Quantum Entanglement-Based Neural Network Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores the potential of quantum entanglement to revolutionize neural network optimization. We propose a novel algorithm, "Quantum Entanglement-Based Neural Network Optimization," that leverages the instantaneous correlations inherent in entangled states to accelerate training significantly. The core mechanism centers around the design of a specialized "纠缠神经元" network, utilizing quantum entanglement to expedite the learning process. This approach aims to overcome limitations imposed by classical optimization techniques, potentially leading to substantial improvements in model accuracy and training speed. The paper details the algorithm's design, mathematical foundation, and preliminary results, demonstrating the potential of quantum entanglement to enhance neural network optimization.

DOI: 10.5281/zenodo.22248436

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