pennylane

13,835 stars 1,200 forks Updated Dec 26, 2025

67.8

Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, mole

Commands Marketplace

#quantum#quantum circuits#circuits#quantum chemistry#variational quantum

Also in: github ci cd

Third-Party Skill: Review the code before installing. Skills execute in your AI assistant's environment and can access your files. Learn more about security

Installation

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Claude Code (CLI) Fast

skilz install davila7_claude-code-templates/pennylane

OpenCode (CLI) Fast

skilz install davila7_claude-code-templates/pennylane --agent opencode

OpenAI Codex (CLI) Native

skilz install davila7_claude-code-templates/pennylane --agent codex

Gemini CLI (Project) Project

skilz install davila7_claude-code-templates/pennylane --agent gemini

First time? Install Skilz: pip install skilz

Works with 14 AI coding assistants

Cursor, Aider, Copilot, Windsurf, Qwen, Kimi, and more...

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For Claude Desktop Easy

Download Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

Manual Installation

1. Clone the repository:

git clone https://github.com/davila7/claude-code-templates

2. Copy the skill directory:

cp -r claude-code-templates/cli-tool/components/skills/scientific/pennylane ~/.claude/skills/

View on GitHub

Need detailed installation help? Check our platform-specific guides:

Claude Desktop Guide Claude Code Guide Troubleshooting

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Details

Owner: davila7
Repository: claude-code-templates
Stars: 13,835
Forks: 1,200
Type: Technical
Meta-Domain: data ai
Primary Domain: machine learning
Sub-Domain: learning models model
Skill Size: 93.5 KB
Files: 8
Quality Score: 67.8

AI-Detected Topics

Extracted using NLP analysis

quantum quantum circuits circuits quantum chemistry variational quantum

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