Quantum Computing-Based Feature Selection for Machine Learning

This technical report presents a novel approach to feature selection in high-dimensional machine learning contexts, leveraging quantum computing techniques. The document outlines the significance of feature selection in enhancing the performance of machine learning models, particularly in scenarios involving large datasets with complex relationships among features. It details the challenges faced in traditional feature selection methods, such as class imbalance, dataset shift, incremental learning, and the presence of noisy data. The report introduces a quantum feature selection methodology, comparing its effectiveness against classical techniques through experiments conducted on datasets related to malicious software detection and Parkinson’s disease detection. The findings demonstrate the potential of quantum computing to optimize feature selection processes, thereby improving model accuracy and generalization. The report concludes with a discussion of next steps for further research and application of the proposed methods in real-world machine learning scenarios.