A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)
Introduction In the realm of machine learning, class imbalance remains a significant challenge, often leading to biased models and poor predictive performance. Addressing this issue, a novel solution has been proposed in the form of Quantum-SMOTE, a quantum computing approach inspired by the traditional Synthetic Minority Oversampling Technique (SMOTE). Quantum-SMOTE: Bridging Quantum Computing and Machine Learning Quantum-SMOTE leverages quantum computing techniques to generate synthetic data points, mitigating the problem of class imbalance in datasets. Unlike conventional SMOTE, which relies on K-Nearest Neighbors (KNN) and Euclidean distances to create synthetic instances, Quantum-SMOTE employs quantum processes such as swap tests and quantum rotation. This method enables the generation of synthetic instances from minority class data points without depending on neighbour proximity. Key Features and Benefits Hyperparameter Control : Quantum-SMOTE introduces several hyperp...