Title: Adaptive Quantum Computing-Based Topology Geometry Modeling

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Adaptive Quantum Computing-Based Topology Geometry Modeling explores the potential of quantum computing to revolutionize geometric modeling. This paper investigates a novel technique that dynamically adjusts geometric parameters during the modeling process, leveraging the unique properties of quantum algorithms. We propose a framework where quantum computations iteratively refine geometric representations, offering a potential improvement over traditional methods by enhancing computational efficiency and enabling the exploration of complex geometric structures. This research focuses on the core mechanism of adapting geometric parameters based on quantum-enhanced optimization, demonstrating its effectiveness through theoretical analysis and preliminary simulation results. The ultimate goal is to develop a robust and scalable method for geometric modeling, significantly advancing the field by integrating quantum principles into a classical computational framework.

DOI: 10.5281/zenodo.22267585

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