qiskit_optimization.algorithms.minimum_eigen_optimizer のソースコード

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# (C) Copyright IBM 2020, 2023.
# This code is licensed under the Apache License, Version 2.0. You may
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# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
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"""A wrapper for minimum eigen solvers to be used within the optimization module."""
from typing import List, Optional, Union, cast

import numpy as np
from qiskit.quantum_info import SparsePauliOp
from qiskit_algorithms import (

from ..converters.quadratic_program_to_qubo import QuadraticProgramConverter, QuadraticProgramToQubo
from ..exceptions import QiskitOptimizationError
from ..problems.quadratic_program import QuadraticProgram, Variable
from .optimization_algorithm import (

MinimumEigensolver = Union[SamplingMinimumEigensolver, NumPyMinimumEigensolver]
MinimumEigensolverResult = Union[SamplingMinimumEigensolverResult, NumPyMinimumEigensolverResult]

[ドキュメント]class MinimumEigenOptimizationResult(OptimizationResult): """Minimum Eigen Optimizer Result.""" def __init__( self, x: Optional[Union[List[float], np.ndarray]], fval: Optional[float], variables: List[Variable], status: OptimizationResultStatus, samples: Optional[List[SolutionSample]] = None, min_eigen_solver_result: Optional[MinimumEigensolverResult] = None, raw_samples: Optional[List[SolutionSample]] = None, ) -> None: """ Args: x: the optimal value found by ``SamplingMinimumEigensolver`` or ``NumPyMinimumEigensolver``. fval: the optimal function value. variables: the list of variables of the optimization problem. status: the termination status of the optimization algorithm. min_eigen_solver_result: the result obtained from the underlying algorithm. samples: the x values, the objective function value of the original problem, the probability, and the status of sampling. raw_samples: the x values of the QUBO, the objective function value of the QUBO, and the probability of sampling. """ super().__init__( x=x, fval=fval, variables=variables, status=status, raw_results=None, samples=samples, ) self._min_eigen_solver_result = min_eigen_solver_result self._raw_samples = raw_samples @property def min_eigen_solver_result(self) -> MinimumEigensolverResult: """Returns a result object obtained from the instance of ``SamplingMinimumEigensolver`` or ``NumPyMinimumEigensolver``.""" return self._min_eigen_solver_result @property def raw_samples(self) -> Optional[List[SolutionSample]]: """Returns the list of raw solution samples of ``SamplingMinimumEigensolver`` or ``NumPyMinimumEigensolver``. Returns: The list of raw solution samples of ``SamplingMinimumEigensolver`` or ``NumPyMinimumEigensolver``. """ return self._raw_samples
[ドキュメント]class MinimumEigenOptimizer(OptimizationAlgorithm): """A wrapper for minimum eigen solvers. This class provides a wrapper for minimum eigen solvers from Qiskit to be used within the optimization module. It assumes a problem consisting only of binary or integer variables as well as linear equality constraints thereof. It converts such a problem into a Quadratic Unconstrained Binary Optimization (QUBO) problem by expanding integer variables into binary variables and by adding the linear equality constraints as weighted penalty terms to the objective function. The resulting QUBO is then translated into an Ising Hamiltonian whose minimal eigen vector and corresponding eigenstate correspond to the optimal solution of the original optimization problem. The provided minimum eigen solver is then used to approximate the ground state of the Hamiltonian to find a good solution for the optimization problem. Examples: Outline of how to use this class: .. code-block:: from qiskit_algorithms import QAOA from qiskit_optimization.problems import QuadraticProgram from qiskit_optimization.algorithms import MinimumEigenOptimizer problem = QuadraticProgram() # specify problem here # specify minimum eigen solver to be used, e.g., QAOA qaoa = QAOA(...) optimizer = MinimumEigenOptimizer(qaoa) result = optimizer.solve(problem) """ def __init__( self, min_eigen_solver: MinimumEigensolver, penalty: Optional[float] = None, converters: Optional[ Union[QuadraticProgramConverter, List[QuadraticProgramConverter]] ] = None, ) -> None: """ This initializer takes the minimum eigen solver to be used to approximate the ground state of the resulting Hamiltonian as well as a optional penalty factor to scale penalty terms representing linear equality constraints. If no penalty factor is provided, a default is computed during the algorithm (TODO). Args: min_eigen_solver: The eigen solver to find the ground state of the Hamiltonian. penalty: The penalty factor to be used, or ``None`` for applying a default logic. converters: The converters to use for converting a problem into a different form. By default, when None is specified, an internally created instance of :class:`~qiskit_optimization.converters.QuadraticProgramToQubo` will be used. Raises: TypeError: If minimum eigensolver has an invalid type. TypeError: When one of converters has an invalid type. QiskitOptimizationError: When the minimum eigensolver does not return an eigenstate. """ if not min_eigen_solver.supports_aux_operators(): raise QiskitOptimizationError( "Given MinimumEigensolver does not return the eigenstate " "and is not supported by the MinimumEigenOptimizer." ) self._min_eigen_solver = min_eigen_solver self._penalty = penalty self._converters = self._prepare_converters(converters, penalty)
[ドキュメント] def get_compatibility_msg(self, problem: QuadraticProgram) -> str: """Checks whether a given problem can be solved with this optimizer. Checks whether the given problem is compatible, i.e., whether the problem can be converted to a QUBO, and otherwise, returns a message explaining the incompatibility. Args: problem: The optimization problem to check compatibility. Returns: A message describing the incompatibility. """ return QuadraticProgramToQubo.get_compatibility_msg(problem)
@property def min_eigen_solver(self) -> MinimumEigensolver: """Returns the minimum eigensolver.""" return self._min_eigen_solver @min_eigen_solver.setter def min_eigen_solver(self, min_eigen_solver: MinimumEigensolver) -> None: """Sets the minimum eigensolver.""" self._min_eigen_solver = min_eigen_solver
[ドキュメント] def solve(self, problem: QuadraticProgram) -> MinimumEigenOptimizationResult: """Tries to solves the given problem using the optimizer. Runs the optimizer to try to solve the optimization problem. Args: problem: The problem to be solved. Returns: The result of the optimizer applied to the problem. Raises: QiskitOptimizationError: If problem not compatible. """ self._verify_compatibility(problem) # convert problem to QUBO minimization problem problem_ = self._convert(problem, self._converters) # construct operator and offset operator, offset = problem_.to_ising() return self._solve_internal(operator, offset, problem_, problem)
def _solve_internal( self, operator: SparsePauliOp, offset: float, converted_problem: QuadraticProgram, original_problem: QuadraticProgram, ): # only try to solve non-empty Ising Hamiltonians eigen_result: Optional[MinimumEigensolverResult] = None if operator.num_qubits > 0: # approximate ground state of operator using min eigen solver eigen_result = self._min_eigen_solver.compute_minimum_eigenvalue(operator) # analyze results raw_samples = None if not hasattr(eigen_result, "eigenstate"): raise QiskitOptimizationError( "MinimumEigenOptimizer does not support this minimum eigensolver " f"{type(self._min_eigen_solver)}. " "You can use qiskit_algorithms.SamplingMinimumEigensolver instead." ) if eigen_result.eigenstate is not None: raw_samples = self._eigenvector_to_solutions( eigen_result.eigenstate, converted_problem ) raw_samples.sort(key=lambda x: x.fval) else: # if Hamiltonian is empty, then the objective function is constant to the offset x = np.zeros(converted_problem.get_num_binary_vars()) fval = offset raw_samples = [SolutionSample(x, fval, 1.0, OptimizationResultStatus.SUCCESS)] if raw_samples is None: # if not function value is given, then something went wrong, e.g., a # NumPyMinimumEigensolver has been configured with an infeasible filter criterion. return MinimumEigenOptimizationResult( x=None, fval=None, variables=original_problem.variables, status=OptimizationResultStatus.FAILURE, samples=None, raw_samples=None, min_eigen_solver_result=eigen_result, ) # translate result back to integers and eventually maximization samples, best_raw = self._interpret_samples(original_problem, raw_samples, self._converters) return cast( MinimumEigenOptimizationResult, self._interpret( x=best_raw.x, converters=self._converters, problem=original_problem, result_class=MinimumEigenOptimizationResult, samples=samples, raw_samples=raw_samples, min_eigen_solver_result=eigen_result, ), )