Quantum Neural Networks with Dynamic Topological Structures

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel architecture for quantum neural networks (QNNs) characterized by dynamically adjustable topological structures. Traditional QNNs often suffer from limitations in scalability and adaptability, hindering their potential for complex computations. The proposed framework leverages quantum entanglement and interference to enable real-time modifications to the network's connectivity and topology. This dynamic capability is hypothesized to significantly enhance the learning efficiency and generalization performance of QNNs. We outline the theoretical foundations, detailing the mathematical formalism underpinning the dynamic topology control and its impact on network behavior. Specifically, we introduce a method for evolving the network's connectivity based on feedback from the output, creating a self-optimizing system. The core claim is that designing a QNN with the ability to dynamically change its topology will allow for more efficient learning and reasoning. The key mechanism relies on quantum entanglement and quantum interference to dynamically adjust the network's connection strength and topological structure. This research contributes to a deeper understanding of QNN design and opens new avenues for developing highly adaptive and powerful quantum computing systems.

DOI: 10.5281/zenodo.22201486

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