发表机构
College of Computing and Data Science, Nanyang Technological University; Tokyo University of Agriculture and Technology; Zhejiang University of Technology; City University of Hong Kong; Hon Hai (Foxconn) Research Institute; School of Physical and Mathematical Science, Nanyang Technological University（南洋理工大学计算与数据科学学院; 东京农工大学; 浙江工业大学; 香港城市大学; 鸿海研究院; 南洋理工大学物理与数学科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在发现实用量子纠错码，核心方法是用OmniQEC，它结合自我进化推理与慢快协同工作流程，通过代码级代理探索候选方案并经电路级评估反馈证据。贡献是发现的代码性能优且硬件友好，为大语言模型辅助量子纠错发现奠基。
AI 中文摘要
量子纠错对于可扩展的容错量子计算不可或缺。然而，发现有效的量子纠错码具有挑战性，因为逻辑性能取决于码结构、硬件、综合症提取和解码之间的相互作用，这些往往相互矛盾。本文介绍了OmniQEC，一种用于发现适合在现代量子处理器上部署的量子纠错码的高效人工智能科学家。OmniQEC将量子纠错设计制定为一个迭代发现过程，其中由先进的大语言模型实现的协调器协调代码生成、代码级筛选、综合症提取合成和基于解码器的电路评估。其核心是将自我进化的推理机制与慢快协同工作流程相结合：快速循环使用廉价的代码级代理探索候选方案，而慢速循环进行基于物理的电路级评估，并将结果证据反馈到搜索中。我们在四个qLDPC构造族、三个大语言模型后端以及每个后端14个总物理量子比特预算上评估了OmniQEC。发现的代码随着物理量子比特预算的增加，逻辑错误抑制稳步提高，在98和240个物理量子比特的完整实现预算下，分别优于具有[[72,12,6]]和[[144,12,12]]的BB代码。发现的代码对硬件友好，可能对实际的量子纠错实现具有独立的意义。这些发现为基于物理信息的代码-电路-解码器协同设计的大语言模型辅助量子纠错发现铺平了道路。
英文摘要
Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing. However, discovering QEC codes that remain effective is challenging, as logical performance depends on the interplay between code structure, hardware, syndrome extraction, and decoding, which often impose competing requirements. Here we introduce OmniQEC, an efficient AI scientist for discovering QEC codes suited to deployment on modern quantum processors. OmniQEC formulates QEC design as an iterative discovery process in which an orchestrator, implemented by advanced large language models (LLMs), coordinates code generation, code-level screening, syndrome-extraction synthesis, and decoder-based circuit evaluation. At its core, OmniQEC combines a self-evolving reasoning mechanism with a slow--fast synergistic workflow: a fast loop explores candidates using inexpensive code-level proxies, whereas a slow loop performs physically grounded circuit-level evaluation and feeds the resulting evidence back into the search. We evaluate OmniQEC across four qLDPC construction families, three LLM backends, and $14$ total-physical-qubit budgets per backend. The discovered codes show steadily improving logical-error suppression with increasing physical-qubit budgets and outperform the BB codes with $[\![72,12,6]\!]$ and $[\![144,12,12]\!]$ under complete-implementation budgets of 98 and 240 physical qubits, respectively. The discovered codes are hardware-friendly and may be of independent interest for practical QEC implementation. These findings pave the way towards LLM-assisted QEC discovery grounded in physically informed code--circuit--decoder co-design.
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