Delay-based photonic reservoir computing on thin-film lithium niobate with time–wavelength-coupled virtual nodes

Reservoir computing (RC) has gained significant attention for the ability to process temporal data with high-dimensional dynamical systems. However, the existing photonic RC architectures have to face limitations in scalability, nonlinearity, and integration. In this work, we have proposed and numerically investigated a delay-based photonic reservoir computing architecture compatible with thin-film lithium niobate (TFLN) integration, which combines high-speed modulators, cascaded microring resonators, periodically poled lithium niobate (PPLN) waveguides, and photodetectors. This architecture exploits both the fundamental and second-harmonic frequency components simultaneously, thereby enhancing the effective dimensionality and dramatically reducing the prediction error. With numerical simulations, the performance of the proposed architecture has been evaluated through time-series prediction tasks, including the Santa Fe chaotic dataset and the 10-order Nonlinear Auto-Regression Moving Average (NARMA-10) benchmarks. According to the results, substantial improvements in prediction accuracy have been achieved with NMSEs of $$2.6\times 1{0}^{-3}$$ and $$3.9\times 1{0}^{-3}$$ , respectively. The architecture reduces the prediction error by up to approximately one order of magnitude compared with single-component configurations, highlighting the benefits of combining fundamental and second-harmonic components. We believe that this work would set the stage for future all-optical reservoir computing systems, with potential applications in real-time data processing, machine learning, and complex system modeling.