Quantum Entanglement Topological Geometry Modeling with Adaptive Parameter Tuning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement offers a fundamentally different approach to geometric modeling compared to traditional methods. This paper explores the utilization of quantum entanglement to automatically adjust geometric modeling parameters, resulting in enhanced modeling precision. We propose a novel method centered around leveraging the unique properties of entangled quantum states to dynamically modify the geometry of a defined space. This approach avoids the need for manual parameter tuning, offering a potentially more efficient and accurate modeling process. The core mechanism involves an entanglement-based algorithm that iteratively refines the geometric configuration based on the inherent correlations within the entangled system. We present a preliminary implementation demonstrating this adaptive parameter adjustment process, showcasing improved accuracy in a specific geometric model. The research aims to advance the field of geometric modeling by integrating quantum entanglement into a sophisticated optimization technique.

DOI: 10.5281/zenodo.22201549

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