Resumen:
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[EN] Population annealing, one of the currently state-of-the-art algorithms for solving spin -glass systems, sometimes finds hard disorder instances for which its equilibration quality at each temperature step is severely ...[+]
[EN] Population annealing, one of the currently state-of-the-art algorithms for solving spin -glass systems, sometimes finds hard disorder instances for which its equilibration quality at each temperature step is severely damaged. In such cases, therefore, one cannot be sure about having reached the true ground state without vastly increasing the computational resources. In this work, we seek to overcome this problem by proposing a quantum -inspired modification of population annealing. Here we focus on the three-dimensional random plaquette gauge model, whose ground -state energy problem seems to be much harder to solve than that of the standard spin -glass Edwards -Anderson model. In analogy to the toric code, by allowing single bond flips we let the system explore nonphysical states, effectively expanding the configurational space by introducing topological defects that are then annealed through an additional field parameter. The dynamics of these defects allow for the effective realization of nonlocal cluster moves, potentially easing the equilibration process. We study the performance of this method in three-dimensional random plaquette gauge model lattices of various sizes, and we compare it against population annealing. With that we conclude that the newly introduced nonlocal moves are able to improve the equilibration of the lattices, in some cases being superior to a normal population annealing algorithm with a computational resource investment that is 16 times higher.
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Agradecimientos:
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D.C. and P.R.G. would like to thank H. Katzgraber and J. Machta for illuminating discussion and comments that helped to improve the manuscript. D.C. acknowledges funding from Generalitat de Catalunya (AGAUR Doctorats ...[+]
D.C. and P.R.G. would like to thank H. Katzgraber and J. Machta for illuminating discussion and comments that helped to improve the manuscript. D.C. acknowledges funding from Generalitat de Catalunya (AGAUR Doctorats Industrials 2019, 2n termini) . ICFO group acknowledges support from ERC AdG NOQIA; Ministerio de Ciencia y Innovation Agencia Estatal de Investigaciones (PGC2018-097027-B-I00/10.13039/501100011033, CEX2019-000910-S/10.13039/501100011033, Plan National FIDEUA PID2019-106901GB-I00, FPI, QUANTERA MAQS PCI2019-111828-2, QUANTERA DYNAMITE PCI2022-132919, Proyectos de I + D + I "Retos Colabo-racion" QUSPIN RTC2019-007196-7) ; MICIIN with funding from European Union NextGenerationEU (PRTR-C17.I1) and by Generalitat de Catalunya; Fundacio Cellex; Fundacio Mir-Puig; Generalitat de Catalunya (European Social Fund FEDER and CERCA program, AGAUR Grant No. 2021 SGR 01452, QuantumCAT U16-011424, cofunded by ERDF Operational Program of Catalonia 2014-2020) ; Barcelona Supercomputing Center MareNostrum (FI-2023-1-0013) ; EU (PASQuanS2.1, 101113690) ; EU Horizon 2020 FET-OPEN OPTOlogic (Grant No. 899794) ; EU Horizon Europe Program (Grant Agreement No. 101080086-NeQST) , National Science Centre, Poland (Symfonia Grant No. 2016/20/W/ST4/00314) ; ICFO Internal "QuantumGaudi" project; European Union's Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie Grant Agreement No. 101029393 (STREDCH) and No. 847648 ("La Caixa" Junior Leaders fellowships ID 100010434: LCF/BQ/PI19/11690013, LCF/BQ/PI20/11760031, LCF/BQ/PR20/11770012, LCF/BQ/PR21/11840013) . Views and opinions expressed are, however, those of the author (s) only and do not necessarily reflect those of the European Union, European Commission, European Climate, Infrastructure and Environment Executive Agency (CINEA) , nor any other granting authority. Neither the European Union nor any granting authority can be held responsible for them. M.& Aacute;.G.-M. acknowledges funding from the Spanish Ministry of Education and Professional Training (MEFP) through the Beatriz Galindo program 2018 (BEAGAL18/00203) , QuantERA II Cofund 2021 PCI2022-133004, Projects of MCIN with funding from European Union NextGenerationEU (PRTR-C17.I1) and by Generalitat Valenciana, with Ref. 20220883 (PerovsQuTe) and COMCUANTICA/007 (QuanTwin) , and Red Tematica RED2022-134391-T.
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