Optical lattice experiments at unobserved conditions with generative adversarial deep learning
Optical lattice experiments with ultracold atoms allow for the experimental realization of contemporary problems in many-body physics. Yet, devising models that faithfully describe experimental observables is often difficult and problem dependent; there is currently no theoretical method which accou...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2021-09-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.3.033267 |
| _version_ | 1797210921654812672 |
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| author | Corneel Casert Kyle Mills Tom Vieijra Jan Ryckebusch Isaac Tamblyn |
| author_facet | Corneel Casert Kyle Mills Tom Vieijra Jan Ryckebusch Isaac Tamblyn |
| author_sort | Corneel Casert |
| collection | DOAJ |
| description | Optical lattice experiments with ultracold atoms allow for the experimental realization of contemporary problems in many-body physics. Yet, devising models that faithfully describe experimental observables is often difficult and problem dependent; there is currently no theoretical method which accounts for all experimental observations. Leveraging the large data volume and presence of strong correlations, machine learning provides a novel avenue for the study of such systems. It has recently been proven successful in analyzing properties of experimental data of ultracold quantum gases. Here we show that generative deep learning succeeds in the challenging task of modeling such an experimental data distribution. Our method is able to produce synthetic experimental snapshots of a doped two-dimensional Fermi-Hubbard model that are indistinguishable from previously reported experimental realizations. We demonstrate how our generative model interprets physical conditions such as temperature at the level of individual configurations. We use our approach to predict snapshots at conditions and scales which are currently experimentally inaccessible, mapping the large-scale behavior of optical lattices at unseen conditions. |
| first_indexed | 2024-04-24T10:18:17Z |
| format | Article |
| id | doaj.art-483e6862cbed4659a36a4f109e2e994a |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:18:17Z |
| publishDate | 2021-09-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-483e6862cbed4659a36a4f109e2e994a2024-04-12T17:14:13ZengAmerican Physical SocietyPhysical Review Research2643-15642021-09-013303326710.1103/PhysRevResearch.3.033267Optical lattice experiments at unobserved conditions with generative adversarial deep learningCorneel CasertKyle MillsTom VieijraJan RyckebuschIsaac TamblynOptical lattice experiments with ultracold atoms allow for the experimental realization of contemporary problems in many-body physics. Yet, devising models that faithfully describe experimental observables is often difficult and problem dependent; there is currently no theoretical method which accounts for all experimental observations. Leveraging the large data volume and presence of strong correlations, machine learning provides a novel avenue for the study of such systems. It has recently been proven successful in analyzing properties of experimental data of ultracold quantum gases. Here we show that generative deep learning succeeds in the challenging task of modeling such an experimental data distribution. Our method is able to produce synthetic experimental snapshots of a doped two-dimensional Fermi-Hubbard model that are indistinguishable from previously reported experimental realizations. We demonstrate how our generative model interprets physical conditions such as temperature at the level of individual configurations. We use our approach to predict snapshots at conditions and scales which are currently experimentally inaccessible, mapping the large-scale behavior of optical lattices at unseen conditions.http://doi.org/10.1103/PhysRevResearch.3.033267 |
| spellingShingle | Corneel Casert Kyle Mills Tom Vieijra Jan Ryckebusch Isaac Tamblyn Optical lattice experiments at unobserved conditions with generative adversarial deep learning Physical Review Research |
| title | Optical lattice experiments at unobserved conditions with generative adversarial deep learning |
| title_full | Optical lattice experiments at unobserved conditions with generative adversarial deep learning |
| title_fullStr | Optical lattice experiments at unobserved conditions with generative adversarial deep learning |
| title_full_unstemmed | Optical lattice experiments at unobserved conditions with generative adversarial deep learning |
| title_short | Optical lattice experiments at unobserved conditions with generative adversarial deep learning |
| title_sort | optical lattice experiments at unobserved conditions with generative adversarial deep learning |
| url | http://doi.org/10.1103/PhysRevResearch.3.033267 |
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