Affiliation:
1. Harbin Institute of Technology
2. Wood Centre for Innovation
Abstract
Deep learning has profoundly reshaped the technology landscape in numerous scientific areas and industrial sectors. This technology advancement is, nevertheless, confronted with severe bottlenecks in digital computing. Optical neural network presents a promising solution due to the ultra-high computing speed and energy efficiency. In this work, we present systematic study of unitary optical neural network (UONN) as an approach towards optical deep learning. Our results show that the UONN can be trained to high accuracy through special unitary gradient descent optimization, and the UONN is robust against physical imperfections and noises, hence it is more suitable for physical implementation than existing ONNs.
Subject
Atomic and Molecular Physics, and Optics