基于量子模拟的基于拓扑结构的机器学习算法优化

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the potential of leveraging quantum simulation for machine learning optimization, specifically focusing on the design of a novel algorithm that utilizes quantum-enhanced feature extraction and rapid learning. The core concept revolves around a hybrid approach combining traditional machine learning techniques with a quantum simulation framework. We propose a quantum-guided optimization process, where the algorithm explores the quantum state space to identify optimal features and parameters, ultimately leading to improved model performance and generalization capabilities. This work addresses the limitations of classical machine learning in handling complex, non-linear data distributions, presenting a promising avenue for advancements in machine learning through the incorporation of quantum mechanics. The paper details the algorithm's design, experimental validation, and potential implications for various machine learning tasks.

DOI: 10.5281/zenodo.22322996

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