Self-Adaptive Topological Modeling

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel approach to geometric modeling based on dynamically-constructed topological structures. The method leverages quantum computing to dynamically adjust modeling parameters, enabling a level of precision unattainable through traditional techniques. We propose a framework where input data is mapped to a dynamic topology, and quantum algorithms are employed to refine the model based on the input's inherent geometric characteristics. This offers a significant advancement in modeling accuracy and adaptability, particularly for complex and intricate shapes. The core mechanism centers around a "quantumization" process that transforms the input data into a dynamically evolving topological representation. The paper details the design of the quantum algorithm, its implementation within a computational framework, and a preliminary evaluation of its performance on a set of synthetic and real-world geometric datasets. We demonstrate that this method surpasses existing methods in terms of geometric fidelity and computational efficiency.

DOI: 10.5281/zenodo.22621186

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