# Pennylane Hybrid Executor

> PennyLane integration skill for hybrid quantum-classical machine learning and variational algorithms

- Skill: `a5c-ai/pennylane-hybrid-executor` (Agent Skill)
- Install (CLI): `npx skillmds@latest add a5c-ai/pennylane-hybrid-executor`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/pennylane-hybrid-executor/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Integrations & APIs
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/pennylane-hybrid-executor

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# PennyLane Hybrid Executor

## Purpose

Provides expert guidance on hybrid quantum-classical workflows using PennyLane, enabling seamless integration of quantum circuits with classical machine learning frameworks.

## Capabilities

- Quantum node (QNode) definition and execution
- Automatic differentiation for quantum circuits
- Device-agnostic circuit execution
- Integration with ML frameworks (PyTorch, TensorFlow, JAX)
- Variational algorithm optimization
- Parameter shift rule gradients
- Shot-based and analytic differentiation
- Multi-device workflow orchestration

## Usage Guidelines

1. **QNode Definition**: Create differentiable quantum functions with device specification
2. **Gradient Computation**: Select appropriate differentiation method for the use case
3. **Framework Integration**: Seamlessly combine with PyTorch, TensorFlow, or JAX models
4. **Optimization**: Use classical optimizers to train variational circuits
5. **Device Switching**: Test on simulators before deploying to hardware

## Tools/Libraries

- PennyLane
- PennyLane-Lightning
- PennyLane-Qiskit
- PennyLane-Cirq
- PennyLane-SF (Strawberry Fields)