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Quantum computing and quantum machine learning classification – a survey
Book chapter   Peer reviewed

Quantum computing and quantum machine learning classification – a survey

P Kuppusamy, N. Yaswanth Kumar, Jyotsna Dontireddy and Celestine Iwendi
2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), pp.200-204
2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE
2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), conference (Goa, India, 08/10/2022–09/10/2022)
22/12/2022

Abstract

quantum computing quantum machine learning quantum neural networks entanglement superposition quantum encoding single qubit gates multi-qubit gates Technology
The rapid development of machine learning technology leads to make the devices in the industries working autonomously. However, the growth of sensors in the industries leads to produce vast data that is utilized by machine learning algorithms to improve the autonomous devices’ performances. However, classical ML algorithms and hardware systems cannot process large data to meet real-time problems. Hence, the researchers have developed Quantum Computing hardware systems and Quantum Machine learning algorithms to speed up the process. This research work presented the review of quantum computing mechanisms and QML algorithms that are applied to classify the images. This work demonstrated the performance comparison of various QML algorithms. It showed that the images are classified using various QML algorithms faster than classical ML algorithms in terms of time.
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