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.
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.
Building large-scale quantum computers, essential to demonstrating quantum advantage, is a key challenge. Quantum networks can help address this challenge by enabling the construction of large, robust, and more capable quantum computing platforms by connecting smaller quantum computers. Moreover, unlike classical systems, quantum networks can enable fully secured long-distance communication. Thus, quantum networks lie at the heart of the success of future quantum information technologies. In quantum networks, multipartite entangled states distributed over the network help implement and support many quantum network applications for communications, sensing, and computing. Our work focuses on developing optimal techniques to generate and distribute multipartite entanglement states efficiently. Prior works on generating general multipartite entanglement states have focused on the objective of minimizing the number of maximally entangled pairs while ignoring the heterogeneity of the network nodes and links as well as the stochastic nature of underlying processes. In this work, we develop a hypergraph-based linear programming framework that delivers optimal (under certain assumptions) generation schemes for general multipartite entanglement represented by graph states, under the network resources, decoherence, and fidelity constraints, while considering the stochasticity of the underlying processes. We illustrate our technique by developing generation schemes for the special cases of path and tree graph states and discuss optimized generation schemes for more general classes of graph states. Using extensive simulations over a quantum network simulator, we demonstrate the effectiveness of our developed techniques and show that they outperform prior known schemes by up to orders of magnitude.
The development of quantum networks (QNs) relies on efficient mechanisms for distributing entanglement among multiple quantum users (QUs) under practical system constraints. This paper investigates the problem of entanglement rate maximization in a dual-connectivity (DC) wireless quantum network comprising multiple quantum base stations (QBSs). Under the DC architecture, each QU can associate with up to two QBSs, thereby enhancing resource utilization compared to conventional single-connectivity (SC) schemes. The joint QBS-QU association and entanglement generation rate allocation problem is formulated as a mixed-integer nonlinear programming problem that incorporates practical constraints, including limited QBS entanglement generation capacity as well as heterogeneous minimum entanglement rate demands and fidelity requirements for QUs. To efficiently solve this challenging problem, an alternating optimization (AO) algorithm is developed, which decomposes the original formulation into entanglement rate allocation and association subproblems. Simulation results demonstrate that the proposed DC architecture significantly outperforms SC schemes, while the AO algorithm achieves near-optimal performance with substantially reduced computational complexity.
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 facilitate numerous applications including secure communication and distributed quantum computation by performing entanglement distribution. For some multi-user quantum applications access to a shared multipartite state is required. We consider the problem of designing protocols for distributing such states, at an increased rate. For this, we propose three protocols that leverage multipath routing to increase the distribution rate for multi-user applications. The protocols are evaluated on quantum networks with NISQ constraints, including limited quantum memories and probabilistic entanglement generation. Simulation results show that the developed protocols achieve an exponential increase in the distribution rate of multipartite states compared to single path routing techniques, with a maximum increase of four orders of magnitude for the cases studied. Further, the relative increase in distribution rate was also found to improve for larger sets of users. When the protocols were tested in scaled-down real-world topologies, it was found that topology had a significant effect on the multipartite state distribution rates achieved by the protocols. Finally, we found that the benefits of multipath routing are maximum for short quantum memory decoherence times and intermediate values of entanglement generation probability. Hence, the protocols developed can benefit NISQ quantum network control and design.
Entanglement distribution is a fundamental building block of quantum communication networks, enabling the secure and reliable exchange of quantum information over long distances. Unlike classical communication, quantum networks exploit the unique properties of quantum entanglement to support applications such as quantum key distribution, quantum teleportation, distributed quantum computing, and networked quantum sensing. Although significant progress has been made in recent years, the efficient generation, transmission, and preservation of entangled states remain major research challenges due to photon loss, decoherence, and limitations in current communication infrastructure.This review provides a comprehensive overview of entanglement distribution in quantum communication networks, focusing on the technologies that enable practical implementation and the latest experimental developments. It discusses key approaches for generating and distributing entangled photons through optical fibers, free-space links, and satellite-based communication, together with supporting technologies such as quantum repeaters, quantum memories, and photonic devices. The review also examines recent experimental demonstrations that have advanced long-distance quantum communication while highlighting the technical barriers that must be overcome for large-scale deployment. Finally, emerging applications and future research directions are presented, including the development of scalable quantum networks, the quantum internet, and secure global communication systems. This review aims to provide researchers and students with a clear understanding of the current state of entanglement distribution and its growing role in next-generation quantum communication technologies.
Recent technological advancements in satellite based quantum communication has made it a promising technology for realizing global scale quantum networks. Due to better loss distance scaling compared to ground based fiber communication, satellite quantum communication can distribute high quality quantum entanglements among ground stations that are geographically separated at very long distances. This work focuses on optimal distribution of bipartite entanglements to a set of pair of ground stations using a constellation of orbiting satellites. In particular, we characterize the optimal satellite-to-ground station transmission scheduling policy with respect to the aggregate entanglement distribution rate subject to various resource constraints at the satellites and ground stations. We cast the optimal transmission scheduling problem as an integer linear programming problem and solve it efficiently for some specific scenarios. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.