High-performance delay-based photonic reservoir computing

Delay-based reservoir computing with temporally multiplexed virtual nodes provides a hardware-efficient platform for photonic implementations, yet parameter selection is often guided by empirical tuning. Here, we report a systematic experimental study of a single-node delay-based photonic reservoir computer that resolves the key physical and dynamical factors governing computational performance using the nonlinear channel equalization (NCE) benchmark. By mapping the bifurcation landscape of the system using port-Hamiltonian formulation, we establish a direct correspondence between operating regime and NCE performance as well as memory capacity, showing that optimal performance occurs near, but below, the onset of oscillatory and chaotic dynamics. We further provide a unified comparison of temporal optimization strategies, including delay-input period desynchronization and controlled local node coupling, within the same physical platform. At a 19 dB signal-to-noise ratio, the symbol error rate (SER) decreases with increasing node count before saturating beyond N ≈ 50, indicating task-dependent performance saturation and practical scaling constraints in delay-based reservoir architectures. A readout shifting strategy further improves the performance, achieving an SER of 0.003. These results provide physics-informed insights into parameter selection and architectural optimization in delay-based photonic reservoir computing for scalable, high-speed operation.