Optical computing systems can provide high-speed and low-energy data
processing but face deficiencies in computationally demanding training and
simulation-to-reality gap. We propose a model-free solution for lightweight in
situ optimization of optical computing systems based on the score gradient
estimation algorithm. This approach treats the system as a black box and
back-propagates loss directly to the optical weights' probabilistic
distributions, hence circumventing the need for computation-heavy and biased
system simulation. We demonstrate a superior classification accuracy on the
MNIST and FMNIST datasets through experiments on a single-layer diffractive
optical computing system. Furthermore, we show its potential for image-free and
high-speed cell analysis. The inherent simplicity of our proposed method,
combined with its low demand for computational resources, expedites the
transition of optical computing from laboratory demonstrations to real-world
applications.

光计算系统的实地优化是无模型的解决方案，基于分数梯度估计算法，其简单性和对计算资源的低需求加速了光计算从实验室演示到现实世界应用的过渡。