发表机构
University of Southern California; Kennesaw State University; Florida State University; Arizona State University（南加州大学; 肯尼索州立大学; 佛罗里达州立大学; 亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
QEMScore通过引入容量匹配且不读取测量的对照组，量化测量对学习型量子纠错的贡献，发现所选学习器大部分增益可由电路描述实现，但准确率差距源于学习器容量而非测量需求。
AI 中文摘要
噪声测量对学习型量子纠错有多大贡献？仅凭准确率表无法回答这个问题，因为一个被赋予电路结构的模型即使完全不读取测量结果，也能获得不错的分数。QEMScore 增加了能够回答这一问题的比较。每个模拟电路都带有精确的理想答案。学习型纠错器与一个容量匹配的对照组进行评分，该对照组同样灵活，读取相同的电路描述但从不读取测量结果。每种方法的测量开销都被计入且未进行均等化处理。我们在模拟电路上开展了一项受控实验，并利用已发布的硬件数据重新分析了两个已发表的学习型纠错器 Q-LEAR 和 QRAFT。有三项发现尤为突出。首先，在熟悉的族内条件（S0）下，跨两个自旋链族和三个随机种子进行评估，连续耦合参数能够识别目标，而从不读取测量结果的对照组达到了纠错器相对于电路描述仿射拟合增益的 87.7% 至 100.5%。在实验后拟合的耦合参数多项式，在所有六项评估中均优于纠错器，这反映了所选学习器的容量。其次，对于这些选定的学习器，匹配大部分增益并不等同于匹配准确率：在六项评估中的五项中，纠错器消除了容量匹配对照组所遗留误差的 19.5% 至 74.5%，这是一个学习器特有的差距，而非测量需求所致。第三，在已发布的硬件数据上，描述符仅部分识别查询，结果有所不同：在 Q-LEAR 中，描述符的灵活模型相对于仿射拟合的平均增益可忽略不计，而在 Q-LEAR 和 QRAFT 中，测量输入均带来了预测增益。这些比较反映了表示和协议特有的行为，而非孤立的跨机制差异。因此，学习型纠错器的准确率应与此类对照组一起报告。
英文摘要
How much does the noisy measurement add to learned quantum error mitigation? An accuracy table cannot say, because a model handed circuit structure can score well without reading the measurement at all. QEMScore adds the comparison that can. Each simulated circuit carries an exact ideal answer. The learned mitigator is scored beside a capacity-matched control, a model just as flexible that reads the same circuit description but never the measurement. Each method's measurement spend is accounted and not equalized. We run a controlled campaign on simulated circuits and reanalyze two published learned mitigators, Q-LEAR and QRAFT, from their released hardware data. Three findings stand out. First, under familiar within-family conditions (S0) evaluated across two spin-chain families and three seeds, continuous couplings identify the target, and the control that never reads the measurement matches 87.7 to 100.5 percent of the mitigator's gain over an affine fit to the circuit description. A plain polynomial in the coupling parameters, fitted after the campaign, beats the mitigator on all six evaluations, reflecting the selected learners' capacity. Second, for these selected learners, matching most of the gain is not matching the accuracy: on five of six evaluations the mitigator removes 19.5 to 74.5 percent of the error the capacity-matched control leaves, a learner-specific gap rather than a measurement requirement. Third, on released hardware data where descriptors only partially identify queries, the findings differ: flexible models of the descriptors show negligible mean gain over affine fits in Q-LEAR, and measurement inputs carry predictive gains in both Q-LEAR and QRAFT. These comparisons reflect representation- and protocol-specific behavior rather than an isolated cross-regime difference. A learned mitigator's accuracy should therefore be reported beside such controls.
Comments55 pages, including references and appendices
