Experimental Realization of Reservoir Computing with Wave Chaotic Systems

The execution of machine learning (ML) algorithms largely depends on the computing `substrate', which is often not optimized for running ML tasks. The invention of ML-tailored hardware greatly improves the computing speed and power efficiency. Photonic devices are well suited for ML due to the parallelism of light. Here we utilize the complicated wave dynamics inside a chaotic-shaped overmoded electromagnetic cavity containing nonlinear elements to emulate the complex dynamics of the Reservoir Computer (RC). We propose unique techniques to create virtual RC nodes by both spectral and spatial perturbation. The computational power of the wave-based RC is experimentally demonstrated with the so-called Observer Task, where we predict the future evolution of chaotic Rossler y(t) and z(t) time series using the x(t) series as the input. Different tasks are executed with a single RC physical device by simply switching output couplers.