  Resource allocation of wide-area internet networks is inherently a
combinatorial optimization problem that if solved quickly, could provide near
real-time adaptive control of internet-protocol traffic ensuring increased
network efficacy and robustness, while minimizing energy requirements coming
from power-hungry transceivers. In recent works we demonstrated how such a
problem could be cast as a quadratic unconstrained binary optimization (QUBO)
problem that can be embedded onto the D-Wave AdvantageTM quantum annealer
system, demonstrating proof of principle. Our initial studies left open the
possibility for improvement of D-Wave solutions via judicious choices of system
run parameters. Here we report on our investigations for optimizing these
system parameters, and how we incorporate machine learning (ML) techniques to
further improve on the quality of solutions. In particular, we use the Hamming
distance to investigate correlations between various system-run parameters and
solution vectors. We then apply a decision tree neural network (NN) to learn
these correlations, with the goal of using the neural network to provide
further guesses to solution vectors. We successfully implement this NN in a
simple integer linear programming (ILP) example, demonstrating how the NN can
fully map out the solution space that was not captured by D-Wave. We find,
however, for the 3-node network problem the NN is not able to enhance the
quality of space of solutions.
