基于量子态的非线性拓扑优化算法

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel quantum-based topological optimization algorithm designed to address complex optimization problems in diverse fields, including physics and machine learning. The algorithm leverages the inherent adaptability of quantum states to dynamically explore and refine potential solutions, offering a fundamentally different approach compared to traditional optimization methods. We present the core mechanism, detailing how the algorithm simulates topological structure evolution and adjusts optimization parameters through the quantum state. The proposed method demonstrates promising results in solving optimization challenges, specifically in optimizing physical systems and machine learning model parameter spaces. This research explores the potential of quantum mechanics to enhance optimization capabilities, offering a fundamentally new algorithmic paradigm.

DOI: 10.5281/zenodo.22637671

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