Skip to main content
GitHub Workflow Status (branch) Codecov coverage CodeFactor Grade Read the Docs PyPI PyPI - Python Version

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.basicaer and qiskit.ibmq. 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 version 3.8 and above, as well as PennyLane and Qiskit. Installation of this plugin, as well as all dependencies, can be done using pip:

pip install pennylane-qiskit

To test that the PennyLane-Qiskit plugin is working correctly you can run

make test

in the source folder. Tests restricted to a specific provider can be run by executing make test-basicaer, make test-aer, and make test-ibmq.

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 contributers 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.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

PennyLane_qiskit-0.30.1-py3-none-any.whl (31.6 kB view details)

Uploaded Python 3

File details

Details for the file PennyLane_qiskit-0.30.1-py3-none-any.whl.

File metadata

File hashes

Hashes for PennyLane_qiskit-0.30.1-py3-none-any.whl
Algorithm Hash digest
SHA256 945cccc7712d05ba2637d4c5877dfb0e236fd2a517302861477ba70ac6b3c9d4
MD5 3ea135aeecef978bf1c739b5ed337d36
BLAKE2b-256 aadc5d1a2449ec2d0afcc812d936ef46d7db9ad111d07255419afab3425edea6

See more details on using hashes here.

Release history Release notifications | RSS feed

0.45.0

1 file

0.44.1

1 file

0.44.0

1 file

0.43.0

1 file

0.42.0

1 file

0.41.0.post0

1 file

0.41.0

1 file

0.40.1

1 file

0.40.0

1 file

0.39.1

1 file

0.39.0

1 file

0.38.1

1 file

0.38.0

1 file

0.37.0

1 file

0.36.0

1 file

0.35.1

1 file

0.35.0

1 file

0.34.1

1 file

0.34.0

1 file

0.33.1

1 file

0.33.0

1 file

0.32.0

1 file

0.31.0

1 file

This release

0.30.1 This release

1 file

0.30.0

1 file

0.29.0

1 file

0.28.0

2 files

0.27.0

1 file

0.24.0

1 file

0.23.0

1 file

0.22.0

1 file

0.21.0

1 file

0.20.0

2 files

0.18.0

1 file

0.17.0

1 file

0.16.0

1 file

0.15.0

1 file

0.14.0

1 file

0.13.0

1 file

0.12.0

1 file

0.11.0

1 file

0.9.0

2 files

0.8.2

2 files

0.8.1

2 files

0.8.0

2 files

0.7.1

2 files

0.7.0

2 files

0.6.0

2 files

0.5.1

2 files

0.5.0

2 files

0.0.8

1 file

0.0.7

2 files

0.0.6

2 files

0.0.5

2 files

0.0.4

2 files

0.0.3

2 files

0.0.2

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page