Design principles for optoelectronic 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 optoelectronic 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 three-axis 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 substrate-independent concepts of criticality and balanced coupling, provide a transferable framework for reconfigurable optical reservoir hardware.