Year: 2025
Type: article
Abstract: Understanding magnetic structures is essential for advancing materials science, spintronics, and geology, but reconstructing magnetization from an experimentally measured magnetic field map has been a challenging inverse problem. The authors introduce a GPU-accelerated method that recovers spatially varying magnetization with minimal prior assumptions and is robust to realistic experimental conditions. This approach reveals magnetic textures such as ferromagnetic domains, topological skyrmions, and a magnetic moir\'e superlattice. This capability provides a versatile tool for decoding complex magnetization profiles and expands the reach of next-generation quantum magnetic sensing.
Source: Physical Review Applied
DOI: 10.1103/q312-kf83
Institutions: Washington University in St. Louis, Stanford University
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: Linking Quantum Sensing Technologies across Disciplines: a Convergent Quantum Sciences and Engineering Graduate Training Program, ExpandQISE: Track 2: Neutral Atom Based Quantum Information Processing, Collaborative Research: CNS Core: Medium: TeTON: A Testbed and a Toolkit for Expediting Investigation of and Accelerating Advancements in All-Optical Neural Networks, Non-Hermitian Physics in Ultracold Atoms and Photonics, Probing Non-Equilibrium Quantum Dynamics with Spins in Diamond and hBN, QuSeC-TAQS: Nanodiamond Quantum Sensing for Four-Dimensional Live-Cell Imaging
OpenAlex ID: W4417088478