Gaussian optical Ising machines

被引:22
|
作者
Clements, William R. [1 ]
Renema, Jelmer J. [1 ]
Wen, Y. Henry [1 ]
Chrzanowski, Helen M. [1 ]
Kolthammer, W. Steven [1 ]
Walmsley, Ian A. [1 ]
机构
[1] Univ Oxford, Dept Phys, Clarendon Lab, Oxford OX1 3PU, England
基金
欧洲研究理事会; 英国工程与自然科学研究理事会; 欧盟地平线“2020”;
关键词
COMPUTATIONAL-COMPLEXITY; OPTIMIZATION; NETWORK;
D O I
10.1103/PhysRevA.96.043850
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
It has recently been shown that optical parametric oscillator (OPO) Ising machines, consisting of coupled optical pulses circulating in a cavity with parametric gain, can be used to probabilistically find low-energy states of Ising spin systems. In this work, we study optical Ising machines that operate under simplified Gaussian dynamics. We show that these dynamics are sufficient for reaching probabilities of success comparable to previous work. Based on this result, we propose modified optical Ising machines with simpler designs that do not use parametric gain yet achieve similar performance, thus suggesting a route to building much larger systems.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Penalty Coefficient Adjustment Technique for Extended Ising Machines
    Yin, Fang
    Tamura, Hirotaka
    Furue, Yuki
    Watanabe, Yasuhiro
    IEEE ACCESS, 2024, 12 : 168303 - 168313
  • [42] Experimental Investigation of the Dynamics of Coupled Oscillators as Ising Machines
    Bashar, Mohammad Khairul
    Mallick, Antik
    Shukla, Nikhil
    IEEE ACCESS, 2021, 9 : 148184 - 148190
  • [43] Scalable almost-linear dynamical Ising machines
    Shukla, Aditya
    Erementchouk, Mikhail
    Mazumder, Pinaki
    NATURAL COMPUTING, 2024,
  • [44] Ising machines as hardware solvers of combinatorial optimization problems
    Naeimeh Mohseni
    Peter L. McMahon
    Tim Byrnes
    Nature Reviews Physics, 2022, 4 : 363 - 379
  • [45] On computational capabilities of Ising machines based on nonlinear oscillators
    Erementchouk, Mikhail
    Shukla, Aditya
    Mazumder, Pinaki
    PHYSICA D-NONLINEAR PHENOMENA, 2022, 437
  • [46] Evaluating Spintronics-Compatible Implementations of Ising Machines
    Grimaldi, Andrea
    Mazza, Luciano
    Raimondo, Eleonora
    Tullo, Pietro
    Rodrigues, Davi
    Camsari, Kerem Y.
    Crupi, Vincenza
    Carpentieri, Mario
    Puliafito, Vito
    Finocchio, Giovanni
    PHYSICAL REVIEW APPLIED, 2023, 20 (02):
  • [47] Optimization, Chaotic Neural Networks, and Coherent Ising Machines
    Aihara, Kazuyuki
    Hasegawa, Mikio
    PROCEEDINGS OF THE IEEE, 2014, 102 (04) : 585 - 585
  • [48] Chemical design with GPU-based Ising machines
    Mao, Zetian
    Matsuda, Yoshiki
    Tamura, Ryo
    Tsuda, Koji
    DIGITAL DISCOVERY, 2023, 2 (04): : 1098 - 1103
  • [49] Training deep Boltzmann networks with sparse Ising machines
    Niazi, Shaila
    Chowdhury, Shuvro
    Aadit, Navid Anjum
    Mohseni, Masoud
    Qin, Yao
    Camsari, Kerem Y.
    NATURE ELECTRONICS, 2024, 7 (07): : 610 - 619
  • [50] Restricted Boltzmann machines for the long range Ising models
    Aoki, Ken-Ichi
    Kobayashi, Tamao
    MODERN PHYSICS LETTERS B, 2016, 30 (34):