Hybrid Quantum Neural Networks (HQNNs) have recently emerged as a promising paradigm for near-term quantum machine learning. However, their practical performance strongly depends on design choices such as classical-to-quantum data encoding, quantum circuit architecture, measurement strategy and shots. In this paper, we present a comprehensive design space exploration of HQNNs for Chronic Kidney Disease (CKD) diagnosis. Using a carefully curated and preprocessed clinical dataset, we benchmark 625 different HQNN models obtained by combining five encoding schemes, five entanglement architectures, five measurement strategies, and five different shot settings. To ensure fair and robust evaluation, all models are trained using 10-fold stratified cross-validation and assessed on a test set using a comprehensive set of metrics, including accuracy, area under the curve (AUC), F1-score, and a composite performance score. Our results reveal strong and non-trivial interactions between encoding choices and circuit architectures, showing that high performance does not necessarily require large parameter counts or complex circuits. In particular, we find that compact architectures combined with appropriate encodings (e.g., IQP with Ring entanglement) can achieve the best trade-off between accuracy, robustness, and efficiency. Beyond absolute performance analysis, we also provide actionable insights into how different design dimensions influence learning behavior in HQNNs.
Hybrid Quantum Neural Networks (HQNNs) have recently emerged as a promising paradigm for near-term quantum machine learning. However, their practical performance strongly depends on design choices such as classical-to-quantum data encoding, quantum circuit architecture, measurement strategy and shots. In this paper, we present a comprehensive design space exploration of HQNNs for Chronic Kidney Disease (CKD) diagnosis. Using a carefully curated and preprocessed clinical dataset, we benchmark 625 different HQNN models obtained by combining five encoding schemes, five entanglement architectures, five measurement strategies, and five different shot settings. To ensure fair and robust evaluation, all models are trained using 10-fold stratified cross-validation and assessed on a test set using a comprehensive set of metrics, including accuracy, area under the curve (AUC), F1-score, and a composite performance score. Our results reveal strong and non-trivial interactions between encoding choices and circuit architectures, showing that high performance does not necessarily require large parameter counts or complex circuits. In particular, we find that compact architectures combined with appropriate encodings (e.g., IQP with Ring entanglement) can achieve the best trade-off between accuracy, robustness, and efficiency. Beyond absolute performance analysis, we also provide actionable insights into how different design dimensions influence learning behavior in HQNNs.
Abstract Chronic Kidney Disease (CKD) is a progressive and potentially fatal condition that is often diagnosed at advanced stages due to its asymptomatic nature in early progression. Accurate early prediction is challenging because of limited, heterogeneous clinical data and complex nonlinear relationships among biomarkers. This paper proposes HCF-QNet, a hybrid quantum–classical learning framework for robust CKD prediction. The proposed approach integrates three complementary components: optimized feature selection, contrastive representation learning, and variational quantum classification. Initially, a Grey Wolf Optimizer (GWO) is employed to select the most informative clinical features, reducing redundancy and dimensionality. The selected features are then transformed into a compact latent space using a supervised contrastive encoder, which enhances intra class compactness and inter class separability. These embeddings are subsequently processed by an 8-qubit Variational Quantum Neural Network (VQNN) with ring-based entanglement, enabling expressive nonlinear modeling beyond classical classifiers. A two-stage training strategy is adopted, where the encoder is contrastively pretrained and frozen during quantum fine-tuning to ensure stable optimization. The model is evaluated on the UCI CKD dataset using five fold cross-validation. Experimental results demonstrate an average accuracy of 99.75%, achieving better predictive performance than most existing classical machine learning and deep learning approaches. The results highlight the potential of hybrid quantum–classical models for accurate medical diagnosis on small and heterogeneous clinical datasets.
Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures have been proposed and demonstrated successfully on benchmark tasks, a significant open question remains regarding the specific contribution of quantum components to the overall performance of these models. In this work, we aim to shed light on the impact of quantum processing within hybrid quantum-classical neural network architectures through a rigorous statistical study under ideal, noise-free simulation. We systematically assess a selection of commonly used HQNN architectures on three binary medical classification tasks covering medical signal data as well as planar and volumetric images. In doing so, we examine the influence attributable to classical and quantum aspects such as encoding schemes, entanglement, and circuit size. Within this experimental setting, we find that in best-case scenarios, hybrid models show performance comparable to their classical counterparts. However, in most cases, performance metrics deteriorate when the investigated QNN components replace matched classical processing layers. Our multi-modal analysis provides empirical insights into the contributions of the tested quantum components and advocates for cautious claims and design choices for hybrid models in near-term applications.
Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale quantum (NISQ) era, as they combine classical learning components with parameterized quantum circuits in an end-to-end trainable framework. However, their performance and efficiency depend strongly on architectural choices such as data encoding, circuit structure, measurement design, and the coupling between classical and quantum modules. This makes manual design increasingly difficult, especially when hardware limitations and resource constraints must also be taken into account. In this paper, we study the foundations of HQNNs and neural architecture search (NAS), discuss how NAS extends to quantum and hybrid settings, and demonstrate FLOPs-aware search (where FLOPs serve as a proxy for computational complexity), as an important hardware-aware direction for building HQNNs that are not only accurate but also computationally efficient and practically deployable.
In this study, we develop and compare quantum and classical machine learning-based chronic kidney disease prediction models. We used the "Chronic_Kidney_Disease Data Set" of the UCI Machine Learning Repository. We performed data preprocessing and applied feature engineering techniques to select the best features. We developed two quantum machine learning-based models and two classical machine learning-based models. We used a hybrid classical-quantum environment for building quantum machine learning models. Finally, we compared the performances of all four models. We found that the Quantum Support Vector Machine performs best among the quantum models. The model’s accuracy was 95% with a k-fold cross-validation score of 94.5% and an ROC-AUC score of 0.987. Among the classical models, the Support Vector Machine showed the highest performance with an accuracy of 92.5%, a k-fold cross-validation score of 93.9%, and a ROC-AUC score of 0.974. Overall, the Quantum Support Vector Machine outperformed all other developed models in terms of accuracy and validation scores. If the quantum models can be executed in a quantum computer instead of a hybrid environment, the models will exhibit higher accuracy and faster execution time. As the models are precisely predicting chronic kidney disease with high accuracy, we believe this study will act as an inspiring framework in the less-investigated field of quantum machine learning-based chronic kidney disease prediction.
Hybrid quantum neural networks (HQNNs) integrate parameterized quantum circuits (PQCs) within classical networks, where the behavior of the underlying PQCs is often the primary focus of analysis. In this context, expressibility and trainability are widely used to characterize PQC's performance and are commonly assumed to exhibit a trade-off, where highly expressive circuits are more susceptible to barren plateaus. However, the validity of this relationship in HQNNs remains unclear. In this paper, we systematically analyze the expressibility--trainability relationship in HQNNs across varying circuit depths, qubit counts, entanglement topologies. We consider different training configurations, including pure PQCs, quantum-only training in hybrid setting, and full end-to-end training of hybrid models. Our results show that pure PQCs exhibit only a weak and regime-dependent trade-off, while hybrid architectures increasingly disrupt and can eliminate this relationship under full hybrid training. This indicates that classical components reshape the optimization landscape, decoupling trainability from PQC expressibility. We further propose a multi-objective neural architecture search (NAS) framework that jointly optimizes expressibility, trainability, and task performance over a combined classical--quantum design space, revealing different Pareto-optimal solutions under full end-to-end and quantum only training in hybrid setting. different trainability definitions. Our results suggest that hybridization is not just an implementation detail, but a defining factor in the performance of quantum machine learning models.
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.