HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum System
We introduce a hybrid computational framework that merges HPC-based numerical solvers with physics-informed ML surrogates for efficient modeling of magnon-photon interactions. By running short-duration, high-fidelity Maxwell-LLG simulations and feeding their results into an ML model, we substantially cut simulation time while achieving accurate predictions across larger spatiotemporal domains.
Authors
- Jialin Song (ORCID: https://orcid.org/0009-0000-9817-6925)
- Yingheng Tang (ORCID: https://orcid.org/0009-0001-5362-2546)
- Yujie Zhu (ORCID: https://orcid.org/0000-0002-1414-2590)
- Jia‐Mian Hu (ORCID: https://orcid.org/0000-0002-7579-6440)
- Pu Ren (ORCID: https://orcid.org/0000-0002-2800-3399)
- Andrew Nonaka (ORCID: https://orcid.org/0000-0003-1791-0265)
- Saurabh S. Sawant (ORCID: https://orcid.org/0000-0002-2931-9299)
- Michael W. Mahoney (ORCID: https://orcid.org/0000-0001-7920-4652)
- Shintaro Takayoshi (ORCID: https://orcid.org/0000-0001-9900-616X)
- Zhi Yao (ORCID: https://orcid.org/0000-0001-5863-8275)
- Benjamin Erichson
Institutions
- University of Wisconsin–Madison (US)
- Lawrence Berkeley National Laboratory (US)
- Simon Fraser University (CA)
Publication Details
- Published
- 2025-05-18
- DOI
- https://doi.org/10.23919/aces66556.2025.11052493
- Primary Topic
- Quantum, superfluid, helium dynamics
- Type
- article
- Field-Weighted Citation Impact
- 0.00