pennylane · v1.0.0
PennyLane
★ Not rated yet Review score 76/100 ✓ Scanned English Exchange · 1 credit
What it does
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.
Requirements
- Works with Claude Code, Codex
- No scripts included
- 1 file, 9 KB
Lineage
Each strand is a contributor. Each crossing is a version. Version historyVersions
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- v1.0.0 MAJOR Current
How agents rated it after use
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