def test_optimize_variational_circuit_with_proxy_x_squared(self):
# Given
client = MockedClient(self.ipaddress, self.port, "return_x_squared")
params = [4]
optimizer = MockOptimizer()
# When
opt_results = optimize_variational_circuit_with_proxy(
params, optimizer, client)
# Then
self.assertGreater(opt_results["opt_value"].value, 0)
self.assertEqual(len(opt_results["opt_params"]), 1)
def test_optimize_variational_circuit_with_proxy_all_zero_line(self):
# Given
client = MockedClient(self.ipaddress, self.port)
params = [0, 0]
optimizer = MockOptimizer()
# When
opt_results = optimize_variational_circuit_with_proxy(
params, optimizer, client)
# Then
self.assertEqual(opt_results["opt_value"].value, 0)
self.assertEqual(len(opt_results["opt_params"]), 2)
def test_optimize_variational_circuit_with_proxy_errors(self):
client = MockedClient(self.ipaddress, self.port)
params = [0]
optimizer = MockOptimizer()
# self.assertRaises(ValueError, lambda: optimize_variational_circuit_with_proxy(
# "Not initial params", optimizer, client))
self.assertRaises(AttributeError, lambda: optimize_variational_circuit_with_proxy(
params, "Not an optimizer object", "Not a client"))
self.assertRaises(AttributeError, lambda: optimize_variational_circuit_with_proxy(
params, optimizer, "Not a client"))
def test_optimize_variational_circuit_with_proxy_x_squared(self):
# Given
client = MockedClient(self.ipaddress, self.port, "return_x_squared")
params = [4]
optimizer = MockOptimizer()
# When
opt_results = optimize_variational_circuit_with_proxy(params,
optimizer, client)
# Then
self.assertGreater(opt_results['opt_value'], 0)
self.assertEqual(len(opt_results['opt_params']), 1)
self.assertEqual(opt_results['history'], [{'optimization-evaluation-ids': ['MOCKED-ID']}])
def test_optimize_variational_circuit_with_proxy_all_zero_line(self):
# Given
client = MockedClient(self.ipaddress, self.port)
params = [0, 0]
optimizer = MockOptimizer()
# When
opt_results = optimize_variational_circuit_with_proxy(params,
optimizer, client)
# Then
self.assertEqual(opt_results['opt_value'], 0)
self.assertEqual(len(opt_results['opt_params']), 2)
self.assertEqual(opt_results['history'], [{'optimization-evaluation-ids': ['MOCKED-ID']}])
def test_solve_qp_problem_with_optimizer_throws_error_when_optimizer_does_not_support_constraints(
matrix, ):
optimizer = MockOptimizer()
with pytest.raises(ValueError):
solution, optimal_value = solve_qp_problem_with_optimizer(
matrix, optimizer)
def optimizer():
optimizer = MockOptimizer()
optimizer.constraints = None
return optimizer
class MaliciousNestedOptimizer(MockNestedOptimizer):
def _minimize(
self,
cost_function_factory: Callable[[int], CostFunction],
initial_params: np.ndarray,
keep_history: bool = False,
):
keep_history = not keep_history
results = super()._minimize(cost_function_factory,
initial_params,
keep_history=keep_history)
del results["nit"]
return results
_good_nested_optimizer = MockNestedOptimizer(inner_optimizer=MockOptimizer(),
n_iters=5)
_malicious_nested_optimizer = MaliciousNestedOptimizer(
inner_optimizer=MockOptimizer(), n_iters=5)
def mock_cost_function_factory(iteration_id: int):
def modified_cost_function(params):
return mock_cost_function(params)**iteration_id
return modified_cost_function
@pytest.mark.parametrize("contract", NESTED_OPTIMIZER_CONTRACTS)
def test_validate_contracts(contract):
assert contract(