The evolution of quantum networks towards distributed quantum computing and sensing necessitates the efficient distribution of multipartite entangled states among multiple users. However, most existing entanglement routing studies primarily focus on efficient entanglement distribution and typically assume that quantum sources are either fixed or available on all network edges. Due to the scarcity and high cost of quantum sources, some prior studies have focused on optimized source placement strategies, but these existing studies are confined to bipartite state distributions. Consequently, how to jointly optimize quantum source placement and entanglement routing for efficient multipartite state distribution under realistic resource constraints remains an open problem. In this work, we propose an Optimized source Placement and Reactive entanglement Routing (OP-RR) framework to maximize the multipartite state distribution rate given a limited amount of quantum sources. Numerical results on a grid and on the Tokyo topology demonstrate that OP-RR consistently outperforms baseline algorithms with non-optimized source placement, achieving an improvement of up to 16.7× in the multipartite distribution rate, while maintaining robustness under varying operation probabilities and scalability as the size of multipartite states increases.
The evolution of quantum networks towards distributed quantum computing and sensing necessitates the efficient distribution of multipartite entangled states among multiple users. However, most existing entanglement routing studies primarily focus on efficient entanglement distribution and typically assume that quantum sources are either fixed or available on all network edges. Due to the scarcity and high cost of quantum sources, some prior studies have focused on optimized source placement strategies, but these existing studies are confined to bipartite state distributions. Consequently, how to jointly optimize quantum source placement and entanglement routing for efficient multipartite state distribution under realistic resource constraints remains an open problem. In this work, we propose an Optimized source Placement and Reactive entanglement Routing (OP-RR) framework to maximize the multipartite state distribution rate given a limited amount of quantum sources. Numerical results on a grid and on the Tokyo topology demonstrate that OP-RR consistently outperforms baseline algorithms with non-optimized source placement, achieving an improvement of up to 16.7× in the multipartite distribution rate, while maintaining robustness under varying operation probabilities and scalability as the size of multipartite states increases.
Entanglement routing plays a vital role in quantum networks. Previous works on entanglement routing did not note that all-optical switching capacity can be used to improve the network throughput or ignored that the placement of Entangled Photon Sources (EPS), another type of precious (and costly) quantum source, which also limits the network throughput. In this paper, we propose OptEPS to jointly optimize all-optical switching and EPS placement in entanglement routing to maximize the quantum network throughput. The main challenge of OptEPS lies in two folds: i). an entanglement may fail to be created; ii). the joint optimization problem suffers from a large time complexity. To overcome these challenges, we first formulate the joint optimization problem as a link-path based model and prune out some candidate paths in order to reduce the time complexity. Then, efficient algorithms are proposed to derive a near-optimal solution in a timely manner. Extensive simulations show that compared with the solutions ignoring EPS placement or without introducing all-optical switching, OptEPS will increase the network throughput by 1557% and 109%, respectively.
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.
Multipartite entangled states (MES) are useful resources for a wide range of quantum applications, including quantum secret sharing, and quantum sensing. In this work, we propose a scalable architecture for distributing multipartite entangled states (MES) using arbitrary bipartite quantum networks. In particular, we address the problem of joint tree selection and rate allocation in multipartite entanglement distribution networks in order to optimize the weighted aggregate MES generation rate across different user groups. To obtain optimal solutions, we develop a mixed-integer programming (MIP) model and employ a heuristic approach based on the Genetic algorithm (GA) to obtain feasible solutions efficiently.
Entanglement routing plays a vital role in distributed quantum computing and quantum networks. Previous works on entanglement routing have addressed several design challenges due to limited quantum resources, failures to establish entanglement, and decoherence of established entanglement, but considered neither the limitations imposed by having a limited number of entangled photon sources (EPSes), nor the benefits of using all-optical (or quantum) switching to distribute entangled photons. In this paper, we first explore the problem of jointly optimizing entanglement routing and EPS assignment, assuming no all-optical switching capability. In other words, a pair of entangled photons generated by one EPS can be distributed to two neighboring quantum nodes. We then relax the above assumption so as to allow a pair of entangled photons generated by one EPS to be distributed to non-adjacent nodes using all-optical switching. We propose two corresponding solutions, namely, entanglement routing and EPS assignment (or ERSA), and ERSA with all-optical switching-based distribution (or ERSA+D) that aim to maximize the number of entanglement connections between the given set of source-destination (SD) pairs while avoiding starvation and achieving fairness among the SD pairs. In order to obtain efficient solutions in a large discrete solution space in a timely manner, we first formulate each optimization problem as an Integer Linear Programming (ILP), and then propose efficient algorithms to derive near-optimal solutions based on relaxation, Lagrangian decomposition, duality iteration, and rounding techniques. Extensive simulations show that ERSA+D can increase network throughput by up to 1652% and 113%, respectively, thanks to EPS assignment optimization, and all-optical switching based entanglement distribution.
The effective routing of entanglement over a quantum network is a fundamental problem in quantum communication. The distribution and reshaping of multipartite entanglement over a quantum network requires scalable protocols that adapt to the underlying topology. Graph states provide a convenient framework for this challenging task, as they encode global entanglement in a structured way that can be locally manipulated to produce a variety of target states, without requiring additional long-distance quantum operations. In this article, we propose graph-state-based routing protocols for sharing GHZ states, achieving larger sizes than those achieved in the existing works for given network topologies. For this improvement, we consider tree structures connecting the users participating in the final GHZ states, as opposed to the linear configurations used in the earlier works. In particular, for grid networks, we show special constructions of such trees that achieve larger GHZ states than the prior works. Moreover, if the user nodes along which the entanglement is to be routed are pre-specified, a strategy is proposed to accomplish this routing.
Quantum Internet has potential capabilities far beyond the traditional Internet and is thus a promising future platform for communication and computation. Entanglement is a cornerstone of quantum mechanics and forms the basis of numerous quantum applications in the quantum Internet. While existing studies primarily focus on two-user entanglement, a plethora of applications necessitates the leap to multi-user entanglement. This paper tackles the fundamental problem of multi-user entanglement routing in the quantum Internet, aiming to entangle multiple quantum users with a high entanglement rate. We abstract the problem as a novel graph routing problem, which is not readily addressed by existing graph problem solutions due to the unique characteristics of the quantum Internet. To address this problem, we first consider a sufficient condition ensuring a feasible solution's existence and design an algorithm with the optimal solution. Given the NP-Completeness and NP- Hardness of determining a feasible solution's existence and deriving an optimal solution in general cases, respectively, we propose two heuristic algorithms to offer efficient solutions, which are shown, via extensive simulations, to outperform the existing algorithms in terms of entanglement rates.