From Reachability to Learnability: Geometric Design Principles for Quantum Neural Networks

This paper reframes Quantum Neural Network design from state reachability to learnability by introducing geometric design principles and the almost Complete Local Selectivity (aCLS) criterion, demonstrating that architectures requiring joint dependence on data and trainable weights enable adaptive feature learning and outperform traditional schemes with greater efficiency.

Vishal S. Ngairangbam, Michael Spannowsky2026-03-03⚛️ quant-ph