Dynamic Topology Optimization Based on Quantum Computing Geometric Model

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel approach to dynamic topology optimization, leveraging quantum computing to accelerate the process of generating and refining geometric models. Traditional topology optimization relies heavily on computationally intensive simulations, often requiring significant time and resources. We propose a quantum-enhanced algorithm that utilizes quantum algorithms to efficiently explore the geometric space, thereby reducing the computational burden and enabling the discovery of optimal designs. The core mechanism focuses on utilizing quantum state representations to represent and manipulate geometric parameters, facilitating faster convergence and more nuanced optimization. The study demonstrates the potential for significant improvements in optimization speed and accuracy compared to classical methods, particularly for complex geometries. The research highlights the promise of quantum computing to revolutionize geometric design and problem-solving.

DOI: 10.5281/zenodo.22488386

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