NTT Research and Tokyo Tech Algorithm Enhances Performance and Scope of Coherent Ising Machine
NTT Research, Inc. has unveiled a new algorithm developed in collaboration with Tokyo Institute of Technology to enhance the performance of Coherent Ising Machines (CIMs). The study, published in Communications Physics, addresses limitations in CIMs by incorporating amplitude control feedback and Zeeman terms, allowing for more effective problem-solving across various optimization scenarios. This advancement is expected to widen the practical applications of CIMs, including challenges in drug discovery and wireless communications, solidifying NTT's role in cutting-edge technology.
- New algorithm enhances Coherent Ising Machine (CIM) performance.
- Increased application range for solving complex optimization problems.
- Collaboration with Tokyo Institute of Technology strengthens research capabilities.
- None.
Proposal combines amplitude control feedback and Zeeman terms to overcome inherent limits and broaden application reach
This article addresses two practical difficulties associated with the CIM, a network of optical parametric oscillators (OPOs) programmed to solve problems that are mapped to an Ising model. (The Ising model is a mathematical abstraction of magnetic systems composed of competitively interacting spins, or angular momentums of fundamental particles.) One challenge relates to the mutual coupling-induced inhomogeneous amplitudes of a CIM’s OPOs, which can take on a range of values other than +1 or -1, and as such, may lack faithful representation and impair the CIM’s performance. CIM is also constrained by its lack of Zeeman terms, a set of hypothetical interacting spins associated with most real-world optimization problems. In its basic form, the Ising model involves pairwise terms between optimization variables, which limits the CIM’s usefulness to benchmarking purposes. The authors of this article address those difficulties by proposing a theoretical scheme/algorithm that allows CIMs to solve optimization problems requiring a more general Ising form, i.e., one modified to include Zeeman terms. The algorithm also corrects for inhomogeneity with an implementation of time-dependent amplitude control feedback that both forces physical amplitudes closer to +1 or -1 and improves performance when CIMs are used to solve Ising problems in the form modified with Zeeman terms.
“We are pleased with the results of our ongoing collaboration with Dr. Aonishi’s Lab at
There have been various attempts to harness a CIM’s optical pulses, which are mutually coupled by dissipative circuits rather than unitary gates, to solve combinatorial optimization problems, such as the traveling salesman problem, lead optimization in drug discovery, multiple-input multiple-output (MIMO) optimization for wireless communications and compressed sensing for medical imaging. In the laboratory realm, a landmark result of an experimental measurement feedback CIM with 100,000 spins was achieved in 2021. The theoretical schemes proposed in this article, by contrast, apply to modeling methodologies that simulate the behavior of a physical CIM, which allow scientists to study a CIM without needing to build one. Some of the article’s more technical findings concern the performances of those models. The primary takeaway, however, is that the proposed amplitude control scheme increases the performance for a 16-spin CIM with Zeeman terms, especially when those terms are competing against mutually coupling coefficients.
The authors also note that this method will be effective in the CIM application for compressed sensing, a topic treated recently in another paper by
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