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Computer Methods in Applied Mechanics and Engineering
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Quantum computing enhanced distance-minimizing data-driven computational mechanics

Authors: Yongchun Xu; Jie Yang; Zengtao Kuang; Qun Huang; Wei Huang; Heng Hu;

Quantum computing enhanced distance-minimizing data-driven computational mechanics

Abstract

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.

22 pages, 14 figures

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Keywords

Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, data-driven computational mechanics, Numerical algorithms for specific classes of architectures, Quantum computation, swap test, nearest-neighbor search, Computer Science - Computational Engineering, Finance, and Science, quantum computing, distance calculation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Average
Top 10%
Green
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