基于量子计算的拓扑优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-Based Topological Optimization represents a novel approach to address challenges in complex decision-making systems, particularly those involving intricate decision trees and workflows. This paper explores the application of quantum computing to optimize these systems, leveraging quantum superposition and entanglement to enhance decision efficiency and accuracy. We introduce a framework centered around a topological optimization algorithm, demonstrating its potential to significantly improve the performance of decision-making processes across diverse domains. The core mechanism involves manipulating quantum states to represent decision boundaries, facilitating exploration of complex solution spaces with enhanced efficiency. We provide a preliminary analysis of the algorithm's potential, outlining key design choices and demonstrating initial results through simplified scenarios. This work underscores the promise of quantum computing to revolutionize optimization, specifically within decision-making contexts.

DOI: 10.5281/zenodo.22266570

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