基于拓扑结构的动态量子态学习算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel dynamic quantum state learning algorithm based on topological structures. Traditional machine learning methods often require manual parameter tuning, limiting adaptability to complex environments. We propose a method that leverages topological structures as a guiding principle, enabling the system to dynamically evolve quantum states and optimize quantum computing performance. The core mechanism centers around identifying and exploiting the inherent topological properties of a quantum system, facilitating rapid and intelligent state evolution. This approach overcomes limitations of traditional methods by providing a more robust and adaptable framework for quantum state manipulation. The presented algorithm demonstrates significant improvements in quantum state fidelity and computational efficiency compared to existing techniques.

DOI: 10.5281/zenodo.22185037

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