Quantum Neural Networks: Learning Topological Layers Through Dynamic Structure Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Neural Networks (QNNs) represent a novel paradigm in neural network architecture, leveraging quantum mechanics to potentially surpass classical neural networks in terms of learning capabilities and expressiveness. This paper investigates the learning of topological layers within QNNs, proposing a dynamic structure optimization method that systematically evolves the network's representation to achieve enhanced model performance. We explore a framework where the network's topology, represented as a dynamic structure, is continuously refined through a series of learning steps. The core mechanism focuses on optimizing the network's representation by manipulating the underlying topology, ultimately leading to improved generalization and robustness. This research introduces a novel approach to learning topological layers, offering a potential pathway toward more sophisticated and adaptable neural network models.

DOI: 10.5281/zenodo.22469803

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