基于量子纠缠的自适应神经网络

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel neural network architecture, the Quantum Entangled Neural Network (QENN), leveraging the unique properties of quantum entanglement to enhance prediction accuracy and generalization capabilities. We explore the potential of quantum entanglement to circumvent computational limitations inherent in traditional neural networks. The QENN utilizes a dynamically adjusted entanglement matrix to promote self-sustained quantum states, resulting in improved performance across a range of tasks. This work presents a theoretical framework and preliminary experimental results demonstrating the effectiveness of this approach, highlighting its potential to advance neural network design. The core claim centers on the enhanced predictive power achieved through the manipulation of quantum entanglement. This advancement offers a promising pathway to create more robust and adaptable neural networks.

DOI: 10.5281/zenodo.22189553

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