基于张量信息的量子机器学习模型

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning model leveraging the power of tensor information. Traditional machine learning models often struggle with complex, non-linear relationships, hindering their ability to generalize effectively. We propose a quantum-enhanced learning framework that encodes data into quantum tensors, utilizing quantum entanglement and superposition to accelerate training and improve model robustness. This work addresses the limitations of classical machine learning by harnessing the unique properties of quantum systems. The core mechanism involves encoding data into quantum tensors, employing quantum machine learning algorithms, and exploiting the inherent properties of quantum entanglement and superposition to enhance learning efficiency and generalization. This research explores the potential of quantum computing to significantly advance the field of machine learning, particularly in scenarios requiring handling intricate data dependencies.

DOI: 10.5281/zenodo.22263739

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