NOTE As of the version 0.7.0 Qiskit Ignis is deprecated and has been superseded by the Qiskit Experiments project. Active development on the project has stopped and only compatibility fixes and other critical bugfixes will be accepted until the project is officially retired and archived.
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.
As of version 0.7.0, Qiskit Ignis has been deprecated and some of its functionality
was migrated into the qiskit-experiments package and into qiskit-terra.
-
Ignis characterization module
- This module was partly migrated to
qiskit-experimentsand split into two different modules:qiskit_experiments.library.calibrationqiskit_experiments.library.characterization AmpCalis now replaced byFineAmplitude.ZZFitterwas not migrated yet.
- This module was partly migrated to
-
Ignis discriminator module
- This module is in the process of migration to
qiskit-experiments
- This module is in the process of migration to
-
Ignis mitigation module
- The readout mitigator will be soon added to
qiskit-terra. - Experiments for generating the readout mitigators will be added to
qiskit-experiments - For use of mitigators with
qiskit.algorithmsand theQuantumInstanceclass this has been integrated intoqiskit-terradirectly with theQuantumInstance.
- The readout mitigator will be soon added to
-
Ignis verification module
- Randomized benchmarking, Quantum Volume and State and Process Tomography were migrated to
qiskit-experiments. - Migration of Gate-set tomography to
qiskit-experimentsis in progress. topological_codeswill continue development under NCCR-SPIN, while the functionality is reintegrated into Qiskit. Some additional functionality can also be found in the offshoot project qtcodes.- Currently the Accredition and Entanglement modules have not been migrated.
The following table gives a more detailed breakdown that relates the function, as it existed in Ignis, to where it now lives after this move.
- Randomized benchmarking, Quantum Volume and State and Process Tomography were migrated to
| Old | New | Library |
|---|---|---|
| qiskit.ignis.characterization.calibrations | qiskit_experiments.library.calibration | qiskit-experiments |
| qiskit.ignis.characterization.coherence | qiskit_experiments.library.characterization | qiskit-experiments |
| qiskit.ignis.mitigation | qiskit_terra.mitigation | qiskit-terra |
| qiskit.ignis.verification.quantum_volume | qiskit_experiments.library.quantum_volume | qiskit-experiments |
| qiskit.ignis.verification.randomized_benchmarking | qiskit_experiments.library.randomized_benchmarking | qiskit-experiments |
| qiskit.ignis.verification.tomography | qiskit_experiments.library.tomography | qiskit-experiments |
We encourage installing Qiskit via the pip tool (a python package manager). The following command installs the core Qiskit components, including Ignis.
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.
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].
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}
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.