Quantum Neural Networks: Dynamic Topology Control

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum Neural Networks (QNNs) represent a promising paradigm shift in artificial intelligence, leveraging the unique properties of quantum mechanics to enhance computational capabilities. This paper explores the concept of dynamic topology control within QNNs, focusing on the design of a novel architecture that dynamically adjusts the network's topology to optimize learning and expressiveness. We propose a quantum neural network architecture incorporating a self-adaptive topology, achieved through the manipulation of quantum states. This dynamic adjustment allows the network to learn and represent data more effectively than traditional neural networks, offering potential improvements in both accuracy and efficiency. The core mechanism hinges on the application of quantum state transformations to dynamically shape the network's topology, fostering a more flexible and adaptable learning process. We present a preliminary implementation demonstrating the benefits of this approach, showcasing increased model performance and a novel approach to represent data.

DOI: 10.5281/zenodo.22157638

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