Dimension Space Geometry Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel algorithm for dimensionality reduction and optimization, leveraging a modified non-Euclidean geometry and quantum entanglement principles to achieve significant improvements in computational efficiency and data storage. Traditional geometric optimization methods struggle with high-dimensional data, necessitating complex and computationally intensive approaches. This paper introduces a new framework that utilizes quantum entanglement to facilitate instantaneous data mapping across a multi-dimensional space, enabling parallel processing and ultimately enhancing performance. We provide a detailed explanation of the algorithm's core concepts, the underlying mathematical framework, and preliminary results demonstrating its effectiveness.

DOI: 10.5281/zenodo.22203470

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