Performance Analysis of Quantum Classifier on Benchmarking Datasets

Quantum machine learning (QML) is an evolving field which capable of surpassing the classical in solving classification and clustering problems. The enormous growth data size started creating barrier for techniques. QML stand out as a best solution to handle big complex data. In this paper quantum support vector (QSVM) based models three benchmarking datasets namely, Iris species, Pumpkin seed Raisin has been constructed. These QSVM are implemented on real-time superconducting computers/simulators. performance these evaluated context execution time accuracy compared with (SVM) models. kernel when run IBMQ_QASM_simulator appeared be 232, 207 186 times faster than SVM model. results indicate that computers/algorithms deliver speed-up.