arXiv AI By Hong-Ye Hu, Zhenlai Wang, Binzhao Luo, Tao Xiang, Chen Zhao

Fast and accurate neural triage decoding of ultra-high-rate quantum LDPC codes

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The paper introduces neural triage decoding, a new framework for high‑rate quantum LDPC codes that adaptively uses neural message‑passing to predict error corrections, followed by iterative refinement and a rare diversification step. It demonstrates that the Triage decoder can handle ultra‑high‑rate codes encoding hundreds of logical qubits, achieving near‑optimal accuracy while reducing decoding latency by one to two orders of magnitude compared to existing decoders. The approach is grounded in a rigorous analysis of bitwise marginal decoding and shows robustness to mismatched error priors.

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Fast and accurate neural triage decoding of ultra-high-rate quantum LDPC codes

The paper introduces neural triage decoding, a new framework that uses syndrome consistency to adaptively allocate neural computation for high-rate quantum LDPC codes. By combining a local neural message-passing network with iterative refinement and a rare diversification stage, the Triage decoder achieves state‑of‑the‑art accuracy while reducing decoding latency by one to two orders of magnitude. The approach is validated on ultra‑high‑rate qLDPC codes such as lifted‑product and Kasai codes, and demonstrates robustness to mismatched error priors.