Quantum Computing Enhanced Distance-Minimizing Data-Driven Computational Mechanics

TL;DR

This paper demonstrates how quantum computing can significantly accelerate distance calculations in data-driven computational mechanics, reducing computational complexity and enabling more efficient engineering simulations.

Contribution

It introduces a quantum computing approach to exponentially speed up distance calculations in data-driven mechanics, validated on both simulators and real quantum hardware.

Findings

Quantum computing reduces distance calculation time exponentially.

Validated approach on Qiskit simulator and OriginQ quantum computer.

Potential to improve efficiency of 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.