发表机构
Google Quantum AI; Department of Physics, Cornell University（谷歌量子人工智能; 康奈尔大学物理系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对逻辑量子电路的残留误差问题，提出仅利用校正子记录的虚拟误差抵消方法，通过双解码器方案实现采样开销降低，误差随样本数增加而减小，可实现三个数量级以上的误差抑制。
AI 中文摘要
早期容错量子算法是可扩展量子计算的关键一步，但最终受限于残留的、无法纠正的逻辑误差。逻辑量子误差缓解可消除这种偏差，但其标准要求包括先验噪声表征、更复杂的实验以及大量采样开销。尽管没有任何缓解技术能同时克服这三个限制，但我们证明，对于逻辑电路，仅利用校正子记录就可完全绕过前两个限制——噪声学习和对修改后电路的需求。具体而言，我们提出虚拟误差抵消：一种与以往无偏差方法不同，完全在通用量子电路的经典后处理中运行的方法。我们开发并数值测试了端到端协议，这些协议将采样开销降低到现有感知校正子的缓解技术（包括基于校正子的后选择）的基本限制之外。特别地，我们提出一种方案，将实验中使用的低延迟解码器与后处理中运行的高复杂度解码器配对。在距离为7的旋转表面码的数值模拟中，我们表明，我们的全栈双解码器算法的误差随样本数量增加而减小，在可实验实现的设置下，与解码后的逻辑误差率相比，能够实现三个数量级以上的抑制。这些结果确立了校正子记录作为量子执行后纠正通用计算估计的一种资源。
英文摘要
Early fault-tolerant quantum algorithms are a key step in scalable quantum computing, but are ultimately constrained by residual, uncorrectable logical errors. Logical quantum error mitigation can eliminate this bias, however its standard requirements include prior noise characterization, more complex experiments, and substantial sampling overhead. Although no mitigation technique can simultaneously overcome all three constraints, we demonstrate that the first two---noise learning and need for modified circuits---can be completely bypassed for logical circuits by leveraging only syndrome records. Specifically, we introduce virtual error cancellation: a method that, unlike previous bias-free approaches, operates entirely in classical post-processing on universal quantum circuits. We develop and numerically test end-to-end protocols that reduce the sampling overhead beyond fundamental limitations of existing syndrome-aware mitigation techniques, including syndrome-based postselection. In particular, we propose a scheme that pairs the low-latency decoder used in the experiment with a high-complexity decoder run in post-processing. In numerical simulations of a distance-7 rotated surface code, we show that our full-stack dual-decoder algorithm exhibits an error that diminishes as the number of samples increases, capable of reaching more than three orders of magnitude of suppression compared to the decoded logical error rate in an experimentally achievable setting. These results establish syndrome records as a resource for correcting universal computational estimates after quantum execution.
