Shift-consistent and explanation-stable distributed hybrid quantum learning for domain-generalized skin lesion classification

Accurate skin lesion classification in real-world deployment remains difficult when models are trained on a single dataset and must generalize across heterogeneous devices and patient cohorts. Quantum computing can enrich representation learning via variational transformations in high-dimensional Hilbert spaces, yet clinically reliable and domain-robust hybrid quantum dermoscopy remains insufficiently explored. A shift-consistent and explanation-stable distributed hybrid quantum network (SCES-QNet) was developed to examine whether source-only cross-domain generalization, calibration, and attribution consistency could be improved within a unified dermoscopic classification framework. Distributed quantum branches were combined with prediction-consistency and attribution-equivariance regularization under strict source-only training. HAM10000 was used for model development, whereas ISIC 2019 and PAD-UFES-20 were reserved for external evaluation after class-taxonomy harmonization. Discrimination, calibration, and explanation-stability measures were assessed over five independently initialized runs. On HAM10000, near-ceiling performance was achieved (Macro-F1 = 98.4%, Balanced Accuracy = 98.6%, macro-AUROC = 0.998). Under source-only transfer, strong retention was maintained on ISIC 2019 and PAD-UFES-20 (Macro-F1 = 96.2% / 94.8%; macro-AUROC = 0.993 / 0.989). Calibration degradation remained controlled (ECE: 0.007 → 0.014 → 0.017), and explanation stability remained high across domains (ESS ≥ 0.872). Under the evaluated protocol, SCES-QNet was associated with strong discrimination, controlled calibration drift, and comparatively stable explanations across the selected datasets. The findings provide preliminary evidence for stability-aware hybrid quantum learning in dermoscopic classification; however, broader multi-center evaluation, verified cross-archive duplicate auditing, and execution on physical NISQ hardware remain necessary before clinical or hardware-level conclusions can be drawn.