Software Development

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.

Publisher / source

KalarisLabs/research-agent-skills (found on the CSV list)

Upstream record

KalarisLabs/research-agent-skills

Version

Version 2

Published

2026-11-02

Publication review

Approved for publication

Content fingerprint

91749ef5d746271af4a317a21e07b8afdffb98d105d5e7988dea711afb89045c