  Digital Annealer (DA) is a computer architecture designed for tackling
combinatorial optimization problems formulated as quadratic unconstrained
binary optimization (QUBO) models. In this paper, we present the results of an
extensive computational study to evaluate the performance of DA in a systematic
way in comparison to multiple state-of-the-art solvers for different problem
classes. We examine pure QUBO models, as well as QUBO reformulations of three
constrained problems, namely quadratic assignment, quadratic cycle partition,
and selective graph coloring, with the last two being new applications for DA.
For the selective graph coloring problem, we also present a size reduction
heuristic that significantly increases the number of eligible instances for DA.
Our experimental results show that despite being in its development stage, DA
can provide high-quality solutions quickly and in that regard rivals the state
of the art, particularly for large instances. Moreover, as opposed to
established solvers, within its limit on the number of decision variables, DA's
solution times are not affected by the increase in instance size. These
findings illustrate that DA has the potential to become a successful technology
in tackling combinatorial optimization problems.
