  Classical machine learning, extensively utilized across diverse domains,
faces limitations in speed, efficiency, parallelism, and processing of complex
datasets. In contrast, quantum machine learning algorithms offer significant
advantages, including exponentially faster computations, enhanced data handling
capabilities, inherent parallelism, and improved optimization for complex
problems. In this study, we used the entanglement-enhanced quantum kernel in
quantum support vector machine to train complex respiratory data sets. Compared
to classical algorithms, our findings reveal that QSVM performs better with 45%
higher accuracy for complex respiratory data sets while maintaining comparable
performance with linear datasets in contrast to their classical counterparts
executed on a 2-qubit system. Through our study, we investigate the efficacy of
the QSVM-Kernel algorithm in harnessing the enhanced dimensionality of the
quantum Hilbert space for effectively training complex datasets.
