Hamiltonian-learning quantum magnets with nonlocal impurity tomography

Loading...
Thumbnail Image

Access rights

openAccess
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Major/Subject

Mcode

Degree programme

Language

en

Pages

14

Series

Physical Review Applied, Volume 23, issue 5, pp. 1-14

Abstract

Impurities in quantum materials have provided successful strategies for learning properties of complex states, ranging from unconventional superconductors to topological insulators. In quantum magnetism, inferring the Hamiltonian of an engineered system becomes a challenging open problem in the presence of complex interactions. Here we show how a supervised machine-learning technique can be used to infer Hamiltonian parameters from atomically engineered quantum magnets by inferring fluctuations of the ground states due to the presence of impurities. We demonstrate our methodology both with a fermionic model with spin-orbit coupling, as well as with many-body spin models with long-range exchange and anisotropic exchange interactions. We show that our approach enables performing Hamiltonian extraction in the presence of significant noise, providing a strategy to perform Hamiltonian learning with experimental observables in atomic-scale quantum magnets. Our results establish a strategy to perform Hamiltonian learning by exploiting the impact of impurities in complex quantum many-body states.

Description

Keywords

Other note

Citation

Lupi, G & Lado, J 2025, 'Hamiltonian-learning quantum magnets with nonlocal impurity tomography', Physical Review Applied, vol. 23, no. 5, 054077, pp. 1-14. https://doi.org/10.1103/PhysRevApplied.23.054077