QDataSet, quantum datasets for machine learning.

Perrier, E; Youssry, A; Ferrie, C

QDataSet, quantum datasets for machine learning.

Perrier, E Youssry, A Ferrie, C

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Sci Data, 2022, 9, (1), pp. 582
Issue Date:: 2022-09-23

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Field	Value	Language
dc.contributor.author	Perrier, E
dc.contributor.author	Youssry, A
dc.contributor.author	Ferrie, C https://orcid.org/0000-0003-2736-9943
dc.date.accessioned	2023-03-14T04:01:15Z
dc.date.available	2022-08-11
dc.date.available	2023-03-14T04:01:15Z
dc.date.issued	2022-09-23
dc.identifier.citation	Sci Data, 2022, 9, (1), pp. 582
dc.identifier.issn	2052-4463
dc.identifier.issn	2052-4463
dc.identifier.uri	http://hdl.handle.net/10453/167297
dc.description.abstract	The availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline. Despite considerable advancements, the field of quantum machine learning has thus far lacked a set of comprehensive large-scale datasets upon which to benchmark the development of algorithms for use in applied and theoretical quantum settings. In this paper, we introduce such a dataset, the QDataSet, a quantum dataset designed specifically to facilitate the training and development of quantum machine learning algorithms. The QDataSet comprises 52 high-quality publicly available datasets derived from simulations of one- and two-qubit systems evolving in the presence and/or absence of noise. The datasets are structured to provide a wealth of information to enable machine learning practitioners to use the QDataSet to solve problems in applied quantum computation, such as quantum control, quantum spectroscopy and tomography. Accompanying the datasets on the associated GitHub repository are a set of workbooks demonstrating the use of the QDataSet in a range of optimisation contexts.
dc.format	Electronic
dc.language	eng
dc.publisher	Springer Nature
dc.relation	http://purl.org/au-research/grants/arc/CE170100012
dc.relation.ispartof	Sci Data
dc.relation.isbasedon	10.1038/s41597-022-01639-1
dc.rights	info:eu-repo/semantics/openAccess
dc.title	QDataSet, quantum datasets for machine learning.
dc.type	Journal Article
utslib.citation.volume	9
utslib.location.activity	England
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-14T04:01:12Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	9
utslib.citation.issue	1

Abstract:

The availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline. Despite considerable advancements, the field of quantum machine learning has thus far lacked a set of comprehensive large-scale datasets upon which to benchmark the development of algorithms for use in applied and theoretical quantum settings. In this paper, we introduce such a dataset, the QDataSet, a quantum dataset designed specifically to facilitate the training and development of quantum machine learning algorithms. The QDataSet comprises 52 high-quality publicly available datasets derived from simulations of one- and two-qubit systems evolving in the presence and/or absence of noise. The datasets are structured to provide a wealth of information to enable machine learning practitioners to use the QDataSet to solve problems in applied quantum computation, such as quantum control, quantum spectroscopy and tomography. Accompanying the datasets on the associated GitHub repository are a set of workbooks demonstrating the use of the QDataSet in a range of optimisation contexts.

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http://hdl.handle.net/10453/167297