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.
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.
A quantum internet aims at harnessing networked quantum technologies, namely by distributing bipartite entanglement between distant nodes. However, multipartite entanglement between the nodes may empower the quantum internet for additional or better applications for communications, sensing, and computation. In this work, we present an algorithm for generating multipartite entanglement between different nodes of a quantum network with noisy quantum repeaters and imperfect quantum memories, where the links are entangled pairs. Our algorithm is optimal for GHZ states with 3 qubits, maximising simultaneously the final state fidelity and the rate of entanglement distribution. Furthermore, we determine the conditions yielding this simultaneous optimality for GHZ states with a higher number of qubits, and for other types of multipartite entanglement. Our algorithm is general also in the sense that it can optimise simultaneously arbitrary parameters. This work opens the way to optimally generate multipartite quantum correlations over noisy quantum networks, an important resource for distributed quantum technologies.
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.
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.
Quantum networks are designed to transmit quantum bits (qubits) among quantum devices to enable new network resources for the applications. Entanglement distribution and entanglement swapping are fundamental procedures that are required in several network operations. However, they are probabilistic operations, which can lead to severe network performance degradation. This article investigates the engineering problem of resource allocation in quantum networks, considering factors like entanglement distribution probability, quantum memory characteristics, and fidelity. We model this as an optimization model to obtain an optimal solution. In particular, we formulate an integer linear programming (ILP) and develop a heuristic algorithm, aiming to minimize the number of required entangled qubit pairs (Bell pairs or EPR pairs) in any adjacent pair in the quantum network. Extensive simulations are performed to compare the performance of proposed ILP and heuristic. In all the cases, the heuristic produces a comparable solution to the optimal one. Simulation results ensure that the value of maximum utilized Bell pairs in a quantum network highly depends on the value of the probability of entangled pairs established, considering the time in the quantum memory besides the number of incoming requests.
Multiparty quantum key distribution (QKD) is useful for many applications that involve secure communication or collaboration among multiple parties. While it can be achieved using pairwise QKD, a more efficient approach is to achieve it using multipartite entanglement distributed over quantum networks that connect the multiple parties. Existing studies on multipartite entanglement distribution, however, are not designed for multiparty QKD, and hence do not aim to maximize secret key generation rate. In this paper, we design efficient strategies for multiparty QKD over quantum networks. For 3-party QKD, we derive closed-form expressions for analyzing key distribution over quantum networks. We then use it to develop an efficient strategy for 3-party QKD by packing multiple stars that connect the 3 parties. For the general form of N-party QKD, we develop an approach that packs multiple trees to connect the N parties, while directly incorporating the estimated key rates on network paths. Extensive evaluation of our strategies, in both grid and random graphs, under a wide range of settings, demonstrates that our schemes achieve high key rate, which degrades gracefully when increasing the number of parties.
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.