Analytical Challenges of Quantum and Classical Computing in Thailand: A Comparative Exploration of Machine Learning Through Classification

Abstract

This research explores the integration of classical and quantum machine learning (QML) for classification tasks, focusing on applications in Thailand. We evaluate QML algorithms, including Variational Quantum Classifier (VQC), Quantum Support Vector Classifier (QSVC), and Quantum Neural Network (QNN), against the classical Support Vector Machine (SVM) using the Iris and Wine datasets on IBM's Qiskit platform. Our analysis highlights the trade-offs between accuracy and preprocessing time for quantum encoding, noting that while some QML algorithms achieve high accuracy, their preprocessing times are significantly longer than those of classical SVM. This underscores current limitations in QML for practical classification tasks. However, the study also identifies future opportunities for QML applications in sectors like healthcare, materials science, logistics, and financial modeling in Thailand. This research contributes to the field by providing a comparative analysis of QML and classical algorithms, revealing that QML, despite its high accuracy, is often hindered by lengthy preprocessing. It also emphasizes QML's potential to address specific technological challenges in Thailand.