Compared with classical communication, quantum communication has many advantages, which can realize absolutely safe and efficient information transmission. The research of quantum secure communication started earlier, and the technology gradually matured. At present, it is rapidly developing into networked applications, and has entered the practical and large-scale networking stage. In order to improve the resource utilization rate of the network, an optimal allocation model of quantum communication resources based on grey clustering algorithm is proposed. The author uses the grey dynamic clustering algorithm based on interval grey number to divide users into a group of clusters, and selects a node in each cluster as the cluster head, as the gateway and the relay node within the cluster, and the cluster head as the gateway within the cluster to manage the mobility of local users. It can cluster users, reduce the number of quantum communication links used when forwarding signaling messages, reduce the cost of using resources and improve network throughput. The simulation results show that the grey dynamic clustering algorithm based on interval grey number performs better when the average number of network connections is high. Under the maximum throughput criterion, compared with other schemes, the maximum system transmission rate and. Under the criterion of proportional fairness, the scheme can ensure fairness among users while maintaining relatively high system throughput.
Compared with classical communication, quantum communication has many advantages, which can realize absolutely safe and efficient information transmission. The research of quantum secure communication started earlier, and the technology gradually matured. At present, it is rapidly developing into networked applications, and has entered the practical and large-scale networking stage. In order to improve the resource utilization rate of the network, an optimal allocation model of quantum communication resources based on grey clustering algorithm is proposed. The author uses the grey dynamic clustering algorithm based on interval grey number to divide users into a group of clusters, and selects a node in each cluster as the cluster head, as the gateway and the relay node within the cluster, and the cluster head as the gateway within the cluster to manage the mobility of local users. It can cluster users, reduce the number of quantum communication links used when forwarding signaling messages, reduce the cost of using resources and improve network throughput. The simulation results show that the grey dynamic clustering algorithm based on interval grey number performs better when the average number of network connections is high. Under the maximum throughput criterion, compared with other schemes, the maximum system transmission rate and. Under the criterion of proportional fairness, the scheme can ensure fairness among users while maintaining relatively high system throughput.
By using quantum packet transmission technology in large scale quantum communication networks, the throughput of transmission node, network link utilization, and the anti-interference performance of communication can be effectively improved. However, the fast transmission of quantum packets is closely related to the performance of router. The bottleneck of the router performance will seriously affect the scalability of the network and the transmission efficiency of the link. In order to reduce the number of quantum packet queues in nodes of the quantum communication network and to reduce the transmission delay of quantum packets, firstly, according to the classical computer communication network structure, in our paper, we divide the quantum communication network into quantum local area network, quantum metropolitan area network and quantum wide area network. Secondly, the quantum packet format and the quantum cluster format compatible with the packet format in the computer network are determined. Then, a quantum information packet transmission scheme based on the hierarchy is proposed, to realize the end-to-end transmission of quantum information. In our scheme, the quantum packets are divided into quantum packet header information and quantum data information. Quantum dense coding mode is used to transmit the quantum packet header information, while the quantum data information uses quantum teleportation to transmit. First, the quantum packets are sent to the router of the quantum local area network at source address, then the quantum LAN router relay the packets to the quantum metropolitan area network router, the router here makes the quantum packets into quantum cluster according to destination address. Quantum clusters are transmitted in the quantum metropolitan area network and quantum wide area network, ending in the quantum metropolitan area network routing. After the quantum clusters are decomposed, they are sent to the destination address through the local area network router of each quantum packet.We analyze the number of quantum entanglement pairs and the total transmission time in our scheme. The results show that the more the routers by the quantum packet and the quantum cluster are, the more the number of quantum entanglement pairs required by the transmission of a certain quantum packet is. When the number of routers is certain, the number of entanglement pairs required in the transmission process of quantum packet and quantum cluster depends on the number of quantum packets. Finally, the theoretical analysis and calculation are carried out by Matlab simulation, the results show that hierarchical quantum packet information transmission scheme can effectively reduce the transmission time of quantum packet information in the quantum communication network, and the reduced time is related to the quantum router performance and the number of quantum packets to send: the more the number of quantum packets to send, the longer the length of packet processing time needs is and the more obvious the advantage of our scheme is. Therefore, the proposed scheme in this paper is suitable for the construction of large scale quantum communication networks.
This paper presents a novel algorithm for optimizing the allocation of entangled resources within quantum networks. The core of the approach lies in employing a reinforcement learning (RL) framework to dynamically manage and distribute entanglement, addressing the inherent challenges of maximizing utilization and enhancing quantum communication efficiency. Unlike traditional static allocation methods, the proposed RL agent learns optimal allocation strategies through iterative interaction with a simulated quantum network environment. The algorithm considers factors such as node connectivity, qubit availability, and communication delays to minimize resource wastage and improve overall network performance. The key performance indicator (KPI) is the entanglement fidelity between communicating nodes. The developed algorithm offers a promising pathway towards realizing the full potential of quantum networks by intelligently managing their most valuable asset: entanglement. The primary goal is to achieve higher fidelity entanglement distribution and improved communication rates. The effectiveness of the algorithm is evaluated through simulations, demonstrating its ability to outperform existing allocation schemes in various network topologies and operational scenarios.
Quantum entanglement networks has ushered in a new era of communication, with the core task of generating long-distance quantum entanglement being crucial for a multitude of quantum applications. Although current research is primarily focused on establishing long-distance entanglement across various network topologies, there is still a lack of attention to the variability of communication requests within quantum networks. This paper introduces the Fair and Restricted Resource Allocation Algorithm (FARA), designed to meet the business-driven resource allocation requirements within quantum networks. This algorithm prioritizes the demands and urgency of requests to establish a fairer response mechanism, leading to an enhanced user experience. We refine the strategy for allocating network resources to maximize their use during multiple concurrent requests and to improve the success rate of business. Simulation results demonstrate that our proposed scheme improves the business success rate by 6.35% to 24.58% and user satisfaction by 0.922 to 4.533 points compared to the Q-CAST algorithm. Additionally, resource wastage is reduced by 473 to 800 units. These enhancements indicate that our research provides effective strategies for business-driven optimization in quantum networks and makes a positive contribution to the development of entanglement networks towards more practical and efficient applications.
Quantum key distribution (QKD) is an encrypted communication technique based on the principles of quantum mechanics that ensures communication security by exploiting the properties of quantum states. Currently, the transmission efficiency of the QKD system is low. Trusted relay technology is used to solve this problem and achieve long-distance transmission. However, trusted relaying alone cannot decrypt the issues of poor link stability and the low utilization of key resources. To further optimize the system performance, we propose a dynamic routing algorithm. One of the improvement schemes includes the following: firstly, an adjustable-size quantum key pool (QKP) is designed, which can dynamically adjust the size of the refreshing pool according to the actual demand. Secondly, the utilization of key resources is improved by using the residual quantum key model to dynamically obtain the remaining key amount in the QKP and set the key amount threshold. We calculate the link-blocking probability and track the blocking intensity and blocking entry by combining the Poisson process, thus realizing the evaluation of the link stability. Finally, the number of remaining keys in the QKP and the link-blocking probability combine with the random wandering model as the basis of the route selection for the QKD dynamic routing algorithm to achieve efficient key path selection. We validated the algorithm by comparing it with other algorithms on the Mininet simulation platform, and the algorithm proved to have a better performance in terms of congestion avoidance, delay reduction, and improved QKD efficiency. This scheme provides a novel and efficient way to solve the problems in existing QKD systems. It effectively improves the transmission efficiency and strengthens the system’s security by dynamically obtaining the critical volume, accurately evaluating the link state, and selecting the optimal critical path.
With the continuous expansion of quantum communication networks and the increasing diversity of their business types, how to achieve efficient configuration under limited quantum resources has become the key to improving system performance. The quantum resources described in this article mainly include entanglement pairs, quantum bits, and channel capacity. Traditional optimization methods typically rely on precise mathematical models, which are difficult to adapt to dynamically evolving network environments and susceptible to noise interference. Therefore, this study proposes a machine learning based framework for optimizing the allocation of quantum communication resources. This framework integrates deep reinforcement learning and graph neural network technology, enabling collaborative optimization of entanglement allocation, routing selection, and time slice scheduling between nodes. Three typical quantum communication scenarios were constructed and tested in a simulation environment, including static topology, dynamic links, and high noise environments. The proposed method was compared and analyzed with integer linear programming, heuristic greedy algorithms, and rule-based strategies. Experimental data shows that this research method achieved an improvement of approximately 12% to 18% in average fidelity retention, a 15% to 22% increase in resource utilization, and a 20% reduction in latency. This study provides useful reference for the intelligent resource management of large-scale quantum networks in the future.
With the development of the Internet of Things (IoT) towards supporting next-generation security and distributed applications, the integration of quantum communication technology provides a promising way to enhance data security, computing power, and perception accuracy. The large-scale interconnection of quantum nodes enables the formation of quantum enhanced IoT networks, with high fidelity entanglement serving as the cornerstone of secure and efficient communication. However, the inherent limitations of quantum resources and channel noise pose significant challenges to routing and resource management. This article constructs the quantum path selection problem as a multi criteria path selection problem, designs a weight scoring function to obtain the optimal path in the current network for each request, and proposes a two-stage routing and allocation strategy TPROA, namely the idea of "guarantee before utilization", aiming to maximize network throughput and improve resource utilization in resource constrained network environments. The simulation results show that compared with traditional methods, TPROA significantly improves quantum resource utilization and network throughput. The proposed method lays the foundation for scalable quantum IoT integration.