  This study addresses the urgent need for improved prostate cancer detection
methods by harnessing the power of advanced technological solutions. We
introduce the application of Quantum Support Vector Machine (QSVM) to this
critical healthcare challenge, showcasing an enhancement in diagnostic
performance over the classical Support Vector Machine (SVM) approach. Our study
not only outlines the remarkable improvements in diagnostic performance made by
QSVM over the classic SVM technique, but it delves into the advancements
brought about by the quantum feature map architecture, which has been carefully
identified and evaluated, ensuring it aligns seamlessly with the unique
characteristics of our prostate cancer dataset. This architecture succeded in
creating a distinct feature space, enabling the detection of complex,
non-linear patterns in the data. The findings reveal not only a comparable
accuracy with classical SVM ($92\%$) but also a $7.14\%$ increase in
sensitivity and a notably high F1-Score ($93.33\%$). This study's important
combination of quantum computing in medical diagnostics marks a pivotal step
forward in cancer detection, offering promising implications for the future of
healthcare technology.
