HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911460691542016 |
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| author | Song, Jialin Tang, Yingheng Ren, Pu Takayoshi, Shintaro Sawant, Saurabh Zhu, Yujie Hu, Jia-Mian Nonaka, Andy Mahoney, Michael W. Erichson, Benjamin Yao, Zhi |
| author_facet | Song, Jialin Tang, Yingheng Ren, Pu Takayoshi, Shintaro Sawant, Saurabh Zhu, Yujie Hu, Jia-Mian Nonaka, Andy Mahoney, Michael W. Erichson, Benjamin Yao, Zhi |
| contents | Simulating hybrid magnonic quantum systems remains a challenge due to the large disparity between the timescales of the two systems. We present a massively parallel GPU-based simulation framework that enables fully coupled, large-scale modeling of on-chip magnon-photon circuits. Our approach resolves the dynamic interaction between ferromagnetic and electromagnetic fields with high spatiotemporal fidelity. To accelerate design workflows, we develop a physics-informed machine learning surrogate trained on the simulation data, reducing computational cost while maintaining accuracy. This combined approach reveals real-time energy exchange dynamics and reproduces key phenomena such as anti-crossing behavior and the suppression of ferromagnetic resonance under strong electromagnetic fields. By addressing the multiscale and multiphysics challenges in magnon-photon modeling, our framework enables scalable simulation and rapid prototyping of next-generation quantum and spintronic devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22221 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems Song, Jialin Tang, Yingheng Ren, Pu Takayoshi, Shintaro Sawant, Saurabh Zhu, Yujie Hu, Jia-Mian Nonaka, Andy Mahoney, Michael W. Erichson, Benjamin Yao, Zhi Quantum Physics Machine Learning Computational Physics Simulating hybrid magnonic quantum systems remains a challenge due to the large disparity between the timescales of the two systems. We present a massively parallel GPU-based simulation framework that enables fully coupled, large-scale modeling of on-chip magnon-photon circuits. Our approach resolves the dynamic interaction between ferromagnetic and electromagnetic fields with high spatiotemporal fidelity. To accelerate design workflows, we develop a physics-informed machine learning surrogate trained on the simulation data, reducing computational cost while maintaining accuracy. This combined approach reveals real-time energy exchange dynamics and reproduces key phenomena such as anti-crossing behavior and the suppression of ferromagnetic resonance under strong electromagnetic fields. By addressing the multiscale and multiphysics challenges in magnon-photon modeling, our framework enables scalable simulation and rapid prototyping of next-generation quantum and spintronic devices. |
| title | HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems |
| topic | Quantum Physics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2510.22221 |