Non-Euclidean Geometry Computation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of adaptive topology computation to non-Euclidean geometry, leveraging the unique properties of quantum computing to automate the adjustment of topological structures. The core claim is that this approach, driven by quantum computations, facilitates the efficient representation and modeling of high-dimensional data structures, thereby enhancing algorithm efficiency. Traditional topological computation methods rely on manual design, presenting limitations for large-scale, complex data. This paper explores a novel method employing quantum mechanics to dynamically shape topological spaces, offering a paradigm shift in data representation and algorithmic optimization. We outline the fundamental mechanism, detailing how quantum algorithms are used to iteratively refine topological configurations, ultimately leading to improved computational efficiency. This research contributes to the advancement of computational geometry and offers a potential solution for tackling challenges associated with high-dimensional data.

DOI: 10.5281/zenodo.22197667

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