Dynamic Topological Embedded Quantum Neural Networks

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper proposes a novel computational architecture termed Dynamic Topological Embedded Quantum Neural Networks (DTEQNNs). The core claim centers around the dynamic adaptation of complex nonlinear systems through real-time adjustment of network-level quantum entanglement topology. This mechanism leverages the dynamic control of entanglement states within topological quantum computing, associating network nodes with specific topological structures via quantum bits. By modulating entanglement strength and topological network structure, the system facilitates node-to-node communication and computation, enabling a neural network capable of automatically adjusting its structure in response to changing input data. The proposed approach addresses the limitations of conventional static neural networks in handling complex, dynamic systems, offering a fundamentally new computational paradigm for real-time system modeling and prediction. This work outlines the theoretical framework, key concepts, and potential applications of DTEQNNs.

DOI: 10.5281/zenodo.22636165

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