Experimental quantum homodyne tomography via machine learning
Complete characterization of states and processes that occur within quantum devices is crucial for understanding and testing their potential to outperform classical technologies for communications and computing. However, solving this task with current state-of-the-art techniques becomes unwieldy for...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Optical Society of America
2020
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| _version_ | 1826284391457882112 |
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| author | Tiunov, ES Tiunova (Vyborova), VV Ulanov, AE Lvovsky, AI Fedorov, AK |
| author_facet | Tiunov, ES Tiunova (Vyborova), VV Ulanov, AE Lvovsky, AI Fedorov, AK |
| author_sort | Tiunov, ES |
| collection | OXFORD |
| description | Complete characterization of states and processes that occur within quantum devices is crucial for understanding and testing their potential to outperform classical technologies for communications and computing. However, solving this task with current state-of-the-art techniques becomes unwieldy for large and complex quantum systems. Here we realize and experimentally demonstrate a method for complete characterization of a quantum harmonic oscillator based on an artificial neural network known as the restricted Boltzmann machine. We apply the method to optical homodyne tomography and show it to allow full estimation of quantum states based on a smaller amount of experimental data compared to state-of-the-art methods. We link this advantage to reduced overfitting. Although our experiment is in the optical domain, our method provides a way of exploring quantum resources in a broad class of large-scale physical systems, such as superconducting circuits, atomic and molecular ensembles, and optomechanical systems. |
| first_indexed | 2024-03-07T01:13:08Z |
| format | Journal article |
| id | oxford-uuid:8dbae052-f012-457e-81cf-6e18965cc152 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:13:08Z |
| publishDate | 2020 |
| publisher | Optical Society of America |
| record_format | dspace |
| spelling | oxford-uuid:8dbae052-f012-457e-81cf-6e18965cc1522022-03-26T22:53:05ZExperimental quantum homodyne tomography via machine learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:8dbae052-f012-457e-81cf-6e18965cc152EnglishSymplectic ElementsOptical Society of America2020Tiunov, ESTiunova (Vyborova), VVUlanov, AELvovsky, AIFedorov, AKComplete characterization of states and processes that occur within quantum devices is crucial for understanding and testing their potential to outperform classical technologies for communications and computing. However, solving this task with current state-of-the-art techniques becomes unwieldy for large and complex quantum systems. Here we realize and experimentally demonstrate a method for complete characterization of a quantum harmonic oscillator based on an artificial neural network known as the restricted Boltzmann machine. We apply the method to optical homodyne tomography and show it to allow full estimation of quantum states based on a smaller amount of experimental data compared to state-of-the-art methods. We link this advantage to reduced overfitting. Although our experiment is in the optical domain, our method provides a way of exploring quantum resources in a broad class of large-scale physical systems, such as superconducting circuits, atomic and molecular ensembles, and optomechanical systems. |
| spellingShingle | Tiunov, ES Tiunova (Vyborova), VV Ulanov, AE Lvovsky, AI Fedorov, AK Experimental quantum homodyne tomography via machine learning |
| title | Experimental quantum homodyne tomography via machine learning |
| title_full | Experimental quantum homodyne tomography via machine learning |
| title_fullStr | Experimental quantum homodyne tomography via machine learning |
| title_full_unstemmed | Experimental quantum homodyne tomography via machine learning |
| title_short | Experimental quantum homodyne tomography via machine learning |
| title_sort | experimental quantum homodyne tomography via machine learning |
| work_keys_str_mv | AT tiunoves experimentalquantumhomodynetomographyviamachinelearning AT tiunovavyborovavv experimentalquantumhomodynetomographyviamachinelearning AT ulanovae experimentalquantumhomodynetomographyviamachinelearning AT lvovskyai experimentalquantumhomodynetomographyviamachinelearning AT fedorovak experimentalquantumhomodynetomographyviamachinelearning |