RecursiveMinimumEigenOptimizer

class RecursiveMinimumEigenOptimizer(optimizer, min_num_vars=1, min_num_vars_optimizer=None, penalty=None, history=IntermediateResult.LAST_ITERATION, converters=None)

Bases: OptimizationAlgorithm

A meta-algorithm that applies a recursive optimization.

The recursive minimum eigen optimizer applies a recursive optimization on top of OptimizationAlgorithm. This optimizer can use MinimumEigenOptimizer as an optimizer that is called at each iteration. The algorithm is introduced in [1].

Ejemplos

Outline of how to use this class:

from qiskit_algorithms import QAOA
from qiskit_optimization.problems import QuadraticProgram
from qiskit_optimization.algorithms import (
    MinimumEigenOptimizer, RecursiveMinimumEigenOptimizer
)

problem = QuadraticProgram()
# specify problem here
# specify minimum eigen solver to be used, e.g., QAOA
qaoa = QAOA(...)
internal_optimizer = MinimumEigenOptimizer(qaoa)

optimizer = RecursiveMinimumEigenOptimizer(internal_optimizer)
result = optimizer.solve(problem)

Referencias

[1] Bravyi et al. (2019), Obstacles to State Preparation and Variational Optimization from Symmetry Protection. arXiv:1910.08980

Initializes the recursive minimum eigen optimizer.

This initializer takes an OptimizationAlgorithm, the parameters to specify until when to to apply the iterative scheme, and the optimizer to be applied once the threshold number of variables is reached.

Parámetros:

• optimizer (OptimizationAlgorithm) – The optimizer to use in every iteration.

• min_num_vars (int) – The minimum number of variables to apply the recursive scheme. If this threshold is reached, the min_num_vars_optimizer is used.

• min_num_vars_optimizer (OptimizationAlgorithm | None) – This optimizer is used after the recursive scheme for the problem with the remaining variables. Default value is MinimumEigenOptimizer created on top of NumPyMinimumEigensolver.

• penalty (float | None) – The factor that is used to scale the penalty terms corresponding to linear equality constraints.

• history (IntermediateResult | None) – Whether the intermediate results are stored. Default value is LAST_ITERATION.

• converters (QuadraticProgramConverter | List[QuadraticProgramConverter] | None) – The converters to use for converting a problem into a different form. By default, when None is specified, an internally created instance of QuadraticProgramToQubo will be used.

Muestra:

• QiskitOptimizationError – In case of invalid parameters (num_min_vars < 1).

• TypeError – When there one of converters is an invalid type.

Methods

get_compatibility_msg(problem)

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.

Parámetros:

problem (QuadraticProgram) – The optimization problem to check compatibility.

Devuelve:

A message describing the incompatibility.

Tipo del valor devuelto:

str

is_compatible(problem)

Checks whether a given problem can be solved with the optimizer implementing this method.

Parámetros:

problem (QuadraticProgram) – The optimization problem to check compatibility.

Devuelve:

Returns True if the problem is compatible, False otherwise.

Tipo del valor devuelto:

bool

solve(problem)

Tries to solve the given problem using the recursive optimizer.

Runs the optimizer to try to solve the optimization problem.

Parámetros:

problem (QuadraticProgram) – The problem to be solved.

Devuelve:

The result of the optimizer applied to the problem.

Muestra:

• QiskitOptimizationError – Incompatible problem.

• QiskitOptimizationError – Infeasible due to variable substitution

Tipo del valor devuelto:

OptimizationResult