=norms2\n",
    ",mode = 'lines+markers',\n",
    "        showlegend=False\n",
    "),  row=1, col=1)\n",
    "x_selection = [1,2,-1]\n",
    "y_selection = [0,1,-1]\n",
    "y_selection2 = [1,2,5,-1]\n",
    "fig.update_layout(\n",
    "    xaxis1=dict(\n",
    "    title=dict(text=r'', standoff=8),\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),\n",
    "        tickmode = 'array',\n",
    "        tickvals = [x2[s] for s in x_selection],\n",
    "        ticktext = [round(x2[s],2) for s in x_selection]\n",
    "    ),\n",
    "    yaxis1=dict(\n",
    "        title=dict(text=r'', standoff=8),\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),\n",
    "        tickmode = 'array',\n",
    "        tickvals = [norms[s] for s in y_selection]+[norms2[s] for s in y_selection2],\n",
    "        ticktext = [round(norms[s],1) for s in y_selection]+[round(norms2[s],1) for s in y_selection2]\n",
    "    ))\n",
    "\n",
    "#fig_norm.update_yaxes(range=[min(norms)-5,max(norms)+6])\n",
    "\n",
    "y_selection = [0,1,2,3,7,13,-1]\n",
    "y_labels_rounded = [ round(y, 1 ) for y in norms ]\n",
    "y_vals = [norms[i] for i in y_selection]\n",
    "y_labels = [ y_labels_rounded[i] for i in y_selection]\n",
    "x_selection = [0,1,3,7,13,-2]\n",
    "x_vals = [x2[i] for i in x_selection]\n",
    "x_labels = [ round(x[i],2)for i in x_selection]\n",
    "\n",
    "\n",
    "\n",
    "x = [x+1 for x in range((flow.nmb_flow_steps))]\n",
    "\n",
    "#Canonical flow\n",
    "\n",
    "for i, k in enumerate(range(2**L)):\n",
    "    e = flow.observable_over_steps( k, flow.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow.observable_over_steps( k,\n",
    "        flow.flow_outputs['expected_energy_fluctuation'] )\n",
    "    )\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "    label = r'$|'+str(flow.binary_label_from_state_number(k)) +r'\\rangle$'\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=x, y=e, line=dict(color=color_discrete_sequence[i])\n",
    "                  , name = label),\n",
    "        row=1, col=2\n",
    "    )\n",
    "    y = e\n",
    "    y_upper = [ E+EF for E,EF in zip( e, ef)]\n",
    "    y_lower = [ E-EF for E,EF in zip( e, ef)]\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "        x=x+x[::-1], # x, then x reversed\n",
    "        y=y_upper+y_lower[::-1], # upper, then lower reversed\n",
    "        fill='toself',\n",
    "        fillcolor=color_discrete_sequence[i],\n",
    "        line=dict(color='rgba(0,0,0,0)'),\n",
    "            opacity=0.2,\n",
    "        hoverinfo=\"skip\",\n",
    "        showlegend=False),\n",
    "        row=1, col=2\n",
    "    )   \n",
    "\n",
    "\n",
    "for i, k in enumerate(range(2**L)):\n",
    "    e = flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow2.observable_over_steps( k,\n",
    "            flow2.flow_outputs['expected_energy_fluctuation'] ))\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=x, y=e,line=dict(color=color_discrete_sequence[i]), name = label,showlegend=False),\n",
    "        row=1, col=3\n",
    "    )\n",
    "    y = e\n",
    "    y_upper = [ E+EF for E,EF in zip( e, ef)]\n",
    "    y_lower = [ E-EF for E,EF in zip( e, ef)]\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "        x=x+x[::-1], # x, then x reversed\n",
    "        y=y_upper+y_lower[::-1], # upper, then lower reversed\n",
    "        fill='toself',\n",
    "        fillcolor=color_discrete_sequence[i],\n",
    "        line=dict(color='rgba(0,0,0,0)'),\n",
    "            opacity=0.2,\n",
    "        hoverinfo=\"skip\",\n",
    "        showlegend=False),\n",
    "        row=1, col=3\n",
    "    )   \n",
    "fig.add_annotation(x=-.045, y=1.1,\n",
    "            text='a)',\n",
    "            xref=\"paper\",\n",
    "    yref=\"paper\",\n",
    "            showarrow=False,font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        )\n",
    ")  \n",
    "fig.add_annotation(x=.315, y=1.1,\n",
    "            text='b)',\n",
    "            xref=\"paper\",\n",
    "    yref=\"paper\",\n",
    "            showarrow=False,font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        )\n",
    ") \n",
    "fig.add_annotation(x=.70, y=1.1,\n",
    "            text='c)',\n",
    "            xref=\"paper\",\n",
    "    yref=\"paper\",\n",
    "            showarrow=False,font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        )\n",
    ") \n",
    "\n",
    "# fig.add_annotation(x=.8, y=8,\n",
    "#             text=\"GWW flow\",\n",
    "#             showarrow=True,\n",
    "#             arrowhead=1)\n",
    "\n",
    "x = [ sum(flow.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow.flow_outputs['minimizing_flow_step']))]\n",
    "fig.add_annotation(dict(\n",
    "                x= x[1],\n",
    "                y= norms[1]+.3,\n",
    "                xref=\"x\", yref=\"y\",\n",
    "                text=\"GWW flow\",\n",
    "                showarrow=True,\n",
    "                axref = \"x\", ayref='y',\n",
    "                ax= .8,\n",
    "                ay= 8,\n",
    "                arrowhead = 3,\n",
    "                arrowwidth=1.5,\n",
    "                )\n",
    "            )\n",
    "fig.add_annotation(dict(\n",
    "                x= x[2],\n",
    "                y= norms[2]+.4,\n",
    "                xref=\"x\", yref=\"y\",\n",
    "                text=\" \",\n",
    "                showarrow=True,\n",
    "                axref = \"x\", ayref='y',\n",
    "                ax= .8,\n",
    "                ay= 7.8,\n",
    "                arrowhead = 3,\n",
    "                arrowwidth=1.5,\n",
    "        yshift=0.2\n",
    "                )\n",
    "            )\n",
    "x2 = [ sum(flow2.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow2.flow_outputs['minimizing_flow_step']))]\n",
    "if 0:\n",
    "    fig.add_annotation(dict(\n",
    "                x= x[5]-.3,\n",
    "                y= norms[5]+.5,\n",
    "                xref=\"x\", yref=\"y\",\n",
    "                text=\" \",\n",
    "                showarrow=True,\n",
    "                axref = \"x\", ayref='y',\n",
    "                ax= .8,\n",
    "                ay= 7.8,\n",
    "                arrowhead = 3,\n",
    "                arrowwidth=1.5,\n",
    "                )\n",
    "            )\n",
    "for i, Z_name in enumerate(flow2.flow_outputs['strs_of_minimizer_Z']):\n",
    "     if i<6 :\n",
    "            fig.add_annotation(dict(     x=x2[i+1], y=norms2[i+1],\n",
    "                    text=Z_name,\n",
    "                             font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=12\n",
    "        ),\n",
    "                    xref=\"x\", yref=\"y\",\n",
    "                    showarrow=True,\n",
    "                    axref = \"x\", ayref='y',\n",
    "                    ax= x2[i+1]-(.8 if Z_name=='Canonical' else .4),\n",
    "                    ay= norms2[i+1]*(1.2 if Z_name=='Canonical' else 1),\n",
    "                    arrowhead = 3,\n",
    "                    arrowwidth=1.5,\n",
    "                    )\n",
    "                )\n",
    "i=7\n",
    "fig.add_annotation(dict(     x=x2[i+1]-.5, y=norms2[i+1],\n",
    "text='...',\n",
    "     font=dict(\n",
    "family='Courier New, monospace',\n",
    "size=12\n",
    "),\n",
    "xref=\"x\", yref=\"y\",\n",
    "showarrow=False\n",
    ")\n",
    ")\n",
    "fig.update_xaxes(title_text=r'$\\text{Total DBI duration } \\ell$', row =1, col = 1)\n",
    "fig.update_xaxes(title_text=r'$\\text{GWW DBI step } k$', row =1, col = 2)\n",
    "fig.update_xaxes(title_text=r'$\\text{DBI step } k$', row =1, col = 3)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\n",
    "fig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\n",
    "fig.update_layout(margin=dict(l=56, r=0, b=0, t=26))\n",
    "fig.update_layout(\n",
    "    autosize=False,\n",
    "    width=800,\n",
    "    height=300)\n",
    "\n",
    "fig.show()\n",
    "print(flow2.flow_outputs['strs_of_minimizer_Z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.write_image( 'figs/simple_eigenstates_comparison_TFIM_J=2.pdf' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checks \n",
    "The landscape for 3 qubits is a bit unusual and exploring large flow steps can speed things up.  Above the brute force check found fairly large steps while binary search may get stuck in local minima, cf this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marek/Projects_git/double_bracket_flow_as_a_diagonalization_quantum_algorithm/class_double_bracket_flow.py:1143: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/home/marek/Projects_git/double_bracket_flow_as_a_diagonalization_quantum_algorithm/class_double_bracket_flow.py:1170: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from class_double_bracket_flow import *\n",
    "\n",
    "\n",
    "flow3 = double_bracket_flow( H_TFIM )\n",
    "\n",
    "\n",
    "flow3.flow_step_max =1\n",
    "flow3.flow_step_min = 0\n",
    "flow3.nmb_search_points_minimizing_s_search = 20\n",
    "flow3.please_use_binary_search  =True #by default binary search is used\n",
    "\n",
    "flow3.nmb_flow_steps = 15\n",
    "flow3.flow_forwards( states = np.eye(2**L).tolist() )\n",
    "flow3.show_flow_forwards_results()\n",
    "flow3.show_flow_forwards_results_fancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}