Coherent measurement of quantum signals used for continuous-variable (CV) quantum key distribution (QKD) across satellite-to-ground channels requires compensation of phase wavefront distortions caused by atmospheric turbulence. One compensation technique involves multiplexing classical reference pulses (RPs) and the quantum signal, with direct phase measurements on the RPs then used to modulate a real local oscillator (RLO) on the ground - a solution that also removes some known attacks on CV-QKD. However, this is a cumbersome task in practice - requiring substantial complexity in equipment requirements and deployment. As an alternative to this traditional practice, here we introduce a new method for estimating phase corrections for an RLO by using only intensity measurements from RPs as input to a convolutional neural network, mitigating completely the necessity to measure phase wavefronts directly. Conventional wisdom dictates such an approach would likely be fruitless. However, we show that the phase correction accuracy needed to provide for non-zero secure key rates through satellite-to-ground channels is achieved by our intensity-only measurements. Our work shows, for the first time, how artificial intelligence algorithms can replace phase-measuring equipment in the context of CV-QKD delivered from space, thereby delivering an alternate deployment paradigm for this global quantum-communication application.

翻译：用于卫星-地面信道的连续变量量子密钥分发系统中，量子信号的相干测量需要补偿大气湍流引起的相位波前畸变。一种补偿技术是将经典参考脉冲与量子信号复用，利用对参考脉冲的直接相位测量来调制地面上的真实本地振荡器——该方案还可消除针对连续变量量子密钥分发的某些已知攻击。然而这在实践中是一项繁琐任务，需要对设备要求和部署投入大量复杂度。作为传统实践的替代方案，本文提出一种新方法，仅将参考脉冲的强度测量值作为卷积神经网络的输入来估算真实本地振荡器的相位校正，完全免除了直接测量相位波前的必要性。传统观点认为此类方法可能徒劳无功，但我们的研究表明：通过仅基于强度的测量，即可获得通过卫星-地面信道实现非零安全密钥率所需的相位校正精度。本工作首次展示了人工智能算法如何替代空间传输连续变量量子密钥分发中的相位测量设备，从而为这一全球量子通信应用提供新的部署范式。