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Quantum-Based Dynamic Entanglement-Based Self-Organizing Network Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

This paper introduces a novel framework for dynamic self-organizing network optimization utilizing quantum entanglement. We propose a novel approach that leverages the unique properties of entanglement to facilitate parameter interactions, enabling adaptive learning and improved robustness in complex optimization problems. Specifically, we demonstrate the framework's effectiveness in addressing machine learning optimization, financial risk management, and other challenging scenarios. This work explores how entanglement can serve as a fundamental mechanism for enhancing the performance of optimization algorithms.

DOI
10.5281/zenodo.22201665

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