标题： 量子网络中纠缠图态的优化分布 显示英文标题

标题： Optimized Distribution of Entanglement Graph States in Quantum Networks

摘要： 构建大规模量子计算机是展示量子优势的关键挑战。 量子网络（QNs）可以通过连接较小的量子计算机来帮助解决这一挑战，从而构建出更大、更强大且功能更强的量子计算平台。 此外，与经典系统不同，量子网络可以实现完全安全的远距离通信。 因此，量子网络是未来量子信息技术成功的核心。 在量子网络中，分布式在网路中的多体纠缠态有助于实现和支撑许多用于通信、传感和计算的量子网络应用。 我们的工作专注于开发生成和高效分发多体纠缠态的最佳技术。 先前关于生成一般多体纠缠态的研究主要集中在最小化最大纠缠对（EPs）的数量上，而忽略了网络节点和链路的异质性以及底层过程的随机性。 在本工作中，我们开发了一个基于超图的线性规划框架，在考虑底层过程的随机性的同时，针对由图态表示的一般多体纠缠态，在网络资源、退相干和保真度约束下，提供最优（在某些假设下）的生成方案。 我们通过为路径和树图态的特殊情况开发生成方案来说明我们的技术，并讨论了更一般的图态类别的优化生成方案。 通过在量子网络模拟器（NetSquid）上进行大量模拟，我们证明了所开发技术的有效性，并展示了它们比之前已知的方案性能优越多达数量级。 显示英文摘要

摘要： Building large-scale quantum computers, essential to demonstrating quantum advantage, is a key challenge. Quantum Networks (QNs) 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, QNs 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 (EPs) 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 (NetSquid), we demonstrate the effectiveness of our developed techniques and show that they outperform prior known schemes by up to orders of magnitude.