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Related Experiment Video

Updated: Apr 14, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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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
PubMed
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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.
Keywords:
Biomarker importance evaluationCovid-19Multi-omics dataQuantum machine learningQuantum support vector machine

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  • 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.