Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

Conference · Fri Nov 01 00:00:00 EDT 2024

OSTI ID:2498441

Kim, Seongmin ^[1]; Suh, In-Saeng ^[1]

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1. ORNL

Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 2498441
• Resource Type: Conference paper/presentation
• Conference Information: SC24: The International Conference for High Performance Computing, Networking, Storage, and Analysis - Atlanta, Georgia, United States of America - 11/17/2024-11/22/2024
• Country of Publication: United States
• Language: English