Nonlinear photonic dynamical systems for unconventional computing

Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experienced a revival. Here, we provide a general overview of progress over the past decade, and sketch a roadmap of important future developments. We focus on photonic implementations of the reservoir computing machine learning paradigm, which offers a conceptually simple approach that is amenable to hardware implementations. In particular, we provide an overview of photonic reservoir computing implemented via either spatio temporal or delay dynamical systems. Going beyond reservoir computing, we discuss recent advances and future challenges of photonic implementations of deep neural networks, of the quest for learning methods that are hardware-friendly as well as realizing autonomous photonic neural networks, i.e. with minimal digital electronic auxiliary hardware.We acknowledge the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711) and through the QUARESC project (PID2019-109094GB-C22/AEI/10.13039/501100011033), and through the Region Bourgogne Franche-Comt ́e, the French National Research Agency via EUR EIPHI program (Contract No. ANR-17-EURE-0002), and the Volkswagen Foundation (NeuroQNet II) and the European Union’s Horizon 2020 research and innovation program under the Marie Sk lodowska-Curie Grant Agreements No. 860360 (POST DIGITAL). The work of MCS has been supported by MICINN/AEI/FEDER and the University of the Balearic Islands through a “Ramon y Cajal” Fellowship (RYC-2015-18140).Peer reviewe