Core Claim: Design a Quantum Entanglement-Based Adaptive Geometry Transformation Algorithm

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper presents a novel algorithm for adaptive geometry transformation based on quantum entanglement. Traditional geometric transformation algorithms often rely on static transformations, limiting their ability to effectively optimize transformations. This work leverages the unique properties of quantum entanglement to dynamically adjust geometric transformations, resulting in significantly improved efficiency and potential for more sophisticated geometric modeling. We propose a framework that utilizes entangled states to represent and manipulate geometric parameters, enabling a form of self-optimization during the transformation process. The core mechanism involves creating entangled mappings between geometric transformations and the underlying parameter space. This allows for a continuous and nuanced optimization of the transformation process, surpassing the limitations of traditional approaches. The paper details the algorithm's design, provides a preliminary implementation, and analyzes its potential benefits and challenges in the context of geometric transformations.

DOI: 10.5281/zenodo.22244157

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