def test_simple_qubo(self):
"""Test on a simple QUBO problem."""
model = Model()
# pylint:disable=invalid-name
u = model.binary_var(name="u")
v = model.binary_var(name="v")
model.minimize((u - v + 2)**2)
problem = QuadraticProgram()
problem.from_docplex(model)
backend = BasicAer.get_backend("statevector_simulator")
qaoa = QAOA(quantum_instance=backend, reps=1)
optimizer = WarmStartQAOAOptimizer(
pre_solver=SlsqpOptimizer(),
relax_for_pre_solver=True,
qaoa=qaoa,
epsilon=0.25,
)
result_warm = optimizer.solve(problem)
self.assertIsNotNone(result_warm)
self.assertIsNotNone(result_warm.x)
np.testing.assert_almost_equal([0, 1], result_warm.x, 3)
self.assertIsNotNone(result_warm.fval)
np.testing.assert_almost_equal(1, result_warm.fval, 3)
def test_max_cut(self):
"""Basic test on the max cut problem."""
graph = np.array([
[0.0, 1.0, 2.0, 0.0],
[1.0, 0.0, 1.0, 0.0],
[2.0, 1.0, 0.0, 1.0],
[0.0, 0.0, 1.0, 0.0],
])
presolver = GoemansWilliamsonOptimizer(num_cuts=10)
problem = Maxcut(graph).to_quadratic_program()
backend = BasicAer.get_backend("statevector_simulator")
qaoa = QAOA(quantum_instance=backend, reps=1)
aggregator = MeanAggregator()
optimizer = WarmStartQAOAOptimizer(
pre_solver=presolver,
relax_for_pre_solver=False,
qaoa=qaoa,
epsilon=0.25,
num_initial_solutions=10,
aggregator=aggregator,
converters=[],
)
result_warm = optimizer.solve(problem)
self.assertIsNotNone(result_warm)
self.assertIsNotNone(result_warm.x)
np.testing.assert_almost_equal([0, 0, 1, 0], result_warm.x, 3)
self.assertIsNotNone(result_warm.fval)
np.testing.assert_almost_equal(4, result_warm.fval, 3)
def test_constrained_binary(self):
"""Constrained binary optimization problem."""
model = Model()
v = model.binary_var(name="v")
w = model.binary_var(name="w")
# pylint:disable=invalid-name
t = model.binary_var(name="t")
model.minimize(v + w + t)
model.add_constraint(2 * v + 10 * w + t <= 3, "cons1")
model.add_constraint(v + w + t >= 2, "cons2")
problem = QuadraticProgram()
problem.from_docplex(model)
backend = BasicAer.get_backend("statevector_simulator")
qaoa = QAOA(quantum_instance=backend, reps=1)
aggregator = MeanAggregator()
optimizer = WarmStartQAOAOptimizer(
pre_solver=SlsqpOptimizer(),
relax_for_pre_solver=True,
qaoa=qaoa,
epsilon=0.25,
aggregator=aggregator,
)
result_warm = optimizer.solve(problem)
self.assertIsNotNone(result_warm)
self.assertIsNotNone(result_warm.x)
np.testing.assert_almost_equal([1, 0, 1], result_warm.x, 3)
self.assertIsNotNone(result_warm.fval)
np.testing.assert_almost_equal(2, result_warm.fval, 3)
def test_max_cut(self):
"""Basic test on the max cut problem."""
try:
graph = np.array([[0., 1., 2., 0.], [1., 0., 1., 0.],
[2., 1., 0., 1.], [0., 0., 1., 0.]])
presolver = GoemansWilliamsonOptimizer(num_cuts=10)
problem = max_cut_qp(graph)
backend = BasicAer.get_backend("statevector_simulator")
qaoa = QAOA(quantum_instance=backend, reps=1)
aggregator = MeanAggregator()
optimizer = WarmStartQAOAOptimizer(pre_solver=presolver,
relax_for_pre_solver=False,
qaoa=qaoa,
epsilon=0.25,
num_initial_solutions=10,
aggregator=aggregator)
result_warm = optimizer.solve(problem)
self.assertIsNotNone(result_warm)
self.assertIsNotNone(result_warm.x)
np.testing.assert_almost_equal([0, 0, 1, 0], result_warm.x, 3)
self.assertIsNotNone(result_warm.fval)
np.testing.assert_almost_equal(4, result_warm.fval, 3)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))