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

arXiv: https://arxiv.org/abs/2403.07856 · HTML: https://mlsanity.com/paper/2403.07856/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [The complexity of quantum support vector machines](https://mlsanity.com/paper/2203.00031/) (2022)
- [Enhancing Quantum Support Vector Machines through Variational Kernel Training](https://mlsanity.com/paper/2305.06063/) (2023)
- [Quantum-enhanced least-square support vector machine: simplified quantum algorithm and sparse solutions](https://mlsanity.com/paper/1908.01462/) (2019)

## Built on by (most cited first, 3 of 3)

- [RobQFL: Robust Quantum Federated Learning in Adversarial Environment](https://mlsanity.com/paper/2509.04914/) (2025)
- [Evaluating the Impact of Different Quantum Kernels on the Classification Performance of Support Vector Machine Algorithm: A Medical Dataset Application](https://mlsanity.com/paper/2407.09930/) (2024)
- [Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning](https://mlsanity.com/paper/2508.00024/) (2025)

## Abstract (arXiv)

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.