Work

Parameter Tuning Patterns for Random Graph Coloring with Quantum Annealing

Year: 2012

Type: article

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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0050060

Authors: Olawale Titiloye, Alan Crispin

Institution: Manchester Metropolitan University

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,382

Funder: Manchester Metropolitan University