Quantum Entanglement-Based Deep Learning Model

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel deep learning model based on quantum entanglement, aiming to enhance both learning efficiency and generalization capability. Traditional deep learning models are inherently limited by the computational power of classical computers. This work explores the potential of leveraging quantum entanglement to overcome these limitations, proposing a new architecture that harnesses the unique properties of entangled particles to accelerate training and improve model robustness. We detail the core mechanism of this approach, outlining its theoretical underpinnings and preliminary experimental validation. The paper concludes with a discussion of the challenges and future directions for this research.

DOI: 10.5281/zenodo.22510957

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