High performance optical reservoir computing based on spatially extended systems

• Main Author: Pauwels, Jaël
• Other Authors: Massar, Serge
• Format: Doctoral Thesis
• Language: en
• Published: Universite Libre de Bruxelles 2021
• Subjects: Optique; Physique; Sciences de l'ingénieur; photonic reservoir computing; spatially extended systems; optical reservoir computing
• Online Access: https://dipot.ulb.ac.be/dspace/bitstream/2013/331699/3/thesis.pdf; https://dipot.ulb.ac.be/dspace/bitstream/2013/331699/4/toc.pdf; https://dipot.ulb.ac.be/dspace/bitstream/2013/331699/5/ContratDiPauwels.pdf; http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/331699

In this thesis we study photonic computation within the framework of reservoir computing. Inspired by the insight that the human brain processes information by generating patterns of transient neuronal activity excited by input sensory signals, reservoir computing exploits the transient dynamics of an analogue nonlinear dynamical system to solve tasks that are hard to solve by algorithmic approaches. Harnessing the massive parallelism offered by optics, we consider a generic class of nonlinear dynamical systems which are suitable for reservoir computing and which we label photonic computing liquids. These are spatially extended systems which exhibit dispersive or diffractive signal coupling and nonlinear signal distortion. We demonstrate that a wide range of optical systems meet these requirements and allow for elegant and performant imple- mentations of optical reservoirs. These advances address the limitations of current photonic reservoirs in terms of scalability, ease of implementation and the transition towards truly all-optical computing systems.We start with an abstract presentation of a photonic computing liquid and an in-depth analysis of what makes these kinds of systems function as potent reservoir computers. We then present an experimental study of two photonic reservoir computers, the first based on a diffractive free-space cavity, the second based on a fiber-loop cavity. These systems allow us to validate the promising concept of photonic computing liquids, to investigate the effects of symme- tries in the neural interconnectivity and to demonstrate the effectiveness of weak and distributed optical nonlinearities. We also investigate the ability to recover performance lost due to uncontrolled parameters variations in unstable operating environments by introducing an easily scalable way to expand a reservoir’s output layer. Finally, we show how to exploit random diffraction in a strongly dispersive optical system, including applications in optical telecom- munications. In the conclusion we discuss future perspectives and identify the characteristic of the optical systems that we consider most promising for the future of photonic reservoir computing. === Doctorat en Sciences === info:eu-repo/semantics/nonPublished