def generate_mitigator(num_qubits, noisy, shots=1024):
meas_calibs, state_labels = complete_meas_cal(range(num_qubits))
experiments = transpile(meas_calibs, backend=noisy, optimization_level=3)
job = noisy.run(assemble(experiments, shots=shots))
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels)
return job, meas_fitter.filter
def mitigated_results(circuit, backend, results):
# Import the required methods
from qiskit.providers.aer.noise import NoiseModel
from qiskit.ignis.mitigation.measurement import (complete_meas_cal,
CompleteMeasFitter)
# 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)
# Plot the calibration matrix
print("Calibration matrix")
meas_fitter.plot_calibration()
# 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)
print("Mitigated", backend, "results:\n", mitigated_counts)
return (mitigated_counts)
def meas_calib_circ_creation():
"""
create measurement calibration circuits and a GHZ state circuit for the tests
Returns:
QuantumCircuit: the measurement calibrations circuits
list[str]: the mitigation pattern
QuantumCircuit: ghz circuit with 5 qubits (3 are used)
"""
qubit_list = [1, 2, 3]
total_number_of_qubit = 5
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list,
qr=total_number_of_qubit)
# Choose 3 qubits
qubit_1 = qubit_list[0]
qubit_2 = qubit_list[1]
qubit_3 = qubit_list[2]
ghz = qiskit.QuantumCircuit(total_number_of_qubit, len(qubit_list))
ghz.h(qubit_1)
ghz.cx(qubit_1, qubit_2)
ghz.cx(qubit_1, qubit_3)
for i in qubit_list:
ghz.measure(i, i - 1)
return meas_calibs, state_labels, ghz
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 _qiskit_generate_samples_MEM(self: QiskitDevice) -> np.array:
"""
This function is used to overrite the default QiskitDevice.generate_samples method.
It adds a simple mechanism to perform measurement error mitigation on the results of
the computation.
"""
# branch out depending on the type of backend
if self.backend_name in self._state_backends:
# software simulator: need to sample from probabilities
return super().generate_samples()
# hardware or hardware simulator
# ***here we actully perform the measurement error mitigation***
meas_calibs, state_labels = complete_meas_cal(qr=self._reg, circlabel="mcal")
mitigation_run_args = self.run_args.copy()
mitigation_run_args["shots"] = NUMBER_OF_SHOTS_FOR_QISKIT_ERROR_MITIGATION
meas_job = qiskit.execute(meas_calibs, backend=self.backend, **mitigation_run_args)
# TODO: Adding a cache here is a free performance boost
meas_fitter = CompleteMeasFitter(meas_job.result(), state_labels, circlabel="mcal")
mitigated_results = meas_fitter.filter.apply(self._current_job.result())
current_job_shots = sum(self._current_job.result().get_counts().values())
summed_counts = sum(mitigated_results.get_counts().values())
probs = [value / summed_counts for value in mitigated_results.get_counts().values()]
samples = np.random.choice(
np.array(list(mitigated_results.get_counts().keys())),
current_job_shots,
p=probs,
replace=True,
)
# reverse qubit order to match PennyLane convention
return np.vstack([np.array([int(i) for i in s[::-1]]) for s in samples])
def matricaZaGreske(brojQubita, noise_model):
qr = QuantumRegister(brojQubita)
meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')
backend = Aer.get_backend('qasm_simulator')
job = execute(meas_calibs, backend=backend, shots=1000, noise_model=noise_model)
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')
return(meas_fitter.cal_matrix)
def build_measurement_error_mitigation_circuits(qubit_list,
fitter_cls,
backend,
backend_config=None,
compile_config=None):
"""Build measurement error mitigation circuits
Args:
qubit_list (list[int]): list of ordered qubits used in the algorithm
fitter_cls (callable): CompleteMeasFitter or TensoredMeasFitter
backend (BaseBackend): backend instance
backend_config (dict, optional): configuration for backend
compile_config (dict, optional): configuration for compilation
Returns:
QasmQobj: the Qobj with calibration circuits at the beginning
list[str]: the state labels for build MeasFitter
list[str]: the labels of the calibration circuits
Raises:
QiskitError: when the fitter_cls is not recognizable.
MissingOptionalLibraryError: Qiskit-Ignis not installed
"""
try:
from qiskit.ignis.mitigation.measurement import (
complete_meas_cal,
CompleteMeasFitter,
TensoredMeasFitter,
)
except ImportError as ex:
raise MissingOptionalLibraryError(
libname="qiskit-ignis",
name="build_measurement_error_mitigation_qobj",
pip_install="pip install qiskit-ignis",
) from ex
circlabel = "mcal"
if not qubit_list:
raise QiskitError("The measured qubit list can not be [].")
if fitter_cls == CompleteMeasFitter:
meas_calibs_circuits, state_labels = complete_meas_cal(
qubit_list=range(len(qubit_list)), circlabel=circlabel)
elif fitter_cls == TensoredMeasFitter:
# TODO support different calibration
raise QiskitError("Does not support TensoredMeasFitter yet.")
else:
raise QiskitError("Unknown fitter {}".format(fitter_cls))
# the provided `qubit_list` would be used as the initial layout to
# assure the consistent qubit mapping used in the main circuits.
tmp_compile_config = copy.deepcopy(compile_config)
tmp_compile_config["initial_layout"] = qubit_list
t_meas_calibs_circuits = compiler.transpile(meas_calibs_circuits, backend,
**backend_config,
**tmp_compile_config)
return t_meas_calibs_circuits, state_labels, circlabel
def measured_qubits(self, qc):
measure_list = []
for op in qc.get_instructions('measure'):
measure_list.append(op[1][0]._index)
meas_calibs, state_labels = complete_meas_cal(qubit_list=measure_list)
info_list = [meas_calibs, state_labels, measure_list]
return info_list
def initialize_meas_calibration(self, N_qubits, layout):
""" Set up the confusion matrix needed for measurement error mitigation.
This is basically just boilerplate code from the Ignis Github
https://github.com/Qiskit/qiskit-ignis
"""
if layout is None:
cal_q = QuantumRegister(N_qubits)
meas_cals, state_labels = complete_meas_cal(qr=cal_q)
else:
meas_cals, state_labels = complete_meas_cal(qubit_list=layout)
# Run the calibration circuits with the device noise model
backend = Aer.get_backend('qasm_simulator')
job = execute(meas_cals,
backend=backend,
shots=10000,
noise_model=self.noise_model)
cal_results = job.result()
return CompleteMeasFitter(cal_results, state_labels).filter
def generate_error_mitigation_filter(q_list, noise_model):
backend = get_sim_backend_from_name("qasm_simulator")
qr = QuantumRegister(5)
meas_cals, state_labels = complete_meas_cal(qubit_list=q_list, qr=qr)
calibration_job = execute(meas_cals,
backend=backend,
shots=8192,
noise_model=noise_model)
cal_results = calibration_job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels)
em_filter = meas_fitter.filter
return em_filter
def add_measurement_error_mitigation_to_qobj(qobj,
fitter_cls,
backend,
backend_config=None,
compile_config=None,
run_config=None,
new_qobj=False):
"""
Args:
qobj (QasmQobj): QasmQobj of the algorithm
fitter_cls (callable): CompleteMeasFitter or TensoredMeasFitter
backend (BaseBackend): backend instance
backend_config (dict, optional): configuration for backend
compile_config (dict, optional): configuration for compilation
run_config (RunConfig, optional): configuration for running a circuit
new_qobj (bool, optional): whether or not combine qobj to the input qobj
Returns:
QasmQobj: the Qobj with calibration circuits at the beginning
list[str]: the state labels for build MeasFitter
list[str]: the labels of the calibration circuits
Raises:
AquaError: when the fitter_cls is not recognizable.
"""
circlabel = 'mcal'
qubits = get_measured_qubits_from_qobj(qobj)
if len(qubits) == 0:
raise AquaError("The measured qubits can not be [].")
if fitter_cls == CompleteMeasFitter:
meas_calibs_circuits, state_labels = complete_meas_cal(
qubit_list=qubits, circlabel=circlabel)
elif fitter_cls == TensoredMeasFitter:
# TODO support different calibration
raise AquaError("Does not support TensoredMeasFitter yet.")
else:
raise AquaError("Unknown fitter {}".format(fitter_cls))
cals_qobj = compile_circuits(meas_calibs_circuits, backend, backend_config,
compile_config, run_config)
if new_qobj:
qobj = cals_qobj
else:
qobj.experiments[0:0] = cals_qobj.experiments
return qobj, state_labels, circlabel
def measurement_error_mitigator(systemsize,
sim,
quantum_com_choice_results,
shots=8192):
if sim == "noisy_qasm":
backend, coupling_map, noise_model = quantum_com_choice_results[sim]
qr = QuantumRegister(systemsize)
meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')
print('Calibrating POVM Matrix')
job = execute(meas_calibs,
backend=backend,
shots=shots,
noise_model=noise_model,
coupling_map=coupling_map)
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results,
state_labels,
circlabel='mcal')
meas_filter = meas_fitter.filter
print("Provider backend: ", backend)
return meas_filter
elif sim == "real":
backend = quantum_com_choice_results[sim]
qr = QuantumRegister(systemsize)
meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')
print('Calibrating POVM Matrix')
job = execute(meas_calibs, backend=backend, shots=shots)
job_monitor(job, interval=2)
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results,
state_labels,
circlabel='mcal')
meas_filter = meas_fitter.filter
print("Provider backend: ", backend)
return meas_filter
def build_measurement_error_mitigation_qobj(qubit_list,
fitter_cls,
backend,
backend_config=None,
compile_config=None,
run_config=None):
"""
Args:
qubit_list (list[int]): list of ordered qubits used in the algorithm
fitter_cls (callable): CompleteMeasFitter or TensoredMeasFitter
backend (BaseBackend): backend instance
backend_config (dict, optional): configuration for backend
compile_config (dict, optional): configuration for compilation
run_config (RunConfig, optional): configuration for running a circuit
Returns:
QasmQobj: the Qobj with calibration circuits at the beginning
list[str]: the state labels for build MeasFitter
list[str]: the labels of the calibration circuits
Raises:
AquaError: when the fitter_cls is not recognizable.
"""
circlabel = 'mcal'
if not qubit_list:
raise AquaError("The measured qubit list can not be [].")
if fitter_cls == CompleteMeasFitter:
meas_calibs_circuits, state_labels = \
complete_meas_cal(qubit_list=range(len(qubit_list)), circlabel=circlabel)
elif fitter_cls == TensoredMeasFitter:
# TODO support different calibration
raise AquaError("Does not support TensoredMeasFitter yet.")
else:
raise AquaError("Unknown fitter {}".format(fitter_cls))
# the provided `qubit_list` would be used as the initial layout to
# assure the consistent qubit mapping used in the main circuits.
tmp_compile_config = copy.deepcopy(compile_config)
tmp_compile_config['initial_layout'] = qubit_list
t_meas_calibs_circuits = compiler.transpile(meas_calibs_circuits, backend,
**backend_config,
**tmp_compile_config)
cals_qobj = compiler.assemble(t_meas_calibs_circuits, backend,
**run_config.to_dict())
return cals_qobj, state_labels, circlabel
def circuits(self, backend=None):
if backend is None:
backend = FakeValencia()
sys.stderr.write('MeasurementErrorExperiment: Using FakeValencia for backend\n')
qreg = QuantumRegister(len(self.physical_qubits))
circuits, state_labels = complete_meas_cal(qubit_list=list(range(qreg.size)), qr=qreg, circlabel='mcal')
for circuit, state_label in zip(circuits, state_labels):
circuit.metadata = {
'experiment_type': self._type,
'physical_qubits': self.physical_qubits,
'state_label': state_label
}
return circuits * self.circuits_per_state
def generate_calibration_circuits(noisy,
simulator,
max_depth,
pc_cirq,
plot=0):
global shots
num_qubits = len(noisy.properties().qubits)
training, _ = complete_meas_cal(range(num_qubits))
for i in range(1, max_depth):
for _ in range(100 * num_qubits):
training.append(random_gated_circuit(noisy, i, 0))
experiments = transpile(training, backend=noisy)
expected_all = execute(experiments, simulator, shots=shots).result()
actual_all = execute(experiments, noisy, shots=shots).result()
errors = []
keys = get_hex_keys(num_qubits)
for i in range(len(training)):
expected = clean_result(expected_all.data(i)['counts'], keys, shots)
actual = clean_result(actual_all.data(i)['counts'], keys, shots)
errors.append([(expected[key] - actual[key])**2 for key in keys])
errors = np.array(errors)
pca = PCA(n_components=math.ceil(2**num_qubits))
pca.fit(errors)
test_cirq_indexes = []
for i in range(num_qubits):
get_proj = lambda e: np.dot(pca.components_[i], e) / np.linalg.norm(e)
projs = [abs(get_proj(error)) for error in errors]
start_len = len(test_cirq_indexes)
pc_sorted = np.argsort(projs)
i = -1
while len(test_cirq_indexes) - start_len < pc_cirq:
ind = pc_sorted[i]
if ind not in test_cirq_indexes:
test_cirq_indexes.append(ind)
i -= 1
if plot != 0:
plot_errors(num_qubits, pca, pc_cirq, errors, test_cirq_indexes, plot)
return [training[ind] for ind in test_cirq_indexes]
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_correction_matrix(qpu, max_age, access_token, used_qubits):
"""Return a correction matrix for the given QPU whit the maximum age in minutes"""
app.logger.info('Getting calibration matrix for QPU ' + qpu + ' with max age of ' + str(max_age) + ' minutes')
# Check for existing calibration matrix
existing_matrix = calibration_matrixes.get(qpu)
if existing_matrix is not None:
age = datetime.now() - existing_matrix['Date']
app.logger.info('Calibration matrix for this QPU exists with age ' + str(age))
if age.total_seconds() < max_age * 60:
app.logger.info('Returning existing calibration matrix!')
return existing_matrix['Calibration_Matrix']
if access_token is None:
app.logger.error('Unable to create new correction matrix without access key...')
return None
# Load account to enable execution of calibration circuits
IBMQ.save_account(access_token, overwrite=True)
IBMQ.load_account()
provider = IBMQ.get_provider(group='open')
backend = provider.get_backend(qpu)
# Generate a calibration circuit for each state
qr = qiskit.QuantumRegister(used_qubits)
meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')
# Execute each calibration circuit and store results
app.logger.info('Executing ' + str(len(meas_calibs)) + ' circuits to create calibration matrix...')
cal_results = []
for circuit in meas_calibs:
app.logger.info('Executing circuit ' + circuit.name)
cal_results.append(execute_job(circuit, backend))
# Generate calibration matrix out of measurement results
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')
app.logger.info('Calibration matrix:')
app.logger.info(meas_fitter.cal_matrix)
# Store calibration matrix for later reuse
calibration_matrixes[qpu] = {"Date": datetime.now(), "Calibration_Matrix": meas_fitter.filter}
return meas_fitter.filter
def build_measurement_error_mitigation_qobj(qubit_list,
fitter_cls,
backend,
backend_config=None,
compile_config=None,
run_config=None):
"""
Args:
qubit_list (list[int]): list of qubits used in the algorithm
fitter_cls (callable): CompleteMeasFitter or TensoredMeasFitter
backend (BaseBackend): backend instance
backend_config (dict, optional): configuration for backend
compile_config (dict, optional): configuration for compilation
run_config (RunConfig, optional): configuration for running a circuit
Returns:
QasmQobj: the Qobj with calibration circuits at the beginning
list[str]: the state labels for build MeasFitter
list[str]: the labels of the calibration circuits
Raises:
AquaError: when the fitter_cls is not recognizable.
"""
circlabel = 'mcal'
if not qubit_list:
raise AquaError("The measured qubit list can not be [].")
if fitter_cls == CompleteMeasFitter:
meas_calibs_circuits, state_labels = \
complete_meas_cal(qubit_list=qubit_list, circlabel=circlabel)
elif fitter_cls == TensoredMeasFitter:
# TODO support different calibration
raise AquaError("Does not support TensoredMeasFitter yet.")
else:
raise AquaError("Unknown fitter {}".format(fitter_cls))
cals_qobj = compile_circuits(meas_calibs_circuits, backend, backend_config,
compile_config, run_config)
return cals_qobj, state_labels, circlabel
def get_measurement_fitter(l, backend, device, shots):
qb = QuantumRegister(l)
qubit_list = list(range(l))
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list,
qr=qb,
circlabel='mcal')
# Execute calibration circuits
job = execute(meas_calibs,
backend=backend,
shots=shots,
noise_model=device.noise_model,
coupling_map=device.coupling_map,
basis_gates=device.basis_gates)
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results,
state_labels,
circlabel='mcal')
#meas_filter = meas_fitter.filter
#mitigated_results = meas_filter.apply(results)
#mitigated_counts = mitigated_results.get_counts(0)
return meas_fitter
def apply_readout_correction(self, counts, qubit_list=None, **kwargs):
if self.readout_correction_filter is None:
for key in counts.keys():
num_qubits = len(key)
break
if qubit_list is None or qubit_list == {}:
qubit_list = [i for i in range(num_qubits)]
qr = QuantumRegister(num_qubits)
meas_cals, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qr)
# Execute the calibration circuits
job = self.execute_with_retries(meas_cals, self.n_samples)
cal_results = job.result()
# Make a calibration matrix
meas_fitter = CompleteMeasFitter(cal_results, state_labels)
# Create a measurement filter from the calibration matrix
self.readout_correction_filter = meas_fitter.filter
mitigated_counts = self.readout_correction_filter.apply(counts)
return mitigated_counts
from qiskit.ignis.mitigation.measurement import (complete_meas_cal,
CompleteMeasFitter,
MeasurementFilter)
import qiskit.execute
# Generates noise model for qubits (percentages?).
aer_noise_model = noise.NoiseModel()
error = [[0.75, 0.25], [0.1, 0.9]]
for qb in range(5):
readout_error = noise.errors.readout_error.ReadoutError(error)
aer_noise_model.add_readout_error(readout_error, [qb])
# Generates measurement calibration circuits for running measurement error
# mitigation.
q_register = QuantumRegister(5)
calibration_circuits, state_labels = complete_meas_cal(qubit_list=[2, 3, 4],
qr=q_register)
# Executes calibration circuits.
qasm_backend = Aer.get_backend("qasm_simulator") # simulates circuit
job = qiskit.execute(calibration_circuits,
backend=qasm_backend,
shots=1000,
noise_model=aer_noise_model)
calibration_results = job.result()
# Makes calibration matrix.
measure_fitter = CompleteMeasFitter(calibration_results, state_labels)
# Makes a 3-qubit GHZ state (a particular entangled state, at least 3 qubits).
# 1. applies Hadamard, putting qubit into superposition
# 2. applies CX (CNOT), putting qubits into Bell state
if label1[l][i] == 1 and label2[l][i] == 0:
drive_idx = 1
target_idx = 0
flip_drive = True
backend_sim = qiskit.providers.aer.PulseSimulator()
# experiments10 = cr_drive_experiments(drive_idx, target_idx, flip_drive)
# compute frequencies from the Hamiltonian
# qubit_lo_freq10 = two_qubit_model.hamiltonian.get_qubit_lo_from_drift()
#assemble qobj 1
rabifit_result = RabiFitter(result1, amps1, qubits, fit_p0 = [0.5,0.5,0.6,1.5])
if l == trainidx[0] and i == 0:
#if flag != 1:
# Generate the calibration circuits
qr = qiskit.QuantumRegister(2)
qubit_list = [0,1]
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qr, circlabel='mcal')
job = qiskit.execute(meas_calibs, backend=backend, shots=10, noise_model=noise_model)
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels)
# meas_fitter.cal_matrix = np.kron(meas_fitter.cal_matrices[1],meas_fitter.cal_matricesc[0])
meas_fitter.cal_matrix
plt.figure(figsize=(15, 10))
for qubit in qubits:
ax = plt.subplot(2, 2, qubit + 1)
#Xvmin, Xvmax = ax.xaxis.get_data_interval()
#Yvmin, Yvmax = ax.yaxis.get_data_interval()
#print(Xvmin, Xvmax,Yvmin, Yvmax)
Xvmin = multiplier * np.floor(0.1 / multiplier)
Xvmax = multiplier * np.ceil(0.5 / multiplier)
ax.set_xlim([Xvmin, Xvmax])
rabifit_result.plot(qubit, ax=ax)
print('\nOptimization level must be an integer between 0 and 3, inclusive. Higher levels generate more optimized circuits, at the expense of longer transpilation time.\n')
exit(1)
except:
print('\nOptimization level must be an integer between 0 and 3, inclusive. Higher levels generate more optimized circuits, at the expense of longer transpilation time.\n')
exit(1)
else:
opt_level = 1
U_f = get_U_f(func_in, n)
list_y = []
circuit = simon_program(U_f, n)
circuit.measure(range(n), range(n))
# Calibration Matrix
meas_calibs, state_labels = complete_meas_cal(qubit_list=range(n), circlabel='mcal')
cal_job = execute(meas_calibs, backend=backend, shots=8192)
cal_results = cal_job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')
start = time.time()
circuit = transpile(circuit, backend, optimization_level=opt_level)
transpiletime = time.time() - start
if trials*n > 2048:
trial_list = [8192]*((trials*n)//2048) + [(4*trials*n)%8192]
else:
trial_list = [4*trials*n]
job = []
for t in trial_list:
def test_ideal_meas_cal(self):
"""Test ideal execution, without noise."""
for nq in self.nq_list:
print("Testing %d qubit measurement calibration" % nq)
for pattern_type in range(1, 2**nq):
# Generate the quantum register according to the pattern
qubits, weight = self.choose_calibration(nq, pattern_type)
# Generate the calibration circuits
meas_calibs, state_labels = \
complete_meas_cal(qubit_list=qubits,
circlabel='test')
# Perform an ideal execution on the generated circuits
backend = Aer.get_backend('qasm_simulator')
job = qiskit.execute(meas_calibs,
backend=backend,
shots=self.shots)
cal_results = job.result()
# Make a calibration matrix
meas_cal = CompleteMeasFitter(cal_results,
state_labels,
circlabel='test')
# Assert that the calibration matrix is equal to identity
IdentityMatrix = np.identity(2**weight)
self.assertListEqual(
meas_cal.cal_matrix.tolist(), IdentityMatrix.tolist(),
'Error: the calibration matrix is \
not equal to identity')
# Assert that the readout fidelity is equal to 1
self.assertEqual(
meas_cal.readout_fidelity(), 1.0,
'Error: the average fidelity \
is not equal to 1')
# Generate ideal (equally distributed) results
results_dict, results_list = \
self.generate_ideal_results(state_labels, weight)
# Output the filter
meas_filter = meas_cal.filter
# Apply the calibration matrix to results
# in list and dict forms using different methods
results_dict_1 = meas_filter.apply(results_dict,
method='least_squares')
results_dict_0 = meas_filter.apply(results_dict,
method='pseudo_inverse')
results_list_1 = meas_filter.apply(results_list,
method='least_squares')
results_list_0 = meas_filter.apply(results_list,
method='pseudo_inverse')
# Assert that the results are equally distributed
self.assertListEqual(results_list, results_list_0.tolist())
self.assertListEqual(results_list,
np.round(results_list_1).tolist())
self.assertDictEqual(results_dict, results_dict_0)
round_results = {}
for key, val in results_dict_1.items():
round_results[key] = np.round(val)
self.assertDictEqual(results_dict, round_results)
def all_methods_data(interested_qubits,
backend,
itr,
QDT_correlated,
shots_per_point=1024,
file_address=''):
"""Collect data for our method, Qiskit method, QDT, and standard Bayesian.
Args:
interested_qubits:
an array of ints. REMEBER TO FOLLOW THE ORDER [LAST QUBIT, ..., FIRST QUBIT]
backend:
backend from provider.get_backend().
itr:
number of iterations of job submission in our method only.
QDT_correlated:
True if want qubits corrlected in QDT method.
file_address:
file address, string ends with '/' if not empty
Returns:
None
"""
with open(file_address + 'interested_qubits.csv', mode='w',
newline='') as sgm:
param_writer = csv.writer(sgm,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
param_writer.writerow(interested_qubits)
# Record data for filters (ourmethod)
print('Our method')
collect_filter_data(backend,
itr=itr,
shots=8192,
if_monitor_job=True,
if_write=True,
file_address=file_address)
# Qiskit Method
print('Qiskit Method')
qr = QuantumRegister(len(interested_qubits))
meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')
for i in range(len(meas_calibs)):
meas_calibs[i] = transpile(meas_calibs[i],
backend,
initial_layout=interested_qubits[::-1])
job = execute(meas_calibs,
backend=backend,
shots=8192,
optimization_level=0)
job_monitor(job)
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results,
state_labels,
circlabel='mcal')
meas_filter = meas_fitter.filter
cal_matrix = meas_fitter.cal_matrix
with open(file_address + 'cal_matrix.csv', mode='w', newline='') as sgm:
param_writer = csv.writer(sgm,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
for row in cal_matrix:
param_writer.writerow(row)
with open(file_address + 'state_labels.csv', mode='w', newline='') as sgm:
param_writer = csv.writer(sgm,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
param_writer.writerow(state_labels)
# QDT
print('QDT, correlation = ', QDT_correlated)
if QDT_correlated:
qdt_qubit_indices = interested_qubits
qdt_probe_kets = povmtools.pauli_probe_eigenkets
qdt_calibration_circuits = detector_tomography_circuits(
qdt_qubit_indices, qdt_probe_kets)
print('Number of Circuits needed is ', len(qdt_calibration_circuits))
# We then execute them on backend prepared earlier.
shots_number = 8192
# Perform a noisy simulation
job = execute(qdt_calibration_circuits,
backend=backend,
shots=shots_number,
optimization_level=0)
job_monitor(job)
result = job.result()
dtf = DetectorTomographyFitter()
calibration_setup = QDTCalibrationSetup.from_qiskit_results(
[result], qdt_probe_kets)
ml_povm_estimator = dtf.get_maximum_likelihood_povm_estimator(
calibration_setup)
mitigator = QDTErrorMitigator()
mitigator.prepare_mitigator(ml_povm_estimator)
trans_mat = mitigator.transition_matrix
with open(file_address + 'trans_matrix.csv', mode='w',
newline='') as sgm:
param_writer = csv.writer(sgm,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
for row in trans_mat:
param_writer.writerow(row)
else:
for q in interested_qubits:
qdt_qubit_indices = [q]
qdt_probe_kets = povmtools.pauli_probe_eigenkets
qdt_calibration_circuits = detector_tomography_circuits(
qdt_qubit_indices, qdt_probe_kets)
print('Number of Circuits needed is ',
len(qdt_calibration_circuits))
# We then execute them on backend prepared earlier.
shots_number = 8192
# Perform a noisy simulation
job = execute(qdt_calibration_circuits,
backend=backend,
shots=shots_number,
optimization_level=0)
job_monitor(job)
result = job.result()
# Create Mitigator
dtf = DetectorTomographyFitter()
calibration_setup = QDTCalibrationSetup.from_qiskit_results(
[result], qdt_probe_kets)
ml_povm_estimator = dtf.get_maximum_likelihood_povm_estimator(
calibration_setup)
mitigator = QDTErrorMitigator()
mitigator.prepare_mitigator(ml_povm_estimator)
trans_mat = mitigator.transition_matrix
with open(file_address + 'trans_matrix' + str(q) + '.csv',
mode='w',
newline='') as sgm:
param_writer = csv.writer(sgm,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
for row in trans_mat:
param_writer.writerow(row)
# Data for Standard Bayesian
# Read data to for measurement error while input is |0>
print('Write data for standard Bayesian')
prop_dict = backend.properties().to_dict()
with open(file_address + 'given_params.csv', mode='w', newline='') as sgm:
param_writer = csv.writer(sgm,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
for q in interested_qubits:
p0m0 = 1 - prop_dict['qubits'][q][5]['value']
if p0m0 == 1 or p0m0 < 0.7:
p0m0 = 0.9
p1m1 = 1 - prop_dict['qubits'][q][4]['value']
if p1m1 == 1 or p1m1 < 0.7:
p1m1 = 0.9
param_writer.writerow([p0m0, p1m1])
with open(file_address + 'Filter_data.csv', mode='r') as measfile:
reader = csv.reader(measfile)
cali01 = np.asarray([row for row in reader][0])
Data = cali01
for q in interested_qubits:
y = getData0(Data, itr * int(8192 / shots_per_point), q)
with open(file_address + 'Qubit{}.csv'.format(q), mode='w',
newline='') as sgr:
read_writer = csv.writer(sgr,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
read_writer.writerow(['x', 'y'])
for i in range(len(y)):
read_writer.writerow([0.5, y[i]])
MeasurementFilter)
provider = IBMQ.load_account()
backend = provider.get_backend('ibmqx2') # calibrate using real hardware
layout = [2, 3, 0, 4]
chip_qubits = 5
# Transpiled circuit for the real hardware
qc_qa_cx = transpile(qc, backend=backend, initial_layout=layout)
# The next step is to create the extra circuits used to mitigate the readout errors<sup>[[3]](#readouterr)</sup>.
# In[18]:
meas_cals, state_labels = complete_meas_cal(qubit_list=layout,
qr=QuantumRegister(chip_qubits))
qcs = meas_cals + [qc_qa_cx]
job = backend.run(qcs, shots=10)
# The following plot<sup>[[5]](#tridi)</sup>, shows the results from running the circuit above on real hardware for $10$ different initial states. The $x$-axis represents the angle $\theta$ defining the initial state in each case. The results where obtained after mitigating the readout error and then extrapolating the errors arising during the run of the circuit from the results with the circuits with $1$, $3$ and $5$ CNOTs.
#
# <img src="images/norm_public.png">
#
# Compare to the results without error mitigation nor extrapolation from the CNOTs<sup>[5](#tridi)</sup>.
#
# <img src="images/noerrmit_public.png">
# ## 8. Problems<a id='problems'></a>
# ##### Real hardware:
register_token()
qubits = 4
circuit = QuantumCircuit(qubits, qubits)
circuit.h(0)
circuit.cx(0, 1)
circuit.cx(1, 2)
circuit.measure([0, 1, 2, 3], [0, 1, 2, 3])
circuit.draw(output='mpl')
simulator = Aer.get_backend('qasm_simulator')
sim_result = execute(circuit, backend=simulator, shots=1024).result()
plot_histogram(sim_result.get_counts(circuit))
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q')
device = provider.get_backend('ibmqx2')
job = execute(circuit, backend=device, shots=1024)
print("Job ID:", job.job_id())
job_monitor(job)
device_result = job.result()
plot_histogram(device_result.get_counts(circuit))
cal_circuits, state_labels = complete_meas_cal(
qr=circuit.qregs[0], circlabel='measerrormitigationcal')
cal_circuits[2].draw(output='mpl')
def test_meas_cal_on_circuit(self):
"""Test an execution on a circuit."""
print("Testing measurement calibration on a circuit")
# Choose 3 qubits
q1 = 1
q2 = 2
q3 = 3
# Generate the quantum register according to the pattern
qr = qiskit.QuantumRegister(5)
# Generate the calibration circuits
meas_calibs, state_labels = \
complete_meas_cal(qubit_list=[1, 2, 3],
qr=qr)
# Run the calibration circuits
backend = Aer.get_backend('qasm_simulator')
job = qiskit.execute(meas_calibs, backend=backend, shots=self.shots)
cal_results = job.result()
# Make a calibration matrix
meas_cal = CompleteMeasFitter(cal_results, state_labels)
# Calculate the fidelity
fidelity = meas_cal.readout_fidelity()
# Make a 3Q GHZ state
cr = ClassicalRegister(3)
ghz = QuantumCircuit(qr, cr)
ghz.h(qr[q1])
ghz.cx(qr[q1], qr[q2])
ghz.cx(qr[q2], qr[q3])
ghz.measure(qr[q1], cr[0])
ghz.measure(qr[q2], cr[1])
ghz.measure(qr[q3], cr[2])
job = qiskit.execute([ghz], backend=backend, shots=self.shots)
results = job.result()
# Predicted equally distributed results
predicted_results = {'000': 0.5, '111': 0.5}
meas_filter = meas_cal.filter
# Calculate the results after mitigation
output_results_pseudo_inverse = meas_filter.apply(
results, method='pseudo_inverse').get_counts(0)
output_results_least_square = meas_filter.apply(
results, method='least_squares').get_counts(0)
# Compare with expected fidelity and expected results
self.assertAlmostEqual(fidelity, 1.0)
self.assertAlmostEqual(output_results_pseudo_inverse['000'] /
self.shots,
predicted_results['000'],
places=1)
self.assertAlmostEqual(output_results_least_square['000'] / self.shots,
predicted_results['000'],
places=1)
self.assertAlmostEqual(output_results_pseudo_inverse['111'] /
self.shots,
predicted_results['111'],
places=1)
self.assertAlmostEqual(output_results_least_square['111'] / self.shots,
predicted_results['111'],
places=1)
def create_qpt_experiment(target_circuits: List[QuantumCircuit],
control: int,
target: int,
backend: BaseBackend,
mit_readout: bool = True,
inst_map: InstructionScheduleMap = None,
basis_gate: List[str] = None,
sanity_check: bool = False,
shots: int = 2048,
return_schedule=False)\
-> Tuple[Union[PulseQobj, Schedule], List[List[QuantumCircuit]], List[str]]:
""" Create circuits and schedules for QPT.
Args:
target_circuits: List of target circuits for QPT experiment.
control: index of control qubit.
target: index of target qubit.
backend: Target quantum system.
mit_readout: If use readout mitigation.
inst_map: instruction mapping object.
basis_gate: basis gates.
sanity_check: check memory slot mapping of generated qobj.
shots: Number of shots.
return_schedule: set ``True`` when return schedule object instead of qobj.
Returns:
Qobj, Schedules, Quantum circuits, Measurement labels
Additional Information:
Bit ordering is little endian as a convention of computational science community,
as the rest of qiskit does. When you measure the CR process tomography of q0 and q1,
you will observe XZ (ZX) interaction when q0 (q1) is control qubit.
"""
qubits = sorted([control, target])
back_config = backend.configuration()
back_defaults = backend.defaults()
if inst_map is None:
inst_map = back_defaults.circuit_instruction_map
if basis_gate is None:
basis_gate = back_config.basis_gates
if isinstance(target_circuits, QuantumCircuit):
target_circuits = [target_circuits]
exp_circs = []
# create the measurement circuits for error mitigation, optional
qr = target_circuits[0].qregs[0]
if mit_readout:
meas_circs, meas_labels = mit.complete_meas_cal(qubit_list=qubits,
qr=qr,
circlabel='mcal')
exp_circs.extend(meas_circs)
else:
meas_labels = []
# create qpt circuit
qpt_qcs_list = []
for target_circuit in target_circuits:
# extract quantum registers from target circuit
qr = target_circuit.qregs[0]
qr0 = qr[qubits[0]]
qr1 = qr[qubits[1]]
qpt_qcs = tomo.process_tomography_circuits(target_circuit,
measured_qubits=[qr0, qr1])
qpt_qcs_list.append(qpt_qcs)
exp_circs.extend(qpt_qcs)
# transpile
exp_circs = qiskit.transpile(exp_circs, backend, basis_gates=basis_gate)
# schedule with measure alignment
exp_scheds = align_measures(qiskit.schedule(exp_circs,
backend=backend,
inst_map=inst_map),
inst_map=inst_map)
if return_schedule:
return exp_scheds, qpt_qcs_list, meas_labels
# assemble pulse qobj
qobj = qiskit.assemble(exp_scheds,
backend=backend,
meas_level=2,
shots=shots)
# sanity check
if sanity_check:
for experiment in qobj.experiments:
for inst in experiment.instructions:
if inst.name == 'acquire':
memory_slot_map = inst.memory_slot
if memory_slot_map[qubits[0]] != __reserved_registers[0] or \
memory_slot_map[qubits[0]] != __reserved_registers[1]:
warnings.warn('Wrong memory slots are assigned. '
'QPT fitter may return invalid result.')
assert len(qobj.experiments) <= back_config.max_experiments
return qobj, qpt_qcs_list, meas_labels
def parametri(N):
res =[ ele for ele in itertools.product([0,1,2,3], repeat = N)]
return res
par=parametri(n)
def merX(kpom,i):
kpom.h(i)
return kpom
def merY(kpom,i):
kpom.s(i)
kpom.h(i)
return kpom
#Pravim matricu M
qr = QuantumRegister(5)
meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')
noise_model = get_noise(0.1)
backend = Aer.get_backend('qasm_simulator')
job = execute(meas_calibs, backend=backend, shots=1000, noise_model=noise_model)
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')
M=meas_fitter.cal_matrix
#Pravim Kolo
for i in range(4**n):
q.append(QuantumRegister(n))
c.append(ClassicalRegister(n))
wKolo.append(QuantumCircuit(q[i],c[i]))
#ovde stavljamo gejtove
wKolo[i].x(q[i][1])
wKolo[i].ry(1.37,q[i][3])
wKolo[i].cx(q[i][3],q[i][1])