基于量子纠缠的机器学习算法设计

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre yet fundamental phenomenon in quantum mechanics, holds immense potential for revolutionizing machine learning. This paper explores the design of a novel machine learning algorithm leveraging quantum entanglement to enhance model learning efficiency and generalization capabilities. We propose a framework centered around manipulating quantum states to facilitate complex pattern recognition and data representation, ultimately offering a potential paradigm shift in machine learning. We detail the underlying principles, mathematical modeling, and implementation considerations of this algorithm. The core claim underscores the transformative potential of this approach, highlighting its ability to overcome limitations inherent in classical machine learning. This work presents a preliminary investigation into the feasibility and potential benefits of applying quantum entanglement to enhance machine learning.

DOI: 10.5281/zenodo.22621911

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