基于量子纠缠的自适应拓扑网络

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of quantum entanglement to construct and optimize adaptive topological networks. Traditional topological network optimization approaches often rely on static parameters, limiting their adaptability to complex systems. We propose a novel architecture leveraging quantum entanglement to dynamically adjust the network's topology, enabling self-adaptive optimization of network performance. The core mechanism involves creating entangled states that represent the network's potential, allowing for continuous, real-time adjustments based on input data. This allows us to simulate intricate topological structures and improve network performance through continuous optimization. This work presents a framework for creating robust and adaptable topological networks, offering a significant advancement over existing methods.

DOI: 10.5281/zenodo.22264888

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