Performance evaluation of quantum support vector machine for COVID-19 biomarker analysis.

BACKGROUND AND

OBJECTIVE

Identifying key biomarkers from multi-omics data is essential for advancing COVID-19 diagnosis and understanding disease mechanisms. Quantum machine learning approaches, particularly the quantum support vector machine, offer potential for biomarker analysis. This study aimed to assess the applicability of the quantum support vector machine for biomarker evaluation under a performance-based feature importance framework.

METHODS

Proteomic and metabolomic biomarker data from two independent cohorts (Cleveland Clinic and Swedish Medical Center) were analyzed. Biomarkers were ranked using ridge regression and grouped into higher- and lower-importance sets. These groups were used to compare classification performance between the classical support vector machine and the quantum support vector machine. Multiple quantum kernels were examined, including amplitude encoding, angle encoding, the ZZ feature map, and the projected quantum kernel.

RESULTS

Across diverse experimental conditions, the quantum support vector machine achieved classification performance comparable to, and in some settings slightly higher than, that of the classical support vector machine. Moreover, the quantum support vector machine performance consistently aligned with biomarker importance rankings derived from ridge regression.

CONCLUSIONS

Within a performance-based feature importance framework, these results highlight the quantum support vector machine as a promising approach for multi-omics data analysis in biomedical research. The findings suggest that the quantum support vector machine can support biomarker importance evaluation in complex diseases such as COVID-19 through model performance-driven analysis.