pennylane-hybrid-executor

name	pennylane-hybrid-executor
description	PennyLane integration skill for hybrid quantum-classical machine learning and variational algorithms
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
metadata	{"specialization":"quantum-computing","domain":"science","category":"quantum-framework","phase":6}
graph	{"domains":["domain:quantum-computing"],"specializations":["specialization:quantum-computing"],"skillAreas":["skill-area:machine-learning-frameworks","skill-area:mathematical-reasoning","skill-area:physics-simulation"],"workflows":["workflow:experiment-design"],"roles":["role:research-engineer","role:ml-engineer"]}

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)