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 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.
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.
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.
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 IoT brings both unprecedented opportunities and significant challenges across 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 (QKD). However, quantum link delay remains one of the key challenges that hinder the effective utilization of these networks. Traffic engineering is a robust method to address this challenge and optimize the performance of quantum networks. This technique involves assessing the current state of the network and making dynamic adjustments based on real-time conditions. Despite its proven benefits in classical systems, its role in quantum networks remains largely unexplored, with no comprehensive framework to date. As a result, in this paper, we propose a framework for quantum traffic engineering and discuss its core components—non-invasive measurements, quantum traffic matrices, data analysis, and performance control. Additionally, we integrate the Particle Swarm Optimization (PSO) algorithm into our framework to minimize the quantum link delay. Ultimately, this work establishes the foundation for researchers in quantum network traffic engineering and sets the stage for continuous, and dynamic monitoring of these networks.
This analytical article rigorously evaluates the revolutionary capacity of quantumcomputing in enhancing network traffic optimisation and data routing, a field historicallycontrolled by conventional techniques. Due to the rapid expansion of data and the growingintricacy of network infrastructures, traditional methods are frequently constrained by theirinadequate capacity to effectively scale and oversee resources. Quantum computing, utilising theconcepts of superposition and entanglement, presents a hopeful solution to surpass theseconstraints by offering unparalleled processing capability and efficiency.The study starts by examining the fundamental concepts of quantum physics that serve as thefoundation of quantum computing. Superposition permits quantum bits (qubits) to concurrentlyembody and manipulate several states, whereas entanglement facilitates the interconnection ofqubits that are physically apart, hence enabling expedited and more intricate computations. Thesefeatures offer substantial benefits compared to classical bits, which are limited to binary states.We explore the precise quantum algorithms that have the ability to optimise networks, with aparticular focus on Grover's search algorithm and the Quantum Approximate OptimisationAlgorithm (QAOA). The Grover's technique provides a quadratic improvement in searchprocedures, enabling more efficient identification of optimal pathways in routing issues.Conversely, QAOA is well-suited for handling combinatorial optimisation issues, namely thosefound in network traffic management.A comprehensive analysis of current literature and case studies is performed to assess the practicaluses of quantum algorithms in network contexts. Metrics such as reducing latency, optimisingbandwidth utilisation, minimising packet loss, and effectively managing congestion are closely examined. Our study suggests that quantum computing has the potential to greatly improve theseperformance indicators. Quantum algorithms have the capability to handle large quantities of datasimultaneously, resulting in quicker identification of the best routes and more effective trafficcontrol. This leads to reduced latency and enhanced total network performance.The report also evaluates the existing technological and theoretical obstacles that impede themainstream implementation of quantum computing in network management. Important concernsencompass the reliability and consistency of qubits, the ability to repair errors, and the capacityfor quantum systems to be expanded. Furthermore, this study investigates the infrastructuralobstacles, such as the requirement for specialised hardware and the incorporation of quantumsystems into current network topologies. We emphasise the continuous progress in quantumtechnology, including the enhancement of qubits' stability and the refinement of quantum errorcorrection techniques, both of which are crucial for tackling these difficulties.Moreover, the research examines the possible long-term consequences of quantum computing onnetwork management paradigms. Quantum computing offers substantial enhancements inprocessing speed and efficiency, as well as presents novel approaches for solving complicatedoptimisation problems that are currently unsolvable using traditional methods. Our proposalinvolves investigating future research areas, specifically focusing on the advancement of hybridquantum-classical algorithms and the examination of quantum machine learning approaches foradaptive network management.
We provide various results about the transmission capacity of quantum networks. Our primary focus is on algorithmic methods to efficiently compute upper-bounds to the traffic that the network can handle at most, and to compute lower-bounds on the likelihood that a customer has to wait for service due to network congestion. This establishes analogous assertions as derived from Erlang B or Erlang C models for standard telecommunications. Our proposed methods, while specifically designed for quantum networks, do neither hinge on a particular quantum key distribution technology nor on any particular routing scheme. We demonstrate the feasibility of our approach using a worked example. Moreover, we explicitly consider two different architectures for quantum key management, one of which employs individual key-buffers for each relay connection, the other using a shared key-buffer for every transmission. We devise specific methods for analyzing the network performance depending on the chosen key-buffer architecture, and our experiments led to quite different results for the two variants.
ABSTRACT Network monitoring is fundamental to effective traffic engineering (TE) in quantum networks, as it provides a timely snapshot of network status. However, quantum measurement imposes intrinsic constraints: direct measurement of entangled states collapses the quantum state, and the no‐cloning theorem prohibits duplication of quantum information, rendering real‐time and continuous monitoring inherently challenging. Although nondestructive techniques such as weak measurement, quantum non‐demolition (QND) measurement, and protective measurement have been proposed, their roles in supporting TE have not been systematically examined within a unified framework. In this paper, we develop a unified analytical framework that integrates weak, QND, and protective measurements into TE‐driven quantum network monitoring, providing the first systematic network‐level analysis of these measurement paradigms in the context of quantum TE. We formulate their measurement models, characterize their information gain and disturbance trade‐offs, and evaluate their applicability in terms of measurement accuracy, real time response, scalability, overhead, and robustness to noise. We observe that although weak, QND, and protective measurement techniques offer theoretically compelling approaches for mitigating measurement‐induced back‐action, they remain constrained by significant practical limitations. Experimental complexity, scalability challenges, resource overhead, and latency considerations collectively limit their immediate suitability for deployment in real‐time quantum network monitoring frameworks supporting TE. We further distinguish between directly measurable and estimable network metrics, discuss their roles in TE, and analyze feasible measurement locations and timescales under coherence constraints. In addition, we present a formal definition of quantum traffic matrices and identify four key types of network knowledge inferred from data analysis and provide a formal definition and framework for the characterization of entanglement availability. Finally, our work lays a solid foundation and outlines a clear roadmap for achieving TE in quantum networks, while carefully accounting for the fundamental limitations imposed by quantum measurements and the no‐cloning theorem.