Hybrid quantum-classical neural networks offer a parameter-efficient path for clinical prediction, yet the field lacks reproducible methodologies for architectural design. Most current models rely on ad hoc circuit choices, complicating replication and comparison. This study introduces a generalizable Hybrid Quantum-Classical Neural Network (HQCNN) framework that replaces trial-and-error design with a principled Bayesian-surprise-guided methodology. Evaluated on the Wisconsin Diagnostic Breast Cancer dataset (n = 569), the framework employs a four-component PCA pipeline feeding a 4-qubit parameterized quantum circuit with two variational layers, integrated within a classical neural pipeline. The model was benchmarked against tuned Support Vector Machine, Random Forest, XGBoost, and Multi-Layer Perceptron baselines under identical 5-fold stratified cross-validation with nested GridSearchCV. The HQCNN achieved 96.49% ± 1.24% accuracy and 99.51% ± 0.38% AUC, outperforming a structurally comparable MLP while using 11.27% fewer trainable parameters (441 versus 497). A circuit-depth ablation identified two variational layers as optimal, consistent with barren-plateau dynamics. KL divergence scores of 0.925, 0.804, and 0.653 nats quantified the epistemic informativeness of competitive accuracy, optimal shallow depth, and parameter efficiency, respectively, while the AI2 AutoDiscovery platform independently validated preprocessing choices post hoc. These results indicate that the primary near-term value of hybrid models in healthcare lies in empirical parameter efficiency rather than raw accuracy gains. Fewer parameters reduce overfitting risk on small medical datasets, lower deployment costs, and produce models that are easier to audit for clinical governance. The Bayesian-surprise methodology finally provides the reproducible, principled design framework that the field has long lacked.

This study presents a reproducible evaluation framework for hybrid quantum-classical neural networks (HQCNNs) in healthcare classification, rather than a new architecture. We assess a four-qubit HQCNN combining a compact classical encoder, a two-layer parameterized quantum circuit (PQC), and a classical readout (441 trainable parameters) against carefully tuned classical baselines on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset under identical five-fold cross-validation. The work is framed as a single-dataset proof-of-concept: the contribution is a documented, shared-fold evaluation protocol with a parameter-matched classical control and a quantified epistemic-informativeness analysis, not a demonstration of general quantum advantage. The HQCNN reached $96.49\pm 1.96\%$ accuracy and $99.44\pm 0.60\%$ ROC-AUC. A parameter-matched classical multilayer perceptron (441 parameters) reached $95.08\pm 1.81\%$ accuracy; the HQCNN’s $+1.41$ percentage-point edge at equal capacity was not statistically significant (paired t, $p=0.056$). Across five shared folds, no HQCNN-versus-classical accuracy difference survived Holm–Bonferroni correction (all adjusted $p\ge 0.625$), so we report the HQCNN as competitive with, not superior to, strong tuned classical baselines. A multi-split depth ablation showed that circuit depth $L\in \{1,2,3\}$ had no statistically detectable effect on accuracy ($L=2$ vs. $L=3$: Wilcoxon $p=1.00$); we therefore adopt two variational layers as a practical default rather than an optimum. Under a low-noise simulator (depolarising and amplitude-damping channels, $p=0.01$), accuracy was $96.49\%$, indicating robustness only at modest uniform error rates; realistic hardware noise is higher. We additionally apply Bayesian surprise as an epistemic-informativeness heuristic—not a formal generative model—to rank which findings are most worth building on. The framework offers a reproducible, documented evaluation procedure that can support cumulative comparison of hybrid quantum-classical models in healthcare.