def test_save_parameter_grid_evaluation(self):
# Given
ansatz = MockAnsatz(2, 2)
grid = build_uniform_param_grid(1, 2, 0, np.pi, np.pi / 10)
backend = create_object({
"module_name": "zquantum.core.interfaces.mock_objects",
"function_name": "MockQuantumSimulator",
})
op = QubitOperator("0.5 [] + 0.5 [Z1]")
(
parameter_grid_evaluation,
optimal_parameters,
) = evaluate_operator_for_parameter_grid(ansatz, grid, backend, op)
# When
save_parameter_grid_evaluation(parameter_grid_evaluation,
"parameter-grid-evaluation.json")
save_circuit_template_params(optimal_parameters,
"optimal-parameters.json")
# Then
# TODO
files_to_remove = ("parameter-grid-evaluation.json",
"optimal-parameters.json")
failed_to_remove = []
for path in files_to_remove:
try:
os.remove(path)
except OSError:
failed_to_remove.append(path)
if failed_to_remove:
raise RuntimeError(f"Failed to remove files: {failed_to_remove}")
def test_noisy_ground_state_cost_function_adds_noise_to_parameters():
target_operator = QubitOperator("Z0")
parametrized_circuit = MockAnsatz(
number_of_layers=2, problem_size=1
).parametrized_circuit
parametrized_circuit.evaluate = mock.Mock(wraps=parametrized_circuit.evaluate)
backend = MockQuantumSimulator()
estimation_method = estimate_expectation_values_by_averaging
estimation_preprocessors = [partial(allocate_shots_uniformly, number_of_shots=1)]
noisy_ground_state_cost_function = get_ground_state_cost_function(
target_operator,
parametrized_circuit,
backend,
estimation_method=estimation_method,
estimation_preprocessors=estimation_preprocessors,
parameter_precision=1e-4,
parameter_precision_seed=RNGSEED,
)
generator = np.random.default_rng(RNGSEED)
# We expect the below to get added to parameters
noise = generator.normal(0, 1e-4, 2)
params = np.array([0.1, 2.3], dtype=float)
expected_symbols_map = [
(Symbol("theta_0"), noise[0] + params[0]),
(Symbol("theta_1"), noise[1] + params[1]),
]
# ansatz based cost function may modify params in place
# and we need original ones - therefore we pass a copy
noisy_ground_state_cost_function(np.array(params))
# We only called our function once, therefore the following should be true
parametrized_circuit.evaluate.assert_called_with(expected_symbols_map)
# and if only everything went right, this only call should be of the form
# noisy_ansatz.ansatz.get_executable_circuit(params+noise)
# Therefore, we extract the single argument and compare it to the
# expected one.
assert np.array_equal(
parametrized_circuit.evaluate.call_args[0][0], expected_symbols_map
)
def ansatz_based_cost_function():
target_operator = QubitOperator("Z0")
ansatz = MockAnsatz(number_of_layers=1, problem_size=1)
backend = MockQuantumSimulator()
estimation_method = estimate_expectation_values_by_averaging
estimation_preprocessors = [partial(allocate_shots_uniformly, number_of_shots=1)]
return AnsatzBasedCostFunction(
target_operator,
ansatz,
backend,
estimation_method=estimation_method,
estimation_preprocessors=estimation_preprocessors,
)
def noisy_ansatz_cost_function_with_ansatz():
target_operator = QubitOperator("Z0")
ansatz = MockAnsatz(number_of_layers=2, problem_size=1)
backend = MockQuantumSimulator()
estimation_method = mock.Mock(wraps=calculate_exact_expectation_values)
return (
AnsatzBasedCostFunction(
target_operator,
ansatz,
backend,
estimation_method=estimation_method,
parameter_precision=1e-4,
parameter_precision_seed=RNGSEED,
),
ansatz,
)
def test_save_parameter_grid_evaluation(self):
# Given
ansatz = MockAnsatz(2, 2)
grid = build_uniform_param_grid(1, 2, 0, np.pi, np.pi / 10)
backend = create_object({
"module_name": "zquantum.core.interfaces.mock_objects",
"function_name": "MockQuantumSimulator",
})
op = QubitOperator("0.5 [] + 0.5 [Z1]")
(
parameter_grid_evaluation,
optimal_parameters,
) = evaluate_operator_for_parameter_grid(ansatz, grid, backend, op)
# When
save_parameter_grid_evaluation(parameter_grid_evaluation,
"parameter-grid-evaluation.json")
save_circuit_template_params(optimal_parameters,
"optimal-parameters.json")
def ansatz():
return MockAnsatz(1, 5)
from zquantum.core.utils import create_symbols_map
from zquantum.core.interfaces.mock_objects import (
MockAnsatz,
MockQuantumSimulator,
)
from zquantum.core.measurement import ExpectationValues
RNGSEED = 1234
@pytest.fixture(
params=[
{
"target_operator": QubitOperator("Z0"),
"parametrized_circuit": MockAnsatz(
number_of_layers=1, problem_size=1
).parametrized_circuit,
"backend": MockQuantumSimulator(),
"estimation_method": estimate_expectation_values_by_averaging,
"estimation_preprocessors": [
partial(allocate_shots_uniformly, number_of_shots=1)
],
},
{
"target_operator": QubitOperator("Z0 Z1"),
"parametrized_circuit": MockAnsatz(
number_of_layers=1, problem_size=2
).parametrized_circuit,
"backend": MockQuantumSimulator(),
"estimation_method": estimate_expectation_values_by_averaging,
"estimation_preprocessors": [