Hybrid Quantum-Classical Neural Network with Bit Cutting for Remote Sensing Image Classification

Remote sensing image classification is crucial for understanding land cover and monitoring environmental changes. Traditional machine learning methods are limited in efficiently processing high-resolution remote sensing data, while quantum machine learning (QML) has the potential for parallel computing and exponential acceleration. However, the noise and limited qubits of quantum hardware in the noisy intermediate-scale quantum (NISQ) era limit the application of QML. To address these challenges, this paper proposes a Cut Hybrid Quantum-Classical Neural Network (CHQC-Net), which combines the classical ResNet-18 and the Matrix Product State (MPS)-based Quantum Neural Network (QNN). By employing a bit cutting method, large quantum circuits are decomposed into smaller subcircuits, reducing the required number of qubits and simplifying circuit implementation. Experiments on the EuroSAT dataset demonstrate the effectiveness of this method, highlighting the potential of quantum computing in remote sensing image classification.