Arshpreet Singh Maan, Francisco-Garcia Herrero, Alexandru Paler, Valentin Savin (Oct 17 2025).
Abstract: We introduce a decoding framework for correlated errors in quantum LDPC codes under circuit-level noise. The core of our approach is a graph augmentation and rewiring for interference (GARI) method, which modifies the correlated detector error model by eliminating 4-cycles involving Y-type errors, while preserving the equivalence of the decoding problem. We test our approach on the bivariate bicycle codes of distances 6, 10, and 12. A normalized min-sum decoder with a hybrid serial-layered schedule is applied on the transformed graph, achieving high accuracy with low latency. Performance is further enhanced through ensemble decoding, where 24 randomized normalized min-sum decoders run in parallel on the transformed graph, yielding the highest reported accuracy (on par with XYZ-Relay-BP) with unprecedented speed for the tested codes under uniform depolarizing circuit level noise. For the distance 12 (gross) code, our approach yields a logical error rate of
(6.70±1.93)×10−9 at a practical physical error rate of
10−3. Furthermore, preliminary FPGA implementation results show that such high accuracy can be achieved in real time, with a per-round average decoding latency of 273 ns and sub-microsecond latency in 99.99% of the decoding instances.
