Hamiltonian learning in quantum field theories
Quantum field theories (QFTs) as relevant for condensed-matter or high-energy physics are formulated in continuous space and time, and typically emerge as effective low-energy descriptions. In atomic physics, an example is given by tunnel-coupled superfluids, which realize the paradigmatic sine-Gord...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Physical Society
2024-12-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.6.043284 |
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| _version_ | 1850255336223539200 |
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| author | Robert Ott Torsten V. Zache Maximilian Prüfer Sebastian Erne Mohammadamin Tajik Hannes Pichler Jörg Schmiedmayer Peter Zoller |
| author_facet | Robert Ott Torsten V. Zache Maximilian Prüfer Sebastian Erne Mohammadamin Tajik Hannes Pichler Jörg Schmiedmayer Peter Zoller |
| author_sort | Robert Ott |
| collection | DOAJ |
| description | Quantum field theories (QFTs) as relevant for condensed-matter or high-energy physics are formulated in continuous space and time, and typically emerge as effective low-energy descriptions. In atomic physics, an example is given by tunnel-coupled superfluids, which realize the paradigmatic sine-Gordon model, and can act as quantum simulators of continuous QFTs. To quantitatively characterize QFT simulators, or to discover the Hamiltonian governing the dynamics of a continuous many-body quantum system, we discuss Hamiltonian learning as a method to systematically extract the operator content and the coupling constants of Hamiltonians from experimental data. In contrast to Hamiltonian learning for lattice models with a given lattice scale, we learn QFT Hamiltonians on a resolution scale set by the experiment. Varying the resolution scale gives access to QFTs at different energy scales, and allows to learn a flow of Hamiltonians reminiscent of the renormalization group. Applying these techniques to available experimental data from a tunnel-coupled quantum gas experiment allows a definite distinction between a free quadratic theory from an interacting sine-Gordon model, as the underlying QFT description of the system. |
| format | Article |
| id | doaj-art-1bc0a6b91c8b46cb9a352d55aebeaa4a |
| institution | OA Journals |
| issn | 2643-1564 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj-art-1bc0a6b91c8b46cb9a352d55aebeaa4a2025-08-20T01:56:53ZengAmerican Physical SocietyPhysical Review Research2643-15642024-12-016404328410.1103/PhysRevResearch.6.043284Hamiltonian learning in quantum field theoriesRobert OttTorsten V. ZacheMaximilian PrüferSebastian ErneMohammadamin TajikHannes PichlerJörg SchmiedmayerPeter ZollerQuantum field theories (QFTs) as relevant for condensed-matter or high-energy physics are formulated in continuous space and time, and typically emerge as effective low-energy descriptions. In atomic physics, an example is given by tunnel-coupled superfluids, which realize the paradigmatic sine-Gordon model, and can act as quantum simulators of continuous QFTs. To quantitatively characterize QFT simulators, or to discover the Hamiltonian governing the dynamics of a continuous many-body quantum system, we discuss Hamiltonian learning as a method to systematically extract the operator content and the coupling constants of Hamiltonians from experimental data. In contrast to Hamiltonian learning for lattice models with a given lattice scale, we learn QFT Hamiltonians on a resolution scale set by the experiment. Varying the resolution scale gives access to QFTs at different energy scales, and allows to learn a flow of Hamiltonians reminiscent of the renormalization group. Applying these techniques to available experimental data from a tunnel-coupled quantum gas experiment allows a definite distinction between a free quadratic theory from an interacting sine-Gordon model, as the underlying QFT description of the system.http://doi.org/10.1103/PhysRevResearch.6.043284 |
| spellingShingle | Robert Ott Torsten V. Zache Maximilian Prüfer Sebastian Erne Mohammadamin Tajik Hannes Pichler Jörg Schmiedmayer Peter Zoller Hamiltonian learning in quantum field theories Physical Review Research |
| title | Hamiltonian learning in quantum field theories |
| title_full | Hamiltonian learning in quantum field theories |
| title_fullStr | Hamiltonian learning in quantum field theories |
| title_full_unstemmed | Hamiltonian learning in quantum field theories |
| title_short | Hamiltonian learning in quantum field theories |
| title_sort | hamiltonian learning in quantum field theories |
| url | http://doi.org/10.1103/PhysRevResearch.6.043284 |
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