  Quantum machine learning (QML) is one of the most promising applications of
quantum computation. However, it is still unclear whether quantum advantages
exist when the data is of a classical nature and the search for practical,
real-world applications of QML remains active. In this work, we apply the
well-studied quantum support vector machine (QSVM), a powerful QML model, to a
binary classification task which classifies peptides as either hemolytic or
non-hemolytic. Using three peptide datasets, we apply and contrast the
performance of the QSVM, numerous classical SVMs, and the best published
results on the same peptide classification task, out of which the QSVM performs
best. The contributions of this work include (i) the first application of the
QSVM to this specific peptide classification task, (ii) an explicit
demonstration of QSVMs outperforming the best published results attained with
classical machine learning models on this classification task and (iii)
empirical results showing that the QSVM is capable of outperforming many (and
possibly all) classical SVMs on this classification task. This foundational
work paves the way to verifiable quantum advantages in the field of
computational biology and facilitates safer therapeutic development.
