  Quantum computing is an emerging field on the multidisciplinary interface
between physics, engineering, and computer science with the potential to make a
large impact on computational intelligence (CI). The aim of this paper is to
introduce quantum approximate optimization methods to the CI community because
of direct relevance to solving combinatorial problems. We introduce quantum
computing and variational quantum algorithms (VQAs). VQAs are an effective
method for the near-term implementation of quantum solutions on noisy
intermediate-scale quantum (NISQ) devices with less reliable qubits and
early-stage error correction. Then, we explain Farhi et al.'s quantum
approximate optimization algorithm (Farhi's QAOA, to prevent confusion). This
VQA is generalized by Hadfield et al. to the quantum alternating operator
ansatz (QAOA), which is a nature-inspired (particularly, adiabatic) quantum
metaheuristic for approximately solving combinatorial optimization problems on
gate-based quantum computers. We discuss connections of QAOA to relevant
domains, such as computational learning theory and genetic algorithms,
discussing current techniques and known results regarding hybrid
quantum-classical intelligence systems. We present a schematic of how QAOA is
constructed, and also discuss how CI techniques can be used to improve QAOA. We
conclude with QAOA implementations for the well-known maximum cut, maximum
bisection, and traveling salesperson problems, which can serve as templates for
CI practitioners interested in using QAOA.
