Research¶

Explore cutting-edge research using PennyLane. The demonstrations below implement ideas from inspiring papers that investigate the training of quantum circuits. You can find research that targets quantum machine learning in the Quantum machine learning section, or discover other variational quantum optimization algorithms under Optimization.

Quantum machine learning¶

Delve into the latest exciting research and cutting-edge ideas in quantum machine learning. Implement and run a vast array of different QML applications on your own computer—using simulators from Xanadu, IBM, Google, Rigetti, and many more—or on real hardware devices.

Quantum Generative Adversarial Networks with Cirq + TensorFlow

Function fitting with a photonic quantum neural network

Data-reuploading classifier

Quantum transfer learning

Quanvolutional Neural Networks

Variational Quantum Linear Solver

Coherent Variational Quantum Linear Solver

Multiclass margin classifier

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The Stochastic Parameter-Shift Rule

The Quantum Graph Recurrent Neural Network

Optimizing a quantum optical neural network

Quantum models as Fourier series

Quantum computation with neutral atoms

Kernel-based training of quantum models with scikit-learn

Learning to learn with quantum neural networks

Training and evaluating quantum kernels

Optimization¶

Here you will find demonstrations showcasing quantum optimization. Explore various topics and ideas, such as the shots-frugal Rosalin optimizer, the variational quantum thermalizer, or the issue of barren plateaus in quantum neural networks.