Source code for qiskit_algorithms.gradients.reverse.reverse_gradient

# This code is part of a Qiskit project.
# (C) Copyright IBM 2022, 2023.
# 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
# Any modifications or derivative works of this code must retain this
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"""Estimator gradients with the classically efficient reverse mode."""

from __future__ import annotations
from import Sequence
from typing import cast
import logging

import numpy as np

from qiskit.circuit import QuantumCircuit, Parameter
from qiskit.quantum_info.operators.base_operator import BaseOperator
from qiskit.quantum_info import Statevector
from qiskit.primitives import Estimator

from .bind import bind
from .derive_circuit import derive_circuit
from .split_circuits import split

from ..base.base_estimator_gradient import BaseEstimatorGradient
from ..base.estimator_gradient_result import EstimatorGradientResult
from ..utils import DerivativeType

logger = logging.getLogger(__name__)

[docs]class ReverseEstimatorGradient(BaseEstimatorGradient): """Estimator gradients with the classically efficient reverse mode. .. note:: This gradient implementation is based on statevector manipulations and scales exponentially with the number of qubits. However, for small system sizes it can be very fast compared to circuit-based gradients. This class implements the calculation of the expectation gradient as described in [1]. By keeping track of two statevectors and iteratively sweeping through each parameterized gate, this method scales only linearly with the number of parameters. **References:** [1]: Jones, T. and Gacon, J. "Efficient calculation of gradients in classical simulations of variational quantum algorithms" (2020). `arXiv:2009.02823 <>`_. """ SUPPORTED_GATES = ["rx", "ry", "rz", "cp", "crx", "cry", "crz"] def __init__(self, derivative_type: DerivativeType = DerivativeType.REAL): """ Args: derivative_type: Defines whether the real, imaginary or real plus imaginary part of the gradient is returned. """ dummy_estimator = Estimator() # this is required by the base class, but not used super().__init__(dummy_estimator, derivative_type=derivative_type) @BaseEstimatorGradient.derivative_type.setter # type: ignore[attr-defined] def derivative_type(self, derivative_type: DerivativeType) -> None: """Set the derivative type.""" self._derivative_type = derivative_type def _run( self, circuits: Sequence[QuantumCircuit], observables: Sequence[BaseOperator], parameter_values: Sequence[Sequence[float]], parameters: Sequence[Sequence[Parameter]], **options, ) -> EstimatorGradientResult: """Compute the gradients of the expectation values by the parameter shift rule.""" g_circuits, g_parameter_values, g_parameters = self._preprocess( circuits, parameter_values, parameters, self.SUPPORTED_GATES ) results = self._run_unique( g_circuits, observables, g_parameter_values, g_parameters, **options ) return self._postprocess(results, circuits, parameter_values, parameters) def _run_unique( self, circuits: Sequence[QuantumCircuit], observables: Sequence[BaseOperator], parameter_values: Sequence[Sequence[float]], parameters: Sequence[Sequence[Parameter]], **options, # pylint: disable=unused-argument ) -> EstimatorGradientResult: num_gradients = len(circuits) gradients = [] metadata = [] for i in range(num_gradients): # temporary variables for easier access circuit = circuits[i] parameters_ = parameters[i] observable = observables[i] values = parameter_values[i] # the metadata only contains the parameters as there are no run configs here metadata.append( { "parameters": parameters_, "derivative_type": self.derivative_type, } ) # keep track of the parameter order of the circuit, as the circuit splitting might # produce a list of unitaries in a different order # original_parameter_order = [p for p in circuit.parameters if p in parameters_] # split the circuit and generate lists of unitaries [U_1, U_2, ...] and # parameters [p_1, p_2, ...] in these unitaries unitaries, paramlist = split(circuit, parameters=parameters_) parameter_binds = dict(zip(circuit.parameters, values)) bound_circuit = bind(circuit, parameter_binds) # initialize state variables -- we use the same naming as in the paper phi = Statevector(bound_circuit) lam = _evolve_by_operator(observable, phi) # store gradients in a dictionary to return them in the correct order grads = {param: 0j for param in parameters_} num_parameters = len(unitaries) for j in reversed(range(num_parameters)): unitary_j = unitaries[j] # We currently only support gates with a single parameter -- which is reflected # in self.SUPPORTED_GATES -- but generally we could also support gates with multiple # parameters per gate parameter_j = paramlist[j][0] # get the analytic gradient d U_j / d p_j and bind the gate deriv = derive_circuit(unitary_j, parameter_j) for _, gate in deriv: bind(gate, parameter_binds, inplace=True) # iterate the state variable unitary_j_dagger = cast(QuantumCircuit, bind(unitary_j, parameter_binds)).inverse() phi = phi.evolve(unitary_j_dagger) # compute current gradient grad = sum( coeff * lam.conjugate() for coeff, gate in deriv ) # Compute the full gradient (real and complex parts) as all information is available. # Later, based on the derivative type, cast to real/imag/complex. grads[parameter_j] += grad if j > 0: lam = lam.evolve(unitary_j_dagger) gradient = np.array(list(grads.values())) gradients.append(self._to_derivtype(gradient)) result = EstimatorGradientResult(gradients, metadata=metadata, options={}) return result def _to_derivtype(self, gradient): # this disable is needed as Pylint does not understand derivative_type is a property if # it is only defined in the base class and the getter is in the child # pylint: disable=comparison-with-callable if self.derivative_type == DerivativeType.REAL: return 2 * np.real(gradient) if self.derivative_type == DerivativeType.IMAG: return 2 * np.imag(gradient) return 2 * gradient
def _evolve_by_operator(operator, state): """Evolve the Statevector state by operator.""" # try casting to sparse matrix and use sparse matrix-vector multiplication, which is # a lot faster than using Statevector.evolve try: spmatrix = operator.to_matrix(sparse=True) evolved = spmatrix @ return Statevector(evolved) except (TypeError, AttributeError):"Operator is not castable to a sparse matrix, using Statevector.evolve.") return state.evolve(operator)