Decoder Dependence in Surface‐Code Threshold Estimation Under Digitized Hybrid Continuous‐Variable and Discrete Noise

Surface‐code threshold estimates depend on the inference pipeline, including decoder and estimator choices. We compare decoders within a single LiDMaS+ workflow under Pauli‐reference and digitized hybrid continuous‐variable/discrete sweeps. In the Pauli‐reference mode, the matching‐style backend outperforms Union‐Find and yields crossing median (bootstrap interval [0.0415,0.0572]) and collapse fit (). For the hybrid mode, a dense transition‐window sweep at uses with step 0.01 and 3000 trials per point. After the initial exact‐zero plateau is excluded from crossing localization, the matching‐style backend gives interior crossing estimates for and for ; the latter lies in a low‐LER region and remains estimator‐sensitive. A targeted extension shows larger Union‐Find LER at moderate‐to‐high and matching‐fallback rates up to 0.747 at . In a neural‐guidance sensitivity sweep, full learned reweighting reduces the sampled mean LER from 0.1773 to 0.1663 over . These results show that estimator resolution and backend fallback diagnostics are part of an auditable decoder comparison.