基于自适应的几何建模的量子算法

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm based on adaptive geometry modeling, designed to address the challenges posed by large-scale computational problems. Traditional geometric modeling techniques often struggle with high computational complexity, limiting their applicability to substantial datasets. Our algorithm leverages adaptive geometry modeling to dynamically adjust model parameters, effectively reducing computational demands and enhancing overall efficiency. The core mechanism centers around a self-optimizing system that continuously refines the geometric representation based on the input data, leading to improved performance. This approach offers a promising pathway for tackling complex problems within the quantum computing landscape, particularly those involving large-scale simulations and optimization tasks. We detail the algorithm's architecture, mathematical formulation, and preliminary experimental results demonstrating its efficacy.

DOI: 10.5281/zenodo.22597826

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