  Leveraging quantum computers for optimization problems holds promise across
various application domains. Nevertheless, utilizing respective quantum
computing solvers requires describing the optimization problem according to the
Quadratic Unconstrained Binary Optimization (QUBO) formalism and selecting a
proper solver for the application of interest with a reasonable setting. Both
demand significant proficiency in quantum computing, QUBO formulation, and
quantum solvers, a background that usually cannot be assumed by end users who
are domain experts rather than quantum computing specialists. While tools aid
in QUBO formulations, support for selecting the best-solving approach remains
absent. This becomes even more challenging because selecting the best solver
for a problem heavily depends on the problem itself. In this work, we are
accepting this challenge and propose a predictive selection approach, which
aids end users in this task. To this end, the solver selection task is first
formulated as a classification task that is suitable to be solved by supervised
machine learning. Based on that, we then propose strategies for adjusting
solver parameters based on problem size and characteristics. Experimental
evaluations, considering more than 500 different QUBO problems, confirm the
benefits of the proposed solution. In fact, we show that in more than 70% of
the cases, the best solver is selected, and in about 90% of the problems, a
solver in the top two, i.e., the best or its closest suboptimum, is selected.
This exploration proves the potential of machine learning in quantum solver
selection and lays the foundations for its automation, broadening access to
quantum optimization for a wider range of users.
