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，但在计算密集型训练以及仿真与现实之间的差距方面存在不足。我们提出了一种基于分数梯度估计算法的无模型解决方案，用于光学计算系统的轻量级原位优化。该方法将系统视为黑箱，并直接将损失反向传播至光学权重的概率分布，从而规避了计算量大且存在偏见的系统仿真需求。通过在单层衍射光学计算系统上进行实验，我们在MNIST和FMNIST数据集上展示了卓越的分类精度。此外，我们还证明了该方法在无图像高速细胞分析中的潜力。所提方法固有的简洁性及其对计算资源的低需求，加速了光学计算从实验室演示向实际应用的过渡。