Neural networks for quantum inverse problems
Quantum inverse problem (QIP) is the problem of estimating an unknown quantum system from a set of measurements, whereas the classical counterpart is the inverse problem of estimating a distribution from a set of observations. In this paper, we present a neural-network-based method for QIPs, which h...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2022-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ac706c |
| _version_ | 1797748410291322880 |
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| author | Ningping Cao Jie Xie Aonan Zhang Shi-Yao Hou Lijian Zhang Bei Zeng |
| author_facet | Ningping Cao Jie Xie Aonan Zhang Shi-Yao Hou Lijian Zhang Bei Zeng |
| author_sort | Ningping Cao |
| collection | DOAJ |
| description | Quantum inverse problem (QIP) is the problem of estimating an unknown quantum system from a set of measurements, whereas the classical counterpart is the inverse problem of estimating a distribution from a set of observations. In this paper, we present a neural-network-based method for QIPs, which has been widely explored for its classical counterpart. The proposed method utilizes the quantumness of the QIPs and takes advantage of the computational power of neural networks to achieve remarkable efficiency for the quantum state estimation. We test the method on the problem of maximum entropy estimation of an unknown state ρ from partial information both numerically and experimentally. Our method yields high fidelity, efficiency and robustness for both numerical experiments and quantum optical experiments. |
| first_indexed | 2024-03-12T16:04:21Z |
| format | Article |
| id | doaj.art-27df921d3fc249d78f28bbe1420d1ab8 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:04:21Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-27df921d3fc249d78f28bbe1420d1ab82023-08-09T14:24:15ZengIOP PublishingNew Journal of Physics1367-26302022-01-0124606300210.1088/1367-2630/ac706cNeural networks for quantum inverse problemsNingping Cao0Jie Xie1Aonan Zhang2https://orcid.org/0000-0002-6310-4769Shi-Yao Hou3https://orcid.org/0000-0001-9739-2263Lijian Zhang4Bei Zeng5Department of Mathematics & Statistics, University of Guelph , Guelph N1G 2W1, Ontario, Canada; Institute for Quantum Computing, University of Waterloo , Waterloo N2L 3G1, Ontario, CanadaNational Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences and School of Physics, Nanjing University , Nanjing 210093, People’s Republic of China; Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, People’s Republic of ChinaNational Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences and School of Physics, Nanjing University , Nanjing 210093, People’s Republic of China; Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, People’s Republic of ChinaCollege of Physics and Electronic Engineering, Center for Computational Sciences, Sichuan Normal University , Chengdu 610068, People’s Republic of ChinaNational Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences and School of Physics, Nanjing University , Nanjing 210093, People’s Republic of China; Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, People’s Republic of ChinaDepartment of Physics, The Hong Kong University of Science and Technology , Clear Water Bay, Kowloon, Hong Kong, People’s Republic of ChinaQuantum inverse problem (QIP) is the problem of estimating an unknown quantum system from a set of measurements, whereas the classical counterpart is the inverse problem of estimating a distribution from a set of observations. In this paper, we present a neural-network-based method for QIPs, which has been widely explored for its classical counterpart. The proposed method utilizes the quantumness of the QIPs and takes advantage of the computational power of neural networks to achieve remarkable efficiency for the quantum state estimation. We test the method on the problem of maximum entropy estimation of an unknown state ρ from partial information both numerically and experimentally. Our method yields high fidelity, efficiency and robustness for both numerical experiments and quantum optical experiments.https://doi.org/10.1088/1367-2630/ac706cquantum informationquantum machine learningquantum inverse problem |
| spellingShingle | Ningping Cao Jie Xie Aonan Zhang Shi-Yao Hou Lijian Zhang Bei Zeng Neural networks for quantum inverse problems New Journal of Physics quantum information quantum machine learning quantum inverse problem |
| title | Neural networks for quantum inverse problems |
| title_full | Neural networks for quantum inverse problems |
| title_fullStr | Neural networks for quantum inverse problems |
| title_full_unstemmed | Neural networks for quantum inverse problems |
| title_short | Neural networks for quantum inverse problems |
| title_sort | neural networks for quantum inverse problems |
| topic | quantum information quantum machine learning quantum inverse problem |
| url | https://doi.org/10.1088/1367-2630/ac706c |
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