Deep Bayesian experimental design for quantum many-body systems
Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ad020d |
| _version_ | 1797644323307651072 |
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| author | Leopoldo Sarra Florian Marquardt |
| author_facet | Leopoldo Sarra Florian Marquardt |
| author_sort | Leopoldo Sarra |
| collection | DOAJ |
| description | Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of this technique to complex high-dimensional situations. In this paper, we show how this approach holds promise for adaptive measurement strategies to characterize present-day quantum technology platforms. In particular, we focus on arrays of coupled cavities and qubit arrays. Both represent model systems of high relevance for modern applications, like quantum simulations and computing, and both have been realized in platforms where measurement and control can be exploited to characterize and counteract unavoidable disorder. Thus, they represent ideal targets for applications of Bayesian experimental design. |
| first_indexed | 2024-03-11T14:29:51Z |
| format | Article |
| id | doaj.art-172609cb95504441a4f7346dd1b152ff |
| institution | Directory Open Access Journal |
| issn | 2632-2153 |
| language | English |
| last_indexed | 2024-03-11T14:29:51Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj.art-172609cb95504441a4f7346dd1b152ff2023-10-31T10:12:48ZengIOP PublishingMachine Learning: Science and Technology2632-21532023-01-014404502210.1088/2632-2153/ad020dDeep Bayesian experimental design for quantum many-body systemsLeopoldo Sarra0https://orcid.org/0000-0001-7504-8656Florian Marquardt1https://orcid.org/0000-0003-4566-1753Max Planck Institute for the Science of Light , Staudtstraße 2, 91058 Erlangen, Germany; Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg , Staudtstraße 5, 91058 Erlangen, GermanyMax Planck Institute for the Science of Light , Staudtstraße 2, 91058 Erlangen, Germany; Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg , Staudtstraße 5, 91058 Erlangen, GermanyBayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of this technique to complex high-dimensional situations. In this paper, we show how this approach holds promise for adaptive measurement strategies to characterize present-day quantum technology platforms. In particular, we focus on arrays of coupled cavities and qubit arrays. Both represent model systems of high relevance for modern applications, like quantum simulations and computing, and both have been realized in platforms where measurement and control can be exploited to characterize and counteract unavoidable disorder. Thus, they represent ideal targets for applications of Bayesian experimental design.https://doi.org/10.1088/2632-2153/ad020dactive learningBayesian optimal experimental designquantum many-body systemsquantum technologies |
| spellingShingle | Leopoldo Sarra Florian Marquardt Deep Bayesian experimental design for quantum many-body systems Machine Learning: Science and Technology active learning Bayesian optimal experimental design quantum many-body systems quantum technologies |
| title | Deep Bayesian experimental design for quantum many-body systems |
| title_full | Deep Bayesian experimental design for quantum many-body systems |
| title_fullStr | Deep Bayesian experimental design for quantum many-body systems |
| title_full_unstemmed | Deep Bayesian experimental design for quantum many-body systems |
| title_short | Deep Bayesian experimental design for quantum many-body systems |
| title_sort | deep bayesian experimental design for quantum many body systems |
| topic | active learning Bayesian optimal experimental design quantum many-body systems quantum technologies |
| url | https://doi.org/10.1088/2632-2153/ad020d |
| work_keys_str_mv | AT leopoldosarra deepbayesianexperimentaldesignforquantummanybodysystems AT florianmarquardt deepbayesianexperimentaldesignforquantummanybodysystems |