Quantum-enhanced magnetometry by phase estimation algorithms with a single artificial atom View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

S. Danilin, A. V. Lebedev, A. Vepsäläinen, G. B. Lesovik, G. Blatter, G. S. Paraoanu

ABSTRACT

Phase estimation algorithms are key protocols in quantum information processing. Besides applications in quantum computing, they can also be employed in metrology as they allow for fast extraction of information stored in the quantum state of a system. Here, we implement two suitably modified phase estimation procedures, the Kitaev and the semiclassical Fourier-transform algorithms, using an artificial atom realized with a superconducting transmon circuit. We demonstrate that both algorithms yield a flux sensitivity exceeding the classical shot-noise limit of the device, allowing one to approach the Heisenberg limit. Our experiment paves the way for the use of superconducting qubits as metrological devices which are potentially able to outperform the best existing flux sensors with a sensitivity enhanced by few orders of magnitude. Quantum computing algorithms can improve the performance of a superconducting magnetic field sensor beyond the classical limit. A qubit’s time evolution is often influenced by environmental factors like magnetic fields; measuring this evolution allows the magnetic field strength to be determined. Using classical methods, improvements in measurement performance can only scale with the square root of the total measurement time. However, by exploiting quantum coherence to use so-called phase estimation algorithms during the measurements, the scaling with measurement time can be driven beyond the classical limits. Andrey Lebedev at ETH Zurich and colleagues in Finland, Switzerland and Russia have applied this approach to superconducting qubits. They demonstrate both superior performance and improved scaling compared to the classical approach, and show that in principle superconducting qubits can become the highest-performing magnetic flux sensors. More... »

PAGES

29

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41534-018-0078-y

DOI

http://dx.doi.org/10.1038/s41534-018-0078-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105107310


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/2008/0204", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Condensed Matter 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": "Aalto University", 
          "id": "https://www.grid.ac/institutes/grid.5373.2", 
          "name": [
            "Low Temperature Laboratory, Department of Applied Physics, Aalto University School of Science, PO Box 15100, FI-00076, Aalto, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Danilin", 
        "givenName": "S.", 
        "id": "sg:person.015424750236.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015424750236.35"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moscow Institute of Physics and Technology", 
          "id": "https://www.grid.ac/institutes/grid.18763.3b", 
          "name": [
            "Theoretische Physik, Wolfgang-Pauli-Strasse 27, ETH Z\u00fcrich, CH-8093, Z\u00fcrich, Switzerland", 
            "Moscow Institute of Physics and Technology, Institutskii per. 9, 141700, Dolgoprudny, Moscow District, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lebedev", 
        "givenName": "A. V.", 
        "id": "sg:person.010702005747.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010702005747.67"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Aalto University", 
          "id": "https://www.grid.ac/institutes/grid.5373.2", 
          "name": [
            "Low Temperature Laboratory, Department of Applied Physics, Aalto University School of Science, PO Box 15100, FI-00076, Aalto, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Veps\u00e4l\u00e4inen", 
        "givenName": "A.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Landau Institute for Theoretical Physics", 
          "id": "https://www.grid.ac/institutes/grid.436090.8", 
          "name": [
            "Moscow Institute of Physics and Technology, Institutskii per. 9, 141700, Dolgoprudny, Moscow District, Russia", 
            "L.D. Landau Institute for Theoretical Physics RAS, Akad. Semenova av., 1-A, 142432, Chernogolovka, Moscow Region, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lesovik", 
        "givenName": "G. B.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Swiss Federal Institute of Technology in Zurich", 
          "id": "https://www.grid.ac/institutes/grid.5801.c", 
          "name": [
            "Theoretische Physik, Wolfgang-Pauli-Strasse 27, ETH Z\u00fcrich, CH-8093, Z\u00fcrich, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Blatter", 
        "givenName": "G.", 
        "id": "sg:person.0776150017.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0776150017.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Aalto University", 
          "id": "https://www.grid.ac/institutes/grid.5373.2", 
          "name": [
            "Low Temperature Laboratory, Department of Applied Physics, Aalto University School of Science, PO Box 15100, FI-00076, Aalto, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Paraoanu", 
        "givenName": "G. S.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1103/physreva.89.012118", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003339705"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.89.012118", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003339705"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nnano.2015.261", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010755954", 
          "https://doi.org/10.1038/nnano.2015.261"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.76.042319", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015118427"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.76.042319", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015118427"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.76.3228", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019663722"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.76.3228", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019663722"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nnano.2011.224", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024849462", 
          "https://doi.org/10.1038/nnano.2011.224"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.aad9480", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025992528"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature07128", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029810190", 
          "https://doi.org/10.1038/nature07128"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.99.187006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030141595"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.99.187006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030141595"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep04677", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032253526", 
          "https://doi.org/10.1038/srep04677"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.97.167001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032801644"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.97.167001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032801644"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00107510802091298", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035028829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.83.052317", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036417991"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.83.052317", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036417991"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nphys566", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039498581", 
          "https://doi.org/10.1038/nphys566"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nphys566", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039498581", 
          "https://doi.org/10.1038/nphys566"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature12290", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040616957", 
          "https://doi.org/10.1038/nature12290"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms2332", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040800091", 
          "https://doi.org/10.1038/ncomms2332"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rspa.1998.0164", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044905446"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature06257", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045335822", 
          "https://doi.org/10.1038/nature06257"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.98.090501", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047274939"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.98.090501", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047274939"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.103.150502", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049813916"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.103.150502", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049813916"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/npjqi.2016.23", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053607998", 
          "https://doi.org/10.1038/npjqi.2016.23"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.100291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057647884"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.110.147002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060761388"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.110.147002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060761388"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/20.92457", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061119831"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1104149", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062451106"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1138007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062455164"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.22331/q-2017-09-06-27", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091642734"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/sfcs.1994.365700", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095740049"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.97.022115", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101247022"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.97.022115", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101247022"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1101840889", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "Phase estimation algorithms are key protocols in quantum information processing. Besides applications in quantum computing, they can also be employed in metrology as they allow for fast extraction of information stored in the quantum state of a system. Here, we implement two suitably modified phase estimation procedures, the Kitaev and the semiclassical Fourier-transform algorithms, using an artificial atom realized with a superconducting transmon circuit. We demonstrate that both algorithms yield a flux sensitivity exceeding the classical shot-noise limit of the device, allowing one to approach the Heisenberg limit. Our experiment paves the way for the use of superconducting qubits as metrological devices which are potentially able to outperform the best existing flux sensors with a sensitivity enhanced by few orders of magnitude. Quantum computing algorithms can improve the performance of a superconducting magnetic field sensor beyond the classical limit. A qubit\u2019s time evolution is often influenced by environmental factors like magnetic fields; measuring this evolution allows the magnetic field strength to be determined. Using classical methods, improvements in measurement performance can only scale with the square root of the total measurement time. However, by exploiting quantum coherence to use so-called phase estimation algorithms during the measurements, the scaling with measurement time can be driven beyond the classical limits. Andrey Lebedev at ETH Zurich and colleagues in Finland, Switzerland and Russia have applied this approach to superconducting qubits. They demonstrate both superior performance and improved scaling compared to the classical approach, and show that in principle superconducting qubits can become the highest-performing magnetic flux sensors.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41534-018-0078-y", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.5363346", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5330404", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.4247346", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.4246407", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1285192", 
        "issn": [
          "2056-6387"
        ], 
        "name": "npj Quantum Information", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "4"
      }
    ], 
    "name": "Quantum-enhanced magnetometry by phase estimation algorithms with a single artificial atom", 
    "pagination": "29", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "e2d460e9f2fb3206f759d9b8118eec77db063734994ec03a2bae2cb536ad7656"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41534-018-0078-y"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1105107310"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41534-018-0078-y", 
      "https://app.dimensions.ai/details/publication/pub.1105107310"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T21:33", 
    "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/v90/0000000001_0000000264/records_8687_00000494.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41534-018-0078-y"
  }
]
 

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.10.1038/s41534-018-0078-y'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41534-018-0078-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41534-018-0078-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41534-018-0078-y'


 

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

206 TRIPLES      21 PREDICATES      56 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41534-018-0078-y schema:about anzsrc-for:02
2 anzsrc-for:0204
3 schema:author Nc2d2d566d97c4e2faece40df7a4c358d
4 schema:citation sg:pub.10.1038/nature06257
5 sg:pub.10.1038/nature07128
6 sg:pub.10.1038/nature12290
7 sg:pub.10.1038/ncomms2332
8 sg:pub.10.1038/nnano.2011.224
9 sg:pub.10.1038/nnano.2015.261
10 sg:pub.10.1038/nphys566
11 sg:pub.10.1038/npjqi.2016.23
12 sg:pub.10.1038/srep04677
13 https://app.dimensions.ai/details/publication/pub.1101840889
14 https://doi.org/10.1063/1.100291
15 https://doi.org/10.1080/00107510802091298
16 https://doi.org/10.1098/rspa.1998.0164
17 https://doi.org/10.1103/physreva.76.042319
18 https://doi.org/10.1103/physreva.83.052317
19 https://doi.org/10.1103/physreva.89.012118
20 https://doi.org/10.1103/physreva.97.022115
21 https://doi.org/10.1103/physrevlett.103.150502
22 https://doi.org/10.1103/physrevlett.110.147002
23 https://doi.org/10.1103/physrevlett.76.3228
24 https://doi.org/10.1103/physrevlett.97.167001
25 https://doi.org/10.1103/physrevlett.98.090501
26 https://doi.org/10.1103/physrevlett.99.187006
27 https://doi.org/10.1109/20.92457
28 https://doi.org/10.1109/sfcs.1994.365700
29 https://doi.org/10.1126/science.1104149
30 https://doi.org/10.1126/science.1138007
31 https://doi.org/10.1126/science.aad9480
32 https://doi.org/10.22331/q-2017-09-06-27
33 schema:datePublished 2018-12
34 schema:datePublishedReg 2018-12-01
35 schema:description Phase estimation algorithms are key protocols in quantum information processing. Besides applications in quantum computing, they can also be employed in metrology as they allow for fast extraction of information stored in the quantum state of a system. Here, we implement two suitably modified phase estimation procedures, the Kitaev and the semiclassical Fourier-transform algorithms, using an artificial atom realized with a superconducting transmon circuit. We demonstrate that both algorithms yield a flux sensitivity exceeding the classical shot-noise limit of the device, allowing one to approach the Heisenberg limit. Our experiment paves the way for the use of superconducting qubits as metrological devices which are potentially able to outperform the best existing flux sensors with a sensitivity enhanced by few orders of magnitude. Quantum computing algorithms can improve the performance of a superconducting magnetic field sensor beyond the classical limit. A qubit’s time evolution is often influenced by environmental factors like magnetic fields; measuring this evolution allows the magnetic field strength to be determined. Using classical methods, improvements in measurement performance can only scale with the square root of the total measurement time. However, by exploiting quantum coherence to use so-called phase estimation algorithms during the measurements, the scaling with measurement time can be driven beyond the classical limits. Andrey Lebedev at ETH Zurich and colleagues in Finland, Switzerland and Russia have applied this approach to superconducting qubits. They demonstrate both superior performance and improved scaling compared to the classical approach, and show that in principle superconducting qubits can become the highest-performing magnetic flux sensors.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree true
39 schema:isPartOf N6872cc93fad54f6fa90b417b1b5d924e
40 Na7429921789b44449ceeb399984fb27f
41 sg:journal.1285192
42 schema:name Quantum-enhanced magnetometry by phase estimation algorithms with a single artificial atom
43 schema:pagination 29
44 schema:productId N1bd5d00c6ac4401486861a0bdded6c37
45 N7e9dcaa7764149bab58bcd7df12d4009
46 N87355fd3e56a4baaae3d4583e46466c8
47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105107310
48 https://doi.org/10.1038/s41534-018-0078-y
49 schema:sdDatePublished 2019-04-10T21:33
50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
51 schema:sdPublisher Ncdd827d5bffd472e905421cca8e15ebe
52 schema:url https://www.nature.com/articles/s41534-018-0078-y
53 sgo:license sg:explorer/license/
54 sgo:sdDataset articles
55 rdf:type schema:ScholarlyArticle
56 N1bd5d00c6ac4401486861a0bdded6c37 schema:name readcube_id
57 schema:value e2d460e9f2fb3206f759d9b8118eec77db063734994ec03a2bae2cb536ad7656
58 rdf:type schema:PropertyValue
59 N357db353b00a4561baf3b27bd7ba3bde rdf:first N8d9ecddedfce4da2b9e693d6e7bfcaa2
60 rdf:rest rdf:nil
61 N3c002742e9b443c4bd4a17399ee84c10 schema:affiliation https://www.grid.ac/institutes/grid.436090.8
62 schema:familyName Lesovik
63 schema:givenName G. B.
64 rdf:type schema:Person
65 N44e771b8b0fe496689f51ded28604afe rdf:first N3c002742e9b443c4bd4a17399ee84c10
66 rdf:rest N9b04ae6b06ec4f7c98414fc3fdf3968e
67 N6872cc93fad54f6fa90b417b1b5d924e schema:volumeNumber 4
68 rdf:type schema:PublicationVolume
69 N7b1c21851e4f48c1bec5501129117d85 schema:affiliation https://www.grid.ac/institutes/grid.5373.2
70 schema:familyName Vepsäläinen
71 schema:givenName A.
72 rdf:type schema:Person
73 N7e9dcaa7764149bab58bcd7df12d4009 schema:name dimensions_id
74 schema:value pub.1105107310
75 rdf:type schema:PropertyValue
76 N87355fd3e56a4baaae3d4583e46466c8 schema:name doi
77 schema:value 10.1038/s41534-018-0078-y
78 rdf:type schema:PropertyValue
79 N8d9ecddedfce4da2b9e693d6e7bfcaa2 schema:affiliation https://www.grid.ac/institutes/grid.5373.2
80 schema:familyName Paraoanu
81 schema:givenName G. S.
82 rdf:type schema:Person
83 N9b04ae6b06ec4f7c98414fc3fdf3968e rdf:first sg:person.0776150017.92
84 rdf:rest N357db353b00a4561baf3b27bd7ba3bde
85 Na7429921789b44449ceeb399984fb27f schema:issueNumber 1
86 rdf:type schema:PublicationIssue
87 Nac511c7c3ec344018ae8a582d4932834 rdf:first N7b1c21851e4f48c1bec5501129117d85
88 rdf:rest N44e771b8b0fe496689f51ded28604afe
89 Nc2d2d566d97c4e2faece40df7a4c358d rdf:first sg:person.015424750236.35
90 rdf:rest Ne672d5e3be244398894479b1976ddefc
91 Ncdd827d5bffd472e905421cca8e15ebe schema:name Springer Nature - SN SciGraph project
92 rdf:type schema:Organization
93 Ne672d5e3be244398894479b1976ddefc rdf:first sg:person.010702005747.67
94 rdf:rest Nac511c7c3ec344018ae8a582d4932834
95 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
96 schema:name Physical Sciences
97 rdf:type schema:DefinedTerm
98 anzsrc-for:0204 schema:inDefinedTermSet anzsrc-for:
99 schema:name Condensed Matter Physics
100 rdf:type schema:DefinedTerm
101 sg:grant.4246407 http://pending.schema.org/fundedItem sg:pub.10.1038/s41534-018-0078-y
102 rdf:type schema:MonetaryGrant
103 sg:grant.4247346 http://pending.schema.org/fundedItem sg:pub.10.1038/s41534-018-0078-y
104 rdf:type schema:MonetaryGrant
105 sg:grant.5330404 http://pending.schema.org/fundedItem sg:pub.10.1038/s41534-018-0078-y
106 rdf:type schema:MonetaryGrant
107 sg:grant.5363346 http://pending.schema.org/fundedItem sg:pub.10.1038/s41534-018-0078-y
108 rdf:type schema:MonetaryGrant
109 sg:journal.1285192 schema:issn 2056-6387
110 schema:name npj Quantum Information
111 rdf:type schema:Periodical
112 sg:person.010702005747.67 schema:affiliation https://www.grid.ac/institutes/grid.18763.3b
113 schema:familyName Lebedev
114 schema:givenName A. V.
115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010702005747.67
116 rdf:type schema:Person
117 sg:person.015424750236.35 schema:affiliation https://www.grid.ac/institutes/grid.5373.2
118 schema:familyName Danilin
119 schema:givenName S.
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015424750236.35
121 rdf:type schema:Person
122 sg:person.0776150017.92 schema:affiliation https://www.grid.ac/institutes/grid.5801.c
123 schema:familyName Blatter
124 schema:givenName G.
125 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0776150017.92
126 rdf:type schema:Person
127 sg:pub.10.1038/nature06257 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045335822
128 https://doi.org/10.1038/nature06257
129 rdf:type schema:CreativeWork
130 sg:pub.10.1038/nature07128 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029810190
131 https://doi.org/10.1038/nature07128
132 rdf:type schema:CreativeWork
133 sg:pub.10.1038/nature12290 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040616957
134 https://doi.org/10.1038/nature12290
135 rdf:type schema:CreativeWork
136 sg:pub.10.1038/ncomms2332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040800091
137 https://doi.org/10.1038/ncomms2332
138 rdf:type schema:CreativeWork
139 sg:pub.10.1038/nnano.2011.224 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024849462
140 https://doi.org/10.1038/nnano.2011.224
141 rdf:type schema:CreativeWork
142 sg:pub.10.1038/nnano.2015.261 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010755954
143 https://doi.org/10.1038/nnano.2015.261
144 rdf:type schema:CreativeWork
145 sg:pub.10.1038/nphys566 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039498581
146 https://doi.org/10.1038/nphys566
147 rdf:type schema:CreativeWork
148 sg:pub.10.1038/npjqi.2016.23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053607998
149 https://doi.org/10.1038/npjqi.2016.23
150 rdf:type schema:CreativeWork
151 sg:pub.10.1038/srep04677 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032253526
152 https://doi.org/10.1038/srep04677
153 rdf:type schema:CreativeWork
154 https://app.dimensions.ai/details/publication/pub.1101840889 schema:CreativeWork
155 https://doi.org/10.1063/1.100291 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057647884
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1080/00107510802091298 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035028829
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1098/rspa.1998.0164 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044905446
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1103/physreva.76.042319 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015118427
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1103/physreva.83.052317 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036417991
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1103/physreva.89.012118 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003339705
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1103/physreva.97.022115 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101247022
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1103/physrevlett.103.150502 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049813916
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1103/physrevlett.110.147002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060761388
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1103/physrevlett.76.3228 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019663722
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1103/physrevlett.97.167001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032801644
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1103/physrevlett.98.090501 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047274939
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1103/physrevlett.99.187006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030141595
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1109/20.92457 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061119831
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1109/sfcs.1994.365700 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095740049
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1126/science.1104149 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062451106
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1126/science.1138007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062455164
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1126/science.aad9480 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025992528
190 rdf:type schema:CreativeWork
191 https://doi.org/10.22331/q-2017-09-06-27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091642734
192 rdf:type schema:CreativeWork
193 https://www.grid.ac/institutes/grid.18763.3b schema:alternateName Moscow Institute of Physics and Technology
194 schema:name Moscow Institute of Physics and Technology, Institutskii per. 9, 141700, Dolgoprudny, Moscow District, Russia
195 Theoretische Physik, Wolfgang-Pauli-Strasse 27, ETH Zürich, CH-8093, Zürich, Switzerland
196 rdf:type schema:Organization
197 https://www.grid.ac/institutes/grid.436090.8 schema:alternateName Landau Institute for Theoretical Physics
198 schema:name L.D. Landau Institute for Theoretical Physics RAS, Akad. Semenova av., 1-A, 142432, Chernogolovka, Moscow Region, Russia
199 Moscow Institute of Physics and Technology, Institutskii per. 9, 141700, Dolgoprudny, Moscow District, Russia
200 rdf:type schema:Organization
201 https://www.grid.ac/institutes/grid.5373.2 schema:alternateName Aalto University
202 schema:name Low Temperature Laboratory, Department of Applied Physics, Aalto University School of Science, PO Box 15100, FI-00076, Aalto, Finland
203 rdf:type schema:Organization
204 https://www.grid.ac/institutes/grid.5801.c schema:alternateName Swiss Federal Institute of Technology in Zurich
205 schema:name Theoretische Physik, Wolfgang-Pauli-Strasse 27, ETH Zürich, CH-8093, Zürich, Switzerland
206 rdf:type schema:Organization
 




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


...