Исходный код qiskit_machine_learning.algorithms.classifiers.vqc

# 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
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"""An implementation of variational quantum classifier."""

from __future__ import annotations
from typing import Callable

import numpy as np

from qiskit import QuantumCircuit
from qiskit.providers import Backend
from qiskit.utils import QuantumInstance
from qiskit.algorithms.optimizers import Optimizer, OptimizerResult
from qiskit.primitives import BaseSampler

from ...deprecation import warn_deprecated, DeprecatedType
from ...neural_networks import CircuitQNN, SamplerQNN
from ...utils import derive_num_qubits_feature_map_ansatz
from ...utils.loss_functions import Loss

from .neural_network_classifier import NeuralNetworkClassifier

[документация]class VQC(NeuralNetworkClassifier): r"""A convenient Variational Quantum Classifier implementation. The variational quantum classifier (VQC) is a variational algorithm where the measured bitstrings are interpreted as the output of a classifier. Constructs a quantum circuit and corresponding neural network, then uses it to instantiate a neural network classifier. Labels can be passed in various formats, they can be plain labels, a one dimensional numpy array that contains integer labels like `[0, 1, 2, ...]`, or a numpy array with categorical string labels. One hot encoded labels are also supported. Internally, labels are transformed to one hot encoding and the classifier is always trained on one hot labels. Multi-label classification is not supported. E.g., :math:`[[1, 1, 0], [0, 1, 1], [1, 0, 1]]`. """ def __init__( self, num_qubits: int | None = None, feature_map: QuantumCircuit | None = None, ansatz: QuantumCircuit | None = None, loss: str | Loss = "cross_entropy", optimizer: Optimizer | None = None, warm_start: bool = False, quantum_instance: QuantumInstance | Backend | None = None, initial_point: np.ndarray | None = None, callback: Callable[[np.ndarray, float], None] | None = None, *, sampler: BaseSampler | None = None, ) -> None: """ Args: num_qubits: The number of qubits for the underlying QNN. If ``None`` is given, the number of qubits is derived from the feature map or ansatz. If neither of those is given, raises an exception. 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`` is given, the ``ZZFeatureMap`` is used if the number of qubits is larger than 1. For a single qubit classification problem the ``ZFeatureMap`` is used by default. ansatz: The (parametrized) circuit to be used as an ansatz for the underlying QNN. If ``None`` is given then the ``RealAmplitudes`` circuit is used. loss: A target loss function to be used in training. Default value is ``cross_entropy``. 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 sent and ``sampler`` is ``None``, the underlying QNN will be of type :class:`~qiskit_machine_learning.neural_networks.CircuitQNN`, and the quantum instance will be used to compute the neural network's results. If a sampler instance is also set, it will override the `quantum_instance` parameter and a :class:`~qiskit_machine_learning.neural_networks.SamplerQNN` 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. sampler: If a sampler instance is sent, the underlying QNN will be of type :class:`~qiskit_machine_learning.neural_networks.SamplerQNN`, and the sampler 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``. """ 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 self._circuit = QuantumCircuit(self._num_qubits) self._circuit.compose(self.feature_map, inplace=True) self._circuit.compose(self.ansatz, inplace=True) # needed for mypy neural_network: SamplerQNN | CircuitQNN = None if quantum_instance is not None and sampler is None: warn_deprecated( "0.5.0", DeprecatedType.ARGUMENT, old_name="quantum_instance", new_name="sampler" ) neural_network = CircuitQNN( self._circuit, input_params=self.feature_map.parameters, weight_params=self.ansatz.parameters, interpret=self._get_interpret(2), output_shape=2, quantum_instance=quantum_instance, input_gradients=False, ) else: # construct sampler QNN by default neural_network = SamplerQNN( sampler=sampler, circuit=self._circuit, input_params=self.feature_map.parameters, weight_params=self.ansatz.parameters, interpret=self._get_interpret(2), output_shape=2, input_gradients=False, ) super().__init__( neural_network=neural_network, loss=loss, one_hot=True, 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 circuit(self) -> QuantumCircuit: """Returns the underlying quantum circuit.""" return self._circuit @property def num_qubits(self) -> int: """Returns the number of qubits used by ansatz and feature map.""" return self.circuit.num_qubits def _fit_internal(self, X: np.ndarray, y: np.ndarray) -> OptimizerResult: """ Fit the model to data matrix X and targets y. Args: X: The input feature values. y: The input target values. Required to be one-hot encoded. Returns: Trained classifier. """ X, y = self._validate_input(X, y) num_classes = self._num_classes # instance check required by mypy (alternative to cast) if isinstance(self._neural_network, (CircuitQNN, SamplerQNN)): self._neural_network.set_interpret(self._get_interpret(num_classes), num_classes) return super()._minimize(X, y) def _get_interpret(self, num_classes: int): def parity(x: int, num_classes: int = num_classes) -> int: return x % num_classes return parity

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