Quantum Machine Learning (QML) which is the integration of Quantum Mechanics (QM) and Machine Learning (ML), comes with immense computational capacity due to principles of superposition and entanglement. The PIMA diabetes dataset is used in this research as a test bed for comparing the results obtained from ML and QML approaches. The assessment shows that Support Vector Machine (SVM) has better results compared to Logistic Regression (LR) with an accuracy of 0.76. On the other hand, among QML algorithms; the Quantum Support Vector Classifier (QSVC) has a higher accuracy than the Variational Quantum Classifier (VQC) with 0.74. The findings of the given research imply that the use of QML methods may be valuable for further developments in diabetes prediction in the future.
Quantum Machine Learning (QML) which is the integration of Quantum Mechanics (QM) and Machine Learning (ML), comes with immense computational capacity due to principles of superposition and entanglement. The PIMA diabetes dataset is used in this research as a test bed for comparing the results obtained from ML and QML approaches. The assessment shows that Support Vector Machine (SVM) has better results compared to Logistic Regression (LR) with an accuracy of 0.76. On the other hand, among QML algorithms; the Quantum Support Vector Classifier (QSVC) has a higher accuracy than the Variational Quantum Classifier (VQC) with 0.74. The findings of the given research imply that the use of QML methods may be valuable for further developments in diabetes prediction in the future.
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.
Blood glucose is one of the key indicators influencing the body’s metabolic performance. Accurate prediction and proactive management of this parameter are of paramount clinical importance. In this context, the analysis and evaluation of quantum machine learning (QML) algorithms for processing medical data and enhancing the predictive accuracy of health-related models have emerged as cutting-edge research directions in recent studies. Leveraging the principles of quantum physics and computation, QML algorithms serve as a promising alternative to classical methods for predicting chronic diseases. By mapping classical data into a quantum feature space and exploiting intrinsic quantum properties such as superposition and entanglement, these algorithms significantly enhance the ability to detect complex and latent patterns within datasets. In this study, the dataset was first preprocessed using Principal Component Analysis (PCA) for dimensionality reduction and noise suppression. Subsequently, the predictive performance of two quantum machine learning models—the Quantum Support Vector Machine (QSVM) and the Variational Quantum Circuit (VQC)—was evaluated and compared with that of the classical Support Vector Machine (SVM) for blood glucose level prediction. The results indicate that the QSVM model achieves a prediction accuracy of 90%, outperforming the classical SVM, which attained an accuracy of 74.4%. Moreover, the hybrid classical–quantum VQC model demonstrated the highest predictive performance, achieving an accuracy of 94.2%, thereby establishing it as the most effective approach for blood glucose level forecasting in this experimental setting.
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.
Quantum Machine Learning (QML) shows how it maintains certain significant advantages over machine learning methods. It now shows that hybrid quantum methods have great scope for deployment and optimisation, and hold promise for future industries. As a weakness, quantum computing does not have enough qubits to justify its potential. This topic of study gives us encouraging results in the improvement of quantum coding, being the data preprocessing an important point in this research we employ two dimensionality reduction techniques LDA and PCA applying them in a hybrid way Quantum Support Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) in the classification of Diabetes.
A potential path forward is Quantum Machine Learning (QML), which aims to leverage quantum computing in conjunction with classical machine learning to enhance computing efficiency and the expressiveness of models. In this paper, two different quantum classifiers - Variational Quantum Classifier (VQC) and Quantum Kernel Support Vector Machine (QSVM) - are compared with three classical classifiers as baseline classifiers - Logistic Regression, Support Vector Machine (SVM), and a Multi-Layer Perceptron (MLP) - on the Breast Cancer Wisconsin dataset. The quantum circuits were created in the PennyLane framework and simulated on a classical backend. However, in terms of accuracy, classical Logistic Regression performed better with an accuracy of 97.8%, classical SVM and QSVM with an accuracy of 95.6% each, although the Quantum VQC achieved a lower accuracy of 88.9% and had a recall of 100% for the benign class, though it correctly identified only 12 of the 17 malignant cases (a malignant-class recall of approximately 70.6%). The drawback of quantum models is the higher training time; however, since the quantum circuit needs to be classically simulated, the quantum SVM took 23.29 seconds compared to less than 0.01 seconds for the classical linear models. These results indicate that for small structured datasets, classifiers based on quantum computing have not yet surpassed well-tuned classical counterparts. In some respects (e.g., benign-class recall), they perform competitively, though not on malignant-class recall, where the VQC in particular performed worse than the classical baselines, which is worth further investigation on real quantum computers.
Quantum information processing introduces novel approaches for classical data encoding to encompass the complex patterns of input data of practical computational challenges using basic principles of quantum mechanics. The classification of diabetes is an example of a problem that can be efficiently resolved by using quantum unitary operations and the variational quantum classifier (VQC). This study demonstrates the effects of the number of qubits, types of feature maps, optimizers’ class, and the number of layers in the parametrized circuit, and the number of learnable parameters in ansatz influences the effectiveness of the VQC. In total, 76 variants of VQC are analyzed for four and eight qubits’ cases and their results are compared with six classical machine learning models to predict diabetes. Three different types of feature maps (Pauli, Z, and ZZ) are implemented during analysis in addition to three different optimizers (COBYLA, SPSA and SLSQP). Experiments are performed using the PIMA Indian Diabetes Dataset (PIDD). The results conclude that VQC with six layers embedded with an error correction scaling factor of 0.01 and having ZZ feature map and COBYLA optimizer outperforms other quantum variants. The optimal proposed model attained the accuracy of 0.85 and 0.80 for eight and four qubits’ cases, respectively. In addition, the final quantum model among 76 variants was compared with six classical machine learning models. The results suggest that the proposed VQC model has outperformed four classical models including SVM, random forest (RF), decision tree (DT), and linear regression (LR).