VQMedNet NISQ aware quantum feature embedding with QFeatEmbed VQC for MRI and CT disease diagnosis

Although automated diagnosis in medical imaging has made tremendous breakthroughs with deep learning, current models also exhibit difficulties with representation stability, calibration, and robustness to domain heterogeneity. Traditional methods that use deeper CNN or transformer networks incur greater computational costs without fully addressing feature separability and uncertainty estimation. To address these shortcomings, the present study proposes a hybrid quantum-classical approach, VQMedNet, that integrates variational quantum circuits with classical feature extraction for supervised classification of MRI and CT images. The suggested QFeatEmbed-VQC module is an NISQ-conscious nonlinear quantum feature embedding that can be trained on tokenised and compressed image features, enabling effective learning with constrained quantum resources. A gradual training approach is followed to achieve consistent optimisation, and calibration-sensitive assessment is included to measure the predictability. The experiments with the brain tumour MRI (BraTS) and lung CT (LIDC-IDRI) datasets show a stable increase over the classical baseline, achieving 95.6% accuracy and 0.947 Macro-F1 on MRI classification, and 93.1% accuracy with improved AUC-PR on CT tasks. Additional analyses show enhanced embedding separability, robustness to domain perturbations, and reduced Expected Calibration Error. Moreover, the findings suggest that moderate-depth quantum circuits offer a good balance between performance and computational complexity. Although tested under simulation-based conditions, the proposed framework can indicate the potential of quantum-enhanced feature embedding for building stable and efficient medical AI systems. It may be seen as a promising approach to future NISQ-aware diagnostics. The implementation is publicly available at: https://github.com/gvvsrk/QuantumMed-FedDx . Clinical trial number: Not applicable.