globalchange  > 气候变化与战略
DOI: 10.1016/j.scib.2019.11.009
论文题名:
Reconstruction of quantum channel via convex optimization
作者: Huang X.-L.; Gao J.; Jiao Z.-Q.; Yan Z.-Q.; Zhang Z.-Y.; Chen D.-Y.; Zhang X.; Ji L.; Jin X.-M.
刊名: Science Bulletin
ISSN: 20959273
出版年: 2020
卷: 65, 期:4
起始页码: 286
结束页码: 292
语种: 英语
中文关键词: Convex optimization ; Quantum channel ; Quantum information ; Quantum process tomography
英文关键词: Convex optimization ; Cost functions ; Machine components ; Machine learning ; Quantum channel ; Quantum communication ; Quantum entanglement ; Quantum optics ; Tomography ; Global optimum ; Optimization approach ; Process matrix ; Process tomography ; Quantum Information ; Quantum process ; Quantum process tomography ; Robust estimation ; Communication channels (information theory)
英文摘要: Quantum process tomography is often used to completely characterize an unknown quantum process. However, it may lead to an unphysical process matrix, which will cause the loss of information with respect to the tomography result. Convex optimization, widely used in machine learning, is able to generate a global optimum that best fits the raw data while keeping the process tomography in a legitimate region. Only by correctly revealing the original action of the process can we seek deeper into its properties like its phase transition and its Hamiltonian. Here, we reconstruct the seawater channel using convex optimization and further test it on the seven fundamental gates. We compare our method to the standard-inversion and norm-optimization approaches using the cost function value and our proposed state deviation. The advantages convince that our method enables a more precise and robust estimation of the elements of the process matrix with less demands on preliminary resources. In addition, we examine on a set of non-unitary channels and the reconstructions reach up to 99.5% accuracy. Our method offers a more universal tool for further analyses on the components of the quantum channels and we believe that the crossover between quantum process tomography and convex optimization may help us move forward to machine learning of quantum channels. © 2019 Science China Press
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169928
Appears in Collections:气候变化与战略

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作者单位: Center for Integrated Quantum Information Technologies (IQIT), School of Physics and Astronomy and State Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, Shanghai, 200240, China; CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, 230026, China

Recommended Citation:
Huang X.-L.,Gao J.,Jiao Z.-Q.,et al. Reconstruction of quantum channel via convex optimization[J]. Science Bulletin,2020-01-01,65(4)
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