基于量子纠缠的神经网络架构

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper investigates the potential of leveraging quantum entanglement to enhance neural network performance and efficiency. We propose a novel neural network architecture incorporating quantum entanglement to facilitate a more efficient and robust information transfer between neurons. The core mechanism centers on exploiting the unique properties of entangled states to minimize computational complexity and improve learning speed. The research explores the feasibility of implementing this architecture within a simulated environment, demonstrating preliminary results suggesting a potential improvement in training speed and accuracy compared to conventional neural networks. The paper concludes with a discussion of future research directions, including exploring the scalability of the quantum entanglement approach and investigating its applicability to diverse neural network tasks.

DOI: 10.5281/zenodo.22153352

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