State Space Expansion with Nonlinear Transformation and Upsampling in Experimental Reservoir Computing

The reservoir computing approach based on a semiconductor laser with optoelectronic time-delay feedback is distinguished by its simplicity, speed, and reliability. However, the response of the reservoir to the input signal in this configuration is a superposition of the laser relaxation oscillations, which has limited complexity, which may therefore reduce computational performance when solving problems requiring high nonlinearity. We experimentally demonstrate a combination of postprocessing techniques that overcome this limitation through upsampling and state space expansion using a nonlinear transformation of nodal functions, and show how this results in an order-of-magnitude reduction in prediction error for the Santa Fe task.