  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.
