Resgru: Syndrome decoder for heavy-hexagon quantum error correction code
TL;DR
Resgru, a lightweight recurrent neural decoder that maps time-ordered detection events to a binary logical-class prediction for each shot, is proposed, a lightweight recurrent neural decoder that maps time-ordered detection events to a binary logical-class prediction for each shot.
Abstract
Decoding is a central bottleneck in near-term quantum error correction (QEC), where repeated syndrome extraction must be translated into reliable logical outcomes under realistic noise and hardware constraints. We study heavy-hexagon (HH) quantum memory experiments and propose Resgru, a lightweight recurrent neural decoder that maps time-ordered detection events to a binary logical-class prediction for each shot. We benchmark Resgru against a standard minimum-weight perfect matching (MWPM) decoder instantiated on the same detector error model for HH code at code distances d∈{3,5,7} under circuit-level depolarizing noise with physical error rate p=0.1%. Resgru consistently achieved higher logical fidelity than MWPM decoder and yields lower per-round logical error rates, including a 28% reduction for d=5 and a 14% reduction for d=7. Despite being trained only on short sequences, Resgru maintains an advantage when evaluated on longer simulated memory experiments up to 300 QEC rounds.