Design Principles for Light‐Scattering Reservoir Computing at the Edge of Chaos

ABSTRACT Physical reservoir computing offers an energy‐efficient route to sequential cognitive inference by outsourcing nonlinear temporal mixing to hardware substrates with rich intrinsic dynamics, with free‐space light‐scattering systems particularly attractive for their parallelism and reconfigurability—yet practical design principles linking hardware control variables to computational performance have remained unestablished. Here, we establish such principles by systematically mapping three physical control axes of a reconfigurable light‐scattering reservoir—reservoir dynamics, input–reservoir coupling, and reservoir interconnectivity—and identifying a quantitative optimum along each axis. Within this design landscape, we observe a memory‐capacity peak that coincides with near‐zero maximal Lyapunov exponent and is quantitatively reproduced in numerical simulation, extending edge‐of‐chaos confirmations previously reported in ion‐gating and spin‐wave reservoirs into the photonic substrate. The two remaining axes exhibit a density–magnitude trade‐off in input coupling and an intermediate optimum in reservoir interconnectivity. Operating at the resulting design optimum, the reservoir achieves stable Mackey–Glass chaotic time‐series prediction in free‐running mode and 84.5% blind classification accuracy on the 10‐class Speech Commands spoken‐digit benchmark; the principles, stated in substrate‐specific units yet rooted in broadly applicable concepts of criticality and balanced coupling, provide a transferable framework for reconfigurable optical reservoir hardware.