Localized statistics decoding for quantum low-density parity-check codes

Abstract

Quantum low-density parity-check codes are a promising candidate for fault-tolerant quantum computing with considerably reduced overhead compared to the surface code. However, the lack of a practical decoding algorithm remains a barrier to their implementation. In this work, we introduce localized statistics decoding, a reliability-guided inversion decoder that is highly parallelizable and applicable to arbitrary quantum low-density parity-check codes. Our approach employs a parallel matrix factorization strategy, which we call on-the-fly elimination, to identify, validate, and solve local decoding regions on the decoding graph. Through numerical simulations, we show that localized statistics decoding matches the performance of state-of-the-art decoders while reducing the runtime complexity for operation in the sub-threshold regime. Importantly, our decoder is more amenable to implementation on specialized hardware, positioning it as a promising candidate for decoding real-time syndromes from experiments. Quantum low-density parity-check (QLDPC) codes offer lower overhead than topological quantum error-correcting codes, but decoding remains a key challenge for scalable fault-tolerant quantum computing. This work introduces a highly parallelizable decoding algorithm for QLDPC codes that matches the accuracy of leading decoders while enabling significantly improved scalability.