Traffic matrices (TM) are commonly used in classical networks to optimize routing and improve overall network performance. These matrices give essential information about the current state of the network and serve as a key tool for efficient and optimal traffic distribution. However, their applicability in quantum networks has not yet been explored. In this paper, we first introduce the concept of TM in quantum networks, called quantum traffic matrices (QTM). Furthermore, we introduce two parameters: QTM based link cost function and QTM based network cost. We then analyze the network cost associated with using QTM for entanglement routing, with the goal of gaining deeper insight into their practical value and effectiveness. Our simulation results indicate that as the size of the network increases, the cost of using these matrices increases. Furthermore, we analyze the cost of the network in relation to the utilization ratio of entanglement (EUR). The result shows that when the EUR is low, the network cost increases, whereas higher EUR values lead to a reduction in network cost. In addition to this, we investigate the cost of the network in relation to the average fidelity. The results indicate a significant decrease in network cost as fidelity increases. Lastly, we analyze the cost of the network in relation to the entanglement distribution rate (EDR). The results show that when the EDR is low, the network cost increases, whereas higher EDR values lead to a reduction in cost. These results highlight the need for techniques that reduce the complexity of QTM construction and reduce storage overhead to enable scalable and efficient entanglement distribution in quantum networks.
Traffic matrices (TM) are commonly used in classical networks to optimize routing and improve overall network performance. These matrices give essential information about the current state of the network and serve as a key tool for efficient and optimal traffic distribution. However, their applicability in quantum networks has not yet been explored. In this paper, we first introduce the concept of TM in quantum networks, called quantum traffic matrices (QTM). Furthermore, we introduce two parameters: QTM based link cost function and QTM based network cost. We then analyze the network cost associated with using QTM for entanglement routing, with the goal of gaining deeper insight into their practical value and effectiveness. Our simulation results indicate that as the size of the network increases, the cost of using these matrices increases. Furthermore, we analyze the cost of the network in relation to the utilization ratio of entanglement (EUR). The result shows that when the EUR is low, the network cost increases, whereas higher EUR values lead to a reduction in network cost. In addition to this, we investigate the cost of the network in relation to the average fidelity. The results indicate a significant decrease in network cost as fidelity increases. Lastly, we analyze the cost of the network in relation to the entanglement distribution rate (EDR). The results show that when the EDR is low, the network cost increases, whereas higher EDR values lead to a reduction in cost. These results highlight the need for techniques that reduce the complexity of QTM construction and reduce storage overhead to enable scalable and efficient entanglement distribution in quantum networks.
Quantum traffic engineering is a revolutionary approach envisioned to give optimum solutions to network-related difficulties in quantum networks such as entanglement routing, resource management and so on. However, the crucial input to these strategies is traffic matrix. The traffic matrices (Fidelity and channel capacity matrices) provide the knowledge about the network which enables network engineers to apply reliable and resilient solutions to optimize the performance of quantum networks. There are extensive works of traffic matrix estimation in classical networks, however there is no work in literature that explain how to construct traffic matrices in quantum networks. In this work, we provide comprehensive step-by-step guidance for creating quantum traffic matrices. Moreover, we examine the effects of data analysis on traffic matrices that have been developed. Based on our findings, we observe that the fidelity matrix is significantly impacted by the methods used to execute quantum state reconstruction during data analysis, while the capacity matrix is significantly impacted by the likelihood that channels will be successfully distinguished.
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.
A network of devices capable of transmitting quantum information called a quantum network has promising applications with vast benefits. One of the most near term achievable application is the quantum key distribution. It could be used for sharing a secret key between two end-users through any insecure authenticated communication channel. Other than sharing a secret key, a quantum network can be used for connecting quantum computers. Quantum computers have the potential to solve computational problems that their classical counterparts would require significantly more time to do so. By connecting such devices, distributed computation problems such as leader election or distributed consensus between nodes can be solved securely. For such applications to be put into practice on a large scale, there are open practical questions still to be answered. In a quantum network, a data qubit containing quantum information is transmitted by an operation called quantum teleportation. For it, two nodes need to share entanglement. Quantum teleportation then ensures the safe transmission of a qubit by using the shared entanglement and the exchange of two classical bits. This operation, however, consumes entanglement between the nodes, changing the network topology with each served request. In quantum networks, certain nodes share entanglement between each other. Routing in quantum networks entails determining which of these shared entanglements are used to transmit quantum information. As this proves to be a difficult task, we study quantum networks and in particular consider the problem of routing entanglement in quantum networks. The average latency for routing entanglement in a quantum network has been studied so far using a distributed routing approach. Thus, we present numerical simulation results of centralised routing for the average latency of demands in two existing entanglement generation models. In the first, shared entanglement between nodes is created on-demand. In the second, certain nodes pre-share entanglement before the demand comes. Our results show the intuition that using a centralised routing approach in the on-demand model results in drastically more average latency than in the model with pre-shared entanglement. Since some nodes might not be informed about the change in topology, we study the effect of the propagation of information about the network topology to nodes in the network. Our observations based on simulation results show that the average latency is significantly higher if the information about the topology is not propagated well in the network. As propagating information to all nodes in a network would be a significant load for the network, we consider information propagation within a radius and still observe a considerable decrease in average latency. At last, we present a technique called link prediction which can be performed by any node without the need for information propagation and still achieve a considerable decrease in average latency. It can be effectively used to predict the change of topology in a quantum network by using knowledge about the network topology for a certain future point in time and previous knowledge about the network traffic.
In today’s fast-evolving technological landscape, the Internet of Things (IoT) is fundamentally reshaping how systems connect, interact, and exchange data. As billions of devices become interconnected, the Internet of Things brings unprecedented opportunities and significant challenges in various domains. Emerging technologies, particularly quantum communication networks, offer transformative solutions by enabling ultra-secure data exchange within IoT infrastructures through advanced techniques such as quantum key distribution. However, a significant obstacle to the efficient use of these networks is the lack of current status knowledge of network parameters, which is essential for continuous and dynamic management through quantum traffic engineering. In this paper, we propose the novel concept of quantum traffic matrices as a foundational framework to capture the dynamic operational state of quantum networks. We begin by distinguishing classical traffic matrices from their quantum counterparts. We then identify and present mathematical models for eleven key quantum network parameters that are essential to enable continuous and dynamic management of quantum networks. Our approach and results serve as crucial input for the development of quantum traffic engineering strategies, paving the way for intelligent control, optimization, and resilience enhancement of future quantum communication infrastructures.
There will be a fast-paced shift from conventional network systems to novel quantum networks that are supported by the quantum entanglement and teleportation, key technologies of the quantum era, to enable secured data transmissions in the next-generation of the Internet. Despite this prospect, migration to quantum networks cannot be done at once, especially on the aspect of quantum routing. In this paper, we study the maximizing entangled routing rate (MERR) problem. In particular, given a set of demands, we try to determine entangled routing paths for the maximum number of demands in the quantum network while meeting the network's fidelity. We first formulate the MERR problem using an integer linear programming (ILP) model to capture the traffic patent for all demands in the network. We then leverage the theory of relaxation of ILP to devise two efficient algorithms including HBRA and RRA with provable approximation ratios for the objective function. To deal with the challenge of the combinatorial optimization problem in big scale networks, we also propose the path-length-based approach (PLBA) to solve the MERR problem. Using both simulations and an open quantum network simulator platform to conduct experiments with real-world topologies and traffic matrices, we evaluate the performance of our algorithms and show up the success of maximizing entangled routing rate.
Long-distance quantum networks are far more than mere channels for data transfer; they are sophisticated systems that harness the counterintuitive features of quantum mechanics to enable secure and high-performance communication over vast distances. In our study, we explore the underlying mechanisms of quantum connectivity by investigating phenomena such as superposition and entanglement, and their roles in sustaining robust information propagation. We focus on the interaction between these quantum effects and routing protocols strategies that, while inspired by classical networks, must overcome challenges associated with quantum decoherence and measurement uncertainty. To evaluate these interactions, we simulated distributed quantum networks with node counts ranging from 10 to 100 using novel routing strategies including Q-Learning, Bell State, and Entanglement Swapping protocols. Our analysis centers on key performance metrics that capture both quantum fidelity and operational efficiency. Notably, our results show that high quantum state fidelity is maintained across all network configurations, confirming the efficacy of entangled states in preserving the integrity of quantum information over long distances. Simultaneously, we observe that increased network complexity introduces trade-offs, such as higher computational overhead and amplified susceptibility to decoherence and error propagation. These findings underscore the necessity for advanced error-correction techniques and adaptive routing strategies to ensure scalability and robustness in long-distance quantum communication. Overall, our work provides critical insights into optimizing quantum superposition and entanglement in practical routing implementations, thereby paving the way for the future realization of a global quantum communication infrastructure.