Related papers: Optimizing Quantum Key Distribution Network Perfor…

Quantum key distribution (QKD) is a cryptographic technique that uses quantum mechanical principles to enable secure key exchange. Practical deployment of QKD requires robust, cost-effective systems that can operate in challenging field…

Efficient entanglement distribution is the foundational challenge in realizing large-scale Quantum Networks. However, state-of-the-art solutions are frequently limited by restrictive operational assumptions, prohibitive computational…

Quantum Key Distribution (QKD) is an innovative quantum communications protocol which exploits the laws of quantum mechanics to generate unconditionally secure cryptographic keying material between two geographically separated parties. The…

The rapid advancement of quantum technologies calls for the design and deployment of quantum-safe cryptographic protocols and communication networks. There are two primary approaches to achieving quantum-resistant security: quantum key…

Quantum key distribution (QKD) represents a cornerstone of secure communication in the quantum era. While discrete-variable QKD (DV-QKD) protocols were historically the first to demonstrate secure key exchange, continuous-variable QKD…

Quantum mechanics is the current best description of the world as we know it. Experiments have shown that quantum predictions are accurate up ten places of decimal. In quantum cryptography much work has been devoted to the study of Quantum…

Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification. However, only few work has addressed the adversarial robustness of…

To perform Quantum Key Distribution, the mastering of the extremely weak signals carried by the quantum channel is required. Transporting these signals without disturbance is customarily done by isolating the quantum channel from any noise…

With the rapid growth of interconnected devices, accurately detecting malicious activities in network traffic has become increasingly challenging. Most existing deep learning-based intrusion detection systems treat network flows as…

Graph Neural Networks (GNNs) are increasingly important given their popularity and the diversity of applications. Yet, existing studies of their vulnerability to adversarial attacks rely on relatively small graphs. We address this gap and…

Communication efficiency is a major bottleneck in the applications of distributed networks. To address the problem, the problem of quantized distributed optimization has attracted a lot of attention. However, most of the existing quantized…

In recent years, continuous-variable quantum key distribution (CV-QKD) has become a promising paradigm for enabling secure communication among multiple end users sharing the same telecommunication backbone. CV-QKD with reverse…

The advance of quantum computing poses a significant threat to classical cryptography, compromising the security of current encryption schemes such as RSA and ECC. In response to this challenge, two main approaches have emerged: quantum…

Quantum computing poses significant threats to conventional cryptographic techniques such as RSA and AES, motivating the need for quantum secure communication methods. Quantum Key Distribution (QKD) offers information theoretic security…

The theoretically proven security of quantum key distribution (QKD) could revolutionise how information exchange is protected in the future. Several field tests of QKD have proven it to be a reliable technology for cryptographic key…

Improvement in secure transmission of information is an urgent practical need for governments, corporations and individuals. Quantum key distribution (QKD) promises security based on the laws of physics and has rapidly grown from…

Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. Here, we recapitulate the graph construction for crystalline (periodic) materials and investigate its impact on the GNNs…

The emergence of quantum computing has introduced unprecedented security challenges to conventional cryptographic systems, particularly in the domain of optical communications. This research addresses these challenges by innovatively…

Multiparty quantum key distribution (QKD) is useful for many applications that involve secure communication or collaboration among multiple parties. While it can be achieved using pairwise QKD, a more efficient approach is to achieve it…

The main approach to hybrid quantum-classical neural networks (QNN) is employing quantum computing to build a neural network (NN) that has quantum features, which is then optimized classically. Here, we propose a different strategy: to use…