Abstract            Volume:10  Issue-7  Year-2022         Original Research Articles

Online ISSN : 2347 - 3215 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcret@gmail.com

The integration of quantum computing with machine learning (ML) is a rapidly emerging field that holds the potential to revolutionize data processing and optimization tasks. Traditional machine learning algorithms often face limitations when dealing with large-scale datasets or complex optimization problems. Quantum computing, leveraging the principles of superposition and entanglement, offers a promising approach to accelerate these tasks by enhancing the efficiency of data processing and improving the performance of optimization algorithms. This paper explores the convergence of quantum computing and machine learning, examining how quantum algorithms can be utilized to enhance classification, clustering, and optimization tasks. We discuss the implementation of quantum-enhanced models, such as Quantum Support Vector Machines and Quantum Neural Networks, and explore hybrid quantum-classical models that combine quantum processing with classical machine learning techniques. While promising, the integration of quantum computing into machine learning faces challenges, including hardware limitations and algorithmic scalability. This paper also addresses these challenges and highlights the potential applications of quantum machine learning in fields such as drug discovery, finance, and artificial intelligence. Our research emphasizes the importance of continued exploration in this area and provides insights into the future directions of quantum machine learning.

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