Coherent Laser Networks as Energy-Based Neural Networks

In recent years, there has been a growing interest in developing new platforms for general-purpose or application-specific computing that offer an advantage over classical processors in terms of computational time, energy efficiency, and scalability. Here, we propose the use of coherently coupled laser networks for neural computing. The proposed scheme is built on harnessing the collective behavior of laser networks for storing a number of phase patterns as stable fixed points of the governing dynamical equations and retrieving such patterns through proper excitation conditions, thus exhibiting an associative memory property. We further show that limitations on the number of images can be overcome by using nonreciprocal coupling between lasers, thus allowing for utilizing the large storage capacity inherent to the laser network. This work opens new possibilities for neural computation with coherent laser networks as a novel physical analog processor. In addition, the underlying dynamical model discussed here suggests a novel energy-based recurrent neural network that can directly handle continuous data as opposed to Hopfield networks and Boltzmann machines which are intrinsically binary systems.