pennylane

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Built by researchers, for research.

https://github.com/pennylaneai/pennylane

Science Score: 46.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
Links to: arxiv.org
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Committers with academic emails
8 of 203 committers (3.9%) from academic institutions
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Scientific vocabulary similarity
Low similarity (13.8%) to scientific vocabulary

Keywords

autograd automatic-differentiation cirq deep-learning differentiable-computing hacktoberfest jax machine-learning neural-network optimization python pytorch qiskit qml quantum quantum-chemistry quantum-computing quantum-machine-learning strawberryfields tensorflow

Keywords from Contributors

parallel mpi rocm openmp distributed-computing llvm quantum-circuit qir quantum-programming-language mlir

Last synced: 6 months ago · JSON representation

Repository

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Built by researchers, for research.

Basic Info

Host: GitHub
Owner: PennyLaneAI
License: apache-2.0
Language: Python
Default Branch: master
Homepage: https://pennylane.ai
Size: 134 MB

Statistics

Stars: 2,795
Watchers: 48
Forks: 690
Open Issues: 481
Releases: 67

Topics

Created almost 8 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Code of conduct Codeowners

README.md

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PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry.

The definitive open-source framework for quantum programming. Built by researchers, for research.

Key Features

Program quantum computers. Build quantum circuits with a wide range of state preparations, gates, and measurements. Run on high-performance simulators or various hardware devices, with advanced features like mid-circuit measurements and error mitigation.
Master quantum algorithms. From NISQ to fault-tolerant quantum computing, unlock algorithms for research and application. Analyze performance, visualize circuits, and access tools for quantum chemistry and algorithm development.
Machine learning with quantum hardware and simulators. Integrate with PyTorch, TensorFlow, JAX, Keras, or NumPy to define and train hybrid models using quantum-aware optimizers and hardware-compatible gradients for advanced research tasks. Quantum machine learning quickstart.
Quantum datasets. Access high-quality, pre-simulated datasets to decrease time-to-research and accelerate algorithm development. Browse the datasets or contribute your own data.
Compilation and performance. Experimental support for just-in-time compilation. Compile your entire hybrid workflow, with support for advanced features such as adaptive circuits, real-time measurement feedback, and unbounded loops. See Catalyst for more details.

For more details and additional features, please see the PennyLane website.

Installation

PennyLane requires Python version 3.11 and above. Installation of PennyLane, as well as all dependencies, can be done using pip:

console python -m pip install pennylane

Docker support

Docker images are found on the PennyLane Docker Hub page, where there is also a detailed description about PennyLane Docker support. See description here for more information.

Getting started

Get up and running quickly with PennyLane by following our quickstart guide, designed to introduce key features and help you start building quantum circuits right away.

Whether you're exploring quantum machine learning (QML), quantum computing, or quantum chemistry, PennyLane offers a wide range of tools and resources to support your research:

Key Resources:

Research-oriented Demos
Learn Quantum Programming with the Codebook and Coding Challenges
Frequently Asked Questions
Glossary
Videos

You can also check out our documentation for quickstart guides to using PennyLane, and detailed developer guides on how to write your own PennyLane-compatible quantum device.

Demos

Take a deeper dive into quantum computing by exploring cutting-edge algorithms using PennyLane and quantum hardware. Explore PennyLane demos.

If you would like to contribute your own demo, see our demo submission guide.

Research Applications

PennyLane is at the forefront of research in quantum computing, quantum machine learning, and quantum chemistry. Explore how PennyLane is used for research in the following publications:

Quantum Computing: Fast quantum circuit cutting with randomized measurements
Quantum Machine Learning: Better than classical? The subtle art of benchmarking quantum machine learning models
Quantum Chemistry: Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization

Impactful research drives PennyLane. Let us know what features you need for your research on GitHub or on our website.

Contributing to PennyLane

We welcome contributions—simply fork the PennyLane repository, and then make a pull request containing your contribution. All contributors to PennyLane will be listed as authors on the releases. All users who contribute significantly to the code (new plugins, new functionality, etc.) will be listed on the PennyLane arXiv paper.

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

See our contributions page and our Development guide for more details.

Support

Source Code: https://github.com/PennyLaneAI/pennylane
Issue Tracker: https://github.com/PennyLaneAI/pennylane/issues

If you are having issues, please let us know by posting the issue on our GitHub issue tracker.

Join the PennyLane Discussion Forum to connect with the quantum community, get support, and engage directly with our team. It’s the perfect place to share ideas, ask questions, and collaborate with fellow researchers and developers!

Note that we are committed to providing a friendly, safe, and welcoming environment for all. Please read and respect the Code of Conduct.

Authors

PennyLane is the work of many contributors.

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

Ville Bergholm et al. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968

License

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

Owner

Name: PennyLaneAI
Login: PennyLaneAI
Kind: organization

Website: https://pennylane.ai
Twitter: PennyLaneAI
Repositories: 24
Profile: https://github.com/PennyLaneAI

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.

Committers

Last synced: 9 months ago

All Time

Total Commits: 5,212
Total Committers: 203
Avg Commits per committer: 25.675
Development Distribution Score (DDS): 0.873

Past Year

Commits: 1,243
Committers: 76
Avg Commits per committer: 16.355
Development Distribution Score (DDS): 0.817

Top Committers

Name	Email	Commits
Josh Izaac	j**6@g**m	661
Christina Lee	c**a@x**i	426
Nathan Killoran	c****y	285
antalszava	a**a@g**m	244
Christian Gogolin	g**t@c**e	233
ringo-but-quantum	g**m@x**i	227
Matthew Silverman	m**s@x**i	209
Maria Schuld	m**d@g**m	202
Mudit Pandey	m**y@x**i	180
David Wierichs	d**s@x**i	164
Romain Moyard	r**d@g**m	147
github-actions[bot]	4****]	128
Astral Cai	a**i@x**i	121
lillian542	3****2	112
Albert Mitjans	a**a@g**m	111
Andrija Paurevic	4****u	98
Korbinian Kottmann	4****n	91
Pietropaolo Frisoni	p**i@x**i	85
Jay Soni	j**i@u**a	82
Ville Bergholm	v**m@i**i	82
Edward Jiang	3****y	79
Tom Bromley	4****y	76
soranjh	4****h	75
Yushao Chen (Jerry)	c**3@o**m	72
Utkarsh	u**8@g**m	71
Guillermo Alonso-Linaje	6****G	49
Lee James O'Riordan	m****d	41
Vincent Michaud-Rioux	v**m@n**m	36
Olivia Di Matteo	2****s	34
agran2018	4****8	32
and 173 more...

Committer Domains (Top 20 + Academic)

xanadu.ai: 19 cgogolin.de: 1 uwaterloo.ca: 1 iki.fi: 1 nanoacademic.com: 1 live.ca: 1 oracle.com: 1 me.com: 1 unibo.it: 1 student.chalmers.se: 1 mail.utoronto.ca: 1 umich.edu: 1 cmu.edu: 1 freenet.de: 1 gmx.de: 1 hawk.iit.edu: 1 pm.me: 1 live.co.uk: 1 myumanitoba.ca: 1 protonmail.ch: 1 g.ecc.u-tokyo.ac.jp: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 648
Total pull requests: 5,402
Average time to close issues: 6 months
Average time to close pull requests: 19 days
Total issue authors: 177
Total pull request authors: 152
Average comments per issue: 2.14
Average comments per pull request: 2.3
Merged pull requests: 3,684
Bot issues: 1
Bot pull requests: 297

Past Year

Issues: 201
Pull requests: 2,629
Average time to close issues: 14 days
Average time to close pull requests: 9 days
Issue authors: 67
Pull request authors: 79
Average comments per issue: 0.81
Average comments per pull request: 2.03
Merged pull requests: 1,720
Bot issues: 1
Bot pull requests: 159

View more stats

Top Authors

Issue Authors

albi3ro (63)
dwierichs (56)
antalszava (30)
KetpuntoG (29)
Qottmann (29)
isaacdevlugt (27)
mudit2812 (21)
lillian542 (18)
CatalinaAlbornoz (17)
cvjjm (17)
trbromley (16)
astralcai (16)
dime10 (10)
JerryChen97 (10)
ankit27kh (8)

Pull Request Authors

albi3ro (610)
mudit2812 (395)
astralcai (357)
andrijapau (300)
github-actions[bot] (288)
PietropaoloFrisoni (257)
JerryChen97 (245)
timmysilv (235)
lillian542 (222)
dwierichs (219)
Qottmann (185)
Jaybsoni (127)
isaacdevlugt (116)
KetpuntoG (103)
obliviateandsurrender (100)

Top Labels

Issue Labels

bug :bug: (414) enhancement :sparkles: (138) good first issue (32) wontfix :see_no_evil: (10) unitaryhack (5) WIP :construction: (4) code quality :mortar_board: (3) autodiff :chart_with_upwards_trend: (3) intermediate issue :hatched_chick: (3) Hacktoberfest (3) question :question: (2) documentation :blue_book: (2) photonics :zap: (1) devices :computer: (1) CI :wrench: (1) performance :timer_clock: (1) unified compiler (1) merge-ready :heavy_check_mark: (1) urgent (1)

Pull Request Labels

merge-ready :heavy_check_mark: (247) do not merge :warning: (142) review-ready :ok_hand: (128) WIP :construction: (83) urgent (81) bug :bug: (59) ci:run-full-test-suite (49) qchem :atom_symbol: (26) gpu (23) enhancement :sparkles: (23) external (21) compiler :factory: (17) documentation :blue_book: (15) deprecation :wastebasket: (12) unified compiler (12) dependencies :pushpin: (9) labs :scientist: (7) tests :test_tube: (6) python compiler (4) ci:run-benchmarks (4) CI :wrench: (4) hacktoberfest-accepted (2) op-arithmetic (2) performance :timer_clock: (2) templates :file_folder: (2) discussion :speech_balloon: (2) core :atom: (1) merge-later (1)

Packages

Total packages: 3
Total downloads:
- pypi 143,853 last-month

Total dependent packages: 53
(may contain duplicates)
Total dependent repositories: 97
(may contain duplicates)
Total versions: 69
Total maintainers: 6

pypi.org: pennylane

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

Documentation: https://pennylane.readthedocs.io/
License: apache-2.0
Latest release: 0.42.3
published 6 months ago

Versions: 61
Dependent Packages: 49
Dependent Repositories: 95
Downloads: 143,853 Last month

Rankings

Dependent packages count: 0.3%

Average: 1.5%

Dependent repos count: 1.5%

Stargazers count: 1.6%

Downloads: 1.6%

Forks count: 2.3%

Maintainers (6)

josh146 PennyLaneAI XanaduAI mlxd trbromley ChristinaLee

Last synced: 6 months ago

conda-forge.org: pennylane

Homepage: https://github.com/PennyLaneAI/pennylane
License: Apache-2.0 AND MIT
Latest release: 0.23.0
published almost 4 years ago

Versions: 7
Dependent Packages: 4
Dependent Repositories: 2

Rankings

Forks count: 7.8%

Stargazers count: 9.7%

Dependent packages count: 12.5%

Average: 12.6%

Dependent repos count: 20.3%

Last synced: 6 months ago

conda-forge.org: pennylane-qchem

Homepage: http://xanadu.ai
License: Apache-2.0
Latest release: 0.17.0
published almost 4 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Forks count: 7.3%

Stargazers count: 9.1%

Average: 25.4%

Dependent repos count: 34.0%

Dependent packages count: 51.2%

Last synced: 6 months ago

Dependencies

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