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Journal Article

Ising machines: Hardware solvers for combinatorial optimization problems

MPS-Authors

Mohseni,  Naeimeh
State Key Laboratory of Precision Spectroscopy, School of Physical and Material Sciences, East China Normal University ;
Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;
Department of Physics, University of Erlangen-Nürnberg ;

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Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-000A-2E98-8
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.