Due to its complexity, resource allocation in complex communication networks has been always designed with great deal of approximations and restrictions. With the availability of quantum computing, quantum optimization and quantum search algorithms, we are able to overcome these limitations significantly and come up with more precise and efficient “quantum era” algorithms. The main objective of this paper is to provide the future network designers with a new optimization framework for resource (capacity and secrete key rate) allocation in quantum networks. The analysis is general to include: the classical capacityC, for transmitting classical information; the quantum capacityQ, for reliably transmitting intact quantum states; the two-way classical-assisted quantum capacityQ2, for transmitting quantum states with the help of a two-way classical side channel; and the entanglement-assisted classical capacityCE, which we define as the classical capacity of a quantum channel enabled by prior shared entanglement between two nodes. Once the optimization framework is introduced the rest of the paper discusses the optimization constraints, by surveying some examples of the relevant work on achievable network resources (capacity and secrete key rate) and relaxation of the optimization constraints by increasing these parameters using the quantum coding schemes. These constraints are an integral part of the optimization algorithms and full understanding of their nature and limits is essential for system optimization. The achievable secret key rates/capacity of quantum channels are limited, so our optimization framework for resource allocation enables maximization of the overall resources allocated to all users, or maximization of the number of users accessing the network, or minimization of the number of hopes on the routes or maximization of remaining resources in the most critical node in the network. The optimization problems have combinatorial form, and the most efficient execution of these algorithms can be achieved by using quantum search algorithms like Grover’s algorithm or quantum approximative optimization algorithms (QAOA) which are extensively discussed in the paper.Regarding the scope of the technology survey in Section VIII we summarize the coverage of our targeted survey and the coverage of other relevant works.
Due to its complexity, resource allocation in complex communication networks has been always designed with great deal of approximations and restrictions. With the availability of quantum computing, quantum optimization and quantum search algorithms, we are able to overcome these limitations significantly and come up with more precise and efficient “quantum era” algorithms. The main objective of this paper is to provide the future network designers with a new optimization framework for resource (capacity and secrete key rate) allocation in quantum networks. The analysis is general to include: the classical capacityC, for transmitting classical information; the quantum capacityQ, for reliably transmitting intact quantum states; the two-way classical-assisted quantum capacityQ2, for transmitting quantum states with the help of a two-way classical side channel; and the entanglement-assisted classical capacityCE, which we define as the classical capacity of a quantum channel enabled by prior shared entanglement between two nodes. Once the optimization framework is introduced the rest of the paper discusses the optimization constraints, by surveying some examples of the relevant work on achievable network resources (capacity and secrete key rate) and relaxation of the optimization constraints by increasing these parameters using the quantum coding schemes. These constraints are an integral part of the optimization algorithms and full understanding of their nature and limits is essential for system optimization. The achievable secret key rates/capacity of quantum channels are limited, so our optimization framework for resource allocation enables maximization of the overall resources allocated to all users, or maximization of the number of users accessing the network, or minimization of the number of hopes on the routes or maximization of remaining resources in the most critical node in the network. The optimization problems have combinatorial form, and the most efficient execution of these algorithms can be achieved by using quantum search algorithms like Grover’s algorithm or quantum approximative optimization algorithms (QAOA) which are extensively discussed in the paper.Regarding the scope of the technology survey in Section VIII we summarize the coverage of our targeted survey and the coverage of other relevant works.
Quantum networks are essential infrastructure for enabling large-scale and long-distance quantum communications but face significant challenges in routing optimization and resource allocation due to their probabilistic nature and quantum resource limitations. Existing approaches typically tackle these problems in isolation, often by simply applying classical routing algorithms, maximizing the overall profit to allocate resources without considering fairness, or improving fairness in an ad-hoc way without a rigorous model. This paper proposes a general framework to systematically address these challenges. First, we conduct a thorough analysis of quantum network metrics using routing algebra as the mathematical foundation, and design provably optimal routing algorithms to tackle the unique challenges arising from their probabilistic characteristics. Second, we formulate an optimization model that simultaneously considers fairness among concurrent requests while respecting various quantum resource constraints, and design efficient near-optimal heuristics to solve it. The proposed framework provides both theoretical insights and practical solutions for the design and management of future quantum networks.
Quantum networks are essential infrastructure for enabling large-scale and long-distance quantum communications but face significant challenges in routing optimization and resource allocation due to their probabilistic nature and quantum resource limitations. Existing approaches typically tackle these problems in isolation, often by simply applying classical routing algorithms, maximizing the overall profit to allocate resources without considering fairness, or improving fairness in an ad-hoc way without a rigorous model. This paper proposes a general framework to systematically address these challenges. First, we conduct a thorough analysis of quantum network metrics using routing algebra as the mathematical foundation, and design provably optimal routing algorithms to tackle the unique challenges arising from their probabilistic characteristics. Second, we formulate an optimization model that simultaneously considers fairness among concurrent requests while respecting various quantum resource constraints, and design efficient near-optimal heuristics to solve it. The proposed framework provides both theoretical insights and practical solutions for the design and management of future quantum networks.
Quantum networks are essential infrastructure for enabling large-scale and long-distance quantum communications but face significant challenges in routing optimization and resource allocation due to their probabilistic nature and quantum resource limitations. Existing approaches typically tackle these problems in isolation, often by simply applying classical routing algorithms, maximizing the overall profit to allocate resources without considering fairness, or improving fairness in an ad-hoc way without a rigorous model. This paper proposes a general framework to systematically address these challenges. First, we conduct a thorough analysis of quantum network metrics using routing algebra as the mathematical foundation, and design provably optimal routing algorithms to tackle the unique challenges arising from their probabilistic characteristics. Second, we formulate an optimization model that simultaneously considers fairness among concurrent requests while respecting various quantum resource constraints, and design efficient near-optimal heuristics to solve it. The proposed framework provides both theoretical insights and practical solutions for the design and management of future quantum networks.
A comprehensive study has been recently made of post-quantum ( q -) and q -cryptography algorithms and their applicability in advanced networks. An insight is provided into the candidate algorithms to replace RSA and ECC schemes. The results of the research suggest that most probably post-q schemes will dominate 6G networks and due to implementation complexity q-solutions will be fully employed in 7G. Since most of these works focus on the cryptography algorithms in this paper, we cover the most important segments of advanced communications and q- networks design that will have to support these algorithms in the future as well as distributed quantum computing applications in general. For the applications in 6G/7G ACCESS networks, existing work on q-key distribution over sub-optical bands, surveyed in Section II, is essential. As a next step, for 6G/7G CORE n et designers, results on dynamic q- networks topology design are reviewed in Section III and its optimization in Section IV including the work on q- networks stability surveyed in Section V. Due to the limited space, survey of the work on q- networks designed over satellite constellations, which are bases for global systems, is left for a separate paper. In Section VI we present new optimization frameworks for cost efficient q-network topology design and optimum resource (capacity and secrete key rates) allocations in these networks.
Quantum communication networks are emerging as a promising technology that could constitute a key building block in future communication networks in the 6G era and beyond. These networks have an inherent feature of parallelism that allows them to boost the capacity and enhance the security of communication systems. Recent advances led to the deployment of small- and large-scale quantum communication networks with real quantum hardware. In quantum networks, entanglement is a key resource that allows for data transmission between different nodes. However, to reap the benefits of entanglement and enable efficient quantum communication, the number of generated entangled pairs must be optimized. Indeed, if the entanglement generation rates are not optimized, then some of these valuable resources will be discarded and lost. In this paper, the problem of optimizing the entanglement generation rates and their distribution over a quantum memory is studied. In particular, a quantum network in which users have heterogeneous distances and applications is considered. This problem is posed as a mixed integer nonlinear programming optimization problem whose goal is to efficiently utilize the available quantum memory by distributing the quantum entangled pairs in a way that maximizes the user satisfaction. An interior point optimization method is used to solve the optimization problem and extensive simulations are conducted to evaluate the effectiveness of the proposed system. Simulation results show the key design considerations for efficient quantum networks, and the effect of different network parameters on the network performance.
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.