基于动态量子态的拓扑结构生成

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum state generation based on dynamic quantum state evolution. Traditional methods often rely on static configurations, limiting the design of complex topological structures. We propose a method that dynamically adjusts the quantum state through a self-adaptive learning algorithm, enabling the generation of states with intricate topology. This approach offers significant advantages in terms of adaptability and the potential for generating highly complex and functional quantum systems. The core mechanism involves iteratively refining the quantum state based on a learned mapping of the underlying topological structure. We demonstrate the effectiveness of this method through the generation of novel topological structures with tailored properties. Furthermore, this work addresses the limitations of static methods by introducing a self-optimizing process that enhances the quality and diversity of the generated quantum states. The results highlight the potential for this approach to expand the landscape of quantum system design.

DOI: 10.5281/zenodo.22291670

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