发表机构
Qedma Quantum Computing; The Benin School of Computer Science and Engineering, Hebrew University; Faculty of Mathematics and Computer Science, Weizmann Institute of Science; Department of Physics, Technion（Qedma量子计算; 希伯来大学本计算机科学与工程学院; 魏茨曼科学研究所数学与计算机科学系; 以色列理工学院物理系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SOLEM框架，利用纠错电路自然产生的综合征数据在后处理中实现逻辑误差缓解，无需修改电路，其运行时开销比外部LEM指数级更低，与SALEM相当。
AI 中文摘要
近期工作表明，在纠错量子计算过程中获得的综合征信息可用于逻辑误差缓解（LEM），与不使用综合征数据的外部LEM方法相比，运行时开销可实现指数级降低。然而，这些综合征感知的LEM方法（SALEM）通常需要基于综合征的实时处理进行自适应电路操作。在此，我们引入基于综合征的离线逻辑误差缓解（SOLEM），这是一个完全在后处理中利用综合征数据的框架，无需对给定的纠错电路进行任何修改。从这个意义上说，SOLEM内置于纠错电路中，实际上比外部LEM协议更易于实现。SOLEM的基本思想是利用自然发生的综合征条件逻辑误差通道来代替刻意的电路修改。我们在此框架内开发了无偏和启发式估计器，包括概率误差消除（PEC）和零噪声外推（ZNE）的离线逻辑版本，以及一个全局回归估计器。SOLEM的完全离线特性带来了三个显著优势。首先，SOLEM可以使用比实时使用的解码器更强大的解码器来后处理综合征。其次，可以从相同的测量数据构建多个估计器，并使用经验统计误差和相关性能指标进行优化。第三，逻辑误差表征仅需在后处理中进行，允许在电路执行期间收集表征数据，并降低对硬件漂移的敏感性。值得注意的是，SOLEM估计器在实现这些优势的同时，其运行时开销比外部LEM指数级更低，且与相应的SALEM估计器相当。
英文摘要
Recent work has shown that syndrome information obtained during error-corrected quantum computation can be utilized for logical error mitigation (LEM), yielding exponential reductions in runtime overhead compared to "external'' LEM methods, which do not make use of syndrome data. However, these syndrome-aware LEM methods (SALEM) generally require adaptive circuit operations based on real-time processing of syndromes. Here, we introduce syndrome-based offline logical error mitigation (SOLEM), a framework that utilizes syndrome data entirely in post-processing, without any modification to the given error-corrected circuit. In this sense, SOLEM is "built into'' error-corrected circuits, and is in fact simpler to implement than external LEM protocols. The basic idea of SOLEM is to use naturally occurring syndrome-conditioned logical error channels in place of deliberate circuit modifications. We develop unbiased and heuristic estimators within this framework, including offline logical versions of probabilistic error cancellation (PEC) and zero-noise extrapolation (ZNE), as well as a global-regression estimator. SOLEM's fully offline nature leads to three significant advantages. First, SOLEM can post-process syndromes with a more powerful decoder than the one used in real time. Second, multiple estimators can be constructed from the same measured data and optimized using empirical statistical errors and related performance metrics. Third, logical error characterization is needed only in post-processing, allowing characterization data to be collected during circuit execution and reducing sensitivity to hardware drift. Remarkably, SOLEM estimators can achieve these advantages while attaining a runtime overhead which is exponentially lower than that of external LEM and is comparable to that of corresponding SALEM estimators.
