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Código fuente para qiskit_machine_learning.algorithms.regressors.vqr

# This code is part of Qiskit.
#
# (C) Copyright IBM 2021, 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""An implementation of quantum neural network regressor."""
from __future__ import annotations

from typing import Callable

import numpy as np
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import Optimizer
from qiskit.opflow import OperatorBase, PauliSumOp
from qiskit.primitives import BaseEstimator
from qiskit.quantum_info.operators.base_operator import BaseOperator
from qiskit.utils import QuantumInstance

from .neural_network_regressor import NeuralNetworkRegressor
from ...deprecation import warn_deprecated, DeprecatedType
from ...neural_networks import TwoLayerQNN, EstimatorQNN
from ...utils import derive_num_qubits_feature_map_ansatz
from ...utils.loss_functions import Loss


[documentos]class VQR(NeuralNetworkRegressor): """A convenient Variational Quantum Regressor implementation.""" def __init__( self, num_qubits: int | None = None, feature_map: QuantumCircuit | None = None, ansatz: QuantumCircuit | None = None, observable: QuantumCircuit | OperatorBase | BaseOperator | PauliSumOp | None = None, loss: str | Loss = "squared_error", optimizer: Optimizer | None = None, warm_start: bool = False, quantum_instance: QuantumInstance | None = None, initial_point: np.ndarray | None = None, callback: Callable[[np.ndarray, float], None] | None = None, *, estimator: BaseEstimator | None = None, ) -> None: r""" Args: num_qubits: The number of qubits for the underlying QNN. If ``None`` then the number of qubits is derived from the feature map or ansatz, but if neither of these are given an error is raised. The number of qubits in the feature map and ansatz are adjusted to this number if required. feature_map: The (parametrized) circuit to be used as a feature map for the underlying QNN. If ``None`` the :class:`~qiskit.circuit.library.ZZFeatureMap` is used if the number of qubits is larger than 1. For a single qubit regression problem the :class:`~qiskit.circuit.library.ZFeatureMap` is used by default. ansatz: The (parametrized) circuit to be used as an ansatz for the underlying QNN. If ``None`` then the :class:`~qiskit.circuit.library.RealAmplitudes` circuit is used. observable: The observable to be measured in the underlying QNN. If ``None``, use the default :math:`Z^{\otimes num\_qubits}` observable. If ``quantum_instance`` is set and the ``estimator`` is ``None`` then the observable must be of type :class:`~qiskit.QuantumCircuit` or :class:`~qiskit.opflow.OperatorBase`. Otherwise, the type must be either :class:`~qiskit.quantum_info.operators.base_operator.BaseOperator` or :class:`~qiskit.opflow.PauliSumOp`. loss: A target loss function to be used in training. Default is squared error. optimizer: An instance of an optimizer to be used in training. When ``None`` defaults to SLSQP. warm_start: Use weights from previous fit to start next fit. quantum_instance: Deprecated: If a quantum instance is set and ``estimator`` is ``None``, the underlying QNN will be of type :class:`~qiskit_machine_learning.neural_networks.TwoLayerQNN`, and the quantum instance will be used to compute the neural network's results. If an estimator instance is also set, it will override the `quantum_instance` parameter and a :class:`~qiskit_machine_learning.neural_networks.EstimatorQNN` will be used instead. initial_point: Initial point for the optimizer to start from. callback: A reference to a user's callback function that has two parameters and returns ``None``. The callback can access intermediate data during training. On each iteration an optimizer invokes the callback and passes current weights as an array and a computed value as a float of the objective function being optimized. This allows to track how well optimization / training process is going on. estimator: An estimator to be used to evaluate expectation values of the observable. If ``None`` the :class:`qiskit.primitives.BaseEstimator` is used. The underlying QNN is :class:`~qiskit_machine_learning.neural_networks.EstimatorQNN`. If an estimator instance is set, the underlying QNN will be of type :class:`~qiskit_machine_learning.neural_networks.EstimatorQNN`, and the estimator primitive will be used to compute the neural network's results. Raises: QiskitMachineLearningError: Needs at least one out of ``num_qubits``, ``feature_map`` or ``ansatz`` to be given. Or the number of qubits in the feature map and/or ansatz can't be adjusted to ``num_qubits``. ValueError: if the type of the observable is not compatible with ``quantum_instance`` or ``estimator``. """ # needed for mypy if quantum_instance is not None and estimator is None: warn_deprecated( "0.5.0", DeprecatedType.ARGUMENT, old_name="quantum_instance", new_name="estimator" ) if observable is not None and not isinstance( observable, (QuantumCircuit, OperatorBase) ): raise ValueError( f"Unsupported type of the observable, expected " f"'QuantumCircuit | OperatorBase', got {type(observable)}" ) self._quantum_instance = quantum_instance self._estimator = None # construct QNN neural_network = TwoLayerQNN( num_qubits=num_qubits, feature_map=feature_map, ansatz=ansatz, observable=observable, quantum_instance=quantum_instance, input_gradients=False, ) self._feature_map = neural_network.feature_map self._ansatz = neural_network.ansatz self._num_qubits = neural_network.num_qubits else: if observable is not None and not isinstance(observable, (BaseOperator, PauliSumOp)): raise ValueError( f"Unsupported type of the observable, expected " f"'BaseOperator | PauliSumOp', got {type(observable)}" ) # construct estimator QNN by default self._quantum_instance = None self._estimator = estimator num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz( num_qubits, feature_map, ansatz ) # construct circuit self._feature_map = feature_map self._ansatz = ansatz self._num_qubits = num_qubits circuit = QuantumCircuit(self._num_qubits) circuit.compose(self._feature_map, inplace=True) circuit.compose(self._ansatz, inplace=True) observables = [observable] if observable is not None else None neural_network = EstimatorQNN( estimator=estimator, circuit=circuit, observables=observables, input_params=feature_map.parameters, weight_params=ansatz.parameters, ) super().__init__( neural_network=neural_network, loss=loss, optimizer=optimizer, warm_start=warm_start, initial_point=initial_point, callback=callback, ) @property def feature_map(self) -> QuantumCircuit: """Returns the used feature map.""" return self._feature_map @property def ansatz(self) -> QuantumCircuit: """Returns the used ansatz.""" return self._ansatz @property def num_qubits(self) -> int: """Returns the number of qubits used by ansatz and feature map.""" return self._num_qubits

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