qiskit.optimization.algorithms.MinimumEigenOptimizer

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

A wrapper for minimum eigen solvers from Qiskit Aqua.

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:

from qiskit.aqua.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).

Parameters

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

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

• converters (Union[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.

Raises

• TypeError – When one of converters has an invalid type.

• QiskitOptimizationError – When the minimum eigensolver does not return an eigenstate.

__init__(min_eigen_solver, penalty=None, converters=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).

Parameters

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

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

• converters (Union[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.

Raises

• TypeError – When one of converters has an invalid type.

• QiskitOptimizationError – When the minimum eigensolver does not return an eigenstate.

Methods

__init__(min_eigen_solver[, penalty, converters])	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.
get_compatibility_msg(problem)	Checks whether a given problem can be solved with this optimizer.
is_compatible(problem)	Checks whether a given problem can be solved with the optimizer implementing this method.
solve(problem)	Tries to solves the given problem using the optimizer.

Attributes

min_eigen_solver

Returns the minimum eigensolver.

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.

Parameters

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

Return type

str

Returns

A message describing the incompatibility.

is_compatible(problem)

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

Parameters

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

Return type

bool

Returns

Returns True if the problem is compatible, False otherwise.

property min_eigen_solver

Returns the minimum eigensolver.

Return type

MinimumEigensolver

solve(problem)

Tries to solves the given problem using the optimizer.

Runs the optimizer to try to solve the optimization problem.

Parameters

problem (QuadraticProgram) – The problem to be solved.

Return type

MinimumEigenOptimizationResult

Returns

The result of the optimizer applied to the problem.

Raises

QiskitOptimizationError – If problem not compatible.