Quantum Machine Learning: An Application of Quantum Support Vector Machines for Financial Data Classification

Nur Amalina Rahmaputri Hilmy, Muhamad Akrom

Quantum Machine Learning (QML) is increasingly attracting attention as a potential solution to improve computational performance, especially in handling complex and big data-driven classification tasks. In this study, the Quantum Support Vector Machine (QSVM) algorithm is applied to prostate cancer classification, with the results compared to the classical Support Vector Machine (SVM) model. QSVM shows superiority in accuracy, reaching 0.93, compared to the classical SVM which has an accuracy of 0.91. In addition, QSVM produces precision, recall, and F1-score values of 0.83, 0.95, and 0.88, respectively, higher than the precision of 0.82, recall of 0.93, and F1-score of 0.87 of the classical SVM. These findings indicate that QSVM is more effective in handling high-dimensional data and complex classification, thus demonstrating the great potential of QML in medical applications, especially in cancer classification and biomarker discovery.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Klasifikasi, Kanker Prostat

S. S. Kavitha, Narasimha Kaulgud

Hong Li, Nan Jiang, Rui Zhang +2 more

Mhlambululi Mafu, Makhamisa Senekane

Machine Learning (ML) is arguably the most ad-vanced sub-field of Artificial Intelligence (AI). It concerns the study of computer algorithms that can improve automatically by learning from data (experience) without being explicitly pre-programmed. On the other hand, the quantum version of ML, Quantum Machine Learning (QML), forms one of the most crucial and recent quantum computing applications. It uses quantum mechanical principles such as superposition, interference, and tunneling to enable quantum computers to learn from data. Some examples of QML algorithms include Quantum Principal Component Analysis (QPCA) and Quantum Support Vector Machine (QSVM). The QSVM is a robust supervised machine learning algorithm used for classification and regression. Therefore, this paper discusses the design and implementation of an efficient QSVM model. The model's efficiency is achieved using Principal Component Analysis (PCA) on the classical data, then loading the classical data into a quantum computer. Experimental results demonstrate the significance and application of the QSVM model reported in this paper.

Shengxin Zhuang, John J. Tanner, Yusen Wu +8 more

Abstract 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 outperform-ing 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 1 task. This foundational work paves the way to verifiable quantum advantages in the field of computational biology and facilitates safer therapeutic development.