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https://github.com/PennyLaneAI/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.
https://github.com/PennyLaneAI/pennylane

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

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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.

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README 

        


  

  

    

  

  

  

    

  

  

  

    

  

  

  

    

  

  

  

    

  

  

  

    

  






  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.

  

    

    




## Key Features






- *Machine learning on quantum hardware*. Connect to quantum hardware using **PyTorch**, **TensorFlow**, **JAX**, **Keras**, or **NumPy**. Build rich and flexible hybrid quantum-classical models.



- *Just in time compilation*. 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](https://github.com/pennylaneai/catalyst) for more details.



- *Device-independent*. Run the same quantum circuit on different quantum backends. Install

  [plugins](https://pennylane.ai/plugins.html) to access even more devices, including **Strawberry

  Fields**, **Amazon Braket**, **IBM Q**, **Google Cirq**, **Rigetti Forest**, **Qulacs**, **Pasqal**, **Honeywell**, and more.



- *Follow the gradient*. Hardware-friendly **automatic differentiation** of quantum circuits.



- *Batteries included*. Built-in tools for **quantum machine learning**, **optimization**, and

  **quantum chemistry**. Rapidly prototype using built-in quantum simulators with

  backpropagation support.



## Installation



PennyLane requires Python version 3.10 and above. Installation of PennyLane, as well as all

dependencies, can be done using pip:



```console

python -m pip install pennylane

```



## Docker support



**Docker** support exists for building using **CPU** and **GPU** (Nvidia CUDA

11.1+) images. [See a more detailed description

here](https://pennylane.readthedocs.io/en/stable/development/guide/installation.html#docker).



## Getting started



For an introduction to quantum machine learning, guides and resources are available on

PennyLane's [quantum machine learning hub](https://pennylane.ai/qml/):






* [What is quantum machine learning?](https://pennylane.ai/qml/whatisqml)

* [QML tutorials and demos](https://pennylane.ai/qml/demonstrations)

* [Frequently asked questions](https://pennylane.ai/faq)

* [Key concepts of QML](https://pennylane.ai/qml/glossary)

* [QML videos](https://pennylane.ai/qml/videos)



You can also check out our [documentation](https://pennylane.readthedocs.io) for [quickstart

guides](https://pennylane.readthedocs.io/en/stable/introduction/pennylane.html) to using PennyLane,

and detailed developer guides on [how to write your

own](https://pennylane.readthedocs.io/en/stable/development/plugins.html) PennyLane-compatible

quantum device.



## Tutorials and demonstrations



Take a deeper dive into quantum machine learning by exploring cutting-edge algorithms on our [demonstrations

page](https://pennylane.ai/qml/demonstrations).





  




All demonstrations are fully executable, and can be downloaded as Jupyter notebooks and Python

scripts.



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

guide](https://pennylane.ai/qml/demos_submission).



## Videos



Seeing is believing! Check out [our videos](https://pennylane.ai/qml/videos) to learn about

PennyLane, quantum computing concepts, and more. 





  




## Contributing to PennyLane



We welcome contributions—simply fork the PennyLane repository, and then make a [pull

request](https://help.github.com/articles/about-pull-requests/) 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](https://github.com/PennyLaneAI/pennylane/blob/master/.github/CONTRIBUTING.md) and our

[developer hub](https://pennylane.readthedocs.io/en/stable/development/guide.html) 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.



We also have a [PennyLane discussion forum](https://discuss.pennylane.ai)—come join the community

and chat with the PennyLane team.



Note that we are committed to providing a friendly, safe, and welcoming environment for all.

Please read and respect the [Code of Conduct](.github/CODE_OF_CONDUCT.md).



## Authors



PennyLane is the work of [many contributors](https://github.com/PennyLaneAI/pennylane/graphs/contributors).



If you are doing research using PennyLane, please cite [our paper](https://arxiv.org/abs/1811.04968):



> 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.