IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v404y2000i6776d10.1038_35006012.html
   My bibliography  Save this article

An algorithmic benchmark for quantum information processing

Author

Listed:
  • E. Knill

    (Los Alamos National Laboratory, MS B265)

  • R. Laflamme

    (Los Alamos National Laboratory, MS B265)

  • R. Martinez

    (Los Alamos National Laboratory, MS B265)

  • C.-H. Tseng

    (MIT)

Abstract

Quantum information processing offers potentially great advantages over classical information processing, both for efficient algorithms1,2 and for secure communication3,4. Therefore, it is important to establish that scalable control of a large number of quantum bits (qubits) can be achieved in practice. There are a rapidly growing number of proposed device technologies5,6,7,8,9,10,11 for quantum information processing. Of these technologies, those exploiting nuclear magnetic resonance (NMR) have been the first to demonstrate non-trivial quantum algorithms with small numbers of qubits12,13,14,15,16. To compare different physical realizations of quantum information processors, it is necessary to establish benchmark experiments that are independent of the underlying physical system, and that demonstrate reliable and coherent control of a reasonable number of qubits. Here we report an experimental realization of an algorithmic benchmark using an NMR technique that involves coherent manipulation of seven qubits. Moreover, our experimental procedure can be used as a reliable and efficient method for creating a standard pseudopure state, the first step for implementing traditional quantum algorithms in liquid state NMR systems. The benchmark and the techniques can be adapted for use with other proposed quantum devices.

Suggested Citation

  • E. Knill & R. Laflamme & R. Martinez & C.-H. Tseng, 2000. "An algorithmic benchmark for quantum information processing," Nature, Nature, vol. 404(6776), pages 368-370, March.
  • Handle: RePEc:nat:nature:v:404:y:2000:i:6776:d:10.1038_35006012
    DOI: 10.1038/35006012
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/35006012
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/35006012?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, Nianqiao & Yan, Fei & Hirota, Kaoru, 2022. "Quantum data visualization: A quantum computing framework for enhancing visual analysis of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).

    More about this item

    Statistics

    Access and download statistics

    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:nat:nature:v:404:y:2000:i:6776:d:10.1038_35006012. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.