> ## Documentation Index
> Fetch the complete documentation index at: https://kalarislabs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# pennylane — AI agent skill for physical sciences

> Hardware-agnostic quantum ML framework with automatic differentiation.

# `pennylane`

> 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.

**Category:** [physical-sciences](/skills#physical-sciences) · **License:** Apache-2.0 license · **Version:** 1.2

## Install

```bash theme={null}
npx research-agent-skills install pennylane
npx skills add KalarisLabs/research-agent-skills --skill pennylane
```

## When to use it

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.

## Full playbook

Read [SKILL.md](https://github.com/KalarisLabs/research-agent-skills/blob/main/skills/pennylane/SKILL.md) for the complete workflow, references and any scripts. The agent installer copies the full skill folder.