发表机构
Beijing Zhongke Qhub Technology Co., Ltd.; Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University; Institute of Computing Technology, Chinese Academy of Sciences（北京中科瓴湖科技有限公司; 清华大学交叉信息研究院量子信息中心; 中国科学院计算技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对容错量子计算中硬件噪声非平稳及分布偏移导致的性能问题，提出噪声自适应神经预解码框架QAdapt，在基准数据上显著降低量子纠错的逻辑错误率与解码延迟。
AI 中文摘要
容错量子计算（FTQC）依赖量子纠错来抑制物理错误并大规模保留逻辑信息。但实际应用中，性能不仅受物理噪声限制，还受经典解码器处理快速生成的症候数据的延迟影响。硬件噪声强、异质且非平稳，加上仿真到硬件的分布偏移会大幅降低固定神经解码器的性能，加剧了这一挑战。本文提出QAdapt，一种用于表面码量子纠错的噪声自适应神经预解码框架。QAdapt捕捉症候数据的局部时空相关性，在缓解灾难性遗忘的同时依次适应不断变化的噪声条件，并将残差症候转发给传统全局解码器。针对旋转表面码存储电路的110种合成分布外噪声配置，QAdapt相比神经预解码基线持续降低逻辑错误率；在Google的Willow基准数据上，无需目标域微调，其在残差症候上实现逻辑错误率降低最高5.79%、后端解码延迟降低9.32%。这些结果表明，QAdapt提供了一种实用且与解码器兼容的方法，可提升不断变化的硬件噪声下量子纠错的鲁棒性和后端解码效率。
英文摘要
Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale. In practice, however, performance is constrained not only by physical noise but also by the latency of classical decoders processing rapidly generated syndrome data. This challenge is exacerbated by hardware noise that is strong, heterogeneous, and nonstationary, as well as by the simulation-to-hardware distribution shift that can substantially degrade fixed neural decoders. We present QAdapt, a noise-adaptive neural pre-decoding framework for surface-code quantum error correction. QAdapt captures local spatiotemporal correlations in syndrome data, sequentially adapts to evolving noise conditions while mitigating catastrophic forgetting, and forwards the residual syndrome to a conventional global decoder. Across 110 synthetic out-of-distribution noise configurations for rotated surface-code memory circuits, QAdapt consistently reduces the logical error rate relative to the neural pre-decoding baseline. On Google's Willow benchmark data, without target-domain fine-tuning, it achieves reductions of up to 5.79 percent in logical error rate and 9.32 percent in backend decoding latency on the residual syndrome. These results demonstrate that QAdapt provides a practical and decoder-compatible approach to improving the robustness and backend decoding efficiency of quantum error correction under evolving hardware noise.
Comments11 pages, 6 figures, 6 tables
