南京大学吴培亨团队研究了接近标准量子极限精度的噪声容忍激光雷达。该研究于2025年3月26日发表在《光：科学与应用》杂志上。

随着单光子探测器和计算算法的进步，量子启发成像技术已被证明对激光雷达是有效的。然而，由于背景噪声的干扰和室外环境中信号的变化，激光雷达的性能仍远未达到相干探测光量子波动所设定的极限。

在这项工作中，研究组从探测的角度提出并演示了一种激光雷达，以接近标准的量子限制性能。回波信号的光子数由光子数分辨探测器记录，并通过激光雷达中的有源光子数滤波器应用于克服重背景噪声。它可以在宽光子通量范围内接近强度估计的标准量子极限，并且当平均信号光子数为10时，Fisher信息仅比量子Fisher信息低0.04dB。

实验上，所提出的激光雷达在白天演示了无噪声目标重建和成像。当基于开/关检测仅进行1/1000的测量时，它在反射率分辨率方面也表现更好。这项工作为构建激光雷达提供了一种基本策略，以在复杂环境中快速提取目标和识别材料，这对自动驾驶汽车等智能代理非常重要。

附：英文原文

Title: Noise-tolerant LiDAR approaching the standard quantum-limited precision

Author: Li, Haochen, Zheng, Kaimin, Ge, Rui, Zhang, Labao, Zhang, Lijian, He, Weiji, Zhang, Biao, Wu, Miao, Wang, Ben, Mi, Minghao, Guan, Yanqiu, Tan, Jingrou, Wang, Hao, Chen, Qi, Tu, Xuecou, Zhao, Qingyuan, Jia, Xiaoqing, Chen, Jian, Kang, Lin, Chen, Qian, Wu, Peiheng

Issue&Volume: 2025-03-26

Abstract: Quantum-inspired imaging techniques have been proven to be effective for LiDAR with the advances of single photon detectors and computational algorithms. However, due to the disturbance of background noise and the varies of signal in outdoor environment, the performance of LiDAR is still far from its ultimate limit set by the quantum fluctuations of coherent probe light. In this work, we propose and demonstrate a LiDAR from the detection perspective for approaching the standard quantum-limited performance. The photon numbers of echo signals are recorded by a photon-number-resolving detector and applied to overcome heavy background noise through an active photon number filter in the LiDAR. It can approach the standard quantum limit in intensity estimation in a wide photon-flux range, and achieve a Fisher information of only 0.04dB less than the quantum Fisher information when the mean signal photon number is 10. Experimentally, a noise-free target reconstruction and imaging is demonstrated in the daytime by the proposed LiDAR. It also performs better in reflectivity resolution when taking only 1/1000 of the measurements based on on/off detection. This work provides a fundamental strategy for constructing a LiDAR to quickly extract targets and identify materials in complex environments, which is important for intelligent agents such as autonomous vehicles.

DOI: 10.1038/s41377-025-01790-5

Source: https://www.nature.com/articles/s41377-025-01790-5