基于量子拓扑的自适应几何模型生成

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel method for generating geometric models based on quantum topology, enabling dynamic adaptation to complex data. Traditional geometric modeling techniques often rely on predefined parameters, limiting their flexibility and hindering the ability to capture intricate relationships within data. We propose a framework that leverages quantum topology to dynamically adjust model parameters, allowing the model to learn from data and efficiently represent complex geometric patterns. The core mechanism involves using quantum topology to create a flexible, adaptable representation of the data, enhancing learning and enabling the generation of models that can effectively capture the underlying structure of the input space. The proposed approach offers a significant advancement over static models by providing a mechanism for continuous refinement and adaptation, addressing the challenges of handling high-dimensional and complex datasets.

DOI: 10.5281/zenodo.22201571

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