Quantum-enhanced classification: an empirical study of quantum support vector machines using the iris and wine datasets

Abstract Quantum computing has demonstrated a potential advantage in advancing artificial intelligence and machine learning applications. Both disciplines leverage higher-dimensional computation that relies on complex linear algebra operations. Quantum Support Vector Machines (QSVMs) uses quantum computation principles to enhance classification performance over traditional SVMs. This paper studies QSVMs applied to the Iris and Wine datasets by exploring various quantum encoding methods, including amplitude encoding, ZZFeatureMap, and PauliFeatureMap, within IBM’s Qiskit framework. These encoding methods enable QSVMs to utilize quantum phenomena like superposition and entanglement to transform classical data into quantum states to increase computational efficiency and accuracy in classification tasks. Numerical results indicate that QSVMs with amplitude encoding with combination of ZZFeatureMap achieve higher classification accuracy than traditional techniques. Also, this study assess the current performance of Noisy Intermediate-Scale Quantum (NISQ) devices to highlight the need for further optimization and advancements in quantum data encoding approches. These findings provide important insights into the application of QSVMs and their potential to address a range of classification problems effectively.