Potential of quantum machine learning for solving the real-world problem of cancer classification

Quantum machine learning (QML) algorithms have demonstrated the power of quantum computing for solving complex problems and big data in certain tasks. In this study, we explore the capabilities of QML for the classification of real-world biological large datasets including ten different cancer types based on gene expression values. By comparing the classification results obtained from the quantum algorithm with those from classical approaches, we disclose that the QML algorithm overall achieves comparable and reliable results. Moreover, we identify novel biomarkers that can contribute to the understanding of cancer biology. Some of these biomarkers are consistent with DNA promoter methylation. Our findings highlight the potential of QML in cancer classification and biomarker discovery, paving the way for future advancements in other disease research and clinical applications. QML could be implemented on real-world datasets to classify cancer types and identify biomarkers.QSVM outperformed some classical models in classification of ten cancer datasets.The novel biomarkers were found using quantum machine learning approach.Findings demonstrate the potential of QML in medical research and biomarker discovery.