  The optimization of the power consumption of antenna networks is a problem
with a potential impact in the field of telecommunications. In this work, we
investigate the application of the quantum approximate optimization algorithm
(QAOA) and the quantum adiabatic algorithm (QAA), to the solution of a
prototypical model in this field. We use statevector emulation in a
high-performance computing environment to compare the performance of these two
algorithms in terms of solution quality, using selected evaluation metrics. We
estimate the circuit depth scaling with the problem size while maintaining a
certain level of solution quality, and we extend our analysis up to 31 qubits,
which is rarely addressed in the literature. Our calculations show that as the
problem size increases, the probability of measuring the exact solution
decreases exponentially for both algorithms. This issue is particularly severe
when we include constraints in the problem, resulting in full connectivity
between the sites. Nonetheless, we observe that the cumulative probability of
measuring solutions close to the optimal one remains high also for the largest
instances considered in this work. Our findings keep the way open to the
application of these algorithms, or variants thereof, to generate suboptimal
solutions at scales relevant to industrial use-cases.
