发表机构
University of Idaho; IonQ Inc.（爱达荷大学; IonQ公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出提升随机基准测试方法，通过扩大群来消除多重性，无需拟合多指数衰减，稳健估计双量子门保真度，适用于捕获离子系统。
AI 中文摘要
随机基准测试包含一套用于评估量子处理器操作保真度的标准技术。这些技术可以可靠地应用于其过程矩阵表示无多重性的群。然而，许多由原生门自然形成的群确实具有多重性，并且从由此产生的数据中难以得出稳健的结论。在本工作中，我们提出了一种针对多重性问题的新的且内在的解决方案，该方案完全消除了拟合多指数衰减的需求，同时仍然对态制备和测量中的误差保持稳健。我们的方法称为提升随机基准测试，它推广了特征随机基准测试和合成随机基准测试的关键要素，以相对于一个扩大的群来基准测试目标量子门群。我们在数值示例中表明，我们的方法能够高效且可靠地估计与实验捕获离子系统相关的双量子门保真度。这些技术可能找到超出随机基准测试的应用，例如在量子层析成像和学习，以及纠错码的表征方面。
英文摘要
Randomized benchmarking comprises a suite of standard techniques for assessing the operational fidelity of quantum processors. These techniques can be reliably applied to groups with process matrix representations that are free of multiplicity. However, many groups that are naturally formed from native gates do have multiplicity, and it is difficult to draw robust conclusions from the resulting data. In this work, we present a new and intrinsic solution to the problem of multiplicity that completely eliminates the need to fit multi-exponential decays while still remaining robust to errors in state preparation and measurement. Our procedure, which we call lifted randomized benchmarking, generalizes key elements of both character and synthetic randomized benchmarking to benchmark a target group of quantum gates with respect to an enlarged group. We illustrate in numerical examples that our method can efficiently and reliably estimate the fidelity of two-qubit gates relevant to experimental trapped-ion systems. These techniques may find applications beyond randomized benchmarking to quantum tomography and learning, as well as the characterization of error-correcting codes.
Comments38 pages, 5 figures
