用轻量神经网络提升卫星量子通信相位估计速度与精度。
Machine Learning for Phase Estimation in Satellite-to-Earth Quantum Communication
- 采用低复杂度LSTM神经网络实现相位误差估计
- 在保持高精度的同时显著降低模型计算开销
- 适合实时量子密钥分发系统部署于星地链路
利用一系列星地量子通道可构建全球连续变量量子密钥分发（CV-QKD）网络。通过使用本地参考光进行相干测量，并借助已知信息编码的参考脉冲对本地振荡器进行校准，结合信号相位误差估计算法，可提升网络性能。算法的速度与精度对实际CV-QKD应用至关重要。本文提出框架，分析长短期记忆神经网络（LSTM NN）架构参数化与信号相位误差估计的量子克拉美-罗极限之间的关系，重点降低模型复杂度。具体而言，我们证明了可采用低复杂度神经网络架构实现相位误差估计，且精度损失可忽略。结果显著提升了星地链路中实用化CV-QKD系统的实时性能，推动量子互联网的发展。
原文摘要 · Abstract (English)
A global continuous-variable quantum key distribution (CV-QKD) network can be established using a series of satellite-to-Earth channels. Increased performance in such a network is provided by performing coherent measurement of the optical quantum signals using a real local oscillator, calibrated locally by encoding known information on transmitted reference pulses and using signal phase error estimation algorithms. The speed and accuracy of the signal phase error estimation algorithm are vital to practical CV-QKD implementation. Our work provides a framework to analyze long short-term memory neural network (NN) architecture parameterization, with respect to the quantum Cramér-Rao uncertainty bound of the signal phase error estimation, with a focus on reducing the model complexity. More specifically, we demonstrate that signal phase error estimation can be achieved using a low-complexity NN architecture, without significantly sacrificing accuracy. Our results significantly improve the real-time performance of practical CV-QKD systems deployed over satellite-to-Earth channels, thereby contributing to the ongoing development of the Quantum Internet.
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