pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
v1.0.0New
Signing
SignedSLSA L2- Signed by
- skills-hub.ai distributor
- Method
- Distributor-signed by skills-hub.aiCryptographically signed by the skills-hub.ai distributor key at publish time.
- Signed
Install this skill
Run this command in your terminal. No account required — it auto-detects your AI tool and installs the skill file.
npx @skills-hub-ai/cli install scientific-skills-pennylaneOr download directly:
Browse all CLI commands →Setup by platform
Install
One-click setup for your editorRun in your project root
npx @skills-hub-ai/cli install scientific-skills-pennylane --target claude-codeInstructions
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