Support Vector Machine (SVM) is a machine learning technique that seeks a hyperplane in a high-dimensional space that optimally divides data points into distinct classes. SVM is very good at dealing with complex data and obtaining high accuracy in binary and multiclass classification problems. It maximizes class separation using a mathematical notion known as “margin.” Additionally, it can handle non-linearly separable data by transferring it into a higher-dimensional space using kernel functions. SVM has been extensively used in many fields, including image classification, text classification, bioinformatics, and finance [1-7].
Quantum Support Vector Machine(QSVM) is a quantum variant of the SVM method widely used in classification and regression problems. Data are represented in a high-dimensional feature space in conventional SVM, and a hyperplane is discovered to distinguish distinct classes of data points. QSVM's quantum version employs quantum techniques to accelerate some calculations, such as the quantum kernel trick. When dealing with large-scale data or jobs involving quantum data, QSVM may provide benefits over traditional SVM [8-13].
This section will discuss the "Online Fraud" Dataset used for the Quantum Support Vector Machine (QSVM) model and the preprocessing techniques used to clean and transform the data in the dataset.
Learning objectives: after completing this module, students will be able to
(i) describe the Quantum Support Vector Machine (QSVM) and its applications
(ii) learn to preprocess the "Online Fraud" Dataset for QSVM
(iii) apply the knowledge learned in this module to analyze more data sets using QSVM
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