def test_load_optimization_results_successfully_loads_optimization_result_from_file():
result_to_serialize = EXAMPLE_OPTIMIZATION_RESULT
optimization_result_filename = "test-optimization-result-io.json"
save_optimization_results(result_to_serialize, optimization_result_filename)
loaded_data = load_optimization_results(optimization_result_filename)
assert optimization_results_equal(result_to_serialize, loaded_data)
os.remove(optimization_result_filename)
def optimize_variational_qcbm_circuit(
distance_measure_specs,
distance_measure_parameters,
n_layers,
n_qubits,
n_samples,
topology,
backend_specs,
optimizer_specs,
initial_parameters,
target_distribution,
keep_history,
gradient_type = "finite_difference",
gradient_kwargs = None,
):
if isinstance(distance_measure_specs, str):
distance_measure_specs = json.loads(distance_measure_specs)
distance_measure = get_func_from_specs(distance_measure_specs)
ansatz = QCBMAnsatz(n_layers, n_qubits, topology)
if isinstance(backend_specs, str):
backend_specs = json.loads(backend_specs)
backend = create_object(backend_specs)
if isinstance(optimizer_specs, str):
optimizer_specs = json.loads(optimizer_specs)
optimizer = create_object(optimizer_specs)
initial_parameters = load_array(initial_parameters)
target_distribution = load_bitstring_distribution(target_distribution)
if isinstance(distance_measure_parameters, str):
distance_measure_parameters = json.loads(distance_measure_parameters)
cost_function = QCBMCostFunction(
ansatz,
backend,
n_samples,
distance_measure,
distance_measure_parameters,
target_distribution,
gradient_type,
gradient_kwargs
)
opt_results = optimizer.minimize(cost_function, initial_parameters, keep_history)
save_optimization_results(opt_results, "qcbm-optimization-results.json")
save_array(opt_results.opt_params, "optimized-parameters.json")
def test_save_optimization_results_successfully_saves_optimization_result():
result_to_serialize = EXAMPLE_OPTIMIZATION_RESULT
expected_deserialized_result = EXPECTED_DESERIALIZED_RESULT
optimization_result_filename = "test-optimization-result-io.json"
# When
save_optimization_results(result_to_serialize, optimization_result_filename)
# Then
with open(optimization_result_filename, "r") as f:
loaded_data = json.load(f)
assert loaded_data == expected_deserialized_result
os.remove(optimization_result_filename)
def optimize_variational_qcbm_circuit(
distance_measure_specs,
distance_measure_parameters,
n_layers,
n_qubits,
topology,
backend_specs,
optimizer_specs,
initial_parameters,
target_distribution,
):
if isinstance(distance_measure_specs, str):
distance_measure_specs = json.loads(distance_measure_specs)
distance_measure = get_func_from_specs(distance_measure_specs)
ansatz = QCBMAnsatz(n_layers, n_qubits, topology)
if isinstance(backend_specs, str):
backend_specs = json.loads(backend_specs)
backend = create_object(backend_specs)
if isinstance(optimizer_specs, str):
optimizer_specs = json.loads(optimizer_specs)
optimizer = create_object(optimizer_specs)
initial_parameters = load_circuit_template_params(initial_parameters)
target_distribution = load_bitstring_distribution(target_distribution)
if isinstance(distance_measure_parameters, str):
distance_measure_parameters = json.loads(distance_measure_parameters)
cost_function = QCBMCostFunction(
ansatz,
backend,
distance_measure,
distance_measure_parameters,
target_distribution,
)
opt_results = optimizer.minimize(cost_function, initial_parameters)
save_optimization_results(opt_results, "qcbm-optimization-results.json")
save_circuit_template_params(opt_results.opt_params,
"optimized-parameters.json")
def optimize_parametrized_circuit_for_ground_state_of_operator(
optimizer_specs: Specs,
target_operator: Union[SymbolicOperator, str],
parametrized_circuit: Union[Circuit, str],
backend_specs: Specs,
estimator_specs: Optional[Specs] = None,
estimator_kwargs: Optional[Union[Dict, str]] = None,
initial_parameters: Union[str, np.ndarray, List[float]] = None,
fixed_parameters: Optional[Union[np.ndarray, str]] = None,
parameter_precision: Optional[float] = None,
parameter_precision_seed: Optional[int] = None,
):
"""Optimize the parameters of a parametrized quantum circuit to prepare the ground state of a target operator.
Args:
optimizer_specs (Union[Dict, str]): The specs of the optimizer to use to refine the parameter values
target_operator (Union[SymbolicOperator, str]): The operator of which to prepare the ground state
parametrized_circuit (Union[Circuit, str]): The parametrized quantum circuit that prepares trial states
backend_specs (Union[Dict, str]): The specs of the quantum backend (or simulator) to use to run the circuits
estimator_specs (Union[Dict, str]): The estimator to use to estimate the expectation value of the operator.
The default is the BasicEstimator.
estimator_kwargs (dict): kwargs required to run get_estimated_expectation_values method of the estimator.
initial_parameters (Union[str, np.ndarray, List[float]]): The initial parameter values to begin optimization
fixed_parameters (Optional[Union[np.ndarray, str]]): values for the circuit parameters that should be fixed.
parameter_precision (float): the standard deviation of the Gaussian noise to add to each parameter, if any.
parameter_precision_seed (int): seed for randomly generating parameter deviation if using parameter_precision
initial_parameters (Union[str, np.ndarray, List[float]] = None,
"""
if estimator_kwargs is not None:
if isinstance(estimator_kwargs, str):
estimator_kwargs = json.loads(estimator_kwargs)
estimator = create_object(estimator_kwargs)
else:
estimator_kwargs = {}
if isinstance(optimizer_specs, str):
optimizer_specs = json.loads(optimizer_specs)
optimizer = create_object(optimizer_specs)
if isinstance(target_operator, str):
with open(target_operator, "r") as f:
target_operator = json.loads(f.read())
if isinstance(parametrized_circuit, str):
parametrized_circuit = load_circuit(parametrized_circuit)
if isinstance(backend_specs, str):
backend_specs = json.loads(backend_specs)
backend = create_object(backend_specs)
if estimator_specs is not None:
if isinstance(estimator_specs, str):
estimator_specs = json.loads(estimator_specs)
estimator = create_object(estimator_specs)
else:
estimator = BasicEstimator()
if initial_parameters is not None:
if isinstance(initial_parameters, str):
initial_parameters = load_circuit_template_params(
initial_parameters)
if fixed_parameters is not None:
if isinstance(fixed_parameters, str):
fixed_parameters = load_circuit_template_params(fixed_parameters)
cost_function = get_ground_state_cost_function(
target_operator,
parametrized_circuit,
backend,
estimator=estimator,
estimator_kwargs=estimator_kwargs,
fixed_parameters=fixed_parameters,
parameter_precision=parameter_precision,
parameter_precision_seed=parameter_precision_seed,
)
optimization_results = optimizer.minimize(cost_function,
initial_parameters)
save_optimization_results(optimization_results,
"optimization_results.json")
def optimize_parametrized_circuit_for_ground_state_of_operator(
optimizer_specs: Specs,
target_operator: Union[SymbolicOperator, str],
parametrized_circuit: Union[Circuit, str],
backend_specs: Specs,
estimation_method_specs: Optional[Specs] = None,
estimation_preprocessors_specs: Optional[List[Specs]] = None,
initial_parameters: Union[str, np.ndarray, List[float]] = None,
fixed_parameters: Optional[Union[np.ndarray, str]] = None,
parameter_precision: Optional[float] = None,
parameter_precision_seed: Optional[int] = None,
keep_history: bool = True,
**kwargs,
):
"""Optimize the parameters of a parametrized quantum circuit to prepare the ground
state of a target operator.
Args:
optimizer_specs: The specs of the optimizer to use to refine the parameter
values
target_operator: The operator of which to prepare the ground state
parametrized_circuit: The parametrized quantum circuit that prepares trial
states
backend_specs: The specs of the quantum backend (or simulator) to use to run the
circuits
estimation_method_specs: A reference to a callable to use to estimate the
expectation value of the operator. The default is the
estimate_expectation_values_by_averaging function.
estimation_preprocessors_specs: A list of Specs that describe callable functions
that adhere to the EstimationPreprocessor protocol.
initial_parameters: The initial parameter values to begin optimization
fixed_parameters: values for the circuit parameters that should be fixed.
parameter_precision: the standard deviation of the Gaussian noise to add to each
parameter, if any.
parameter_precision_seed: seed for randomly generating parameter deviation if
using parameter_precision
keep_history: flag indicating whether to store optimization history.
kwargs: unused, exists for compatibility
"""
if isinstance(optimizer_specs, str):
optimizer_specs = json.loads(optimizer_specs)
optimizer = create_object(optimizer_specs)
if isinstance(target_operator, str):
target_operator = load_qubit_operator(target_operator)
if isinstance(parametrized_circuit, str):
with open(parametrized_circuit) as f:
parametrized_circuit = new_circuits.circuit_from_dict(json.load(f))
if isinstance(backend_specs, str):
backend_specs = json.loads(backend_specs)
backend = create_object(backend_specs)
if estimation_method_specs is not None:
if isinstance(estimation_method_specs, str):
estimation_method_specs = json.loads(estimation_method_specs)
estimation_method = create_object(estimation_method_specs)
else:
estimation_method = estimate_expectation_values_by_averaging
estimation_preprocessors = []
if estimation_preprocessors_specs is not None:
for estimation_preprocessor_specs in estimation_preprocessors_specs:
if isinstance(estimation_preprocessor_specs, str):
estimation_preprocessor_specs = json.loads(
estimation_preprocessor_specs)
estimation_preprocessors.append(
create_object(estimation_preprocessor_specs))
if initial_parameters is not None:
if isinstance(initial_parameters, str):
initial_parameters = load_array(initial_parameters)
if fixed_parameters is not None:
if isinstance(fixed_parameters, str):
fixed_parameters = load_array(fixed_parameters)
cost_function = get_ground_state_cost_function(
target_operator,
parametrized_circuit,
backend,
estimation_method=estimation_method,
estimation_preprocessors=estimation_preprocessors,
fixed_parameters=fixed_parameters,
parameter_precision=parameter_precision,
parameter_precision_seed=parameter_precision_seed,
)
optimization_results = optimizer.minimize(cost_function,
initial_parameters, keep_history)
save_optimization_results(optimization_results,
"optimization-results.json")
save_array(optimization_results.opt_params, "optimized-parameters.json")
def optimize_ansatz_based_cost_function(
optimizer_specs: Specs,
target_operator: Union[SymbolicOperator, str],
ansatz_specs: Specs,
backend_specs: Specs,
estimation_method_specs: Optional[Specs] = None,
estimation_preprocessors_specs: Optional[List[Specs]] = None,
initial_parameters: Union[str, np.ndarray, List[float]] = None,
fixed_parameters: Optional[Union[np.ndarray, str]] = None,
parameter_precision: Optional[float] = None,
parameter_precision_seed: Optional[int] = None,
keep_history: bool = False,
**kwargs,
):
"""Optimize the parameters of an ansatz circuit to prepare the ground state of a
target operator.
Args:
optimizer_specs: The specs of the optimizer to use to refine the parameter
values
target_operator: The operator of which to prepare the ground state
ansatz_specs: The specs describing an Ansatz which will prepare the quantum
circuit
backend_specs: The specs of the quantum backend (or simulator) to use to run the
circuits
estimation_method_specs: A reference to a callable to use to estimate the
expectation value of the operator. The default is the
estimate_expectation_values_by_averaging function.
estimation_preprocessors_specs: A list of Specs that describe callable functions
that adhere to the EstimationPreprocessor protocol.
initial_parameters: The initial parameter values to begin optimization
fixed_parameters: values for the circuit parameters that should be fixed.
parameter_precision: the standard deviation of the Gaussian noise to add to each
parameter, if any.
parameter_precision_seed: seed for randomly generating parameter deviation if
using parameter_precision
keep_history: flag indicating whether to store optimization history.
kwargs:
The following key word arguments are handled explicitly when appropriate:
- thetas: A list of thetas used to initialize the WarmStartQAOAAnsatz
"""
if isinstance(optimizer_specs, str):
optimizer_specs = json.loads(optimizer_specs)
optimizer = create_object(optimizer_specs)
if isinstance(target_operator, str):
target_operator = load_qubit_operator(target_operator)
if isinstance(ansatz_specs, str):
ansatz_specs = json.loads(ansatz_specs)
if "WarmStartQAOAAnsatz" in ansatz_specs["function_name"]:
ansatz_specs["thetas"] = np.array(load_list(kwargs.pop("thetas")))
ansatz_specs["cost_hamiltonian"] = target_operator
elif "QAOA" in ansatz_specs["function_name"]:
ansatz_specs["cost_hamiltonian"] = target_operator
ansatz = create_object(ansatz_specs)
if isinstance(backend_specs, str):
backend_specs = json.loads(backend_specs)
backend = create_object(backend_specs)
if estimation_method_specs is not None:
if isinstance(estimation_method_specs, str):
estimation_method_specs = json.loads(estimation_method_specs)
estimation_method = create_object(estimation_method_specs)
else:
estimation_method = estimate_expectation_values_by_averaging
estimation_preprocessors = []
if estimation_preprocessors_specs is not None:
for estimation_preprocessor_specs in estimation_preprocessors_specs:
if isinstance(estimation_preprocessor_specs, str):
estimation_preprocessor_specs = json.loads(
estimation_preprocessor_specs)
estimation_preprocessors.append(
create_object(estimation_preprocessor_specs))
if initial_parameters is not None:
if isinstance(initial_parameters, str):
initial_parameters = load_array(initial_parameters)
if fixed_parameters is not None:
if isinstance(fixed_parameters, str):
fixed_parameters = load_array(fixed_parameters)
cost_function = AnsatzBasedCostFunction(
target_operator,
ansatz,
backend,
estimation_method=estimation_method,
estimation_preprocessors=estimation_preprocessors,
fixed_parameters=fixed_parameters,
parameter_precision=parameter_precision,
parameter_precision_seed=parameter_precision_seed,
)
optimization_results = optimizer.minimize(cost_function,
initial_parameters, keep_history)
save_optimization_results(optimization_results,
"optimization-results.json")
save_array(optimization_results.opt_params, "optimized-parameters.json")
def optimize_variational_circuit(
ansatz_specs,
backend_specs,
optimizer_specs,
cost_function_specs,
qubit_operator,
initial_parameters="None",
fixed_parameters="None",
noise_model="None",
device_connectivity="None",
parameter_grid="None",
constraint_operator="None",
):
if initial_parameters != "None":
initial_params = load_circuit_template_params(initial_parameters)
else:
initial_params = None
if fixed_parameters != "None":
fixed_params = load_circuit_template_params(fixed_parameters)
else:
fixed_params = None
# Load qubit operator
operator = load_qubit_operator(qubit_operator)
if isinstance(ansatz_specs, str):
ansatz_specs_dict = yaml.load(ansatz_specs, Loader=yaml.SafeLoader)
else:
ansatz_specs_dict = ansatz_specs
if ansatz_specs_dict["function_name"] == "QAOAFarhiAnsatz":
ansatz = create_object(ansatz_specs_dict, cost_hamiltonian=operator)
else:
ansatz = create_object(ansatz_specs_dict)
# Load parameter grid
if parameter_grid != "None":
grid = load_parameter_grid(parameter_grid)
else:
grid = None
# Load optimizer specs
if isinstance(optimizer_specs, str):
optimizer_specs_dict = yaml.load(optimizer_specs,
Loader=yaml.SafeLoader)
else:
optimizer_specs_dict = optimizer_specs
if (grid is not None and optimizer_specs_dict["function_name"]
== "GridSearchOptimizer"):
optimizer = create_object(optimizer_specs_dict, grid=grid)
else:
optimizer = create_object(optimizer_specs_dict)
# Load backend specs
if isinstance(backend_specs, str):
backend_specs_dict = yaml.load(backend_specs, Loader=yaml.SafeLoader)
else:
backend_specs_dict = backend_specs
if noise_model != "None":
backend_specs_dict["noise_model"] = load_noise_model(noise_model)
if device_connectivity != "None":
backend_specs_dict["device_connectivity"] = load_circuit_connectivity(
device_connectivity)
backend = create_object(backend_specs_dict)
# Load cost function specs
if isinstance(cost_function_specs, str):
cost_function_specs_dict = yaml.load(cost_function_specs,
Loader=yaml.SafeLoader)
else:
cost_function_specs_dict = cost_function_specs
estimator_specs = cost_function_specs_dict.pop("estimator-specs", None)
if estimator_specs is not None:
cost_function_specs_dict["estimator"] = create_object(estimator_specs)
cost_function_specs_dict["target_operator"] = operator
cost_function_specs_dict["ansatz"] = ansatz
cost_function_specs_dict["backend"] = backend
cost_function_specs_dict["fixed_parameters"] = fixed_params
cost_function = create_object(cost_function_specs_dict)
if constraint_operator != "None":
constraint_op = load_qubit_operator(constraint_operator)
constraints_cost_function_specs = yaml.load(cost_function_specs,
Loader=yaml.SafeLoader)
constraints_estimator_specs = constraints_cost_function_specs.pop(
"estimator-specs", None)
if constraints_estimator_specs is not None:
constraints_cost_function_specs["estimator"] = create_object(
constraints_estimator_specs)
constraints_cost_function_specs["ansatz"] = ansatz
constraints_cost_function_specs["backend"] = backend
constraints_cost_function_specs["target_operator"] = constraint_op
constraint_cost_function = create_object(
constraints_cost_function_specs)
constraint_cost_function_wrapper = (
lambda params: constraint_cost_function.evaluate(params).value)
constraint_functions = ({
"type": "eq",
"fun": constraint_cost_function_wrapper
}, )
optimizer.constraints = constraint_functions
opt_results = optimizer.minimize(cost_function, initial_params)
save_optimization_results(opt_results, "optimization-results.json")
save_circuit_template_params(opt_results.opt_params,
"optimized-parameters.json")
def optimize_variational_circuit(
ansatz_specs,
backend_specs,
optimizer_specs,
cost_function_specs,
qubit_operator,
initial_parameters="None",
fixed_parameters="None",
noise_model="None",
device_connectivity="None",
parameter_grid="None",
parameter_values_list=None,
constraint_operator="None",
prior_expectation_values: Optional[str] = None,
keep_history=False,
thetas=None,
):
warnings.warn(
"optimize_variational_circuit will be depreciated in favor of optimize_ansatz_based_cost_function in steps/optimize.py in z-quantum-core.",
DeprecationWarning,
)
if initial_parameters != "None":
initial_params = load_array(initial_parameters)
else:
initial_params = None
if fixed_parameters != "None":
fixed_params = load_array(fixed_parameters)
else:
fixed_params = None
# Load qubit operator
operator = load_qubit_operator(qubit_operator)
if isinstance(ansatz_specs, str):
ansatz_specs_dict = yaml.load(ansatz_specs, Loader=yaml.SafeLoader)
else:
ansatz_specs_dict = ansatz_specs
if "WarmStartQAOAAnsatz" in ansatz_specs_dict["function_name"]:
thetas = np.array(load_list(thetas))
ansatz = create_object(ansatz_specs_dict,
cost_hamiltonian=operator,
thetas=thetas)
elif "QAOA" in ansatz_specs_dict["function_name"]:
ansatz = create_object(ansatz_specs_dict, cost_hamiltonian=operator)
else:
ansatz = create_object(ansatz_specs_dict)
# Load parameter grid
if parameter_grid != "None":
grid = load_parameter_grid(parameter_grid)
else:
grid = None
# Load parameter values list
if parameter_values_list is not None:
parameter_values_list = load_array(parameter_values_list)
# Load optimizer specs
if isinstance(optimizer_specs, str):
optimizer_specs_dict = yaml.load(optimizer_specs,
Loader=yaml.SafeLoader)
else:
optimizer_specs_dict = optimizer_specs
if (grid is not None and optimizer_specs_dict["function_name"]
== "GridSearchOptimizer"):
optimizer = create_object(optimizer_specs_dict, grid=grid)
elif (parameter_values_list is not None and
optimizer_specs_dict["function_name"] == "SearchPointsOptimizer"):
optimizer = create_object(optimizer_specs_dict,
parameter_values_list=parameter_values_list)
else:
optimizer = create_object(optimizer_specs_dict)
# Load backend specs
if isinstance(backend_specs, str):
backend_specs_dict = yaml.load(backend_specs, Loader=yaml.SafeLoader)
else:
backend_specs_dict = backend_specs
if noise_model != "None":
backend_specs_dict["noise_model"] = load_noise_model(noise_model)
if device_connectivity != "None":
backend_specs_dict["device_connectivity"] = load_circuit_connectivity(
device_connectivity)
backend = create_object(backend_specs_dict)
# Load cost function specs
if isinstance(cost_function_specs, str):
cost_function_specs_dict = yaml.load(cost_function_specs,
Loader=yaml.SafeLoader)
else:
cost_function_specs_dict = cost_function_specs
estimation_method_specs = cost_function_specs_dict.pop(
"estimation_method_specs", None)
if estimation_method_specs is not None:
if isinstance(estimation_method_specs, str):
estimation_method_specs = yaml.loads(estimation_method_specs)
estimation_method = create_object(estimation_method_specs)
else:
estimation_method = estimate_expectation_values_by_averaging
cost_function_specs_dict["estimation_method"] = estimation_method
estimation_preprocessors_specs_list = cost_function_specs_dict.pop(
"estimation_preprocessors_specs", None)
if estimation_preprocessors_specs_list is not None:
estimation_preprocessors = []
for estimation_preprocessor_specs in estimation_preprocessors_specs_list:
if isinstance(estimation_preprocessor_specs, str):
estimation_preprocessor_specs = yaml.loads(
estimation_preprocessor_specs)
estimation_preprocessors.append(
create_object(estimation_preprocessor_specs))
cost_function_specs_dict[
"estimation_preprocessors"] = estimation_preprocessors
cost_function_specs_dict["target_operator"] = operator
cost_function_specs_dict["ansatz"] = ansatz
cost_function_specs_dict["backend"] = backend
cost_function_specs_dict["fixed_parameters"] = fixed_params
cost_function = create_object(cost_function_specs_dict)
if prior_expectation_values is not None:
if isinstance(prior_expectation_values, str):
cost_function.estimator.prior_expectation_values = load_expectation_values(
prior_expectation_values)
if constraint_operator != "None":
constraint_op = load_qubit_operator(constraint_operator)
constraints_cost_function_specs = yaml.load(cost_function_specs,
Loader=yaml.SafeLoader)
constraints_estimator_specs = constraints_cost_function_specs.pop(
"estimator-specs", None)
if constraints_estimator_specs is not None:
constraints_cost_function_specs["estimator"] = create_object(
constraints_estimator_specs)
constraints_cost_function_specs["ansatz"] = ansatz
constraints_cost_function_specs["backend"] = backend
constraints_cost_function_specs["target_operator"] = constraint_op
constraint_cost_function = create_object(
constraints_cost_function_specs)
constraint_cost_function_wrapper = (
lambda params: constraint_cost_function.evaluate(params).value)
constraint_functions = ({
"type": "eq",
"fun": constraint_cost_function_wrapper
}, )
optimizer.constraints = constraint_functions
opt_results = optimizer.minimize(cost_function, initial_params,
keep_history)
save_optimization_results(opt_results, "optimization-results.json")
save_array(opt_results.opt_params, "optimized-parameters.json")