Low-rank combinatorial optimization and statistical learning by spatial photonic Ising machine

Abstract

The spatial photonic Ising machine (SPIM) [D. Pierangeli et al., Phys. Rev. Lett. 122, 213902 (2019)] is a promising optical architecture utilizing spatial light modulation for solving large-scale combinatorial optimization problems efficiently. The primitive version of the SPIM, however, can accommodate Ising problems with only rank-one interaction matrices. In this Letter, we propose a new computing model for the SPIM that can accommodate any Ising problem without changing its optical implementation. The proposed model is particularly efficient for Ising problems with low-rank interaction matrices, such as knapsack problems. Moreover, it acquires the learning ability of Boltzmann machines. We demonstrate that learning, classification, and sampling of the MNIST handwritten digit images are achieved efficiently using the model with low-rank interactions. Thus, the proposed model exhibits higher practical applicability to various problems of combinatorial optimization and statistical learning, without losing the scalability inherent in the SPIM architecture.

6 pages, 5 figures (with a 4-page supplemental); accepted version

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• Noise-augmented Chaotic Ising Machines for Combinatorial Optimization and Sampling
• Spin Hamiltonians in the Modulated Momenta of Light
• Spatial Optical Simulator for Classical Statistical Models
• Dense Associative Memory in a Nonlinear Optical Hopfield Neural Network