Quantum Neural Networks Adopt Algebraic Inverse Learning

Jaemin Seo et al. (arXiv, Jan 23, 2026) propose an inverse-probability algebraic learning method for quantum neural networks that maps Born-rule probability discrepancies to parameter corrections via a Jacobian pseudo-inverse. The learning-rate-free, covariant updates converge faster, escape plateaus, and achieve lower errors with near-optimal finite-shot scaling and robustness to dephasing noise, suggesting practical gains for near-term quantum devices.