Application of Quantum Machine Learning in Genomic Data Analysis Using Quantum Support Vector Machines (QSVM)
Abstract
Quantum computing has reintroduced itself as a breakthrough technology for machine learning, especially in the analysis of massive datasets, especially the genomic ones. The present research focuses on using QML, namely QSVM, to parse sequences, discover features relating to multiple genetic alterations, biomarkers, and gene expression patterns. The data type under consideration, namely genomic data, is high-dimensional and complex, which presents some problems to the computational methods originating from traditional mathematics. QSVM is designed to work with the mathematical foundations of quantum mechanics including superposition and entanglement and for this reason, it solves problems associated with large scale genomic data more efficiently and accurately. The application of QSVM in MEPs confirms its superiority over classical counterparts in classifying gene mutations, clustering of expression profiles or prediction of disease susceptibility. In addition, through feature mapping, QSVM offers better insights into biological information contained on genomic data, as compared to linear correlations. Furthermore, the ability of QML frameworks to expand to fit the increasing amount of data produced by next-generation sequencing technologies is assessed. Leveraging on this research QSVM has been recognised to redefine precision medicine by rapidly diagnosing diseases and providing unique therapeutic options. The results re-echo the potential of QML in transforming CG and promoting future innovations in biomedicine and health.