Source code for pennylane.devices.default_qubit_torch

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at


# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
"""This module contains a PyTorch implementation of the :class:`~.DefaultQubitLegacy`
reference plugin.
import warnings
import inspect
import logging
import semantic_version

    import torch

    VERSION_SUPPORT = semantic_version.match(">=1.8.1", torch.__version__)
    if not VERSION_SUPPORT:  # pragma: no cover
        raise ImportError("default.qubit.torch device requires Torch>=1.8.1")

except ImportError as e:  # pragma: no cover
    raise ImportError("default.qubit.torch device requires Torch>=1.8.1") from e

import numpy as np
from pennylane.ops.qubit.attributes import diagonal_in_z_basis
from . import DefaultQubitLegacy

logger = logging.getLogger(__name__)

[docs]class DefaultQubitTorch(DefaultQubitLegacy): """Simulator plugin based on ``"default.qubit.legacy"``, written using PyTorch. **Short name:** ``default.qubit.torch`` This device provides a pure-state qubit simulator written using PyTorch. As a result, it supports classical backpropagation as a means to compute the Jacobian. This can be faster than the parameter-shift rule for analytic quantum gradients when the number of parameters to be optimized is large. To use this device, you will need to install PyTorch: .. code-block:: console pip install torch>=1.8.0 **Example** The ``default.qubit.torch`` is designed to be used with end-to-end classical backpropagation (``diff_method="backprop"``) and the PyTorch interface. This is the default method of differentiation when creating a QNode with this device. Using this method, the created QNode is a 'white-box', and is tightly integrated with your PyTorch computation: .. code-block:: python dev = qml.device("default.qubit.torch", wires=1) @qml.qnode(dev, interface="torch", diff_method="backprop") def circuit(x): qml.RX(x[1], wires=0) qml.Rot(x[0], x[1], x[2], wires=0) return qml.expval(qml.PauliZ(0)) >>> weights = torch.tensor([0.2, 0.5, 0.1], requires_grad=True) >>> res = circuit(weights) >>> res.backward() >>> print(weights.grad) tensor([-2.2527e-01, -1.0086e+00, 1.3878e-17]) Autograd mode will also work when using classical backpropagation: >>> def cost(weights): ... return torch.sum(circuit(weights)**3) - 1 >>> res = circuit(weights) >>> res.backward() >>> print(weights.grad) tensor([-4.5053e-01, -2.0173e+00, 5.9837e-17]) Executing the pipeline in PyTorch will allow the whole computation to be run on the GPU, and therefore providing an acceleration. Your parameters need to be instantiated on the same device as the backend device. .. code-block:: python dev = qml.device("default.qubit.torch", wires=1, torch_device='cuda') @qml.qnode(dev, interface="torch", diff_method="backprop") def circuit(x): qml.RX(x[1], wires=0) qml.Rot(x[0], x[1], x[2], wires=0) return qml.expval(qml.PauliZ(0)) >>> weights = torch.tensor([0.2, 0.5, 0.1], requires_grad=True, device='cuda') >>> res = circuit(weights) >>> res.backward() >>> print(weights.grad) tensor([-2.2527e-01, -1.0086e+00, 2.9919e-17], device='cuda:0') There are a couple of things to keep in mind when using the ``"backprop"`` differentiation method for QNodes: * You must use the ``"torch"`` interface for classical backpropagation, as PyTorch is used as the device backend. * Only exact expectation values, variances, and probabilities are differentiable. When instantiating the device with ``shots!=None``, differentiating QNode outputs will result in an error. If you wish to use a different machine-learning interface, or prefer to calculate quantum gradients using the ``parameter-shift`` or ``finite-diff`` differentiation methods, consider using the ``default.qubit`` device instead. Args: wires (int, Iterable): Number of subsystems represented by the device, or iterable that contains unique labels for the subsystems. Default 1 if not specified. shots (None, int): How many times the circuit should be evaluated (or sampled) to estimate the expectation values. Defaults to ``None`` if not specified, which means that the device returns analytical results. If ``shots > 0`` is used, the ``diff_method="backprop"`` QNode differentiation method is not supported and it is recommended to consider switching device to ``default.qubit`` and using ``diff_method="parameter-shift"``. torch_device='cpu' (str): the device on which the computation will be run, e.g., ``'cpu'`` or ``'cuda'`` """ name = "Default qubit (Torch) PennyLane plugin" short_name = "default.qubit.torch" _abs = staticmethod(torch.abs) _einsum = staticmethod(torch.einsum) _flatten = staticmethod(torch.flatten) _reshape = staticmethod(torch.reshape) _roll = staticmethod(torch.roll) _stack = staticmethod(lambda arrs, axis=0, out=None: torch.stack(arrs, axis=axis, out=out)) _tensordot = staticmethod( lambda a, b, axes: torch.tensordot( a, b, axes if isinstance(axes, int) else tuple(map(list, axes)) ) ) _transpose = staticmethod(lambda a, axes=None: a.permute(*axes)) _asnumpy = staticmethod(lambda x: x.cpu().numpy()) _real = staticmethod(torch.real) _imag = staticmethod(torch.imag) _norm = staticmethod(torch.norm) _flatten = staticmethod(torch.flatten) _const_mul = staticmethod(torch.mul) _size = staticmethod(torch.numel) _ndim = staticmethod(lambda tensor: tensor.ndim) def __init__(self, wires, *, shots=None, analytic=None, torch_device=None): # Store if the user specified a Torch device. Otherwise the execute # method attempts to infer the Torch device from the gate parameters. self._torch_device_specified = torch_device is not None self._torch_device = torch_device r_dtype = torch.float64 c_dtype = torch.complex128 super().__init__(wires, r_dtype=r_dtype, c_dtype=c_dtype, shots=shots, analytic=analytic) # Move state to torch device (e.g. CPU, GPU, XLA, ...) self._state.requires_grad = True self._state = self._pre_rotated_state = self._state @staticmethod def _get_parameter_torch_device(ops): """An auxiliary function to determine the Torch device specified for the gate parameters of the input operations. Returns the first CUDA Torch device found (if any) using a string format. Does not handle tensors put on multiple CUDA Torch devices. Such a case raises an error with Torch. If CUDA is not used with any of the parameters, then specifies the CPU if the parameters are on the CPU or None if there were no parametric operations. Args: ops (list[Operator]): list of operations to check Returns: str or None: The string of the Torch device determined or None if there is no data for any operations. """ par_torch_device = None for op in ops: for data in # Using hasattr in case we don't have a Torch tensor as input if hasattr(data, "is_cuda"): if data.is_cuda: # pragma: no cover return ":".join([data.device.type, str(data.device.index)]) par_torch_device = "cpu" return par_torch_device
[docs] def execute(self, circuit, **kwargs): if logger.isEnabledFor(logging.DEBUG): logger.debug( "Entry with args=(circuit=%s, kwargs=%s) called by=%s", circuit, kwargs, "::L".join( str(i) for i in inspect.getouterframes(inspect.currentframe(), 2)[1][1:3] ), ) ops_and_obs = circuit.operations + circuit.observables par_torch_device = self._get_parameter_torch_device(ops_and_obs) if not self._torch_device_specified: self._torch_device = par_torch_device # If we've changed the device of the parameters between device # executions, need to move the state to the correct Torch device if self._state.device != self._torch_device: self._state = else: if par_torch_device is not None: # pragma: no cover params_cuda_device = "cuda" in par_torch_device specified_device_cuda = "cuda" in self._torch_device # Raise a warning if there's a mismatch between the specified and # used Torch devices if params_cuda_device != specified_device_cuda: warnings.warn( f"Torch device {self._torch_device} specified " "upon PennyLane device creation does not match the " "Torch device of the gate parameters; " f"{self._torch_device} will be used." ) return super().execute(circuit, **kwargs)
def _asarray(self, a, dtype=None): if isinstance(a, list): # Handle unexpected cases where we don't have a list of tensors if not isinstance(a[0], torch.Tensor): res = np.asarray(a) res = torch.from_numpy(res) res =[torch.reshape(i, (-1,)) for i in res], dim=0) elif len(a) == 1 and len(a[0].shape) > 1: res = a[0] else: res =[torch.reshape(i, (-1,)) for i in a], dim=0) res =[torch.reshape(i, (-1,)) for i in res], dim=0) else: res = torch.as_tensor(a, dtype=dtype) res = torch.as_tensor(res, device=self._torch_device) return res _cast = _asarray @staticmethod def _dot(x, y): if x.device != y.device: # GPU-specific cases if x.device != "cpu": # pragma: no cover return torch.tensordot(x,, dims=1) if y.device != "cpu": # pragma: no cover return torch.tensordot(, y, dims=1) return torch.tensordot(x, y, dims=1) @staticmethod def _reduce_sum(array, axes): if not axes: return array return torch.sum(array, dim=axes) @staticmethod def _conj(array): if isinstance(array, torch.Tensor): return torch.conj(array) return np.conj(array) @staticmethod def _scatter(indices, array, new_dimensions): # `array` is now a torch tensor tensor = array new_tensor = torch.zeros(new_dimensions, dtype=tensor.dtype, device=tensor.device) new_tensor[indices] = tensor return new_tensor
[docs] @classmethod def capabilities(cls): capabilities = super().capabilities().copy() capabilities.update(passthru_interface="torch") return capabilities
def _get_unitary_matrix(self, unitary): """Return the matrix representing a unitary operation. Args: unitary (~.Operation): a PennyLane unitary operation Returns: torch.Tensor[complex]: Returns a 2D matrix representation of the unitary in the computational basis, or, in the case of a diagonal unitary, a 1D array representing the matrix diagonal. """ if unitary in diagonal_in_z_basis: return self._asarray(unitary.eigvals(), dtype=self.C_DTYPE) return self._asarray(unitary.matrix(), dtype=self.C_DTYPE)
[docs] def sample_basis_states(self, number_of_states, state_probability): """Sample from the computational basis states based on the state probability. This is an auxiliary method to the ``generate_samples`` method. Args: number_of_states (int): the number of basis states to sample from state_probability (torch.Tensor[float]): the computational basis probability vector Returns: List[int]: the sampled basis states """ return super().sample_basis_states( number_of_states, state_probability.cpu().detach().numpy() )