用于……的多角度量子近似优化算法的性能分析（你提供的原文不完整，这里的译文根据现有内容尽量准确翻译）

Performance analysis of multi-angle QAOA for .

作者信息

Gaidai Igor, Herrman Rebekah

机构信息

Department of Industrial and Systems Engineering, University of Tennessee at Knoxville, 37996, Knoxville, TN, USA.

出版信息

Sci Rep. 2024 Aug 14;14(1):18911. doi: 10.1038/s41598-024-69643-6.

DOI:10.1038/s41598-024-69643-6

PMID:39143274

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11324650/

Abstract

In this paper we consider the scalability of multi-angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets. Moreover, MA-QAOA is less sensitive to system size, therefore we predict that this factor will be even larger for big graphs. However, MA-QAOA was found to be not optimal for minimization of the total QPU time. Different optimization initialization strategies are considered and compared for both QAOA and MA-QAOA. Among them, a new initialization strategy is suggested for MA-QAOA that is able to consistently and significantly outperform random initialization used in the previous studies.

摘要

在本文中，我们考虑了多角度量子近似优化算法（QAOA）相对于QAOA层数的可扩展性。我们发现，对于所考虑的数据集，多角度QAOA（MA-QAOA）能够显著减少QAOA电路的深度，最多可减少四倍。此外，MA-QAOA对系统规模不太敏感，因此我们预测对于大型图，这个因子会更大。然而，发现MA-QAOA在最小化总量子处理单元（QPU）时间方面并非最优。针对QAOA和MA-QAOA，考虑并比较了不同的优化初始化策略。其中，为MA-QAOA提出了一种新的初始化策略，该策略能够始终如一地显著优于先前研究中使用的随机初始化。