Sharp Target-Domain Certificates for Quantum-Kernel Advantage under Distribution Shift
This work derives a sharp, assumption-free interval for quantum predictive advantage when comparing a fixed candidate model against a set of classical kernel models under distribution shift. It provides exact bounds by solving a finite max-min problem, establishing minimal certificates for advantage based only on audited labels and fixed data sets.