An improved quantum network communication model based on compressed tensor network states View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-07-26

AUTHORS

Qiang Zhang, Hong Lai, Josef Pieprzyk, Lei Pan

ABSTRACT

Almost currently published quantum key distribution (QKD) protocols are variants of the first protocol proposed by Bennett and Brassard, and the generic entanglement-based protocol. These protocols, however, are not very efficient. An improvement of key generation rate is possible by using quantum many-body systems, and tensor network states provides a compact model for them. The work presents an improved QKD protocol, which first uses partial isometries to compress a matrix product state (MPS) |Ψ⟩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|\Psi \rangle $$\end{document} into its compressed MPS |Ψ(n)⟩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|\Psi ^{(n)}\rangle $$\end{document}. Then, Alice uses |Ψ(n)⟩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|\Psi ^{(n)}\rangle $$\end{document} to communicate with Bob via a quantum channel. Next, Alice transmits the number of compressed operations to Bob via a secure classical channel. Finally, according to the measured results, Alice and Bob share a cryptographic key from the MPS |Ψ⟩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|\Psi \rangle $$\end{document}. Our protocol can obtain a higher key generation capability and a longer communication distance. We apply the flow network model to obtain the upper bound of the dimension of the geometric index. More... »

PAGES

253

References to SciGraph publications

  • 2017-05-09. Reversible circuit synthesis by genetic programming using dynamic gate libraries in QUANTUM INFORMATION PROCESSING
  • 2018-02-14. The quantum internet has arrived (and it hasn’t) in NATURE
  • 2019-08-05. Tensor networks for complex quantum systems in NATURE REVIEWS PHYSICS
  • 2021-04-20. Modular zero modes and sewing the states of QFT in JOURNAL OF HIGH ENERGY PHYSICS
  • 2014-05-29. Controlled remote state preparation protocols via AKLT states in QUANTUM INFORMATION PROCESSING
  • 2020-02-15. Entanglement accessibility measures for the quantum Internet in QUANTUM INFORMATION PROCESSING
  • 2019-07-30. Modeling and simulation of practical quantum secure communication network in QUANTUM INFORMATION PROCESSING
  • 2010-10-17. Optical one-way quantum computing with a simulated valence-bond solid in NATURE PHYSICS
  • 2019-02-14. Quantum Algorithm Design: Techniques and Applications in JOURNAL OF SYSTEMS SCIENCE AND COMPLEXITY
  • 2020. Tensor Network Contractions, Methods and Applications to Quantum Many-Body Systems in NONE
  • 2017-11-23. Simulating quantum light propagation through atomic ensembles using matrix product states in NATURE COMMUNICATIONS
  • 2020-12-11. Surface growth scheme for bulk reconstruction and tensor network in JOURNAL OF HIGH ENERGY PHYSICS
  • 2017-02-23. In situ click chemistry generation of cyclooxygenase-2 inhibitors in NATURE COMMUNICATIONS
  • 2019-05-17. End-to-end capacities of a quantum communication network in COMMUNICATIONS PHYSICS
  • 2017-03-09. Fundamental tensor operations for large-scale data analysis using tensor network formats in MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
  • 2016-07-26. Entanglement classification with matrix product states in SCIENTIFIC REPORTS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11128-022-03609-3

    DOI

    http://dx.doi.org/10.1007/s11128-022-03609-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1149786267


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0802", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Computation Theory and Mathematics", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "School of Computer and Information Science, Southwest University, 400715, Chongqing, China", 
              "id": "http://www.grid.ac/institutes/grid.263906.8", 
              "name": [
                "School of Computer and Information Science, Southwest University, 400715, Chongqing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "Qiang", 
            "id": "sg:person.016226571774.18", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016226571774.18"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of Computer and Information Science, Southwest University, 400715, Chongqing, China", 
              "id": "http://www.grid.ac/institutes/grid.263906.8", 
              "name": [
                "School of Computer and Information Science, Southwest University, 400715, Chongqing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lai", 
            "givenName": "Hong", 
            "id": "sg:person.01022504133.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01022504133.46"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute of Computer Science, Polish Academy of Sciences, 01-248, Warsaw, Poland", 
              "id": "http://www.grid.ac/institutes/grid.425308.8", 
              "name": [
                "Data61, CSIRO, 2122, Sydney, NSW, Australia", 
                "Institute of Computer Science, Polish Academy of Sciences, 01-248, Warsaw, Poland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pieprzyk", 
            "givenName": "Josef", 
            "id": "sg:person.014746733025.45", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014746733025.45"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of Information Technology, Deakin University, 3220, Geelong, VIC, Australia", 
              "id": "http://www.grid.ac/institutes/grid.1021.2", 
              "name": [
                "School of Information Technology, Deakin University, 3220, Geelong, VIC, Australia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pan", 
            "givenName": "Lei", 
            "id": "sg:person.010204736445.52", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010204736445.52"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/jhep04(2021)189", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1137341352", 
              "https://doi.org/10.1007/jhep04(2021)189"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-017-01416-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092756073", 
              "https://doi.org/10.1038/s41467-017-01416-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s42005-019-0147-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1114527873", 
              "https://doi.org/10.1038/s42005-019-0147-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep30188", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043827511", 
              "https://doi.org/10.1038/srep30188"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11128-017-1609-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085382978", 
              "https://doi.org/10.1007/s11128-017-1609-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/jhep12(2020)083", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1133497191", 
              "https://doi.org/10.1007/jhep12(2020)083"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11424-019-9008-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1112141836", 
              "https://doi.org/10.1007/s11424-019-9008-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11128-014-0757-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037589226", 
              "https://doi.org/10.1007/s11128-014-0757-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-34489-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1124344992", 
              "https://doi.org/10.1007/978-3-030-34489-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11128-019-2394-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1119975632", 
              "https://doi.org/10.1007/s11128-019-2394-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nphys1777", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026005647", 
              "https://doi.org/10.1038/nphys1777"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s42254-019-0086-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1120129455", 
              "https://doi.org/10.1038/s42254-019-0086-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/d41586-018-01835-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101044982", 
              "https://doi.org/10.1038/d41586-018-01835-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11128-020-2605-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1124916685", 
              "https://doi.org/10.1007/s11128-020-2605-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-016-0009-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083776852", 
              "https://doi.org/10.1038/s41467-016-0009-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11045-017-0481-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084029019", 
              "https://doi.org/10.1007/s11045-017-0481-0"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-07-26", 
        "datePublishedReg": "2022-07-26", 
        "description": "Almost currently published quantum key distribution (QKD) protocols are variants of the first protocol proposed by Bennett and Brassard, and the generic entanglement-based protocol. These protocols, however, are not very efficient. An improvement of key generation rate is possible by using quantum many-body systems, and tensor network states provides a compact model for them. The work presents an improved QKD protocol, which first uses partial isometries to compress a matrix product state (MPS) |\u03a8\u27e9\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$|\\Psi \\rangle $$\\end{document} into its compressed MPS |\u03a8(n)\u27e9\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$|\\Psi ^{(n)}\\rangle $$\\end{document}. Then, Alice uses |\u03a8(n)\u27e9\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$|\\Psi ^{(n)}\\rangle $$\\end{document} to communicate with Bob via a quantum channel. Next, Alice transmits the number of compressed operations to Bob via a secure classical channel. Finally, according to the measured results, Alice and Bob share a cryptographic key from the MPS |\u03a8\u27e9\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$|\\Psi \\rangle $$\\end{document}. Our protocol can obtain a higher key generation capability and a longer communication distance. We apply the flow network model to obtain the upper bound of the dimension of the geometric index.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11128-022-03609-3", 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.7074517", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.8308487", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.8518083", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1052742", 
            "issn": [
              "1570-0755", 
              "1573-1332"
            ], 
            "name": "Quantum Information Processing", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "7", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "21"
          }
        ], 
        "keywords": [
          "matrix product states", 
          "tensor network states", 
          "quantum key distribution protocol", 
          "entanglement-based protocols", 
          "key distribution protocol", 
          "key generation rate", 
          "QKD protocol", 
          "quantum channel", 
          "classical channel", 
          "distribution protocol", 
          "product states", 
          "body systems", 
          "long communication distance", 
          "Alice", 
          "Bob", 
          "quantum", 
          "generation rate", 
          "communication distance", 
          "compact model", 
          "Brassard", 
          "state", 
          "generation capability", 
          "Alice transmits", 
          "channels", 
          "cryptographic keys", 
          "distance", 
          "network state", 
          "first protocol", 
          "model", 
          "capability", 
          "system", 
          "work", 
          "Bennett", 
          "dimensions", 
          "operation", 
          "transmit", 
          "results", 
          "index", 
          "number", 
          "rate", 
          "key", 
          "improvement", 
          "protocol", 
          "isometries", 
          "partial isometries", 
          "network model", 
          "network communication model", 
          "variants", 
          "communication model", 
          "flow network model", 
          "geometric indices"
        ], 
        "name": "An improved quantum network communication model based on compressed tensor network states", 
        "pagination": "253", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1149786267"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11128-022-03609-3"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11128-022-03609-3", 
          "https://app.dimensions.ai/details/publication/pub.1149786267"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-09-02T16:07", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_930.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11128-022-03609-3"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11128-022-03609-3'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11128-022-03609-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11128-022-03609-3'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11128-022-03609-3'


     

    This table displays all metadata directly associated to this object as RDF triples.

    206 TRIPLES      21 PREDICATES      91 URIs      67 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11128-022-03609-3 schema:about anzsrc-for:08
    2 anzsrc-for:0802
    3 schema:author N54911086e86141d99c2bd238d0562388
    4 schema:citation sg:pub.10.1007/978-3-030-34489-4
    5 sg:pub.10.1007/jhep04(2021)189
    6 sg:pub.10.1007/jhep12(2020)083
    7 sg:pub.10.1007/s11045-017-0481-0
    8 sg:pub.10.1007/s11128-014-0757-3
    9 sg:pub.10.1007/s11128-017-1609-8
    10 sg:pub.10.1007/s11128-019-2394-3
    11 sg:pub.10.1007/s11128-020-2605-y
    12 sg:pub.10.1007/s11424-019-9008-0
    13 sg:pub.10.1038/d41586-018-01835-3
    14 sg:pub.10.1038/nphys1777
    15 sg:pub.10.1038/s41467-016-0009-6
    16 sg:pub.10.1038/s41467-017-01416-4
    17 sg:pub.10.1038/s42005-019-0147-3
    18 sg:pub.10.1038/s42254-019-0086-7
    19 sg:pub.10.1038/srep30188
    20 schema:datePublished 2022-07-26
    21 schema:datePublishedReg 2022-07-26
    22 schema:description Almost currently published quantum key distribution (QKD) protocols are variants of the first protocol proposed by Bennett and Brassard, and the generic entanglement-based protocol. These protocols, however, are not very efficient. An improvement of key generation rate is possible by using quantum many-body systems, and tensor network states provides a compact model for them. The work presents an improved QKD protocol, which first uses partial isometries to compress a matrix product state (MPS) |Ψ⟩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|\Psi \rangle $$\end{document} into its compressed MPS |Ψ(n)⟩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|\Psi ^{(n)}\rangle $$\end{document}. Then, Alice uses |Ψ(n)⟩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|\Psi ^{(n)}\rangle $$\end{document} to communicate with Bob via a quantum channel. Next, Alice transmits the number of compressed operations to Bob via a secure classical channel. Finally, according to the measured results, Alice and Bob share a cryptographic key from the MPS |Ψ⟩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|\Psi \rangle $$\end{document}. Our protocol can obtain a higher key generation capability and a longer communication distance. We apply the flow network model to obtain the upper bound of the dimension of the geometric index.
    23 schema:genre article
    24 schema:isAccessibleForFree false
    25 schema:isPartOf Naafa1289746b43488a82c55faf331c7e
    26 Ne960b480489d43b38c12d971451cea2d
    27 sg:journal.1052742
    28 schema:keywords Alice
    29 Alice transmits
    30 Bennett
    31 Bob
    32 Brassard
    33 QKD protocol
    34 body systems
    35 capability
    36 channels
    37 classical channel
    38 communication distance
    39 communication model
    40 compact model
    41 cryptographic keys
    42 dimensions
    43 distance
    44 distribution protocol
    45 entanglement-based protocols
    46 first protocol
    47 flow network model
    48 generation capability
    49 generation rate
    50 geometric indices
    51 improvement
    52 index
    53 isometries
    54 key
    55 key distribution protocol
    56 key generation rate
    57 long communication distance
    58 matrix product states
    59 model
    60 network communication model
    61 network model
    62 network state
    63 number
    64 operation
    65 partial isometries
    66 product states
    67 protocol
    68 quantum
    69 quantum channel
    70 quantum key distribution protocol
    71 rate
    72 results
    73 state
    74 system
    75 tensor network states
    76 transmit
    77 variants
    78 work
    79 schema:name An improved quantum network communication model based on compressed tensor network states
    80 schema:pagination 253
    81 schema:productId N9dd766cbc5eb436eb7a59c633809721e
    82 Nf411cf2e5043449abac93e020008f260
    83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1149786267
    84 https://doi.org/10.1007/s11128-022-03609-3
    85 schema:sdDatePublished 2022-09-02T16:07
    86 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    87 schema:sdPublisher Nf1f82b1ce71c40e38f1298b50429e578
    88 schema:url https://doi.org/10.1007/s11128-022-03609-3
    89 sgo:license sg:explorer/license/
    90 sgo:sdDataset articles
    91 rdf:type schema:ScholarlyArticle
    92 N54911086e86141d99c2bd238d0562388 rdf:first sg:person.016226571774.18
    93 rdf:rest Ne34b82afe4e043db88976637968fca07
    94 N9dd766cbc5eb436eb7a59c633809721e schema:name doi
    95 schema:value 10.1007/s11128-022-03609-3
    96 rdf:type schema:PropertyValue
    97 Na1d40bb23ec84b90988937d7073701e7 rdf:first sg:person.014746733025.45
    98 rdf:rest Nce3d7c893f204945be65674f5bc6b7fa
    99 Naafa1289746b43488a82c55faf331c7e schema:volumeNumber 21
    100 rdf:type schema:PublicationVolume
    101 Nce3d7c893f204945be65674f5bc6b7fa rdf:first sg:person.010204736445.52
    102 rdf:rest rdf:nil
    103 Ne34b82afe4e043db88976637968fca07 rdf:first sg:person.01022504133.46
    104 rdf:rest Na1d40bb23ec84b90988937d7073701e7
    105 Ne960b480489d43b38c12d971451cea2d schema:issueNumber 7
    106 rdf:type schema:PublicationIssue
    107 Nf1f82b1ce71c40e38f1298b50429e578 schema:name Springer Nature - SN SciGraph project
    108 rdf:type schema:Organization
    109 Nf411cf2e5043449abac93e020008f260 schema:name dimensions_id
    110 schema:value pub.1149786267
    111 rdf:type schema:PropertyValue
    112 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    113 schema:name Information and Computing Sciences
    114 rdf:type schema:DefinedTerm
    115 anzsrc-for:0802 schema:inDefinedTermSet anzsrc-for:
    116 schema:name Computation Theory and Mathematics
    117 rdf:type schema:DefinedTerm
    118 sg:grant.7074517 http://pending.schema.org/fundedItem sg:pub.10.1007/s11128-022-03609-3
    119 rdf:type schema:MonetaryGrant
    120 sg:grant.8308487 http://pending.schema.org/fundedItem sg:pub.10.1007/s11128-022-03609-3
    121 rdf:type schema:MonetaryGrant
    122 sg:grant.8518083 http://pending.schema.org/fundedItem sg:pub.10.1007/s11128-022-03609-3
    123 rdf:type schema:MonetaryGrant
    124 sg:journal.1052742 schema:issn 1570-0755
    125 1573-1332
    126 schema:name Quantum Information Processing
    127 schema:publisher Springer Nature
    128 rdf:type schema:Periodical
    129 sg:person.010204736445.52 schema:affiliation grid-institutes:grid.1021.2
    130 schema:familyName Pan
    131 schema:givenName Lei
    132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010204736445.52
    133 rdf:type schema:Person
    134 sg:person.01022504133.46 schema:affiliation grid-institutes:grid.263906.8
    135 schema:familyName Lai
    136 schema:givenName Hong
    137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01022504133.46
    138 rdf:type schema:Person
    139 sg:person.014746733025.45 schema:affiliation grid-institutes:grid.425308.8
    140 schema:familyName Pieprzyk
    141 schema:givenName Josef
    142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014746733025.45
    143 rdf:type schema:Person
    144 sg:person.016226571774.18 schema:affiliation grid-institutes:grid.263906.8
    145 schema:familyName Zhang
    146 schema:givenName Qiang
    147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016226571774.18
    148 rdf:type schema:Person
    149 sg:pub.10.1007/978-3-030-34489-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1124344992
    150 https://doi.org/10.1007/978-3-030-34489-4
    151 rdf:type schema:CreativeWork
    152 sg:pub.10.1007/jhep04(2021)189 schema:sameAs https://app.dimensions.ai/details/publication/pub.1137341352
    153 https://doi.org/10.1007/jhep04(2021)189
    154 rdf:type schema:CreativeWork
    155 sg:pub.10.1007/jhep12(2020)083 schema:sameAs https://app.dimensions.ai/details/publication/pub.1133497191
    156 https://doi.org/10.1007/jhep12(2020)083
    157 rdf:type schema:CreativeWork
    158 sg:pub.10.1007/s11045-017-0481-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084029019
    159 https://doi.org/10.1007/s11045-017-0481-0
    160 rdf:type schema:CreativeWork
    161 sg:pub.10.1007/s11128-014-0757-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037589226
    162 https://doi.org/10.1007/s11128-014-0757-3
    163 rdf:type schema:CreativeWork
    164 sg:pub.10.1007/s11128-017-1609-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085382978
    165 https://doi.org/10.1007/s11128-017-1609-8
    166 rdf:type schema:CreativeWork
    167 sg:pub.10.1007/s11128-019-2394-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1119975632
    168 https://doi.org/10.1007/s11128-019-2394-3
    169 rdf:type schema:CreativeWork
    170 sg:pub.10.1007/s11128-020-2605-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1124916685
    171 https://doi.org/10.1007/s11128-020-2605-y
    172 rdf:type schema:CreativeWork
    173 sg:pub.10.1007/s11424-019-9008-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112141836
    174 https://doi.org/10.1007/s11424-019-9008-0
    175 rdf:type schema:CreativeWork
    176 sg:pub.10.1038/d41586-018-01835-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101044982
    177 https://doi.org/10.1038/d41586-018-01835-3
    178 rdf:type schema:CreativeWork
    179 sg:pub.10.1038/nphys1777 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026005647
    180 https://doi.org/10.1038/nphys1777
    181 rdf:type schema:CreativeWork
    182 sg:pub.10.1038/s41467-016-0009-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083776852
    183 https://doi.org/10.1038/s41467-016-0009-6
    184 rdf:type schema:CreativeWork
    185 sg:pub.10.1038/s41467-017-01416-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092756073
    186 https://doi.org/10.1038/s41467-017-01416-4
    187 rdf:type schema:CreativeWork
    188 sg:pub.10.1038/s42005-019-0147-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1114527873
    189 https://doi.org/10.1038/s42005-019-0147-3
    190 rdf:type schema:CreativeWork
    191 sg:pub.10.1038/s42254-019-0086-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1120129455
    192 https://doi.org/10.1038/s42254-019-0086-7
    193 rdf:type schema:CreativeWork
    194 sg:pub.10.1038/srep30188 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043827511
    195 https://doi.org/10.1038/srep30188
    196 rdf:type schema:CreativeWork
    197 grid-institutes:grid.1021.2 schema:alternateName School of Information Technology, Deakin University, 3220, Geelong, VIC, Australia
    198 schema:name School of Information Technology, Deakin University, 3220, Geelong, VIC, Australia
    199 rdf:type schema:Organization
    200 grid-institutes:grid.263906.8 schema:alternateName School of Computer and Information Science, Southwest University, 400715, Chongqing, China
    201 schema:name School of Computer and Information Science, Southwest University, 400715, Chongqing, China
    202 rdf:type schema:Organization
    203 grid-institutes:grid.425308.8 schema:alternateName Institute of Computer Science, Polish Academy of Sciences, 01-248, Warsaw, Poland
    204 schema:name Data61, CSIRO, 2122, Sydney, NSW, Australia
    205 Institute of Computer Science, Polish Academy of Sciences, 01-248, Warsaw, Poland
    206 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...