Qiskit Ignis

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.

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.

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, Aer
from qiskit.providers.aer import noise # import AER noise model
# Measurement correction functions
import qiskit.ignis.measurement_correction as meas_corr

# 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 calibration circuits
qr = qiskit.QuantumRegister(5)
meas_calibs, state_labels = meas_corr.measurement_calibration([qr[2],qr[3],qr[4]])

# Run the calibration circuits
backend = qiskit.Aer.get_backend('qasm_simulator')
qobj = qiskit.compile(meas_calibs, backend=backend, shots=1000)
job = backend.run(qobj, noise_model=noise_model)
cal_results = job.result()

# Make a calibration matrix
MeasCal = meas_corr.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 correction
print("Results without correction:", results.get_counts(0))

# Results with correction
print("Results with correction:", MeasCal.calibrate(results.get_counts(0), method=1))

Results without correction: {'000': 220, '001': 79, '010': 67, '011': 62, '100': 87, '101': 67, '110': 57, '111': 361}
Results with correction: {'000': 520.2870508054327, '011': 3.940910098254591e-13, '101': 0.3251956072435034, '111': 479.3877535873258}

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 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 Overflow.

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