Demos¶

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

Getting started

Begin your journey into quantum computing using PennyLane.

Optimization

Learn how to train quantum circuits like neural networks, using the latest tips and tricks from the literature.

QML

Explore cutting-edge research in quantum machine learning using PennyLane.

Quantum Chemistry

Study the properties of molecules and materials using quantum computing.

Quantum Computing

Explore general quantum computing concepts and algorithms, from quantum volume to boson sampling.

Community

Discover PennyLane demonstrations created by other users, or submit one yourself.

Do you want to make your own demo using PennyLane? Read the guidelines and submission instructions here, and have your demo featured on our community page.

To cite a PennyLane demo, please refer to it as: Author. Title (PennyLane).Date of publication (updated on Date of last update). URL address (accessed on Date of access).

Featured

Quantum transfer learning

Learn how to apply a machine learning method, known as transfer learning, to a hybrid classical-quantum image classifier.

The quantum graph recurrent neural network

Use a quantum graph recurrent neural network to learn quantum dynamics.

Data re-uploading classifier

A universal single-qubit quantum classifier using the idea of 'data re-uploading' by Pérez-Salinas et al. (2019), akin to a single hidden-layered neural network.

All content above is free, open-source, and available as executable code downloads. If you would like to contribute a demo, please make a pull request over at our GitHub repository.