def test_t2(self):
"""
Run the simulator with dephasing noise.
Then verify that the calculated T2 matches the dephasing parameter.
"""
# 25 numbers ranging from 1 to 200, linearly spaced
num_of_gates = (np.linspace(1, 300, 35)).astype(int)
gate_time = 0.11
num_of_qubits = 2
qubit = 0
circs, xdata = t2_circuits(num_of_gates, gate_time, num_of_qubits,
qubit)
expected_t2 = 20
gamma = 1 - np.exp(-2 * gate_time / expected_t2)
error = phase_damping_error(gamma)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error, 'id')
# TODO: Include SPAM errors
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 300
backend_result = qiskit.execute(circs,
backend,
shots=shots,
backend_options={
'max_parallel_experiments': 0
},
noise_model=noise_model).result()
initial_t2 = expected_t2
initial_a = 1
initial_c = 0.5 * (-1)
fit = T2Fitter(backend_result,
shots,
xdata,
num_of_qubits,
qubit,
fit_p0=[initial_a, initial_t2, initial_c],
fit_bounds=([0, 0, -1], [2, expected_t2 * 1.2, 1]))
self.assertAlmostEqual(fit.time,
expected_t2,
delta=4,
msg='Calculated T2 is inaccurate')
self.assertTrue(
fit.time_err < 5,
'Confidence in T2 calculation is too low: ' + str(fit.time_err))
def main():
# Get backends
IBMQ.load_account()
provider = IBMQ.get_provider(hub="ibm-q-ornl", group="anl", project="chm168")
backend = provider.get_backend("ibmq_burlington")
plugin_backend = Quac.get_backend("fake_burlington_density_simulator", t1=True, t2=True, meas=False, zz=False)
# Get calibration circuits
hardware_properties = FakeBurlington().properties()
num_gates = np.linspace(10, 500, 30, dtype='int')
qubits = list(range(len(backend.properties().qubits)))
t1_circs, t1_delay = t1_circuits(num_gates,
hardware_properties.gate_length('id', [0]) * 1e9,
qubits)
t2_circs, t2_delay = t2_circuits((num_gates / 2).astype('int'),
hardware_properties.gate_length('id', [0]) * 1e9,
qubits)
# Formulate real noise model
real_noise_model = QuacNoiseModel(
t1_times=[1234, 2431, 2323, 2222, 3454],
t2_times=[12000, 14000, 14353, 20323, 30232]
)
# Formulate initial guess noise model (only same order of magnitude)
guess_noise_model = QuacNoiseModel(
t1_times=[1000, 1000, 1000, 1000, 1000],
t2_times=[10000, 10000, 10000, 10000, 10000]
)
# Calculate calibration circuit reference results
reference_job = execute(t1_circs + t2_circs, plugin_backend,
quac_noise_model=real_noise_model,
optimization_level=0)
reference_result = reference_job.result()
# Calculate optimized noise model
new_noise_model = optimize_noise_model_ng(
guess_noise_model=guess_noise_model,
circuits=t1_circs + t2_circs,
backend=plugin_backend,
reference_result=reference_result,
loss_function=angle_objective_function
)
# Show original noise model and optimized noise model
print(f"Original noise model: {real_noise_model}")
print(f"New noise model: {new_noise_model}")
def test_t2(self):
"""
Run the simulator with thermal relaxation noise.
Then verify that the calculated T2 matches the t2 parameter.
"""
num_of_gates = (np.linspace(1, 30, 30)).astype(int)
gate_time = 0.11
qubits = [0]
n_echos = 5
alt_phase_echo = True
circs, xdata = t2_circuits(num_of_gates, gate_time, qubits,
n_echos, alt_phase_echo)
expected_t2 = 20
error = thermal_relaxation_error(np.inf, expected_t2, gate_time, 0.5)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error, 'id')
# TODO: Include SPAM errors
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 300
backend_result = qiskit.execute(
circs, backend,
shots=shots,
backend_options={'max_parallel_experiments': 0},
noise_model=noise_model).result()
initial_t2 = expected_t2
initial_a = 1
initial_c = 0.5*(-1)
fit = T2Fitter(backend_result, xdata, qubits,
fit_p0=[initial_a, initial_t2, initial_c],
fit_bounds=([0, 0, -1], [2, expected_t2*1.2, 1]))
self.assertAlmostEqual(fit.time(qid=0), expected_t2, delta=5,
msg='Calculated T2 is inaccurate')
self.assertTrue(
fit.time_err(qid=0) < 5,
'Confidence in T2 calculation is too low: ' + str(fit.time_err))
def test_t2(self):
"""
Run the simulator with thermal relaxation noise.
Then verify that the calculated T2 matches the t2 parameter.
"""
num_of_gates = (np.linspace(1, 30, 10)).astype(int)
gate_time = 0.11
qubits = [0]
n_echos = 5
alt_phase_echo = True
circs, xdata = t2_circuits(num_of_gates, gate_time, qubits, n_echos,
alt_phase_echo)
expected_t2 = 20
error = thermal_relaxation_error(np.inf, expected_t2, gate_time, 0.5)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error, 'id')
# TODO: Include SPAM errors
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 100
backend_result = qiskit.execute(circs,
backend,
shots=shots,
seed_simulator=SEED,
backend_options={
'max_parallel_experiments': 0
},
noise_model=noise_model,
optimization_level=0).result()
initial_t2 = expected_t2
initial_a = 1
initial_c = 0.5 * (-1)
T2Fitter(backend_result,
xdata,
qubits,
fit_p0=[initial_a, initial_t2, initial_c],
fit_bounds=([0, 0, -1], [2, expected_t2 * 1.2, 1]))
def t2_circuit_execution(
) -> Tuple[qiskit.result.Result, np.array, List[int], float]:
"""
Create T2 circuits and simulate them.
Returns:
* Backend result.
* xdata.
* Qubits for the T2 measurement.
* T2 that was used in the circuits creation.
"""
num_of_gates = (np.linspace(1, 30, 10)).astype(int)
gate_time = 0.11
qubits = [0]
n_echos = 5
alt_phase_echo = True
circs, xdata = t2_circuits(num_of_gates, gate_time, qubits, n_echos,
alt_phase_echo)
t2_value = 20
error = thermal_relaxation_error(np.inf, t2_value, gate_time, 0.5)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error, 'id')
# TODO: Include SPAM errors
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 100
backend_result = qiskit.execute(circs,
backend,
shots=shots,
seed_simulator=SEED,
backend_options={
'max_parallel_experiments': 0
},
noise_model=noise_model,
optimization_level=0).result()
return backend_result, xdata, qubits, t2_value