def test_all_cuts(self):
"""Basic test of the Goemans-Williamson optimizer."""
try:
graph = np.array([[0., 1., 2., 0.], [1., 0., 1., 0.],
[2., 1., 0., 1.], [0., 0., 1., 0.]])
optimizer = GoemansWilliamsonOptimizer(num_cuts=10, seed=0)
problem = Maxcut(graph).to_quadratic_program()
self.assertIsNotNone(problem)
results = optimizer.solve(problem)
self.assertIsNotNone(results)
self.assertIsInstance(results, GoemansWilliamsonOptimizationResult)
self.assertIsNotNone(results.x)
np.testing.assert_almost_equal([0, 1, 1, 0], results.x, 3)
self.assertIsNotNone(results.fval)
np.testing.assert_almost_equal(4, results.fval, 3)
self.assertIsNotNone(results.samples)
self.assertEqual(3, len(results.samples))
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_all_cuts(self):
"""Basic test of the Goemans-Williamson optimizer."""
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],
])
optimizer = GoemansWilliamsonOptimizer(num_cuts=10, seed=0)
problem = Maxcut(graph).to_quadratic_program()
self.assertIsNotNone(problem)
results = optimizer.solve(problem)
self.assertIsNotNone(results)
self.assertIsInstance(results, GoemansWilliamsonOptimizationResult)
self.assertIsNotNone(results.x)
np.testing.assert_almost_equal([0, 1, 1, 0], results.x, 3)
self.assertIsNotNone(results.fval)
np.testing.assert_almost_equal(4, results.fval, 3)
self.assertIsNotNone(results.samples)
self.assertEqual(3, len(results.samples))
def test_minimization_problem(self):
"""Tests the optimizer with a minimization problem"""
optimizer = GoemansWilliamsonOptimizer(num_cuts=10, seed=0)
problem = Maxcut(self.graph).to_quadratic_program()
# artificially convert to minimization
max2min = MaximizeToMinimize()
problem = max2min.convert(problem)
results = optimizer.solve(problem)
np.testing.assert_almost_equal([0, 1, 1, 0], results.x, 3)
np.testing.assert_almost_equal(4, results.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_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))