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 or JAX. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
- Version
- 1.2
- License
- Apache-2.0 license
Pinned to revision 70a605012eee, so it is the text this page describes rather than whatever the author pushed since.
Pre-approved tools experimental
Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.
- Read
- Bash
- Python
Files
- skills/pennylane/SKILL.md
- skills/pennylane/references/advanced_features.md
- skills/pennylane/references/devices_backends.md
- skills/pennylane/references/getting_started.md
- skills/pennylane/references/optimization.md
- skills/pennylane/references/quantum_chemistry.md
- skills/pennylane/references/quantum_circuits.md
- skills/pennylane/references/quantum_ml.md
Every link opens the file at its source, pinned to the revision this page describes.