Title: Adaptive Quantum Entanglement for Nonlinear Topological Geometry Transformations

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper investigates the application of adaptive quantum entanglement to nonlinear topological geometry transformations, aiming to achieve high-precision geometric manipulation. Traditional topological transformations often rely on computationally intensive methods, limiting their applicability to complex models. We propose a novel algorithm leveraging the unique properties of quantum entanglement to facilitate accurate transformations, offering enhanced computational efficiency. The core mechanism centers around utilizing entanglement to represent and manipulate geometric primitives, enabling the exploration of intricate topological structures with unprecedented precision. This work presents a framework for implementing this algorithm, demonstrating its potential for advanced geometric modeling and data processing.

DOI: 10.5281/zenodo.22635606

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