Related papers: Optimizing Quantum Key Distribution Network Perfor…
Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhead. Unfortunately, the performance gains of QGNN have never…
During the last 20 years, the advance of communication technologies has generated multiple exciting applications. However, classical cryptography, commonly adopted to secure current communication systems, can be jeopardized by the advent of…
Quantum key distribution (QKD), providing a way to generate secret keys with information-theoretic security,is arguably one of the most significant achievements in quantum information. The continuous-variable QKD (CV-QKD) offers the…
Quantum Key Distribution (QKD) is a physical layer encryption technique that enables two distant parties to exchange secure keys with information-theoretic security. In the last two decades, QKD has transitioned from laboratory research to…
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However, the sparse nature of GNN computation poses new challenges…
As an extension of quantum key distribution, secure communication among multiple users is an essential task in a quantum network. When the quantum network structure becomes complicated with a large number of users, it is important to…
Ensuring secure and efficient data processing in mobile edge computing (MEC) systems is a critical challenge. While quantum key distribution (QKD) offers unconditionally secure key exchange and homomorphic encryption (HE) enables…
Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured semantic information communication (QKD-SIC). In QKD-SIC systems, edge devices connected via quantum channels can…
Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured federated learning (QKD-FL), in which data owners connected via quantum channels can train an FL global model…
Graph Neural Networks (GNNs) are powerful tools for learning graph-structured data, but their scalability is hindered by inefficient mini-batch generation, data transfer bottlenecks, and costly inter-GPU synchronization. Existing training…
Continuous-variable quantum key distribution (CV QKD) with discrete modulation has attracted increasing attention due to its experimental simplicity, lower-cost implementation and compatibility with classical optical communication.…
Deep learning-based approaches have been developed to solve challenging problems in wireless communications, leading to promising results. Early attempts adopted neural network architectures inherited from applications such as computer…
Quantum key distribution (QKD) has been emerged as a promising solution for guaranteeing information-theoretic security. Inspired by this, a great amount of research effort has been recently put on designing and testing QKD systems as well…
Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data. We present an automated framework that discovers and refines variational quantum circuits (VQCs) using graph-based Bayesian…
We develop a novel key routing algorithm for quantum key distribution (QKD) networks that utilizes a distribution of keys between remote, i.e., not directly connected by a QKD link, nodes through multiple non-overlapping paths. This…
Graph Neural Networks (GNNs) achieve an impressive performance on structured graphs by recursively updating the representation vector of each node based on its neighbors, during which parameterized transformation matrices should be learned…
This paper presents a federated learning framework secured by quantum key distribution (QKD) for wireless channel estimation and radar spectrum sensing in the next generation networks (NextG or Beyond 6G). A BB84-style protocol abstraction…
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph might not be optimal in terms of specific downstream tasks,…
Quantum Key Distribution (QKD) enables two parties to securely share encryption keys by leveraging the principles of quantum mechanics, offering protection against eavesdropping. In practical implementations, QKD systems often rely on a…
Quantum key distribution (QKD) offers unconditional information security by allowing two distant users to establish a common encryption key resilient to hacking. Resultingly, QKD networks interconnecting critical infrastructure and enabling…