Commit or Defer: A Checkable Neural Pre-Decoder for Quantum LDPC Codes

Quantum low-density parity-check (qLDPC) codes offer a route to lower qubit overhead in fault-tolerant quantum computing. However, algorithmic decoders can require costly iterative inference and candidate search, while standalone neural decoders often rely on large models and extensive training data. Accordingly, we propose CoD (Commit-or-Defer), a novel unified neural pre-decoding framework. Its lightweight predictor estimates fault posteriors by combining physical priors with gated recurrent unit (GRU) message passing on $K$-hop active subgraphs around fired detectors in a detector error model. A commit-or-defer strategy bypasses the classical backend when a candidate passes a zero-residual syndrome check and otherwise uses the same posteriors to guide backend inference. We bound the logical risk of committed shots in terms of posterior uncertainty and the DEM logical distance, and give a sufficient condition under which CoD lowers overall logical risk below the unassisted backend. A common circuit-level benchmark spanning four code families and three backends shows up to $42.8$% lower mean logical error rates (LERs) and $55$–$93$% fewer backend calls than unassisted decoding. On BB72 and BB144 under code-capacity noise, CoD achieves lower mean LERs than a standard graph neural decoder using roughly three million training shots and $42$–$56\times$ fewer neural inference FLOPs