def t2star_circuit_execution(
) -> Tuple[qiskit.result.Result, np.array, List[int], float, float]:
"""
Create T2* circuits and simulate them.
Returns:
* Backend result.
* xdata.
* Qubits for the T2* measurement.
* T2* that was used in the circuits creation.
* Frequency.
"""
# Setting parameters
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]
t2_value = 10
error = thermal_relaxation_error(np.inf, t2_value, 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
# 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()
return backend_result, xdata, qubits, t2_value, omega
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]))
def test_t2star(self):
"""
Run the simulator with phase damping noise.
Then verify that the calculated T2star matches the phase damping
parameter.
"""
# Setting parameters
# 25 numbers ranging from 100 to 1000, linearly spaced
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
num_of_qubits = 1
qubit = 0
expected_t2 = 10
p = 1 - np.exp(-2 * gate_time / expected_t2)
error = phase_damping_error(p)
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,
num_of_qubits, qubit)
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 = T2StarExpFitter(backend_result,
shots,
xdata,
num_of_qubits,
qubit,
fit_p0=[initial_a, initial_t2, initial_c],
fit_bounds=([-0.5, 0, -0.5],
[1.5, expected_t2 * 1.2, 1.5]))
self.assertAlmostEqual(fit.time,
expected_t2,
delta=2,
msg='Calculated T2 is inaccurate')
self.assertTrue(
fit.time_err < 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,
num_of_qubits, qubit, 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 = T2StarOscFitter(
backend_result,
shots,
xdata,
num_of_qubits,
qubit,
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,
expected_t2,
delta=2,
msg='Calculated T2 is inaccurate')
self.assertTrue(
fit.time_err < 2,
'Confidence in T2 calculation is too low: ' + str(fit.time_err))
print(result_real2.get_counts(0))
# # End of Run on Real Hardware
# End Circuit 2
# Question 2
print("Question 2:")
# T2* Charactirization circuit
SEED = 0
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, 1]
circs_osc, xdata, omega = t2star_circuits(num_of_gates, gate_time, qubits, 5)
shots = 2048
# Start of T2* characteristic whithout noise - simulation
# T2_cha_result = qiskit.execute(
# circs_osc, backend,
# shots=shots,
# seed_simulator=SEED,
# optimization_level=0).result()
# print("T2* characteristic - simulation without noise - NOT GOOD NEED TO CHANGE TO GRAPH")
# print(T2_cha_result.get_counts(0))
# End of T2* characteristic whithout noise - simulation