HAQCCN: A Hybrid Quantum–Classical Convolutional Network with Asymmetric Kernels for Remote Sensing Image Classification

Lianghai Chen1,Yuzhen Liu2,Yi Lu3, Xiaoliang Wang4 and Huaning Song5

  1. School of Computer Science and Engineering, Hunan University of Science and Technology
    Xiangtan, China
    Sanya Institute of Hunan University of Science and Technology
    Sanya, China
    chenlh@mail.hnust.edu.cn
  2. School of Computer Science and Engineering, Hunan University of Science and Technology
    Xiangtan, China
    Sanya Institute of Hunan University of Science and Technology
    Sanya, China
    yzhenliu@126.com
  3. School of Computer Science and Engineering, Hunan University of Science and Technology
    Xiangtan, China
    Sanya Institute of Hunan University of Science and Technology
    Sanya, China
    24010502004@mail.hnust.edu.cn
  4. School of Computer Science and Engineering, Hunan University of Science and Technology
    Xiangtan, China
    Sanya Institute of Hunan University of Science and Technology
    Sanya, China
    fengwxl@hnust.edu.cn
  5. School of Artificial Intelligence and Manufacturing, Hechi University
    Hechi, Guangxi
    2008@163.com

Abstract

Remote sensing image classification is a fundamental task for Earth observation and environmental monitoring. However, conventional convolutional neural networks (CNNs) are limited by computational capacity and struggle to efficiently process the rapidly growing volume of remote sensing data. To address this limitation, we propose HAQCCN (Hybrid Asymmetric Quantum–Classical Convolutional Network), a novel hybrid architecture that integrates quantum computation into the classical convolutional framework through asymmetric quantum convolutional circuits. In HAQCCN, the asymmetric quantum circuits enable a limited number of qubits to process more classical data while maintaining excellent feature extraction capability. Experiments conducted on the IBM Qiskit platform using the Overhead-MNIST, PatternNet, and RSI-CB256 datasets demonstrate that HAQCCN outperforms conventional CNNs and existing quantum models. Furthermore, we systematically investigate the effects of encoding schemes, the number of quantum convolutional kernels, and the number of qubits on model performance, confirming the effectiveness and scalability of the proposed method for remote sensing image classification.

Key words

Remote Sensing Image Classification, Quantum Computing, Convolutional Neural Networks, Quantum Convolutional Network, Feature Extraction, Deep Learning

How to cite

Chen, L., Liu, Y., Lu, Y., Wang, X., Song, H.: HAQCCN: A Hybrid Quantum–Classical Convolutional Network with Asymmetric Kernels for Remote Sensing Image Classification. Computer Science and Information Systems