# This code is part of a Qiskit project.
#
# (C) Copyright IBM 2022, 2024.
#
# 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.
"""Estimator quantum neural network class"""
from __future__ import annotations
import logging
from copy import copy
from typing import Sequence
import numpy as np
from qiskit.circuit import Parameter, QuantumCircuit
from qiskit.primitives import BaseEstimator, Estimator, EstimatorResult
from qiskit.quantum_info import SparsePauliOp
from qiskit.quantum_info.operators.base_operator import BaseOperator
from qiskit_algorithms.gradients import (
BaseEstimatorGradient,
EstimatorGradientResult,
ParamShiftEstimatorGradient,
)
from qiskit_machine_learning.circuit.library import QNNCircuit
from qiskit_machine_learning.exceptions import QiskitMachineLearningError
from .neural_network import NeuralNetwork
logger = logging.getLogger(__name__)
[문서]class EstimatorQNN(NeuralNetwork):
"""A neural network implementation based on the Estimator primitive.
The ``EstimatorQNN`` is a neural network that takes in a parametrized quantum circuit
with designated parameters for input data and/or weights, an optional observable(s) and outputs
their expectation value(s). Quite often, a combined quantum circuit is used. Such a circuit is
built from two circuits: a feature map, it provides input parameters for the network, and an
ansatz (weight parameters).
In this case a :class:`~qiskit_machine_learning.circuit.library.QNNCircuit` can be passed as
circuit to simplify the composition of a feature map and ansatz.
If a :class:`~qiskit_machine_learning.circuit.library.QNNCircuit` is passed as circuit, the
input and weight parameters do not have to be provided, because these two properties are taken
from the :class:`~qiskit_machine_learning.circuit.library.QNNCircuit`.
Example:
.. code-block::
from qiskit import QuantumCircuit
from qiskit.circuit.library import ZZFeatureMap, RealAmplitudes
from qiskit_machine_learning.circuit.library import QNNCircuit
from qiskit_machine_learning.neural_networks import EstimatorQNN
num_qubits = 2
# Using the QNNCircuit:
# Create a parameterized 2 qubit circuit composed of the default ZZFeatureMap feature map
# and RealAmplitudes ansatz.
qnn_qc = QNNCircuit(num_qubits)
qnn = EstimatorQNN(
circuit=qnn_qc
)
qnn.forward(input_data=[1, 2], weights=[1, 2, 3, 4, 5, 6, 7, 8])
# Explicitly specifying the ansatz and feature map:
feature_map = ZZFeatureMap(feature_dimension=num_qubits)
ansatz = RealAmplitudes(num_qubits=num_qubits)
qc = QuantumCircuit(num_qubits)
qc.compose(feature_map, inplace=True)
qc.compose(ansatz, inplace=True)
qnn = EstimatorQNN(
circuit=qc,
input_params=feature_map.parameters,
weight_params=ansatz.parameters
)
qnn.forward(input_data=[1, 2], weights=[1, 2, 3, 4, 5, 6, 7, 8])
The following attributes can be set via the constructor but can also be read and
updated once the EstimatorQNN object has been constructed.
Attributes:
estimator (BaseEstimator): The estimator primitive used to compute the neural network's results.
gradient (BaseEstimatorGradient): The estimator gradient to be used for the backward
pass.
"""
def __init__(
self,
*,
circuit: QuantumCircuit,
estimator: BaseEstimator | None = None,
observables: Sequence[BaseOperator] | BaseOperator | None = None,
input_params: Sequence[Parameter] | None = None,
weight_params: Sequence[Parameter] | None = None,
gradient: BaseEstimatorGradient | None = None,
input_gradients: bool = False,
):
r"""
Args:
estimator: The estimator used to compute neural network's results.
If ``None``, a default instance of the reference estimator,
:class:`~qiskit.primitives.Estimator`, will be used.
circuit: The quantum circuit to represent the neural network. If a
:class:`~qiskit_machine_learning.circuit.library.QNNCircuit` is passed, the
`input_params` and `weight_params` do not have to be provided, because these two
properties are taken from the
:class:`~qiskit_machine_learning.circuit.library.QNNCircuit`.
observables: The observables for outputs of the neural network. If ``None``,
use the default :math:`Z^{\otimes num\_qubits}` observable.
input_params: The parameters that correspond to the input data of the network.
If ``None``, the input data is not bound to any parameters.
If a :class:`~qiskit_machine_learning.circuit.library.QNNCircuit` is provided the
`input_params` value here is ignored. Instead the value is taken from the
:class:`~qiskit_machine_learning.circuit.library.QNNCircuit` input_parameters.
weight_params: The parameters that correspond to the trainable weights.
If ``None``, the weights are not bound to any parameters.
If a :class:`~qiskit_machine_learning.circuit.library.QNNCircuit` is provided the
`weight_params` value here is ignored. Instead the value is taken from the
:class:`~qiskit_machine_learning.circuit.library.QNNCircuit` weight_parameters.
gradient: The estimator gradient to be used for the backward pass.
If None, a default instance of the estimator gradient,
:class:`~qiskit_algorithms.gradients.ParamShiftEstimatorGradient`, will be used.
input_gradients: Determines whether to compute gradients with respect to input data.
Note that this parameter is ``False`` by default, and must be explicitly set to
``True`` for a proper gradient computation when using
:class:`~qiskit_machine_learning.connectors.TorchConnector`.
Raises:
QiskitMachineLearningError: Invalid parameter values.
"""
if estimator is None:
estimator = Estimator()
self.estimator = estimator
self._org_circuit = circuit
if observables is None:
observables = SparsePauliOp.from_list([("Z" * circuit.num_qubits, 1)])
if isinstance(observables, BaseOperator):
observables = (observables,)
self._observables = observables
if isinstance(circuit, QNNCircuit):
self._input_params = list(circuit.input_parameters)
self._weight_params = list(circuit.weight_parameters)
else:
self._input_params = list(input_params) if input_params is not None else []
self._weight_params = list(weight_params) if weight_params is not None else []
if gradient is None:
gradient = ParamShiftEstimatorGradient(self.estimator)
self.gradient = gradient
self._input_gradients = input_gradients
super().__init__(
num_inputs=len(self._input_params),
num_weights=len(self._weight_params),
sparse=False,
output_shape=len(self._observables),
input_gradients=input_gradients,
)
self._circuit = self._reparameterize_circuit(circuit, input_params, weight_params)
@property
def circuit(self) -> QuantumCircuit:
"""The quantum circuit representing the neural network."""
return copy(self._org_circuit)
@property
def observables(self) -> Sequence[BaseOperator] | BaseOperator:
"""Returns the underlying observables of this QNN."""
return copy(self._observables)
@property
def input_params(self) -> Sequence[Parameter] | None:
"""The parameters that correspond to the input data of the network."""
return copy(self._input_params)
@property
def weight_params(self) -> Sequence[Parameter] | None:
"""The parameters that correspond to the trainable weights."""
return copy(self._weight_params)
@property
def input_gradients(self) -> bool:
"""Returns whether gradients with respect to input data are computed by this neural network
in the ``backward`` method or not. By default such gradients are not computed."""
return self._input_gradients
@input_gradients.setter
def input_gradients(self, input_gradients: bool) -> None:
"""Turn on/off computation of gradients with respect to input data."""
self._input_gradients = input_gradients
def _forward_postprocess(self, num_samples: int, result: EstimatorResult) -> np.ndarray:
"""Post-processing during forward pass of the network."""
return np.reshape(result.values, (-1, num_samples)).T
def _forward(
self, input_data: np.ndarray | None, weights: np.ndarray | None
) -> np.ndarray | None:
"""Forward pass of the neural network."""
parameter_values_, num_samples = self._preprocess_forward(input_data, weights)
job = self.estimator.run(
[self._circuit] * num_samples * self.output_shape[0],
[op for op in self._observables for _ in range(num_samples)],
np.tile(parameter_values_, (self.output_shape[0], 1)),
)
try:
results = job.result()
except Exception as exc:
raise QiskitMachineLearningError("Estimator job failed.") from exc
return self._forward_postprocess(num_samples, results)
def _backward_postprocess(
self, num_samples: int, result: EstimatorGradientResult
) -> tuple[np.ndarray | None, np.ndarray]:
"""Post-processing during backward pass of the network."""
num_observables = self.output_shape[0]
if self._input_gradients:
input_grad = np.zeros((num_samples, num_observables, self._num_inputs))
else:
input_grad = None
weights_grad = np.zeros((num_samples, num_observables, self._num_weights))
gradients = np.asarray(result.gradients)
for i in range(num_observables):
if self._input_gradients:
input_grad[:, i, :] = gradients[i * num_samples : (i + 1) * num_samples][
:, : self._num_inputs
]
weights_grad[:, i, :] = gradients[i * num_samples : (i + 1) * num_samples][
:, self._num_inputs :
]
else:
weights_grad[:, i, :] = gradients[i * num_samples : (i + 1) * num_samples]
return input_grad, weights_grad
def _backward(
self, input_data: np.ndarray | None, weights: np.ndarray | None
) -> tuple[np.ndarray | None, np.ndarray]:
"""Backward pass of the network."""
# prepare parameters in the required format
parameter_values, num_samples = self._preprocess_forward(input_data, weights)
input_grad, weights_grad = None, None
if np.prod(parameter_values.shape) > 0:
num_observables = self.output_shape[0]
num_circuits = num_samples * num_observables
circuits = [self._circuit] * num_circuits
observables = [op for op in self._observables for _ in range(num_samples)]
param_values = np.tile(parameter_values, (num_observables, 1))
job = None
if self._input_gradients:
job = self.gradient.run(circuits, observables, param_values)
elif len(parameter_values[0]) > self._num_inputs:
params = [self._circuit.parameters[self._num_inputs :]] * num_circuits
job = self.gradient.run(circuits, observables, param_values, parameters=params)
if job is not None:
try:
results = job.result()
except Exception as exc:
raise QiskitMachineLearningError("Estimator job failed.") from exc
input_grad, weights_grad = self._backward_postprocess(num_samples, results)
return input_grad, weights_grad