An optical neural chip for implementing complex-valued neural network

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, most optical neural network demonstrations still rely on real-valued frameworks designed for digital computers, forfeiting advantages such as efficient complex-valued operations. Here we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks by integrating input generation, weight multiplication and output generation on a single photonic chip. We benchmark performance in Boolean tasks, Iris species classification, nonlinear datasets (Circle and Spiral), and handwriting recognition. The complex-valued ONC achieves strong learning capabilities—high accuracy, fast convergence, and nonlinear decision boundaries—compared to real-valued counterparts.

H. Zhang, M. Gu, X. D. Jiang, J. Thompson, H. Cai, S. Paesani, R. Santagati, A. Laing, Y. Zhang, M. H. Yung, Y. Z. Shi, F. K. Muhammad, G. Q. Lo, X. S. Luo, B. Dong, D. L. Kwong, L. C. Kwek, A. Q. Liu