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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.

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

New demos

  • Compilation
  • Quantum Computing

The KAK decomposition

  • Algorithms
  • How-to
  • Quantum Computing

How to use quantum arithmetic operators

  • Algorithms
  • Quantum Computing

Intro to quantum read-only memory (QROM)

  • Quantum Machine Learning

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

  • Algorithms
  • Optimization

Fast optimization of instantaneous quantum polynomial circuits

  • Compilation
  • Quantum Computing

The KAK decomposition

  • Algorithms
  • How-to
  • Quantum Computing

How to use quantum arithmetic operators

  • Algorithms
  • Quantum Computing

Intro to quantum read-only memory (QROM)

  • Quantum Machine Learning

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

  • Algorithms
  • Optimization

Fast optimization of instantaneous quantum polynomial circuits

  • Compilation
  • Quantum Computing

The KAK decomposition

  • Algorithms
  • How-to
  • Quantum Computing

How to use quantum arithmetic operators

  • Algorithms
  • Quantum Computing

Intro to quantum read-only memory (QROM)

Demos based on papers

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

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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)

How to use wire registers

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Algorithms

See all (6)

Fast optimization of instantaneous quantum polynomial circuits

How to use quantum arithmetic operators

Intro to quantum read-only memory (QROM)

Intro to Amplitude Amplification

Linear combination of unitaries and block encodings

Intro to QSVT

See all (6)

Compilation

See all (1)

The KAK decomposition

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Devices and Performance

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Using JAX with PennyLane

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How-to

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How to use quantum arithmetic operators

How to use wire registers

Learning dynamics incoherently: Variational learning using classical shadows

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Optimization

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Fast optimization of instantaneous quantum polynomial circuits

Learning shallow quantum circuits with local inversions and circuit sewing

Implicit differentiation of variational quantum algorithms

3-qubit Ising model in PyTorch

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Quantum Chemistry

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Initial State Preparation for Quantum Chemistry

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Quantum Computing

See all (7)

The KAK decomposition

How to use quantum arithmetic operators

Intro to quantum read-only memory (QROM)

Intro to Amplitude Amplification

Learning shallow quantum circuits with local inversions and circuit sewing

Linear combination of unitaries and block encodings

Intro to QSVT

See all (7)

Quantum Machine Learning

See all (7)

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

Learning dynamics incoherently: Variational learning using classical shadows

Learning shallow quantum circuits with local inversions and circuit sewing

Generalization in QML from few training data

Quantum advantage in learning from experiments

Training and evaluating quantum kernels

Kernel-based training of quantum models with scikit-learn

See all (7)
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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