Feedback-Free Photonic Reservoir Computing via Time-Concatenated Dimensional Expansion and Hybrid Pruning

Reservoir computing (RC) offers an efficient framework for temporal processing by combining nonlinear mapping with short-term memory. However, conventional implementations are often hindered by the high hardware costs of spatial arrays or the speed and integration bottlenecks associated with feedback delay lines and input masking in time-delayed RC. In this paper, we propose and experimentally demonstrate a single-node photonic reservoir computing system without physical delay lines and input masking operations. To enhance the representational capability of the single-node system, we introduce a time-concatenated dimensional expansion technique to construct a high-dimensional state space. Furthermore, a hybrid pruning strategy is employed to quantitatively optimize the trade-off between computational resources and forecasting performance. Our results demonstrate a feedback-free photonic reservoir computing framework that combines physical photonic feature generation with digital state construction and optimization for temporal processing tasks with diverse dynamical characteristics.