Useful Quantum Signal Processing via Hybrid Quantum–Classical Reservoir Computing on Neutral Atom Processors

We present a hybrid quantum–classical reservoir computing (hQCRC) framework [1] aimed at demonstrating useful quantum signal processing on near-term analog hardware. Building on recent perspectives linking quantum feature maps and reservoir computing [2], we implement a modular approach where quantum evolution generates expressive nonlinear features and a classical stage manages training and memory. The scheme is run in Pasqal's neutral-atom processors currently in operation but applies broadly across analog and digital Rydberg architectures. We investigate how finite-shot measurement protocols and tunable interactions act as computational resources enhancing expressivity and stability. Benchmark studies on memory capacity and temporal prediction tasks will be shown to illustrate performance trends and scaling. The results suggest that hQCRC can serve as practical testbeds for connecting quantum feature-map ideas with neuromorphic information processing and deliver useful performance on current devices without requiring fault-tolerance.