发表机构
North Carolina State University（北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对样本级量子误差缓解问题，提出优势感知（DA）优化方法，结合责任阈值与局部优势筛选，经MaxCut-QAOA实验验证可提升中心恢复效果，且无需额外量子电路执行。
AI 中文摘要
许多用于经典难以解决的优化任务的量子算法，必须从有限次电路执行中返回高质量的比特串，而大多数量子误差缓解方法针对的是期望值。我们研究当测量到的概率质量分布在多个潜在比特串（称为中心）周围时的样本级恢复，测量到的概率质量的每个组成部分称为一个源，我们假设每个中心与一个源相关联。我们确定了“在每个坐标上，保留区域中超过一半的概率质量来自一个源且与其中心一致”这一条件，该条件是多数投票以指数级减小的误差概率恢复该中心的充分条件。我们表明，即使已知真实中心，k-modes等聚类算法中使用的最近中心分配也可能无法产生受优势支配的区域。这一失败促使我们提出责任阈值和局部优势筛选，二者的组合称为优势感知（DA）优化。合成和模拟的MaxCut-QAOA实验表明，DA优化有利于精度，而带有DA优化的k-modes则提高了整体中心恢复效果。所有过程均为经典后处理，无需额外的量子电路执行。
英文摘要
Many quantum algorithms for classically difficult optimization tasks must return high-quality bitstrings from finitely many circuit executions, whereas most quantum error-mitigation methods target expectation values. We study sample-level recovery when measured probability mass is distributed around multiple latent bitstrings, called centers. Each component of the measured probability mass is called a source and we assume that each center is associated with one source. We identify dominance-at every coordinate, more than half of a retained region's probability mass comes from one source and agrees with its center-as a sufficient condition under which majority voting recovers that center with exponentially decreasing error probability. We show that nearest-center assignment, as used in clustering algorithms such as the $k$-modes algorithm, can fail to produce dominated regions even when the true centers are known. This failure motivates responsibility thresholding and a local dominance screen, whose combination we call dominance-aware (DA) refinement. Synthetic and simulated MaxCut-QAOA experiments show that DA refinement favors precision, while $k$-modes with DA refinement improves overall center recovery. All procedures are classical post-processing and require no additional quantum-circuit executions.
