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ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS A comprehensive presentation of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more Discussions of explainable neural networks and XAI Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.
ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS A comprehensive presentation of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more Discussions of explainable neural networks and XAI Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.
This is a comprehensive description of the principles, algorithms, design technology, quantum machine learning and quantum physics in quantum computing and communications. Current work on quantum computers is motivated by the need for significantly faster and more powerful computers, and there are already in use so-called near-term quantum computers, with initial, reduced capabilities used for research and further enhancement of the technology. The outcome is distributed quantum computing which requires quantum networks to enable connections between such computers, which is a focus of this book. Moving from introductory to advanced level, this is a step-by-step approach, with numerous design examples throughout for students to work on. This is a stand-alone volume, and onward reading is available in Wireless Quantum Networks Volume 1: Intelligent Continuous Variable Technology. Topics covered in Volume 2 include: Quantum Key Distribution; Reinforcement Learning Based on Protocols; Satellite QN; Post-Quantum Cryptography; qubit Physics; Implementation Examples; and more.
Quantum Artificial Intelligence (QAI) is a new interdisciplinary research field that combines quantum computing with Artificial Intelligence (AI), aiming to use the unique properties of quantum computers to enhance the capabilities of AI systems. Quantum Artificial Intelligence with Qiskit provides a cohesive overview of the field of QAI, providing the tools for readers to create and manipulate quantum programs on devices as accessible as a laptop computer. Introducing symbolical quantum algorithms, sub-symbolical quantum algorithms, and quantum Machine Learning (ML) algorithms, this book explains each process step by step with associated Qiskit listings. All examples are additionally available for download at https://github.com/andrzejwichert/qai. Allowing readers to learn the basic concepts of quantum computing on their home computers, this book is accessible to both the general readership as well as students and instructors of courses relating to computer science and AI.
The union of quantum networks and artificial intelligence marks a pivotal moment in the trajectory of technological advancement. This encompasses data security, optimization, finance, high-precision sensors, simulations, and computer applications. Numerous quantum information and processing systems have been created and proven in labs, fields, and commercial settings during the last few decades. Quantum technologies have received considerable support for research and development from corporations and governments. However, considerable work is required to bring quantum technology-based gadgets and systems to consumers' homes. Quantum Networks and Their Applications in AI investigates the potential uses of artificial intelligence and related technologies in quantum networks and to educate the computational intelligence community about current advances in quantum information technology. The purpose of this research topic is to bring together individuals from academia and industry, from the classical and quantum artificial intelligence communities in order to discuss the theory, technology, and applications of quantum technologies, and to exchange ideas on how to efficiently advance the engineering and development of this fascinating field. Covering topics such as machine learning, management systems, and quantum networks, this book is a valuable resource for computer scientists, engineers, professionals, researchers, academicians, government officials, policy makers, and more.
Since the launch of Second-Generation Networks (2G), planning for each future mobile service was initiated many years before its commercial launch. In 2019, 5G Networks begun to be deployed commercially after almost ten years of planning. Similarly, the race for the 6G wireless networks that will be operational in 2030 has already started. To fulfill its potential in the upcoming decade, 6G will undoubtedly require an architectural orchestration based on the amalgamation of existing solutions and innovative technologies. The book will begin by evaluating the state of the art of all current mobile generations' while looking into their core building blocks. 6G implementation will require fundamental support from Artificial Intelligence (AI) and Machine Learning on the network's edge and core, including a new Radio Frequency (RF) spectrum. The 6G use cases will require advanced techniques for enabling the future wireless network to be human-centric, ensuring enhanced quality of experience (QoE) for most of its applications. The concept of Human Bond Communication Beyond 2050 (Knowledge Home) and Communication, Navigation, Sensing, and Services (CONASENSE) will also profit from future wireless communication. Terahertz domains will exploit the ultra-Massive Multiple Input Multiple Output Antennas (UM-MIMO) technologies to support Terabits' data throughputs.Moreover, optical wireless communications (OWC) will also come into play to support indoor and outdoor high-data rates. Further expansion of 6G core entities will support the novel concept of Society 5.0. Quantum computing processing and communications is also likely to be added into the 6G ecosystem with security managed by blockchain orchestration for a robust network.
This is a comprehensive description of the principles, algorithms, design technology, quantum machine learning and quantum physics in quantum computing and communications. Unifying several disciplines in the field, especially computing and communication, the book also covers computing and artificial intelligence (quantum machine learning), quantum cyber security, quantum circuit design, and relative quantum physics phenomena. Based on the latest results in the field, enabling the reader/researcher to understand these interrelations of a complex system in practice, the book moves from introductory to advanced level, step by step, providing a self-contained text. Undergraduate and postgraduate students will find several DESIGN EXAMPLES to replace the classical concept of using “problems and solutions” addendums at the end of the chapters/book. This enables offering more sophisticated assignments for the teamwork of the students. This is a stand-alone volume, and onward reading is available in Volume 2 Quantum Vs Post Quantum Security/Algorithms and Design Technology. Topics covered in Volume 1 include: Visible Light Communication; Terahertz Communications; Optical Wireless Communications; Quantum Key Distribution over THz band; Deep Neural Networks; Quantum Network Routing and Technology Design; Quantum Machine Learning; and more.
Quantum computers are unparalleled in terms of computational power, and they have a multitude of promising applications. However, these computers are prone to noise and instability caused by environmental interactions, making the use of these advanced machines rather impractical in most scenarios. Despite these challenges, Real-World Challenges in Quantum Electronics and Machine Computing provides innovative solutions to navigate the complexities of quantum computation, thus offering hope during this time of turbulence. By delving into the intricacies of quantum electronics and machine computing, this book equips readers with the tools to overcome the hurdles obstructing the path to practical quantum computing. It serves as a roadmap for students, practitioners, and professionals, guiding them through the intricacies of error correction techniques and hardware development. With its comprehensive coverage of cutting-edge topics and innovative solutions, the book empowers readers to tackle the most pressing challenges facing the quantum computing landscape. As researchers and engineers strive to unlock the full potential of quantum computation, this book stands as an indispensable resource, guiding them toward a future where quantum computing transcends the realm of theory and becomes a tangible reality.
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.
This book constitutes the proceedings of the 14th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2019, held in Honolulu, HI, USA, in June 2019. The 43 full and 11 short papers presented were carefully reviewed and selected from 143 submissions. The papers deal with new ideas and recent advances in computer systems, wireless networks, distributed applications, and advanced algorithms that are pushing forward the new technologies for better information sharing, computer communication, and universal connected devices in various environments, especially in wireless networks.