title: Parameter tuning patterns for random graph coloring with quantum annealing.
abstract: Quantum annealing is a combinatorial optimization technique inspired by quantum mechanics. Here we show that a spin model for the k-coloring of large dense random graphs can be field tuned so that its acceptance ratio diverges during Monte Carlo quantum annealing, until a ground state is reached. We also find that simulations exhibiting such a diverging acceptance ratio are generally more effective than those tuned to the more conventional pattern of a declining and/or stagnating acceptance ratio. This observation facilitates the discovery of solutions to several well-known benchmark k-coloring instances, some of which have been open for almost two decades.
authors.affiliation: School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, United Kingdom.
authors.forename: Olawale
authors.initials: O
authors.lastname: Titiloye
authors.forename: Alan
authors.initials: A
authors.lastname: Crispin
mesh_terms: D000465:Algorithms; D003116:Color; D003196:Computer Graphics; D003198:Computer Simulation; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011789:Quantum Theory; D013696:Temperature