Quantum Machine Learning and Recent Advancements
| dc.contributor.author | Manjunath, T.D. | |
| dc.contributor.author | Bhowmik, B. | |
| dc.date.accessioned | 2026-02-06T06:35:01Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Quantum 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.citation | 2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023, 2023, Vol., , p. 206-211 | |
| dc.identifier.uri | https://doi.org/10.1109/AISC56616.2023.10085586 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/29609 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | Classical and Quantum Computing | |
| dc.subject | QSVM | |
| dc.subject | Quantum Kernel | |
| dc.subject | Quantum Machine Learning | |
| dc.subject | Quantum Perceptron | |
| dc.title | Quantum Machine Learning and Recent Advancements |
