Parallel Time-Delay Reservoir Computing With Quantum Dot Lasers

Abstract

A semiconductor laser with optical feedback and optical injection is an appealing scheme to construct the time-delay reservoir computing (TDRC) networks. Quantum dot (QD) lasers are compatible to the silicon platform, and hence is helpful to develop fully on-chip TDRCs. This work theoretically demonstrates a parallel TDRC based on a Fabry-Perot QD laser with multiple longitudinal modes. These modes act as connected physical neurons, which process the input signal in parallel. The interaction strength of the modes is characterized by the cross-gain saturation effect. We show that the neuron interaction strength affects the performance of various benchmark tasks, including the memory capacity, time series prediction, nonlinear channel equalization, and spoken digit recognition. In comparison with the one-channel TDRC with the same number of nodes, the parallel TDRC runs faster and its performance is improved on multiple benchmark tasks.