from qiskit.aqua.algorithms import NumPyMinimumEigensolver
from qiskit.optimization.algorithms import GroverOptimizer, MinimumEigenOptimizer
from qiskit.optimization.problems import QuadraticProgram
from qiskit import BasicAer
from docplex.mp.model import Model
backend = BasicAer.get_backend("statevector_simulator")
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 = QuadraticProgram()
qp.from_docplex(model)
print(qp.export_as_lp_string())
from qiskit.aqua import QuantumInstance
grover_optimizer = GroverOptimizer(6, num_iterations=10, quantum_instance=backend)
results = grover_optimizer.solve(qp)
print("x={}".format(results.x))
print("fval={}".format(results.fval))
exact_solver = MinimumEigenOptimizer(NumPyMinimumEigensolver())
exact_result = exact_solver.solve(qp)
print("x={}".format(exact_result.x))
print("fval={}".format(exact_result.fval))
# Since the `pytket` extension modules provide an interface to the widest variety of devices and simulators out of all major quantum software platforms, the simplest advantage to obtain through `pytket` is to try using some alternative backends.
#
if (ii != jj):
quadraticCoeffDict[(z[ii], z[jj])] = penalty
else:
quadraticCoeffDict[(z[ii], z[jj])] = quadraticCoeffs[ii][jj]
else:
quadraticCoeffDict[(z[ii], z[jj])] = quadraticCoeffs[ii][jj]
#backend = BasicAer.get_backend('statevector_simulator')
qubo = QuadraticProgram()
for ii in range(numOfItems + backPackCap):
qubo.binary_var(z[ii])
qubo.minimize(linear=linearCoeff, quadratic=quadraticCoeffDict)
print(qubo.export_as_lp_string())
"""qaoa_mes = QAOA(quantum_instance=BasicAer.get_backend('statevector_simulator'));
qaoa = MinimumEigenOptimizer(qaoa_mes) # using QAOA
qaoa_result = qaoa.solve(qubo)
print(qaoa_result)"""
quantumBackend = QuantumInstance(provider.get_backend('ibmq_qasm_simulator'),
shots=2048,
skip_qobj_validation=False)
qaoa_mes = QAOA(quantum_instance=quantumBackend)
qaoa = MinimumEigenOptimizer(qaoa_mes)
# using QAOA
qaoa_result = qaoa.solve(qubo)
print(qaoa_result)
