RT Journal Article
T1 Quantum Metropolis Solver: a quantum walks approach to optimization problems
A1 Campos, Roberto
A1 Moreno Casares, Pablo Antonio
A1 Martín-Delgado Alcántara, Miguel Ángel
AB The efficient resolution of optimization problems is one of the key issues in today’s industry. This task relies mainly on classical algorithms that present scalability problems and processing limitations. Quantum computing has emerged to challenge these types of problems. In this paper, we focus on the Metropolis-Hastings quantum algorithm, which is based on quantum walks. We use this algorithm to build a quantum software tool called Quantum Metropolis Solver (QMS). We validate QMS with the N-Queen problem to show a potential quantum advantage in an example that can be easily extrapolated to an Artificial Intelligence domain. We carry out different simulations to validate the performance of QMS and its configuration.
PB Springer Nature
SN 2524-4906
YR 2023
FD 2023
LK https://hdl.handle.net/20.500.14352/100536
UL https://hdl.handle.net/20.500.14352/100536
LA eng
NO Campos, R., Casares, P.A.M. & Martin-Delgado, M.A. Quantum Metropolis Solver: a quantum walks approach to optimization problems. Quantum Mach. Intell. 5, 28 (2023). https://doi.org/10.1007/s42484-023-00119-y
NO Ministerio de Asuntos Económicos y Transformación Digital (España)
NO Comunidad de Madrid.
NO European Commission
NO U.S. Army Research Office
NO Ministerio de Educación, Cultura y Deporte (España)
DS Docta Complutense
RD 21 dic 2025