Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1501.07030 · Lecture notes in physics

A Coherent Ising Machine for MAX-CUT Problems: Performance Evaluation against Semidefinite Programming and Simulated Annealing

Yoshitaka Haribara, Shoko Utsunomiya, Y. Yamamoto

Published Dec 29, 2015• 64 citations
arXivHTMLPDF
DOI
10.1007/978-4-431-55756-2_12

Related papers

Benchmarking coherent Ising machines and quantum annealers with MAX-CUT and SK problems

Ryan Hamerly, Takahiro Inagaki, Peter L. McMahon +10 more

Bulletin of the American Physical Society•Mar 6, 2019•0 citations

Approximation bounds for quadratic maximization and max-cut problems with semidefinite programming relaxation

Dachuan Xu, Shu-zhong Zhang

Science in China Series A Mathematics•Jul 30, 2007•7 citations

A coherent Ising machine with quantum measurement and feedback control

Yoshitaka Haribara, Y. Yamamoto, Ken‐ichi Kawarabayashi +1 more

Combinatorial optimization problems are computationally hard in general, but they are ubiquitous in our modern life. A coherent Ising machine (CIM) based on a multiple-pulse degenerate optical parametric oscillator (DOPO) is an alternative approach to solve these problems by a specialized physical computing system. To evaluate its potential performance, computational experiments are performed on maximum cut (MAX-CUT) problems against traditional algorithms such as semidefinite programming relaxation of Goemans-Williamson and simulated annealing by Kirkpatrick, et al. The numerical results empirically suggest that the almost constant computation time is required to obtain the reasonably accurate solutions of MAX-CUT problems on a CIM with the number of vertices up to $2 \times 10^4$ and the number of edges up to $10^8$.

•Jan 28, 2015•5 citations

Combinatorial optimization with coherent Ising machine

Takahiro Inagaki

The Japan Society of Applied Physics•Jul 10, 2018•0 citations

Experimental investigation of performance differences between coherent Ising machines and a quantum annealer

Ryan Hamerly, Takahiro Inagaki, Peter L. McMahon +20 more

Physical annealing systems provide heuristic approaches to solving combinatorial optimization problems. Here, we benchmark two types of annealing machines—a quantum annealer built by D-Wave Systems and measurement-feedback coherent Ising machines (CIMs) based on optical parametric oscillators—on two problem classes, the Sherrington-Kirkpatrick (SK) model and MAX-CUT. The D-Wave quantum annealer outperforms the CIMs on MAX-CUT on cubic graphs. On denser problems, however, we observe an exponential penalty for the quantum annealer [exp(–α_(DW)N^2)] relative to CIMs [exp(–α_(CIM)N)] for fixed anneal times, both on the SK model and on 50% edge density MAX-CUT. This leads to a several orders of magnitude time-to-solution difference for instances with over 50 vertices. An optimal–annealing time analysis is also consistent with a substantial projected performance difference. The difference in performance between the sparsely connected D-Wave machine and the fully-connected CIMs provides strong experimental support for efforts to increase the connectivity of quantum annealers.

•Jan 1, 2019•257 citations

A coherent Ising machine for solving complex combinatorial optimization problems

Hiroki Takesue

The Japan Society of Applied Physics•Jul 21, 2017•0 citations
Combinatorial optimization problems are computationally hard in general, but they are ubiquitous in our modern life. A coherent Ising machine (CIM) based on a multiple-pulse degenerate optical parametric oscillator (DOPO) is an alternative approach to solve these problems by a specialized physical computing system. To evaluate its potential performance, computational experiments are performed on maximum cut (MAX-CUT) problems against traditional algorithms such as semidefinite programming relaxation of Goemans-Williamson and simulated annealing by Kirkpatrick, et al. The numerical results empirically suggest that the almost constant computation time is required to obtain the reasonably accurate solutions of MAX-CUT problems on a CIM with the number of vertices up to 2×1042 \times 10^42×104 and the number of edges up to 10810^8108.
Physical annealing systems provide heuristic approaches to solving combinatorial optimization problems. Here, we benchmark two types of annealing machines—a quantum annealer built by D-Wave Systems and measurement-feedback coherent Ising machines (CIMs) based on optical parametric oscillators—on two problem classes, the Sherrington-Kirkpatrick (SK) model and MAX-CUT. The D-Wave quantum annealer outperforms the CIMs on MAX-CUT on cubic graphs. On denser problems, however, we observe an exponential penalty for the quantum annealer [exp(–α_(DW)N^2)] relative to CIMs [exp(–α_(CIM)N)] for fixed anneal times, both on the SK model and on 50% edge density MAX-CUT. This leads to a several orders of magnitude time-to-solution difference for instances with over 50 vertices. An optimal–annealing time analysis is also consistent with a substantial projected performance difference. The difference in performance between the sparsely connected D-Wave machine and the fully-connected CIMs provides strong experimental support for efforts to increase the connectivity of quantum annealers.