t-imamichi / qiskit-ignis

Ignis provides tools for quantum hardware verification, noise characterization, and error correction.

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 or when you install ignis with pip install qiskit-ignis[visualization]. If you're going to use a cvx fitter for running tomogography you'll need to install cvxpy. You can do this with pip install cvxpy or when you install ignis with pip install qiskit-ignis[cvx]. When performing expectation value measurement error mitigation using the CTMP method performance can be improved using just-in-time compiling if Numbda is installed. You can do this with pip install numba or when you install ignis with pip install qiskit-ignis[jit]. For using the discriminator classes in qiskit.ignis.measurement scikit-learn needs to be installed. You can do this with pip install scikit-learn or when you install ignis with pip install qiskit-ignis[iq]. If you want to install all extra requirements when you install ignis you can run 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