利用衍射光学神经网络实现量子极限成像
Quantum-limited imaging using diffractive optical neural networks
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中文总结 AI 辅助
该研究将成像转化为多参数量子估计，提出基于衍射光学神经网络与光子计数的测量架构，可达到Nagaoka-Hayashi Cramér-Rao界，实现量子极限下的精细图像重建，性能优于直接成像，为相关领域应用开辟可扩展途径。
中文摘要 AI 辅助
我们将一般成像问题转化为对带限空间频率振幅的多参数量子估计。针对可分离（单副本）测量，我们利用半定规划计算精度极限，以评估Nagaoka-Hayashi Cramér-Rao界。随后，我们提出一种基于衍射光学神经网络（diffractive optical neural networks）与光子计数的测量装置架构，该架构可达到此精度极限。将该框架扩展至任意物体与多振幅场景后，我们展示了图像重建结果：此架构在量子极限下恢复了精细特征，性能优于直接成像。这些结果共同为超分辨率显微镜、望远镜及遥感领域中实现多参数量子极限的可扩展应用开辟了途径。
英文摘要
We cast general imaging as multiparameter quantum estimation of band-limited spatial-frequency amplitudes. For separable (single-copy) measurements, we compute precision limits using semidefinite programming to evaluate the Nagaoka-Hayashi Cramér-Rao bound. We then introduce an architecture for a measurement apparatus based on diffractive optical neural networks and photon counting that saturates this bound. Extending the framework to arbitrary objects and many amplitudes, we show image reconstructions in which our architecture recovers fine features at the quantum limit, outperforming direct imaging. Together, these results open a scalable route to saturating multiparameter quantum limits in superresolution microscopy, telescopy, and remote sensing.
