自适应拓扑量子态的几何拓扑优化算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel algorithm for optimizing quantum states, specifically focusing on adaptive topology optimization. Traditional quantum computing approaches often rely on static configurations, limiting the ability to dynamically adjust and control quantum states. We introduce a new algorithm, termed "Dynamic Topological Optimization," that leverages the inherent geometric evolution of quantum states to dynamically adjust and control these states. The core mechanism involves learning a mapping between the current quantum state and its geometric transformation, allowing for controlled evolution through a tailored optimization process. This approach promises to enhance computational efficiency and provide novel capabilities in quantum systems. We detail the algorithm's design, provide a theoretical analysis, and demonstrate its effectiveness through simulation results. The paper concludes with a discussion of the potential implications and future research directions.

DOI: 10.5281/zenodo.22495814

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