Nonlinear Photonics

Within this line of research, we explore complex phenomena in photonics,filling the gap between Modern Photonic Sources and Functional Complex Systems. Our Nonlinear Photonics Lab, working alongside a strong theoretical team, aims to gain an in-depth understanding of complex phenomena and to provide novel solutions from communication to information processing, transferring knowledge to the Information and Communication Technologies (ICT) domain.

We study nonlinear and spatio-temporal emission properties of semiconductor lasers, implement optical complex networks based on lasers, advance characterization techniques, and demonstrate the utility of optical complexity for information technologies including encryption and ultra-fast neuro-inspired photonic information processing.


Researchers

  • Pere Colet

    Pere Colet

  • Ingo Fischer

    Ingo Fischer

  • Damià Gomila

    Damià Gomila

  • Claudio Mirasso

    Claudio Mirasso

  • Roberta Zambrini

    Roberta Zambrini

  • Apostolos Argyris

    Apostolos Argyris

  • Miguel C. Soriano

    Miguel C. Soriano

Recent and ongoing Research projects

POST-DIGITAL

POST-DIGITAL: Neuromorphic computing in photonic and other nonlinear media

P.I.: Ingo Fischer, Claudio Mirasso
POST-DIGITAL is a Marie Skłodowska-Curie Innovative Training Network, funded by the European Union’s Horizon 2020 research and innovation programme. POST-DIGITAL is committed to form a new generation of engineers and researchers, affording ...

ADOPD

Adaptive Optical Dendrites

P.I.: Ingo Fischer, Claudio Mirasso
The increased demand for computation with low energy consumption requires entirely novel hardware concepts. In ADOPD we develop ultra-fast computational units based on optical-fiber technologies exploiting information processing principles used by neurons ...

Decaph

Dendrite-based Computation Applied to PHotonics systems

P.I.: Apostolos Argyris, Ingo Fischer, Claudio Mirasso
The DECAPH project addresses fundamental aspects of cognitive computing introducing a disruptive architecture that mimics in more detail the actual operations that are performed in the brain. By mimicking such neuron structures ...

QuaResC

Quantum machine learning using reservoir computing

P.I.: Miguel C. Soriano, Roberta Zambrini
The QuaResC project engages in a new collaboration UIB and CSIC researchers at IFISC with the objective to address an interdisciplinary topic between artificial intelligence and quantum physics: quantum machine learning using ...

Recent Publications

Boosting the output power of large-aperture lasers by breaking their circular symmetry

Brejnak, A.; Gębski, M.; Sokół, A. K.; Marciniak, M.; Wasiak, M.; Muszalski, J.; Lott, J. A.; Fischer, I.; Czyszanowski, T.
Optica 8 (9), 1167-1175 (2021)

High-Performance Reservoir Computing With Fluctuations in Linear Networks

Nokkala, Johannes; Martínez-Peña, Rodrigo; Zambrini, Roberta; Soriano, Miguel C.
IEEE Transactions on Neural Networks and Learning Systems , (2021)

Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

Stelzer, Florian; Röhm, Andre; Vicente, Raul; Fischer, Ingo; Yanchuk, Serhiy
Nature Communications 12, 5164 (1-10) (2021)

Reservoir Computing: Theory, Physical Implementations, and Applications

Nakajima, Kohei; Fischer, Ingo (Editors)
, Springer Singapore (2021)

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

Mujal, Pere; Martínez-Peña, Rodrigo; Nokkala, Johannes; García-Beni, Jorge; Giorgi, Gian Luca; Soriano, Miguel C.; Zambrini, Roberta
Advanced Quantum Technologies , 2100027 (2021)

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