Automation framework using TensorFlow
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, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
Core Features
Ready to Use
Quick integration into your workflow with minimal setup
Community Verified
Active open-source community with continuous updates
Completely Free
MIT/Apache licensed for commercial and personal use
Flexible Extension
Customizable and extendable based on your needs
How to Use
2Install to Claude
Place the skill file in Claude's skills directory (usually ~/.claude/skills/）。
3Start Using
Restart Claude or run the reload command to load the skill
Tip: Read the documentation and code carefully before first use to understand functionality and permission requirements
Technical Information
All Skills from open-source community, preserving original authors' copyrights
K-Dense-AI__claude-scientific-skills/scientific-skills/pennylane/SKILL.mdRelated Skills
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