IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v596y2022ics0378437122001546.html
   My bibliography  Save this article

Nonlocal operation enhanced entanglement detection and classification

Author

Listed:
  • Li, Yan
  • Ren, Zhihong

Abstract

Based on the nature that nonlocal operation will change the fixed quantum statistical speed limited by the local operation, we study the performance of Ising-type nonlocal operation in multipartite entanglement detection and classification. We first present the formula of quantum statistical speed for general quantum state under the two-body nonlocal operation, and then investigate several important and experimentally relevant states, including N-qubit W state, Twin-Fock state, Q state and N-qubit GHZ state. The results show that the optimal nonlocal operation can be used to address some intractable problems encountered under the local operation, such as the classification of N-qubit W state and entanglement detection of Q state at the edges. Meanwhile, some defects are presented and discussed. Interestingly, the N-qubit GHZ state is found to be stable when it is probed by weaker nonlocal operation (γ≤N−1) and it maybe helpful to the experimental research. Our work provides an alternative route to investigate entanglement detection and classification, especially for the novel and complex quantum system.

Suggested Citation

  • Li, Yan & Ren, Zhihong, 2022. "Nonlocal operation enhanced entanglement detection and classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    • Handle: RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001546
    DOI: 10.1016/j.physa.2022.127137
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122001546
    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.2022.127137?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Yan & Ren, Zhihong, 2023. "Quantum Fisher information of an N-qubit maximal sliced state in decoherence channels and Ising-type interacting model," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

    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:596:y:2022:i:c:s0378437122001546. 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.