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Quantum Machine Learning
Enhancing collective entanglement witnesses through correlation with state purity
arXiv
Authors: Kateřina Jiráková, Antonín Černoch, Artur Barasiński, Karel Lemr
Year
2023
Paper ID
52694
Status
Preprint
Abstract Read
~2 min
Abstract Words
129
Citations
N/A
Abstract
This paper analyzes the adverse impact of white noise on collective quantum measurements and argues that such noise poses a significant obstacle to the otherwise straightforward deployment of collective measurements in quantum communications. The paper then suggests addressing this issue by correlating the outcomes of these measurements with quantum state purity. To test the concept, a support vector machine is employed to boost the performance of several collective entanglement witnesses by incorporating state purity into the classification task of distinguishing entangled states from separable ones. Furthermore, the application of machine learning allows to optimize selectivity of entanglement detection given a target value of sensitivity. A response operating characteristic curve is reconstructed based on this optimization and the area under curve calculated to assess the efficacy of the proposed model.
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- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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- This paper analyzes the adverse impact of white noise on collective quantum measurements and argues that such noise poses a significant obstacle to the otherwise...
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