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Integrating deep learning to achieve phase compensation for free-space orbital-angular-momentum-encoded quantum key distribution under atmospheric turbulence
Photonics Research ( IF 7.6 ) Pub Date : 2021-01-13
Xingyu Wang, Tianyi Wu, Chen Dong, Haonan Zhu, Zhuodan Zhu, and Shanghong Zhao

A high-dimensional quantum key distribution (QKD), which adopts degrees of freedom of the orbital angular momentum (OAM) states, is beneficial to realize secure and high-speed QKD. However, the helical phase of a vortex beam that carries OAM is sensitive to the atmospheric turbulence and easily distorted. In this paper, an adaptive compensation method using deep learning technology is developed to improve the performance of OAM-encoded QKD schemes. A convolutional neural network model is first trained to learn the mapping relationship of intensity profiles of inputs and the turbulent phase, and such mapping is used as feedback to control a spatial light modulator to generate a phase screen to correct the distorted vortex beam. Then an OAM-encoded QKD scheme with the capability of real-time phase correction is designed, in which the compensation module only needs to extract the intensity distributions of the Gaussian probe beam and thus ensures that the information encoded on OAM states would not be eavesdropped. The results show that our method can efficiently improve the mode purity of the encoded OAM states and extend the secure distance for the involved QKD protocols in the free-space channel, which is not limited to any specific QKD protocol.

中文翻译:

集成深度学习以实现大气湍流下自由空间轨道角动量编码量子密钥分布的相位补偿

采用轨道角动量(OAM)状态的自由度的高维量子密钥分发(QKD)有助于实现安全和高速的QKD。但是,携带OAM的涡旋光束的螺旋相位对大气湍流敏感并且容易变形。本文提出了一种使用深度学习技术的自适应补偿方法,以提高OAM编码QKD方案的性能。首先训练卷积神经网络模型,以学习输入强度分布和湍流相位的映射关系,并将这种映射用作反馈,以控制空间光调制器以生成相位屏幕以校正扭曲的涡旋光束。然后设计了一种具有实时相位校正功能的OAM编码QKD方案,其中补偿模块仅需要提取高斯探测光束的强度分布,从而确保不会窃听在OAM状态下编码的信息。结果表明,我们的方法可以有效地提高编码后的OAM状态的模式纯度,并扩展自由空间信道中涉及的QKD协议的安全距离,而不限于任何特定的QKD协议。
更新日期:2021-01-13
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