HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems

TL;DR

This paper introduces a GPU-accelerated simulation framework combined with machine learning surrogates to efficiently model large-scale magnon-photon interactions in hybrid quantum systems, capturing complex dynamics with high fidelity.

Contribution

It presents a novel, scalable simulation approach that integrates physics-informed machine learning to reduce computational costs in modeling magnon-photon systems.

Findings

Reproduces key phenomena like anti-crossing behavior.

Enables real-time energy exchange analysis.

Reduces simulation time with ML surrogates.

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.