demonstrations.rst

Demos

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Take a deeper dive into quantum machine learning by exploring cutting-edge algorithms using PennyLane and near-term quantum hardware.

Basics

Begin your journey into quantum machine learning using PennyLane.

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Research

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

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Community

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

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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.

Featured

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  Quantum transfer learning

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

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  The quantum graph recurrent neural network

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

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  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.

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  A brief overview of VQE

  Calculate the ground-state energy of the hydrogen molecule by sampling from terms in the VQE Hamiltonian.

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  Quantum natural gradient

  Achieve faster optimization convergence using the quantum natural gradient.

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  QAOA for MaxCut

  Implement the QAOA algorithm using PennyLane to solve the MaxCut problem.

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  The variational quantum thermalizer

  Learn about the variational quantum thermalizer algorithm, an extension of VQE.

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  Frugal shot optimization with Rosalin

  Optimize variational quantum algorithms with a minimized number of shots by using the Rosalin optimizer.

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.

.. toctree::
    :maxdepth: 2
    :caption: QML Demos
    :hidden:

    demos_basics
    demos_research
    demos_community
    demos_submission