  This article empirically examines the computational cost of solving a known
hard problem, graph clustering, using novel purpose-built computer hardware. We
express the graph clustering problem as an intra-cluster distance or
dissimilarity minimization problem. We formulate our poblem as a quadratic
unconstrained binary optimization problem and employ a novel computer
architecture to obtain a numerical solution. Our starting point is a clustering
formulation from the literature. This formulation is then converted to a
quadratic unconstrained binary optimization formulation. Finally, we use a
novel purpose-built computer architecture to obtain numerical solutions. For
benchmarking purposes, we also compare computational performances to those
obtained using a commercial solver, Gurobi, running on conventional hardware.
Our initial results indicate the purpose-built hardware provides equivalent
solutions to the commercial solver, but in a very small fraction of the time
required.
