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arXiv 2402.08952quant-phcs.SYeess.SY
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量子过程层析的两阶段解决方案:误差分析与最优设计

A two-stage solution to quantum process tomography: error analysis and optimal design

Shuixin Xiao, Yuanlong Wang, Jun Zhang, Daoyi Dong, Gary J. Mooney, Ian R. Petersen, Hidehiro Yonezawa

AI总结:

提出一种适用于保迹与非保迹量子过程层析的两阶段解决方案,利用张量结构降低计算复杂度,建立解析误差上界并据此设计最优输入态与测量算符,数值实验与IBM量子设备测试验证了其性能。

摘要:

量子过程层析是表征量子系统动力学和实现精确量子控制的关键任务。在本文中,我们提出了一种适用于保迹和非保迹量子过程层析的两阶段解决方案。利用张量结构,我们的算法计算复杂度为$O(MLd^2)$,其中$d$是量子系统的维度,$M$、$L$分别表示不同输入态和测量算符的数量。我们建立了解析误差上界,进而设计了最优输入态和最优测量算符,二者均基于最小化误差上界和最大化由条件数表征的鲁棒性。给出了数值示例和在IBM量子设备上的测试,以证明我们算法的性能和效率。

英文摘要:

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-trace-preserving quantum process tomography. Utilizing a tensor structure, our algorithm exhibits a computational complexity of $O(MLd^2)$ where $d$ is the dimension of the quantum system and $ M $, $ L $ represent the numbers of different input states and measurement operators, respectively. We establish an analytical error upper bound and then design the optimal input states and the optimal measurement operators, which are both based on minimizing the error upper bound and maximizing the robustness characterized by the condition number. Numerical examples and testing on IBM quantum devices are presented to demonstrate the performance and efficiency of our algorithm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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