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IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)

Combining Dimensionality Reduction and Feature Map in Quantum Support Vector Machines

Chien‐Cheng Tseng, Su‐Ling Lee

Published Jul 1, 2026• 0 citations
Publisher
DOI
10.1109/icce-taiwan71481.2026.11652636

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Obesity, characterized by an excessive increase in body fat, is not only a significant disease but also a condition that serves as the basis for many other illnesses. Therefore, early intervention and necessary precautions for diagnosing and treating obesity are of paramount importance. Classical machine learning algorithms are actively utilized in medical fields to expedite prediction processes. However, the increasing volume of data renders even effective classification algorithms inadequate for experts to diagnose diseases. Quantum computing-based algorithms come into play at this point, offering a new perspective in machine learning by utilizing quantum physics, which is contrary to the rules of classical physics. Dimensionality reduction techniques required for the use of quantum-based algorithms play an essential role in both classical and quantum applications. In this study, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), classical dimensionality reduction methods, were applied to the obesity dataset and analyzed with Quantum Support Vector Machine (QSVM) and Support Vector Machine (SVM) algorithms. To conduct QSVM studies, the comparison of three different quantum feature maps providing the qubit transformation of classical bit data is also included in this study. As a result of the analysis, it was determined that the proposed method as LDA-QSVM achieved 100% success when used with Z and Pauli X feature maps. This success, which is rare in literature studies on obesity data, emphasizes the future potential of quantum-based algorithms in obesity diagnosis and treatment.
Quantum Support Vector Machines (QSVMs) have recently emerged as a promising approach for biomedical classification tasks. However, their practical deployment remains constrained by limited qubit availability and sensitivity to high-dimensional feature spaces. This study proposes a hybrid framework integrating Pearson correlation–based feature selection and Principal Component Analysis (PCA) with QSVM for breast cancer diagnosis. First, Pearson correlation analysis is employed to remove redundant and weakly relevant features. Subsequently, PCA projects the selected attributes into a compact subspace while preserving most of the original data variance. This dimensionality reduction strategy decreases the number of qubits required for quantum encoding and improves computational efficiency. Experiments conducted on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset demonstrate that the proposed hybrid QSVM achieves 98% classification accuracy, outperforming or matching existing classical and quantum-based approaches. The results confirm that combining classical preprocessing techniques with quantum classifiers provides a robust and resource-efficient solution for biomedical data analysis.