  Graph partitioning is a fundamental combinatorial optimization problem that
attracts a lot of attention from theoreticians and practitioners due to its
broad applications. From multilevel graph partitioning to more general-purpose
optimization solvers such as Gurobi and CPLEX, a wide range of approaches have
been developed. Limitations of these approaches are important to study in order
to break the computational optimization barriers of this problem. As we
approach the limits of Moore's law, there is now a need to explore ways of
solving such problems with special-purpose hardware such as quantum computers
or quantum-inspired accelerators. In this work, we experiment with solving the
graph partitioning on the Fujitsu Digital Annealer (a special-purpose hardware
designed for solving combinatorial optimization problems) and compare it with
the existing top solvers. We demonstrate limitations of existing solvers on
many dense graphs as well as those of the Digital Annealer on sparse graphs
which opens an avenue to hybridize these approaches.
