Artificial Intelligence and Quantum Computing for Advanced Wirel...
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SAVO GLISIC
BEATRIZ LORENZO
GLISIC
LORENZO
ARTIFICIAL INTELLIGENCE AND
QUANTUM COMPUTING FOR
ADVANCED WIRELESS NETWORKS
ARTIFICIAL INTELLIGENCE AND
QUANTUM COMPUTING FOR
ADVANCED WIRELESS NETWORKS
www.wiley.com
Cover Design: Wiley
Cover Images: © AF-studio/Getty Images; Courtesy of
Savo Glisic; © Yuichiro Chino/Moment/Getty Images
49.6 mm 178 x 254 mm
A practical overview of the implementation of articial intelligence and quantum
computing technology in large-scale communication networks
Increasingly dense and exible wireless networks require the use of articial intelligence (AI) for planning
network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to
predict trafc and network state in order to reserve resources for smooth communication with high reliability
and low latency.
In Articial 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
specic applications in wireless networks, like channel, network state, and trafc prediction. Additional chapters
include Fundamentals of ML, Articial 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 benet 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 articial 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, Articial 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.
Savo G. Glisic is Research Professor at Worcester Polytechnic Institute, Massachusetts, USA. His research
interests include network optimization theory, network topology control and graph theory, cognitive networks,
game theory, articial intelligence, and quantum computing technology.
Beatriz Lorenzo is Assistant Professor in the Department of Electrical and Computer Engineering at the
University of Massachusetts Amherst, USA. Her research interests include the areas of communication
networks, wireless networks, and mobile computing.
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