Estimation of entanglement in bipartite systems directly from tomograms

  • B. SharmilaEmail author
  • S. Lakshmibala
  • V. Balakrishnan


We investigate the advantages of extracting the degree of entanglement in bipartite systems directly from tomograms, as it is the latter that are readily obtained from experiments. This would provide a superior alternative to the standard procedure of assessing the extent of entanglement between subsystems after employing the machinery of state reconstruction from the tomogram. The latter is both cumbersome and involves statistical methods, while a direct inference about entanglement from the tomogram circumvents these limitations. In an earlier paper, we had identified a procedure to obtain a bipartite entanglement indicator directly from tomograms. To assess the efficacy of this indicator, we now carry out a detailed investigation using two nonlinear bipartite models by comparing this tomographic indicator with standard markers of entanglement such as the subsystem linear entropy and the subsystem von Neumann entropy and also with a commonly used indicator obtained from inverse participation ratios. The two-model systems selected for this purpose are a multilevel atom interacting with a radiation field, and a double-well Bose–Einstein condensate. The role played by the specific initial states of these two systems in the performance of the tomographic indicator is also examined. Further, the efficiency of the tomographic entanglement indicator during the dynamical evolution of the system is assessed from a time-series analysis of the difference between this indicator and the subsystem von Neumann entropy.


Entanglement indicator Tomogram Inverse participation ratios Bipartite systems Time-series analysis 



One of the authors (SL) thanks M. Santhanam of IISER Pune for discussions pertaining to inverse participation ratios.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of PhysicsIndian Institute of Technology MadrasChennaiIndia

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