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arXiv:2312.04290 · International Conference on Communication Systems & Networks (COMSNETS)

Convergence Analysis of Opto-Electronic Oscillator Based Coherent Ising Machines

Sayantan Pramanik, Sourav Chatterjee, Harshkumar Oza

Published Jan 3, 2024• 5 citations
arXivHTMLPDF

Abstract

Ising machines are purported to be better at solving large-scale combinatorial optimisation problems better than conventional von Neumann computers. However, these Ising machines are widely believed to be heuristics, whose promise is observed empirically rather than obtained theoretically. We bridge this gap by considering an opto-electronic oscillator based coherent Ising machine, and providing the first analytical proof that under reasonable assumptions, the OEO-CIM is not a heuristic approach. We find and prove bounds on its performance in terms of the expected difference between the objective value at the final iteration and the optimal one, and on the number of iterations required by it. In the process, we emphasise on some of its limitations such as the inability to handle asymmetric coupling between spins, and the absence of external magnetic field applied on them (both of which are necessary in many optimisation problems), along with some issues in its convergence. We overcome these limitations by proposing suitable adjustments and prove that the improved architecture is guaranteed to converge to the optimum of the relaxed objective function.

DOI
10.1109/comsnets59351.2024.10427235

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Ising machines are purported to be better at solving large-scale combinatorial optimisation problems better than conventional von Neumann computers. However, these Ising machines are widely believed to be heuristics, whose promise is observed empirically rather than obtained theoretically. We bridge this gap by considering an opto-electronic oscillator based coherent Ising machine, and providing the first analytical proof that under reasonable assumptions, the OEO-CIM is not a heuristic approach. We find and prove bounds on its performance in terms of the expected difference between the objective value at the final iteration and the optimal one, and on the number of iterations required by it. In the process, we emphasise on some of its limitations such as the inability to handle asymmetric coupling between spins, and the absence of external magnetic field applied on them (both of which are necessary in many optimisation problems), along with some issues in its convergence. We overcome these limitations by proposing suitable adjustments and prove that the improved architecture is guaranteed to converge to the optimum of the relaxed objective function.
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.
Coherent Ising machines (CIMs) constitute a promising approach to solve computationally hard optimization problems by mapping them to ground state searches of the Ising model and implementing them with optical artificial spin-networks. However, while CIMs promise speed-ups over conventional digital computers, they are still challenging to build and operate. Here, we propose and test a concept for a fully programmable CIM, which is based on opto-electronic oscillators subjected to self-feedback. Contrary to current CIM designs, the artificial spins are generated in a feedback induced bifurcation and encoded in the intensity of coherent states. This removes the necessity for nonlinear optical processes or large external cavities and offers significant advantages regarding stability, size and cost. We demonstrate a compact setup for solving MAXCUT optimization problems on regular and frustrated graphs with 100 spins and can report similar or better performance compared to CIMs based on degenerate optical parametric oscillators.
Analog computing using bosonic computational states is a leading approach to surpassing the computational speed and energy limitations of von Neumann architectures. But the challenges of manufacturing large-scale photonic integrated circuits (PIC) has led to hybrid solutions that integrate optical analog and electronic digital components. A notable example is the coherent Ising machine (CIM), that was primarily invented for solving quadratic binary optimization problems. In this paper, we focus on a mean-field interpretation of the dynamics of optical pulses in the CIM as solutions to Langevin dynamics, a stochastic differential equation (SDE) that plays a key role in non-convex optimization and generative AI. This interpretation establishes a computational framework for understanding the system's operation, the computational role of each component, and its performance, strengths, and limitations. We then infer that the CIM is inherently a continuous state machine, capable of integrating a broad range of SDEs, in particular for solving a continuous global (or mildly constrained) optimization problems. Nevertheless, we observe that the iterative digital-to-analog and analog-to-digital conversions within the protocol create a bottleneck for the low power and high speed of optics to shine. This observation underscores the need for major advances in PIC technologies as we envision that fully analog opto-electronic realizations of such experiments can open doors for broader applications, and orders of magnitude improvements in speed and energy consumption.
Coherent Ising machines (CIMs) are an experimentally promising class of physics-based computational architectures that embed hard combinatorial optimization problems into systems of coupled nonlinear optical oscillators. The solution-finding mechanisms employed by CIMs feature complicated dynamical bifurcations occurring on a network scale, posing significant challenges to the development of theory and models for their underlying principles of operation. These difficulties are especially pronounced in the ultra-low-power or quantum regimes where the benefits in computational efficiency over conventional optimization algorithms are expected to be largest. We discuss some of our recent approaches and results at this intersection of dynamical systems theory and quantum model reduction, which have highlighted some potentially useful architectures and applications on the horizon for CIMs.
Computers based on physical systems are increasingly anticipated to overcome the impending limitations on digital computer performance. One such computer is a coherent Ising machine (CIM) for solving combinatorial optimization problems. Here, we report a CIM with 100,512 degenerate optical parametric oscillator pulses working as the Ising spins. We show that the CIM delivers fine solutions to maximum cut problems of 100,000-node graphs drastically faster than standard simulated annealing. Moreover, the CIM, when operated near the phase transition point, provides some extremely good solutions and a very broad distribution. This characteristic will be useful for applications that require fast random sampling such as machine learning.
With the growing demand for efficient computing to tackle complex real-world challenges like combinatorial optimization, both academia and industry are exploring alternative computing paradigms. The Ising machine (IM) is one such approach, solving optimization problems by mapping their cost functions onto the Ising model. In this framework, finding the optimal solution equates to determining the Ising spin configuration corresponding to the ground state of the Ising Hamiltonian. Analog IMs, relying on artificial spins that can take analogue rather than binary values, have shown great potential in reaching this ground at high speeds. Optical implementations, such as the opto-electronic IM developed within our research group, are particularly promising due to their higher bandwidth, energy efficiency, and lower latency compared to electronic IMs. However, scaling remains a challenge for large problems. To overcome this, we propose a novel analog optical IM utilizing a spatial light modulator (SLM). Our design differs from other IMs relying on SLMs by employing analog spin variables instead of binary ones and leveraging gradient descent-based feedback rather than a computer-driven Metropolis–Hastings sampling process. By exploiting the inherent parallelism of light, this SLM-based IM is expected to efficiently handle optimization problems with millions of spins while still maintaining high energy efficiency. At the conference, we will also offer more insight into the performance of these analogue IMs and will delve deeper in the bit resolution required for the different optical components such as the employed SLM. Based on the dynamical evolution of the IM, we will discuss strategies to further improve the problem-solving capacity and speed of these optical IMs.