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Performance evaluation of quantum support vector machine for COVID-19 biomarker analysis.
Junggu Choi1, Chansu Yu2, Kyle L Jung1
1Department of Microbial Sciences in Health, Global Center for Pathogen and Human Health Research, Cleveland Clinic Research, Cleveland Clinic, OH 44915, USA.
Computer Methods and Programs in Biomedicine
|April 12, 2026
Summary
Quantum support vector machines show promise for analyzing multi-omics data in COVID-19 research. This quantum machine learning approach effectively evaluates biomarkers, matching classical methods and aiding disease mechanism understanding.
Area of Science:
- Biomedical research
- Quantum machine learning
- Multi-omics data analysis
Background:
- Identifying key biomarkers from multi-omics data is crucial for COVID-19 diagnosis and understanding disease mechanisms.
- Quantum machine learning, specifically the quantum support vector machine (QSVM), presents a novel approach for biomarker analysis.
Purpose of the Study:
- To assess the applicability of the quantum support vector machine (QSVM) for biomarker evaluation.
- To evaluate QSVM performance within a performance-based feature importance framework.
Main Methods:
- Analyzed proteomic and metabolomic data from two independent cohorts.
- Ranked biomarkers using ridge regression and compared classification performance between classical and quantum support vector machines.
- Investigated various quantum kernels, including amplitude encoding, angle encoding, ZZ feature map, and projected quantum kernel.
Main Results:
- The quantum support vector machine (QSVM) achieved classification performance comparable to, and sometimes exceeding, the classical support vector machine.
- QSVM performance consistently correlated with biomarker importance rankings derived from ridge regression across different conditions.
Conclusions:
- The quantum support vector machine (QSVM) is a promising tool for multi-omics data analysis in biomedical research.
- QSVM can aid in evaluating biomarker importance for complex diseases like COVID-19 using model performance-driven analysis.
