Xanadu

PennyLane

PennyLane is the cross-platform Python framework for quantum computing, quantum machine learning, and quantum chemistry maintained by Xanadu. It provides automatic differentiation of hybrid quantum-classical computations across PyTorch, JAX, TensorFlow, and NumPy with a device-agnostic plugin model so the same circuit runs against simulators (Lightning, default.qubit) and hardware (IBM Q, IonQ, Rigetti, AWS Braket, Strawberry Fields). Apache-2.0, latest version 0.45.0 (May 2026), 3.2k+ stars.

GitHub

Documentation

📖
Documentation
https://docs.pennylane.ai/en/stable/

SDKs

📦
GitHubRepository
https://github.com/PennyLaneAI/pennylane
📦
SDK
https://pypi.org/project/PennyLane/

Code Examples

💻
CodeExamples
https://pennylane.ai/qml/demonstrations/

Schemas & Data

📊
JSONSchema
https://raw.githubusercontent.com/api-evangelist/xanadu/refs/heads/main/json-schema/pennylane-device-schema.json

Other Resources

🔗
Portal
https://pennylane.ai
🔗
Forum
https://discuss.pennylane.ai/
🔗
JSONLD
https://raw.githubusercontent.com/api-evangelist/xanadu/refs/heads/main/json-ld/xanadu-context.jsonld

API entry from apis.yml

apis.yml Raw ↑ 
aid: xanadu:pennylane
name: PennyLane
tags:
- Quantum Computing
- Quantum Machine Learning
- Open Source
- Python
- Automatic Differentiation
humanURL: https://pennylane.ai
properties:
- url: https://pennylane.ai
  type: Portal
- url: https://docs.pennylane.ai/en/stable/
  type: Documentation
- url: https://github.com/PennyLaneAI/pennylane
  type: GitHubRepository
- url: https://pypi.org/project/PennyLane/
  type: SDK
- url: https://pennylane.ai/qml/demonstrations/
  type: CodeExamples
- url: https://discuss.pennylane.ai/
  type: Forum
- url: json-schema/pennylane-device-schema.json
  type: JSONSchema
- url: json-ld/xanadu-context.jsonld
  type: JSONLD
description: PennyLane is the cross-platform Python framework for quantum computing, quantum machine learning,
  and quantum chemistry maintained by Xanadu. It provides automatic differentiation of hybrid quantum-classical
  computations across PyTorch, JAX, TensorFlow, and NumPy with a device-agnostic plugin model so the same
  circuit runs against simulators (Lightning, default.qubit) and hardware (IBM Q, IonQ, Rigetti, AWS Braket,
  Strawberry Fields). Apache-2.0, latest version 0.45.0 (May 2026), 3.2k+ stars.