Beyond Quantum Noise Spectroscopy: modelling and mitigating noise with quantum feature engineering

Youssry, A; Paz-Silva, GA; Ferrie, C

Beyond Quantum Noise Spectroscopy: modelling and mitigating noise with quantum feature engineering

Youssry, A Paz-Silva, GA Ferrie, C

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Publication Type:: Journal Article
Citation:: npj Quantum Inf, 2020, 6, pp. 95
Issue Date:: 2020-03-16

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Field	Value	Language
dc.contributor.author	Youssry, A
dc.contributor.author	Paz-Silva, GA
dc.contributor.author	Ferrie, C
dc.date.accessioned	2021-05-13T02:43:36Z
dc.date.available	2021-05-13T02:43:36Z
dc.date.issued	2020-03-16
dc.identifier.citation	npj Quantum Inf, 2020, 6, pp. 95
dc.identifier.uri	http://hdl.handle.net/10453/148885
dc.description.abstract	The ability to use quantum technology to achieve useful tasks, be they scientific or industry related, boils down to precise quantum control. In general it is difficult to assess a proposed solution due to the difficulties in characterising the quantum system or device. These arise because of the impossibility to characterise certain components in situ, and are exacerbated by noise induced by the environment and active controls. Here we present a general purpose characterisation and control solution making use of a novel deep learning framework composed of quantum features. We provide the framework, sample data sets, trained models, and their performance metrics. In addition, we demonstrate how the trained model can be used to extract conventional indicators, such as noise power spectra.
dc.language	en
dc.relation.ispartof	npj Quantum Inf
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Beyond Quantum Noise Spectroscopy: modelling and mitigating noise with quantum feature engineering
dc.type	Journal Article
utslib.citation.volume	6
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-05-13T02:43:35Z
pubs.volume	6

Abstract:

The ability to use quantum technology to achieve useful tasks, be they scientific or industry related, boils down to precise quantum control. In general it is difficult to assess a proposed solution due to the difficulties in characterising the quantum system or device. These arise because of the impossibility to characterise certain components in situ, and are exacerbated by noise induced by the environment and active controls. Here we present a general purpose characterisation and control solution making use of a novel deep learning framework composed of quantum features. We provide the framework, sample data sets, trained models, and their performance metrics. In addition, we demonstrate how the trained model can be used to extract conventional indicators, such as noise power spectra.

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http://hdl.handle.net/10453/148885