Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction

Quantum Physics arXiv:2601.09921 (quant-ph) [Submitted on 14 Jan 2026] Title:Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction Authors:Kai Zhang, Zhengzhong Yi, Shaojun Guo, Linghang Kong, Situ Wang, Xiaoyu Zhan, Tan He, Weiping Lin, Tao Jiang, Dongxin Gao, Yiming Zhang, Fangming Liu, Fang Zhang, Zhengfeng Ji, Fusheng Chen, Jianxin Chen View a PDF of the paper titled Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction, by Kai Zhang and 15 other authors View PDF HTML (experimental) Abstract:Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time. Moreover, integrating AlphaQubit with sliding window-based parallel decoding schemes presents non-trivial challenges: AlphaQubit is trained solely to output a single bit corresponding to the global logical correction for an entire memory experiment, rather than local physical corrections that can be easily integrated. We address this issue by training a recurrent, transformer-based neural network specifically tailored for parallel window decoding. While it still outputs a single bit, we derive training labels from a consistent set of local corrections and train on various types of decoding windows simultaneously. This approach enables the network to self-coordinate across neighboring windows, facilitating high-accuracy parallel decoding of arbitrarily long memory experiments. As a result, we overcome the throughput bottleneck that previously precluded the use of AlphaQubit-type decoders in FTQC. Our work presents the first scalable, neural-network-based parallel decoding framework that simultaneously achieves SOTA accuracy and the stringent throughput required for real-time quantum error correction. Using an end-to-end experimental workflow, we benchmark our decoder on the Zuchongzhi 3.2 superconducting quantum processor on surface codes with distances up to 7, demonstrating its superior accuracy. Moreover, we demonstrate that, using our approach, a single TPU v6e is capable of decoding surface codes with distances up to 25 within 1us per decoding round. Comments: Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI) Cite as: arXiv:2601.09921 [quant-ph] (or arXiv:2601.09921v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.09921 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jianxin Chen [view email] [v1] Wed, 14 Jan 2026 23:04:25 UTC (2,944 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction, by Kai Zhang and 15 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 Change to browse by: cs cs.AI References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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