A PAC-Bayesian approach to generalization for quantum models

ArXi:2603.22964v1 Announce Type: cross Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacity rather than the specific function learned. These capacity-based uniform bounds are often too loose and entirely insensitive to the actual