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Efficient optimization with higher-order ising machines
University of California, USA.
RISE Research Institutes of Sweden, Digitala system, Datavetenskap. University of California, USA.ORCID-id: 0000-0002-6032-6155
Intel, USA.
University of California, USA; Intel, USA.
Vise andre og tillknytning
2023 (engelsk)Inngår i: Nature Communications, E-ISSN 2041-1723, Vol. 14, artikkel-id 6033Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Boolean k-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines. 

sted, utgiver, år, opplag, sider
Nature Research , 2023. Vol. 14, artikkel-id 6033
Emneord [en]
metal oxide, benchmarking; data set; hardware; optimization, Article; data availability; decomposition; machine; model; process optimization; satisfaction; simulated annealing
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-67762DOI: 10.1038/s41467-023-41214-9Scopus ID: 2-s2.0-85172783609OAI: oai:DiVA.org:ri-67762DiVA, id: diva2:1815666
Merknad

C.B. acknowledges support from the National Science Foundation (NSF) through an NSF Graduate Research Fellowships Program (GRFP) fellowship (DGE 1752814) and an Intel Corporation research grant. D.K. acknowledges support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 839179. F.T.S. was supported by NSF Grant IIS1718991 and NIH Grant 1R01EB026955. B.A.O. was supported by NSF EAGER grant 2147640.

Tilgjengelig fra: 2023-11-29 Laget: 2023-11-29 Sist oppdatert: 2023-12-12bibliografisk kontrollert

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