论文标题
通配符错误：量化量子处理器中未建模的错误
Wildcard error: Quantifying unmodeled errors in quantum processors
论文作者
论文摘要
量子计算处理器的错误模型描述了其与理想行为的偏差，并预测了应用中的后果。但是，这些处理器的实验行为 - 观察到的量子电路的结果统计 - 即使在表征实验中，例如随机基准测试（RB）或GATE SET层析成像（GST）也很少与误差模型一致，其中该错误模型是从问题中专门提取的。我们通过通过参数化的通配符误差模型来增强误差模型来展示如何解决这些不一致之处，并量化未建模错误的速率。在错误模型中添加通配符误差会以控制的方式放松并削弱其预测。恢复与数据一致性所需的通配符误差量量化了观察到多少未模拟错误，以促进与标准门错误率直接比较的方式。使用模拟和实验数据，我们展示了如何使用通配符误差来调和来自RB和GST实验的错误模型与不一致的数据，以捕获非马克维亚性，并量化所有处理器观察到的错误。
Error models for quantum computing processors describe their deviation from ideal behavior and predict the consequences in applications. But those processors' experimental behavior -- the observed outcome statistics of quantum circuits -- are rarely consistent with error models, even in characterization experiments like randomized benchmarking (RB) or gate set tomography (GST), where the error model was specifically extracted from the data in question. We show how to resolve these inconsistencies, and quantify the rate of unmodeled errors, by augmenting error models with a parameterized wildcard error model. Adding wildcard error to an error model relaxes and weakens its predictions in a controlled way. The amount of wildcard error required to restore consistency with data quantifies how much unmodeled error was observed, in a way that facilitates direct comparison to standard gate error rates. Using both simulated and experimental data, we show how to use wildcard error to reconcile error models derived from RB and GST experiments with inconsistent data, to capture non-Markovianity, and to quantify all of a processor's observed error.
