HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems
🤖 AI Summary
Multiscale dynamic simulation of magnon–photon hybrid systems has long been hindered by the vast timescale disparity (GHz vs. THz) and strong nonlinear coupling. This work introduces a GPU-accelerated, fully coupled multiphysics simulation framework that integrates physics-informed neural network (PINN) surrogate models to achieve high spatiotemporal resolution while drastically improving computational efficiency. To our knowledge, this is the first method enabling large-scale, fully dynamic on-chip simulation of magnon–photon circuits. It accurately reproduces key quantum phenomena—including avoided crossings, real-time energy exchange, and suppression of ferromagnetic resonance under strong fields. Compared with conventional numerical approaches, the proposed framework reduces computational cost by one to two orders of magnitude. It thus facilitates rapid prototyping and scalable parameter optimization for complex spin–photon devices, establishing an efficient simulation paradigm for next-generation quantum and spintronic integrated systems.
Application Category
📝 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 scalable simulation and rapid prototyping of next-generation quantum and spintronic devices.
Problem
Research questions and friction points this paper is trying to address.
Simulating magnon-photon dynamics across disparate timescales
Developing GPU-accelerated framework for coupled magnon-photon circuits
Creating ML surrogates to reduce computational costs while maintaining accuracy
Innovation
Methods, ideas, or system contributions that make the work stand out.
GPU-based parallel framework for magnon-photon circuit modeling
Physics-informed ML surrogate trained on simulation data
Resolves multiscale magnon-photon dynamics with high fidelity
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