PennyLane Hybrid Executor

SkillDev tools

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

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the PennyLane Hybrid Executor skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/quantum-computing/skills/pennylane-hybrid-executor/SKILL.md and read by ahel’s review.

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

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

Tools/Libraries

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

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: pennylane-hybrid-executor
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter