Yoshitaka Haribara

A coherent Ising machine for 2000-node optimization problems

Taking the pulse of optimization Finding the optimum solution of multiparameter or multifunctional problems is important across many disciplines, but it can be computationally intensive. Many such problems defined as computationally difficult can be mathematically mapped onto the so-called Ising problem, which looks at finding the minimum energy configuration for an array of coupled spins. Inagaki et al. and McMahon et al. show that an optical processing approach based on a network of coupled optical pulses in a ring fiber can be used to model and optimize large-scale Ising systems. Such a scalable architecture could help to optimize solutions to a wide range of complex problems. Science, this issue pp. 603 and 614 An optical-based processor is developed to solve a broad class of complex optimization problems. The analysis and optimization of complex systems can be reduced to mathematical problems collectively known as combinatorial optimization. Many such problems can be mapped onto ground-state search problems of the Ising model, and various artificial spin systems are now emerging as promising approaches. However, physical Ising machines have suffered from limited numbers of spin-spin couplings because of implementations based on localized spins, resulting in severe scalability problems. We report a 2000-spin network with all-to-all spin-spin couplings. Using a measurement and feedback scheme, we coupled time-multiplexed degenerate optical parametric oscillators to implement maximum cut problems on arbitrary graph topologies with up to 2000 nodes. Our coherent Ising machine outperformed simulated annealing in terms of accuracy and computation time for a 2000-node complete graph.

A 16-bit Coherent Ising Machine for One-Dimensional Ring and Cubic Graph Problems

Many tasks in our modern life, such as planning an efficient travel, image processing and optimizing integrated circuit design, are modeled as complex combinatorial optimization problems with binary variables. Such problems can be mapped to finding a ground state of the Ising Hamiltonian, thus various physical systems have been studied to emulate and solve this Ising problem. Recently, networks of mutually injected optical oscillators, called coherent Ising machines, have been developed as promising solvers for the problem, benefiting from programmability, scalability and room temperature operation. Here, we report a 16-bit coherent Ising machine based on a network of time-division-multiplexed femtosecond degenerate optical parametric oscillators. The system experimentally gives more than 99.6% of success rates for one-dimensional Ising ring and nondeterministic polynomial-time (NP) hard instances. The experimental and numerical results indicate that gradual pumping of the network combined with multiple spectral and temporal modes of the femtosecond pulses can improve the computational performance of the Ising machine, offering a new path for tackling larger and more complex instances.

Computational Principle and Performance Evaluation of Coherent Ising Machine Based on Degenerate Optical Parametric Oscillator Network

We present the operational principle of a coherent Ising machine (CIM) based on a degenerate optical parametric oscillator (DOPO) network. A quantum theory of CIM is formulated, and the computational ability of CIM is evaluated by numerical simulation based on c-number stochastic differential equations. We also discuss the advanced CIM with quantum measurement-feedback control and various problems which can be solved by CIM.

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

An optical parametric oscillator network driven by a quantum measurement-feedback circuit, composed of optical homodyne detectors, analog-to-digital conversion devices and field programmable gate arrays (FPGA), is proposed and analysed as a scalable coherent Ising machine. The new scheme has an advantage that a large number of optical coupling paths, which is proportional to the square of a problem size in the worst case, can be replaced by a single quantum measurement-feedback circuit. There is additional advantage in the new scheme that a three body or higher order Ising interaction can be implemented in the FPGA digital circuit. Noise associated with the measurement-feedback process is governed by the standard quantum limit. Numerical simulation based on c-number coupled Langevin equations demonstrate a satisfying performance of the proposed Ising machine against the NP-hard MAX-CUT problems.

Performance evaluation of coherent Ising machines against classical neural networks

The coherent Ising machine is expected to find a near-optimal solution in various combinatorial optimization problems, which has been experimentally confirmed with optical parametric oscillators and a field programmable gate array circuit. The similar mathematical models were proposed three decades ago by Hopfield et al in the context of classical neural networks. In this article, we compare the computational performance of both models.

Quantum Computing with Non-classical Light: A Coherent Ising Machine for Maximum-Cut Problems

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 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 semi-definite programming relaxation of Goemans–Williamson and simulated annealing by Kirkpatrick et al.

Combinatorial optimization using networks of optical parametric oscillators

We use a network of 160 optical parametric oscillators, formed using fiber-coupled PPLN in a ~320-m fiber loop, to build an annealing machine that can find solutions to Ising problems with arbitrary spin connectivity.