Scalable Multipartite Entanglement Distribution in Quantum Networks.

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.