Hybrid Variational Quantum Circuit for Satellite Image Classification

Abstract (excerpt). The requirement for classifying terrain images is increasing with a fast pace due to various important applications such as agriculture, urban planning, and environmental monitoring. Therefore, a variety of pre-trained classification models such as VGG, ResNet, MobileNet, and also Swin-Transformer V2, have been adopted to enhance the accuracy for classifying the satellite images. However, these improvements often come at the cost of increased computational resources, which can be infeasible for edge devices with limited re-training capabilities. In this paper, we integrate the variational quantum circuits into the deep learning model for image classification, which offers superior classification performance with a reduced number of retraining parameters. By utilizing the parameter shift rule, we can compute the gradients of the variational quantum circuit’s parameters without relying on traditional backpropagation …

Read the full paper

Recommended citation: Trung Thanh Pham, Loc X Nguyen, Phuong-Nam Tran, Eui-Nam Huh, Choong Seon Hong. (2026). "Hybrid Variational Quantum Circuit for Satellite Image Classification." 2026 40th International Conference on Information Networking (ICOIN), 776-781.
Download Paper