标题： 基于集成高性能计算和量子系统的超材料设计优化算法性能分析 显示英文标题

标题： Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems

摘要： 优化具有复杂几何结构的超材料是一个巨大的挑战。 尽管一种结合机器学习（ML）、量子计算和光学模拟的主动学习算法已作为一种高效的优化工具出现，但它在优化可能具有高性能的复杂结构时仍然面临困难。 在本工作中，我们全面分析了在集成HPC和量子系统上用于超材料设计的优化算法的性能。 我们通过在高性能计算（HPC）系统上的消息传递接口（MPI）并行化展示了显著的时间优势，显示ML任务大约快了54%，光学模拟比串行工作负载快了67倍。 此外，我们分析了专为优化设计的量子算法的性能，该算法在本地计算机或HPC-量子系统上的各种量子模拟器上运行。 结果表明，在HPC-量子混合系统上执行优化算法时，速度提升了约24倍。 这项研究为使用集成的HPC-量子系统优化复杂的超材料铺平了道路。 显示英文摘要

摘要： Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system.