Xanadu

PennyLane

PennyLane is the flagship open-source Python framework for quantum differentiable programming — train and optimize variational quantum circuits with the same automatic differentiation engines used in classical machine learning. PennyLane unifies quantum machine learning, quantum chemistry, and hybrid quantum-classical workflows behind a single device-abstraction API, interoperates with NumPy, PyTorch, JAX, and TensorFlow/Keras, and runs against 40+ simulators and hardware backends through its plugin ecosystem. Apache 2.0 licensed. 3,200+ GitHub stars.

GitHub

Documentation

📖
Documentation
https://docs.pennylane.ai/en/stable/
📖
APIReference
https://docs.pennylane.ai/en/stable/code/qml.html

SDKs

📦
GitHubRepository
https://github.com/PennyLaneAI/pennylane

Code Examples

💻
CodeExamples
https://github.com/PennyLaneAI/demos

Other Resources

🔗
SDKs
https://pypi.org/project/PennyLane/
🔗
Tutorials
https://pennylane.ai/qml/demonstrations
🔗
APIsJSON
https://raw.githubusercontent.com/api-evangelist/xanadu-ai/refs/heads/main/apis.yml

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API entry from apis.yml

apis.yml Raw ↑ 
aid: xanadu-ai:pennylane
name: PennyLane
tags:
- Quantum Machine Learning
- Differentiable Programming
- Hybrid
- Python
humanURL: https://pennylane.ai
properties:
- type: Documentation
  url: https://docs.pennylane.ai/en/stable/
- type: APIReference
  url: https://docs.pennylane.ai/en/stable/code/qml.html
- type: GitHubRepository
  url: https://github.com/PennyLaneAI/pennylane
- type: SDKs
  url: https://pypi.org/project/PennyLane/
- type: Tutorials
  url: https://pennylane.ai/qml/demonstrations
- type: CodeExamples
  url: https://github.com/PennyLaneAI/demos
description: PennyLane is the flagship open-source Python framework for quantum differentiable programming
  — train and optimize variational quantum circuits with the same automatic differentiation engines used
  in classical machine learning. PennyLane unifies quantum machine learning, quantum chemistry, and hybrid
  quantum-classical workflows behind a single device-abstraction API, interoperates with NumPy, PyTorch,
  JAX, and TensorFlow/Keras, and runs against 40+ simulators and hardware backends through its plugin
  ecosystem. Apache 2.0 licensed. 3,200+ GitHub stars.