MinimumEigenOptimizer

class MinimumEigenOptimizer(min_eigen_solver, penalty=None, converters=None)

Bases: 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.

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

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).

Parámetros:

• min_eigen_solver (SamplingMinimumEigensolver | NumPyMinimumEigensolver) – The eigen solver to find the ground state of the Hamiltonian.

• penalty (float | None) – The penalty factor to be used, or None for applying a default logic.

• 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:

• 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.

Attributes

min_eigen_solver

Returns the minimum eigensolver.

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 solves the given problem using the 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 – If problem not compatible.

Tipo del valor devuelto:

MinimumEigenOptimizationResult