Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146596
Title: Multipartite entanglement analysis from random correlations
Authors: Knips, Lukas
Dziewior, Jan
Kłobus, Waldemar
Laskowski, Wiesław
Paterek, Tomasz
Shadbolt, Peter J.
Weinfurter, Harald
Meinecke, Jasmin D. A.
Keywords: Science::Mathematics
Issue Date: 2020
Source: Knips, L., Dziewior, J., Kłobus, W., Laskowski, W., Paterek, T., Shadbolt, P. J., . . . Meinecke, J. D. A. (2020). Multipartite entanglement analysis from random correlations. npj Quantum Information, 6(1), 51-. doi:10.1038/s41534-020-0281-5
Project: MOE2015-T2-2-034
Journal: npj Quantum Information
Abstract: Quantum entanglement is usually revealed via a well aligned, carefully chosen set of measurements. Yet, under a number of experimental conditions, for example in communication within multiparty quantum networks, noise along the channels or fluctuating orientations of reference frames may ruin the quality of the distributed states. Here, we show that even for strong fluctuations one can still gain detailed information about the state and its entanglement using random measurements. Correlations between all or subsets of the measurement outcomes and especially their distributions provide information about the entanglement structure of a state. We analytically derive an entanglement criterion for two-qubit states and provide strong numerical evidence for witnessing genuine multipartite entanglement of three and four qubits. Our methods take the purity of the states into account and are based on only the second moments of measured correlations. Extended features of this theory are demonstrated experimentally with four photonic qubits. As long as the rate of entanglement generation is sufficiently high compared to the speed of the fluctuations, this method overcomes any type and strength of localized unitary noise.
URI: https://hdl.handle.net/10356/146596
ISSN: 2056-6387
DOI: 10.1038/s41534-020-0281-5
Rights: © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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