基于量子纠缠的拓扑学几何的自适应建模

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to geometry modeling utilizing quantum entanglement. The core concept revolves around a dynamically adjustable, quantum-enhanced modeling framework that adapts to input data, allowing for unprecedented precision and efficiency in geometric prediction. We explore the potential of this method to overcome limitations in traditional geometric modeling techniques, particularly in scenarios requiring high accuracy and complex transformations. The paper details the underlying principles of entanglement-based geometry and introduces a robust modeling algorithm, demonstrating its effectiveness through illustrative examples and preliminary results. The focus is on achieving a self-adaptive and computationally efficient method for geometric representation.

DOI: 10.5281/zenodo.22195659

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