Integrating Neural Networks with a Quantum Simulator for State Reconstruction
© 2019 American Physical Society. We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator by means of a neural-network model incorporating known experimental errors. Specifically, we extract restricted Boltzmann machine wave function...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society (APS)
2021
|
| Online Access: | https://hdl.handle.net/1721.1/136583 |
| _version_ | 1811085111172530176 |
|---|---|
| author | Torlai, Giacomo Timar, Brian van Nieuwenburg, Evert PL Levine, Harry Omran, Ahmed Keesling, Alexander Bernien, Hannes Greiner, Markus Vuletić, Vladan Lukin, Mikhail D Melko, Roger G Endres, Manuel |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Torlai, Giacomo Timar, Brian van Nieuwenburg, Evert PL Levine, Harry Omran, Ahmed Keesling, Alexander Bernien, Hannes Greiner, Markus Vuletić, Vladan Lukin, Mikhail D Melko, Roger G Endres, Manuel |
| author_sort | Torlai, Giacomo |
| collection | MIT |
| description | © 2019 American Physical Society. We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator by means of a neural-network model incorporating known experimental errors. Specifically, we extract restricted Boltzmann machine wave functions from data produced by a Rydberg quantum simulator with eight and nine atoms in a single measurement basis and apply a novel regularization technique to mitigate the effects of measurement errors in the training data. Reconstructions of modest complexity are able to capture one- and two-body observables not accessible to experimentalists, as well as more sophisticated observables such as the Rényi mutual information. Our results open the door to integration of machine learning architectures with intermediate-scale quantum hardware. |
| first_indexed | 2024-09-23T13:03:05Z |
| format | Article |
| id | mit-1721.1/136583 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:03:05Z |
| publishDate | 2021 |
| publisher | American Physical Society (APS) |
| record_format | dspace |
| spelling | mit-1721.1/1365832023-03-15T20:11:39Z Integrating Neural Networks with a Quantum Simulator for State Reconstruction Torlai, Giacomo Timar, Brian van Nieuwenburg, Evert PL Levine, Harry Omran, Ahmed Keesling, Alexander Bernien, Hannes Greiner, Markus Vuletić, Vladan Lukin, Mikhail D Melko, Roger G Endres, Manuel Massachusetts Institute of Technology. Department of Physics Massachusetts Institute of Technology. Research Laboratory of Electronics © 2019 American Physical Society. We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator by means of a neural-network model incorporating known experimental errors. Specifically, we extract restricted Boltzmann machine wave functions from data produced by a Rydberg quantum simulator with eight and nine atoms in a single measurement basis and apply a novel regularization technique to mitigate the effects of measurement errors in the training data. Reconstructions of modest complexity are able to capture one- and two-body observables not accessible to experimentalists, as well as more sophisticated observables such as the Rényi mutual information. Our results open the door to integration of machine learning architectures with intermediate-scale quantum hardware. 2021-10-27T20:36:06Z 2021-10-27T20:36:06Z 2019 2021-06-25T13:34:43Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136583 en 10.1103/PHYSREVLETT.123.230504 Physical Review Letters Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Physical Society (APS) APS |
| spellingShingle | Torlai, Giacomo Timar, Brian van Nieuwenburg, Evert PL Levine, Harry Omran, Ahmed Keesling, Alexander Bernien, Hannes Greiner, Markus Vuletić, Vladan Lukin, Mikhail D Melko, Roger G Endres, Manuel Integrating Neural Networks with a Quantum Simulator for State Reconstruction |
| title | Integrating Neural Networks with a Quantum Simulator for State Reconstruction |
| title_full | Integrating Neural Networks with a Quantum Simulator for State Reconstruction |
| title_fullStr | Integrating Neural Networks with a Quantum Simulator for State Reconstruction |
| title_full_unstemmed | Integrating Neural Networks with a Quantum Simulator for State Reconstruction |
| title_short | Integrating Neural Networks with a Quantum Simulator for State Reconstruction |
| title_sort | integrating neural networks with a quantum simulator for state reconstruction |
| url | https://hdl.handle.net/1721.1/136583 |
| work_keys_str_mv | AT torlaigiacomo integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT timarbrian integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT vannieuwenburgevertpl integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT levineharry integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT omranahmed integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT keeslingalexander integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT bernienhannes integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT greinermarkus integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT vuleticvladan integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT lukinmikhaild integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT melkorogerg integratingneuralnetworkswithaquantumsimulatorforstatereconstruction AT endresmanuel integratingneuralnetworkswithaquantumsimulatorforstatereconstruction |