IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v594y2022ics0378437122000838.html
   My bibliography  Save this article

Quantum metrics based upon classical Jensen–Shannon divergence

Author

Listed:
  • Osán, T.M.
  • Bussandri, D.G.
  • Lamberti, P.W.

Abstract

Jensen–Shannon divergence is an important distinguishability measure between probability distributions that finds interesting applications within the context of Information Theory. In particular, this classical divergence belongs to a remarkable class of divergences known as Csiszár or f-divergences. In this paper we analyze the problem of obtaining a distance measure between two quantum states starting from the classical Jensen–Shannon divergence between two probability distributions. Considering the Jensen–Shannon divergence as a Csiszár divergence, we first focus on the problem of distinguishability between two pure quantum states. We find a quantum version of the classical Jensen–Shannon divergence that differs from the previously introduced Quantum Jensen–Shannon Divergence. The two quantum versions of Jensen–Shannon divergence have different interpretations within the framework of Quantum Information Theory. Whereas the former quantum version of Jensen–Shannon divergence can be interpreted as the Holevo bound, the alternative quantum version obtained in this work equals the accessible information. Furthermore, we obtain a monoparametric family of metrics between two quantum pure states. Finally, we extend this family of metrics to the case of mixed quantum states by means of the concept of purification.

Suggested Citation

  • Osán, T.M. & Bussandri, D.G. & Lamberti, P.W., 2022. "Quantum metrics based upon classical Jensen–Shannon divergence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
  • Handle: RePEc:eee:phsmap:v:594:y:2022:i:c:s0378437122000838
    DOI: 10.1016/j.physa.2022.127001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122000838
    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.2022.127001?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.

    References listed on IDEAS

    as
    1. Ferdinand Österreicher & Igor Vajda, 2003. "A new class of metric divergences on probability spaces and its applicability in statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(3), pages 639-653, September.
    2. Osán, Tristán M. & Bussandri, Diego G. & Lamberti, Pedro W., 2018. "Monoparametric family of metrics derived from classical Jensen–Shannon divergence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 336-344.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sagron, Ruth & Pugatch, Rami, 2021. "Universal distribution of batch completion times and time-cost tradeoff in a production line with arbitrary buffer size," European Journal of Operational Research, Elsevier, vol. 293(3), pages 980-989.
    2. Papastamoulis Panagiotis & Rattray Magnus, 2017. "Bayesian estimation of differential transcript usage from RNA-seq data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(5-6), pages 387-405, December.
    3. Yu, Xisheng, 2021. "A unified entropic pricing framework of option: Using Cressie-Read family of divergences," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    4. Yan Zhihua & Tang Xijin, 2020. "Exploring Evolution of Public Opinions on Tianya Club Using Dynamic Topic Models," Journal of Systems Science and Information, De Gruyter, vol. 8(4), pages 309-324, August.
    5. Leila M Naeni & Hugh Craig & Regina Berretta & Pablo Moscato, 2016. "A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-27, August.
    6. Boussalis, Constantine & Dukalskis, Alexander & Gerschewski, Johannes, 2022. "Why It Matters What Autocrats Say: Assessing Competing Theories of Propaganda," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 70(3), pages 241-252.
    7. Tsagris, Michail & Preston, Simon & T.A. Wood, Andrew, 2016. "Improved classi cation for compositional data using the $\alpha$-transformation," MPRA Paper 67657, University Library of Munich, Germany.
    8. Osán, Tristán M. & Bussandri, Diego G. & Lamberti, Pedro W., 2018. "Monoparametric family of metrics derived from classical Jensen–Shannon divergence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 336-344.
    9. Michail Tsagris & Simon Preston & Andrew T. A. Wood, 2016. "Improved Classification for Compositional Data Using the α-transformation," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 243-261, July.
    10. Tsagris, Michail, 2014. "The k-NN algorithm for compositional data: a revised approach with and without zero values present," MPRA Paper 65866, University Library of Munich, Germany.
    11. Tsagris, Michail, 2015. "A novel, divergence based, regression for compositional data," MPRA Paper 72769, University Library of Munich, Germany.
    12. Topsøe, Flemming, 2004. "Entropy and equilibrium via games of complexity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(1), pages 11-31.
    13. Gómez-Lopera, J.F. & Martínez-Aroza, J. & Rodríguez-Valverde, M.A. & Cabrerizo-Vílchez, M.A. & Montes-Ruíz-Cabello, F.J., 2015. "Entropic image segmentation of sessile drops over patterned acetate," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 118(C), pages 239-247.

    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:594:y:2022:i:c:s0378437122000838. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.