---
title: "QAdapt：面向量子纠错的噪声自适应神经预解码框架"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.28422v1"
permalink: "https://feed.astralor.com/items/itm_7befa0d32fc2988011a0bdf6"
published: "2026-07-30T16:02:00.000Z"
score: 72
channel: "tech"
tags: ["QAdapt","Willow","神经预解码","表面码","谷歌","量子纠错"]
---

# QAdapt：面向量子纠错的噪声自适应神经预解码框架

## AI 点评

> 这篇论文最值得关注的点是它不试图取代全局解码器，而是做预解码，拿真实硬件数据验证了“适配噪声漂移”的价值。对做量子控制或纠错系统的人来说，这是把 ML 塞进现有量子栈的低风险路径。

## 速览

量子容错计算依赖纠错码抑制噪声，但经典解码器的延迟与硬件噪声的强异质性、非平稳性成为瓶颈。QAdapt 提出一种噪声自适应神经预解码框架，通过捕捉综合征数据局部时空关联、顺序适应噪声变化并缓解灾难性遗忘，将残差综合征交给传统全局解码器。在 110 种合成分布外噪声配置和谷歌 Willow 基准数据上，QAdapt 无需目标域微调即可降低逻辑错误率并减少后端解码延迟，为可实用化量子纠错提供兼容方案。

## 关键点

- QAdapt 利用神经预解码器捕捉综合征数据中的局部时空相关性，天然适配表面码纠错场景。
- 通过顺序适应机制与缓解灾难性遗忘，QAdapt 能跟随硬件噪声动态变化，无需目标域微调。
- 在 110 种合成分布外噪声配置及谷歌 Willow 数据上，逻辑错误率最多降低 5.79%，后端解码延迟最高降低 9.32%。

## 全文

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.

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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_7befa0d32fc2988011a0bdf6