Adaptive Quantum Topology Geometric Modeling

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of quantum topology to the modeling of geometric morphology, presenting a novel technique for dynamically adjusting geometric features based on data. Traditional modeling approaches are static, requiring manual feature engineering and often lacking the ability to adapt to complex datasets. Quantum topology offers a fundamentally different approach – a system of interconnected, conserved states – that allows for continuous, self-adaptive geometric representation. We detail the theoretical framework, algorithm implementation, and preliminary results demonstrating the efficacy of this method in analyzing and manipulating geometric patterns within complex datasets. The core claim centers around the creation of a quantum topology-based self-adaptive geometric modeling technique that dynamically adjusts geometric morphology based on data. This approach promises to enhance data analysis and representation through continuous, intelligent geometric adjustment.

DOI: 10.5281/zenodo.22264257

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