自适应拓扑结构优化算法的量子计算实现

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper explores the potential of utilizing quantum computing to revolutionize the field of topology optimization. Traditional topology optimization methods often struggle with complex geometries and require significant computational resources. We propose a novel algorithm, "Quantum-Driven Adaptive Topology Optimization," (QDATO) that leverages quantum phenomena, specifically superposition and entanglement, to accelerate the optimization process and enhance efficiency. The core mechanism of QDATO involves mapping the optimization problem to a quantum algorithm, effectively harnessing quantum properties to explore the solution space more effectively. This paper details the algorithm's design, its mathematical underpinnings, and preliminary experimental results demonstrating its potential for significant improvements in optimization speed and accuracy. We conclude with a discussion of future research directions and the broader implications of this quantum-enhanced approach to topology optimization.

DOI: 10.5281/zenodo.22295493

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