000061617 001__ 61617 000061617 005__ 20190709135445.0 000061617 0247_ $$2doi$$a10.1038/s41598-017-01096-6 000061617 0248_ $$2sideral$$a98932 000061617 037__ $$aART-2017-98932 000061617 041__ $$aeng 000061617 100__ $$aZhang, B.H. 000061617 245__ $$aAdvantages of Unfair Quantum Ground-State Sampling 000061617 260__ $$c2017 000061617 5060_ $$aAccess copy available to the general public$$fUnrestricted 000061617 5203_ $$aThe debate around the potential superiority of quantum annealers over their classical counterparts has been ongoing since the inception of the field. Recent technological breakthroughs, which have led to the manufacture of experimental prototypes of quantum annealing optimizers with sizes approaching the practical regime, have reignited this discussion. However, the demonstration of quantum annealing speedups remains to this day an elusive albeit coveted goal. We examine the power of quantum annealers to provide a different type of quantum enhancement of practical relevance, namely, their ability to serve as useful samplers from the ground-state manifolds of combinatorial optimization problems. We study, both numerically by simulating stoquastic and non-stoquastic quantum annealing processes, and experimentally, using a prototypical quantum annealing processor, the ability of quantum annealers to sample the ground-states of spin glasses differently than thermal samplers. We demonstrate that (i) quantum annealers sample the ground-state manifolds of spin glasses very differently than thermal optimizers (ii) the nature of the quantum fluctuations driving the annealing process has a decisive effect on the final distribution, and (iii) the experimental quantum annealer samples ground-state manifolds significantly differently than thermal and ideal quantum annealers. We illustrate how quantum annealers may serve as powerful tools when complementing standard sampling algorithms. 000061617 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000061617 590__ $$a4.122$$b2017 000061617 591__ $$aMULTIDISCIPLINARY SCIENCES$$b12 / 64 = 0.188$$c2017$$dQ1$$eT1 000061617 592__ $$a1.533$$b2017 000061617 593__ $$aMultidisciplinary$$c2017$$dQ1 000061617 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000061617 700__ $$aWagenbreth, G. 000061617 700__ $$0(orcid)0000-0002-3376-0327$$aMartin-Mayor, V. 000061617 700__ $$aHen, I. 000061617 773__ $$g7, 1 (2017), 1044 [12 pp]$$pSci. rep.$$tScientific reports$$x2045-2322 000061617 8564_ $$s2295904$$uhttps://zaguan.unizar.es/record/61617/files/texto_completo.pdf$$yVersión publicada 000061617 8564_ $$s115425$$uhttps://zaguan.unizar.es/record/61617/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000061617 909CO $$ooai:zaguan.unizar.es:61617$$particulos$$pdriver 000061617 951__ $$a2019-07-09-11:38:07 000061617 980__ $$aARTICLE