近日，法国索邦大学的Sylvain Gigan及其研究小组与美国耶鲁大学的Hui Cao等人合作并取得一项新进展。经过不懈努力，他们发现循环线性散射能够实现的线性光学编码。相关研究成果已于2024年7月31日在国际知名学术期刊《自然—光子学》上发表。
本文介绍了一种创新设计，该设计利用多散射腔在低功率连续波激光中被动诱导光学非线性随机映射。每个散射事件都能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形成高度非线性的映射关系。研究人员证明，即使在读出维度降低的情况下，他们的设计也能保留关键信息，从而实现光学数据的有效压缩。这一特性使得他们的光学平台能够在多种应用中提供高效的光学信息处理解决方案。
研究人员进一步验证了该设计在多种任务中的有效性，包括分类、图像重建、关键点检测和目标检测，所有这些都是通过将光学数据压缩与数字解码器相结合来实现的。特别是在极端压缩比下，该设计在实时行人检测任务中展现出了卓越的性能。这一研究成果为光学计算领域的新算法和非常规建筑设计开辟了新的道路。
据悉，光信息处理和计算可以潜在地提供增强的性能、可扩展性和能源效率。然而，实现非线性——计算的关键组成部分——在光学领域仍然具有挑战性。
附：英文原文
Title: Nonlinear optical encoding enabled by recurrent linear scattering
Author: Xia, Fei, Kim, Kyungduk, Eliezer, Yaniv, Han, SeungYun, Shaughnessy, Liam, Gigan, Sylvain, Cao, Hui
Issue&Volume: 2024-07-31
Abstract: 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.
DOI: 10.1038/s41566-024-01493-0
Source: https://www.nature.com/articles/s41566-024-01493-0