Performance Evaluation of Quantum Support Vector Machine for COVID-19 Biomarker Analysis
Document Type
Article
Publication Date
7-2026
Publication Title
Computer Programs and Methods in Biomedicine
Abstract
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.
Repository Citation
Choi, Junggu; Yu, Chansu; Jung, Kyle L.; Foo, Suan-Sin; Chen, Weiqiang; Comhair, Suzy A.A.; Jehi, Lara; and Junf, Jae U., "Performance Evaluation of Quantum Support Vector Machine for COVID-19 Biomarker Analysis" (2026). Electrical and Computer Engineering Faculty Publications. 541.
https://engagedscholarship.csuohio.edu/enece_facpub/541
DOI
10.1016/j.cmpb.2026.109343
Volume
281
Comments
This research was supported by the grant the Korea-US Collaborative Research Project funded by the Ministry of Science, ICT & Future Planning and Ministry of Health and Welfare of Korea (RS-2024-00,467,046) to JUJ and the education and training program of the Quantum Information Research Support Center, funded through the National research foundation of Korea (NRF) by the Ministry of science and ICT (MSIT) of the Korean government (No.2021M3H3A1036573) to JC.