Quantum annealing-based structural optimization with a multiplicative design update

• URL: http://arxiv.org/abs/2406.18833v1
• Date: Thu, 27 Jun 2024 02:07:38 GMT
• Title: Quantum annealing-based structural optimization with a multiplicative design update
• Authors: Naruethep Sukulthanasorn, Junsen Xiao, Koya Wagatsuma, Shuji Moriguchi, Kenjiro Terada,
• Abstract summary: This paper presents a new structural design framework, developed based on iterative optimization via quantum annealing (QA) The novelty lies in its successful design update using an unknown design multiplier obtained by iteratively solving the optimization problems with QA. To align with density-based approaches in structural optimization, multipliers are multiplicative to represent design material and serve as design variables.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a new structural design framework, developed based on iterative optimization via quantum annealing (QA). The novelty lies in its successful design update using an unknown design multiplier obtained by iteratively solving the optimization problems with QA. In addition, to align with density-based approaches in structural optimization, multipliers are multiplicative to represent design material and serve as design variables. In particular, structural analysis is performed on a classical computer using the finite element method, and QA is utilized for topology updating. The primary objective of the framework is to minimize compliance under an inequality volume constraint, while an encoding process for the design variable is adopted, enabling smooth iterative updates to the optimized design. The proposed framework incorporates both penalty methods and slack variables to transform the inequality constraint into an equality constraint and is implemented in a quadratic unconstrained binary optimization (QUBO) model through QA. To demonstrate its performance, design optimization is performed for both truss and continuum structures. Promising results from these applications indicate that the proposed framework is capable of creating an optimal shape and topology similar to those benchmarked by the optimality criteria (OC) method on a classical computer.

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