Quantum Machine Learning - Models and Algorithms: Studying quantum machine learning models and algorithms for leveraging quantum computing advantages in data analysis, pattern recognition, and optimization
Keywords:
Quantum Machine LearningAbstract
Quantum Machine Learning (QML) merges quantum computing and machine learning to potentially revolutionize data analysis, pattern recognition, and optimization. This paper explores various QML models and algorithms, highlighting their advantages and challenges. We delve into quantum algorithms like quantum annealing, quantum approximate optimization, and quantum neural networks, discussing their applications in classification, clustering, regression, and optimization tasks. Furthermore, we examine the potential of quantum-enhanced machine learning in handling large datasets and improving computational efficiency. This paper aims to provide a comprehensive overview of QML, shedding light on its future implications and challenges.
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