Based on Multi-Scale Geometric Information Quantum Algorithms

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm designed to enhance machine learning precision through the synergistic optimization of multi-scale geometric information. Traditional machine learning approaches often rely on single-scale data, limiting their potential for robust and accurate models. Our algorithm introduces a multi-scale geometric information encoding technique, utilizing quantum entanglement and quantum gates to facilitate coordinated optimization of diverse geometric features. The core mechanism focuses on leveraging the inherent correlations and patterns within multi-scale datasets to improve model performance across various levels of complexity. We demonstrate the algorithm's effectiveness through a series of simulations and preliminary experiments, showcasing significant improvements in accuracy compared to existing state-of-the-art machine learning models. This work offers a promising path towards more sophisticated and data-driven machine learning solutions.

DOI: 10.5281/zenodo.22272017

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