Year: 2025
Type: article
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.
Source: Nature Communications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: UK Research and Innovation, European Commission, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Knut och Alice Wallenbergs Stiftelse, Bundesministerium für Digitalisierung und Wirtschaftsstandort, Simons Institute for the Theory of Computing, University of California Berkeley, Engineering and Physical Sciences Research Council, Wallenberg Centre for Molecular and Translational Medicine, Wallenberg Center for Quantum Technology, Chalmers University of Technology
OpenAlex ID: W4413927467