  Optical computing systems provide high-speed and low-energy data processing
but face deficiencies in computationally demanding training and
simulation-to-reality gaps. We propose a gradient-based model-free optimization
(G-MFO) method based on a Monte Carlo gradient estimation algorithm for
computationally efficient in situ training of optical computing systems. This
approach treats an optical computing system as a black box and back-propagates
the loss directly to the optical computing weights' probability distributions,
circumventing the need for a computationally heavy and biased system
simulation. Our experiments on diffractive optical computing systems show that
G-MFO outperforms hybrid training on the MNIST and FMNIST datasets.
Furthermore, we demonstrate image-free and high-speed classification of cells
from their marker-free phase maps. Our method's model-free and high-performance
nature, combined with its low demand for computational resources, paves the way
for accelerating the transition of optical computing from laboratory
demonstrations to practical, real-world applications.
