Qiskit Ignis

Qiskit is an open-source framework for working with noisy quantum computers 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.

Installation

We encourage installing Qiskit via the PIP tool (a python package manager), which installs all Qiskit elements, including this one.

pip install qiskit

PIP 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.

Extra Requirements

Some functionality has extra optional requirements. If you're going to use any visualization functions for fitters you'll need to install matplotlib. You can do this with

pip install matplotlib

pip install qiskit-ignis[visualization]

pip install cvxpy

pip install qiskit-ignis[cvx]

pip install numba

pip install qiskit-ignis[jit]

qiskit.ignis.measurement

pip install scikit-learn

pip install qiskit-ignis[iq]

pip install qiskit-ignis[visualization,cvx,jit,iq]

Creating your first quantum experiment with Qiskit Ignis

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.mitigation.measurement import (complete_meas_cal,
                                                 CompleteMeasFitter, 
                                                 MeasurementFilter)
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 = complete_meas_cal(qubit_list=[2,3,4], qr=qr)
Execute the calibration circuits
backend = qiskit.Aer.get_backend('qasm_simulator')
job = qiskit.execute(meas_cals, backend=backend, shots=1000, noise_model=noise_model)
cal_results = job.result()
Make a calibration matrix
meas_fitter = CompleteMeasFitter(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])
Execute the GHZ circuit (with the same noise model)
job = qiskit.execute(ghz, backend=backend, shots=1000, noise_model=noise_model)
results = job.result()
Results without mitigation
raw_counts = results.get_counts()
print("Results without mitigation:", raw_counts)
Create a measurement filter from the calibration matrix
meas_filter = meas_fitter.filter
Apply the filter to the raw counts to mitigate
the measurement errors
mitigated_counts = meas_filter.apply(raw_counts)
print("Results with mitigation:", {l:int(mitigated_counts[l]) for l in 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': 421, '001': 2, '011': 1, '100': 53, '110': 13, '111': 510}

Contribution Guidelines

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.

Next Steps

Now you're set up and ready to check out some of the other examples from our Qiskit Tutorials repository.

Authors and Citation

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.

License

Apache License 2.0