A photonics perspective on computing with physical substrates

Abreu; Boikov; Goldmann, M.; Jonuzi; Lupo; Masaad; Nguyen; Picco; Pourcel; Skalli; Talandier, L.; Vettelschoss; Vlieg; Argyris, A.; Bienstman; Brunner; Dambre; Daudet; Domenech; Fischer, I.; Horst; Massar; Mirasso, C.R.; Offrein; Rossi; Soriano, M.C.; Sygletos; Turitsyn
Reviews in Physics 12, 100093 (2024)

We provide a perspective on the fundamental relationship between physics and computation, exploring the conditions under which a physical system can be harnessed for computation and the practical means to achieve this. Unlike traditional digital computers that impose discreteness on continuous substrates, unconventional computing embraces the inherent properties of physical systems. Exploring simultaneously the intricacies of physical implementations and applied computational paradigms, we discuss the interdisciplinary developments of unconventional computing. Here, we focus on the potential of photonic substrates for unconventional computing, implementing artificial neural networks to solve data-driven machine learning tasks. Several photonic neural network implementations are discussed, highlighting their potential advantages over electronic counterparts in terms of speed and energy efficiency. Finally, we address the challenges of achieving learning and programmability within physical substrates, outlining key strategies for future research.

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