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Optical computing systems provide high-speed and low-energy data processingbut face deficiencies in computationally demanding training andsimulation-to-reality gaps. We propose a model-free optimization (MFO) methodbased on a score gradient estimation algorithm for computationally efficient insitu training of optical computing systems. This approach treats an opticalcomputing system as a black box and back-propagates the loss directly to theoptical computing weights' probability distributions, circumventing the needfor a computationally heavy and biased system simulation. Our experiments on asingle-layer diffractive optical computing system show that MFO outperformshybrid training on the MNIST and FMNIST datasets. Furthermore, we demonstrateimage-free and high-speed classification of cells from their phase maps. Ourmethod's model-free and high-performance nature, combined with its low demandfor computational resources, expedites the transition of optical computing fromlaboratory demonstrations to real-world applications.

High-performance real-world optical computing trained by in situ model-free optimization