Neural networks for quantum state tomography with constrained measurements
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| _version_ | 1866917969233182720 |
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| author | Ma, Hailan Dong, Daoyi Petersen, Ian R. Huang, Chang-Jiang Xiang, Guo-Yong |
| author_facet | Ma, Hailan Dong, Daoyi Petersen, Ian R. Huang, Chang-Jiang Xiang, Guo-Yong |
| contents | Quantum state tomography (QST) aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Recognizing the challenges posed by imperfect measurement data, we develop a unified neural network(NN)-based approach for QST under constrained measurement scenarios, including limited measurement copies, incomplete measurements, and noisy measurements. Through comprehensive comparison with other estimation methods, we demonstrate that our method improves the estimation accuracy in scenarios with limited measurement resources, showcasing notable robustness in noisy measurement settings. These findings highlight the capability of NNs to enhance QST with constrained measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_09504 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Neural networks for quantum state tomography with constrained measurements Ma, Hailan Dong, Daoyi Petersen, Ian R. Huang, Chang-Jiang Xiang, Guo-Yong Quantum Physics Systems and Control Quantum state tomography (QST) aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Recognizing the challenges posed by imperfect measurement data, we develop a unified neural network(NN)-based approach for QST under constrained measurement scenarios, including limited measurement copies, incomplete measurements, and noisy measurements. Through comprehensive comparison with other estimation methods, we demonstrate that our method improves the estimation accuracy in scenarios with limited measurement resources, showcasing notable robustness in noisy measurement settings. These findings highlight the capability of NNs to enhance QST with constrained measurements. |
| title | Neural networks for quantum state tomography with constrained measurements |
| topic | Quantum Physics Systems and Control |
| url | https://arxiv.org/abs/2111.09504 |