Ontology type: schema:ScholarlyArticle Open Access: True
2019-12
AUTHORSDaniel K. Park, Francesco Petruccione, June-Koo Kevin Rhee
ABSTRACTA prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of M entries, each represented by n bits, the method requires O(n) qubits and O(Mn) steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking. More... »
PAGES3949
http://scigraph.springernature.com/pub.10.1038/s41598-019-40439-3
DOIhttp://dx.doi.org/10.1038/s41598-019-40439-3
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1112641530
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30850658
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": "Korea Advanced Institute of Science and Technology",
"id": "https://www.grid.ac/institutes/grid.37172.30",
"name": [
"School of Electrical Engineering, KAIST, 34141, Daejeon, Republic of Korea",
"ITRC of Quantum Computing for AI, KAIST, 34141, Daejeon, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Park",
"givenName": "Daniel K.",
"type": "Person"
},
{
"affiliation": {
"alternateName": "National Institute for Theoretical Physics",
"id": "https://www.grid.ac/institutes/grid.494663.a",
"name": [
"School of Electrical Engineering, KAIST, 34141, Daejeon, Republic of Korea",
"ITRC of Quantum Computing for AI, KAIST, 34141, Daejeon, Republic of Korea",
"Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, 4000, Durban, South Africa",
"National Institute for Theoretical Physics, 4000, Durban, KwaZulu-Natal, South Africa"
],
"type": "Organization"
},
"familyName": "Petruccione",
"givenName": "Francesco",
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of KwaZulu-Natal",
"id": "https://www.grid.ac/institutes/grid.16463.36",
"name": [
"School of Electrical Engineering, KAIST, 34141, Daejeon, Republic of Korea",
"ITRC of Quantum Computing for AI, KAIST, 34141, Daejeon, Republic of Korea",
"Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, 4000, Durban, South Africa"
],
"type": "Organization"
},
"familyName": "Rhee",
"givenName": "June-Koo Kevin",
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1103/physrevlett.113.080501",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001475100"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.113.080501",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001475100"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.64.014303",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004994041"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.64.014303",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004994041"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.93.032318",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005673397"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.93.032318",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005673397"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.78.052310",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008279495"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.78.052310",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008279495"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nphys3029",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008585200",
"https://doi.org/10.1038/nphys3029"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1088/1367-2630/17/12/123010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010672117"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.93.130502",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015823980"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.93.130502",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015823980"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.92.012327",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021122573"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.92.012327",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021122573"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.87.167902",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021843335"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.87.167902",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021843335"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/361011.361061",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023289580"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.52.3457",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036486855"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.52.3457",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036486855"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.112.010505",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041684218"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.112.010505",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041684218"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.100.160501",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047276432"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.100.160501",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047276432"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.75.022313",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049311908"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.75.022313",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049311908"
],
"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": "https://doi.org/10.1103/physrevlett.113.130503",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050008666"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.113.130503",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050008666"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/237814.237866",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053319325"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1049/el:20031202",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1056794037"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.111.090505",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060761977"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.111.090505",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060761977"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/s41534-017-0017-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1084537799",
"https://doi.org/10.1038/s41534-017-0017-3"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevx.7.031041",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1091594619"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevx.7.031041",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1091594619"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature23474",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1091594880",
"https://doi.org/10.1038/nature23474"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature23474",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1091594880",
"https://doi.org/10.1038/nature23474"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1209/0295-5075/119/60002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093149342"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.97.052329",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1104307283"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.97.052329",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1104307283"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.1801723115",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1106833158"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.1801723115",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1106833158"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.1801723115",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1106833158"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevx.8.041015",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1107803452"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevx.8.041015",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1107803452"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-12",
"datePublishedReg": "2019-12-01",
"description": "A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of M entries, each represented by n bits, the method requires O(n) qubits and O(Mn) steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.",
"genre": "research_article",
"id": "sg:pub.10.1038/s41598-019-40439-3",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isPartOf": [
{
"id": "sg:journal.1045337",
"issn": [
"2045-2322"
],
"name": "Scientific Reports",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "9"
}
],
"name": "Circuit-Based Quantum Random Access Memory for Classical Data",
"pagination": "3949",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"7a290120305ded848b9c46bc5ff1796660086f66a0b11a5f9a688a96d1a4f46f"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"30850658"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"101563288"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/s41598-019-40439-3"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1112641530"
]
}
],
"sameAs": [
"https://doi.org/10.1038/s41598-019-40439-3",
"https://app.dimensions.ai/details/publication/pub.1112641530"
],
"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_78959_00000001.jsonl",
"type": "ScholarlyArticle",
"url": "https://www.nature.com/articles/s41598-019-40439-3"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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/s41598-019-40439-3'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-40439-3'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-40439-3'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-40439-3'
This table displays all metadata directly associated to this object as RDF triples.
172 TRIPLES
21 PREDICATES
55 URIs
21 LITERALS
9 BLANK NODES