def test_qv_sigma_decreasing(self):
"""
Test that the sigma is decreasing after adding more trials
"""
num_of_qubits = 3
backend = Aer.get_backend("aer_simulator")
qv_exp = QuantumVolume(num_of_qubits, seed=SEED)
# set number of trials to a low number to make the test faster
qv_exp.set_experiment_options(trials=2)
expdata1 = qv_exp.run(backend)
expdata1.block_for_results()
result_data1 = expdata1.analysis_results(0)
expdata2 = qv_exp.run(backend, experiment_data=expdata1)
expdata2.block_for_results()
result_data2 = expdata2.analysis_results(0)
self.assertTrue(result_data1.extra["trials"] == 2,
"number of trials is incorrect")
self.assertTrue(
result_data2.extra["trials"] == 4,
"number of trials is incorrect"
" after adding more trials",
)
self.assertTrue(
result_data2.value.stderr <= result_data1.value.stderr,
"sigma did not decreased after adding more trials",
)
def create_qv_data_high_confidence(dir_path: str):
"""
create quantum volume experiment_data using seed, and save it as a json
the circuit is generated with moderate noise, so the mean hop is above 2/3,
and also with enough trials, so the confidence is above threshold
Args:
dir_path(str): The directory which the data will be saved to.
"""
num_of_qubits = 4
backend = AerSimulator(seed_simulator=SEED)
basis_gates = ["id", "rz", "sx", "x", "cx", "reset"]
noise = create_noise_model()
qv_exp = QuantumVolume(range(num_of_qubits), seed=SEED)
qv_exp.set_experiment_options(trials=300)
qv_exp.set_transpile_options(basis_gates=basis_gates)
qv_data = qv_exp.run(backend, noise_model=noise, basis_gates=basis_gates)
qv_data.block_for_results()
result_file_path = os.path.join(dir_path, "qv_data_moderate_noise_300_trials.json")
with open(result_file_path, "w") as json_file:
json.dump(qv_data.data(), json_file, cls=ExperimentEncoder)
result_file_path = os.path.join(dir_path, "qv_result_moderate_noise_300_trials.json")
with open(result_file_path, "w") as json_file:
result_dicts = []
for result in qv_data.analysis_results():
result_dicts.append(
{
"name": result.name,
"value": result.value,
"extra": result.extra,
}
)
json.dump(result_dicts, json_file, cls=ExperimentEncoder)
def create_qv_data_70_trials(dir_path: str):
"""
create quantum volume experiment_data using seed, and save it as a json
Args:
dir_path(str): The directory which the data will be saved to.
"""
num_of_qubits = 3
backend = AerSimulator(seed_simulator=SEED)
qv_exp = QuantumVolume(range(num_of_qubits), seed=SEED)
qv_exp.set_experiment_options(trials=70)
qv_data = qv_exp.run(backend)
qv_data.block_for_results()
result_file_path = os.path.join(dir_path, "qv_data_70_trials.json")
with open(result_file_path, "w") as json_file:
json.dump(qv_data.data(), json_file, cls=ExperimentEncoder)
def create_qv_data_low_hop(dir_path: str):
"""
create quantum volume experiment_data using seed, and save it as a json
the circuit is generated with high noise, so the mean hop is below 2/3
Args:
dir_path(str): The directory which the data will be saved to.
"""
num_of_qubits = 4
backend = AerSimulator(seed_simulator=SEED)
basis_gates = ["id", "rz", "sx", "x", "cx", "reset"]
noise = create_high_noise_model()
qv_exp = QuantumVolume(range(num_of_qubits), seed=SEED)
qv_exp.set_transpile_options(basis_gates=basis_gates)
qv_data = qv_exp.run(backend, noise_model=noise, basis_gates=basis_gates)
qv_data.block_for_results()
result_file_path = os.path.join(dir_path, "qv_data_high_noise.json")
with open(result_file_path, "w") as json_file:
json.dump(qv_data.data(), json_file, cls=ExperimentEncoder)
def create_qv_data_low_confidence(dir_path: str):
"""
create quantum volume experiment_data using seed, and save it as a json
the circuit is generated with moderate noise, so the mean hop is above 2/3,
but there are not enough trials, so the confidence is below threshold
Args:
dir_path(str): The directory which the data will be saved to.
"""
num_of_qubits = 4
backend = Aer.get_backend("aer_simulator")
basis_gates = ["id", "rz", "sx", "x", "cx", "reset"]
noise = create_noise_model()
qv_exp = QuantumVolume(num_of_qubits, seed=SEED)
qv_exp.set_transpile_options(basis_gates=basis_gates)
qv_data = qv_exp.run(backend, noise_model=noise, basis_gates=basis_gates)
qv_data.block_for_results()
result_file_path = os.path.join(dir_path,
"qv_data_moderate_noise_100_trials.json")
with open(result_file_path, "w") as json_file:
json.dump(qv_data.data(), json_file, cls=ExperimentEncoder)