---
title: "OmniQEC：AI科学家驱动的实用量子纠错码发现系统"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.25865v1"
permalink: "https://feed.astralor.com/items/itm_c9f9a56e1047279987fc3877"
published: "2026-07-28T15:31:26.000Z"
score: 78
channel: "tech"
tags: ["AI科学家","OmniQEC","qLDPC码","大型语言模型","量子纠错"]
---

# OmniQEC：AI科学家驱动的实用量子纠错码发现系统

## AI 点评

> 这篇论文展示了LLM在科学发现中的巨大潜力，尤其对量子计算研究者而言，OmniQEC提供了一种自动化探索量子纠错码的新范式。它不仅限于理论，还给出了具体的硬件友好码字和性能对比，值得关注量子硬件和纠错编码的工程师仔细研读。

## 速览

OmniQEC是一种由大型语言模型驱动的AI科学家系统，专门用于发现适用于现代量子处理器的量子纠错码。它通过迭代优化流程，协调代码生成、筛选、综合征提取和解码器评估，结合自演进推理与慢-快双循环机制。在多种qLDPC码族和不同物理量子比特预算下，OmniQEC发现的代码在逻辑错误抑制上持续优于传统BB码，且硬件友好，为实用量子纠错提供了新路径。

## 关键点

- OmniQEC利用大型语言模型作为协调器，自动发现量子纠错码。
- 系统采用慢-快双循环机制：快循环使用廉价代理筛选，慢循环进行电路级评估并反馈。
- 在98和240个物理量子比特预算下，发现的码分别优于[[72,12,6]]和[[144,12,12]] BB码。
- 发现的码硬件友好，可直接部署于现代量子处理器。

## 全文

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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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_c9f9a56e1047279987fc3877