Quantum machine learning (QML) provides a framework for benchmarking wearable biosignal classification relevant to stress detection. Motivated by the burden of stress-related conditions, this study compares three quantum classifiers with seven classical baselines using heart rate and respiration rate features as inputs under noise-free and noisy conditions. Uncertainty was quantified using Nadeau–Bengio-corrected confidence intervals and percentile bootstrap (B=1000). The variational quantum classifier (VQC) achieved an accuracy of 99.47%/97.30% (noise-free/noisy), the quantum support vector classifier (QSVC) achieved 99.90%/99.37%, and PegasosQSVC achieved 99.80%/99.70%. Additionally, under the assessed proof-of-concept conditions, statistical equivalence between the QSVC and the best-performing classical model was established at Δ=1 pp; PegasosQSVC under noise achieved equivalence at Δ=2 pp with accuracy degradation of less than 0.10 pp. The time feature was identified as the primary separability driver in a post hoc classical ablation. Tree-based models were robust on physiological features alone. The surveyed methods provide a reproducible, noise-aware benchmark for wearable physiological signal classification; however, the reported high accuracies are based on a deliberately separable proof-of-concept benchmark and do not demonstrate clinical utility or a quantum advantage.
Quantum machine learning (QML) provides a framework for benchmarking wearable biosignal classification relevant to stress detection. Motivated by the burden of stress-related conditions, this study compares three quantum classifiers with seven classical baselines using heart rate and respiration rate features as inputs under noise-free and noisy conditions. Uncertainty was quantified using Nadeau–Bengio-corrected confidence intervals and percentile bootstrap (B=1000). The variational quantum classifier (VQC) achieved an accuracy of 99.47%/97.30% (noise-free/noisy), the quantum support vector classifier (QSVC) achieved 99.90%/99.37%, and PegasosQSVC achieved 99.80%/99.70%. Additionally, under the assessed proof-of-concept conditions, statistical equivalence between the QSVC and the best-performing classical model was established at Δ=1 pp; PegasosQSVC under noise achieved equivalence at Δ=2 pp with accuracy degradation of less than 0.10 pp. The time feature was identified as the primary separability driver in a post hoc classical ablation. Tree-based models were robust on physiological features alone. The surveyed methods provide a reproducible, noise-aware benchmark for wearable physiological signal classification; however, the reported high accuracies are based on a deliberately separable proof-of-concept benchmark and do not demonstrate clinical utility or a quantum advantage.
Quantum Machine Learning (QML) has emerged as an interdisciplinary field that harnesses the potential of quantum computing to revolutionize classical machine learning paradigms. This paper presents a benchmarking study focused on four quantum classifier algorithms: Quantum Support Vector Classifier (QSVC), Variational Quantum Circuit (VQC), Quantum Neural Network Classifier (QNNC) and Pegasos Quantum Support Vector Classifier (PegasosQSVC). These quantum classifiers hold promise for unlocking quantum advantages in classification tasks. However, a systematic assessment of their performance across diverse datasets is yet to be fully explored. In this research study, we evaluate and compare the classification performance of quantum machine learning classifiers on various datasets (Breast Cancer, Diabetes, and Titanic) and compare them to a classical model. Our findings revealed that PegasosQSVC consistently maintained high performance across all datasets with the highest average accuracy, recall, and F1-score values of 0.81, 0.72, and 0.71, respectively, followed by VQC, followed by VQC, while QSVC and QNNC perform variably. Nevertheless, QSVC was found to balance performance and training time, achieving an average accuracy of 0.81 and a training time of 177 seconds. Overall, the classical model surpassed QML models as it was up to 10% higher in average accuracy.
Diabetes remains one of the common chronic diseases around the world and finding a good early prediction from clinical and lifestyle data is very important for timely help. Classical machine learning models such as regression, decision trees and random forests have been used for this purpose but the complicated non‑linear connections among clinical features might need richer ways of representation. Quantum Machine Learning (QML) gives an approach using quantum feature encoding and variational circuits to find patterns that are expensive to express with classical methods. In this study we show a diabetes prediction pipeline that compares baseline models—logistic regression, decision tree and random forest—with two quantum classifiers built in Qiskit: a Quantum Support Vector Classifier (QSVC) and a Variational Quantum Classifier (VQC). Using the Pima Indians Diabetes dataset we test all models on accuracy, precision, recall and F1‑score. The results show that the classical Random Forest classifier reaches the accuracy (0.73) beating both quantum classifiers. The Quantum Support Vector Classifier (QSVC 0.66) and the Variational Quantum Classifier (VQC 0.65) are still competitive. Do not outdo the classical baselines. These results agree with reports that in the present Noisy Intermediate‑Scale Quantum (NISQ) era quantum models usually match rather than surpass well‑tuned classical approaches on standard health‑care data and that a real quantum advantage will need larger data, deeper circuits and testing, on real quantum hardware.
Abstract Claims of quantum advantage in machine learning often rest on classical comparators that lack the same features, on simulation alone, and on unreported hardware cost. QBioBench is a benchmark of quantum machine learning on peripheral biosignals that holds quantum and classical models to the same features and reports calibration and hardware cost. It comprises three clinical tasks, 12-lead ECG diagnosis (PTB-XL), single-lead apnea detection (Apnea-ECG), and wearable stress detection, each reduced to six to eight shared dimensions. A factorial grid of projected and fidelity quantum kernels and variational classifiers is screened in noisy simulation, and the promoted configurations are evaluated in an exploratory study on an IBM Nighthawk r2 processor on the full test pools of 510 apnea minutes and 512 PTB-XL records. Every prediction is recovered from the device records, intervals resample recordings or patients, and classical baselines are trained on the same units and tested on the same executed inputs. No quantum model reaches its matched support vector machine in ROC-AUC on either primary task, and none is separably better in balanced accuracy. The angle-encoded fidelity and projected kernels reach ROC-AUC 0.697 and 0.693 on apnea, paired differences of − 0.127 and − 0.119 to the support vector machine, and both are classical by construction, since the entangling layer of the encoding cancels from the fidelity kernel and leaves closed-form local expectations in the projected kernel. A variational classifier comes closest, reaching 0.797 on apnea (difference − 0.012 , 95% interval [ − 0.052 , + 0.027 ] ), and falls to chance without its two-qubit gates. Test sets of the size that circuit budgets favor are unreliable, as random 80-minute subsets of the apnea pool give the fidelity kernel a central 95% range of ROC-AUC from 0.589 to 0.797. Readout mitigation changed ROC-AUC by at most 0.015.
Cardiovascular diseases remain a leading global health challenge, driving the need for advanced, accurate diagnostic tools. While classical machine learning (ML) models like XGBoost have achieved significant success in medical diagnosis, they often face challenges related to accuracy plateaus and model explainability in complex, high-dimensional data spaces. Quantum machine learning (QML) emerges as a promising paradigm to potentially overcome these limitations by leveraging quantum computational advantages. This study aims to develop and evaluate a hybrid quantum-classical pipeline for the early detection of heart disease. We integrate robust data preprocessing—including missing value imputation, feature engineering, SMOTE for class balancing, and PCA for dimensionality reduction—with two classification approaches: a classical XGBoost model and a quantum Variational Quantum Classifier (VQC). Our results demonstrate that the XGBoost model achieves strong performance with an accuracy of 88.52% and an F1-score of 0.8852 on an imbalanced test set. While the VQC model did not surpass the classical baseline in the current noisy simulation environment, it shows theoretical promise. This work highlights the practical challenges of current QML and outlines a clear pathway for future research towards achieving a quantum advantage in clinical decision support systems.
Cardiovascular disease remains one of the leading causes of death worldwide, making early and accurate risk prediction essential. Conventional machine learning models often struggle to handle complex medical data, resulting in limited accuracy and reduced interpretability. To address these challenges, this study presents a hybrid prediction framework that combines Quantum Machine Learning (QML) with ensemble learning techniques. Classical models such as Support Vector Classifier (SVC) and Artificial Neural Network (ANN) are used as baseline approaches. In addition, quantum models including QSVC, Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN) are employed to capture complex non-linear patterns present in clinical data. To further enhance performance, a Bagging-QSVC ensemble model is proposed, improving robustness and overall classification accuracy. Interpretability is incorporated using SHAP, allowing the model’s predictions to be more transparent and easier to understand in a clinical context. Experimental results indicate that the proposed quantum ensemble achieves an accuracy of around 90%, outperforming both classical models and individual quantum models. Furthermore, a real-time user interface is developed, enabling clinicians to input patient data and receive immediate risk predictions. Overall, the system provides a reliable and interpretable solution suitable for practical clinical decision support.
This study presents a noise-aware evaluation framework for hybrid quantum–classical machine learning models applied to medical binary classification. Using three healthcare datasets—Pima Indians Diabetes, Heart Disease, and Breast Cancer Wisconsin—the performance of a Variational Quantum Classifier (VQC) was analyzed under multiple depolarizing noise levels (0.00–0.10). Classical baselines, Logistic Regression (LR) and Support Vector Machine (SVM), were implemented for comparison. The research investigates the impact of noise on predictive accuracy, F1-score, and training time, highlighting the computational overhead of hybrid quantum–classical models. Zero Noise Extrapolation (ZNE), using linear and Richardson methods, was applied to mitigate noise effects, revealing that while ZNE improves performance under certain conditions, extrapolation instability can occur. Transpiler optimization also contributed to improved VQC stability by reducing circuit complexity. Results show that classical models consistently outperformed the VQC in accuracy, stability, and training efficiency. VQC exhibited fluctuating performance under noise, requiring significantly higher training time, and increasing SPSA iterations did not always yield stable accuracy improvements. These findings underscore the challenges of noise robustness, optimization instability, and computational cost in the NISQ era, and suggest directions for future work, including alternative optimizers, adaptive ansatz design, advanced error mitigation, and real hardware benchmarking. This research provides a systematic benchmarking framework for evaluating training time versus noise-level trade-offs, contributing to the development of more reliable quantum machine learning models for medical applications. Keywords: Quantum Machine Learning, Variational Quantum Classifier, Depolarizing Noise, Zero Noise Extrapolation, Medical Classification, Hybrid Quantum–Classical Models, Training Time Analysis