Exploring the Fusion of Quantum Computing and Machine Learning

Exploring the Fusion of Quantum Computing and Machine Learning

Minu R.I., Nagarajan G., Martin Margala, Siva Shankar S., Logashanmugam E.
Pages: 300
DOI: 10.4018/979-8-3693-6225-9
ISBN13: 9798369362259|ISBN13 Softcover: 9798369362266|EISBN13: 9798369362273
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Description & Coverage
Description:

"Exploring the Fusion of Quantum Computing and Machine Learning" is a pioneering publication that intricately explores the revolutionary fusion of quantum computing and machine learning. The book navigates through the fundamentals of quantum computing, elucidating the unparalleled capabilities offered by quantum bits (qubits) and delves into the intricacies of quantum algorithms. The primary focus is on practical applications, demonstrating how the integration of quantum computing and machine learning algorithms can unravel new solutions for complex problems, paving the way for unprecedented advancements in various fields.

This publication is poised to have a profound impact on the research community by providing a comprehensive synthesis of the latest developments at the intersection of quantum computing and machine learning. Researchers will benefit from a detailed exploration of quantum algorithms, their potential applications, and the transformative possibilities they introduce. The book aims to catalyze further innovation and collaboration within the research community, fostering a deeper understanding of the synergies between quantum computing and machine learning. As quantum technologies continue to evolve, this publication seeks to serve as a foundational resource, inspiring new avenues of research and exploration.

Intended Audience: This book is designed to cater to a diverse audience, including but not limited to: Researchers and Academics: Providing a comprehensive overview of the latest advancements in quantum computing and machine learning, the book aims to be a valuable resource for researchers and academics looking to deepen their understanding of this interdisciplinary field. Industry Professionals: Professionals in the technology, finance, healthcare, and artificial intelligence sectors will find practical insights into how quantum computing and machine learning integration can impact and transform their respective industries. Tech Enthusiasts: The publication is crafted to be accessible to tech enthusiasts with a keen interest in emerging technologies, offering an engaging exploration of the exciting possibilities at the forefront of quantum computing and machine learning. Students: Whether pursuing undergraduate or graduate studies, students in computer science, physics, or related disciplines can use "Exploring the Fusion of Quantum Computing and Machine Learning" as a foundational text to explore the intersection of quantum computing and machine learning, gaining insights into the future of computational technologies. In essence, this book aims to be a versatile and informative resource for anyone intrigued by the convergence of quantum computing and machine learning, contributing to the dissemination of knowledge in this transformative field.

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Editor/Author Biographies

Prof. Martin Margala, PhD joined the School of Computing and Informatics as Professor and Director in August 2021. Before joining UL Lafayette, from September 2011 to July 2021, Dr. Margala was Professor and Chair of the Electrical and Computer Engineering Department at the University of Massachusetts Lowell and a Co-Director of the Center for Smart Cyber-Physical Systems (SCyPS). He received his PhD degree in Electrical and Computer Engineering from the University of Alberta, Canada (#61 in Global Ranking in North America region; #13 in Global subject specific ranking Electrical and Electronic Engineering in North America region USNews) in the spring of 1998. He is a senior member of ACM, IEEE, and SPIE with more than 50 journal and 200 peer reviewed conference publications in the areas of Design for Testability for Energy Efficient Architectures and Systems, High-Performance Reliable Low-Power Architectures and Reconfigurable Secure Architectures and Systems. Dr. Margala has directed 22 PhD students and 19 MS students, many of whom now hold leading positions in academia and industry. He has served on numerous program committees of international conferences and on workgroups (such as the International Technology Roadmap for Semiconductors) that have a great impact on the future direction of academia and industry.

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