Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis

TL;DR

This 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 the prostate cancer dataset.

Abstract

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.

Figures (8)

• Figure 1: Illustration of the maximum-margin hyperplane for SVM classification. The margin hyperplanes for support vectors are depicted by the dashed lines.
• Figure 2: Quantum circuit to compute the kernel function, which can be approximated by assessing the occurrence frequency of $|0\rangle^{\otimes n}$ in the output.
• Figure 3: ZZ-Feature Map quantum circuit
• Figure 4: Kernel matrix for SVM training data using RBF kernel
• Figure 5: Kernel matrix for QSVM training data using the ZZFeatureMap with full entanglement