Networks of random lasers: current perspective and future challenges

Abstract

Artificial neural networks are widely used in many different applications because of their ability to deal with a range of complex problems generally involving massive data sets. These networks are made up of nodes, connections, and nonlinear response connections, which are typically implemented as software code running on ordinary electronic computers. In such systems, electrons, with their advantages and drawbacks, are in charge of storing, processing, and transmitting information. Signal processing in the optical domain can provide ultrafast, parallel operation, nonlinear dynamics, and high energy efficiency, making photonics a suitable technology for the realization of neuroinspired computing platforms. This advantage stimulated the development of photonics neural networks based on single and multiple lasers with classical optical cavities. Recently, networks made of random lasers emerged as a novel concept that uses randomly placed scattering elements to create nonlinearity and complexity in photonics neural networks. In this review paper, we present the general framework for networks of coupled lasers, discuss recent advances in networks of random lasers, and outline future directions in this area. We also examine the challenges and limitations of using random lasers in photonic networks, as well as potential solutions. By harnessing the properties of random lasers, such as their unique spectral characteristics in pulsed emission mode and their robustness against noise, networks of interacting random lasers can explore new and exciting possibilities for photonics technology that could find applications in a variety of fields, including image recognition and encryption.