def test_t2(self):
"""
Run the simulator with thermal relaxation noise.
Then verify that the calculated T2 matches the t2 parameter.
"""
backend_result, xdata, qubits, t2_value = t2_circuit_execution()
T2Fitter(backend_result, xdata, qubits,
fit_p0=[1, t2_value, -0.5],
fit_bounds=([0, 0, -1], [2, t2_value*1.2, 1]))
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 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 test_t2star(self):
"""
Run the simulator with thermal relaxation noise.
Then verify that the calculated T2star matches the t2
parameter.
"""
# Setting parameters
num_of_gates = num_of_gates = np.append(
(np.linspace(10, 150, 30)).astype(int),
(np.linspace(160, 450, 20)).astype(int))
gate_time = 0.1
qubits = [0]
expected_t2 = 10
error = thermal_relaxation_error(np.inf, expected_t2, gate_time, 0.5)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error, 'id')
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 300
# Estimating T2* via an exponential function
circs, xdata, _ = t2star_circuits(num_of_gates, gate_time,
qubits)
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 = 0.5
initial_c = 0.5
fit = T2Fitter(backend_result, xdata,
qubits,
fit_p0=[initial_a, initial_t2, initial_c],
fit_bounds=([-0.5, 0, -0.5],
[1.5, expected_t2*1.2, 1.5]),
circbasename='t2star')
self.assertAlmostEqual(fit.time(qid=0), expected_t2, delta=2,
msg='Calculated T2 is inaccurate')
self.assertTrue(
fit.time_err(qid=0) < 2,
'Confidence in T2 calculation is too low: ' + str(fit.time_err))
# Estimate T2* via an oscilliator function
circs_osc, xdata, omega = t2star_circuits(num_of_gates, gate_time,
qubits, 5)
backend_result = qiskit.execute(
circs_osc, backend,
shots=shots,
backend_options={'max_parallel_experiments': 0},
noise_model=noise_model).result()
initial_a = 0.5
initial_c = 0.5
initial_f = omega
initial_phi = 0
fit = T2StarFitter(backend_result, xdata, qubits,
fit_p0=[initial_a, initial_t2, initial_f,
initial_phi, initial_c],
fit_bounds=([-0.5, 0, omega-0.02, -np.pi, -0.5],
[1.5, expected_t2*1.2, omega+0.02,
np.pi, 1.5]))
self.assertAlmostEqual(fit.time(qid=0), expected_t2, delta=2,
msg='Calculated T2 is inaccurate')
self.assertTrue(
fit.time_err(qid=0) < 2,
'Confidence in T2 calculation is too low: ' + str(fit.time_err))
def test_t2star(self):
"""
Run the simulator with thermal relaxation noise.
Then verify that the calculated T2star matches the t2
parameter.
"""
# Setting parameters
num_of_gates = num_of_gates = np.append(
(np.linspace(10, 150, 10)).astype(int),
(np.linspace(160, 450, 5)).astype(int))
gate_time = 0.1
qubits = [0]
expected_t2 = 10
error = thermal_relaxation_error(np.inf, expected_t2, gate_time, 0.5)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error, 'id')
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 200
# Estimating T2* via an exponential function
circs, xdata, _ = t2star_circuits(num_of_gates, gate_time, qubits)
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 = 0.5
initial_c = 0.5
T2Fitter(backend_result,
xdata,
qubits,
fit_p0=[initial_a, initial_t2, initial_c],
fit_bounds=([-0.5, 0, -0.5], [1.5, expected_t2 * 1.2, 1.5]),
circbasename='t2star')
# Estimate T2* via an oscilliator function
circs_osc, xdata, omega = t2star_circuits(num_of_gates, gate_time,
qubits, 5)
backend_result = qiskit.execute(circs_osc,
backend,
shots=shots,
seed_simulator=SEED,
backend_options={
'max_parallel_experiments': 0
},
noise_model=noise_model,
optimization_level=0).result()
initial_a = 0.5
initial_c = 0.5
initial_f = omega
initial_phi = 0
T2StarFitter(
backend_result,
xdata,
qubits,
fit_p0=[initial_a, initial_t2, initial_f, initial_phi, initial_c],
fit_bounds=([-0.5, 0, omega - 0.02, -np.pi, -0.5],
[1.5, expected_t2 * 1.2, omega + 0.02, np.pi, 1.5]))