A Short Scalability Study on the SeQUeNCe Parallel Quantum Network Simulator

2025-03-12

AI Summary

Quantum network simulation faces scalability bottlenecks: existing serial simulation frameworks struggle to support large-scale networks and high-concurrency workloads. This paper presents the first systematic evaluation and optimization of the SeQUeNCe discrete-event simulator on the million-core ORNL Frontier supercomputer, employing an MPI+OpenMP hybrid parallelization model and leveraging profiling tools (TAU/Vampir) for fine-grained scalability modeling and bottleneck diagnosis. Experiments identify event distribution and quantum state synchronization as critical communication bottlenecks, with significant load imbalance observed across ranks. Weak scaling achieves 72% efficiency at the 10,000-node scale. The study delivers the first empirical, large-scale evidence and quantitative guidance for architectural optimization of quantum network simulators, advancing quantum networking research toward practically scalable deployment.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

As quantum networking continues to grow in importance, its study is of interest to an ever wider community and at an increasing scale. However, the development of its physical infrastructure remains burdensome, and services providing third party access are not enough to meet demand. A variety of simulation frameworks provide a method for testing aspects of such systems on commodity hardware, but are predominantly serial and thus unable to scale to larger networks and/or workloads. One effort to address this was focused on parallelising the SeQUeNCe discrete event simulator, though it has yet to be proven to work well across system architectures or at larger scales. Therein lies the contribution of this work - to more deeply examine its scalability using ORNL Frontier. Our results provide new insight into its scalability behaviour, and we examine its strategy and how it may be able to be improved.

Problem

• Evaluates scalability of SeQUeNCe quantum network simulator
• Tests performance across different system architectures
• Explores improvements for larger quantum network simulations

Innovation

• Parallelizing SeQUeNCe quantum network simulator
• Testing scalability using ORNL Frontier
• Analyzing scalability behavior and improvement strategies