自适应拓扑量子计算的几何拓扑分析

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum computation centered around the concept of adaptive topology, specifically within the realm of quantum geometry. We introduce a framework that dynamically learns the geometric characteristics of quantum states, enabling precise analysis and modeling of complex topological structures. This approach aims to significantly enhance computational precision by shifting away from static calculations towards a more flexible and adaptable method. The core mechanism involves a hybrid of quantum mechanics and geometric analysis, utilizing a self-learning system to extract and represent the essential topological properties of the system. This work explores the potential of this framework for tackling problems currently limited by computational cost and precision in quantum computation. The paper concludes with a discussion of the theoretical implications and potential applications of this approach.

DOI: 10.5281/zenodo.22496077

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