def run(self, qobj):
"""Main job in simulator"""
if HAS_AER:
if qobj.type == "PULSE":
from qiskit.providers.aer.pulse import PulseSystemModel
system_model = PulseSystemModel.from_backend(self)
sim = aer.Aer.get_backend("pulse_simulator")
job = sim.run(qobj, system_model)
else:
sim = aer.Aer.get_backend("qasm_simulator")
if self.properties():
from qiskit.providers.aer.noise import NoiseModel
noise_model = NoiseModel.from_backend(self, warnings=False)
job = sim.run(qobj, noise_model=noise_model)
else:
job = sim.run(qobj)
out_job = fake_job.FakeLegacyJob(self, job.job_id, None)
out_job._future = job._future
else:
if qobj.type == "PULSE":
raise QiskitError("Unable to run pulse schedules without " "qiskit-aer installed")
warnings.warn("Aer not found using BasicAer and no noise", RuntimeWarning)
def run_job():
sim = basicaer.BasicAer.get_backend("qasm_simulator")
return sim.run(qobj).result()
job_id = uuid.uuid4()
out_job = fake_job.FakeLegacyJob(self, job_id, run_job)
out_job.submit()
return out_job
def run(self, qobj):
"""Main job in simulator"""
if HAS_AER:
if qobj.type == 'PULSE':
from qiskit.providers.aer.pulse import PulseSystemModel
system_model = PulseSystemModel.from_backend(self)
sim = Aer.get_backend('pulse_simulator')
job = sim.run(qobj, system_model)
else:
sim = Aer.get_backend('qasm_simulator')
if self.properties():
from qiskit.providers.aer.noise import NoiseModel
noise_model = NoiseModel.from_backend(self, warnings=False)
job = sim.run(qobj, noise_model=noise_model)
else:
job = sim.run(qobj)
else:
if qobj.type == 'PULSE':
raise QiskitError("Unable to run pulse schedules without "
"qiskit-aer installed")
warnings.warn("Aer not found using BasicAer and no noise",
RuntimeWarning)
sim = BasicAer.get_backend('qasm_simulator')
job = sim.run(qobj)
return job
def test_simulator_with_noise_model(self, backend):
"""Test using simulator with a noise model."""
noise_model = NoiseModel.from_backend(backend)
result = self.sim_backend.run(
transpile(ReferenceCircuits.bell(), backend=self.sim_backend),
noise_model=noise_model).result()
self.assertTrue(result)
def mitigated_results(backend,circuit,results,results_sim):
from qiskit import Aer, execute
from qiskit import QuantumRegister
# Import the required methods
from qiskit.providers.aer.noise import NoiseModel
from qiskit.ignis.mitigation.measurement import (complete_meas_cal,CompleteMeasFitter)
from qiskit.tools.visualization import plot_histogram
import numpy
numpy.set_printoptions(formatter={'float': lambda x: "{0:0.2f}".format(x)})
# Get noise model for backend
noise_model = NoiseModel.from_backend(backend)
# Create the measurement fitter
qr = QuantumRegister(circuit.num_qubits)
meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')
job = execute(meas_calibs, backend=Aer.get_backend('qasm_simulator'), shots=8192, noise_model=noise_model)
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')
print(meas_fitter.cal_matrix)
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(results)
mitigated_counts = mitigated_results.get_counts(0)
title = "Mitigated Grover on "+str(ibmqbackend.name())
display(plot_histogram([results_sim.get_counts(),results.get_counts(), mitigated_counts], title=title, legend=['sim','noisy', 'mitigated']))
return(mitigated_counts)
def simulate_qc(qc, shots=1000, bname='qasm_simulator', noise=None):
'''simulates a quantum circuit, and
returns a dictionary with the keys being
the binary representation of the measurement
and the values being the number of counts that
measurement recieved. The total number of counts
equals shots
'''
if noise == None:
job = execute(qc, Aer.get_backend(bname), shots=shots)
else:
backend = noise()
noise_model = NoiseModel.from_backend(backend)
coupling_map = backend.configuration().coupling_map
basis_gates = noise_model.basis_gates
job = execute(qc,
Aer.get_backend(bname),
shots=shots,
coupling_map=coupling_map,
basis_gates=basis_gates,
noise_model=noise_model)
result = job.result()
counts = result.get_counts(qc)
return counts
def test_zz(self):
"""
Run the simulator with unitary noise.
Then verify that the calculated ZZ matches the zz parameter.
"""
num_of_gates = np.arange(0, 60, 10)
gate_time = 0.1
qubits = [0]
spectators = [1]
# Generate experiments
circs, xdata, osc_freq = zz_circuits(num_of_gates,
gate_time,
qubits,
spectators,
nosc=2)
# Set the simulator with ZZ
zz_expected = 0.1
zz_unitary = np.eye(4, dtype=complex)
zz_unitary[3, 3] = np.exp(1j * 2 * np.pi * zz_expected * gate_time)
error = coherent_unitary_error(zz_unitary)
noise_model = NoiseModel()
noise_model.add_nonlocal_quantum_error(error, 'id', [0], [0, 1])
# Run the simulator
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 100
# For demonstration purposes split the execution into two jobs
backend_result = qiskit.execute(circs,
backend,
shots=shots,
noise_model=noise_model).result()
initial_a = 0.5
initial_c = 0.5
initial_f = osc_freq
initial_phi = 0.0
ZZFitter(backend_result,
xdata,
qubits,
spectators,
fit_p0=[initial_a, initial_f, initial_phi, initial_c],
fit_bounds=([-0.5, 0, -np.pi,
-0.5], [1.5, 2 * osc_freq, np.pi, 1.5]))
def zz_circuit_execution() -> Tuple[qiskit.result.Result,
np.array,
List[int],
List[int],
float,
float]:
"""
Create ZZ circuits and simulate them.
Returns:
* Backend result.
* xdata.
* Qubits for the ZZ measurement.
* Spectators.
* ZZ parameter that used in the circuit creation
* Frequency.
"""
num_of_gates = np.arange(0, 60, 10)
gate_time = 0.1
qubits = [0]
spectators = [1]
# Generate experiments
circs, xdata, omega = zz_circuits(num_of_gates,
gate_time, qubits,
spectators, nosc=2)
# Set the simulator with ZZ
zz_value = 0.1
zz_unitary = np.eye(4, dtype=complex)
zz_unitary[3, 3] = np.exp(1j*2*np.pi*zz_value*gate_time)
error = coherent_unitary_error(zz_unitary)
noise_model = NoiseModel()
noise_model.add_nonlocal_quantum_error(error, 'id', [0], [0, 1])
# Run the simulator
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 100
backend_result = qiskit.execute(circs, backend,
shots=shots,
seed_simulator=SEED,
noise_model=noise_model,
optimization_level=0).result()
return backend_result, xdata, qubits, spectators, zz_value, omega
def generate_data_noisy():
"""generaete a noisy data pickle"""
seed_accreditation = 1435754
seed_simulator = 877924554
accsys = make_accred_system(seed=seed_accreditation)
simulator = qiskit.Aer.get_backend('qasm_simulator')
noise_model = NoiseModel()
p1q = 0.002
noise_model.add_all_qubit_quantum_error(depolarizing_error(p1q, 1), 'u1')
noise_model.add_all_qubit_quantum_error(depolarizing_error(p1q, 1), 'u2')
noise_model.add_all_qubit_quantum_error(depolarizing_error(p1q, 1), 'u3')
p2q = 0.02
noise_model.add_all_qubit_quantum_error(depolarizing_error(p2q, 2), 'cx')
basis_gates = ['u1', 'u2', 'u3', 'cx']
# Number of runs
d = 20
v = 10
test_3 = AccreditationFitter()
all_results = []
all_postp_list = []
all_v_zero = []
all_acc = []
for run in range(d):
print([run, d])
# Create target and trap circuits with random Pauli gates
circuit_list, postp_list, v_zero = accsys.generate_circuits(v)
# Implement all these circuits with noise
job = execute(circuit_list,
simulator,
noise_model=noise_model,
basis_gates=basis_gates,
shots=1,
seed_simulator=seed_simulator + run)
all_results.append(job.result())
all_postp_list.append(postp_list)
all_v_zero.append(v_zero)
# Post-process the outputs and see if the protocol accepts
test_3.single_protocol_run(job.result(), postp_list, v_zero)
all_acc.append(test_3.flag)
theta = 5 / 100
test_3.bound_variation_distance(theta)
bound = test_3.bound
outputdict = {
'all_results': all_results,
'all_postp_list': all_postp_list,
'all_v_zero': all_v_zero,
'all_acc': all_acc,
'theta': theta,
'bound': bound
}
f = open('accred_noisy_results.pkl', 'wb')
pickle.dump(outputdict, f)
f.close()
def test_noise_models_not_equal(self):
"""Test two noise models are not equal"""
error = pauli_error([['X', 1]])
model1 = NoiseModel()
model1.add_all_qubit_quantum_error(error, ['u3'], False)
model2 = NoiseModel(basis_gates=['u3', 'cx'])
model2.add_all_qubit_quantum_error(error, ['u3'], False)
def test_noise_model_from_invalid_t2_backend(self):
"""Test if issue user warning when creating a noise model from invalid t2 backend"""
from qiskit.providers.models.backendproperties import BackendProperties, Gate, Nduv
import datetime
t1_ns, invalid_t2_ns = 75_1000, 200_1000
u3_time_ns = 320
frequency = 4919.96800692
class InvalidT2Fake1Q(mock.FakeBackend):
def __init__(self):
mock_time = datetime.datetime.now()
dt = 1.3333
configuration = BackendProperties(
backend_name="invalid_t2",
backend_version="0.0.0",
num_qubits=1,
basis_gates=["u3"],
qubits=[
[
Nduv(date=mock_time, name="T1", unit="µs", value=t1_ns/1000),
Nduv(date=mock_time, name="T2", unit="µs", value=invalid_t2_ns/1000),
Nduv(date=mock_time, name="frequency", unit="MHz", value=frequency),
],
],
gates=[
Gate(
gate="u3",
name="u3_0",
qubits=[0],
parameters=[
Nduv(date=mock_time, name="gate_error", unit="", value=0.001),
Nduv(date=mock_time, name="gate_length", unit="ns", value=u3_time_ns),
],
),
],
last_update_date=mock_time,
general=[],
)
super().__init__(configuration)
def defaults(self):
"""defaults == configuration"""
return self._configuration
def properties(self):
"""properties == configuration"""
return self._configuration
backend = InvalidT2Fake1Q()
with self.assertWarns(UserWarning):
noise_model = NoiseModel.from_backend(backend, gate_error=False)
expected = thermal_relaxation_error(
t1=t1_ns,
t2=2*t1_ns,
time=u3_time_ns,
excited_state_population=_excited_population(frequency, temperature=0)
)
self.assertEqual(expected, noise_model._local_quantum_errors["u3"][(0, )])
def t2_star_noise_model(gate_time=0.1, t=[70.5, 85.0, 80.0, 90.5, 77.5]):
"""
Return a NoiseModel object for T2*.
Parameters
- gate_time: gate time (in microseconds) for a single-qubit gate
- t: simulated times (in microseconds) for a set of five qubits
"""
t2_star_noise_model = NoiseModel()
error = [0 for i in range(5)]
for i in range(5):
error[i] = thermal_relaxation_error(np.inf, t[i], gate_time, 0.5)
t2_star_noise_model.add_quantum_error(error[i], 'id', [i])
return t2_star_noise_model
def test_option_order_basis_gates(self, method):
"""Test order of setting method and noise model gives same basis gates"""
noise_model = NoiseModel(basis_gates=['id', 'sx', 'x', 'cx'])
sim1 = self.backend(method=method, noise_model=noise_model)
basis_gates1 = sim1.configuration().basis_gates
sim2 = self.backend(noise_model=noise_model, method=method)
basis_gates2 = sim2.configuration().basis_gates
self.assertEqual(sorted(basis_gates1), sorted(basis_gates2))
def f_average(params, shots = NB_SHOTS, noise_model = NoiseModel()):
""" estimate of the fidelity"""
if np.ndim(params) >1 : res = np.array([f_average(p, shots=shots, noise_model=noise_model) for p in params])
else:
res = qcirc.F(params, shots = shots, noise_model=noise_model)
print(res)
res = 1 - np.atleast_1d(res)
return res
def test_auto_method_nonclifford_circuit_and_kraus_noise(self):
"""Test statevector method is used for Clifford circuit"""
# Noise Model
error = amplitude_damping_error(0.5)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(
error, ['id', 'x', 'y', 'z', 'h', 's', 'sdg'])
backend = self.backend(noise_model=noise_model)
# Test circuits
shots = 100
circuits = ref_non_clifford.ccx_gate_circuits_deterministic(
final_measure=True)
result = backend.run(circuits, shots=shots).result()
success = getattr(result, 'success', False)
self.compare_result_metadata(result, circuits, 'method',
"density_matrix")
def test_auto_method_clifford_circuits_and_unitary_noise(self):
"""Test statevector method is used for Clifford circuit"""
# Noise Model
error = mixed_unitary_error([(Pauli('XX').to_matrix(), 0.5),
(Pauli('II').to_matrix(), 0.5)])
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error, ['cz', 'cx'])
backend = self.backend(noise_model=noise_model)
# Test circuits
shots = 100
circuits = ref_2q_clifford.cz_gate_circuits_deterministic(
final_measure=True)
result = backend.run(circuits, shots=shots).result()
success = getattr(result, 'success', False)
self.compare_result_metadata(result, circuits, 'method',
"density_matrix")
def _test_noise_model(self, backend_properties: BackendProperties):
noise_model: NoiseModel = NoiseModel()
for qubits, error in basic_device_readout_errors(backend_properties):
noise_model.add_readout_error(error, qubits)
for name, qubits, error in basic_device_gate_errors(properties=backend_properties):
noise_model.add_quantum_error(error, name, qubits)
LOG.info(noise_model)
def test_measure_sampling_with_readouterror(self):
"""Test QasmSimulator measure with deterministic counts with sampling and readout-error"""
readout_error = [0.01, 0.1]
noise_model = NoiseModel()
readout = [[1.0 - readout_error[0], readout_error[0]],
[readout_error[1], 1.0 - readout_error[1]]]
noise_model.add_all_qubit_readout_error(ReadoutError(readout))
shots = 1000
circuits = ref_measure.measure_circuits_deterministic(
allow_sampling=True)
targets = ref_measure.measure_counts_deterministic(shots)
qobj = assemble(circuits, self.SIMULATOR, shots=shots)
result = self.SIMULATOR.run(qobj,
noise_model=noise_model,
**self.BACKEND_OPTS).result()
self.assertSuccess(result)
def __init__(self,
make_circuit,
nbqbits,
nbparams,
backend,
cbuilder=qkBuilder,
noise_model=None,
coupling_map=None,
noise_backend=None,
save_path=None):
super().__init__(make_circuit, nbqbits, nbparams, cbuilder)
self.save_path = save_path
if isinstance(backend, Backends):
self.__backend__ = backend
self.backend = self.__backend__.backends
self.noise_model = self.__backend__.noise_models
self.coupling_map = self.__backend__.coupling_maps
self.job_limit = backend.job_limit
else:
backend = backend if isinstance(backend, list) else [backend]
try:
self.job_limit = min(map(lambda x: x.job_limit(), backend))
except AttributeError:
self.job_limit = None
self.backend = cycle(backend)
if noise_model is not None and noise_backend is not None:
raise ValueError(
"Only one between 'noise_model' and 'noise_backend' can \
be passed to the constructor")
if isinstance(noise_model, list):
self.noise_model = cycle(noise_model)
else:
self.noise_model = cycle([noise_model])
if isinstance(coupling_map, list):
self.coupling_map = cycle(coupling_map)
else:
self.coupling_map = cycle([coupling_map])
if noise_backend is not None:
_noise_back = noise_backend
if not isinstance(noise_backend, list):
_noise_back = [noise_backend]
self.noise_model = cycle([
NoiseModel.from_backend(_backend)
for _backend in _noise_back
])
self.coupling_map = cycle([
_backend.configuration().coupling_map
for _backend in _noise_back
])
def get_noise_model(self, name):
if name in IBMQ_NAMES:
backend = self.provider.get_backend(name)
self.properties = backend.properties()
noise_model = NoiseModel.from_backend(backend)
else:
noise_model = None
return noise_model
def test_nonlocal_quantum_errors(self):
qr = QuantumRegister(3, 'qr')
circuit = QuantumCircuit(qr)
circuit.x(qr[0])
circuit.x(qr[2])
error_x = pauli_error([('Y', 0.25), ('I', 0.75)])
noise_model = NoiseModel()
noise_model.add_nonlocal_quantum_error(error_x, 'x', [0], [1])
target_circuit = QuantumCircuit(qr)
target_circuit.x(qr[0])
target_circuit.append(error_x.to_instruction(), [qr[1]])
target_circuit.x(qr[2])
result_circuit = insert_noise(circuit, noise_model)
self.assertEqual(target_circuit, result_circuit)
def meas_calibration_circ_execution(nqunits: int, shots: int, seed: int):
"""
create measurement calibration circuits and simulates them with noise
Args:
nqunits (int): number of qubits to run the measurement calibration on
shots (int): number of shots per simulation
seed (int): the seed to use in the simulations
Returns:
list: list of Results of the measurement calibration simulations
list: list of all the possible states with this amount of qubits
dict: dictionary of results counts of bell circuit simulation with measurement errors
"""
# define the circuits
qr = qiskit.QuantumRegister(nqunits)
meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='test')
# define noise
prob = 0.2
error_meas = pauli_error([('X', prob), ('I', 1 - prob)])
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error_meas, "measure")
# run the circuits multiple times
backend = qiskit.Aer.get_backend('qasm_simulator')
cal_results = qiskit.execute(meas_calibs,
backend=backend,
shots=shots,
noise_model=noise_model,
seed_simulator=seed).result()
# create bell state and get it's results
qc = qiskit.QuantumCircuit(nqunits, nqunits)
qc.h(0)
qc.cx(0, 1)
qc.cx(0, 2)
qc.measure(qc.qregs[0], qc.cregs[0])
bell_results = qiskit.execute(qc,
backend=backend,
shots=shots,
noise_model=noise_model,
seed_simulator=seed).result().get_counts()
return cal_results, state_labels, bell_results
def get_noise(p_meas, p_gate):
error_meas = pauli_error([('X', p_meas), ('I', 1 - p_meas)])
error_gate1 = depolarizing_error(p_gate, 1)
error_gate2 = error_gate1.tensor(error_gate1)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error_meas, "measure")
noise_model.add_all_qubit_quantum_error(error_gate1, ["x"])
noise_model.add_all_qubit_quantum_error(error_gate2, ["cx"])
return noise_model
def test_w_noise(self):
"""with noise test"""
# build noise model
# Asymmetric readout error on qubit-0 only
try:
from qiskit.providers.aer.noise import NoiseModel
from qiskit import Aer
self.backend = Aer.get_backend("qasm_simulator")
except ImportError as ex:
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
probs_given0 = [0.9, 0.1]
probs_given1 = [0.3, 0.7]
noise_model = NoiseModel()
noise_model.add_readout_error([probs_given0, probs_given1], [0])
quantum_instance = QuantumInstance(
self.backend,
seed_transpiler=self.random_seed,
seed_simulator=self.random_seed,
shots=1024,
noise_model=noise_model,
)
res_w_noise = quantum_instance.execute(self.qc).get_counts(self.qc)
quantum_instance.skip_qobj_validation = True
res_w_noise_skip_validation = quantum_instance.execute(
self.qc).get_counts(self.qc)
self.assertTrue(_compare_dict(res_w_noise,
res_w_noise_skip_validation))
# BasicAer should fail:
with self.assertRaises(QiskitError):
_ = QuantumInstance(BasicAer.get_backend("qasm_simulator"),
noise_model=noise_model)
with self.assertRaises(QiskitError):
quantum_instance = QuantumInstance(
BasicAer.get_backend("qasm_simulator"))
quantum_instance.set_config(noise_model=noise_model)
def gate_error_noise_model(dev_name):
# regular noise model for the backend
device = provider.get_backend(dev_name)
properties = device.properties()
gate_lengths = noise.device.parameters.gate_length_values(properties)
noise_model = NoiseModel.from_backend(properties,
gate_lengths=gate_lengths)
basis_gates = noise_model.basis_gates
coupling_map = device.configuration().coupling_map
return device, noise_model, basis_gates, coupling_map
def test_readout_error_all_qubit(self):
"""Test 100% readout error on all qubits"""
# Test circuit: ideal bell state
qr = QuantumRegister(2, 'qr')
cr = ClassicalRegister(2, 'cr')
circuit = QuantumCircuit(qr, cr)
circuit.h(qr[0])
circuit.cx(qr[0], qr[1])
# Ensure qubit 0 is measured before qubit 1
circuit.barrier(qr)
circuit.measure(qr[0], cr[0])
circuit.barrier(qr)
circuit.measure(qr[1], cr[1])
backend = QasmSimulator()
# Asymetric readout error on qubit-0 only
probs_given0 = [0.9, 0.1]
probs_given1 = [0.3, 0.7]
noise_model = NoiseModel()
noise_model.add_all_qubit_readout_error([probs_given0, probs_given1])
# Expected counts
shots = 2000
p00 = 0.5 * (probs_given0[0]**2 + probs_given1[0]**2)
p01 = 0.5 * (probs_given0[0] * probs_given0[1] +
probs_given1[0] * probs_given1[1])
p10 = 0.5 * (probs_given0[0] * probs_given0[1] +
probs_given1[0] * probs_given1[1])
p11 = 0.5 * (probs_given0[1]**2 + probs_given1[1]**2)
target = target = {
'0x0': p00 * shots,
'0x1': p01 * shots,
'0x2': p10 * shots,
'0x3': p11 * shots
}
qobj = compile([circuit],
backend,
shots=shots,
basis_gates=noise_model.basis_gates)
result = backend.run(qobj, noise_model=noise_model).result()
self.is_completed(result)
self.compare_counts(result, [circuit], [target], delta=0.05 * shots)
def test_w_noise(self):
# build noise model
# Asymetric readout error on qubit-0 only
probs_given0 = [0.9, 0.1]
probs_given1 = [0.3, 0.7]
noise_model = NoiseModel()
noise_model.add_readout_error([probs_given0, probs_given1], [0])
quantum_instance = QuantumInstance(self.backend,
seed_mapper=self.random_seed,
run_config=self.run_config,
noise_model=noise_model)
res_w_noise = quantum_instance.execute(self.qc).get_counts(self.qc)
quantum_instance.skip_qobj_validation = True
res_w_noise_skip_validation = quantum_instance.execute(
self.qc).get_counts(self.qc)
self.assertTrue(_compare_dict(res_w_noise,
res_w_noise_skip_validation))
def test_auto_method_clifford_circuits_and_reset_noise(self):
"""Test statevector method is used for Clifford circuit"""
# Test noise model
noise_circs = [Reset(), IGate()]
noise_probs = [0.5, 0.5]
error = QuantumError(zip(noise_circs, noise_probs))
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(
error, ['id', 'x', 'y', 'z', 'h', 's', 'sdg'])
backend = self.backend(noise_model=noise_model)
# Test circuits
shots = 100
circuits = ref_2q_clifford.cz_gate_circuits_deterministic(
final_measure=True)
result = backend.run(circuits, shots=shots).result()
success = getattr(result, 'success', False)
self.assertTrue(success)
self.compare_result_metadata(result, circuits, 'method', 'stabilizer')
def get_noise(p_meas, p_gate):
"""Define a noise model."""
error_meas = pauli_error([("X", p_meas), ("I", 1 - p_meas)])
error_gate1 = depolarizing_error(p_gate, 1)
error_gate2 = error_gate1.tensor(error_gate1)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error_meas, "measure")
noise_model.add_all_qubit_quantum_error(error_gate1, ["u1", "u2", "u3"])
noise_model.add_all_qubit_quantum_error(error_gate2, ["cx"])
return noise_model
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 = circuits.t2(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]))
print(fit.time)
print(fit.time_err)
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 RXDataset(angle_step=10, probability_step=10, shots=1024, save_dir=None):
# define constants
basis_gates = ['u3']
simulator = QasmSimulator()
df = pd.DataFrame()
# generate circuits
for angle in np.linspace(0, np.pi, angle_step, endpoint=True):
# define circuit
circ = QuantumCircuit(1, 1)
rotate = RXGate(angle)
circ.append(rotate, [0])
circ.measure(0, 0)
new_circ = qiskit.compiler.transpile(circ,
basis_gates=basis_gates,
optimization_level=0)
print(new_circ)
# generate noise models:
for probability in np.linspace(0, 1, probability_step, endpoint=True):
# add noise
noise_model = NoiseModel()
error = depolarizing_error(probability, 1)
noise_model.add_all_qubit_quantum_error(error,
['x', 'u1', 'u2', 'u3'])
# execution - Noisy
job = execute(new_circ,
simulator,
shots=shots,
noise_model=noise_model)
result = job.result()
# add to Pandas DF
data = {
'rx_theta': angle,
'p': probability,
'E': result.get_counts(0).get('0', 0) / shots
}
df = df.append(data, ignore_index=True)
df = df[['rx_theta', 'E', 'p']]
df.to_csv(save_dir + "/dataframe_RX.csv")
return df
