PUBLICATION DATE

2017-09-13

AUTHORS

Peter Wittek, Nathan Wiebe, Nicola Pancotti, Seth Lloyd, Patrick Rebentrost, Jacob Biamonte

TITLE

Quantum machine learning

ISSUE

7671

VOLUME

549

ISSN (print)

0028-0836

ISSN (electronic)

1476-4687

ABSTRACT

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

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


45 TRIPLES      29 PREDICATES      44 URIs      17 LITERALS

Subject Predicate Object
1 articles:c7a19d2446e6857ce3db720a88e62cf8 sg:abstract Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.
2 sg:coverDate 2017-09-14
3 sg:coverYear 2017
4 sg:coverYearMonth 2017-09
5 sg:ddsIdJournalBrand 41586
6 sg:doi 10.1038/nature23474
7 sg:doiLink http://dx.doi.org/10.1038/nature23474
8 sg:hasArticleType article-types:reviews
9 sg:hasContributingOrganization grid-institutes:grid.116068.8
10 grid-institutes:grid.419815.0
11 grid-institutes:grid.450272.6
12 grid-institutes:grid.454320.4
13 grid-institutes:grid.46078.3d
14 grid-institutes:grid.5853.b
15 sg:hasContribution contributions:6ff33fbe07fdd9359e7b85bf8663160b
16 contributions:790ab268f94ea06c880a5518911f1f3b
17 contributions:ade2d4342ed603df1518344b393afbb6
18 contributions:ae5dfabaed651bb3f1e9a44568132de4
19 contributions:f7433e85de201ce39c2566181bde841d
20 contributions:f9a9eabfc82180dbb7dfdb53752e1dc5
21 sg:hasFieldOfResearchCode anzsrc-for:08
22 anzsrc-for:0801
23 anzsrc-for:0802
24 anzsrc-for:0803
25 sg:hasJournal journals:5ea8996a5bb089dd0562d3bfe24eaad9
26 journals:723ba46cf7980ad6089b3da0ba4b0b47
27 sg:hasJournalBrand journal-brands:012496b06989edb434c6b8e1d0b0a7db
28 sg:hasSubject subjects:computer-science
29 subjects:quantum-information
30 subjects:quantum-simulation
31 sg:issnElectronic 1476-4687
32 sg:issnPrint 0028-0836
33 sg:issue 7671
34 sg:license http://scigraph.springernature.com/explorer/license/
35 sg:npgId nature23474
36 sg:pageEnd 202
37 sg:pageStart 195
38 sg:publicationDate 2017-09-13
39 sg:publicationYear 2017
40 sg:publicationYearMonth 2017-09
41 sg:scigraphId c7a19d2446e6857ce3db720a88e62cf8
42 sg:title Quantum machine learning
43 sg:volume 549
44 rdf:type sg:Article
45 rdfs:label Article: Quantum machine learning
HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular JSON format for linked data.

curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/articles/c7a19d2446e6857ce3db720a88e62cf8'

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

curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/articles/c7a19d2446e6857ce3db720a88e62cf8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/articles/c7a19d2446e6857ce3db720a88e62cf8'

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

curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/articles/c7a19d2446e6857ce3db720a88e62cf8'






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


...