INFOLANET
INFORMATION PROCESSING WITH COUPLED LASER NETWORKS
In the INFOLANET project, we will combine the expertise of the PIs on dynamical systems and machine learning to advance information processing concepts, based on a high-speed photonic implementation. We anticipate that synchronization, understood in a broad sense, will be key to boost information processing performance. We aim to establish a network of coupled lasers that is able to generate diverse responses to the input stimulus (high-dimensionality of the responses) while keeping these responses reproducible for supervised learning (consistency of the responses). In order to comply with these apparently contradictory requirements, different aspects related to identical or generalized synchronization, stimulus-induced or autonomous synchronization, and transient or stable synchronization properties will need to be investigated to ultimately understand their impact on the performance.
Investigadores
Apostolos Argyris
Miguel C. Soriano
Moritz Pflüger
Publicaciones recientes
Hardware friendly deep reservoir computing
Gallicchio, Claudio; Soriano, Miguel C.Neural Networks 193, 108079 (2026)
Speedup of an all-optical delay-based reservoir computer via input rate increase and bandwidth enhancement
Goldmann, Mirko; Estébanez, Irene; Mirasso, Claudio R.; Fischer, Ingo; Argyris, Apostolos; Soriano, Miguel C.Chaos 35, 073131 (2025)
Temporal pattern generation with tunable repetition rate using semiconductor laser periodic dynamics
Argyris, ApostolosSubmitted (2025)
Quantum machine learning via continuous-variable cluster states and teleportation
García-Beni, Jorge; Paparelle, Iris; Parigi, Valentina; Giorgi, Gian Luca; Soriano, Miguel C.; Zambrini, RobertaEPJ Quantum Technology 12, 63 (2025)
Temperature stabilization with Hebbian learning using an autonomous optoelectronic dendritic unit
Ortín, Silvia; Pflüger, Moritz; Argyris, ApostolosFrontiers of Optoelectronics 18, 7 (1-12) (2025)