pennylane

•

18.1k19953 months ago

Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.

Quick Install

npxskills add k-dense-ai/claude-scientific-skills--skill pennylane

Instructions

No instructions available

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Tags & Topics

ai-scientistbioinformaticschemoinformaticsclaudeclaude-skillsclaudecode