Minimum complexity integrated photonic architecture for delay-based reservoir computing

Reservoir computing is an analog bio-inspired computation scheme for efficiently processing time-dependent signals, the photonic implementations of which promise a combination of massive parallel information processing, low power consumption, and high-speed operation. However, most of these implementations, especially for the case of time-delay reservoir computing, require extensive multi-dimensional parameter optimization to find the optimal combination of parameters for a given task. We propose a novel, largely passive integrated photonic TDRC scheme based on an asymmetric Mach-Zehnder interferometer in a self-feedback configuration, where the nonlinearity is provided by the photodetector, and with only one tunable parameter in the form of a phase shifting element that, as a result of our configuration, allows also to tune the feedback strength, consequently tuning the memory capacity in a lossless manner. Through numerical simulations, we show that the proposed scheme achieves good performance-when compared to other integrated photonic architectures-on the temporal bitwise XOR task and various time series prediction tasks, while greatly reducing hardware and operational complexity.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Reservoir computing is an analog bio-inspired computation scheme that efficiently processes time-dependent signals. Photonic implementations of reservoir computing offer advantages such as massive parallel information processing, low power consumption, and high-speed operation. However, most implementations, especially for time-delay reservoir computing, require extensive parameter optimization. Here, we propose a novel, largely passive integrated photonic TDRC scheme based on an asymmetric Mach-Zehnder interferometer in a self-feedback configuration. This scheme achieves good performance on various tasks while reducing hardware and operational complexity.