Machine Learning for Phase Estimation in Satellite-to-Earth Quantum Communication

TL;DR

This paper demonstrates that low-complexity neural networks can effectively estimate signal phase errors in satellite-to-Earth quantum communication, enhancing real-time performance of quantum key distribution systems.

Contribution

It introduces a framework for optimizing neural network architecture for phase error estimation, achieving near-optimal accuracy with reduced complexity.

Findings

Low-complexity neural networks can approach the quantum Cramér-Rao bound.

Enhanced real-time phase error estimation improves CV-QKD over satellite channels.

Framework guides neural network design for quantum communication applications.

Abstract

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 Cram\'er-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.