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.

翻译：光学计算系统能够提供高速低能耗的数据处理，但面临计算密集型训练和模拟-现实差距两大缺陷。我们提出一种基于分数梯度估计算法的轻量级原位优化无模型方案。该方法将系统视为黑箱，直接将损失反向传播至光学权重的概率分布，从而避免计算密集且存在偏倚的系统模拟。通过单层衍射光学计算系统的实验，我们在MNIST和FMNIST数据集上展示了卓越的分类精度。此外，我们证明了该方法在无图像高速细胞分析中的应用潜力。所提方法的固有简洁性及低计算资源需求，将加速光学计算从实验室演示向真实世界应用的转化进程。