Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive optical networks, in particular, enable large-scale parallel computation through the use of passive structured phase masks and the propagation of light. However, one major challenge remains: systems trained in model-based simulations often fail to perform optimally in real experimental settings, where misalignments, noise, and model inaccuracies are difficult to capture.
Reinforcement learning accelerates model-free training of optical AI systems
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