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Quantum-Inspired Geometric Modeling

Jincheng Zhang

Published Aug 31, 2026• 0 citations
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Abstract

This paper explores the application of quantum-inspired algorithms to geometric modeling, aiming to create more efficient representations compared to traditional methods. We propose a novel technique predicated on the inherent properties of quantum mechanics, specifically superposition, to generate geometric representations. The core mechanism involves designing algorithms that exploit these properties to transform geometric data into a form that is inherently more amenable to computational analysis and optimization. The research focuses on establishing a framework for leveraging quantum concepts to enhance geometric modeling, with potential implications for various fields including computer graphics, data visualization, and scientific simulations.

DOI
10.5281/zenodo.22186129

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