Hamiltonian-encoded quantum feature maps augment classical CNNs for pulmonary tuberculosis detection

Introduction Early detection of pulmonary tuberculosis (TB) from chest radiographs remains challenging due to subtle and low-contrast pulmonary lesions. This study investigates whether quantum-enhanced feature representations can improve TB detection compared with classical deep learning models. Methods We developed a hybrid quantum-classical deep learning framework integrating 4-qubit parameterized quantum circuits with six classical CNN backbones: VGG16, ResNet50, DenseNet121, InceptionV3, EfficientNetB0, and EfficientNetV2-S. A controlled ablation study using classical Sobel-texture feature extraction was conducted. All models were evaluated using 5-fold stratified cross-validation on 662 chest X-ray images (326 normal and 336 TB-positive). Pairwise McNemar tests were used for statistical comparisons. Results QuantumHybrid_EfficientNetB0 achieved the highest AUC of 0.9686 ± 0.0074 (95% CI: [0.934, 0.968]), with a sensitivity of 0.9315 and specificity of 0.8624. Quantum-hybrid models consistently outperformed their classical and ablated counterparts across the evaluated backbone families, with statistically significant improvements in most comparisons ( p < 0.05). Discussion These findings demonstrate the potential of Hamiltonian-encoded quantum feature maps to enhance classical CNN-based pulmonary TB detection and provide a benchmark for further investigation of quantum-augmented diagnostic systems.