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

TL;DR

This paper evaluates an optimization algorithm for complex metamaterial design, demonstrating significant speedups through HPC parallelization and quantum computing integration, advancing the potential for efficient high-performance metamaterial development.

Contribution

It provides a comprehensive performance analysis of a hybrid HPC-quantum optimization algorithm for metamaterials, highlighting substantial computational speedups over traditional methods.

Findings

54% faster ML tasks with MPI parallelization

67 times faster optical simulation on HPC

24 times speedup using HPC-quantum hybrid system

Abstract

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.