Исходный код qiskit_machine_learning.algorithms.distribution_learners.qgan.generative_network

# This code is part of Qiskit.
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# (C) Copyright IBM 2019, 2022.
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# 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.
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""" Generative Quantum and Classical Neural Networks."""

from typing import Optional, List, Tuple
from abc import ABC, abstractmethod
import numpy as np
from qiskit.utils import QuantumInstance

from ....deprecation import deprecate_function


[документация]class GenerativeNetwork(ABC): """ Base class for generative Quantum and Classical Neural Networks. This method should initialize the module, but raise an exception if a required component of the module is not available. """ @abstractmethod @deprecate_function( "0.5.0", additional_msg="with no direct replacement for it. " "Instead, please refer to the new QGAN tutorial", stack_level=3, ) def __init__(self): super().__init__() self._num_parameters = 0 self._num_qubits = 0 self._bounds = [] @property @abstractmethod def parameter_values(self): """ Get parameter values from the generator Raises: NotImplementedError: not implemented """ raise NotImplementedError()
[документация] @abstractmethod def get_output( self, quantum_instance: QuantumInstance, params: Optional[np.ndarray] = None, shots: Optional[int] = None, ) -> Tuple[List, List]: """ Apply quantum/classical neural network to given input and get the respective output Args: quantum_instance: Quantum Instance, used to run the generator circuit. params: parameters which should be used to run the generator, if None use self._params shots: if not None use a number of shots that is different from the number set in quantum_instance Returns: Neural network output Raises: NotImplementedError: not implemented """ raise NotImplementedError()
[документация] @abstractmethod def loss(self): """ Loss function used for optimization """ raise NotImplementedError()
[документация] @abstractmethod def train(self, quantum_instance=None, shots=None): """ Perform one training step w.r.t to the generator's parameters Args: quantum_instance (QuantumInstance): used to run generator network. Ignored for a classical network. shots (int): Number of shots for hardware or qasm execution. Ignored for classical network Returns: dict: generator loss and updated parameters. Raises: NotImplementedError: not implemented """ raise NotImplementedError()