qiskit-noise-learning

A Python toolkit for randomization-based quantum noise characterization.

Note

Alpha software. This library is in the 0.x stage of development and under active development. No part of the public interface is yet stable: while the major version is 0, expect breaking changes between releases and pin your dependency accordingly (for example, qiskit-noise-learning==0.1.*). We do not currently issue deprecation warnings, but all changes are recorded in the changelog. See the deprecation policy for details. All feedback is appreciated.

Installation

You can install qiskit-noise-learning via pip from PyPI:

pip install qiskit-noise-learning

For visualization support, include the visualization dependencies:

pip install "qiskit-noise-learning[vis]"

See the contribution guidelines for developer dependencies and editable installations.

Interfaces and Documentation

This library has two levels of interface. The first is the low-level interface where the user directly interacts with objects representing core concepts in noise learning, enabling custom design of every aspect of a noise learning protocol. The second is a higher-level interface that wraps a stock workflow into an easy-to-use NoiseLearner object. You can see both demonstrated in the following tutorials, which run locally against a fake backend and need no IBM Quantum credentials:

• docs/tutorials/noise_learner.md — end-to-end use of NoiseLearner
• docs/tutorials/workflow.md — step-by-step walkthrough of the internal pipeline

Development

python -m pytest          # run tests (includes doctests)
ruff check --fix . && ruff format .   # lint and format
pre-commit run --all-files            # all checks

See the contribution guidelines for details on developer setup, testing, building the documentation, and the changelog workflow.

License

Apache 2.0

Citing this package

If you use this package in your research, use the CITATION.bib file in this project’s repository to cite the appropriate reference(s).