A hybrid framework integrating classical computers and quantum annealers for optimisation of truss structures

TL;DR

This paper introduces a hybrid classical-quantum framework for optimizing truss structures, leveraging quantum annealing to solve complex minimization problems iteratively, demonstrating potential for advanced structural design.

Contribution

It presents a novel hybrid approach combining classical and quantum computing for structural optimization, utilizing a quantum annealing-assisted sequential programming strategy.

Findings

Successfully applied to several truss optimization case studies.

Demonstrates quantum advantage in solving large-scale minimization problems.

Highlights potential for future quantum-enhanced structural optimization.

Abstract

This work proposes a hybrid framework combining classical computers with quantum annealers for structural optimisation. At each optimisation iteration of an iterative process, two minimisation problems are formulated one for the underlying mechanical boundary value problem through the minimisation potential energy principle and one for the minimisation problem to update the design variables. Our hybrid approach leverages the strength of quantum computing to solve these two minimisation problems at each step, thanks to the developed quantum annealing-assisted sequential programming strategy introduced in [Nguyen, Wu, Remacle, and Noels. A quantum annealing-sequential quadratic programming assisted finite element simulation for non-linear and history-dependent mechanical problems. European Journal of Mechanics-A/Solids 105 (2024): 105254]. The applicability of the proposed framework is demonstrated through several case studies of truss optimisation, highlighting its capability to perform optimisation with quantum computers. The proposed framework offers a promising direction for future structural optimisation applications, particularly in scenarios where the quantum computer could resolve the size limitations of the classical computers due to problem complexities.