Quantum Entanglement Neural Networks: A Probabilistic Approach

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre phenomenon in quantum mechanics, presents a novel approach to neural network training. This paper explores the potential of leveraging entanglement to enhance the efficiency and adaptability of neural networks, specifically focusing on reducing computational errors and enabling more effective parameter adjustment. We propose a novel network architecture that utilizes entanglement to propagate information between neurons, thereby mitigating the limitations of classical computation. The core mechanism centers around introducing quantum entangled states within the network's layers, leading to a fundamentally different training paradigm. This work presents a preliminary investigation into this concept, highlighting potential benefits and outlining future research directions.

DOI: 10.5281/zenodo.22638229

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