Reliable Sample-Level Quantum Error Mitigation via Dominance-Aware Clustering

What the math gives to ML

The paper provides a concrete post-processing principle for recovering discrete modes from noisy samples: a cluster is trustworthy only when its retained probability mass is dominated coordinate-by-coordinate by one latent source. This is stronger than nearest-center assignment, which can absorb samples from overlapping components and produce apparently good but unreliable prototypes. The transferable asset is a responsibility-weighted local purity test for binary samples, requiring no additional model evaluations. A useful neural-network application is extracting reliable modes from stochastic combinatorial predictors, binary latent models, or discrete diffusion samplers before selecting final outputs.