Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments
We introduce a novel interpretable neural network (NN) model designed to perform precision bulk reconstruction under the AdS/CFT correspondence. According to the correspondence, a specific condensed matter system on a ring is holographically equivalent to a gravitational system on a bulk disk, throu...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adb09f |
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| author | Koji Hashimoto Koshiro Matsuo Masaki Murata Gakuto Ogiwara Daichi Takeda |
| author_facet | Koji Hashimoto Koshiro Matsuo Masaki Murata Gakuto Ogiwara Daichi Takeda |
| author_sort | Koji Hashimoto |
| collection | DOAJ |
| description | We introduce a novel interpretable neural network (NN) model designed to perform precision bulk reconstruction under the AdS/CFT correspondence. According to the correspondence, a specific condensed matter system on a ring is holographically equivalent to a gravitational system on a bulk disk, through which tabletop quantum gravity experiments may be possible as reported in (Hashimoto et al 2023 Phys. Rev. Res. 5 023168). The purpose of this paper is to reconstruct a higher-dimensional gravity metric from the condensed matter system data via machine learning using the NN. Our machine reads spatially and temporarily inhomogeneous linear response data of the condensed matter system, and incorporates a novel layer that implements the Runge–Kutta method to achieve better numerical control. We confirm that our machine can let a higher-dimensional gravity metric be automatically emergent as its interpretable weights, using a linear response of the condensed matter system as data, through supervised machine learning. The developed method could serve as a foundation for generic bulk reconstruction, i.e. a practical solution to the AdS/CFT correspondence, and would be implemented in future tabletop quantum gravity experiments. |
| format | Article |
| id | doaj-art-b9b332073aba43f088bd13cd5eef924b |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-b9b332073aba43f088bd13cd5eef924b2025-02-10T10:51:19ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101503010.1088/2632-2153/adb09fMachine-learning emergent spacetime from linear response in future tabletop quantum gravity experimentsKoji Hashimoto0https://orcid.org/0000-0001-5619-9096Koshiro Matsuo1https://orcid.org/0009-0009-6729-5978Masaki Murata2https://orcid.org/0000-0002-0449-2554Gakuto Ogiwara3https://orcid.org/0009-0000-4448-229XDaichi Takeda4https://orcid.org/0000-0002-1263-8656Department of Physics, Kyoto University , Kyoto 606-8502, JapanDepartment of Information Systems, Saitama Institute of Technology , Saitama 369-0293, JapanDepartment of Information Systems, Saitama Institute of Technology , Saitama 369-0293, JapanDepartment of Information Systems, Saitama Institute of Technology , Saitama 369-0293, JapanDepartment of Physics, Kyoto University , Kyoto 606-8502, JapanWe introduce a novel interpretable neural network (NN) model designed to perform precision bulk reconstruction under the AdS/CFT correspondence. According to the correspondence, a specific condensed matter system on a ring is holographically equivalent to a gravitational system on a bulk disk, through which tabletop quantum gravity experiments may be possible as reported in (Hashimoto et al 2023 Phys. Rev. Res. 5 023168). The purpose of this paper is to reconstruct a higher-dimensional gravity metric from the condensed matter system data via machine learning using the NN. Our machine reads spatially and temporarily inhomogeneous linear response data of the condensed matter system, and incorporates a novel layer that implements the Runge–Kutta method to achieve better numerical control. We confirm that our machine can let a higher-dimensional gravity metric be automatically emergent as its interpretable weights, using a linear response of the condensed matter system as data, through supervised machine learning. The developed method could serve as a foundation for generic bulk reconstruction, i.e. a practical solution to the AdS/CFT correspondence, and would be implemented in future tabletop quantum gravity experiments.https://doi.org/10.1088/2632-2153/adb09fmachine-learningemergent spacetimeAdS/CFTneural networkquantum gravity |
| spellingShingle | Koji Hashimoto Koshiro Matsuo Masaki Murata Gakuto Ogiwara Daichi Takeda Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments Machine Learning: Science and Technology machine-learning emergent spacetime AdS/CFT neural network quantum gravity |
| title | Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments |
| title_full | Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments |
| title_fullStr | Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments |
| title_full_unstemmed | Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments |
| title_short | Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments |
| title_sort | machine learning emergent spacetime from linear response in future tabletop quantum gravity experiments |
| topic | machine-learning emergent spacetime AdS/CFT neural network quantum gravity |
| url | https://doi.org/10.1088/2632-2153/adb09f |
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