# Pennylane

> Hardware-agnostic quantum ML framework with automatic differentiation.

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.

- Page: https://ss33.ai/skills/oimiragieo/pennylane-20493972985
- Publisher: oimiragieo
- Category: [Data & Analytics](https://ss33.ai/skills/category/data-and-analytics-m1r3i9) › [Machine learning & training](https://ss33.ai/skills/category/data-and-analytics-m1r3i9/machine-learning-and-training-d194fk)
- Source: https://github.com/oimiragieo/agent-studio/blob/64b580eab27ba37673f042ecc12ac53413cd9dd5/.claude/skills/scientific-skills/skills/pennylane/SKILL.md

## Skill

This skill's licence does not allow redistribution, so the store does not serve its text. Get it from the author: https://github.com/oimiragieo/agent-studio/blob/64b580eab27ba37673f042ecc12ac53413cd9dd5/.claude/skills/scientific-skills/skills/pennylane/SKILL.md