Hybrid Quantum-Classical NN for Chest X-Ray Classification Using 2-Qubit Quantum Circuits

This study investigates the integration of a 2-qubit quantum system into a classical neural network for chest X-ray classification. Three hybrid quantum-classical neural network circuits are evaluated: U-gate with CNOT, IBM QCNN, and RealAmplitudes. A classical convolutional neural network (CNN), specifically ResNet-18, serves as a benchmark. The results show accuracies of 0.9038 for ResNet-18, 0.7981 for U-gate with CNOT, 0.6250 for IBM QCNN, and 0.6250 for RealAmplitudes. The classical ResNet-18 model outperforms all hybrid models, but the U-gate with CNOT circuit demonstrates promising performance, suggesting potential for quantum-enhanced machine learning in medical image classification.