Quantum Computing Enhanced Distance-Minimizing Data-Driven Computational Mechanics

Autor:	Xu, Yongchun, Yang, Jie, Kuang, Zengtao, Huang, Qun, Huang, Wei, Hu, Heng
Rok vydání:	2023
Předmět:	Computer Science - Computational Engineering Finance and Science
Druh dokumentu:	Working Paper
Popis:	The distance-minimizing data-driven computational mechanics has great potential in engineering 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. Comment: 22 pages, 14 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.08305 Zobrazit plný text záznamu View this record from Arxiv