Quantum computing models as a tool box for controlling and understanding the nanoscopic world View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-10

AUTHORS

Dominik Janzing

ABSTRACT

Progress in controlling quantum systems is the major pre-requisite for the realization of quantum computing, yet the results of quantum computing research can also be useful in solving quantum control problems that are not related to computational problems. We arguethat quantum computing provides clear concepts and simple models for discussing quantum theoretical problems. In this article we describe examples from completely different fields where models of quantum computing and quantum communication shed light on quantum theory. First we address quantum limits of classical low power computation and argue that the terms of quantum information theory allows us to discuss device-independent bounds. We argue that a classical bit behaves to some extent like a quantum bit in the time period where it switches its logical value. This implies that a readout during the switching process generates entropy. A related problem is the distribution of timing information like clock signals in low power devices. For low signal energy, the situation is close to phase-covariant cloning problems in quantum information theory. Second we rephrase a classical statistical method to draw causal conclusions from data of a clinical drug-testing experiment. Since this method, as it is described in the literature, relies on a hidden-variable model of patient’s behaviour it leads to misconclusions if quantum theory infact does play a role in the human mind. The toy model we use to illustrate this is formally a quantum communication protocol in the presence of entanglement. We argue that quantum information theory could put classical statistical reasoning on a safer basis because it does not need hidden-variable models of nature. More... »

PAGES

83-90

References to SciGraph publications

  • 2005-06. Ergodic Quantum Computing in QUANTUM INFORMATION PROCESSING
  • 1980. Reversible computing in AUTOMATA, LANGUAGES AND PROGRAMMING
  • 1980-05. The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines in JOURNAL OF STATISTICAL PHYSICS
  • 1982-10. A single quantum cannot be cloned in NATURE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00450-006-0013-x

    DOI

    http://dx.doi.org/10.1007/s00450-006-0013-x

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1038698523


    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/2008/0206", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Quantum Physics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Physical Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Karlsruhe Institute of Technology", 
              "id": "https://www.grid.ac/institutes/grid.7892.4", 
              "name": [
                "Institut f\u00fcr Algorithmen und Kognitive Systeme, Arbeitsgruppe Quantum Computing, Universit\u00e4t Karlsruhe, Am Fasanengarten 5, 76131, Karlsruhe, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Janzing", 
            "givenName": "Dominik", 
            "id": "sg:person.010302530541.21", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010302530541.21"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1103/physreva.62.012302", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003633017"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.62.012302", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003633017"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0167-2789(98)00054-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003708670"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11128-005-4482-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004894678", 
              "https://doi.org/10.1007/s11128-005-4482-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11128-005-4482-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004894678", 
              "https://doi.org/10.1007/s11128-005-4482-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/299802a0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005824196", 
              "https://doi.org/10.1038/299802a0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/revmodphys.75.715", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006328423"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/revmodphys.75.715", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006328423"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-10003-2_104", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006731052", 
              "https://doi.org/10.1007/3-540-10003-2_104"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.90.157904", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008615785"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.90.157904", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008615785"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf01011339", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008923572", 
              "https://doi.org/10.1007/bf01011339"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1063/1.1827924", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028052353"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.67.062104", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029059765"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.67.062104", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029059765"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.67.012320", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043995573"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.67.012320", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043995573"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.76.2818", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049540858"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.76.2818", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049540858"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.69.2881", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060805641"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.69.2881", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060805641"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tit.2002.806162", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061649711"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/s0097539795293172", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062880065"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1142/s0219749904000298", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063005612"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1142/s0219749906001827", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063005765"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1147/rd.176.0525", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063180324"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1147/rd.53.0183", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063183065"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1214/aos/1034276631", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064406349"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1364/on.11.2.000011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1065242236"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.21236/ada082021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091582442"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1613/jair.202", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105538404"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2006-10", 
        "datePublishedReg": "2006-10-01", 
        "description": "Progress in controlling quantum systems is the major pre-requisite for the realization of quantum computing, yet the results of quantum computing research can also be useful in solving quantum control problems that are not related to computational problems. We arguethat quantum computing provides clear concepts and simple models for discussing quantum theoretical problems. In this article we describe examples from completely different fields where models of quantum computing and quantum communication shed light on quantum theory. First we address quantum limits of classical low power computation and argue that the terms of quantum information theory allows us to discuss device-independent bounds. We argue that a classical bit behaves to some extent like a quantum bit in the time period where it switches its logical value. This implies that a readout during the switching process generates entropy. A related problem is the distribution of timing information like clock signals in low power devices. For low signal energy, the situation is close to phase-covariant cloning problems in quantum information theory. Second we rephrase a classical statistical method to draw causal conclusions from data of a clinical drug-testing experiment. Since this method, as it is described in the literature, relies on a hidden-variable model of patient\u2019s behaviour it leads to misconclusions if quantum theory infact does play a role in the human mind. The toy model we use to illustrate this is formally a quantum communication protocol in the presence of entanglement. We argue that quantum information theory could put classical statistical reasoning on a safer basis because it does not need hidden-variable models of nature.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00450-006-0013-x", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1052616", 
            "issn": [
              "1865-2034", 
              "0949-2925"
            ], 
            "name": "Computer Science - Research and Development", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1-2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "21"
          }
        ], 
        "name": "Quantum computing models as a tool box for controlling and understanding the nanoscopic world", 
        "pagination": "83-90", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "b857e3a8dbf4a6c02bd887863d6bde4dfaf374b034f16007085ba1b484ed302f"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00450-006-0013-x"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1038698523"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00450-006-0013-x", 
          "https://app.dimensions.ai/details/publication/pub.1038698523"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T14:26", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000373_0000000373/records_13071_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs00450-006-0013-x"
      }
    ]
     

    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/s00450-006-0013-x'

    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/s00450-006-0013-x'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00450-006-0013-x'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00450-006-0013-x'


     

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

    134 TRIPLES      21 PREDICATES      50 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00450-006-0013-x schema:about anzsrc-for:02
    2 anzsrc-for:0206
    3 schema:author Ne8864b39834b48019acadb5ad31e01b3
    4 schema:citation sg:pub.10.1007/3-540-10003-2_104
    5 sg:pub.10.1007/bf01011339
    6 sg:pub.10.1007/s11128-005-4482-9
    7 sg:pub.10.1038/299802a0
    8 https://doi.org/10.1016/s0167-2789(98)00054-2
    9 https://doi.org/10.1063/1.1827924
    10 https://doi.org/10.1103/physreva.62.012302
    11 https://doi.org/10.1103/physreva.67.012320
    12 https://doi.org/10.1103/physreva.67.062104
    13 https://doi.org/10.1103/physrevlett.69.2881
    14 https://doi.org/10.1103/physrevlett.76.2818
    15 https://doi.org/10.1103/physrevlett.90.157904
    16 https://doi.org/10.1103/revmodphys.75.715
    17 https://doi.org/10.1109/tit.2002.806162
    18 https://doi.org/10.1137/s0097539795293172
    19 https://doi.org/10.1142/s0219749904000298
    20 https://doi.org/10.1142/s0219749906001827
    21 https://doi.org/10.1147/rd.176.0525
    22 https://doi.org/10.1147/rd.53.0183
    23 https://doi.org/10.1214/aos/1034276631
    24 https://doi.org/10.1364/on.11.2.000011
    25 https://doi.org/10.1613/jair.202
    26 https://doi.org/10.21236/ada082021
    27 schema:datePublished 2006-10
    28 schema:datePublishedReg 2006-10-01
    29 schema:description Progress in controlling quantum systems is the major pre-requisite for the realization of quantum computing, yet the results of quantum computing research can also be useful in solving quantum control problems that are not related to computational problems. We arguethat quantum computing provides clear concepts and simple models for discussing quantum theoretical problems. In this article we describe examples from completely different fields where models of quantum computing and quantum communication shed light on quantum theory. First we address quantum limits of classical low power computation and argue that the terms of quantum information theory allows us to discuss device-independent bounds. We argue that a classical bit behaves to some extent like a quantum bit in the time period where it switches its logical value. This implies that a readout during the switching process generates entropy. A related problem is the distribution of timing information like clock signals in low power devices. For low signal energy, the situation is close to phase-covariant cloning problems in quantum information theory. Second we rephrase a classical statistical method to draw causal conclusions from data of a clinical drug-testing experiment. Since this method, as it is described in the literature, relies on a hidden-variable model of patient’s behaviour it leads to misconclusions if quantum theory infact does play a role in the human mind. The toy model we use to illustrate this is formally a quantum communication protocol in the presence of entanglement. We argue that quantum information theory could put classical statistical reasoning on a safer basis because it does not need hidden-variable models of nature.
    30 schema:genre research_article
    31 schema:inLanguage en
    32 schema:isAccessibleForFree false
    33 schema:isPartOf N6e6b0fbbb1b3474aa7541e7f2b630180
    34 Nd7e9f33b5f24428f9a5359a94df06fc5
    35 sg:journal.1052616
    36 schema:name Quantum computing models as a tool box for controlling and understanding the nanoscopic world
    37 schema:pagination 83-90
    38 schema:productId N17c2a89dd85b4aeb82d78216a46eb58e
    39 N5ddb8ea1e250432fa6e6d75d63860a05
    40 Na23c8f89cd5744c78ce57540d3c9a3d3
    41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038698523
    42 https://doi.org/10.1007/s00450-006-0013-x
    43 schema:sdDatePublished 2019-04-11T14:26
    44 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    45 schema:sdPublisher N338fe7f32b9047f1a3900365c544b465
    46 schema:url http://link.springer.com/10.1007%2Fs00450-006-0013-x
    47 sgo:license sg:explorer/license/
    48 sgo:sdDataset articles
    49 rdf:type schema:ScholarlyArticle
    50 N17c2a89dd85b4aeb82d78216a46eb58e schema:name dimensions_id
    51 schema:value pub.1038698523
    52 rdf:type schema:PropertyValue
    53 N338fe7f32b9047f1a3900365c544b465 schema:name Springer Nature - SN SciGraph project
    54 rdf:type schema:Organization
    55 N5ddb8ea1e250432fa6e6d75d63860a05 schema:name readcube_id
    56 schema:value b857e3a8dbf4a6c02bd887863d6bde4dfaf374b034f16007085ba1b484ed302f
    57 rdf:type schema:PropertyValue
    58 N6e6b0fbbb1b3474aa7541e7f2b630180 schema:volumeNumber 21
    59 rdf:type schema:PublicationVolume
    60 Na23c8f89cd5744c78ce57540d3c9a3d3 schema:name doi
    61 schema:value 10.1007/s00450-006-0013-x
    62 rdf:type schema:PropertyValue
    63 Nd7e9f33b5f24428f9a5359a94df06fc5 schema:issueNumber 1-2
    64 rdf:type schema:PublicationIssue
    65 Ne8864b39834b48019acadb5ad31e01b3 rdf:first sg:person.010302530541.21
    66 rdf:rest rdf:nil
    67 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
    68 schema:name Physical Sciences
    69 rdf:type schema:DefinedTerm
    70 anzsrc-for:0206 schema:inDefinedTermSet anzsrc-for:
    71 schema:name Quantum Physics
    72 rdf:type schema:DefinedTerm
    73 sg:journal.1052616 schema:issn 0949-2925
    74 1865-2034
    75 schema:name Computer Science - Research and Development
    76 rdf:type schema:Periodical
    77 sg:person.010302530541.21 schema:affiliation https://www.grid.ac/institutes/grid.7892.4
    78 schema:familyName Janzing
    79 schema:givenName Dominik
    80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010302530541.21
    81 rdf:type schema:Person
    82 sg:pub.10.1007/3-540-10003-2_104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006731052
    83 https://doi.org/10.1007/3-540-10003-2_104
    84 rdf:type schema:CreativeWork
    85 sg:pub.10.1007/bf01011339 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008923572
    86 https://doi.org/10.1007/bf01011339
    87 rdf:type schema:CreativeWork
    88 sg:pub.10.1007/s11128-005-4482-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004894678
    89 https://doi.org/10.1007/s11128-005-4482-9
    90 rdf:type schema:CreativeWork
    91 sg:pub.10.1038/299802a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005824196
    92 https://doi.org/10.1038/299802a0
    93 rdf:type schema:CreativeWork
    94 https://doi.org/10.1016/s0167-2789(98)00054-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003708670
    95 rdf:type schema:CreativeWork
    96 https://doi.org/10.1063/1.1827924 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028052353
    97 rdf:type schema:CreativeWork
    98 https://doi.org/10.1103/physreva.62.012302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003633017
    99 rdf:type schema:CreativeWork
    100 https://doi.org/10.1103/physreva.67.012320 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043995573
    101 rdf:type schema:CreativeWork
    102 https://doi.org/10.1103/physreva.67.062104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029059765
    103 rdf:type schema:CreativeWork
    104 https://doi.org/10.1103/physrevlett.69.2881 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060805641
    105 rdf:type schema:CreativeWork
    106 https://doi.org/10.1103/physrevlett.76.2818 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049540858
    107 rdf:type schema:CreativeWork
    108 https://doi.org/10.1103/physrevlett.90.157904 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008615785
    109 rdf:type schema:CreativeWork
    110 https://doi.org/10.1103/revmodphys.75.715 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006328423
    111 rdf:type schema:CreativeWork
    112 https://doi.org/10.1109/tit.2002.806162 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061649711
    113 rdf:type schema:CreativeWork
    114 https://doi.org/10.1137/s0097539795293172 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062880065
    115 rdf:type schema:CreativeWork
    116 https://doi.org/10.1142/s0219749904000298 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063005612
    117 rdf:type schema:CreativeWork
    118 https://doi.org/10.1142/s0219749906001827 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063005765
    119 rdf:type schema:CreativeWork
    120 https://doi.org/10.1147/rd.176.0525 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063180324
    121 rdf:type schema:CreativeWork
    122 https://doi.org/10.1147/rd.53.0183 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063183065
    123 rdf:type schema:CreativeWork
    124 https://doi.org/10.1214/aos/1034276631 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064406349
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1364/on.11.2.000011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065242236
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1613/jair.202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105538404
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.21236/ada082021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091582442
    131 rdf:type schema:CreativeWork
    132 https://www.grid.ac/institutes/grid.7892.4 schema:alternateName Karlsruhe Institute of Technology
    133 schema:name Institut für Algorithmen und Kognitive Systeme, Arbeitsgruppe Quantum Computing, Universität Karlsruhe, Am Fasanengarten 5, 76131, Karlsruhe, Germany
    134 rdf:type schema:Organization
     




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


    ...