Machine Learning for Phase Estimation in Satellite-to-Earth Quantum Communication
This work addresses a bottleneck in practical continuous-variable quantum key distribution systems for secure global networks, though it appears incremental as it focuses on optimizing an existing method.
The paper tackles the problem of improving real-time performance in satellite-to-Earth quantum communication by developing a low-complexity neural network for signal phase error estimation, achieving this without significantly sacrificing accuracy.
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.