  The support vector machine algorithm with a quantum kernel estimator
(QSVM-Kernel), as a leading example of a quantum machine learning technique,
has undergone significant advancements. Nevertheless, its integration with
classical data presents unique challenges. While quantum computers primarily
interact with data in quantum states, embedding classical data into quantum
states using feature mapping techniques is essential for leveraging quantum
algorithms Despite the recognized importance of feature mapping, its specific
impact on data classification outcomes remains largely unexplored. This study
addresses this gap by comprehensively assessing the effects of various feature
mapping methods on classification results, taking medical data analysis as a
case study. In this study, the QSVM-Kernel method was applied to classification
problems in two different and publicly available medical datasets, namely, the
Wisconsin Breast Cancer (original) and The Cancer Genome Atlas (TCGA) Glioma
datasets. In the QSVM-Kernel algorithm, quantum kernel matrices obtained from 9
different quantum feature maps were used. Thus, the effects of these quantum
feature maps on the classification results of the QSVM-Kernel algorithm were
examined in terms of both classifier performance and total execution time. As a
result, in the Wisconsin Breast Cancer (original) and TCGA Glioma datasets,
when Rx and Ry rotational gates were used, respectively, as feature maps in the
QSVM-Kernel algorithm, the best classification performances were achieved both
in terms of classification performance and total execution time. The
contributions of this study are that (1) it highlights the significant impact
of feature mapping techniques on medical data classification outcomes using the
QSVM-Kernel algorithm, and (2) it also guides undertaking research for improved
QSVM classification performance.
