Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity—a critical component of computation—remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design’s efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing.

光学信息处理和计算有可能提供增强的性能、可扩展性和能源效率。然而，在光领域实现非线性（计算的关键组成部分）仍然具有挑战性。在这里，我们介绍了一种设计，它利用多散射腔以低功率使用连续波激光器被动感应光学非线性随机映射。每个散射事件都会有效地混合来自空间光调制器不同区域的信息，从而在输入数据和输出模式之间产生高度非线性的映射。我们证明，即使读出维度降低，我们的设计也能保留重要信息，从而实现光学数据压缩。此功能使我们的光学平台能够跨应用提供高效的光学信息处理解决方案。我们展示了我们的设计在各个任务中的有效性，包括分类、图像重建、关键点检测和对象检测，所有这些都是通过光学数据压缩与数字解码器相结合来实现的。特别是，在实时行人检测中观察到极端压缩比下的高性能。我们的发现为光计算的新算法和非常规架构设计开辟了途径。