Optimizing Quantum Key Distribution Network Performance using Graph Neural Networks
Akshit Pramod Anchan, Ameiy Acharya, Leki Chom Thungon

TL;DR
This paper introduces a GNN-based framework to optimize Quantum Key Distribution networks, significantly improving key rates and reducing errors while maintaining network integrity across various scales.
Contribution
It presents a novel GNN approach for modeling and optimizing QKD networks, addressing dynamic conditions and resource utilization challenges.
Findings
Key rate increased from 27.1 Kbits/s to 470 Kbits/s
Average QBER reduced from 6.6% to 6.0%
Improved link prediction accuracy in medium-sized networks
Abstract
This paper proposes an optimization of Quantum Key Distribution (QKD) Networks using Graph Neural Networks (GNN) framework. Today, the development of quantum computers threatens the security systems of classical cryptography. Moreover, as QKD networks are designed for protecting secret communication, they suffer from multiple operational difficulties: adaptive to dynamic conditions, optimization for multiple parameters and effective resource utilization. In order to overcome these obstacles, we propose a GNN-based framework which can model QKD networks as dynamic graphs and extracts exploitable characteristics from these networks' structure. The graph contains not only topological information but also specific characteristics associated with quantum communication (the number of edges between nodes, etc). Experimental results demonstrate that the GNN-optimized QKD network achieves a…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Advanced Statistical Modeling Techniques
