[论文解读] Adaptive Aborting Schemes for Quantum Error Correction Decoding

Sanidhay Bhambay, Prakash Murali+2 more|arXiv (Cornell University)|Feb 18, 2026

Quantum Computing Algorithms and Architecture被引用 0

一句话总结

论文提出自适应中止模块，在解码表面码和颜色码时可提前终止不具潜力的综合性测量 shots，从而提高解码器在现实噪声下的效率。

ABSTRACT

Quantum error correction (QEC) is essential for realizing fault-tolerant quantum computation. Current QEC controllers execute all scheduled syndrome (parity-bit) measurement rounds before decoding, even when early syndrome data indicates that the run will result in an error. The resulting excess measurements increase the decoder's workload and system latency. To address this, we introduce an adaptive abort module that simultaneously reduces decoder overhead and suppresses logical error rates in surface codes and color codes under an existing QEC controller. The key idea is that initial syndrome information allows the controller to terminate risky shots early before additional resources are spent. An effective scheme balances the cost of further measurement against the restart cost and thus increases decoder efficiency. Adaptive abort schemes dynamically adjust the number of syndrome measurement rounds per shot using real-time syndrome information. We consider three schemes: fixed-depth (FD) decoding (the standard non-adaptive approach used in current state-of-the-art QEC controllers), and two adaptive schemes, AdAbort and One-Step Lookahead (OSLA) decoding. For surface and color codes under a realistic circuit-level depolarizing noise model, AdAbort substantially outperforms both OSLA and FD, yielding higher decoder efficiency across a broad range of code distances. Numerically, as the code distance increases from 5 to 15, AdAbort yields an improvement that increases from 5% to 35% for surface codes and from 7% to 60% for color codes. To our knowledge, these are the first adaptive abort schemes considered for QEC. Our results highlight the potential importance of abort rules for increasing efficiency as we scale to large, resource-intensive quantum architectures.

研究动机与目标

• 将量子误差纠正框定为一个序贯决策问题，其中在解码前可能中止测量次数
• 开发两种自适应方案（OSLA 和 AdAbort）以决定何时停止综合测量
• 在表面码和颜色码上针对电路级噪声模型比较固定深度、OSLA 与 AdAbort
• 展示在更大码距下的显著效率提升和扩展性潜力

提出的方法

• 将测量视为具有固定轮深度 T_d 和中止成本的序贯决策问题
• 提出三种解码方案：固定深度（FD）、一步前瞻（OSLA）和自适应阈值中止（AdAbort）
• OSLA 使用学习得到的 g(s_t) 和 m(s_t) 来比较停止成本与继续再进行一轮的成本
• AdAbort 使用神经网络估计最终轮逻辑错误概率，当超过阈值 θ 时中止
• 两种自适应方案均位于解码器与硬件之间，与底层解码器与码字解耦
• 在表面码和颜色码上，使用电路级去极化噪声模型的 Stim 进行评估

实验结果

研究问题

• RQ1 自适应中止机制是否能在不牺牲逻辑保真度的前提下降低解码工作量？
• RQ2 相较于固定深度解码，自适应方案在解码效率方面对表面码和颜色码有何差异？
• RQ3 随着码距增加，解码效率提升的程度为何？
• RQ4 在现实噪声下，OSLA 与 AdAbort 如何权衡中止频率与解码保真度？
• RQ5 中止模块是否可以在不修改现有解码器或码字的情况下集成？

主要发现

• AdAbort 在表面码和颜色码的解码效率方面显著优于 OSLA 与 FD
• 效率提升随着码距增加而增强，从表面码在距离从5到15时的提升从5%到35%
• 在相同距离范围内，颜色码的效率提升从7%增加到60%
• OSLA 与 AdAbort 在广泛的码距范围内均显著优于 FD 解码
• 中止模块可在最小开销下与解码器和码字无关地集成