Decoder Dependence in Surface‐Code Threshold Estimation Under Digitized Hybrid Continuous‐Variable and Discrete Noise
Fortschritte der Physik, vol. 74
Abstract
ABSTRACT 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.
Authors 4
-
Georgia Institute of Technology · TU Bergakademie Freiberg
Affiliation as printed
College of Computing Georgia Institute of Technology Atlanta USA
Institute of Computer Science, Faculty of Mathematics and Computer Science TU Bergakademie Freiberg Freiberg Germany
-
Volkswagen Group (Germany) · RWTH Aachen University
Affiliation as printed
Department of Physics RWTH Aachen Germany
Volkswagen AG Wolfsburg Germany
-
Universidade Federal de Juiz de Fora
Affiliation as printed
Department of Computational and Applied Mechanics Federal University of Juiz de Fora Juiz de Fora Brazil
-
Affiliation as printed
Institute of Computer Science, Faculty of Mathematics and Computer Science TU Bergakademie Freiberg Freiberg Germany
Cited by 0 stored of 0
No patents citing this paper on Lens.org (checked 2026-10-06).
References 34
-
W4281479206details pending0citations
-
W3217173135details pending0citations