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NTT technical review

The Future of Problem Solving: The Coherent Ising Machine Approach

Y. Yamamoto

Published Jan 1, 2022• 0 citations
Publisher

Abstract

Several new computing models tackle combinatorial optimization problems.These include adiabatic quantum computation, also known as quantum annealing, and the coherent Ising machine (CIM).The Physics and Informatics Laboratories at NTT Research launched a CIM initiative, and NTT Basic Research Laboratories built large-scale CIM prototypes.This article reviews the CIM approach, results to date, and future research agenda.

DOI
10.53829/ntr202201fa3

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Several new computing models tackle combinatorial optimization problems.These include adiabatic quantum computation, also known as quantum annealing, and the coherent Ising machine (CIM).The Physics and Informatics Laboratories at NTT Research launched a CIM initiative, and NTT Basic Research Laboratories built large-scale CIM prototypes.This article reviews the CIM approach, results to date, and future research agenda.
Today, world-wide efforts are being undertaken to explore non-von Neumann computation methods to overcome "the end of Moore's law". A promising approach is the coherent Ising machine (CIM), which utilizes a network of degenerate optical parametric oscillators for solving ground-state-search problems of the Ising model. Since its proposal in 2013 and the first proof-of-principle experiment in 2014, research on experimental CIMs has made rapid progress and now reached the level where we can find solutions to large-scale combinatorial optimization problems using a CIM system. In this paper, we review recent progress on CIMs, mainly focusing on the experimental side.
Abstract Coherent Ising machines (CIM) have shown promising potential as hardware solvers for tackling combinatorial optimization problems, where discrete variables are encoded onto physical states to form an Ising Hamiltonian. Various platforms, starting from optical parametric oscillators, have been proposed and demonstrated to implement an actual physical CIM. In this review article, we briefly introduce the basic operation principle of CIMs and review physical schemes focusing on MEMS-based nanophotonics.
Coherent Ising machines (CIMs) are increasingly anticipated as a new computer that can solve combinatorial optimization problems. CIMs use degenerate optical parametric oscillator pulses as artificial spins. We constructed a 100,512-spin coherent Ising machine, the world's largest. Our CIM can find good solutions to maximum cut problems of 100,000-node graphs ~1000 times faster than simulated annealing. Furthermore, it provides both very good scores and a broad distribution when we operate it near the phase transition point.
The coherent Ising machine (CIM) implemented by degenerate optical parametric oscillator (DOPO) networks is a novel optical platform to accelerate computation of hard combinatorial optimization problems. Nevertheless, with the increase of the problem size, the probability of the machine being trapped by local minima increases exponentially. According to the quantum adiabatic theorem, a physical system will remain in its instantaneous ground state if the time-dependent Hamiltonian varies slowly enough. Here, we propose a method to help the machine partially avoid getting stuck in local minima by introducing quantum adiabatic evolution to the ground-state-search process of the CIM, which we call A-CIM. Numerical simulation results demonstrate that A-CIM can obtain improved solution accuracy in solving MAXCUT problems of vertices ranging from 10 to 2000 than CIM. The proposed machine that is based on quantum adiabatic theorem is expected to solve optimization problems more correctly.
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.