04 - Grover Optimizer

# This code is from the tutorial at:
# https://qiskit-community.github.io/qiskit-optimization/tutorials/04_grover_optimizer.html

from qiskit_algorithms import NumPyMinimumEigensolver

# Switch to Quantum Rings's Sampler
#from qiskit.primitives import Sampler
from quantumrings.toolkit.qiskit import QrSamplerV1 as Sampler

from qiskit_optimization.algorithms import GroverOptimizer, MinimumEigenOptimizer
from qiskit_optimization.translators import from_docplex_mp
from docplex.mp.model import Model

model = Model()
x0 = model.binary_var(name="x0")
x1 = model.binary_var(name="x1")
x2 = model.binary_var(name="x2")
model.minimize(-x0 + 2 * x1 - 3 * x2 - 2 * x0 * x2 - 1 * x1 * x2)
qp = from_docplex_mp(model)
print(qp.prettyprint())

Problem name: docplex_model1

Minimize
  -2*x0*x2 - x1*x2 - x0 + 2*x1 - 3*x2

Subject to
  No constraints

  Binary variables (3)
    x0 x1 x2

grover_optimizer = GroverOptimizer(6, num_iterations=10, sampler=Sampler())
results = grover_optimizer.solve(qp)
print(results.prettyprint())

objective function value: -6.0
variable values: x0=1.0, x1=0.0, x2=1.0
status: SUCCESS

exact_solver = MinimumEigenOptimizer(NumPyMinimumEigensolver())
exact_result = exact_solver.solve(qp)
print(exact_result.prettyprint())

objective function value: -6.0
variable values: x0=1.0, x1=0.0, x2=1.0
status: SUCCESS