Spatial Mode Correction of Single Photons Using Machine Learning
Spatial Mode Correction of Single Photons Using Machine Learning
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DOI：
10.1002/qute.202000103
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发表时间：
2021-01-22
影响因子：
4.4
通讯作者：
Magana-Loaiza, Omar S.
中科院分区：
文献类型：
--
作者：
Bhusal, Narayan;Lohani, Sanjaya;Magana-Loaiza, Omar S.
光的空间模式构成了从量子通信、量子成像到遥感等各种量子技术的宝贵资源。然而，它们对随机介质引起的相位失真的脆弱性，对许多量子光子技术的实际实施造成了极大的限制。不幸的是，这个问题在单光子水平上加剧了。在过去的二十年里，这一具有挑战性的问题已经通过利用光学非线性、量子关联和自适应光学的传统方案得到了解决。本文利用人工神经网络的自学习和自演化特性，在单光子水平上对扭曲拉盖尔-高斯模的复杂空间轮廓进行校正。此外，该技术的潜力被用于提高依赖于结构化单光子的光通信协议的信道容量。研究结果对结构光子和单光子图像的实时湍流校正具有重要意义。
Spatial modes of light constitute valuable resources for a variety of quantum technologies ranging from quantum communication and quantum imaging to remote sensing. Nevertheless, their vulnerabilities to phase distortions, induced by random media, impose significant limitations on the realistic implementation of numerous quantum-photonic technologies. Unfortunately, this problem is exacerbated at the single-photon level. Over the last two decades, this challenging problem has been tackled through conventional schemes that utilize optical nonlinearities, quantum correlations, and adaptive optics. In this article, the self-learning and self-evolving features of artificial neural networks are exploited to correct the complex spatial profile of distorted Laguerre-Gaussian modes at the single-photon level. Furthermore, the potential of this technique is used to improve the channel capacity of an optical communication protocol that relies on structured single photons. The results have important implications for real-time turbulence correction of structured photons and single-photon images.