Compact Optical Reservoir Computing via Luminescence Dynamics in Rare-Earth Ions-Doped Nanocrystals

Optical neuromorphic computing offers a promising route to high-speed, energy-efficient information processing. However, photonic neurons, as the critical components for enhancing computational expressivity, still face significant bottlenecks in nonlinear mapping and memory capacity. Here, a functionally compact optical reservoir computing system based on rare-earth ions-doped nanocrystals is demonstrated, leveraging their intrinsic nonlinear luminescence dynamics and multi-timescale memory. Unlike traditional schemes that require bulky optical delays or intricate resonant structures, the platform exploits the material's inherent properties: nonlinear cross-relaxation processes enable nonlinear mapping while long-lived metastable energy levels provide fading memory. As a proof of concept, 90.7% accuracy is achieved in MNIST digit classification and low-error chaotic time-series prediction (NRMSE < 0.1) using the rare-earth ions-based system. This work significantly reduces system footprint and complexity, offering a scalable, fully optical solution for edge computing and real-time neuromorphic applications.