标题： 非溶血肽分类使用量子支持向量机 显示英文标题

标题： Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine

摘要： 量子机器学习（QML）是量子计算最有前途的应用之一。 然而，当数据是经典性质时，是否存在量子优势仍不清楚，寻找QML在现实世界中的实际应用仍然活跃。 在本工作中，我们将经过深入研究的量子支持向量机（QSVM），一种强大的QML模型，应用于二分类任务，该任务将肽分类为溶血性或非溶血性。 使用三个肽数据集，我们应用并对比了QSVM、多种经典SVM以及同一肽分类任务中最佳的已发表结果，其中QSVM表现最佳。 本工作的贡献包括（i）首次将QSVM应用于这一特定的肽分类任务，（ii）明确展示了QSVM在该分类任务上优于使用经典机器学习模型获得的最佳已发表结果，以及（iii）实证结果表明QSVM能够在这项分类任务上优于许多（甚至可能所有）经典SVM。 这项基础性工作为计算生物学领域可验证的量子优势铺平了道路，并促进了更安全的治疗开发。 显示英文摘要

摘要： 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.