Applying a simulated quantum support vector machine to proteomic and metabolomic COVID-19 data gives classification performance comparable to a classical SVM in selected settings, but the paper's biomarker-identification claims are not established.
By ranking biomarker using ridge-regression and grouping them by importance, we evaluated classification performance of both CSVM and QSVM across different experimental conditions
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Can a Quantum Support Vector Machine algorithm be utilized to identify Key Biomarkers from Multi-Omics data of COVID19 patients?
Applying a simulated quantum support vector machine to proteomic and metabolomic COVID-19 data gives classification performance comparable to a classical SVM in selected settings, but the paper's biomarker-identification claims are not established.