期刊
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
卷 1, 期 3, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/2632-2153/ab9a21
关键词
machine learning; quantum tomography; convolutional neural networks; deep learning; quantum optics
类别
资金
- Army Research Laboratory
- Army Research Office [W911NF-19-2-0087, W911NF-20-2-0168]
We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For a wide range of pure and mixed input states we demonstrate via simulations that our method produces functionally equivalent reconstructed states to that of traditional methods with the added benefit that expensive computations are front-loaded with our system. Further, by training our system with measurement results that include simulated noise sources we are able to demonstrate a significantly enhanced average fidelity when compared to typical reconstruction methods. These enhancements in average fidelity are also shown to persist when we consider state reconstruction from partial tomography data where several measurements are missing. We anticipate that the present results combining the fields of machine intelligence and quantum state estimation will greatly improve and speed up tomography-based quantum experiments.
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