IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v600y2022ics0378437122003661.html
   My bibliography  Save this article

MaxEnt principle and reduced density matrix estimation

Author

Listed:
  • Losada, Marcelo
  • Penas, Víctor A.
  • Holik, Federico
  • Lamberti, Pedro W.

Abstract

In this work we study the reduced density matrices of sublattices of fermionic, bosonic and spin lattice models. Firstly, we consider fermionic and bosonic lattice models, and we show that the reduced density matrix associated with a sublattice coincides with the state obtained by applying the maximum entropy principle under suitably chosen constraints. Secondly, for informationally incomplete scenarios, we considered spin lattice models. We study the performance of the MaxEnt method for estimating the reduced density matrix of sublattices of the lattice system. We find that the performance of the MaxEnt estimation improves not only with the number of measured observables (as expected), but also with the lattice length. In these cases, the MaxEnt solution can be considered, not as an exact solution, but as a good estimator.

Suggested Citation

  • Losada, Marcelo & Penas, Víctor A. & Holik, Federico & Lamberti, Pedro W., 2022. "MaxEnt principle and reduced density matrix estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    • Handle: RePEc:eee:phsmap:v:600:y:2022:i:c:s0378437122003661
    DOI: 10.1016/j.physa.2022.127517
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122003661
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127517?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:600:y:2022:i:c:s0378437122003661. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.