Quantum Machine Learning and Recent Advancements

dc.contributor.authorManjunath, T.D.
dc.contributor.authorBhowmik, B.
dc.date.accessioned2026-02-06T06:35:01Z
dc.date.issued2023
dc.description.abstractQuantum Computing is a fastly growing area with many applications, including quantum machine learning (QML). Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning enhances the performance in multiple ways. This paper studies different aspects of quantum machine learning. It introduces quantum computing over classical computation, followed by the recent tools and techniques developed in the area. We look at multiple QML models like quantum kernel, quantum support vector machine (QSVM), etc. Finally, we present the literature survey to encourage researchers and academicians. © 2023 IEEE.
dc.identifier.citation2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023, 2023, Vol., , p. 206-211
dc.identifier.urihttps://doi.org/10.1109/AISC56616.2023.10085586
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29609
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectClassical and Quantum Computing
dc.subjectQSVM
dc.subjectQuantum Kernel
dc.subjectQuantum Machine Learning
dc.subjectQuantum Perceptron
dc.titleQuantum Machine Learning and Recent Advancements

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