Self-Organizing Network Learning: A Quantum Model

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel self-organizing network learning model based on quantum hybridization. Leveraging the unique properties of quantum entanglement and superposition, we propose a framework for automatically learning and optimizing complex network structures. The core mechanism involves transforming network nodes and edges into quantum states, utilizing entanglement to facilitate self-organization and optimization. This approach offers a fundamentally different paradigm compared to traditional network learning techniques, potentially leading to more robust and adaptable learning algorithms. We discuss the theoretical foundations, experimental validation, and potential applications of this quantum-enhanced network learning model.

DOI: 10.5281/zenodo.22332084

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