Comparative Analysis of Quantum Encoding Techniques for Biomarker Classification

Biomarker classification represents a significant task in biological, medical diagnostics, and bioinformatics, where high-dimensional datasets represent challenges to classical machine learning models. The study presented in this paper focuses on the impact of data encoding and explores quantum machine learning algorithms for biomarker classification. Three quantum embedding methods; Angle Embedding, Amplitude Embedding, and IQP (Instantaneous Quantum Polynomial) Embedding, were combined with Quantum Support Vector Machines (QSVM) and Quantum k-Nearest Neighbors (QkNN) and compared against classical SVM and KNN baselines. Principal Component Analysis (PCA) was applied as a preprocessing step to reduce dimensionality. Experiments were conducted on the CuMiDa breast cancer dataset using quantum simulators. Results show that IQP embedding and Angle embedding significantly outperform classical models with QSVM, achieving the best performance, where IQP embedding reaching 93.1% accuracy and an F1-score of 0.93 in higher-dimensional settings, outperforming both the other quantum embeddings and the classical SVM. For QkNN, performance depended on dimensionality, with IQP achieving the strongest results in lower dimensions, including a precision of 87.1%, surpassing both the other quantum embeddings and classical KNN. In higher dimensions, Angle embedding yielded the best quantum results, though classical KNN remained superior. Amplitude embedding consistently underperformed in QSVM but was more competitive in QkNN. These findings demonstrate that advanced quantum data encoding, such as IQP and Angle, can enhance biomarker classification performance and highlight the promise of hybrid quantum-classical approaches in biomedical applications.