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The PennyLane-Qiskit plugin integrates the Qiskit quantum computing framework with PennyLane’s quantum machine learning capabilities.

PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.

Qiskit is an open-source framework for quantum computing.

Features

  • Provides three devices to be used with PennyLane: qiskit.aer, qiskit.basicsim and qiskit.remote. These devices provide access to the various backends, including the IBM hardware accessible through the cloud.

  • Supports a wide range of PennyLane operations and expectation values across the providers.

  • Combine Qiskit’s high performance simulator and hardware backend support with PennyLane’s automatic differentiation and optimization.

Installation

This plugin requires Python >= 3.11, as well as PennyLane and Qiskit. Installation of this plugin, as well as all dependencies, can be done using pip:

python -m pip install pennylane-qiskit

To test that the PennyLane-Qiskit plugin is working correctly you can install the development requirements with,

python -m pip install -r requirements-dev.txt

With this, you can run the tests with,

make test

in the source folder.

Development

If you wish to develop the PennyLane-Qiskit plugin, please first install the developer requirements with

python -m pip install -r requirements-dev.txt

followed by an edittable installation of the plugin with

python -m pip install -e .

Optionally, you can install the pre-commit hooks with

pre-commit install

This will set up pre-commit hooks to automatically format and lint your code before each commit.

Please refer to the plugin documentation as well as to the PennyLane documentation for further reference.

Contributing

We welcome contributions - simply fork the repository of this plugin, and then make a pull request containing your contribution. All contributors to this plugin will be listed as authors on the releases.

We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on PennyLane.

Authors

PennyLane-Qiskit is the work of many contributors.

If you are doing research using PennyLane and PennyLane-Qiskit, please cite our paper:

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed, Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer, Zeyue Niu, Antal Száva, and Nathan Killoran. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968

Support

If you are having issues, please let us know by posting the issue on our Github issue tracker, or by asking a question in the forum.

License

The PennyLane qiskit plugin is free and open source, released under the Apache License, Version 2.0.

Metadata

Release files for PennyLane-qiskit 0.45.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for PennyLane-qiskit 0.45.0
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pennylane_qiskit-0.45.0-py3-none-any.whl Python 3 none any Details

Release files / pennylane_qiskit-0.45.0-py3-none-any.whl

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