qkan

Onboarding guide for the qkan package (Quantum-inspired Kolmogorov-Arnold Networks): installing qkan, training a first QKAN, choosing solver backends, mixed precision, circuit packing, and real QPU deployment. Not for generic PyTorch/CUDA setup.

Maintainer note for releasing qkan: hash-stamped dev versions, the branch-then-tag release flow via the release-prep workflow, and publish behavior. For the repo owner — not end-user onboarding.