The advancement of mobile quantum networks unlocks new opportunities for secure communication in dynamic environments, such as drone-based networks equipped with quantum repeaters. This work introduces Q-CAST Mobility, an adaptation of the Q-CAST quantum routing protocol, specifically optimized for mobile quantum nodes. By integrating concepts from classical reactive and proactive routing protocols, inspired by the Zone Routing Protocol (ZRP), and leveraging Q-CAST’s quantum routing framework, Q-CAST Mobility combines multipath routing, dynamic route updates, and efficient qubit management to maintain robust quantum entanglements in highly mobile scenarios. To assess its performance, we conducted simulations across various mobility levels, comparing Q-CAST Mobility with state-of-the-art methods. The results demonstrate that Q-CAST Mobility significantly outperforms existing protocols regarding throughput, underscoring its efficiency and adaptability in mobile quantum communication networks.
The advancement of mobile quantum networks unlocks new opportunities for secure communication in dynamic environments, such as drone-based networks equipped with quantum repeaters. This work introduces Q-CAST Mobility, an adaptation of the Q-CAST quantum routing protocol, specifically optimized for mobile quantum nodes. By integrating concepts from classical reactive and proactive routing protocols, inspired by the Zone Routing Protocol (ZRP), and leveraging Q-CAST’s quantum routing framework, Q-CAST Mobility combines multipath routing, dynamic route updates, and efficient qubit management to maintain robust quantum entanglements in highly mobile scenarios. To assess its performance, we conducted simulations across various mobility levels, comparing Q-CAST Mobility with state-of-the-art methods. The results demonstrate that Q-CAST Mobility significantly outperforms existing protocols regarding throughput, underscoring its efficiency and adaptability in mobile quantum communication networks.
The problem of entanglement routing in quantum networks has found much interest in recent times. Phased protocols have been proposed wherein the entire process of routing and establishment of end-to-end entanglement between nodes of a quantum network is accomplished in phased time-slots. Examples of such protocols are Q-PASS and Q-CAST [Shi et al. Concurrent Entanglement Routing for Quantum Networks: Model and Designs. (IEEE/ACM Transactions on Networking, 32(3):2205–2220, 2024)].In this work, our contribution is two-fold. We design, implement and make available an extensible tool called QPhase that will help quantum network routing protocol designers implement and analyze against various metrics, their (synchronous and phased) protocols.Secondly, we propose a hybrid protocol called Q-CAST-Enhanced that uses the best features of the Q-PASS and Q-CAST protocols. In addition, the protocol incorporates the capability of learning from historical patterns and being adaptive to network changes. Q-CAST-Enhanced uses a hybrid path selection strategy wherein the expected throughput metric is combined with a newly introduced metric called reliability, which is responsible for accounting for historical success. The protocol also employs an adaptive recovery mechanism that dynamically switches between the XOR-based and segment-based recovery methods based on path information. We report simulation results for Q-CAST-Enhanced along with comparisons with Q-CAST using our simulator, QPhase.
The emergence of sixth-generation (6G) wireless networks introduces unprecedented requirements for ultra-secure, low-latency communication across heterogeneous space–air–ground integrated network (SAGIN). Existing drone communication frameworks including LoRaWAN, Long Term Evolution, and Ad Hoc mesh architectures exhibit critical vulnerabilities to eavesdropping, jamming, and quantum-computational attacks due to their reliance on classical cryptographic primitives. To address these challenges, this work presents the Quantum-Secured Adaptive Routing Algorithm (QSARA), a novel framework designed for 6G-enabled unmanned aerial vehicle (UAV) networks that integrates Quantum Key Distribution (QKD), Reconfigurable Intelligent Surfaces (RIS), and Joint Communication and Sensing (JCAS) to enhance information-theoretic security and real-time performance. The proposed framework employs a quantum-augmented dynamic graph model to represent UAV swarm networking and uses Proximal Policy Optimisation (PPO)-based deep reinforcement learning to optimise routing under adversarial and uncertain conditions. A multi-objective cost function jointly captures classical quality of service metrics, such as latency, bandwidth, and energy consumption alongside with quantum-layer security indicators, including quantum bit error rate, key pool entropy, and key availability. High-fidelity simulations with 500 mobile drones under diverse adversarial threats demonstrate that the proposed framework achieves a key establishment success rate of 96.2%, end-to-end latency of 23.7 milliseconds, energy consumption of 7.8 watt-hours, and a packet delivery ratio of 94.1%, outperforming state-of-the-art classical and quantum-aware baselines. These results position the QSARA as a scalable and quantum-resilient routing solution for mission-critical UAV networking in next-generation 6G smart mobility ecosystems.
In recent years, quantum communication networks have emerged as a promising alternative to classical communication networks for the transmission of quantum information. One of the challenges for achieving optimal performance in quantum communication networks is the efficient routing of quantum signals across multiple paths. To address this, a variety of routing protocols have been proposed, including multi-path routing protocols. Multi-path routing involves sending a quantum signal through multiple distinct paths simultaneously to reduce delays in the transmission of quantum information. By distributing the quantum information over multiple paths, this approach can provide significant performance improvements, including reduced latency and higher throughput. In addition, multi-path routing can enable new possibilities for the security of quantum communication networks, such as more secure entanglement swapping protocols and improved protection against eavesdropping. In this paper, we investigate the performance of multi-path routing protocols in multi-node quantum communication networks. We consider two approaches to multi-path routing, a basic routing approach based on shortest path optimization, and a more advanced approach based on graph partitions. We evaluate the performance of these protocols in a range of multi-node networks, with focus on the trade-off between latency and throughput. The results show that multi-path routing yields significant performance improvements in such networks and provides an efficient way for routing quantum signals across multiple paths.
This paper explores the optimization of multi-path communication within quantum networks, aiming to enhance communication efficiency and reliability. The core concept involves distributing quantum information across multiple paths and employing an optimized algorithm to manage this distribution. The algorithm focuses on minimizing latency, maximizing throughput, and mitigating the effects of channel loss inherent in quantum communication. We present a novel approach utilizing a layered optimization strategy, combining shortest path routing with adaptive power allocation. Mathematical formulations are detailed, including metrics for path quality, latency, and overall network throughput. The proposed method offers a significant improvement over traditional single-path communication strategies, particularly in complex quantum network topologies. Simulation results (though not presented through experimental data) demonstrate a substantial increase in communication performance under various network conditions. Further research will explore integration with quantum error correction codes and dynamic network topology adaptation.
Quantum network can provide end-to-end entanglements through quantum operations, supporting various quantum applications such as quantum key distribution and distributed quantum computing. However, many previous proposals for quantum networks assumed the division of quantum network into time slots and batch processing of network communication tasks, performing poorly in dynamic network scenarios and not fully utilizing network resources. This work focuses on the routing problem in entanglement quantum networks, aiming to provide more efficient end-to-end entanglement establishment for upper-layer applications. We propose an on-demand routing algorithm that dynamically adjusts the optimal path based on the current network conditions. Specifically, we consider the benefits of path width for quantum networks and propose an algorithm for finding the shortest wide path. Using this algorithm, each node establishes its own routing table. Additionally, to dynamically avoid network hotspots, we introduce the concept of traffic to reflect the busyness of nodes. Finally, using a dynamic cost calculation method as the routing metric, we select the optimal path for the current request. To evaluate the performance of the proposed quantum routing algorithm, we conducted simulation experiments on SimQN quantum simulation platform. The results show that our approach improves network throughput by 14 percent compared to existing solutions.
Mobile adhoc networks (MANETs) are characterized by their dynamic and decentralized nature, making them inherently vulnerable to security threats. This paper proposes a novel security framework that integrates Quantum Key Distribution (QKD) with error correction, privacy amplification, and distributed key management to enhance the security of MANETs. The experimental analysis was conducted using simulations to evaluate the effectiveness of the proposed framework across various node densities and mobility patterns. Our results demonstrate improved security in key exchanges but also highlight trade-offs in network performance metrics such as throughput and latency.