Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues for building networks that scale with the number of users, communication distance, and application demands by simulating alternative hardware designs and control protocols. Several quantum network simulators have been recently developed with these goals in mind. As the size of the simulated networks increases, however, sequential execution becomes time-consuming. Parallel execution presents a suitable method for scalable simulations of large-scale quantum networks, but the unique attributes of quantum information create unexpected challenges. In this work, we identify requirements for parallel simulation of quantum networks and develop the first parallel discrete-event quantum network simulator by modifying the existing serial simulator SeQUeNCe. Our contributions include the design and development of a quantum state manager (QSM) that maintains shared quantum information distributed across multiple processes. We also optimize our parallel code by minimizing the overhead of the QSM and decreasing the amount of synchronization needed among processes. Using these techniques, we observe a speedup of 2 to 25 times when simulating a 1,024-node linear network topology using 2 to 128 processes. We also observe an efficiency greater than 0.5 for up to 32 processes in a linear network topology of the same size and with the same workload. We repeat this evaluation with a randomized workload on a caveman network. We also introduce several methods for partitioning networks by mapping them to different parallel simulation processes. We have released the parallel SeQUeNCe simulator as an open source tool alongside the existing sequential version.

量子网络模拟器提供了机会，通过模拟替代硬件设计和控制协议，经济高效地研究构建可根据用户数量、通信距离和应用需求进行扩展的网络的潜在途径。最近，出于这些目标，开发了几种量子网络模拟器。然而，随着模拟网络规模的增加，顺序执行变得非常耗时。并行执行为大规模量子网络的可扩展模拟提供了一种合适的方法，但量子信息的独特属性带来了意想不到的挑战。在这项工作中，我们确定了量子网络并行模拟的要求，并通过修改现有的串行模拟器 SeQUeNCe 开发了第一个并行离散事件量子网络模拟器。我们的贡献包括设计和开发量子状态管理器（QSM），该管理器维护分布在多个进程中的共享量子信息。我们还通过最小化 QSM 的开销并减少进程之间所需的同步量来优化并行代码。使用这些技术，我们在使用 2 到 128 个进程模拟 1,024 节点线性网络拓扑时观察到加速了 2 到 25 倍。我们还观察到，在相同规模和相同工作负载的线性网络拓扑中，最多 32 个进程的效率大于 0.5。我们在穴居人网络上使用随机工作负载重复此评估。我们还介绍了几种通过将网络映射到不同的并行模拟过程来划分网络的方法。我们已经将并行序列模拟器作为开源工具与现有的序列版本一起发布。