Unlocking quantum SVM potential: optimal feature map generation and feature selection

Abstract

The study proposes a mechanism to generate effective feature maps with optimal feature selection using the Tabu Search algorithm. It compares the performance of classical support vector machines (SVM), quantum support vector machines (QSVM) with only gate selection, and QSVM with both gate selection and feature selection (QSVM-FS) across various datasets. The results indicate that classical SVMs excel with several benchmark datasets, while QSVMs show superior performance on synthetic datasets with non-linear separability. Notably, QSVM-FS consistently outperforms QSVM without feature selection, highlighting the importance of feature selection in enhancing model accuracy. These findings suggest that while both quantum and classical SVMs have unique advantages, quantum methods offer particular benefits in specific scenarios. In the NISQ era, classical simulations are a primary tool for assessing quantum experiments, though they face challenges such as design impacts, limited scales, and biases. Ultimately, no definitive winner exists between quantum and classical methods, as both have their own strengths.