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karthikeyan s quantum machine learning modern approach 2025

Type:: E-books
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Textbook in PDF format 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. In summary, the book: Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects. 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. PART I Introduction to Statistical and Quantum Learning Fundamentals of Statistics Fundamentals of Quantum Machines PART II Introduction to Quantum Machine Learning Machine Learning with Supervised Quantum Models Machine Learning with Unsupervised Quantum Models Artificial Neural Networks PART III Quantum Models Quantum Information Science: Bridging the Gap between the Classical and Quantum Worlds Quantum Machine Learning Approaches Quantum Classification Boosting in QML PART IV Quantum Evaluation Models Deep Quantum Learning Ensembles and QBoost Quantum Process Tomography and Regression

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Karthikeyan S. Quantum Machine Learning. A Modern Approach 2025.pdf 15.9 MB

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karthikeyan s quantum machine learning modern approach 2025

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