Take a deeper dive into quantum computing by exploring cutting-edge algorithms using PennyLane and quantum hardware.
Learn how to train quantum circuits like neural networks, using the latest tips and tricks from the literature.
Study the properties of molecules and materials using quantum computing.
Explore general quantum computing concepts and algorithms, from quantum volume to boson sampling.
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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).
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.
A brief overview of VQE
Calculate the ground-state energy of the hydrogen molecule by sampling from terms in the VQE Hamiltonian.
Quantum natural gradient
Achieve faster optimization convergence using the quantum natural gradient.
QAOA for MaxCut
Implement the QAOA algorithm using PennyLane to solve the MaxCut problem.
The variational quantum thermalizer
Learn about the variational quantum thermalizer algorithm, an extension of VQE.
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.