Indefinite causal order aids quantum depolarizing channel identification View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Michael Frey

ABSTRACT

Quantum channel identification is the metrological determination of one or more parameters of a quantum channel. This is accomplished by passing probes in prepared states through the channel and then statistically estimating the parameter(s) from the measured channel outputs. In quantum channel identification, the channel parameters’ quantum Fisher information is a means to assess and compare different probing schemes. We use quantum Fisher information to study a probing scheme in which the channel is put in indefinite causal order (ICO) with copies of itself, focusing our investigation on probing the qudit (d-dimensional) depolarizing channel to estimate its state preservation probability. This ICO arrangement is one in which both the eigenvectors and eigenvalues of the channel output depend on the channel’s state preservation probability. We overcome this complication to obtain the quantum Fisher information in analytical form. This result shows that ICO-assisted probing yields greater information than does the comparable probe re-circulation scheme with definite causal order, that the information gained is greater when the channel ordering is more indefinite, and that the information gained is greatest when the channel ordering is maximally indefinite. This leads us to conclude that ICO is acting here in a strong sense as an aid to channel probing. The effectiveness of ICO for probing the depolarizing channel decreases with probe dimension, being most effective for qubits. More... »

PAGES

96

References to SciGraph publications

  • 2012-01. Quantum correlations with no causal order in NATURE COMMUNICATIONS
  • 2015-12. Experimental superposition of orders of quantum gates in NATURE COMMUNICATIONS
  • 2009. Quantum Gravity Computers: On the Theory of Computation with Indefinite Causal Structure in QUANTUM REALITY, RELATIVISTIC CAUSALITY, AND CLOSING THE EPISTEMIC CIRCLE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11128-019-2186-9

    DOI

    http://dx.doi.org/10.1007/s11128-019-2186-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112220608


    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": "National Institute of Standards and Technology", 
              "id": "https://www.grid.ac/institutes/grid.94225.38", 
              "name": [
                "Statistical Engineering Division, National Institute of Standards and Technology, 80305, Boulder, CO, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Frey", 
            "givenName": "Michael", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/ncomms8913", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008315432", 
              "https://doi.org/10.1038/ncomms8913"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4020-9107-0_21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011726315", 
              "https://doi.org/10.1007/978-1-4020-9107-0_21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4020-9107-0_21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011726315", 
              "https://doi.org/10.1007/978-1-4020-9107-0_21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.88.022318", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018708894"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.88.022318", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018708894"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms2076", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028438280", 
              "https://doi.org/10.1038/ncomms2076"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0375-9601(67)90366-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030517889"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0375-9601(67)90366-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030517889"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1117/12.818668", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033263769"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/1751-8113/44/20/205306", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036219172"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ic.2016.02.008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042326043"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.86.040301", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046543288"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.86.040301", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046543288"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.63.042304", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060497056"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreva.63.042304", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060497056"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.117.100502", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060766232"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.117.100502", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060766232"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1142/s0219749909004839", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063006065"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/cbo9780511976667", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1098774954"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.120.120502", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101710197"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.120.120502", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101710197"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-04", 
        "datePublishedReg": "2019-04-01", 
        "description": "Quantum channel identification is the metrological determination of one or more parameters of a quantum channel. This is accomplished by passing probes in prepared states through the channel and then statistically estimating the parameter(s) from the measured channel outputs. In quantum channel identification, the channel parameters\u2019 quantum Fisher information is a means to assess and compare different probing schemes. We use quantum Fisher information to study a probing scheme in which the channel is put in indefinite causal order (ICO) with copies of itself, focusing our investigation on probing the qudit (d-dimensional) depolarizing channel to estimate its state preservation probability. This ICO arrangement is one in which both the eigenvectors and eigenvalues of the channel output depend on the channel\u2019s state preservation probability. We overcome this complication to obtain the quantum Fisher information in analytical form. This result shows that ICO-assisted probing yields greater information than does the comparable probe re-circulation scheme with definite causal order, that the information gained is greater when the channel ordering is more indefinite, and that the information gained is greatest when the channel ordering is maximally indefinite. This leads us to conclude that ICO is acting here in a strong sense as an aid to channel probing. The effectiveness of ICO for probing the depolarizing channel decreases with probe dimension, being most effective for qubits.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s11128-019-2186-9", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1052742", 
            "issn": [
              "1570-0755", 
              "1573-1332"
            ], 
            "name": "Quantum Information Processing", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "4", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "18"
          }
        ], 
        "name": "Indefinite causal order aids quantum depolarizing channel identification", 
        "pagination": "96", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "e8c3b38191a90d2adc694cfe6ce7f49b8e8df77a1a17218f24578f51327cb122"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11128-019-2186-9"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112220608"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11128-019-2186-9", 
          "https://app.dimensions.ai/details/publication/pub.1112220608"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T13:19", 
        "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/v90/0000000368_0000000368/records_78956_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs11128-019-2186-9"
      }
    ]
     

    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/s11128-019-2186-9'

    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/s11128-019-2186-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11128-019-2186-9'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11128-019-2186-9'


     

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

    105 TRIPLES      21 PREDICATES      41 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11128-019-2186-9 schema:about anzsrc-for:02
    2 anzsrc-for:0206
    3 schema:author N5f242416229d42bd978f287e7822f44a
    4 schema:citation sg:pub.10.1007/978-1-4020-9107-0_21
    5 sg:pub.10.1038/ncomms2076
    6 sg:pub.10.1038/ncomms8913
    7 https://doi.org/10.1016/0375-9601(67)90366-0
    8 https://doi.org/10.1016/j.ic.2016.02.008
    9 https://doi.org/10.1017/cbo9780511976667
    10 https://doi.org/10.1088/1751-8113/44/20/205306
    11 https://doi.org/10.1103/physreva.63.042304
    12 https://doi.org/10.1103/physreva.86.040301
    13 https://doi.org/10.1103/physreva.88.022318
    14 https://doi.org/10.1103/physrevlett.117.100502
    15 https://doi.org/10.1103/physrevlett.120.120502
    16 https://doi.org/10.1117/12.818668
    17 https://doi.org/10.1142/s0219749909004839
    18 schema:datePublished 2019-04
    19 schema:datePublishedReg 2019-04-01
    20 schema:description Quantum channel identification is the metrological determination of one or more parameters of a quantum channel. This is accomplished by passing probes in prepared states through the channel and then statistically estimating the parameter(s) from the measured channel outputs. In quantum channel identification, the channel parameters’ quantum Fisher information is a means to assess and compare different probing schemes. We use quantum Fisher information to study a probing scheme in which the channel is put in indefinite causal order (ICO) with copies of itself, focusing our investigation on probing the qudit (d-dimensional) depolarizing channel to estimate its state preservation probability. This ICO arrangement is one in which both the eigenvectors and eigenvalues of the channel output depend on the channel’s state preservation probability. We overcome this complication to obtain the quantum Fisher information in analytical form. This result shows that ICO-assisted probing yields greater information than does the comparable probe re-circulation scheme with definite causal order, that the information gained is greater when the channel ordering is more indefinite, and that the information gained is greatest when the channel ordering is maximally indefinite. This leads us to conclude that ICO is acting here in a strong sense as an aid to channel probing. The effectiveness of ICO for probing the depolarizing channel decreases with probe dimension, being most effective for qubits.
    21 schema:genre research_article
    22 schema:inLanguage en
    23 schema:isAccessibleForFree false
    24 schema:isPartOf Naec03ddd8c334c46b8515882313fe266
    25 Nebb6070f2f934af68da553061bcc0088
    26 sg:journal.1052742
    27 schema:name Indefinite causal order aids quantum depolarizing channel identification
    28 schema:pagination 96
    29 schema:productId N19575acbb58942e893336629dc38091e
    30 N80e62cd372294e86adb336d6153a9c62
    31 N85083688fe3e4d05a0b6f14fbfe99031
    32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112220608
    33 https://doi.org/10.1007/s11128-019-2186-9
    34 schema:sdDatePublished 2019-04-11T13:19
    35 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    36 schema:sdPublisher N18059cdae0be4a7aa5482efa0f7be2cf
    37 schema:url https://link.springer.com/10.1007%2Fs11128-019-2186-9
    38 sgo:license sg:explorer/license/
    39 sgo:sdDataset articles
    40 rdf:type schema:ScholarlyArticle
    41 N18059cdae0be4a7aa5482efa0f7be2cf schema:name Springer Nature - SN SciGraph project
    42 rdf:type schema:Organization
    43 N19575acbb58942e893336629dc38091e schema:name dimensions_id
    44 schema:value pub.1112220608
    45 rdf:type schema:PropertyValue
    46 N5f242416229d42bd978f287e7822f44a rdf:first Ne1a625b2fb5e4d2e8a1e3762e5cf4a1f
    47 rdf:rest rdf:nil
    48 N80e62cd372294e86adb336d6153a9c62 schema:name doi
    49 schema:value 10.1007/s11128-019-2186-9
    50 rdf:type schema:PropertyValue
    51 N85083688fe3e4d05a0b6f14fbfe99031 schema:name readcube_id
    52 schema:value e8c3b38191a90d2adc694cfe6ce7f49b8e8df77a1a17218f24578f51327cb122
    53 rdf:type schema:PropertyValue
    54 Naec03ddd8c334c46b8515882313fe266 schema:issueNumber 4
    55 rdf:type schema:PublicationIssue
    56 Ne1a625b2fb5e4d2e8a1e3762e5cf4a1f schema:affiliation https://www.grid.ac/institutes/grid.94225.38
    57 schema:familyName Frey
    58 schema:givenName Michael
    59 rdf:type schema:Person
    60 Nebb6070f2f934af68da553061bcc0088 schema:volumeNumber 18
    61 rdf:type schema:PublicationVolume
    62 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
    63 schema:name Physical Sciences
    64 rdf:type schema:DefinedTerm
    65 anzsrc-for:0206 schema:inDefinedTermSet anzsrc-for:
    66 schema:name Quantum Physics
    67 rdf:type schema:DefinedTerm
    68 sg:journal.1052742 schema:issn 1570-0755
    69 1573-1332
    70 schema:name Quantum Information Processing
    71 rdf:type schema:Periodical
    72 sg:pub.10.1007/978-1-4020-9107-0_21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011726315
    73 https://doi.org/10.1007/978-1-4020-9107-0_21
    74 rdf:type schema:CreativeWork
    75 sg:pub.10.1038/ncomms2076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028438280
    76 https://doi.org/10.1038/ncomms2076
    77 rdf:type schema:CreativeWork
    78 sg:pub.10.1038/ncomms8913 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008315432
    79 https://doi.org/10.1038/ncomms8913
    80 rdf:type schema:CreativeWork
    81 https://doi.org/10.1016/0375-9601(67)90366-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030517889
    82 rdf:type schema:CreativeWork
    83 https://doi.org/10.1016/j.ic.2016.02.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042326043
    84 rdf:type schema:CreativeWork
    85 https://doi.org/10.1017/cbo9780511976667 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098774954
    86 rdf:type schema:CreativeWork
    87 https://doi.org/10.1088/1751-8113/44/20/205306 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036219172
    88 rdf:type schema:CreativeWork
    89 https://doi.org/10.1103/physreva.63.042304 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060497056
    90 rdf:type schema:CreativeWork
    91 https://doi.org/10.1103/physreva.86.040301 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046543288
    92 rdf:type schema:CreativeWork
    93 https://doi.org/10.1103/physreva.88.022318 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018708894
    94 rdf:type schema:CreativeWork
    95 https://doi.org/10.1103/physrevlett.117.100502 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060766232
    96 rdf:type schema:CreativeWork
    97 https://doi.org/10.1103/physrevlett.120.120502 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101710197
    98 rdf:type schema:CreativeWork
    99 https://doi.org/10.1117/12.818668 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033263769
    100 rdf:type schema:CreativeWork
    101 https://doi.org/10.1142/s0219749909004839 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063006065
    102 rdf:type schema:CreativeWork
    103 https://www.grid.ac/institutes/grid.94225.38 schema:alternateName National Institute of Standards and Technology
    104 schema:name Statistical Engineering Division, National Institute of Standards and Technology, 80305, Boulder, CO, USA
    105 rdf:type schema:Organization
     




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


    ...