In Optica, The Optical Society’s journal for high-impact research, researchers from The Hong Kong University of Science and Technology, Hong Kong detail their two-layer all-optical neural network and successfully apply it to a complex classification task. In conventional hybrid optical neural networks, optical components are typically used for linear operations while nonlinear activation functions — the functions that simulate the way neurons in the human brain respond — are usually implemented electronically because nonlinear optics typically require high-power lasers that are difficult to implement in an optical neural network.

Fully functioned 2-layer all optical neural network (AONN)

To overcome this challenge, the researchers used cold atoms with electromagnetically induced transparency to perform nonlinear functions. “This light-induced effect can be achieved with very weak laser power,” said Shengwang Du, a member of the research team. “Because this effect is based on nonlinear quantum interference, it might be possible to extend our system into a quantum neural network that could solve problems intractable by classical methods.”

To confirm the capability and feasibility of the new approach, the researchers constructed a two-layer fully-connected all optical neural network with 16 inputs and two outputs. The researchers used their all-optical network to classify the order and disorder phases of the Ising model, a statistical model of magnetism. The results showed that the all-optical neural network was as accurate as a well-trained computer-based neural network.

Source (The Optical Society. “All-optical neural network for deep learning: New approach could enable parallel computation with light.” ScienceDaily. ScienceDaily, 29 August 2019.)

Original article: Zuo, Y., Li, B., Zhao, Y., Jiang, Y., Chen, Y.C., Chen, P., Jo, G.B., Liu, J. and Du, S., 2019. All-optical neural network with nonlinear activation functions. Optica, 6(9), pp.1132-1137.