Parallel Simulation of Quantum Networks with Distributed Quantum State Management

• URL: http://arxiv.org/abs/2111.03918v1
• Date: Sat, 6 Nov 2021 16:51:17 GMT
• Title: Parallel Simulation of Quantum Networks with Distributed Quantum State Management
• Authors: Xiaoliang Wu, Alexander Kolar, Joaquin Chung, Dong Jin, Rajkumar Kettimuthu, Martin Suchara
• Abstract summary: We identify requirements for parallel simulation of quantum networks and develop the first parallel discrete event quantum network simulator. Our contributions include the design and development of a quantum state manager that maintains shared quantum information distributed across multiple processes. We release the parallel SeQUeNCe simulator as an open-source tool alongside the existing sequential version.
• Score: 56.24769206561207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues to 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. However, as the size of the simulated networks increases, 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 some 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 SeQUeNCe simulator. 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 among processes. Using these techniques, we observe a speedup of 2 to 25 times when simulating a 1,024-node linear network with 2 to 128 processes. We also observe 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. Finally, we also introduce several methods for partitioning networks by mapping them to different parallel simulation processes. We released the parallel SeQUeNCe simulator as an open-source tool alongside the existing sequential version.

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