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/Rigett…
---
name: pennylane
description: 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 or JAX. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
license: Apache-2.0 license
allowed-tools:
- Read
- Bash
- Python
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---
# PennyLane
## Overview
PennyLane is a quantum computing library that enables training quantum computers like neural networks. It provides automatic differentiation of quantum circuits, device-independent programming, and seamless integration with classical machine learning frameworks.
## Installation
PennyLane 0.45.0 requires Python 3.11 or newer. Install using uv with pinned versions for reproducible environments:
```bash
uv pip install "pennylane==0.45.0"
```
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