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DTSTAMP:20250626T233532Z
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UID:submissions.supercomputing.org_SC24_sess534_post151@linklings.com
SUMMARY:Active Learning for Metamaterial Optimization on HPC and QC Integr
 ated Systems
DESCRIPTION:Seongmin Kim and In-Saeng Suh (Oak Ridge National Laboratory (
 ORNL))\n\nActive learning algorithms, integrating machine learning, quantu
 m computing and optics simulation in an iterative loop, offer a promising 
 approach to optimizing metamaterials. However, these algorithms can face d
 ifficulties in optimizing highly complex structures due to computational l
 imitations. High-performance computing (HPC) and quantum computing (QC) in
 tegrated systems can address these issues by enabling parallel computing. 
 In this study, we develop an active learning algorithm working on HPC-QC i
 ntegrated systems. We evaluate the performance of optimization processes w
 ithin active learning (i.e., training a machine learning model, problem-so
 lving with quantum computing, and evaluating optical properties through wa
 ve-optics simulation) for highly complex metamaterial cases. Our results s
 howcase that utilizing multiple cores on the integrated system can signifi
 cantly reduce computational time, thereby enhancing the efficiency of opti
 mization processes. Therefore, we expect that leveraging HPC-QC integrated
  systems helps effectively tackle large-scale optimization challenges in g
 eneral.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass
 \n\nSession Chairs: Ayesha Afzal (Friedrich-Alexander University, Erlangen
 -Nuremberg; Erlangen National High Performance Computing Center); Sally El
 lingson (University of Kentucky); and Alan Sussman (University of Maryland
 )\n\n
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