• Teja Reddy Gatla

This paper outlines an extensive analysis of an emerging concept of QML and provides a panoramic view of its disruptive power within machine learning. The quantum computing bracket has been a new and prevalent metaphor for a series of fields, such as machine learning, that will be transformed and revolutionized. The paper will perform a research analysis that examines algorithms' capability in quantum computing via machine learning as the primary focus [1]. The fact that quantum mechanics is the principle that quantum machine learning algorithms use is such that they could provide a faster process based on the exponent of the problem being solved. A quantitative machine learning (QML) algorithm review and analysis are performed in a literature review and analysis, exploring the current status, capabilities, constraints, and possible ramifications of the technology across sectors. The conventional algorithms can no longer keep pace with a growing rate of data complexity, which causes asymptotic growth in the number of calculations needed for processing. To cope with those difficulties, this research is designed to provide a thorough investigation capable of using quantum algorithms in dealing with massive datasets and complex optimization challenges [1]. The classical and quantum machine learning paradigms will be compared in our study. Since quantum computing has many advantages, the issues that will come up with its integration into machine learning will be highlighted. Besides, the path this research hopes to find for utilizing quantum calculations to help the current data-reliant applications become compatible with the system will be crystal clear. A deep analysis of QML and its capabilities will reveal insights into effecting change and the future of machine learning [1]. By clarifying the unprecedented collaboration between quantum principles and machine learning algorithms, our research aims to pave the way for advancements in data analysis, predictive modeling, and optimization in multidisciplinary domains and ultimately enhance innovation. By analyzing QML as a whole, its innovative techniques, and the barriers that lay on the path, we strive to help researchers and practitioners exploit the unique properties of quantum computing to reach the potential of machine learning and data analytics.

Machine Learning, Quantum Computing, Artificial Intelligence, Innovation, Decentralized devices, Secure aggregation, Data mining, Performance, Speed, Scalability

"AN EXPLORATIVE STUDY INTO QUANTUM MACHINE LEARNING: ANALYZING THE POWER OF ALGORITHMS IN QUANTUM COMPUTING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 1, page no.175-178, January-2018, Available :http://www.jetir.org/papers/JETIR1801346.pdf

"AN EXPLORATIVE STUDY INTO QUANTUM MACHINE LEARNING: ANALYZING THE POWER OF ALGORITHMS IN QUANTUM COMPUTING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 1, page no. pp175-178, January-2018, Available at : http://www.jetir.org/papers/JETIR1801346.pdf