Reservoir computing based on multicore fibers

Abstract

Photonic reservoir computing offers a hardware-efficient route to processing temporal and sequential data, but delay-based implementations often rely heavily on temporal multiplexing, where long temporal masks are required to generate a sufficiently rich reservoir state. Here we show numerically that the spatial degrees of freedom of an active multicore fiber placed inside a delayed optical feedback loop can reduce this dependence on serial temporal encoding. The input signal is encoded by temporal and spatial masks, the pump distribution across the cores controls the reservoir operating point through the core-dependent effective gain and saturation energy, and the detected core intensities serve as readout features for a single trained linear layer. The system is modeled by linearly coupled nonlinear Schr\"odinger equations with saturable gain and solved using a split-step Fourier method. On the Mackey-Glass one-step-ahead prediction benchmark, a seven-core reservoir with equal temporal masks reduces the validation normalized root mean square error from 0.5956 for the single-core baseline to 0.0651 at a modulation rate of 40 GHz. At 1 GHz, spatial-only encoding reaches an error of 0.0323 using one temporal sample per symbol and no temporal mask. These results show that an active multicore fiber can provide both parallel readout channels and a tunable nonlinear transformation, offering a route to photonic reservoirs with reduced reliance on temporal multiplexing.