PennyLane
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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.

Choose a category, or have a look at demos made by our community.

All Demos
Demos based on papers
Algorithms
Devices and Performance
Getting Started
How-to
Optimization
Quantum Chemistry
Quantum Computing
Quantum Machine Learning

New demos

  • Algorithms
  • Quantum Computing

Fixed depth Hamiltonian simulation via Cartan decomposition

  • Algorithms
  • Quantum Computing

Constant-depth preparation of matrix product states with dynamic circuits

  • Algorithms
  • How-to
  • Quantum Computing

How to track algorithmic error using PennyLane

  • How-to
  • Quantum Chemistry

Density Matrix Embedding Theory

  • Algorithms

Decoded Quantum Interferometry

  • Algorithms
  • Quantum Computing

Fixed depth Hamiltonian simulation via Cartan decomposition

  • Algorithms
  • Quantum Computing

Constant-depth preparation of matrix product states with dynamic circuits

  • Algorithms
  • How-to
  • Quantum Computing

How to track algorithmic error using PennyLane

  • How-to
  • Quantum Chemistry

Density Matrix Embedding Theory

  • Algorithms

Decoded Quantum Interferometry

  • Algorithms
  • Quantum Computing

Fixed depth Hamiltonian simulation via Cartan decomposition

  • Algorithms
  • Quantum Computing

Constant-depth preparation of matrix product states with dynamic circuits

  • Algorithms
  • How-to
  • Quantum Computing

How to track algorithmic error using PennyLane

Demos based on papers

See all (64)

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

See all (64)

Getting Started

See all (1)

Gaussian transformation

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Algorithms

See all (6)

Decoded Quantum Interferometry

Fixed depth Hamiltonian simulation via Cartan decomposition

Constant-depth preparation of matrix product states with dynamic circuits

How to track algorithmic error using PennyLane

Estimating observables with classical shadows in the Pauli basis

Error mitigation with Mitiq and PennyLane

See all (6)

Devices and Performance

See all (1)

Efficient Simulation of Clifford Circuits

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How-to

See all (2)

Density Matrix Embedding Theory

How to track algorithmic error using PennyLane

See all (2)

Optimization

See all (5)

Differentiating quantum error mitigation transforms

Generalized parameter-shift rules

Feedback-Based Quantum Optimization (FALQON)

Coherent Variational Quantum Linear Solver

Doubly stochastic gradient descent

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Quantum Chemistry

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Density Matrix Embedding Theory

Symmetry-invariant quantum machine learning force fields

Fermionic operators

Differentiable Hartree-Fock

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Quantum Computing

See all (5)

Fixed depth Hamiltonian simulation via Cartan decomposition

Constant-depth preparation of matrix product states with dynamic circuits

How to track algorithmic error using PennyLane

Estimating observables with classical shadows in the Pauli basis

Error mitigation with Mitiq and PennyLane

See all (5)

Quantum Machine Learning

See all (5)

Symmetry-invariant quantum machine learning force fields

Contextuality and inductive bias in QML

An equivariant graph embedding

Quantum models as Fourier series

Data-reuploading classifier

See all (5)
PennyLane

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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