pennylane-hybrid-executor
PennyLane integration skill for hybrid quantum-classical machine learning and variational algorithms
Profile →npx skills add a5c-ai/babysitter --skill pennylane-hybrid-executor --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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)
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the pennylane-hybrid-executor skill do?
PennyLane integration skill for hybrid quantum-classical machine learning and variational algorithms
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill pennylane-hybrid-executor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.