SARS-CoV-2 Classification Using Classical vs. Quantum Machine Learning: A Performance Comparison of SVM, QSVM, and Pegasos

Quantum Machine Learning (QML) integrates principles of quantum computation with statistical learning to address classification tasks. This paper conducts a comparative study of three paradigms—classical Support Vector Classification (SVC), Quantum Support Vector Classification (QSVC) using quantum kernels, and QSVC enhanced via the Pegasos stochastic algorithm. We benchmark classification accuracy on a SARS-CoV-2 dataset, analyzing model robustness and resource requirements. Our results indicate that while classical SVC maintains superior scalability and stability for large datasets, QSVC variants show competitive accuracy across simulator backends (Central Processing Units or CPUs and Graphical Processor Units or GPUs) and may achieve quantum advantage as hardware matures. Implications for hybrid workflows and future research directions are discussed.