Qiskit is an open-source framework for working with noisy intermediate-scale quantum computers (NISQ) at the level of pulses, circuits, and algorithms.
Qiskit is made up of elements that each work together to enable quantum computing. This element is Ignis, which provides tools for quantum hardware verification, noise characterization, and error correction.
We encourage installing Qiskit via the PIP tool (a python package manager), which installs all Qiskit elements, including this one.
pip install qiskitPIP will handle all dependencies automatically for us and you will always install the latest (and well-tested) version.
To install from source, follow the instructions in the contribution guidelines.
Now that you have Qiskit Ignis installed, you can start creating experiments, to reveal information about the device quality. Here is a basic example:
$ python
# Import Qiskit classes
import qiskit
from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister
from qiskit.providers.aer import noise # import AER noise model
# Measurement error mitigation functions
from qiskit.ignis.error_mitigation.measurement import (measurement_calibration,
MeasurementFitter)
# Generate a noise model for the qubits
noise_model = noise.NoiseModel()
for qi in range(5):
read_err = noise.errors.readout_error.ReadoutError([[0.75, 0.25],[0.1, 0.9]])
noise_model.add_readout_error(read_err, [qi])
# Generate the measurement calibration circuits
# for running measurement error mitigation
qr = QuantumRegister(5)
meas_cals, state_labels = measurement_calibration(qubit_list=[2,3,4], qr=qr)
# Run the calibration circuits
backend = qiskit.Aer.get_backend('qasm_simulator')
qobj = qiskit.compile(meas_cals, backend=backend, shots=1000)
job = backend.run(qobj, noise_model=noise_model)
cal_results = job.result()
# Make a calibration matrix
meas_fitter = MeasurementFitter(cal_results, state_labels)
# Make a 3Q GHZ state
cr = ClassicalRegister(3)
ghz = QuantumCircuit(qr, cr)
ghz.h(qr[2])
ghz.cx(qr[2], qr[3])
ghz.cx(qr[3], qr[4])
ghz.measure(qr[2],cr[0])
ghz.measure(qr[3],cr[1])
ghz.measure(qr[4],cr[2])
qobj = qiskit.compile(ghz, backend=backend, shots=1000)
job = backend.run(qobj, noise_model=noise_model)
results = job.result()
# Results without mitigation
raw_counts = results.get_counts()
print("Results without mitigation:", raw_counts)
# Results with mitigation
mitigated_counts = meas_fitter.calibrate(raw_counts)
print("Results with mitigation:", mitigated_counts)Results without mitigation: {'000': 181, '001': 83, '010': 59, '011': 65, '100': 101, '101': 48, '110': 72, '111': 391}
Results with mitigation: {'000': 420.866934, '001': 2.1002, '011': 1.30314, '100': 53.0165, '110': 13.1834, '111': 509.5296}
If you'd like to contribute to Qiskit Ignis, please take a look at our contribution guidelines. This project adheres to Qiskit's code of conduct. By participating, you are expect to uphold to this code.
We use GitHub issues for tracking requests and bugs. Please use our slack for discussion and simple questions. To join our Slack community use the link. For questions that are more suited for a forum we use the Qiskit tag in the Stack Exchange.
Now you're set up and ready to check out some of the other examples from our Qiskit Tutorials repository.
Qiskit Ignis is the work of many people who contribute to the project at different levels. If you use Qiskit, please cite as per the included BibTeX file.