PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
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Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Python
Category
Model Development
Subcategory
Domain-specific training
Stars
3,499
Forks
883
Contributors
100
Created date
2018-04-17
Updated date
2026-10-16
Stars in 1 day
2
Stars in 7 days
10
Rank
3,537
Previous rank
2,974
Rank change
-563
7-day stars rank
3,955
Previous 7-day stars rank
4,384
7-day stars rank change
429
Total stars rank
3,903
Previous total stars rank
3,900
Total stars rank change
-3
Country
Canada
README summary
PennyLane is an open-source quantum software platform for quantum computing , quantum machine learning , and quantum chemistry . Create meaningful quantum algorithms, from inspiration to implementation. Key Features *Inspiration to implementation, quickly. Leverage the world’s largest library of research demos, interactive tutorials, and state-of-the-art components to build algorithms in quantum chemistry, quantum information, optimization, and quantum machine learning. * Whether executing, compiling, or analyzing, PennyLane is fast. Unlock production-grade performance with industrial resource estimation and the Catalyst
compiler. Scale up your workflows with the high-performance Lightning simulators on GPUs, supercomputers, and the cloud. * PennyLane integrates with a wide range of quantum hardware devices. * PennyLane is the world’s most active quantum community. You're part of a global network of researchers, developers, and educators actively defining the frontier of quantum computing. Whether quantum is your day job or you’re getting your first taste at a hackathon, you’re backed by the most responsive community in the
field. For more details and additional features, please see the PennyLane website and our most recent release notes. Installation PennyLane requires Python version 3. Installation of PennyLane, as well as all dependencies, can be done using pip: 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 interactive tutorials
and quickstart guide, designed to introduce key features and help you start building quantum circuits right away. Whether you're exploring quantum machine learning, quantum computing, or quantum chemistry, PennyLane offers a wide range of tools and resources to support your research. Key Resources Library of research demos Learn Quantum Programming with the Codebook and Coding Challenges PennyLane Discussion Forum You can also check out our documentation, and detailed developer guides. If you would like to contribute your own demo, see
our demo submission guide. All contributors to PennyLane 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. See our contributions page and our Development guide for more details. Support **Source Code:** **Issue Tracker:** 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. 04968 License PennyLane is **free** and **open source**, released under the Apache License, Version 2.
Frequently asked questions
How many stars and forks are recorded?
PennyLaneAI/pennylane has 3,499 stars and 883 forks.
What is the repository rank?
PennyLaneAI/pennylane is ranked 3,537.
What language and category are recorded?
PennyLaneAI/pennylane has 3,499 stars, uses Python and belongs to Model Development.
Which repository is closest in rank?
PennyLaneAI/pennylane is compared with autorope/donkeycar, ranked 3,534 with 3,526 stars.
Get up and running quickly with PennyLane by following our interactive tutorials and quickstart guide, designed to introduce key features and help you start building quantum circuits right away.
Whether you're exploring quantum machine learning, quantum computing, or quantum chemistry, PennyLane offers a wide range of tools and resources to support your research.
Take a deeper dive into quantum computing by exploring quantum computing research with the PennyLane Demos—covering fundamental quantum concepts alongside the latest quantum algorithm research results.
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.
We also encourage bug reports, suggestions for new features and enhancements, and even links to cool
projects or applications built on PennyLane.
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.
