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rabi_schedules(amp_list, qubits, pulse_width)Generates schedules for a rabi experiment using a Gaussian pulse
drag_schedules(beta_list, qubits, pulse_amp, …)Generates schedules for a drag experiment doing a pulse then the - pulse
RabiFitter(backend_result, xdata, qubits, fit_p0)Rabi Experiment fitter
DragFitter(backend_result, xdata, qubits, fit_p0)Drag Experiment fitter
get_single_q_pulse(inst_map, qubits)Get the DRAG parameters for the single qubit pulse
update_u_gates(drag_params[, …])Update the cmd_def with new single qubit gate values


Design and analyze experiments for characterizing device coherence (e.g. T1, T2). See the following example of T1 estimation.

Generation of coherence circuits: these circuits set the qubit in the excited state, wait different time intervals, then measure the qubit.

import numpy as np
from qiskit.ignis.characterization.coherence import t1_circuits
num_of_gates = np.linspace(10, 300, 5, dtype='int')
gate_time = 0.1
# Note that it is possible to measure several qubits in parallel
qubits = [0, 2]
t1_circs, t1_xdata = t1_circuits(num_of_gates, gate_time, qubits)

Backend execution: actually performing the experiment on the device (or simulator).

import qiskit
from qiskit.providers.aer.noise.errors.standard_errors \
            import thermal_relaxation_error
from qiskit.providers.aer.noise import NoiseModel
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 400
# Let the simulator simulate the following times for qubits 0 and 2:
t_q0 = 25.0
t_q2 = 15.0
# Define T\ :sub:`1` noise:
t1_noise_model = NoiseModel()
thermal_relaxation_error(t_q0, 2*t_q0, gate_time),
                        'id', [0])
    thermal_relaxation_error(t_q2, 2*t_q2, gate_time),
    'id', [2])
# Run the simulator
t1_backend_result = qiskit.execute(t1_circs, backend, shots=shots,

Analysis of results: deduction of T1, based on the experiments outcomes.

import matplotlib.pyplot as plt
from qiskit.ignis.characterization.coherence import T1Fitter
plt.figure(figsize=(15, 6))
t1_fit = T1Fitter(t1_backend_result, t1_xdata, qubits,
                  fit_p0=[1, t_q0, 0],
                  fit_bounds=([0, 0, -1], [2, 40, 1]))
for i in range(2):
    ax = plt.subplot(1, 2, i+1)
    t1_fit.plot(i, ax=ax)
[24.037383005090874, 15.135887350545305]
[7.8548254799468005, 2.1017848602903797]
{'0': [array([ 0.97493845, 24.03738301,  0.02706287]), array([ 0.99312089, 15.13588735,  0.0285408 ])]}
{'0': [array([0.17826439, 7.85482548, 0.18522525]), array([0.05276076, 2.10178486, 0.05765867])]}

Combine with new results:

t1_backend_result_new = qiskit.execute(t1_circs, backend,
plt.figure(figsize=(15, 6))
for i in range(2):
    ax = plt.subplot(1, 2, i+1)
    t1_fit.plot(i, ax=ax)
t1_circuits(num_of_gates, gate_time, qubits)Generate circuits for T1 measurement.
t2_circuits(num_of_gates, gate_time, qubits)Generate circuits for T2 (echo) measurement, by a CPMG sequence.
t2star_circuits(num_of_gates, gate_time, qubits)Generate circuits for T2* measurement.
T1Fitter(backend_result, xdata, qubits, …)Estimate T1, based on experiments outcomes,
T2Fitter(backend_result, xdata, qubits, …)Estimate T2, based on experiments outcomes.
T2StarFitter(backend_result, xdata, qubits, …)Estimate T2*, based on experiments outcomes.


ampcal_1Q_circuits(max_reps, qubits)Generates circuit for measuring the amplitude error of the single qubit gates
anglecal_1Q_circuits(max_reps, qubits[, …])Generates circuit for measuring the angle error of the single qubit gate
ampcal_cx_circuits(max_reps, qubits, …)Generates circuit for measuring the amplitude error of the cx gate
anglecal_cx_circuits(max_reps, qubits, …)Generates circuit for measuring the angle error of the cx gate
AmpCalFitter(backend_result, xdata, qubits, …)Amplitude error fitter
AngleCalFitter(backend_result, xdata, …)Amplitude error fitter
AmpCalCXFitter(backend_result, xdata, …)Amplitude error fitter
AngleCalCXFitter(backend_result, xdata, …)Amplitude error fitter


zz_circuits(num_of_gates, gate_time, qubits, …)Generates circuit for measuring ZZ.
ZZFitter(backend_result, xdata, qubits, …)ZZ fitter

Base Fitters

BaseCoherenceFitter(description, …[, …])Base class for fitters of characteristic times
BaseGateFitter(description, backend_result, …)Base class for fitters of gate errors
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