基于量子自旋的机器学习模型训练算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel machine learning model training algorithm based on quantum spin dynamics, aiming to enhance both training efficiency and generalization ability. Traditional machine learning methods often suffer from static training procedures, limiting their adaptability. We propose a "Quantum Spin Training" algorithm that leverages the inherent adaptability of quantum spin states to dynamically adjust the learning process. This approach allows for more efficient and robust model training, potentially addressing limitations inherent in existing algorithms. The core mechanism involves implementing a quantum state evolution process, where the spin dynamics are controlled to guide the optimization process. The resulting algorithm demonstrates improved performance across a range of benchmark datasets, suggesting a significant advancement in machine learning training techniques. This research explores the potential of quantum-enhanced machine learning for enhanced model performance.

DOI: 10.5281/zenodo.22462042

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