基于量子计算的自适应几何建模

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel approach to geometric modeling utilizing quantum computing for adaptive parameter adjustment. Traditional geometric modeling methods rely heavily on manual parameter tuning, often leading to suboptimal results and increased computational cost. We propose a quantum-enhanced modeling framework that leverages quantum phenomena to dynamically adjust geometric parameters during the modeling process. The core mechanism involves utilizing quantum state manipulation to represent and evolve geometric parameters, enabling automated parameter optimization and ultimately, improved model accuracy and efficiency. We demonstrate the effectiveness of this approach through a series of simulations and analysis, showcasing significant gains in precision and computational speed compared to traditional methods. The paper concludes with a discussion of the potential applications and future research directions.

DOI: 10.5281/zenodo.22199146

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