Quantum computing enhanced distance-minimizing data-driven computational mechanics

被引:7
|
作者
Xu, Yongchun [1 ]
Yang, Jie [1 ]
Kuang, Zengtao [1 ]
Huang, Qun [1 ]
Huang, Wei [1 ]
Hu, Heng [1 ,2 ]
机构
[1] Wuhan Univ, Sch Civil Engn, 8 South Rd East Lake, Wuchang 430072, Wuhan, Peoples R China
[2] Ningxia Univ, Sch Math & Stat, Yinchuan 750021, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Data-driven computational mechanics; Quantum computing; Distance calculation; Swap test; Nearest-neighbor search;
D O I
10.1016/j.cma.2023.116675
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The distance-minimizing data-driven computational mechanics has great potential in engineer-ing applications by eliminating material modeling error and uncertainty. In this computational framework, the solution-seeking procedure relies on minimizing the distance between the constitutive database and the conservation law. However, the distance calculation is time-consuming and often takes up most of the computational time in the case of a huge database. In this paper, we show how to use quantum computing to enhance data-driven computational mechanics by exponentially reducing the computational complexity of distance calculation. The proposed method is not only validated on the quantum computer simulator Qiskit, but also on the real quantum computer from OriginQ. We believe that this work represents a promising step towards integrating quantum computing into data-driven computational mechanics.
引用
收藏
页数:14
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