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Quantum machine learning for support vector machine classification

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Abstract

Quantum machine learning aims to execute machine learning algorithms in quantum computers by utilizing powerful laws like superposition and entanglement for solving problems more efficiently. Support vector machine (SVM) is proved to be one of the most efficient classification machine learning algorithms in today’s world. Since in classical systems, as datasets become complex or mixed up, the SVM kernel approach tends to slow and might fail. Hence our research is focused to examine the execution speed and accuracy of quantum support vector machines classification compared to classical SVM classification by proper quantum feature mapping selection. As the size of the dataset becomes complex, a proper feature map has to be selected to outperform or equally perform the classification. Hence the paper focuses on the selection of the best feature map for some benchmark datasets. Additionally experimental results show that the processing time of the algorithm is considerably reduced concerning classical machine learning. For evaluation of quantum computation over the classical computer, Quantum labs from the IBMQ quantum computer cloud have been used.

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Data availability

Yes, data and material for transparency will be made available. The datasets analyzed during the current study of Wine, HCV and Electrical Grid simulated data are available in the [40, 41]

Code availability

N/A.

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Acknowledgements

Authors thank IBMQ for providing a Quantum lab to explore machine learning algorithms on cloud quantum processors.

Funding

The work presented came from the Ph.D. research undertaken at the Department of Electronics and Communication Engineering, The National Institute of Engineering, Mysuru, Karnataka, India.

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Author notes

  1. Narasimha Kaulgud has contributed equally to this work.

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, The National Institute of Engineering, Manandavadi Road, Mysuru, Karnataka, 570008, India

    S. S. Kavitha & Narasimha Kaulgud

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  1. S. S. Kavitha
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  2. Narasimha Kaulgud
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Kavitha S S and Narasimha Kaulgud.

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Correspondence to S. S. Kavitha.

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Kavitha, S.S., Kaulgud, N. Quantum machine learning for support vector machine classification. Evol. Intel. 17, 819–828 (2024). https://doi.org/10.1007/s12065-022-00756-5

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  • DOI: https://doi.org/10.1007/s12065-022-00756-5

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