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.