用机器学习加速量子自旋电子器件仿真，实现高精度实时模拟。
HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems
- 基于GPU并行计算与物理信息神经网络，构建混合系统仿真框架。
- 成功捕捉能量交换、反交叉等关键量子现象，计算效率提升显著。
- 适合量子芯片设计、自旋电子学研究者快速原型开发使用。
由于磁子与光子系统时间尺度差异巨大，混合磁子量子系统的模拟仍具挑战。本文提出一种大规模并行的GPU仿真框架，实现片上磁子-光子电路的全耦合、大尺度建模。该方法以高时空保真度解析铁磁场与电磁场的动态相互作用。为加速设计流程，我们基于仿真数据构建了物理信息驱动的机器学习代理模型，在保持精度的同时大幅降低计算成本。该联合方法揭示了实时能量交换动态，复现了反交叉行为及强电磁场下铁磁共振抑制等关键现象。通过解决磁子-光子建模中的多尺度与多物理场难题，本框架实现了可扩展仿真与快速原型设计，助力下一代量子与自旋电子器件研发。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。