Hamiltonian learning quantum magnets with dynamical impurity tomography
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911224215633920 |
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| author | Karjalainen, Netta Lupi, Greta Koch, Rouven Fumega, Adolfo O. Lado, Jose L. |
| author_facet | Karjalainen, Netta Lupi, Greta Koch, Rouven Fumega, Adolfo O. Lado, Jose L. |
| contents | Nanoscale engineered spin systems, ranging from spins on surfaces to nanographenes, provide flexible platforms to realize entangled quantum magnets from a bottom up approach. However, assessing the quantum many-body Hamiltonian realized in a specific experiment remains an exceptional open challenge, due to the difficulty of disentangling competing terms accounting for the many-body excitations. Here, we demonstrate a machine learning strategy to learn a quantum many-body spin Hamiltonian from scanning spectroscopy measurements of spin excitations. Our methodology leverages the spatially-resolved reconstruction of the many-body excitations induced by depositing quantum impurities next to the quantum magnet. We demonstrate that our algorithm allows us to predict long-range Heisenberg exchange interactions, anisotropic exchange, as well as antisymmetric Dzyaloshinskii-Moriya interaction, including in the presence of sizable noise. Our methodology establishes defect-induced spatially-resolved dynamical excitations in quantum magnets as a powerful strategy to understand the nature of quantum spin many-body models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hamiltonian learning quantum magnets with dynamical impurity tomography Karjalainen, Netta Lupi, Greta Koch, Rouven Fumega, Adolfo O. Lado, Jose L. Mesoscale and Nanoscale Physics Strongly Correlated Electrons Nanoscale engineered spin systems, ranging from spins on surfaces to nanographenes, provide flexible platforms to realize entangled quantum magnets from a bottom up approach. However, assessing the quantum many-body Hamiltonian realized in a specific experiment remains an exceptional open challenge, due to the difficulty of disentangling competing terms accounting for the many-body excitations. Here, we demonstrate a machine learning strategy to learn a quantum many-body spin Hamiltonian from scanning spectroscopy measurements of spin excitations. Our methodology leverages the spatially-resolved reconstruction of the many-body excitations induced by depositing quantum impurities next to the quantum magnet. We demonstrate that our algorithm allows us to predict long-range Heisenberg exchange interactions, anisotropic exchange, as well as antisymmetric Dzyaloshinskii-Moriya interaction, including in the presence of sizable noise. Our methodology establishes defect-induced spatially-resolved dynamical excitations in quantum magnets as a powerful strategy to understand the nature of quantum spin many-body models. |
| title | Hamiltonian learning quantum magnets with dynamical impurity tomography |
| topic | Mesoscale and Nanoscale Physics Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2510.18613 |