Quantum Neural Network-Based Approach for Optimizing Road Network Selection

Road network selection is a pivotal yet challenging task in cartographic generalization. As neural network technology continues to advance, intelligent methods for road network selection have emerged as a key research area. However, the expanding scale of road networks has led to concerns regarding model training efficiency and resource consumption. Quantum neural networks, leveraging their unique properties of superposition and entanglement, present remarkable advantages for handling large-scale, complex, and nonlinear data. In this paper, we propose a novel framework for road network selection based on quantum neural networks. We design a comprehensive feature set that takes into account various factors, including terrain, settlements, and surrounding density. Our study delves into the impact of feature encoding methods and circuit structures on the performance of quantum neural networks in road selection. We also evaluate the proposed model's performance across different scales, regions, and data volumes. The results demonstrate the feasibility and effectiveness of our approach when compared to existing classical neural network models, offering a promising solution for large-scale road network selection.