Source code for qiskit_algorithms.time_evolvers.variational.var_qrte

# This code is part of a Qiskit project.
# (C) Copyright IBM 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
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"""Variational Quantum Real Time Evolution algorithm."""
from __future__ import annotations

from import Mapping, Sequence
from typing import Type, Callable

import numpy as np
from scipy.integrate import OdeSolver

from qiskit import QuantumCircuit
from qiskit.circuit import Parameter
from qiskit.primitives import BaseEstimator

from .solvers.ode.forward_euler_solver import ForwardEulerSolver

from .variational_principles import RealVariationalPrinciple, RealMcLachlanPrinciple
from .var_qte import VarQTE

from ..real_time_evolver import RealTimeEvolver

[docs]class VarQRTE(VarQTE, RealTimeEvolver): """Variational Quantum Real Time Evolution algorithm. .. code-block::python import numpy as np from qiskit_algorithms import TimeEvolutionProblem, VarQRTE from qiskit.circuit.library import EfficientSU2 from qiskit_algorithms.time_evolvers.variational import RealMcLachlanPrinciple from qiskit.quantum_info import SparsePauliOp from qiskit.quantum_info import SparsePauliOp, Pauli from qiskit.primitives import Estimator observable = SparsePauliOp.from_list( [ ("II", 0.2252), ("ZZ", 0.5716), ("IZ", 0.3435), ("ZI", -0.4347), ("YY", 0.091), ("XX", 0.091), ] ) ansatz = EfficientSU2(observable.num_qubits, reps=1) init_param_values = np.ones(len(ansatz.parameters)) * np.pi/2 var_principle = RealMcLachlanPrinciple() time = 1 # without evaluating auxiliary operators evolution_problem = TimeEvolutionProblem(observable, time) var_qrte = VarQRTE(ansatz, init_param_values, var_principle) evolution_result = var_qrte.evolve(evolution_problem) # evaluating auxiliary operators aux_ops = [Pauli("XX"), Pauli("YZ")] evolution_problem = TimeEvolutionProblem(observable, time, aux_operators=aux_ops) var_qrte = VarQRTE(ansatz, init_param_values, var_principle, Estimator()) evolution_result = var_qrte.evolve(evolution_problem) """ def __init__( self, ansatz: QuantumCircuit, initial_parameters: Mapping[Parameter, float] | Sequence[float], variational_principle: RealVariationalPrinciple | None = None, estimator: BaseEstimator | None = None, ode_solver: Type[OdeSolver] | str = ForwardEulerSolver, lse_solver: Callable[[np.ndarray, np.ndarray], np.ndarray] | None = None, num_timesteps: int | None = None, imag_part_tol: float = 1e-7, num_instability_tol: float = 1e-7, ) -> None: r""" Args: ansatz: Ansatz to be used for variational time evolution. initial_parameters: Initial parameter values for an ansatz. variational_principle: Variational Principle to be used. Defaults to ``RealMcLachlanPrinciple``. estimator: An estimator primitive used for calculating expectation values of TimeEvolutionProblem.aux_operators. ode_solver: ODE solver callable that implements a SciPy ``OdeSolver`` interface or a string indicating a valid method offered by SciPy. lse_solver: Linear system of equations solver callable. It accepts ``A`` and ``b`` to solve ``Ax=b`` and returns ``x``. If ``None``, the default ``np.linalg.lstsq`` solver is used. num_timesteps: The number of timesteps to take. If ``None``, it is automatically selected to achieve a timestep of approximately 0.01. Only relevant in case of the ``ForwardEulerSolver``. imag_part_tol: Allowed value of an imaginary part that can be neglected if no imaginary part is expected. num_instability_tol: The amount of negative value that is allowed to be rounded up to 0 for quantities that are expected to be non-negative. """ if variational_principle is None: variational_principle = RealMcLachlanPrinciple() super().__init__( ansatz, initial_parameters, variational_principle, estimator, ode_solver, lse_solver=lse_solver, num_timesteps=num_timesteps, imag_part_tol=imag_part_tol, num_instability_tol=num_instability_tol, )