AI-Driven Adaptive Control for Scalable Twin-Field Quantum Key Distribution Networks

Abstract

Quantum Key Distribution (QKD) has turned out to be a promising approach to secure communication systems. Twin-field QKD (TF-QKD) is one of the protocols that allow long-range secure communication to be provided beyond the traditional rate-distance constraints. Nevertheless, the instability of the phases, noises in the channels, and fixed mechanisms to control the system pose challenges to implementing large-scale TF-QKD networks in practice. The present paper suggests an AI-assisted adaptive framework of TF-QKD networks to enhance the performance of robustness, scalability, and communication. The proposed solution incorporates a predictive model which is based on machine learning to estimate phase drift, optimize correction parameters in real time. The hybrid quantum-classical architecture is designed in which an intelligent control layer is continuous to monitor channel conditions and optimize system adaptability. Simulation outcomes have shown that proposed framework can reduce Quantum Bit Error Rate (QBER) of the system by about 20-30% and increase the rate of secure key generation and overall system performance when compared to traditional methods of these systems where the system is controlled by a static system. A better scalability and stability of the proposed system to different channel conditions is also observed. These findings indicate the possibilities of incorporating artificial intelligence into the future intelligent quantum communication networks.