Computing & Internet      Computer Science

Generative AI and Quantum Computing: A Practical Guide

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Book Details
Language
English
Publishers
Independently published (26 Jun. 2024)
Weight
0.12 KG
Publication Date
26/06/2024
Pages
76 pages
ISBN-13
9798329495591
Dimensions
15.24 x 0.46 x 22.86 cm
SKU
9798329495591
Author Name
Anand Vemula (Author)
Anand Vemula is a technology, business, ESG and Risk governance Evangelist. He has more than 27 plus years of experience. Has worked in MNC at a CXO level. Has been a part of various projects and forums across customers in BFSI, Healthcare, Retail, Manufacuring, Lifesciences, Energy Industry Verticals. Certified in all the technologies and Enterprise Digital ArchitectRead more about this authorRead less about this author
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"Generative AI and Quantum Computing: A Practical Guide" provides a comprehensive and accessible introduction to two of the most transformative technologies of our time. This book bridges the gap between the theoretical foundations and practical implementations of generative AI and quantum computing, making it an essential resource for students, researchers, and professionals in the field.

Part I: Foundations starts with an overview of generative AI, exploring its applications, impact, and historical context. It then delves into the basics of quantum computing, covering fundamental concepts such as quantum mechanics, quantum bits, and quantum gates.

The section also explains various types of generative models, their mathematical foundations, and key algorithms. Part II: Quantum Computing Algorithms discusses the quantum algorithms that have the potential to accelerate AI, including Shor's Algorithm and Grover's Algorithm.

It provides detailed examples, use cases, and hands-on exercises to illustrate how these algorithms work and their practical applications. Part III: Bridging Generative AI and Quantum Computing explores the synergy between these technologies.

It covers quantum-enhanced machine learning, quantum generative models, and hybrid quantum-classical systems. The section includes real-world case studies and performance evaluations to demonstrate the benefits and challenges of integrating quantum computing with AI.

Part IV: Practical Implementation and Future Directions focuses on the practical aspects of quantum programming. It offers step-by-step implementations, practical coding examples, and explores tools and libraries such as Qiskit, Cirq, and PyQuil.

The book also discusses the ethical and social implications of quantum AI, addressing concerns such as data privacy, algorithmic bias, and the societal impact. Finally, it looks at emerging trends, long-term prospects, and the vision for the future of generative AI and quantum computing.

This guide is packed with tutorials, code examples, and hands-on exercises, making it a valuable resource for anyone looking to understand and apply these cutting-edge technologies. Whether you are a beginner or an advanced practitioner, this book will help you navigate the exciting landscape of generative AI and quantum computing.

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