| Περίληψη: |
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 pc=0.0531$p_c=0.0531$ (bootstrap interval [0.0415,0.0572]) and collapse fit pc=0.052$p_c=0.052$ (ν=1.35$\nu =1.35$). For the hybrid mode, a dense transition‐window sweep at d=3,5,7$d=3,5,7$ uses σ∈[0.30,0.50]$\sigma \in [0.30,0.50]$ 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 σc=0.4707$\sigma _c=0.4707$ for (d=3,5)$(d=3,5)$ and σc=0.3275$\sigma _c=0.3275$ for (d=5,7)$(d=5,7)$; the latter lies in a low‐LER region and remains estimator‐sensitive. A targeted d=9$d=9$ extension shows larger Union‐Find LER at moderate‐to‐high σ$\sigma$ and matching‐fallback rates up to 0.747 at σ=0.50$\sigma =0.50$. In a d=5$d=5$ neural‐guidance sensitivity sweep, full learned reweighting reduces the sampled mean LER from 0.1773 to 0.1663 over σ∈[0.35,0.55]$\sigma \in [0.35,0.55]$. These results show that estimator resolution and backend fallback diagnostics are part of an auditable decoder comparison. [ABSTRACT FROM AUTHOR] |