From 41e0b85c7e0974d623f52372ed62cecdd8d1d315 Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Wed, 30 Oct 2024 10:45:39 +0100
Subject: [PATCH 01/13] feat: migration of pytorch backend effects in qibo
 source code

---
 src/qibo/backends/__init__.py         |   8 +-
 src/qibo/backends/pytorch.py          | 281 --------------------------
 src/qibo/hamiltonians/hamiltonians.py |   3 +-
 src/qibo/models/error_mitigation.py   |   2 +-
 src/qibo/optimizers.py                |   2 +-
 src/qibo/quantum_info/basis.py        |   2 +-
 6 files changed, 6 insertions(+), 292 deletions(-)
 delete mode 100644 src/qibo/backends/pytorch.py

diff --git a/src/qibo/backends/__init__.py b/src/qibo/backends/__init__.py
index f8b10e413c..0774db0872 100644
--- a/src/qibo/backends/__init__.py
+++ b/src/qibo/backends/__init__.py
@@ -8,10 +8,9 @@
 from qibo.backends.clifford import CliffordBackend
 from qibo.backends.npmatrices import NumpyMatrices
 from qibo.backends.numpy import NumpyBackend
-from qibo.backends.pytorch import PyTorchBackend
 from qibo.config import log, raise_error
 
-QIBO_NATIVE_BACKENDS = ("numpy", "pytorch", "qulacs")
+QIBO_NATIVE_BACKENDS = ("numpy", "qulacs")
 
 
 class MissingBackend(ValueError):
@@ -40,9 +39,6 @@ def load(backend: str, **kwargs) -> Backend:
                 + f"The native qibo backends are {QIBO_NATIVE_BACKENDS + ('clifford',)}",
             )
 
-        if backend == "pytorch":
-            return PyTorchBackend()
-
         if backend == "clifford":
             engine = kwargs.pop("platform", None)
             kwargs["engine"] = engine
@@ -80,7 +76,7 @@ class _Global:
         {"backend": "qibojit", "platform": "numba"},
         {"backend": "numpy"},
         {"backend": "qiboml", "platform": "tensorflow"},
-        {"backend": "pytorch"},
+        {"backend": "qiboml", "platform": "pytorch"},
     ]
 
     @classmethod
diff --git a/src/qibo/backends/pytorch.py b/src/qibo/backends/pytorch.py
deleted file mode 100644
index 48404248d2..0000000000
--- a/src/qibo/backends/pytorch.py
+++ /dev/null
@@ -1,281 +0,0 @@
-"""PyTorch backend."""
-
-from typing import Union
-
-import numpy as np
-
-from qibo import __version__
-from qibo.backends.npmatrices import NumpyMatrices
-from qibo.backends.numpy import NumpyBackend
-
-
-class TorchMatrices(NumpyMatrices):
-    """Matrix representation of every gate as a torch Tensor.
-
-    Args:
-        dtype (torch.dtype): Data type of the matrices.
-    """
-
-    def __init__(self, dtype):
-        import torch  # pylint: disable=import-outside-toplevel  # type: ignore
-
-        super().__init__(dtype)
-        self.np = torch
-        self.dtype = dtype
-
-    def _cast(self, x, dtype):
-        flattened = [item for sublist in x for item in sublist]
-        tensor_list = [self.np.as_tensor(i, dtype=dtype) for i in flattened]
-        return self.np.stack(tensor_list).reshape(len(x), len(x))
-
-    def Unitary(self, u):
-        return self._cast(u, dtype=self.dtype)
-
-
-class PyTorchBackend(NumpyBackend):
-    def __init__(self):
-        super().__init__()
-        import torch  # pylint: disable=import-outside-toplevel  # type: ignore
-
-        self.np = torch
-
-        self.name = "pytorch"
-        self.versions = {
-            "qibo": __version__,
-            "numpy": np.__version__,
-            "torch": self.np.__version__,
-        }
-
-        # Default data type used for the gate matrices is complex128
-        self.dtype = self._torch_dtype(self.dtype)
-        # Default data type used for the real gate parameters is float64
-        self.parameter_dtype = self._torch_dtype("float64")
-        self.matrices = TorchMatrices(self.dtype)
-        self.device = self.np.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        self.nthreads = 0
-        self.tensor_types = (self.np.Tensor, np.ndarray)
-
-        # These functions in Torch works in a different way than numpy or have different names
-        self.np.transpose = self.np.permute
-        self.np.copy = self.np.clone
-        self.np.power = self.np.pow
-        self.np.expand_dims = self.np.unsqueeze
-        self.np.mod = self.np.remainder
-        self.np.right_shift = self.np.bitwise_right_shift
-        self.np.sign = self.np.sgn
-        self.np.flatnonzero = lambda x: self.np.nonzero(x).flatten()
-
-    def _torch_dtype(self, dtype):
-        if dtype == "float":
-            dtype += "32"
-        return getattr(self.np, dtype)
-
-    def set_device(self, device):  # pragma: no cover
-        self.device = device
-
-    def cast(
-        self,
-        x,
-        dtype=None,
-        copy: bool = False,
-    ):
-        """Casts input as a Torch tensor of the specified dtype.
-
-        This method supports casting of single tensors or lists of tensors
-        as for the :class:`qibo.backends.PyTorchBackend`.
-
-        Args:
-            x (Union[torch.Tensor, list[torch.Tensor], np.ndarray, list[np.ndarray], int, float, complex]):
-                Input to be casted.
-            dtype (Union[str, torch.dtype, np.dtype, type]): Target data type.
-                If ``None``, the default dtype of the backend is used.
-                Defaults to ``None``.
-            copy (bool, optional): If ``True``, the input tensor is copied before casting.
-                Defaults to ``False``.
-        """
-
-        if dtype is None:
-            dtype = self.dtype
-        elif isinstance(dtype, type):
-            dtype = self._torch_dtype(dtype.__name__)
-        elif not isinstance(dtype, self.np.dtype):
-            dtype = self._torch_dtype(str(dtype))
-
-        if isinstance(x, self.np.Tensor):
-            x = x.to(dtype)
-        elif (
-            isinstance(x, list)
-            and len(x) > 0
-            and all(isinstance(row, self.np.Tensor) for row in x)
-        ):
-            x = self.np.stack(x)
-        else:
-            x = self.np.tensor(x, dtype=dtype)
-
-        if copy:
-            return x.clone()
-        return x
-
-    def matrix_parametrized(self, gate):
-        """Convert a parametrized gate to its matrix representation in the computational basis."""
-        name = gate.__class__.__name__
-        _matrix = getattr(self.matrices, name)
-        if name == "GeneralizedRBS":
-            for parameter in ["theta", "phi"]:
-                if not isinstance(gate.init_kwargs[parameter], self.np.Tensor):
-                    gate.init_kwargs[parameter] = self._cast_parameter(
-                        gate.init_kwargs[parameter], trainable=gate.trainable
-                    )
-
-            _matrix = _matrix(
-                qubits_in=gate.init_args[0],
-                qubits_out=gate.init_args[1],
-                theta=gate.init_kwargs["theta"],
-                phi=gate.init_kwargs["phi"],
-            )
-            return _matrix
-        else:
-            new_parameters = []
-            for parameter in gate.parameters:
-                if not isinstance(parameter, self.np.Tensor):
-                    parameter = self._cast_parameter(
-                        parameter, trainable=gate.trainable
-                    )
-                elif parameter.requires_grad:
-                    gate.trainable = True
-                new_parameters.append(parameter)
-            gate.parameters = tuple(new_parameters)
-        _matrix = _matrix(*gate.parameters)
-        return _matrix
-
-    def _cast_parameter(self, x, trainable):
-        """Cast a gate parameter to a torch tensor.
-
-        Args:
-            x (Union[int, float, complex]): Parameter to be casted.
-            trainable (bool): If ``True``, the tensor requires gradient.
-        """
-        if isinstance(x, int) and trainable:
-            return self.np.tensor(x, dtype=self.parameter_dtype, requires_grad=True)
-        if isinstance(x, float):
-            return self.np.tensor(
-                x, dtype=self.parameter_dtype, requires_grad=trainable
-            )
-        return self.np.tensor(x, dtype=self.dtype, requires_grad=trainable)
-
-    def is_sparse(self, x):
-        if isinstance(x, self.np.Tensor):
-            return x.is_sparse
-
-        return super().is_sparse(x)
-
-    def to_numpy(self, x):
-        if isinstance(x, list):
-            return np.asarray([self.to_numpy(i) for i in x])
-
-        if isinstance(x, self.np.Tensor):
-            return x.numpy(force=True)
-
-        return x
-
-    def _order_probabilities(self, probs, qubits, nqubits):
-        """Arrange probabilities according to the given ``qubits`` ordering."""
-        if probs.dim() == 0:  # pragma: no cover
-            return probs
-        unmeasured, reduced = [], {}
-        for i in range(nqubits):
-            if i in qubits:
-                reduced[i] = i - len(unmeasured)
-            else:
-                unmeasured.append(i)
-        return self.np.transpose(probs, [reduced.get(i) for i in qubits])
-
-    def calculate_probabilities(self, state, qubits, nqubits):
-        rtype = self.np.real(state).dtype
-        unmeasured_qubits = tuple(i for i in range(nqubits) if i not in qubits)
-        state = self.np.reshape(self.np.abs(state) ** 2, nqubits * (2,))
-        if len(unmeasured_qubits) == 0:
-            probs = self.cast(state, dtype=rtype)
-        else:
-            probs = self.np.sum(self.cast(state, dtype=rtype), axis=unmeasured_qubits)
-        return self._order_probabilities(probs, qubits, nqubits).ravel()
-
-    def set_seed(self, seed):
-        self.np.manual_seed(seed)
-        np.random.seed(seed)
-
-    def sample_shots(self, probabilities, nshots):
-        return self.np.multinomial(
-            self.cast(probabilities, dtype="float"), nshots, replacement=True
-        )
-
-    def calculate_eigenvalues(self, matrix, k: int = 6, hermitian: bool = True):
-        if hermitian:
-            return self.np.linalg.eigvalsh(matrix)  # pylint: disable=not-callable
-        return self.np.linalg.eigvals(matrix)  # pylint: disable=not-callable
-
-    def calculate_eigenvectors(self, matrix, k: int = 6, hermitian: int = True):
-        if hermitian:
-            return self.np.linalg.eigh(matrix)  # pylint: disable=not-callable
-        return self.np.linalg.eig(matrix)  # pylint: disable=not-callable
-
-    def calculate_matrix_exp(self, a, matrix, eigenvectors=None, eigenvalues=None):
-        if eigenvectors is None or self.is_sparse(matrix):
-            return self.np.linalg.matrix_exp(  # pylint: disable=not-callable
-                -1j * a * matrix
-            )
-        expd = self.np.diag(self.np.exp(-1j * a * eigenvalues))
-        ud = self.np.conj(eigenvectors).T
-        return self.np.matmul(eigenvectors, self.np.matmul(expd, ud))
-
-    def calculate_matrix_power(
-        self,
-        matrix,
-        power: Union[float, int],
-        precision_singularity: float = 1e-14,
-    ):
-        copied = self.cast(matrix, copy=True)
-        copied = self.to_numpy(copied) if power >= 0.0 else copied.detach()
-        copied = super().calculate_matrix_power(copied, power, precision_singularity)
-        return self.cast(copied, dtype=copied.dtype)
-
-    def calculate_jacobian_matrix(
-        self, circuit, parameters=None, initial_state=None, return_complex: bool = True
-    ):
-        copied = circuit.copy(deep=True)
-
-        def func(parameters):
-            """torch requires object(s) to be wrapped in a function."""
-            copied.set_parameters(parameters)
-            state = self.execute_circuit(copied, initial_state=initial_state).state()
-            if return_complex:
-                return self.np.real(state), self.np.imag(state)
-            return self.np.real(state)
-
-        return self.np.autograd.functional.jacobian(func, parameters)
-
-    def _test_regressions(self, name):
-        if name == "test_measurementresult_apply_bitflips":
-            return [
-                [0, 0, 0, 0, 2, 3, 0, 0, 0, 0],
-                [0, 0, 0, 0, 2, 3, 0, 0, 0, 0],
-                [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
-                [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],
-            ]
-
-        if name == "test_probabilistic_measurement":
-            if self.device == "cuda":  # pragma: no cover
-                return {0: 273, 1: 233, 2: 242, 3: 252}
-            return {1: 270, 2: 248, 3: 244, 0: 238}
-
-        if name == "test_unbalanced_probabilistic_measurement":
-            if self.device == "cuda":  # pragma: no cover
-                return {0: 196, 1: 153, 2: 156, 3: 495}
-            return {3: 492, 2: 176, 0: 168, 1: 164}
-
-        if name == "test_post_measurement_bitflips_on_circuit":
-            return [
-                {5: 30},
-                {5: 17, 4: 5, 7: 4, 1: 2, 6: 2},
-                {4: 9, 2: 5, 5: 5, 3: 4, 6: 4, 0: 1, 1: 1, 7: 1},
-            ]
diff --git a/src/qibo/hamiltonians/hamiltonians.py b/src/qibo/hamiltonians/hamiltonians.py
index 015de265d7..a51ff9f9f8 100644
--- a/src/qibo/hamiltonians/hamiltonians.py
+++ b/src/qibo/hamiltonians/hamiltonians.py
@@ -6,7 +6,6 @@
 import numpy as np
 import sympy
 
-from qibo.backends import PyTorchBackend, _check_backend
 from qibo.config import EINSUM_CHARS, log, raise_error
 from qibo.hamiltonians.abstract import AbstractHamiltonian
 from qibo.symbols import Z
@@ -262,7 +261,7 @@ def __mul__(self, o):
             if self.backend.np.real(o) >= 0:  # TODO: check for side effects K.qnp
                 r._eigenvalues = o * self._eigenvalues
             elif not self.backend.is_sparse(self.matrix):
-                axis = (0,) if isinstance(self.backend, PyTorchBackend) else 0
+                axis = (0,) if (self.backend.platform == "pytorch") else 0
                 r._eigenvalues = o * self.backend.np.flip(self._eigenvalues, axis)
         if self._eigenvectors is not None:
             if self.backend.np.real(o) > 0:  # TODO: see above
diff --git a/src/qibo/models/error_mitigation.py b/src/qibo/models/error_mitigation.py
index 7aef4db5b9..ccbec81293 100644
--- a/src/qibo/models/error_mitigation.py
+++ b/src/qibo/models/error_mitigation.py
@@ -328,7 +328,7 @@ def _curve_fit(
     Returns:
         ndarray: the optimal parameters.
     """
-    if backend.name == "pytorch":
+    if backend.platform == "pytorch":
         # pytorch has some problems with the `scipy.optim.curve_fit` function
         # thus we use a `torch.optim` optimizer
         params.requires_grad = True
diff --git a/src/qibo/optimizers.py b/src/qibo/optimizers.py
index dbd0331deb..5789d4da69 100644
--- a/src/qibo/optimizers.py
+++ b/src/qibo/optimizers.py
@@ -282,7 +282,7 @@ def sgd(
             callback=callback,
         )
 
-    if backend.name == "pytorch":
+    if backend.platform == "pytorch":
         if compile:
             log.warning(
                 "PyTorch does not support compilation of the optimization graph."
diff --git a/src/qibo/quantum_info/basis.py b/src/qibo/quantum_info/basis.py
index 7efab48547..673f7e48ca 100644
--- a/src/qibo/quantum_info/basis.py
+++ b/src/qibo/quantum_info/basis.py
@@ -105,7 +105,7 @@ def pauli_basis(
         basis_full = basis_single
 
     if vectorize and sparse:
-        if backend.name == "pytorch":
+        if backend.platform == "pytorch":
             nonzero = lambda x: backend.np.nonzero(x, as_tuple=True)
         else:
             nonzero = backend.np.nonzero

From c1eef238369740eb2f4f394234b85b1563f171bb Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Wed, 30 Oct 2024 10:46:14 +0100
Subject: [PATCH 02/13] feat: adapt tests to pytorch migration

---
 tests/conftest.py                      |  2 +-
 tests/test_backends.py                 |  3 +-
 tests/test_backends_clifford.py        |  4 +-
 tests/test_backends_torch_gradients.py |  3 +-
 tests/test_hamiltonians.py             | 62 +++++++++++++-------------
 tests/test_models_variational.py       | 14 +++---
 tests/test_quantum_info_metrics.py     |  2 +-
 tests/test_quantum_info_operations.py  |  2 +-
 tests/test_quantum_info_random.py      |  2 +-
 9 files changed, 45 insertions(+), 49 deletions(-)

diff --git a/tests/conftest.py b/tests/conftest.py
index d15e5164d1..9e0dd07af8 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -12,11 +12,11 @@
 # backends to be tested
 BACKENDS = [
     "numpy",
-    "pytorch",
     "qibojit-numba",
     "qibojit-cupy",
     "qibojit-cuquantum",
     "qiboml-tensorflow",
+    "qiboml-pytorch",
 ]
 # multigpu configurations to be tested (only with qibojit-cupy)
 ACCELERATORS = [
diff --git a/tests/test_backends.py b/tests/test_backends.py
index 85fb18f3a3..03069e390f 100644
--- a/tests/test_backends.py
+++ b/tests/test_backends.py
@@ -119,13 +119,12 @@ def test_list_available_backends():
     )
     available_backends = {
         "numpy": True,
-        "pytorch": True,
         "qulacs": qulacs,
         "qibojit": {"numba": True, "cupy": False, "cuquantum": False},
         "qibolab": False,
         "qibo-cloud-backends": False,
         "qibotn": {"cutensornet": False, "qutensornet": True},
-        "qiboml": {"tensorflow": tensorflow},
+        "qiboml": {"tensorflow": tensorflow, "pytorch": True},
     }
     assert available_backends == list_available_backends(
         "qibojit", "qibolab", "qibo-cloud-backends", "qibotn", "qiboml"
diff --git a/tests/test_backends_clifford.py b/tests/test_backends_clifford.py
index 6e0faf27cc..6cd1cd9e2c 100644
--- a/tests/test_backends_clifford.py
+++ b/tests/test_backends_clifford.py
@@ -4,10 +4,10 @@
 
 import numpy as np
 import pytest
-from qiboml.backends import TensorflowBackend
+from qiboml.backends import PyTorchBackend, TensorflowBackend
 
 from qibo import Circuit, gates, get_backend, set_backend
-from qibo.backends import CliffordBackend, NumpyBackend, PyTorchBackend
+from qibo.backends import CliffordBackend, NumpyBackend
 from qibo.backends.clifford import _get_engine_name
 from qibo.noise import DepolarizingError, NoiseModel, PauliError
 from qibo.quantum_info.random_ensembles import random_clifford
diff --git a/tests/test_backends_torch_gradients.py b/tests/test_backends_torch_gradients.py
index 2549e6bd45..21620b0871 100644
--- a/tests/test_backends_torch_gradients.py
+++ b/tests/test_backends_torch_gradients.py
@@ -2,9 +2,10 @@
 
 import numpy as np
 import pytest
+from qiboml.backends import PyTorchBackend
 
 from qibo import gates, models
-from qibo.backends import PyTorchBackend, construct_backend
+from qibo.backends import construct_backend
 from qibo.quantum_info import infidelity
 
 
diff --git a/tests/test_hamiltonians.py b/tests/test_hamiltonians.py
index 9f2f593617..fcc5fc92d6 100644
--- a/tests/test_hamiltonians.py
+++ b/tests/test_hamiltonians.py
@@ -68,10 +68,10 @@ def transformation_d(a, b, use_eye=False):
         H2 = hamiltonians.XXZ(nqubits=2, delta=1, backend=backend)
         mH1, mH2 = backend.to_numpy(H1.matrix), backend.to_numpy(H2.matrix)
     else:
-        if backend.platform == "tensorflow":
-            pytest.skip("Tensorflow does not support operations with sparse matrices.")
-        elif backend.name == "pytorch":
-            pytest.skip("Pytorch does not support operations with sparse matrices.")
+        if backend.platform in ["tensorflow", "pytorch"]:
+            pytest.skip(
+                "Tensorflow and Pytorch do not support operations with sparse matrices."
+            )
 
         mH1 = random_sparse_matrix(backend, 64, sparse_type=sparse_type)
         mH2 = random_sparse_matrix(backend, 64, sparse_type=sparse_type)
@@ -100,10 +100,10 @@ def test_hamiltonian_addition(backend, sparse_type):
         H1 = hamiltonians.Y(nqubits=3, backend=backend)
         H2 = hamiltonians.TFIM(nqubits=3, h=1.0, backend=backend)
     else:
-        if backend.platform == "tensorflow":
-            pytest.skip("Tensorflow does not support operations with sparse matrices.")
-        elif backend.name == "pytorch":
-            pytest.skip("Pytorch does not support operations with sparse matrices.")
+        if backend.platform in ["tensorflow", "pytorch"]:
+            pytest.skip(
+                "Tensorflow and Pytorch do not support operations with sparse matrices."
+            )
         H1 = hamiltonians.Hamiltonian(
             6,
             random_sparse_matrix(backend, 64, sparse_type=sparse_type),
@@ -148,10 +148,10 @@ def test_hamiltonian_operation_errors(backend):
 @pytest.mark.parametrize("sparse_type", [None, "coo", "csr", "csc", "dia"])
 def test_hamiltonian_matmul(backend, sparse_type):
     """Test matrix multiplication between Hamiltonians."""
-    if backend.name == "pytorch":
-        pytest.skip("Pytorch does not support operations with sparse matrices.")
-    if backend.platform == "tensorflow":
-        pytest.skip("Tensorflow does not support operations with sparse matrices.")
+    if backend.platform in ["tensorflow", "pytorch"]:
+        pytest.skip(
+            "Tensorflow and Pytorch do not support operations with sparse matrices."
+        )
     if sparse_type is None:
         nqubits = 3
         H1 = hamiltonians.TFIM(nqubits, h=1.0, backend=backend)
@@ -192,10 +192,10 @@ def test_hamiltonian_matmul_states(backend, sparse_type):
         nqubits = 3
         H = hamiltonians.TFIM(nqubits, h=1.0, backend=backend)
     else:
-        if backend.platform == "tensorflow":
-            pytest.skip("Tensorflow does not support operations with sparse matrices.")
-        elif backend.name == "pytorch":
-            pytest.skip("Pytorch does not support operations with sparse matrices.")
+        if backend.platform in ["tensorflow", "pytorch"]:
+            pytest.skip(
+                "Tensorflow and Pytorch do not support operations with sparse matrices."
+            )
         nqubits = 3
         nstates = 2**nqubits
         matrix = random_sparse_matrix(backend, nstates, sparse_type)
@@ -229,10 +229,10 @@ def test_hamiltonian_expectation(backend, dense, density_matrix, sparse_type):
     if sparse_type is None:
         h = hamiltonians.XXZ(nqubits=3, delta=0.5, dense=dense, backend=backend)
     else:
-        if backend.platform == "tensorflow":
-            pytest.skip("Tensorflow does not support operations with sparse matrices.")
-        elif backend.name == "pytorch":
-            pytest.skip("Pytorch does not support operations with sparse matrices.")
+        if backend.platform in ["tensorflow", "pytorch"]:
+            pytest.skip(
+                "Tensorflow and Pytorch do not support operations with sparse matrices."
+            )
         h = hamiltonians.Hamiltonian(
             6, random_sparse_matrix(backend, 64, sparse_type), backend=backend
         )
@@ -317,10 +317,10 @@ def test_hamiltonian_eigenvalues(backend, dtype, sparse_type, dense):
     if sparse_type is None:
         H1 = hamiltonians.XXZ(nqubits=2, delta=0.5, dense=dense, backend=backend)
     else:
-        if backend.platform == "tensorflow":
-            pytest.skip("Tensorflow does not support operations with sparse matrices.")
-        elif backend.name == "pytorch":
-            pytest.skip("Pytorch does not support operations with sparse matrices.")
+        if backend.platform in ["tensorflow", "pytorch"]:
+            pytest.skip(
+                "Tensorflow and Pytorch do not support operations with sparse matrices."
+            )
         from scipy import sparse
 
         H1 = hamiltonians.XXZ(nqubits=5, delta=0.5, backend=backend)
@@ -394,10 +394,10 @@ def test_hamiltonian_ground_state(backend, sparse_type, dense):
     if sparse_type is None:
         H = hamiltonians.XXZ(nqubits=2, delta=0.5, dense=dense, backend=backend)
     else:
-        if backend.platform == "tensorflow":
-            pytest.skip("Tensorflow does not support operations with sparse matrices.")
-        elif backend.name == "pytorch":
-            pytest.skip("Pytorch does not support operations with sparse matrices.")
+        if backend.platform in ["tensorflow", "pytorch"]:
+            pytest.skip(
+                "Tensorflow and Pytorch do not support operations with sparse matrices."
+            )
         from scipy import sparse
 
         H = hamiltonians.XXZ(nqubits=5, delta=0.5, backend=backend)
@@ -426,12 +426,10 @@ def construct_hamiltonian():
         if sparse_type is None:
             return hamiltonians.XXZ(nqubits=2, delta=0.5, dense=dense, backend=backend)
         else:
-            if backend.platform == "tensorflow":
+            if backend.platform in ["tensorflow", "pytorch"]:
                 pytest.skip(
-                    "Tensorflow does not support operations with sparse matrices."
+                    "Tensorflow and Pytorch do not support operations with sparse matrices."
                 )
-            elif backend.name == "pytorch":
-                pytest.skip("Pytorch does not support operations with sparse matrices.")
             from scipy import sparse
 
             ham = hamiltonians.XXZ(nqubits=5, delta=0.5, backend=backend)
diff --git a/tests/test_models_variational.py b/tests/test_models_variational.py
index 8704aaa31f..d02014683c 100644
--- a/tests/test_models_variational.py
+++ b/tests/test_models_variational.py
@@ -102,13 +102,11 @@ def myloss(parameters, circuit, target):
 def test_vqe(backend, method, options, compile, filename):
     """Performs a VQE circuit minimization test."""
     if (method == "sgd" or compile) and (
-        (backend.name != "pytorch") or (backend.platform != "tensorflow")
+        backend.platform not in ["tensorflow", "pytorch"]
     ):
         pytest.skip("Skipping SGD test for unsupported backend.")
-    if method != "sgd" and backend.name == "pytorch":
-        pytest.skip("Skipping scipy optimizers for pytorch.")
-    if method != "sgd" and backend.platform == "tensorflow":
-        pytest.skip("Skipping scipy optimizers for tensorflow.")
+    if method != "sgd" and (backend.platform not in ["tensorflow", "pytorch"]):
+        pytest.skip("Skipping scipy optimizers for pytorch and tensorflow.")
     n_threads = backend.nthreads
     backend.set_threads(1)
     nqubits = 3
@@ -131,7 +129,7 @@ def test_vqe(backend, method, options, compile, filename):
     initial_parameters = backend.cast(
         np.random.uniform(0, 2 * np.pi, 2 * nqubits * layers + nqubits), dtype="float64"
     )
-    if backend.name == "pytorch":
+    if backend.platform == "pytorch":
         initial_parameters.requires_grad = True
     v = models.VQE(circuit, hamiltonian)
 
@@ -274,9 +272,9 @@ def test_qaoa_errors(backend):
 
 @pytest.mark.parametrize(test_names, test_values)
 def test_qaoa_optimization(backend, method, options, dense, filename):
-    if (method == "sgd") and (backend.name not in ["tensorflow", "pytorch"]):
+    if (method == "sgd") and (backend.platform not in ["tensorflow", "pytorch"]):
         pytest.skip("Skipping SGD test for unsupported backend.")
-    if method != "sgd" and backend.name in ("tensorflow", "pytorch"):
+    if method != "sgd" and backend.platform in ("tensorflow", "pytorch"):
         pytest.skip("Skipping scipy optimizers for tensorflow and pytorch.")
     h = hamiltonians.XXZ(3, dense=dense, backend=backend)
     qaoa = models.QAOA(h)
diff --git a/tests/test_quantum_info_metrics.py b/tests/test_quantum_info_metrics.py
index 4e5b9ea849..a4c53dfdfa 100644
--- a/tests/test_quantum_info_metrics.py
+++ b/tests/test_quantum_info_metrics.py
@@ -394,7 +394,7 @@ def test_frame_potential(backend, nqubits, power_t, samples):
 @pytest.mark.parametrize("return_complex", [False, True])
 @pytest.mark.parametrize("nqubits", [4, 8])
 def test_qfim(backend, nqubits, return_complex, params_flag):
-    if backend.name == "pytorch" or backend.platform == "tensorflow":
+    if backend.platform in ["tensorflow", "pytorch"]:
         # QFIM from https://arxiv.org/abs/2405.20408 is known analytically
         data = np.random.rand(nqubits)
         data = backend.cast(data, dtype=data.dtype)
diff --git a/tests/test_quantum_info_operations.py b/tests/test_quantum_info_operations.py
index d25643f9f8..4791ff5df0 100644
--- a/tests/test_quantum_info_operations.py
+++ b/tests/test_quantum_info_operations.py
@@ -260,7 +260,7 @@ def test_singular_value_decomposition(backend):
 
     S_sorted = backend.np.sort(S)
     coeffs_sorted = backend.np.sort(coeffs)
-    if backend.name == "pytorch":
+    if backend.platform == "pytorch":
         S_sorted, coeffs_sorted = S_sorted[0], coeffs_sorted[0]
 
     backend.assert_allclose(S_sorted, coeffs_sorted)
diff --git a/tests/test_quantum_info_random.py b/tests/test_quantum_info_random.py
index d522f1458d..3d2b6d92dd 100644
--- a/tests/test_quantum_info_random.py
+++ b/tests/test_quantum_info_random.py
@@ -174,7 +174,7 @@ def test_random_unitary(backend, measure):
     matrix_dagger = backend.np.conj(matrix).T
     matrix_inv = (
         backend.np.inverse(matrix)
-        if backend.name == "pytorch"
+        if backend.platform == "pytorch"
         else np.linalg.inv(matrix)
     )
     norm = float(

From d3255c1e0e28eab6dbe8e64f5894fdf1f9958959 Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Wed, 30 Oct 2024 10:57:10 +0100
Subject: [PATCH 03/13] chore: point to qiboml torch migration branch

---
 poetry.lock    | 171 ++++++++++++++++++++++++-------------------------
 pyproject.toml |   2 +-
 2 files changed, 86 insertions(+), 87 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 0d4b72c7b2..183d3794a9 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -201,21 +201,20 @@ lxml = ["lxml"]
 
 [[package]]
 name = "bleach"
-version = "6.1.0"
+version = "6.2.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
-    {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
+    {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
+    {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
 ]
 
 [package.dependencies]
-six = ">=1.9.0"
 webencodings = "*"
 
 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.3)"]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
 
 [[package]]
 name = "cachetools"
@@ -614,13 +613,13 @@ files = [
 
 [[package]]
 name = "colorlog"
-version = "6.8.2"
+version = "6.9.0"
 description = "Add colours to the output of Python's logging module."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "colorlog-6.8.2-py3-none-any.whl", hash = "sha256:4dcbb62368e2800cb3c5abd348da7e53f6c362dda502ec27c560b2e58a66bd33"},
-    {file = "colorlog-6.8.2.tar.gz", hash = "sha256:3e3e079a41feb5a1b64f978b5ea4f46040a94f11f0e8bbb8261e3dbbeca64d44"},
+    {file = "colorlog-6.9.0-py3-none-any.whl", hash = "sha256:5906e71acd67cb07a71e779c47c4bcb45fb8c2993eebe9e5adcd6a6f1b283eff"},
+    {file = "colorlog-6.9.0.tar.gz", hash = "sha256:bfba54a1b93b94f54e1f4fe48395725a3d92fd2a4af702f6bd70946bdc0c6ac2"},
 ]
 
 [package.dependencies]
@@ -1559,13 +1558,13 @@ files = [
 
 [[package]]
 name = "google-api-core"
-version = "2.21.0"
+version = "2.22.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google_api_core-2.21.0-py3-none-any.whl", hash = "sha256:6869eacb2a37720380ba5898312af79a4d30b8bca1548fb4093e0697dc4bdf5d"},
-    {file = "google_api_core-2.21.0.tar.gz", hash = "sha256:4a152fd11a9f774ea606388d423b68aa7e6d6a0ffe4c8266f74979613ec09f81"},
+    {file = "google_api_core-2.22.0-py3-none-any.whl", hash = "sha256:a6652b6bd51303902494998626653671703c420f6f4c88cfd3f50ed723e9d021"},
+    {file = "google_api_core-2.22.0.tar.gz", hash = "sha256:26f8d76b96477db42b55fd02a33aae4a42ec8b86b98b94969b7333a2c828bf35"},
 ]
 
 [package.dependencies]
@@ -1732,85 +1731,85 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "grpcio"
-version = "1.67.0"
+version = "1.67.1"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "grpcio-1.67.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:2b7183c80b602b0ad816315d66f2fb7887614ead950416d60913a9a71c12560d"},
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 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.67.0)"]
+protobuf = ["grpcio-tools (>=1.67.1)"]
 
 [[package]]
 name = "grpcio-status"
-version = "1.67.0"
+version = "1.67.1"
 description = "Status proto mapping for gRPC"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "grpcio_status-1.67.0-py3-none-any.whl", hash = "sha256:0e79e2e01ba41a6ca6ed9d7a825323c511fe1653a646f8014c7e3c8132527acc"},
-    {file = "grpcio_status-1.67.0.tar.gz", hash = "sha256:c3e5a86fa007e9e263cd5f988a8a907484da4caab582874ea2a4a6092734046b"},
+    {file = "grpcio_status-1.67.1-py3-none-any.whl", hash = "sha256:16e6c085950bdacac97c779e6a502ea671232385e6e37f258884d6883392c2bd"},
+    {file = "grpcio_status-1.67.1.tar.gz", hash = "sha256:2bf38395e028ceeecfd8866b081f61628114b384da7d51ae064ddc8d766a5d11"},
 ]
 
 [package.dependencies]
 googleapis-common-protos = ">=1.5.5"
-grpcio = ">=1.67.0"
+grpcio = ">=1.67.1"
 protobuf = ">=5.26.1,<6.0dev"
 
 [[package]]
@@ -4523,7 +4522,7 @@ toml = ">=0.10.2,<0.11.0"
 
 [[package]]
 name = "qibojit"
-version = "0.1.7"
+version = "0.1.8"
 description = "Simulation tools based on numba and cupy."
 optional = false
 python-versions = "^3.9,<3.13"
@@ -4533,7 +4532,7 @@ develop = false
 [package.dependencies]
 numba = ">=0.59.0"
 psutil = "^5.9.5"
-qibo = {git = "https://github.com/qiboteam/qibo.git"}
+qibo = "^0.2.13"
 scipy = "^1.10.1"
 
 [package.extras]
@@ -4544,7 +4543,7 @@ cuquantum = ["cuquantum-python-cu12 (>=23.10.0,<24.0.0)"]
 type = "git"
 url = "https://github.com/qiboteam/qibojit.git"
 reference = "HEAD"
-resolved_reference = "e236bb0a47082593e55bf805372e053174f51395"
+resolved_reference = "290b1776c2ba272f1c9eb59cba1d31964a239f2a"
 
 [[package]]
 name = "qiboml"
@@ -4567,8 +4566,8 @@ torch = "^2.2.0"
 [package.source]
 type = "git"
 url = "https://github.com/qiboteam/qiboml.git"
-reference = "HEAD"
-resolved_reference = "d1df69bb493522c634a49c364421bc4c821d827f"
+reference = "pytorch_migration"
+resolved_reference = "6ee13bb0171a9916dcc84b2efc25542edb0b4768"
 
 [[package]]
 name = "qibotn"
@@ -4590,7 +4589,7 @@ cuda = ["cupy-cuda11x (>=11.6.0,<12.0.0)", "cuquantum-python-cu11 (>=23.3.0,<24.
 type = "git"
 url = "https://github.com/qiboteam/qibotn.git"
 reference = "HEAD"
-resolved_reference = "1eb3817bf4f8770bc6622d3070fea2eca85e6b24"
+resolved_reference = "4f547726b616f73d074abf77811644cf2cb54383"
 
 [[package]]
 name = "quimb"
@@ -4962,23 +4961,23 @@ stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
 [[package]]
 name = "setuptools"
-version = "75.2.0"
+version = "75.3.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "setuptools-75.2.0-py3-none-any.whl", hash = "sha256:a7fcb66f68b4d9e8e66b42f9876150a3371558f98fa32222ffaa5bced76406f8"},
-    {file = "setuptools-75.2.0.tar.gz", hash = "sha256:753bb6ebf1f465a1912e19ed1d41f403a79173a9acf66a42e7e6aec45c3c16ec"},
+    {file = "setuptools-75.3.0-py3-none-any.whl", hash = "sha256:f2504966861356aa38616760c0f66568e535562374995367b4e69c7143cf6bcd"},
+    {file = "setuptools-75.3.0.tar.gz", hash = "sha256:fba5dd4d766e97be1b1681d98712680ae8f2f26d7881245f2ce9e40714f1a686"},
 ]
 
 [package.extras]
 check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.5.2)"]
-core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.collections", "jaraco.functools", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.collections", "jaraco.functools", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.11.*)", "pytest-mypy"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.12.*)", "pytest-mypy"]
 
 [[package]]
 name = "six"
@@ -6043,4 +6042,4 @@ torch = ["torch"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "731880ba3f30c5c16b15692d782ced337173dac75fc8990bea1cd7baaef7f01b"
+content-hash = "a701dab6c07e2b858235407c6c3df0219c5e401bb264d25ee0f8aece308488a9"
diff --git a/pyproject.toml b/pyproject.toml
index 113a087f95..99c9105103 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -72,7 +72,7 @@ matplotlib = "^3.7.0"
 torch = "^2.1.1,<2.4"
 qibojit = { git = "https://github.com/qiboteam/qibojit.git" }
 qibotn = { git = "https://github.com/qiboteam/qibotn.git" }
-qiboml = { git  = "https://github.com/qiboteam/qiboml.git" }
+qiboml = { git  = "https://github.com/qiboteam/qiboml.git" , branch = "pytorch_migration"}
 stim = "^1.12.0"
 qulacs = { version = "^0.6.4", markers = "(sys_platform == 'darwin' and python_version > '3.9') or sys_platform != 'darwin'" }
 

From fdaf4c8fa1744c6962c033b7859c9f18e66cfbc1 Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Wed, 30 Oct 2024 11:04:58 +0100
Subject: [PATCH 04/13] fix: import torch backend from qiboml

---
 tests/test_quantum_info_clifford.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_quantum_info_clifford.py b/tests/test_quantum_info_clifford.py
index 0c90039c1a..e39232e264 100644
--- a/tests/test_quantum_info_clifford.py
+++ b/tests/test_quantum_info_clifford.py
@@ -3,10 +3,10 @@
 
 import numpy as np
 import pytest
-from qiboml.backends import TensorflowBackend
+from qiboml.backends import PyTorchBackend, TensorflowBackend
 
 from qibo import Circuit, gates, matrices
-from qibo.backends import CliffordBackend, PyTorchBackend
+from qibo.backends import CliffordBackend
 from qibo.backends.clifford import _get_engine_name
 from qibo.quantum_info._clifford_utils import (
     _cnot_cost,

From 86e1574b98570505b393e3433462f706b654fe8d Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Wed, 30 Oct 2024 12:46:42 +0100
Subject: [PATCH 05/13] fix: adapt vqe and qaoa optimization tests to pytorch
 migration

---
 tests/test_models_variational.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_models_variational.py b/tests/test_models_variational.py
index d02014683c..baf740c9a1 100644
--- a/tests/test_models_variational.py
+++ b/tests/test_models_variational.py
@@ -105,7 +105,7 @@ def test_vqe(backend, method, options, compile, filename):
         backend.platform not in ["tensorflow", "pytorch"]
     ):
         pytest.skip("Skipping SGD test for unsupported backend.")
-    if method != "sgd" and (backend.platform not in ["tensorflow", "pytorch"]):
+    if method != "sgd" and (backend.platform in ["tensorflow", "pytorch"]):
         pytest.skip("Skipping scipy optimizers for pytorch and tensorflow.")
     n_threads = backend.nthreads
     backend.set_threads(1)
@@ -279,7 +279,7 @@ def test_qaoa_optimization(backend, method, options, dense, filename):
     h = hamiltonians.XXZ(3, dense=dense, backend=backend)
     qaoa = models.QAOA(h)
     initial_p = backend.cast([0.05, 0.06, 0.07, 0.08], dtype="float64")
-    if backend.name == "pytorch":
+    if backend.platform == "pytorch":
         initial_p.requires_grad = True
     best, params, _ = qaoa.minimize(initial_p, method=method, options=options)
     if filename is not None:

From ce623b183f1e1ca055123d95b2e9f6d6ffe1548d Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Fri, 1 Nov 2024 10:25:18 +0100
Subject: [PATCH 06/13] docs: update docs after moving pytorch to qiboml

---
 doc/source/code-examples/advancedexamples.rst |  6 +-
 doc/source/code-examples/examples.rst         |  8 +-
 doc/source/getting-started/installation.rst   | 88 ++++++++-----------
 3 files changed, 47 insertions(+), 55 deletions(-)

diff --git a/doc/source/code-examples/advancedexamples.rst b/doc/source/code-examples/advancedexamples.rst
index 69466d17a7..925f3e9369 100644
--- a/doc/source/code-examples/advancedexamples.rst
+++ b/doc/source/code-examples/advancedexamples.rst
@@ -543,7 +543,9 @@ refer to the :ref:`Optimizers <Optimizers>` section of the documentation.
 Note that if the Stochastic Gradient Descent optimizer is used then the user
 has to use a backend based on tensorflow or pytorch primitives and not the default custom
 backend, as custom operators currently do not support automatic differentiation.
-To switch the backend one can do ``qibo.set_backend("tensorflow")`` or ``qibo.set_backend("pytorch")``.
+To switch the backend one can do ``qibo.set_backend(backend="qiboml", platform="tensorflow")``
+or ``qibo.set_backend(backend="qiboml", platform="pytorch")``, after ensuring the
+Qiboml package has been installed.
 Check the :ref:`How to use automatic differentiation? <autodiff-example>`
 section for more details.
 
@@ -782,7 +784,7 @@ The following script optimizes the parameters of the variational circuit of the
 .. code-block:: python
 
     import qibo
-    qibo.set_backend("pytorch")
+    qibo.set_backend(backend="qiboml", platform="pytorch")
     import torch
     from qibo import gates, models
 
diff --git a/doc/source/code-examples/examples.rst b/doc/source/code-examples/examples.rst
index ca5bdc190c..a41b3c9328 100644
--- a/doc/source/code-examples/examples.rst
+++ b/doc/source/code-examples/examples.rst
@@ -39,9 +39,9 @@ evaluation performance, e.g.:
 .. code-block::  python
 
     import numpy as np
-    # switch backend to "tensorflow"
+    # switch backend to "tensorflow" through the Qiboml provider
     import qibo
-    qibo.set_backend("tensorflow")
+    qibo.set_backend(backend="qiboml", platform="tensorflow")
     from qibo import Circuit, gates
 
     c = Circuit(2)
@@ -54,7 +54,7 @@ evaluation performance, e.g.:
         init_state = np.ones(4) / 2.0 + i
         c(init_state)
 
-Note that compiling is only supported when the native ``tensorflow`` backend is
+Note that compiling is only supported when the ``tensorflow`` backend is
 used. This backend is much slower than ``qibojit`` which uses custom operators
 to apply gates.
 
@@ -226,7 +226,7 @@ For applications that require the state vector to be collapsed during measuremen
 we refer to the :ref:`How to collapse state during measurements? <collapse-examples>`
 
 The measured shots are obtained using pseudo-random number generators of the
-underlying backend (numpy or Tensorflow). If the user has installed a custom
+underlying backend. If the user has installed a custom
 backend (eg. qibojit) and asks for frequencies with more than 100000 shots,
 a custom Metropolis algorithm will be used to obtain the corresponding samples,
 for increase performance. The user can change the threshold for which this
diff --git a/doc/source/getting-started/installation.rst b/doc/source/getting-started/installation.rst
index 8c356b2592..a6a9213a69 100644
--- a/doc/source/getting-started/installation.rst
+++ b/doc/source/getting-started/installation.rst
@@ -7,15 +7,15 @@ Operating systems support
 In the table below we summarize the status of *pre-compiled binaries
 distributed with pypi* for the packages listed above.
 
-+------------------+------+---------+------------+
-| Operating System | qibo | qibojit | tensorflow |
-+==================+======+=========+============+
-| Linux x86        | Yes  | Yes     | Yes        |
-+------------------+------+---------+------------+
-| MacOS >= 10.15   | Yes  | Yes     | Yes        |
-+------------------+------+---------+------------+
-| Windows          | Yes  | Yes     | Yes        |
-+------------------+------+---------+------------+
++------------------+------+---------+-----------+---------+
+| Operating System | qibo | qibojit |Tensorflow | Pytorch |
++==================+======+=========+===========+=========+
+| Linux x86        | Yes  | Yes     | Yes       | Yes     |
++------------------+------+---------+-----------+---------+
+| MacOS >= 10.15   | Yes  | Yes     | Yes       | Yes     |
++------------------+------+---------+-----------+---------+
+| Windows          | Yes  | Yes     | Yes       | Yes     |
++------------------+------+---------+-----------+---------+
 
 .. note::
       All packages are supported for Python >= 3.9.
@@ -148,35 +148,6 @@ Then proceed with the ``qibojit`` installation using ``pip``
 
 _______________________
 
-.. _installing-tensorflow:
-
-tensorflow
-^^^^^^^^^^
-
-If the `TensorFlow <https://www.tensorflow.org>`_ package is installed Qibo
-will detect and provide to the user the possibility to use ``tensorflow``
-backend.
-
-This backend is used by default if ``qibojit`` is not installed, however, if
-needed, in order to switch to the ``tensorflow`` backend please do:
-
-.. code-block::  python
-
-      import qibo
-      qibo.set_backend("tensorflow")
-
-In order to install the package, we recommend the installation using:
-
-.. code-block:: bash
-
-      pip install qibo tensorflow
-
-.. note::
-      TensorFlow can be installed following its `documentation
-      <https://www.tensorflow.org/install>`_.
-
-_______________________
-
 .. _installing-numpy:
 
 numpy
@@ -197,26 +168,45 @@ please do:
 _______________________
 
 
-.. _installing-pytorch:
+.. _installing-qml-backends:
 
-pytorch
-^^^^^^^
+Backends with automatic differentiation support
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If you need automatic differentiation support, for tracing gradients of your
+quantum algorithm or for building some quantum machine learning routine,
+the right backends for you are those provided by the `Qiboml <https://github.com/qiboteam/qiboml>`__
+package.
 
-If the `PyTorch <https://pytorch.org/>`_ package is installed Qibo
-will detect and provide to the user the possibility to use ``pytorch``
-backend.
+In particular, Qiboml currently support `Pytorch <https://pytorch.org/>`_ and
+`Tensorflow <https://www.tensorflow.org>`_ interfaces, integrating the qibo functionalities
+into these well-known machine learning frameworks. Quantum layers can be constructed
+and added to your Pytorch or Tensorflow models, and trained using any supported
+optimization routine.
 
-In order to switch to the ``pytorch`` backend please do:
+In order to use these quantum machine learning backends please make sure the
+preferred package is installed following `Tensorflow's <https://www.tensorflow.org/install>`_
+or `Pytorch's <https://pytorch.org/get-started/locally/>`_ installation instructions.
+
+To switch to Tensorflow or Pytorch backend please do:
 
 .. code-block::  python
 
       import qibo
-      qibo.set_backend("pytorch")
+      # in case of Tensorflow
+      qibo.set_backend(backend="qiboml", platform="tensorflow")
+      # in case of Pytorch
+      qibo.set_backend(backend="qiboml", platform="pytorch")
 
-In order to install the package, we recommend the installation using:
+In order to start using automatic differentiation tools with Qibo,
+we recommend the installation using:
 
 .. code-block:: bash
 
-      pip install qibo torch
+      pip install qibo qiboml tensorflow
 
-_______________________
+or
+
+.. code-block:: bash
+
+      pip install qibo qiboml torch

From 77008a8db61c1208a18d5497797aac4e00540ef3 Mon Sep 17 00:00:00 2001
From: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com>
Date: Tue, 12 Nov 2024 11:38:56 +0100
Subject: [PATCH 07/13] Update doc/source/code-examples/advancedexamples.rst

Co-authored-by: Renato Mello <renato.msf@gmail.com>
---
 doc/source/code-examples/advancedexamples.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/code-examples/advancedexamples.rst b/doc/source/code-examples/advancedexamples.rst
index 925f3e9369..5ef85fd5a1 100644
--- a/doc/source/code-examples/advancedexamples.rst
+++ b/doc/source/code-examples/advancedexamples.rst
@@ -545,7 +545,7 @@ has to use a backend based on tensorflow or pytorch primitives and not the defau
 backend, as custom operators currently do not support automatic differentiation.
 To switch the backend one can do ``qibo.set_backend(backend="qiboml", platform="tensorflow")``
 or ``qibo.set_backend(backend="qiboml", platform="pytorch")``, after ensuring the
-Qiboml package has been installed.
+``qiboml`` package has been installed.
 Check the :ref:`How to use automatic differentiation? <autodiff-example>`
 section for more details.
 

From 299d78ab8d6e20ceb9222da3b8d1b096481ce935 Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Tue, 12 Nov 2024 13:47:19 +0100
Subject: [PATCH 08/13] docs: correct backends subection in api-reference
 documentation section

---
 doc/source/api-reference/qibo.rst | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/doc/source/api-reference/qibo.rst b/doc/source/api-reference/qibo.rst
index 9ee57772ee..a8d60e118c 100644
--- a/doc/source/api-reference/qibo.rst
+++ b/doc/source/api-reference/qibo.rst
@@ -2596,12 +2596,11 @@ The main calculation engine is defined in the abstract backend object
 :class:`qibo.backends.abstract.Backend`. This object defines the methods
 required by all Qibo models to perform simulation.
 
-Qibo currently provides two different calculation backends, one based on
-numpy and one based on Tensorflow. It is possible to define new backends by
-inheriting :class:`qibo.backends.abstract.Backend` and implementing
-its abstract methods.
+Qibo supports several backends (see the :ref:`Backend drivers section <backend-drivers>`),
+which can be used depending on the specific needs:
+lightweight simulation, quantum machine learning, hardware execution, etc.
 
-An additional backend is shipped as the separate library qibojit.
+Among them, the default choice is a backend provided by the qibojit library.
 This backend is supplemented by custom operators defined under which can be
 used to efficiently apply gates to state vectors or density matrices.
 

From 976278853a87f69eda995e701d7c01f48ab736f7 Mon Sep 17 00:00:00 2001
From: Renato Mello <renato.msf@gmail.com>
Date: Thu, 14 Nov 2024 10:08:11 +0400
Subject: [PATCH 09/13] fix test

---
 tests/test_backends_torch_gradients.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_backends_torch_gradients.py b/tests/test_backends_torch_gradients.py
index 3ea2a6ee03..abb6339ed5 100644
--- a/tests/test_backends_torch_gradients.py
+++ b/tests/test_backends_torch_gradients.py
@@ -5,7 +5,7 @@
 from qiboml.backends import PyTorchBackend
 
 from qibo import Circuit, gates
-from qibo.backends import PyTorchBackend, construct_backend
+from qibo.backends import construct_backend
 from qibo.quantum_info.metrics import infidelity
 
 

From aa22507a5d3fa213ae119f6d0a12850de0d887ce Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Mon, 18 Nov 2024 09:24:14 +0100
Subject: [PATCH 10/13] chore: point to qiboml main branch

---
 poetry.lock    | 1229 +++++++++++++++---------------------------------
 pyproject.toml |    2 +-
 2 files changed, 369 insertions(+), 862 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 183d3794a9..9f11ee4748 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,16 +1,5 @@
 # This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
 
-[[package]]
-name = "absl-py"
-version = "2.1.0"
-description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"},
-    {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
-]
-
 [[package]]
 name = "alabaster"
 version = "0.7.16"
@@ -24,13 +13,13 @@ files = [
 
 [[package]]
 name = "alembic"
-version = "1.13.3"
+version = "1.14.0"
 description = "A database migration tool for SQLAlchemy."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "alembic-1.13.3-py3-none-any.whl", hash = "sha256:908e905976d15235fae59c9ac42c4c5b75cfcefe3d27c0fbf7ae15a37715d80e"},
-    {file = "alembic-1.13.3.tar.gz", hash = "sha256:203503117415561e203aa14541740643a611f641517f0209fcae63e9fa09f1a2"},
+    {file = "alembic-1.14.0-py3-none-any.whl", hash = "sha256:99bd884ca390466db5e27ffccff1d179ec5c05c965cfefc0607e69f9e411cb25"},
+    {file = "alembic-1.14.0.tar.gz", hash = "sha256:b00892b53b3642d0b8dbedba234dbf1924b69be83a9a769d5a624b01094e304b"},
 ]
 
 [package.dependencies]
@@ -117,21 +106,6 @@ six = ">=1.12.0"
 astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
 test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
-[[package]]
-name = "astunparse"
-version = "1.6.3"
-description = "An AST unparser for Python"
-optional = false
-python-versions = "*"
-files = [
-    {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
-    {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
-]
-
-[package.dependencies]
-six = ">=1.6.1,<2.0"
-wheel = ">=0.23.0,<1.0"
-
 [[package]]
 name = "attrs"
 version = "21.4.0"
@@ -764,73 +738,73 @@ test = ["altair", "baytune", "chocolate", "dask", "distributed", "kahypar", "mat
 
 [[package]]
 name = "coverage"
-version = "7.6.4"
+version = "7.6.7"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "coverage-7.6.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5f8ae553cba74085db385d489c7a792ad66f7f9ba2ee85bfa508aeb84cf0ba07"},
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 ]
 
 [[package]]
@@ -1195,20 +1169,20 @@ files = [
 
 [[package]]
 name = "deprecated"
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+version = "1.2.15"
 description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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 [package.dependencies]
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 [package.extras]
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+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "jinja2 (>=3.0.3,<3.1.0)", "setuptools", "sphinx (<2)", "tox"]
 
 [[package]]
 name = "dill"
@@ -1407,72 +1381,63 @@ docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.
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-[[package]]
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 [[package]]
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 description = "Tools to manipulate font files"
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 python-versions = ">=3.8"
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 [package.extras]
@@ -1545,26 +1510,15 @@ pygments = ">=2.7"
 sphinx = ">=5.0,<7.0"
 sphinx-basic-ng = "*"
 
-[[package]]
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-
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 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -1590,13 +1544,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.35.0"
+version = "2.36.0"
 description = "Google Authentication Library"
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 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
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 reauth = ["pyu2f (>=0.1.5)"]
 requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
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 [[package]]
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 version = "0.16.3"
@@ -2190,22 +2091,22 @@ scipy = [
 
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 python-versions = ">=3.6"
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+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
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 [[package]]
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 [[package]]
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 docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
 testing = ["coverage", "pyyaml"]
 
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-description = "Python port of markdown-it. Markdown parsing, done right!"
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-profiling = ["gprof2dot"]
-rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
-testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
-
 [[package]]
 name = "markupsafe"
 version = "3.0.2"
@@ -2757,17 +2595,6 @@ files = [
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-description = "Markdown URL utilities"
-optional = false
-python-versions = ">=3.7"
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-
 [[package]]
 name = "mistune"
 version = "3.0.2"
@@ -2799,34 +2626,38 @@ tbb = "==2021.*"
 
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 name = "ml-dtypes"
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 description = ""
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
 numpy = [
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-    {version = ">1.20", markers = "python_version < \"3.10\""},
+    {version = ">=1.21", markers = "python_version < \"3.10\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">=1.23.3", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
 ]
@@ -2924,17 +2755,6 @@ files = [
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 ]
 
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-description = "A simple utility to separate the implementation of your Python package and its public API surface."
-optional = false
-python-versions = "*"
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-
 [[package]]
 name = "nbclient"
 version = "0.10.0"
@@ -3066,37 +2886,37 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "numba"
-version = "0.59.1"
+version = "0.60.0"
 description = "compiling Python code using LLVM"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -3993,13 +3704,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyjwt"
-version = "2.9.0"
+version = "2.10.0"
 description = "JSON Web Token implementation in Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
@@ -4557,17 +4268,14 @@ develop = false
 [package.dependencies]
 jax = "^0.4.25"
 jaxlib = "^0.4.25"
-numba = "^0.59.0"
 numpy = "^1.26.4"
-qibo = {git = "https://github.com/qiboteam/qibo.git"}
-tensorflow = {version = "^2.16.1", markers = "sys_platform == \"linux\" or sys_platform == \"darwin\""}
-torch = "^2.2.0"
+qibo = {git = "https://github.com/qiboteam/qibo"}
 
 [package.source]
 type = "git"
 url = "https://github.com/qiboteam/qiboml.git"
-reference = "pytorch_migration"
-resolved_reference = "6ee13bb0171a9916dcc84b2efc25542edb0b4768"
+reference = "HEAD"
+resolved_reference = "800e800d54f4962f4890716c4b1539014c69c1b5"
 
 [[package]]
 name = "qibotn"
@@ -4739,25 +4447,6 @@ idna = {version = "*", optional = true, markers = "extra == \"idna2008\""}
 [package.extras]
 idna2008 = ["idna"]
 
-[[package]]
-name = "rich"
-version = "13.9.3"
-description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
-optional = false
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-
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-
-[package.extras]
-jupyter = ["ipywidgets (>=7.5.1,<9)"]
-
 [[package]]
 name = "rpcq"
 version = "3.11.0"
@@ -4959,26 +4648,6 @@ dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest
 docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
 stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
-[[package]]
-name = "setuptools"
-version = "75.3.0"
-description = "Easily download, build, install, upgrade, and uninstall Python packages"
-optional = false
-python-versions = ">=3.8"
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-[package.extras]
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-type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.12.*)", "pytest-mypy"]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -5446,138 +5115,6 @@ files = [
 doc = ["reno", "sphinx"]
 test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
-[[package]]
-name = "tensorboard"
-version = "2.18.0"
-description = "TensorBoard lets you watch Tensors Flow"
-optional = false
-python-versions = ">=3.9"
-files = [
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-numpy = ">=1.12.0"
-packaging = "*"
-protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
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-six = ">1.9"
-tensorboard-data-server = ">=0.7.0,<0.8.0"
-werkzeug = ">=1.0.1"
-
-[[package]]
-name = "tensorboard-data-server"
-version = "0.7.2"
-description = "Fast data loading for TensorBoard"
-optional = false
-python-versions = ">=3.7"
-files = [
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-termcolor = ">=1.1.0"
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-
-[package.extras]
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-
-[[package]]
-name = "tensorflow-io-gcs-filesystem"
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-description = "TensorFlow IO"
-optional = false
-python-versions = "<3.13,>=3.7"
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-]
-
-[package.extras]
-tensorflow = ["tensorflow (>=2.16.0,<2.17.0)"]
-tensorflow-aarch64 = ["tensorflow-aarch64 (>=2.16.0,<2.17.0)"]
-tensorflow-cpu = ["tensorflow-cpu (>=2.16.0,<2.17.0)"]
-tensorflow-gpu = ["tensorflow-gpu (>=2.16.0,<2.17.0)"]
-tensorflow-rocm = ["tensorflow-rocm (>=2.16.0,<2.17.0)"]
-
-[[package]]
-name = "termcolor"
-version = "2.5.0"
-description = "ANSI color formatting for output in terminal"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "termcolor-2.5.0-py3-none-any.whl", hash = "sha256:37b17b5fc1e604945c2642c872a3764b5d547a48009871aea3edd3afa180afb8"},
-    {file = "termcolor-2.5.0.tar.gz", hash = "sha256:998d8d27da6d48442e8e1f016119076b690d962507531df4890fcd2db2ef8a6f"},
-]
-
-[package.extras]
-tests = ["pytest", "pytest-cov"]
-
 [[package]]
 name = "threadpoolctl"
 version = "3.5.0"
@@ -5620,13 +5157,13 @@ files = [
 
 [[package]]
 name = "tomli"
-version = "2.0.2"
+version = "2.1.0"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
-    {file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
+    {file = "tomli-2.1.0-py3-none-any.whl", hash = "sha256:a5c57c3d1c56f5ccdf89f6523458f60ef716e210fc47c4cfb188c5ba473e0391"},
+    {file = "tomli-2.1.0.tar.gz", hash = "sha256:3f646cae2aec94e17d04973e4249548320197cfabdf130015d023de4b74d8ab8"},
 ]
 
 [[package]]
@@ -5727,13 +5264,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.6"
+version = "4.67.0"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63"},
-    {file = "tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090"},
+    {file = "tqdm-4.67.0-py3-none-any.whl", hash = "sha256:0cd8af9d56911acab92182e88d763100d4788bdf421d251616040cc4d44863be"},
+    {file = "tqdm-4.67.0.tar.gz", hash = "sha256:fe5a6f95e6fe0b9755e9469b77b9c3cf850048224ecaa8293d7d2d31f97d869a"},
 ]
 
 [package.dependencies]
@@ -5741,6 +5278,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
 dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
@@ -5785,13 +5323,13 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
 [[package]]
 name = "types-deprecated"
-version = "1.2.9.20240311"
+version = "1.2.15.20241117"
 description = "Typing stubs for Deprecated"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-Deprecated-1.2.9.20240311.tar.gz", hash = "sha256:0680e89989a8142707de8103f15d182445a533c1047fd9b7e8c5459101e9b90a"},
-    {file = "types_Deprecated-1.2.9.20240311-py3-none-any.whl", hash = "sha256:d7793aaf32ff8f7e49a8ac781de4872248e0694c4b75a7a8a186c51167463f9d"},
+    {file = "types-Deprecated-1.2.15.20241117.tar.gz", hash = "sha256:924002c8b7fddec51ba4949788a702411a2e3636cd9b2a33abd8ee119701d77e"},
+    {file = "types_Deprecated-1.2.15.20241117-py3-none-any.whl", hash = "sha256:a0cc5e39f769fc54089fd8e005416b55d74aa03f6964d2ed1a0b0b2e28751884"},
 ]
 
 [[package]]
@@ -5877,37 +5415,6 @@ files = [
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
 
-[[package]]
-name = "werkzeug"
-version = "3.0.6"
-description = "The comprehensive WSGI web application library."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "werkzeug-3.0.6-py3-none-any.whl", hash = "sha256:1bc0c2310d2fbb07b1dd1105eba2f7af72f322e1e455f2f93c993bee8c8a5f17"},
-    {file = "werkzeug-3.0.6.tar.gz", hash = "sha256:a8dd59d4de28ca70471a34cba79bed5f7ef2e036a76b3ab0835474246eb41f8d"},
-]
-
-[package.dependencies]
-MarkupSafe = ">=2.1.1"
-
-[package.extras]
-watchdog = ["watchdog (>=2.3)"]
-
-[[package]]
-name = "wheel"
-version = "0.44.0"
-description = "A built-package format for Python"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "wheel-0.44.0-py3-none-any.whl", hash = "sha256:2376a90c98cc337d18623527a97c31797bd02bad0033d41547043a1cbfbe448f"},
-    {file = "wheel-0.44.0.tar.gz", hash = "sha256:a29c3f2817e95ab89aa4660681ad547c0e9547f20e75b0562fe7723c9a2a9d49"},
-]
-
-[package.extras]
-test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
-
 [[package]]
 name = "widgetsnbextension"
 version = "4.0.13"
@@ -6017,13 +5524,13 @@ files = [
 
 [[package]]
 name = "zipp"
-version = "3.20.2"
+version = "3.21.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
-    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
 ]
 
 [package.extras]
@@ -6042,4 +5549,4 @@ torch = ["torch"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "a701dab6c07e2b858235407c6c3df0219c5e401bb264d25ee0f8aece308488a9"
+content-hash = "731880ba3f30c5c16b15692d782ced337173dac75fc8990bea1cd7baaef7f01b"
diff --git a/pyproject.toml b/pyproject.toml
index 99c9105103..113a087f95 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -72,7 +72,7 @@ matplotlib = "^3.7.0"
 torch = "^2.1.1,<2.4"
 qibojit = { git = "https://github.com/qiboteam/qibojit.git" }
 qibotn = { git = "https://github.com/qiboteam/qibotn.git" }
-qiboml = { git  = "https://github.com/qiboteam/qiboml.git" , branch = "pytorch_migration"}
+qiboml = { git  = "https://github.com/qiboteam/qiboml.git" }
 stim = "^1.12.0"
 qulacs = { version = "^0.6.4", markers = "(sys_platform == 'darwin' and python_version > '3.9') or sys_platform != 'darwin'" }
 

From eed754f42044c6e928edc046e0ab7a65de4695cd Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Wed, 20 Nov 2024 09:13:23 +0100
Subject: [PATCH 11/13] chore: updating lock

---
 poetry.lock | 38 +++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 9f11ee4748..1ce3aa0f83 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -922,24 +922,24 @@ numpy = ">=1.21,<2.0"
 
 [[package]]
 name = "custatevec-cu11"
-version = "1.6.0.post1"
+version = "1.7.0"
 description = "cuStateVec - a component of NVIDIA cuQuantum SDK"
 optional = false
 python-versions = "*"
 files = [
-    {file = "custatevec_cu11-1.6.0.post1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:6840bd957455d33f9df1f2e266fe33079e429b2442f5ff6ef5eae6c1b618108e"},
-    {file = "custatevec_cu11-1.6.0.post1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:8d57ada4d3cc77b24b3be83b55929942186698e71edadaa952448c1b0af43539"},
+    {file = "custatevec_cu11-1.7.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:dabdd80bab8e19413904c8377268dcfbc0468b3a8438a04cca422502c70b34d0"},
+    {file = "custatevec_cu11-1.7.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:11b3a2a79de52afaab1acc970e3d810d219e90170405aaded78ac6dcfa1bb389"},
 ]
 
 [[package]]
 name = "custatevec-cu12"
-version = "1.6.0.post1"
+version = "1.7.0"
 description = "cuStateVec - a component of NVIDIA cuQuantum SDK"
 optional = false
 python-versions = "*"
 files = [
-    {file = "custatevec_cu12-1.6.0.post1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4f38175cb6cb9dfa0008e5109e22bf92eeedd3aad843be3ce27ad41b53318f95"},
-    {file = "custatevec_cu12-1.6.0.post1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:0c875981de852091f5f3c040d705b284424171aa5df07f843728ff2fae450016"},
+    {file = "custatevec_cu12-1.7.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0e9c5d810285ddcdf97c980725580a0f549fccec4475a76d28779c112c79c2aa"},
+    {file = "custatevec_cu12-1.7.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:df81b4eeeadc841b4c3ac4816e6b9e3dde6b288f13a2c0005fecbd0501f9ea6d"},
 ]
 
 [[package]]
@@ -968,31 +968,31 @@ files = [
 
 [[package]]
 name = "cutensornet-cu11"
-version = "2.5.0"
+version = "2.6.0"
 description = "cuTensorNet - a component of NVIDIA cuQuantum SDK"
 optional = false
 python-versions = "*"
 files = [
-    {file = "cutensornet_cu11-2.5.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:144f0f804a06e86efba15900fc79c234a5de09466c2245be82a795cc945c41e2"},
-    {file = "cutensornet_cu11-2.5.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:be33d47531a965233c73b9ad0cbec5ce399271372d62d77393e167cb9da2894d"},
+    {file = "cutensornet_cu11-2.6.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:ded8b2b3124fd2acd6ce9c13060b0ea17692bcbca727314b2930c6ab341035ef"},
+    {file = "cutensornet_cu11-2.6.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f7be9781de96cd897b59ac81cfb5c407a79a9781d236a25358656e24c9e81b71"},
 ]
 
 [package.dependencies]
-cutensor-cu11 = ">=2.0.1,<3"
+cutensor-cu11 = ">=2.0.2,<3"
 
 [[package]]
 name = "cutensornet-cu12"
-version = "2.5.0"
+version = "2.6.0"
 description = "cuTensorNet - a component of NVIDIA cuQuantum SDK"
 optional = false
 python-versions = "*"
 files = [
-    {file = "cutensornet_cu12-2.5.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:3d0c55b39b5fb0696c7b219f35a8af91c14fc557f836153d2801c4e31bb3fe70"},
-    {file = "cutensornet_cu12-2.5.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:c2937ff3e2fd2fd984e5bb0a098919981c145ce59ada18857e9f7f4923a3e6c8"},
+    {file = "cutensornet_cu12-2.6.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:39b6f50b8bd47e5a4fcb745472c1005fc9d3889b907c92a91ea76e30a91e11cd"},
+    {file = "cutensornet_cu12-2.6.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:64b582679c4e16faecd9143103272376e847e4b7ff02c97cde72f39ea6243f5e"},
 ]
 
 [package.dependencies]
-cutensor-cu12 = ">=2.0.1,<3"
+cutensor-cu12 = ">=2.0.2,<3"
 
 [[package]]
 name = "cycler"
@@ -4275,7 +4275,7 @@ qibo = {git = "https://github.com/qiboteam/qibo"}
 type = "git"
 url = "https://github.com/qiboteam/qiboml.git"
 reference = "HEAD"
-resolved_reference = "800e800d54f4962f4890716c4b1539014c69c1b5"
+resolved_reference = "3e52004c497ca738730e21aa5a2ca8c2f6044398"
 
 [[package]]
 name = "qibotn"
@@ -4301,13 +4301,13 @@ resolved_reference = "4f547726b616f73d074abf77811644cf2cb54383"
 
 [[package]]
 name = "quimb"
-version = "1.8.4"
+version = "1.9.0"
 description = "Quantum information and many-body library."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "quimb-1.8.4-py3-none-any.whl", hash = "sha256:357c42c5c1a696ba8234e1d7e1361aabc182ac76ff046a85fabb37f63d989d2d"},
-    {file = "quimb-1.8.4.tar.gz", hash = "sha256:51b7b6f09a451f44468d7b41ac990635b2e718b437aee8db992f892c3e5d30b5"},
+    {file = "quimb-1.9.0-py3-none-any.whl", hash = "sha256:edb2f621948410e8ba6fa30b023888a4b6fe46682ebf0cdb7acafe2d87b964d9"},
+    {file = "quimb-1.9.0.tar.gz", hash = "sha256:f5a769e0403094f4fbccd9833648e48e7fac209a43b329b0586e7c4332a1b963"},
 ]
 
 [package.dependencies]

From b9e340b84a0d23a45180444ce9eeb9d0549313c4 Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Wed, 20 Nov 2024 09:34:34 +0100
Subject: [PATCH 12/13] fix: set tensorflow to False in
 test_list_available_backends since removed

---
 tests/test_backends.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tests/test_backends.py b/tests/test_backends.py
index 03069e390f..383fdb7c17 100644
--- a/tests/test_backends.py
+++ b/tests/test_backends.py
@@ -113,7 +113,6 @@ def test_construct_backend(backend):
 
 
 def test_list_available_backends():
-    tensorflow = False if platform.system() == "Windows" else True
     qulacs = (
         False if platform.system() == "Darwin" and sys.version_info[1] == 9 else True
     )
@@ -124,7 +123,7 @@ def test_list_available_backends():
         "qibolab": False,
         "qibo-cloud-backends": False,
         "qibotn": {"cutensornet": False, "qutensornet": True},
-        "qiboml": {"tensorflow": tensorflow, "pytorch": True},
+        "qiboml": {"tensorflow": False, "pytorch": True},
     }
     assert available_backends == list_available_backends(
         "qibojit", "qibolab", "qibo-cloud-backends", "qibotn", "qiboml"

From 832d43cec03fdf2ed5ada09dd5d0e9df56e9be8d Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Wed, 20 Nov 2024 11:02:45 +0100
Subject: [PATCH 13/13] feat: rm torch and tf gradients test from qibo

---
 tests/test_backends_torch_gradients.py | 86 --------------------------
 1 file changed, 86 deletions(-)
 delete mode 100644 tests/test_backends_torch_gradients.py

diff --git a/tests/test_backends_torch_gradients.py b/tests/test_backends_torch_gradients.py
deleted file mode 100644
index abb6339ed5..0000000000
--- a/tests/test_backends_torch_gradients.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import sys
-
-import numpy as np
-import pytest
-from qiboml.backends import PyTorchBackend
-
-from qibo import Circuit, gates
-from qibo.backends import construct_backend
-from qibo.quantum_info.metrics import infidelity
-
-
-def test_torch_gradients():
-    backend = PyTorchBackend()
-    backend.np.manual_seed(42)
-    nepochs = 400
-    optimizer = backend.np.optim.Adam
-    target_state = backend.np.rand(4, dtype=backend.np.complex128)
-    target_state = target_state / backend.np.norm(target_state)
-    params = backend.np.rand(4, dtype=backend.np.float64, requires_grad=True)
-    circuit = Circuit(2)
-    circuit.add(gates.RX(0, params[0]))
-    circuit.add(gates.RY(1, params[1]))
-    circuit.add(gates.U2(1, params[2], params[3]))
-
-    initial_params = params.clone()
-    initial_loss = infidelity(
-        target_state, backend.execute_circuit(circuit).state(), backend=backend
-    )
-
-    optimizer = optimizer([params], lr=0.01)
-    for _ in range(nepochs):
-        optimizer.zero_grad()
-        circuit.set_parameters(params)
-        final_state = backend.execute_circuit(circuit).state()
-        loss = infidelity(target_state, final_state, backend=backend)
-        loss.backward()
-        grad = params.grad.clone().norm()
-        optimizer.step()
-
-    assert initial_loss > loss
-    assert initial_params[0] != params[0]
-    assert grad.item() < 10e-3
-
-
-@pytest.mark.skipif(
-    sys.platform != "linux", reason="Tensorflow available only when testing on linux."
-)
-def test_torch_tensorflow_gradients():
-
-    backend = PyTorchBackend()
-    tf_backend = construct_backend(backend="qiboml", platform="tensorflow")
-
-    target_state = backend.np.tensor([0.0, 1.0], dtype=backend.np.complex128)
-    param = backend.np.tensor([0.1], dtype=backend.np.float64, requires_grad=True)
-    circuit = Circuit(1)
-    circuit.add(gates.RX(0, param[0]))
-
-    optimizer = backend.np.optim.SGD
-    optimizer = optimizer([param], lr=1)
-    circuit.set_parameters(param)
-    final_state = backend.execute_circuit(circuit).state()
-    loss = infidelity(target_state, final_state, backend=backend)
-    loss.backward()
-    torch_param_grad = param.grad.clone().item()
-    optimizer.step()
-    torch_param = param.clone().item()
-
-    target_state = tf_backend.tf.constant([0.0, 1.0], dtype=tf_backend.tf.complex128)
-    param = tf_backend.tf.Variable([0.1], dtype=tf_backend.tf.float64)
-    circuit = Circuit(1)
-    circuit.add(gates.RX(0, param[0]))
-
-    optimizer = tf_backend.tf.optimizers.SGD(learning_rate=1.0)
-
-    with tf_backend.tf.GradientTape() as tape:
-        circuit.set_parameters(param)
-        final_state = tf_backend.execute_circuit(circuit).state()
-        loss = infidelity(target_state, final_state, backend=tf_backend)
-
-    grads = tape.gradient(loss, [param])
-    tf_param_grad = grads[0].numpy()[0]
-    optimizer.apply_gradients(zip(grads, [param]))
-    tf_param = param.numpy()[0]
-
-    assert np.allclose(torch_param_grad, tf_param_grad, atol=1e-7, rtol=1e-7)
-    assert np.allclose(torch_param, tf_param, atol=1e-7, rtol=1e-7)