ONE-VOLUME INTRODUCTION TO QUANTUM COMPUTING Clearly explains core concepts, terminology, and techniques Covers the foundational physics, math, and information theory you need Provides hands-on practice with quantum programming The perfect beginner's guide for anyone interested in a quantum computing career Dr. Chuck Easttom brings together complete coverage of basic quantum computing concepts, terminology, and issues, along with key skills to get you started. Drawing on 30+ years as a computer science instructor, consultant, and researcher, Easttom demystifies the field's underlying technical concepts and math, shows how quantum computing systems are designed and built, explains their implications for cyber security, and previews advances in quantum-resistant cryptography. Writing clearly and simply, he introduces two of today's leading quantum programming languages, Microsoft Q# and QASM, and guides you through sample projects. Throughout, tests, projects, and review questions help you deepen and apply your knowledge. Whether you're a student, professional, or manager, this guide will prepare you for the quantum computing revolution--and expand your career options, too. Master the linear algebra and other mathematical skills you'll need Explore key physics ideas such as quantum states and uncertainty Review data structures, algorithms, and computing complexity Work with probability and set theory in quantum computing Familiarize yourself with basic quantum theory and formulae Understand quantum entanglement and quantum key distribution Discover how quantum computers are architected and built Explore several leading quantum algorithms Compare quantum and conventional asymmetric algorithms See how quantum computing might break traditional cryptography Discover several approaches to quantum-resistant cryptography Start coding with Q#, Microsoft's quantum programming language Simulate quantum gates and algorithms with QASM

Video has rich information including meta-data, visual, audio, spatial and temporal data which can be analysed to extract a variety of low and high-level features to build predictive computational models using machine-learning algorithms to discover interesting patterns, concepts, relations, and associations. This book includes a review of essential topics and discussion of emerging methods and potential applications of video data mining and analytics. It integrates areas like intelligent systems, data mining and knowledge discovery, big data analytics, machine learning, neural network, and deep learning with focus on multimodality video analytics and recent advances in research/applications. Features: Provides up-to-date coverage of the state-of-the-art techniques in intelligent video analytics. Explores important applications that require techniques from both artificial intelligence and computer vision. Describes multimodality video analytics for different applications. Examines issues related to multimodality data fusion and highlights research challenges. Integrates various techniques from video processing, data mining and machine learning which has many emerging indoors and outdoors applications of smart cameras in smart environments, smart homes, and smart cities. This book aims at researchers, professionals and graduate students in image processing, video analytics, computer science and engineering, signal processing, machine learning, and electrical engineering.

The Second Edition of Quantum Information Processing, Quantum Computing, and Quantum Error Correction: An Engineering Approach presents a self-contained introduction to all aspects of the area, teaching the essentials such as state vectors, operators, density operators, measurements, and dynamics of a quantum system. In additional to the fundamental principles of quantum computation, basic quantum gates, basic quantum algorithms, and quantum information processing, this edition has been brought fully up to date, outlining the latest research trends. These include: Key topics include: - Quantum error correction codes (QECCs), including stabilizer codes, Calderbank-Shor-Steane (CSS) codes, quantum low-density parity-check (LDPC) codes, entanglement-assisted QECCs, topological codes, and surface codes - Quantum information theory, and quantum key distribution (QKD) - Fault-tolerant information processing and fault-tolerant quantum error correction, together with a chapter on quantum machine learning. Both quantum circuits- and measurement-based quantum computational models are described - The next part of the book is spent investigating physical realizations of quantum computers, encoders and decoders; including photonic quantum realization, cavity quantum electrodynamics, and ion traps - In-depth analysis of the design and realization of a quantum information processing and quantum error correction circuits This fully up-to-date new edition will be of use to engineers, computer scientists, optical engineers, physicists and mathematicians. - A self-contained introduction to quantum information processing, and quantum error correction - Integrates quantum information processing, quantum computing, and quantum error correction - Describes the latest trends in the quantum information processing, quantum error correction and quantum computing - Presents the basic concepts of quantum mechanics - In-depth presentation of the design and realization of a quantum information processing and quantum error correction circuit

In a world driven by technology and data, classical computing faces limitations in tackling complex challenges like climate modeling and financial risk assessment. These barriers impede our aspirations to revolutionize industries and solve intricate real-world problems. To bridge this gap, we must embrace quantum computing. Edited by Alex Khang PH, Principles and Applications of Quantum Computing is a transformative solution to this challenge. It delves into the interdisciplinary realms of computer science, physics, and mathematics, unveiling the incredible potential of quantum computing, which outperforms supercomputers by 158 million times. This technology, rooted in quantum mechanics, offers solutions to global problems and opens new frontiers in AI, cybersecurity, finance, drug development, and more. By engaging with this book, you become a pioneer in the quantum revolution, contributing to reshaping the limits of what's achievable in our digital age.

This introduction to quantum computing from a classical programmer's perspective is meant for students and practitioners alike. Over 25 fundamental algorithms are explained with full mathematical derivations and classical code for simulation, using an open-source code base developed from the ground up in Python and C++. After presenting the basics of quantum computing, the author focuses on algorithms and the infrastructure to simulate them efficiently, beginning with quantum teleportation, superdense coding, and Deutsch-Jozsa. Coverage of advanced algorithms includes the quantum supremacy experiment, quantum Fourier transform, phase estimation, Shor's algorithm, Grover's algorithm with derivatives, quantum random walks, and the Solovay–Kitaev algorithm for gate approximation. Quantum simulation is explored with the variational quantum eigensolver, quantum approximate optimization, and the Max-Cut and Subset-Sum algorithms. The book also discusses issues around programmer productivity, quantum noise, error correction, and challenges for quantum programming languages, compilers, and tools, with a final section on compiler techniques for transpilation.

Today, computation is an essential component of every technology. However, there has not been much research on quantum computing, even though it has the capability to solve complex problems in an efficient way. Further study is required to fully understand the uses and benefits of this technology. The Handbook of Research on Quantum Computing for Smart Environments presents investigating physical realizations of quantum computers, encoders, and decoders, including photonic quantum realization, cavity quantum electrodynamics, and many more topics on Bits to Qubits. Covering key topics such as machine learning, software, quantum algorithms, and neural networks, this major reference work is ideal for engineers, computer scientists, physicists, mathematicians, researchers, academicians, scholars, practitioners, instructors, and students.

"Quantum Computing: Basic Concepts" is a beginner-friendly guide that introduces readers to the fascinating world of quantum computing. Written for absolute beginners, this book provides clear explanations of key concepts in quantum computing, such as qubits, superposition, and quantum gates, without assuming any prior knowledge of quantum mechanics or advanced mathematics. Through easy-to-understand language and practical examples, readers will gain a foundational understanding of quantum computing principles and explore the potential applications of this cutting-edge technology. Whether you're a curious novice or a seasoned enthusiast, this book offers an accessible entry point into the captivating realm of quantum computing.

Explore the transformative potential of quantum computing in stock market analysis with 'Stock Marketing: Quantum Computing Methods'. This book provides a comprehensive overview of how quantum technologies are reshaping financial strategies, offering practical insights and future implications for investors and technologists alike.

"Dive into 'Forensic: Quantum Computing Methods', exploring how quantum technologies are revolutionizing forensic science. This book covers everything from encryption to legal implications, offering a clear path through the evolving landscape of investigative techniques and data security. Perfect for researchers and practitioners alike, it's a must-read for anyone curious about the future of forensic science in the quantum age."

Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of machine learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more.