发表机构
Quantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE; Campus Arapiraca, Federal University of Alagoas, Arapiraca-AL, Brazil（量子研究中心、技术创新研究所、阿布扎比、阿联酋; 阿拉戈斯联邦大学阿帕里卡校区、巴西）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何通过机器学习从综合征数据中学习来增强量子纠错。核心方法是训练监督分类器区分不同噪声状态综合征，将其输出用作中止分数。通过多种设置验证，该方法能降低逻辑错误率、揭示后选择转变，提高输出保真度，提供可扩展且硬件兼容的量子纠错可靠性提升途径。
AI 中文摘要
量子纠错可通过后选择可能产生逻辑故障的运行来增强，但最准确的方法需要昂贵的解码器级信息。我们引入了一种实用的、与解码器无关的后选择方法，该方法直接从综合征数据中学习。该方法训练一个监督分类器来区分低噪声和高噪声状态下的综合征，然后将分类器的输出用作新运行的中止分数，无需逻辑错误标签、校正算子或特定代码的似然计算。我们在三种互补设置中验证了该方法：Gross双变量双循环码的电路级模拟、表面码的码容量模拟以及来自QuEra中性原子处理器的实验逻辑魔术态蒸馏数据。在Gross码和表面码中，学习到的综合征后选择在固定接受率下降低了条件逻辑错误率，性能与综合征权重过滤相当。对于表面码，学习到的分类器揭示了一个不同于传统解码阈值的后选择转变。在实验数据中，机器学习分数优于综合征权重后选择，并且与逻辑间隙过滤相结合时，提高了输出保真度。这些结果表明，仅综合征学习为提高量子纠错的可靠性提供了一条可扩展且与硬件兼容的途径。
英文摘要
Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-level information. We introduce a practical, decoder-agnostic post-selection method that learns directly from syndrome data. The method trains a supervised classifier to distinguish between syndromes from low- and high-noise regimes, and then uses the classifier's output as an abort score for new runs, without requiring logical-error labels, correction operators, or code-specific likelihood calculations. We validate the approach in three complementary settings: circuit-level simulations of the Gross bivariate-bicycle code, code-capacity simulations of the surface code, and experimental logical magic-state distillation data from the QuEra neutral-atom processor. In the Gross and surface codes, learned syndrome post-selection reduces the conditional logical error rate at a fixed acceptance rate, with performance comparable to syndrome-weight filtering. For the surface code, the learned classifier reveals a post-selection transition distinct from the conventional decoding threshold. In the experimental data, the machine-learning score outperforms syndrome-weight post-selection and, when combined with logical-gap filtering, improves the output fidelity beyond using the logical gap alone. These results show that syndrome-only learning provides a scalable and hardware-compatible route to improving the reliability of quantum error correction.
Comments12 pages, 7 figures
