Fetching the paper…
Reading the bibliography…
We present a classical algorithm to find approximate solutions to instances of quadratic unconstrained binary optimisation.
E. Boros and P. L. Hammer, The max-cut problem and quadratic 0–1 optimization; polyhedral aspects, relaxations and bounds, Annals of Operations Research 33
1991
Earlier work this paper cites.
T. Kadowaki and H. Nishimori, Quantum annealing in the transverse ising model, Physical Review E 58
1998
Earlier work this paper cites.
N. Qian, On the momentum term in gradient descent learning algorithms, Neural networks 12
1999
Earlier work this paper cites.
E. Farhi, J. Goldstone, S. Gutmann, J. Lapan, A. Lundgren, and D. Preda, A quantum adiabatic evolution algorithm applied to random instances of an np-complete problem, Science 292
2001
Earlier work this paper cites.
2005
Earlier work this paper cites.
Z.-Y. Liu and H. Qiao, Gnccp—graduated nonconvexityand concavity procedure, IEEE transactions on pattern analysis and machine intelligence 36
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. A. Smolin and G. Smith, Classical signature of quantum annealing, Frontiers in physics 2
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. Lucas, Ising formulations of many np problems, Frontiers in physics 2
2014
Earlier work this paper cites.
D. Venturelli, S. Mandrà, S. Knysh, B. O’Gorman, R. Biswas, and V. Smelyanskiy, Quantum optimization of fully connected spin glasses, Physical Review X 5
2015
Earlier work this paper cites.
M. Yamaoka, C. Yoshimura, M. Hayashi, T. Okuyama, H. Aoki, and H. Mizuno, A 20k-spin ising chip to solve combinatorial optimization problems with cmos annealing, IEEE Journal of Solid-State Circuits 51
2015
Earlier work this paper cites.
W. Wang, J. Machta, and H. G. Katzgraber, Comparing monte carlo methods for finding ground states of ising spin glasses: Population annealing, simulated annealing, and parallel tempering, Physical Review E 92
2015
Earlier work this paper cites.
H. Mobahi and J. Fisher III, A theoretical analysis of optimization by gaussian continuation, in Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 29 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
T. Inagaki, Y. Haribara, K. Igarashi, T. Sonobe, S. Tamate, T. Honjo, A. Marandi, P. L. McMahon, T. Umeki, K. Enbutsu, et al. , A coherent ising machine for 2000-node optimization problems, Science 354
2016
Earlier work this paper cites.
E. Hazan, K. Y. Levy, and S. Shalev-Shwartz, On graduated optimization for stochastic non-convex problems, in International conference on machine learning (PMLR, 2016) pp. 1833–1841
2016
Earlier work this paper cites.
Y. Haribara, H. Ishikawa, S. Utsunomiya, K. Aihara, and Y. Yamamoto, Performance evaluation of coherent ising machines against classical neural networks, Quantum Science and Technology 2
2017
Earlier work this paper cites.
K. Yamamoto, W. Huang, S. Takamaeda-Yamazaki, M. Ikebe, T. Asai, and M. Motomura, A time-division multiplexing ising machine on fpgas, in Proceedings of the 8th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies (2017) pp. 1–6
2017
Cited alongside, same era.
Y. Kihara, M. Ito, T. Saito, M. Shiomura, S. Sakai, and J. Shirakashi, A new computing architecture using ising spin model implemented on fpga for solving combinatorial optimization problems, in 2017 IEEE 17th International Conference on Nanotechnology (IEEE-NANO) (IEEE, 2017) pp. 256–258
2017
Cited alongside, same era.
S. Tsukamoto, M. Takatsu, S. Matsubara, and H. Tamura, An accelerator architecture for combinatorial optimization problems, Fujitsu Sci. Tech. J 53
2017
Cited alongside, same era.
C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, and S. Wrobel, Ising models for binary clustering via adiabatic quantum computing, in International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (Springer, 2017) pp. 3–17
C. Papalitsas, T. Andronikos, K. Giannakis, G. Theocharopoulou, and S. Fanarioti, A qubo model for the traveling salesman problem with time windows, Algorithms 12
2019
Later among the works it cites.
M. Aramon, G. Rosenberg, E. Valiante, T. Miyazawa, H. Tamura, and H. G. Katzgraber, Physics-inspired optimization for quadratic unconstrained problems using a digital annealer, Frontiers in Physics 7
2019
Later among the works it cites.
2019
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
C. S. Calude, M. J. Dinneen, and R. Hua, Qubo formulations for the graph isomorphism problem and related problems, Theoretical Computer Science 701
2017
Cited alongside, same era.
H. Nishimori and K. Takada, Exponential enhancement of the efficiency of quantum annealing by non-stoquastic hamiltonians, Frontiers in ICT 4
2017
Cited alongside, same era.
G. Carleo and M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355
2017
Cited alongside, same era.
T. Hatomura and T. Mori, Shortcuts to adiabatic classical spin dynamics mimicking quantum annealing, Physical Review E 98
2018
Cited alongside, same era.
Y. Susa, Y. Yamashiro, M. Yamamoto, and H. Nishimori, Exponential speedup of quantum annealing by inhomogeneous driving of the transverse field, Journal of the Physical Society of Japan 87
2018
Cited alongside, same era.
F. Glover, G. Kochenberger, and Y. Du, Quantum bridge analytics i: a tutorial on formulating and using qubo models, 4OR 17
2019
Cited alongside, same era.
P. Date, R. Patton, C. Schuman, and T. Potok, Efficiently embedding qubo problems on adiabatic quantum computers, Quantum Information Processing 18
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Later among the works it cites.
K. Yamamoto, K. Ando, N. Mertig, T. Takemoto, M. Yamaoka, H. Teramoto, A. Sakai, S. Takamaeda-Yamazaki, and M. Motomura, 7.3 statica: A 512-spin 0.25 m-weight full-digital annealing processor with a near-memory all-spin-updates-at-once architecture for combinatorial optimization with complete spin-spin interactions, in 2020 IEEE International Solid-State Circuits Conference-(ISSCC) (IEEE, 2020) pp. 138–140
2020
Later among the works it cites.
K. P. Kalinin, A. Amo, J. Bloch, and N. G. Berloff, Polaritonic xy-ising machine, Nanophotonics 9
2020
Later among the works it cites.
T. Zhao, G. Carleo, J. Stokes, and S. Veerapaneni, Natural evolution strategies and variational monte carlo, Machine Learning: Science and Technology 2
2020
Later among the works it cites.
F. Hamze, J. Raymond, C. A. Pattison, K. Biswas, and H. G. Katzgraber, Wishart planted ensemble: A tunably rugged pairwise ising model with a first-order phase transition, Physical Review E 101
2020
Later among the works it cites.
2020
Later among the works it cites.
2021
Closest in time.
H. Goto, K. Endo, M. Suzuki, Y. Sakai, T. Kanao, Y. Hamakawa, R. Hidaka, M. Yamasaki, and K. Tatsumura, High-performance combinatorial optimization based on classical mechanics, Science Advances 7
2021
Closest in time.
P. Date, D. Arthur, and L. Pusey-Nazzaro, Qubo formulations for training machine learning models, Scientific Reports 11
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
H. Irie, H. Liang, T. Doi, S. Gongyo, and T. Hatsuda, Hybrid quantum annealing via molecular dynamics, Scientific reports 11
2021
Closest in time.