Analysis of Quantum SVM and KNN on Azure Quantum

Integrating quantum computing with machine learning proposes promising advancement by harnessing quantum parallelism and entanglement to enhance computational efficiency. This study investigates the implementation of a quantum support vector machine and quantum k nearest neighbor using azure quantum Ionq and Quantinuum simulators, utilizing Z, ZZ and Pauli feature maps to transform traditional data into quantum states, enabling the quantum enhanced learning. Four datasets are being carried out for this experiment: iris, wine, Indian liver patient and Rain dataset. To assess classification effectiveness, model performance is assessed using accuracy, F1 score, precision and recall. The findings thoroughly examine the influence of quantum simulators on quantum model performance, emphasizing the compromises of computational feasibility. This research contributes to the ongoing development of quantum machine learning by evaluating its practical applicability and current limitations.