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The efficient resolution of optimization problems is one of the key issues intoday's industry. This task relies mainly on classical algorithms that presentscalability problems and processing limitations. Quantum computing has emergedto challenge these types of problems. In this paper, we focus on theMetropolis-Hastings quantum algorithm that is based on quantum walks. We usethis algorithm to build a quantum software tool called Quantum MetropolisSolver (QMS). We validate QMS with the N-Queen problem to show a potentialquantum advantage in an example that can be easily extrapolated to anArtificial Intelligence domain. We carry out different simulations to validatethe performance of QMS and its configuration.

Quantum Metropolis Solver: A Quantum Walks Approach to Optimization Problems