Quantum Entanglement-Based Non-Local Data Dependency Learning

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper proposes a novel approach to deep learning leveraging the phenomenon of quantum entanglement for enhanced training efficiency and generalization capabilities. The core idea is to construct a neural network architecture where neurons are interconnected via entangled quantum states, enabling non-local data dependency learning. We introduce a framework where input data is encoded into quantum states, subjected to quantum gate transformations, and utilizes entanglement for information propagation. This fundamentally shifts the paradigm from classical, locally-connected networks to a system exploiting inherent quantum parallelism. The theoretical underpinning of this approach suggests potential advantages in terms of computational speed and model performance, offering a new direction for deep learning research. We explore the mathematical formalization of this process, emphasizing the key components and their relationships. The primary goal is to demonstrate the feasibility and potential benefits of integrating quantum mechanics into the architecture and training of deep neural networks. The exploration focuses on the design principles and initial mathematical framework for this entanglement-based learning system. ---

DOI: 10.5281/zenodo.22243222

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