一句话：提出 AlphaSyndrome，用 MCTS 为一般交换 stabilizer 码自动调度 syndrome 测量电路，大幅降低逻辑错误率。
量子纠错是可扩展量子计算的前提，但反复的 syndrome 测量循环主导了其时空与硬件开销。虽然 stabilizer 彼此对易、存在大量合法执行顺序，不同调度在真实噪声下会诱发不同的错误传播路径，导致逻辑错误率相差悬殊；然而除 surface code 之外，syndrome 测量调度问题基本未被研究。本文提出 AlphaSyndrome，在仅假设 stabilizer 相互对易并给定启发式解码器的前提下，为一般交换 stabilizer 码自动综合 syndrome 测量调度。该方法把调度形式化为优化问题，主动塑造错误传播路径：一是避开接近逻辑算子的错误模式，二是使错误停留在解码器可纠正区域内；并用 Monte Carlo Tree Search 探索稳定子测量顺序与并行度，由码结构与解码器反馈引导搜索。在多种码族、规模与解码器上，AlphaSyndrome 相比深度最优基线平均降低逻辑错误率 80.6%（最高 96.2%），与 Google 手工设计的 surface code 调度持平，并优于 IBM 为 Bivariate Bicycle 码给出的调度。
Quantum error correction (QEC) is essential for scalable quantum computing, yet repeated syndrome-measurement cycles dominate its spacetime and hardware cost. Although stabilizers commute and admit many valid execution orders, different schedules induce distinct error-propagation paths under realistic noise, leading to large variations in logical error rate. Outside of surface codes, effective syndrome-measurement scheduling remains largely unexplored. We present AlphaSyndrome, an automated synthesis framework for scheduling syndrome-measurement circuits in general commuting-stabilizer codes under minimal assumptions: mutually commuting stabilizers and a heuristic decoder. AlphaSyndrome formulates scheduling as an optimization problem that shapes error propagation to (i) avoid patterns close to logical operators and (ii) remain within the decoder's correctable region. The framework uses Monte Carlo Tree Search (MCTS) to explore ordering and parallelism, guided by code structure and decoder feedback. Across diverse code families, sizes, and decoders, AlphaSyndrome reduces logical error rates by 80.6% on average (up to 96.2%) relative to depth-optimal baselines, matches Google's hand-crafted surface-code schedules, and outperforms IBM's schedule for the Bivariate Bicycle code.
@inproceedings{abs-2601-12509,
title = {AlphaSyndrome: Tackling the Syndrome Measurement Circuit Scheduling Problem for QEC Codes},
author = {Yuhao Liu and Shuohao Ping and Junyu Zhou and Ethan Decker and Justin Kalloor and Mathias Weiden and Kean Chen and Yunong Shi and Ali Javadi-Abhari and Costin Iancu and Gushu Li},
booktitle = {ASPLOS},
year = {2026},
doi = {10.1145/3779212.3790123}
}