I. J. of Electrical & Electronics Research
Research Article | 
Performance Analysis of Quantum Classifier on Benchmarking Datasets
Author(s) : Tarun Kumar1, Dilip Kumar2 and Gurmohan Singh3
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 2, Special Issue on IEEE-SD
Publisher : FOREX Publication
Published : 30 June 2022
e-ISSN : 2347-470X
Page(s) : 375-380
Abstract
Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models. The kernel based QSVM models for the classification of datasets when run on IBMQ_QASM_simulator appeared to be 232, 207 and 186 times faster than the SVM based classification model. The results indicate that quantum computers/algorithms deliver quantum speed-up
Keywords: QML, Iris species, pumpkin seeds, QSVM, SVM, quantum computing
Tarun Kumar, PhD scholar, Department of ECE, SLIET, Longowal, Punjab, India; Email: tarunkumar2992@gmail.com
Dilip Kumar, Professor, Department of ECE, SLIET, Longowal, Punjab, India; Email: dilip.k78@gmail.com
Gurmohan Singh, Joint director, CSTD, Centre for Development of Advanced Computing (C-DAC), Mohali, India; Email: gurmohan@cdac.in
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Tarun Kumar, Dilip Kumar and Gurmohan Singh (2022), Performance Analysis of Quantum Classifier on Benchmarking Datasets. IJEER 10(2), 375-380. DOI: 10.37391/IJEER.100252.