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Babysitter pennylane-hybrid-executor

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

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/quantum-computing/skills/pennylane-hybrid-executor" ~/.claude/skills/a5c-ai-babysitter-pennylane-hybrid-executor && rm -rf "$T"
manifest: library/specializations/domains/science/quantum-computing/skills/pennylane-hybrid-executor/SKILL.md
tags
#quantum-computing#variational-algorithms#ml-integration#pennylane#differentiation#qnode
source content

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)