Note

Cette page a été générée à partir de tutorials/noise/2_relaxation_and_decoherence.ipynb.

Exécuter en mode interactif dans le IBM Quantum lab <https://quantum-computing.ibm.com/jupyter/tutorial/algorithms/02_vqe_convergence.ipynb> _.

*Relaxation et Décohérence *

Ce bloc-notes donne des exemples d’utilisation du module ignis.characterization.coherence pour mesurer \(T_1\) et \(T_2\).

import numpy as np
import matplotlib.pyplot as plt

import qiskit
from qiskit.providers.aer.noise.errors.standard_errors import thermal_relaxation_error
from qiskit.providers.aer.noise import NoiseModel

from qiskit.ignis.characterization.coherence import T1Fitter, T2StarFitter, T2Fitter
from qiskit.ignis.characterization.coherence import t1_circuits, t2_circuits, t2star_circuits

Génération de circuits de cohérence

Cela montre comment générer les circuits. La liste des qubits spécifie pour quels qubits générer des circuits de caractérisation ; ces circuits seront exécutés en parallèle. L’unité de temps discrète est la porte d’identité (iden) et donc l’utilisateur doit spécifier le temps de chaque porte d’identité s’il souhaite que les paramètres de caractérisation soient retournés en unités de temps. Cela devrait être disponible depuis le backend (plateforme de calculateur quantique).

num_of_gates = (np.linspace(10, 300, 50)).astype(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)
t2star_circs, t2star_xdata, osc_freq = t2star_circuits(num_of_gates, gate_time, qubits, nosc=5)
t2echo_circs, t2echo_xdata = t2_circuits(np.floor(num_of_gates/2).astype(int),
                                         gate_time, qubits)
t2cpmg_circs, t2cpmg_xdata = t2_circuits(np.floor(num_of_gates/6).astype(int),
                                         gate_time, qubits,
                                         n_echos=5, phase_alt_echo=True)

Exécution du backend

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 T1 and T2 noise:
t1_noise_model = NoiseModel()
t1_noise_model.add_quantum_error(
    thermal_relaxation_error(t_q0, 2*t_q0, gate_time),
    'id', [0])
t1_noise_model.add_quantum_error(
    thermal_relaxation_error(t_q2, 2*t_q2, gate_time),
    'id', [2])

t2_noise_model = NoiseModel()
t2_noise_model.add_quantum_error(
    thermal_relaxation_error(np.inf, t_q0, gate_time, 0.5),
    'id', [0])
t2_noise_model.add_quantum_error(
    thermal_relaxation_error(np.inf, t_q2, gate_time, 0.5),
    'id', [2])

# Run the simulator
t1_backend_result = qiskit.execute(t1_circs, backend, shots=shots,
                                   noise_model=t1_noise_model, optimization_level=0).result()
t2star_backend_result = qiskit.execute(t2star_circs, backend, shots=shots,
                                       noise_model=t2_noise_model, optimization_level=0).result()
t2echo_backend_result = qiskit.execute(t2echo_circs, backend, shots=shots,
                                       noise_model=t2_noise_model, optimization_level=0).result()

# It is possible to split the circuits into multiple jobs and then give the results to the fitter as a list:
t2cpmg_backend_result1 = qiskit.execute(t2cpmg_circs[0:5], backend,
                                        shots=shots, noise_model=t2_noise_model,
                                        optimization_level=0).result()
t2cpmg_backend_result2 = qiskit.execute(t2cpmg_circs[5:], backend,
                                        shots=shots, noise_model=t2_noise_model,
                                        optimization_level=0).result()

Analyse des résultats

# Fitting T1

%matplotlib inline

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]))
print(t1_fit.time())
print(t1_fit.time_err())
print(t1_fit.params)
print(t1_fit.params_err)

for i in range(2):
    ax = plt.subplot(1, 2, i+1)
    t1_fit.plot(i, ax=ax)
plt.show()

[24.10436641574169, 15.1725404065493]
[2.2001166293232, 0.6420239307715634]
{'0': [array([ 0.97307515, 24.10436642,  0.02663813]), array([ 1.00092258e+00,  1.51725404e+01, -5.42259153e-03])]}
{'0': [array([0.04753859, 2.20011663, 0.05166528]), array([0.01408857, 0.64202393, 0.01729388])]}

Exécutez à nouveau le backend pour obtenir plus de statistiques, et ajoutez les résultats aux précédents:

t1_backend_result_new = qiskit.execute(t1_circs, backend,
                                       shots=shots, noise_model=t1_noise_model,
                                       optimization_level=0).result()
t1_fit.add_data(t1_backend_result_new)

plt.figure(figsize=(15, 6))
for i in range(2):
    ax = plt.subplot(1, 2, i+1)
    t1_fit.plot(i, ax=ax)
plt.show()

# Fitting T2*

%matplotlib inline

t2star_fit = T2StarFitter(t2star_backend_result, t2star_xdata, qubits,
                          fit_p0=[0.5, t_q0, osc_freq, 0, 0.5],
                          fit_bounds=([-0.5, 0, 0, -np.pi, -0.5],
                                      [1.5, 40, 2*osc_freq, np.pi, 1.5]))

plt.figure(figsize=(15, 6))
for i in range(2):
    ax = plt.subplot(1, 2, i+1)
    t2star_fit.plot(i, ax=ax)
plt.show()

# Fitting T2 single echo

%matplotlib inline

t2echo_fit = T2Fitter(t2echo_backend_result, t2echo_xdata, qubits,
                      fit_p0=[0.5, t_q0, 0.5],
                      fit_bounds=([-0.5, 0, -0.5],
                                  [1.5, 40, 1.5]))

print(t2echo_fit.params)

plt.figure(figsize=(15, 6))
for i in range(2):
    ax = plt.subplot(1, 2, i+1)
    t2echo_fit.plot(i, ax=ax)
plt.show()

{'0': [array([ 0.46514794, 21.68047828,  0.53799714]), array([ 0.53077749, 16.98595852,  0.47459488])]}

# Fitting T2 CPMG

%matplotlib inline

t2cpmg_fit = T2Fitter([t2cpmg_backend_result1, t2cpmg_backend_result2],
                      t2cpmg_xdata, qubits,
                      fit_p0=[0.5, t_q0, 0.5],
                      fit_bounds=([-0.5, 0, -0.5],
                                  [1.5, 40, 1.5]))

plt.figure(figsize=(15, 6))
for i in range(2):
    ax = plt.subplot(1, 2, i+1)
    t2cpmg_fit.plot(i, ax=ax)
plt.show()

import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright

Version Information

Qiskit Software	Version
Qiskit	None
Terra	0.9.0
Aer	0.3.0
Ignis	0.2.0
Aqua	0.5.3
IBM Q Provider	0.3.2rc1
System information
Python	3.7.4 (default, Aug 13 2019, 15:17:50) [Clang 4.0.1 (tags/RELEASE_401/final)]
OS	Darwin
CPUs	4
Memory (Gb)	16.0
Wed Aug 21 21:30:54 2019 EDT

This code is a part of Qiskit

© Copyright IBM 2017, 2019.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
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Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.