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

2025-02-14

AI Summary

Real-time, high-precision phase error estimation remains a critical challenge in satellite-to-ground continuous-variable quantum key distribution (CV-QKD). Method: We propose a novel hybrid approach integrating long short-term memory (LSTM) neural networks with quantum parameter estimation theory. Specifically, we jointly design the LSTM architecture and analyze its performance against the quantum Cramér–Rao bound (QCRB), enabling near-optimal estimation accuracy under low computational complexity. A physics-informed phase error model is constructed, and the real-time signal processing pipeline is optimized. Contribution/Results: The method achieves sub-milliradian estimation accuracy while reducing inference latency by an order of magnitude. It enables practical local-oscillator-based coherent detection, significantly enhancing the real-time performance and robustness of satellite-to-ground CV-QKD systems. As a key enabling technology, it advances the feasibility of scalable, global quantum internet infrastructure.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: State Estimation
• Computer Vision: Learning & Optimization for CV

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Enhance satellite-to-Earth quantum communication
• Optimize phase error estimation accuracy
• Reduce neural network model complexity

Innovation

• Long short-term memory NN
• Low-complexity architecture
• Real-time CV-QKD enhancement