Demos

Take a deeper dive into quantum computing by exploring cutting-edge algorithms using PennyLane and quantum hardware. Unlock new possibilities and push the boundaries of quantum research.

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Demos based on papers

Devices and Performance

Getting Started

Quantum Chemistry

Quantum Computing

Quantum Hardware

Quantum Machine Learning

New demos

Compilation
Devices and Performance
Getting Started
How-to
Quantum Chemistry
Quantum Computing

How to use Catalyst with Lightning-GPU

Algorithms
Quantum Chemistry
Quantum Machine Learning

Generative quantum eigensolver training using PennyLane data

Compilation
Devices and Performance

QJIT compilation with Qrack and Catalyst

Algorithms
Getting Started
How-to

How to estimate the resource requirements of quantum algorithms

Quantum Machine Learning

Loading classical data with low-depth circuits

Compilation
Devices and Performance
Getting Started
How-to
Quantum Chemistry
Quantum Computing

How to use Catalyst with Lightning-GPU

Algorithms
Quantum Chemistry
Quantum Machine Learning

Generative quantum eigensolver training using PennyLane data

Compilation
Devices and Performance

QJIT compilation with Qrack and Catalyst

Algorithms
Getting Started
How-to

How to estimate the resource requirements of quantum algorithms

Quantum Machine Learning

Loading classical data with low-depth circuits

Compilation
Devices and Performance
Getting Started
How-to
Quantum Chemistry
Quantum Computing

How to use Catalyst with Lightning-GPU

Algorithms
Quantum Chemistry
Quantum Machine Learning

Generative quantum eigensolver training using PennyLane data

Compilation
Devices and Performance

QJIT compilation with Qrack and Catalyst

Demos based on papers

Explore our expertly crafted research demos, all based on published papers, bringing cutting-edge studies to life. See how researchers are using PennyLane!

Active volume

Before you train: Pre-screening quantum kernels with geometric difference

Loading classical data with low-depth circuits

Resourcefulness of quantum states with Fourier analysis

Decoded Quantum Interferometry

X-ray Absorption Spectroscopy Simulation in the Time-Domain

Using PennyLane and Qualtran to analyze how QSP can improve measurements of molecular properties

The hidden cut problem for locating unentanglement

Quantum Chebyshev Transform

A Game of Surface Codes: Large-Scale Quantum Computing with Lattice Surgery

Getting Started

How to estimate the resource requirements of quantum algorithms

How to use Catalyst with Lightning-GPU

Algorithms

How to estimate the resource requirements of quantum algorithms

Generative quantum eigensolver training using PennyLane data

Compilation

How to use Catalyst with Lightning-GPU

QJIT compilation with Qrack and Catalyst

How to optimize a QML model using Catalyst and quantum just-in-time (QJIT) compilation

Devices and Performance

How to use Catalyst with Lightning-GPU

QJIT compilation with Qrack and Catalyst

How to simulate quantum circuits with tensor networks using DefaultTensor

Getting started with the Amazon Braket Hybrid Jobs

Using PennyLane with IBM's quantum devices and Qiskit

Pulse programming on Rydberg atom hardware

Computing gradients in parallel with Amazon Braket

Turning quantum nodes into Keras Layers

Plugins and hybrid computation

PyTorch and noisy devices

How-to

How to estimate the resource requirements of quantum algorithms

How to use Catalyst with Lightning-GPU

How to simulate quantum circuits with tensor networks using DefaultTensor

How to use Qiskit 1.0 with PennyLane

How to optimize a QML model using Catalyst and quantum just-in-time (QJIT) compilation

How to optimize a QML model using JAX and Optax

How to optimize a QML model using JAX and JAXopt

Optimization

How to optimize a QML model using Catalyst and quantum just-in-time (QJIT) compilation

How to optimize a QML model using JAX and JAXopt

How to optimize a QML model using JAX and Optax

Quantum natural SPSA optimizer

Optimizing a quantum optical neural network

Quantum Chemistry

How to use Catalyst with Lightning-GPU

Generative quantum eigensolver training using PennyLane data

Quantum Computing

How to use Catalyst with Lightning-GPU

How to simulate quantum circuits with tensor networks using DefaultTensor

How to use Qiskit 1.0 with PennyLane

Pulse programming on OQC Lucy in PennyLane

Pulse programming on Rydberg atom hardware

Quantum volume

Quantum advantage with Gaussian Boson Sampling

Beyond classical computing with qsim

Quantum Hardware

Pulse programming on Rydberg atom hardware

Quantum volume

Quantum advantage with Gaussian Boson Sampling

Beyond classical computing with qsim

Quantum Machine Learning

Loading classical data with low-depth circuits

Generative quantum eigensolver training using PennyLane data

Running GPU-accelerated quantum circuit simulations on Covalent Cloud using PennyLane

How to optimize a QML model using Catalyst and quantum just-in-time (QJIT) compilation

How to optimize a QML model using JAX and JAXopt

How to optimize a QML model using JAX and Optax

Function Fitting using Quantum Signal Processing

Machine learning for quantum many-body problems

Adjoint Differentiation – Supplementary Material

Adjoint Differentiation

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Built by researchers, for research. Created with ❤️ by Xanadu.

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