  The Quantum Approximate Optimization Algorithm (QAOA) is one of the most
promising Noisy Intermediate Quantum Algorithms (NISQ) in solving combinatorial
optimizations and displays potential over classical heuristic techniques.
Unfortunately, QAOA performance depends on the choice of parameters and
standard optimizers often fail to identify key parameters due to the complexity
and mystery of these optimization functions. In this paper, we benchmark QAOA
circuits modified with metaheuristic optimizers against classical and quantum
heuristics to identify QAOA parameters. The experimental results reveal
insights into the strengths and limitations of both Quantum Annealing and
metaheuristic-integrated QAOA across different problem domains. The findings
suggest that the hybrid approach can leverage classical optimization strategies
to enhance the solution quality and convergence speed of QAOA, particularly for
problems with rugged landscapes and limited quantum resources. Furthermore, the
study provides guidelines for selecting the most appropriate approach based on
the specific characteristics of the optimization problem at hand.
