An optical neural chip for implementing complex-valued neural network

TLDR

Complex‑valued neural networks offer advantages over real‑valued ones, yet conventional digital platforms cannot perform true complex operations, whereas optical computing can, but most optical neural network demonstrations still rely on real‑valued frameworks. The article presents an optical neural chip that implements truly complex‑valued neural networks. The chip’s performance was benchmarked on Boolean logic, Iris species classification, nonlinear Circle and Spiral datasets, and handwriting recognition. The complex‑valued chip achieved higher accuracy, faster convergence, and better nonlinear decision boundaries than its real‑valued counterpart.

Abstract

Abstract Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.