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Overview: This book presents the research into and application of Machine Learning in quantum computation, known as Quantum Machine Learning (QML). It presents a comparison of Quantum Machine Learning, classical Machine Learning, and traditional programming, along with the usage of quantum computing, toward improving traditional Machine Learning algorithms through case studies. Machine Learning (ML) with supervised quantum models is a cutting-edge field that combines the power of ML algorithms with the potential of quantum computing. This approach aims to leverage the unique properties of quantum systems to enhance the performance of supervised learning tasks. In this paradigm, quantum models are utilized as the underlying framework for data processing and analysis. By harnessing the principles of superposition and entanglement, these models can handle complex computations more efficiently than classical counterparts. This opens up new possibilities for solving intricate problems in various domains, such as optimization, pattern recognition, and data classification. Quantum computers offer the potential for exponential speedup in certain computations compared to classical counterparts. Quantum machine learning algorithms aim to harness this speedup to perform computations more efficiently, especially for problems with large datasets or complex feature spaces. This reference text is primarily written for scholars and researchers working in the fields of Computer Science and engineering, information technology, electrical engineering, and electronics and communication engineering.
Genre: Non-Fiction > Tech & Devices
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