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  Ising machines: Hardware solvers for combinatorial optimization problems

Mohseni, N., McMahon, P., & Byrnes, T. (2022). Ising machines: Hardware solvers for combinatorial optimization problems. Nature Reviews Physics, 4, 363-379. doi:10.1038/S42254-022-00440-8.

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 Creators:
Mohseni, Naeimeh1, 2, 3, Author
McMahon, Peter4, Author
Byrnes, Tim1, 5, 6, 7, 8, Author
Affiliations:
1State Key Laboratory of Precision Spectroscopy, School of Physical and Material Sciences, East China Normal University , Shanghai 200062, China, ou_persistent22              
2Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society, Staudtstraße 2, 91058 Erlangen, DE, ou_2421700              
3Department of Physics, University of Erlangen-Nürnberg , Staudtstr. 5, 91058 Erlangen, DE, ou_persistent22              
4School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA, ou_persistent22              
5New York University Shanghai, 1555 Century Ave, Pudong, Shanghai 200122, China, ou_persistent22              
6NYU-ECNU Institute of Physics at NYU Shanghai, Shanghai 200062, China, ou_persistent22              
7National Institute of Information and Communications Technology, Tokyo 184-8795, Japan, ou_persistent22              
8Department of Physics, New York University, New York, NY 10003, USA, ou_persistent22              

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 Abstract: Ising machines are hardware solvers which aim to find the absolute or approximate ground states of the Ising model. The Ising model is of fundamental computational interest because it is possible to formulate any problem in the complexity class NP as an Ising problem with only polynomial overhead. A scalable Ising machine that outperforms existing standard digital computers could have a huge impact for practical applications for a wide variety of optimization problems. In this review, we survey the current status of various approaches to constructing Ising machines and explain their underlying operational principles. The types of Ising machines considered here include classical thermal annealers based on technologies such as
spintronics, optics, memristors, and digital hardware accelerators; dynamical-systems solvers implemented with optics and electronics; and superconducting-circuit quantum annealers. We compare and contrast their performance using standard metrics such as the ground-state success probability and time-to-solution, give their scaling relations with problem size, and
discuss their strengths and weaknesses.

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Language(s): eng - English
 Dates: 2022-05-04
 Publication Status: Published online
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 Identifiers: DOI: 10.1038/S42254-022-00440-8
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Title: Nature Reviews Physics
  Abbreviation : Nat. Rev. Phys.
Source Genre: Journal
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Publ. Info: London, UK : Nature Research
Pages: - Volume / Issue: 4 Sequence Number: - Start / End Page: 363 - 379 Identifier: ISSN: 2522-5820
CoNE: https://pure.mpg.de/cone/journals/resource/2522-5820