Quellcode fĂŒr qiskit.algorithms.time_evolvers.variational.variational_principles.imaginary_mc_lachlan_principle

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# (C) Copyright IBM 2023.
# This code is licensed under the Apache License, Version 2.0. You may
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"""Class for an Imaginary McLachlan's Variational Principle."""
from __future__ import annotations

import warnings

from collections.abc import Sequence

import numpy as np

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

from .imaginary_variational_principle import ImaginaryVariationalPrinciple

from ....exceptions import AlgorithmError
from ....gradients import (

[Doku]class ImaginaryMcLachlanPrinciple(ImaginaryVariationalPrinciple): """Class for an Imaginary McLachlan's Variational Principle. It aims to minimize the distance between both sides of the Wick-rotated Schrödinger equation with a quantum state given as a parametrized trial state. The principle leads to a system of linear equations handled by a linear solver. The imaginary variant means that we consider imaginary time dynamics. """ def __init__( self, qgt: BaseQGT | None = None, gradient: BaseEstimatorGradient | None = None, ) -> None: """ Args: qgt: Instance of a the GQT class used to compute the QFI. If ``None`` provided, ``LinCombQGT`` is used. gradient: Instance of a class used to compute the state gradient. If ``None`` provided, ``LinCombEstimatorGradient`` is used. Raises: AlgorithmError: If the gradient instance does not contain an estimator. """ self._validate_grad_settings(gradient) if gradient is not None: try: estimator = gradient._estimator except Exception as exc: raise AlgorithmError( "The provided gradient instance does not contain an estimator primitive." ) from exc else: estimator = Estimator() gradient = LinCombEstimatorGradient(estimator) if qgt is None: qgt = LinCombQGT(estimator) super().__init__(qgt, gradient)
[Doku] def evolution_gradient( self, hamiltonian: BaseOperator, ansatz: QuantumCircuit, param_values: Sequence[float], gradient_params: Sequence[Parameter] | None = None, ) -> np.ndarray: """ Calculates an evolution gradient according to the rules of this variational principle. Args: hamiltonian: Operator used for Variational Quantum Time Evolution. ansatz: Quantum state in the form of a parametrized quantum circuit. param_values: Values of parameters to be bound. gradient_params: List of parameters with respect to which gradients should be computed. If ``None`` given, gradients w.r.t. all parameters will be computed. Returns: An evolution gradient. Raises: AlgorithmError: If a gradient job fails. """ try: evolution_grad_lse_rhs = ( self.gradient.run([ansatz], [hamiltonian], [param_values], [gradient_params]) .result() .gradients[0] ) except Exception as exc: raise AlgorithmError("The gradient primitive job failed!") from exc return -0.5 * evolution_grad_lse_rhs
@staticmethod def _validate_grad_settings(gradient): if ( gradient is not None and hasattr(gradient, "_derivative_type") and gradient._derivative_type != DerivativeType.REAL ): warnings.warn( "A gradient instance with a setting for calculating imaginary part of " "the gradient was provided. This variational principle requires the" "real part. The setting to real was changed automatically." ) gradient._derivative_type = DerivativeType.REAL