Photonics Research, Vol. 9, Issue 2, 020000B9 (2021)
Integrating deep learning to achieve phase compensation for free-space orbital-angular-momentum-encoded quantum key distribution under atmospheric turbulence
Xingyu Wang1,2, Tianyi Wu2, Chen Dong2,*, Haonan Zhu1, Zhuodan Zhu3, and Shanghong Zhao1
- 1School of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
- 2Information and Communication College, National University of Defense Technology, Xi’an 710006, China
- 3No. 94782 Unit of PLA, Hangzhou 310021, China
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.
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