Entropy, and Effective Noise Scaling
Understanding how noise propagates through quantum circuits is a central problem in near-term quantum computing. While gate error rates and circuit depth provide partial explanations of circuit degradation, the relationship between circuit structure, state properties, and observable noise sensitivity remains poorly characterized. In this work we introduce a predictive framework for estimating local noise sensitivity in quantum circuits. We combine (i) an effective noise accumulation coordinate derived from gate counting, (ii) entropy-based descriptors of local state mixing, and (iii) machine learning models trained on circuit structural features. Using simulations on QASMBench circuits under depolarizing and readout noise, we show that a simple exponential saturation model based on an effective noise coordinate explains moderate variance in local total variation distance (TVD).
Keywords: quantum circuit noise sensitivity, effective noise scaling, entropy-based descriptors, total variation distance, machine learning prediction