Relationship between Lyapunov Stability and Reservoir Computing Performance in an Optically Injected Semiconductor Laser with Optical Feedback
Kazutaka Kanno, Atsushi Uchida · The Review of Laser Engineering · 2020
Recently, an information- processing method was proposed based on a semiconductor laser with time-delayed optical feedback and optical injection. This method is called reservoir computing, which is a machine learning paradigm based on information processing in the human brain. In this scheme, consistency is a critical characteristic and represents the reproducibility of the responses of a dynamical system when repeatedly driven by similar inputs. The convergence of consistent laser outputs is also important for reservoir computing performance. In this study, we investigate the dependence of the convergence of laser outputs on the initial optical frequency detuning between the two lasers. The convergence is quantitatively evaluated using a conditional Lyapunov exponent. We also demonstrate reservoir computing based on a semi-conductor laser and investigate the relationship between the performances of reservoir computing and convergence of consistent laser output.