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[2510.22221] HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems
Summary
This article presents a GPU-based simulation framework for modeling magnon-photon dynamics in hybrid quantum systems, utilizing machine learning surrogates to enhance computational efficiency and accuracy.
Why It Matters
The research addresses significant challenges in simulating complex quantum systems, which is crucial for advancing quantum technologies and spintronic devices. By integrating high-performance computing with machine learning, the study offers a pathway to more efficient design and prototyping in quantum physics.
Key Takeaways
- Introduces a GPU-based framework for simulating magnon-photon interactions.
- Utilizes machine learning surrogates to reduce computational costs while maintaining accuracy.
- Reveals key phenomena in energy exchange dynamics relevant to quantum systems.
Quantum Physics arXiv:2510.22221 (quant-ph) [Submitted on 25 Oct 2025 (v1), last revised 22 Feb 2026 (this version, v3)] Title:HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems Authors:Jialin Song, Yingheng Tang, Pu Ren, Shintaro Takayoshi, Saurabh Sawant, Yujie Zhu, Jia-Mian Hu, Andy Nonaka, Michael W. Mahoney, Benjamin Erichson, Zhi Yao View a PDF of the paper titled HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems, by Jialin Song and 10 other authors View PDF HTML (experimental) Abstract: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 s...
