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 imple-
mentation, 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
b
2
combinations is represented at any time. By contrast, in a quantum register associated with b
qubits, the composit e quantum state may be found in a superposition of all b
2
values simultane-
ously. Therefore, applying a quantum operation to the quantum register would result in altering
all b
2
values at the same time. This repres ents the parallel processing capability of quantum
computing.
In addition to superior computing capabilities, multiple quantum algorithms have been pro-
posed, 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 algo-
rithms 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 (RL) methods for QML is
presented in this segment of the book.
Currently, 5G networks have entered into the commercialization phase, which makes it appro-
priate to launch a strong effort to conceptualize the future vision of the next generation of wireless
networks. The increasing size, complexi ty, services, and performance demands of communication
networks necessitate planning and consultation for envisioning new technologies to enable and
harmonize future heterogeneous networks. An overwhelming interest in AI methods is seen in
recent years, which has motivated the provision of essential intelligence to 5G networks. Ho wever,
this provision is limited to the performance of different isolated tasks of optimization, control, and
management. The recent success of quantum-assisted and data-driven learning methods in com-
munication networks has led to their candidature as enablers of future heterogeneous networks.
This section reviews a novel framework for 6G/7G networks, where quantum-assisted ML and
QML are proposed as the core enablers along with some promising communication technology
innovations.
The relevance of the research fields integrated throughout this book can be easily recognized
within the National Science Foundation (NSF) list of research priorities in science and technology:
These 10 areas specified by NSF include (i) AI and ML; (ii) high performance computing, semicon-
ductors, and advanced computer hardware; (iii) quantum computing and information systems;
(iv) robotics, automation, and adva nced manufacturing; (v) natural or anthropogenic disaster pre-
vention; (vi) advanced communications technology; (vii) biotechnology, genomics, and synthetic
biology; (viii) cybersecurity, data storag e, and data management technologies; (ix) advanced
4 1 Introduction
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