IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v569y2019i7756d10.1038_s41586-019-1177-4.html
   My bibliography  Save this article

Self-verifying variational quantum simulation of lattice models

Author

Listed:
  • C. Kokail

    (University of Innsbruck
    Austrian Academy of Sciences)

  • C. Maier

    (University of Innsbruck
    Austrian Academy of Sciences)

  • R. van Bijnen

    (University of Innsbruck
    Austrian Academy of Sciences)

  • T. Brydges

    (University of Innsbruck
    Austrian Academy of Sciences)

  • M. K. Joshi

    (University of Innsbruck
    Austrian Academy of Sciences)

  • P. Jurcevic

    (University of Innsbruck
    Austrian Academy of Sciences)

  • C. A. Muschik

    (University of Innsbruck
    Austrian Academy of Sciences)

  • P. Silvi

    (University of Innsbruck
    Austrian Academy of Sciences)

  • R. Blatt

    (University of Innsbruck
    Austrian Academy of Sciences)

  • C. F. Roos

    (University of Innsbruck
    Austrian Academy of Sciences)

  • P. Zoller

    (University of Innsbruck
    Austrian Academy of Sciences)

Abstract

Hybrid classical–quantum algorithms aim to variationally solve optimization problems using a feedback loop between a classical computer and a quantum co-processor, while benefiting from quantum resources. Here we present experiments that demonstrate self-verifying, hybrid, variational quantum simulation of lattice models in condensed matter and high-energy physics. In contrast to analogue quantum simulation, this approach forgoes the requirement of realizing the targeted Hamiltonian directly in the laboratory, thus enabling the study of a wide variety of previously intractable target models. We focus on the lattice Schwinger model, a gauge theory of one-dimensional quantum electrodynamics. Our quantum co-processor is a programmable, trapped-ion analogue quantum simulator with up to 20 qubits, capable of generating families of entangled trial states respecting the symmetries of the target Hamiltonian. We determine ground states, energy gaps and additionally, by measuring variances of the Schwinger Hamiltonian, we provide algorithmic errors for the energies, thus taking a step towards verifying quantum simulation.

Suggested Citation

  • C. Kokail & C. Maier & R. van Bijnen & T. Brydges & M. K. Joshi & P. Jurcevic & C. A. Muschik & P. Silvi & R. Blatt & C. F. Roos & P. Zoller, 2019. "Self-verifying variational quantum simulation of lattice models," Nature, Nature, vol. 569(7756), pages 355-360, May.
  • Handle: RePEc:nat:nature:v:569:y:2019:i:7756:d:10.1038_s41586-019-1177-4
    DOI: 10.1038/s41586-019-1177-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-019-1177-4
    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/s41586-019-1177-4?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. Yasar Y. Atas & Jinglei Zhang & Randy Lewis & Amin Jahanpour & Jan F. Haase & Christine A. Muschik, 2021. "SU(2) hadrons on a quantum computer via a variational approach," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. M.-L. Cai & Y.-K. Wu & Q.-X. Mei & W.-D. Zhao & Y. Jiang & L. Yao & L. He & Z.-C. Zhou & L.-M. Duan, 2022. "Observation of supersymmetry and its spontaneous breaking in a trapped ion quantum simulator," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    3. Giacomo Torlai & Christopher J. Wood & Atithi Acharya & Giuseppe Carleo & Juan Carrasquilla & Leandro Aolita, 2023. "Quantum process tomography with unsupervised learning and tensor networks," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Donald R. Jones & Joaquim R. R. A. Martins, 2021. "The DIRECT algorithm: 25 years Later," Journal of Global Optimization, Springer, vol. 79(3), pages 521-566, March.
    5. Grigory E. Astrakharchik & Luis A. Peña Ardila & Krzysztof Jachymski & Antonio Negretti, 2023. "Many-body bound states and induced interactions of charged impurities in a bosonic bath," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    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:569:y:2019:i:7756:d:10.1038_s41586-019-1177-4. 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.