Virtual reservoir computer using an optical resonator

Reservoir computing is a machine learning approach that enables us to use recurrent neural networks without involving the complexity of training algorithms and make hardware implementation possible. We present a novel photonic architecture of a reservoir computer that employs a nonlinear node and a resonator to implement a virtual recurrent neural network. This resonator behaves as an echo generator component that substitutes the delay line in delay-based reservoir computers available in the literature. The virtual neural network formed in our implementation is fundamentally different from the delay-based reservoir computers. Different virtual architectures based on the FSR and the Finesse of the resonator are investigated to provide higher performance depending on the task. We test the performance of our proposed reservoir by 3 benchmark tasks, signal classification, nonlinear channel equalization, and memory capacity. Our system enhances the memory capacity of the reservoir compared to the delay-based systems and shows reliable results in signal classification and nonlinear channel equalization. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Reservoir computing is a machine learning approach that allows the use of recurrent neural networks without the complexity of training algorithms and enables hardware implementation. Researchers present a novel photonic architecture for a reservoir computer using a nonlinear node and a resonator to implement a virtual recurrent neural network. The performance of the proposed reservoir is tested on three benchmark tasks and shows improved memory capacity and reliable results compared to delay-based systems.