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Bidirectional Information Flow Quantum State Tomography |
| Huikang Huang1, Haozhen Situ1*, and Shenggen Zheng2* |
1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
2Circuits and Systems Research Center, Peng Cheng Laboratory, Shenzhen 518055, China
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| Cite this article: |
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Huikang Huang, Haozhen Situ, and Shenggen Zheng 2021 Chin. Phys. Lett. 38 040303 |
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Abstract The exact reconstruction of many-body quantum systems is one of the major challenges in modern physics, because it is impractical to overcome the exponential complexity problem brought by high-dimensional quantum many-body systems. Recently, machine learning techniques are well used to promote quantum information research and quantum state tomography has also been developed by neural network generative models. We propose a quantum state tomography method, which is based on a bidirectional gated recurrent unit neural network, to learn and reconstruct both easy quantum states and hard quantum states in this study. We are able to use fewer measurement samples in our method to reconstruct these quantum states and to obtain high fidelity.
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Received: 25 December 2020
Published: 06 April 2021
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| PACS: |
03.65.Wj
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(State reconstruction, quantum tomography)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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| Fund: Supported by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2020A1515011204), and the National Natural Science Foundation of China (Grant No. 61602532). |
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