Artificial Intelligence and Quantum Computing for Advanced Wireless Networks. Savo G. Glisic
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Library of Congress Cataloging‐in‐Publication Data Applied for:
HB ISBN: 9781119790297
Cover Design: Wiley
Cover Image: © AF-studio/Getty Images; Courtesy of Savo Glisic; © Yuichiro Chino/Moment/Getty Images
Preface
At this stage, it is anticipated that 6G wireless networks will be based on massive use of machine learning (ML) and artificial intelligence (AI), while 7G will already include hybrids of classical and quantum computing (QC) technologies. In anticipation of this evolution‚ we have structured the book to continuously move, through a series of chapters, from the presentation of ML algorithms to the final chapter covering the principles of quantum internet. In this process, we also provide chapters covering the complex relationship between the two technologies, on topics such as quantum ML, quantum game theory, and quantum decision theory. The focus of the book is not on the problem how to construct a quantum computer but rather how QC technology enables new paradigms in the modeling, analysis, and design of communication networks, what is nowadays referred to as QC‐enabled communications. These new paradigms benefit from the significant computation speedup enabled by the computing parallelism of quantum computers and the new quantum search algorithms developed so far for big data processing. Quantum cryptography and quantum key distribution (QKD) enable new solutions to the problem of security in advanced networks.
This book is also designed to facilitate a new concept in education in this field. Instead of the classical approach of providing a list of problems at the end of a chapter, we introduce a series of design examples throughout the book that require teamwork by a group of students for solving complex design problems, including reproduction of the results presented in the book. This approach turned out to be rather popular with our students at University of Massachusetts at Amherst. We hope that the book provides useful material for not only students but also for researchers, educators, and regulatory professionals in this field.
The Authors
January 2021
Amherst, Massachusetts
1 Introduction
1.1 Motivation
Owing to the increase in the density and number of different functionalities in wireless networks, there is an increasing need for the use of artificial intelligence (AI) in planning the network deployment, running their optimization, and dynamically controlling their operation. Machine learning (ML) algorithms are used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency in a timely fashion. Big data mining is used to predict customer behavior and pre‐distribute (caching) the information content across the network in a timely fashion so that it can be efficiently delivered as soon as it is requested. Intelligent agents can search the Internet on behalf of the customer in order to find the best options when it comes to buying any product online. This book reviews ML‐based algorithms with a number of case studies supported by Python and R programs. It discusses the learning algorithms used in decision making based on game theory and a number of specific applications in wireless networks such as channel, network state, and traffic prediction.
We begin the book with a comprehensive survey of AI learning algorithms. These algorithms are used in the prediction of the network parameters for efficient network slicing, customer behavior for content caching across the network, or for efficient network control and management. Subsequently, we focus on network applications with an emphasis on AI‐based learning algorithms used for reaching equilibria in games used among different parties in a variety of new business models in communication networks. This includes competition between network operators, service providers, or even users in dynamic network architectures of user‐provided networks.
The book also covers in detail a number of specific applications of AI for dynamic readjusting network behavior based on the observation of its state, traffic variation, and user behavior. This includes channel and power level selection in cellular networks, network self‐organization, proactive caching, big data learning, graph neural network (GNN), and multi‐armed bandit estimators.
Why quantum computing? The ever‐reducing transistor size following Moore’s law is approaching the point where quantum effects predominate in transistor operation. This specific trend implies that quantum effects become unavoidable, hence making research on quantum computing (QC) systems an urgent necessity. In fact, a quantum annealing chipset is already commercially available from D‐Wave1.
Apart from the quantum annealing architecture, gate‐based architecture, which relies on building computational blocks using quantum gates in a similar fashion to classical logic gates, is attracting increasing attention due to the recent advances in quantum stabilizer codes, which are capable of mitigating the de‐coherence effects encountered by quantum circuits. In terms of implementation, IBM has initially produced 53-qubits quantum computer [1] and plans to have 1-million qubits by 2030 [2]. D-Wave Two 512 qubit processors [3] are built in Google and NASA quantum computer. With this recent developments, Quantum computing has become a commercial reality and it may be used in wireless communications systems in order to speed up specific processes due to its inherent parallelization capabilities.
Whereas a classical bit may adopt the values 0 or 1, a quantum bit, or qubit, may have the values |0>, |1>, or any superposition of the two, where the notation |> is the column vector of a quantum state. If two qubits are used, then the composite quantum state may have the values |00>, |01>, |10>, and |11> simultaneously. In general, by employing b bits in a classical register, one out of b2 combinations is represented at any time. By contrast, in a quantum register associated with b qubits, the composite quantum state may be found in a superposition of all b2 values simultaneously. Therefore, applying a quantum operation to the quantum register would result in altering all b2 values at the same time. This represents the parallel processing capability of quantum computing.
In addition to superior computing capabilities, multiple quantum algorithms have been proposed, which are capable of outperforming their classical counterparts in the same categories of problems, by either requiring fewer computational steps, or by finding a better solution to the specific problem. In this book, we will focus on the employment of quantum algorithms in classical communication systems, which is nowadays referred to as quantum‐assisted communications.
In the following sections, we revisit the ML methods in the context of quantum‐assisted algorithms for ML and the quantum machine learning (QML) framework. Quantum principles based on emerging computing technologies will bring in entirely new modes of information processing. An overview of supervised, unsupervised, and reinforcement learning