基于量子几何的自适应机器学习算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel self-adaptive machine learning algorithm based on quantum geometry. Traditional machine learning algorithms often rely on handcrafted models, limiting their adaptability and scalability. Quantum geometry, a theoretical framework that describes the fundamental structure of spacetime, offers a unique perspective for representing and manipulating complex data. This work proposes a new algorithm that leverages quantum geometry to enhance the accuracy and efficiency of machine learning models, focusing on model scalability and adaptability. The core mechanism involves mapping data points onto a quantum geometric representation, enabling the algorithm to dynamically adjust its parameters to optimize performance. We demonstrate the effectiveness of this approach through a series of experiments and analysis, highlighting its potential to overcome limitations of conventional machine learning. This research explores the benefits of utilizing quantum geometry in accelerating and improving machine learning processes.

DOI: 10.5281/zenodo.22289485

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