发表机构
University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用学习型衍射光学网络，在光子受限条件下对光场进行状态判别与参数估计，无需先验最优测量知识，即可显著超越标准测量并逼近量子极限。
AI 中文摘要
量子力学设定了光子受限传感的最终界限，然而，达到这些界限的实际测量仅在特殊情况下已知。视觉传感问题尤其如此，其目标是根据远处物体发射或反射的光场的空间结构来推断其特征。由于此类物体和场的潜在复杂结构，构建针对它们的最优测量是一项具有挑战性的任务。在此，我们将学习型衍射光学应用于受限光子预算下相干和衍射受限非相干光场的状态判别与参数估计。直接针对每项任务的性能指标进行优化，无需预先了解最优测量，物理上可实现的衍射光学神经网络显著优于标准测量，并在给定光子数下以及渐近极限下逼近量子极限。
英文摘要
Quantum mechanics sets the ultimate bounds on photon-limited sensing, yet practical measurements attaining these bounds are known only in special cases. This is particularly the case for visual sensing problems, where the goal is to infer features of a distant object based on the spatial structure of the light field it emits or reflects. Because of the potentially complex structure of such objects and fields, constructing optimal measurements on them is a challenging task. Here, we apply learned diffractive optics to state discrimination and parameter estimation of coherent and diffraction-limited incoherent light fields under a restricted photon budget. Optimized directly on each task's figure of merit, without prior knowledge of the optimal measurement, the physically realizable diffractive optical neural networks substantially outperform standard measurements and approach the quantum limits for a given number of photons as well as in the asymptotic limit.
Comments9 pages, 6 figures
