Harnessing data augmentation to quantify uncertainty in the early estimation of single-photon source quality

Kedziora, DJ; Musial, A; Rudno-Rudzinski, W; Gabrys, B

Harnessing data augmentation to quantify uncertainty in the early estimation of single-photon source quality

Kedziora, DJ Musial, A Rudno-Rudzinski, W Gabrys, B

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Publisher:: IOP Publishing Ltd
Publication Type:: Journal Article
Citation:: MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2023, 4, (4)
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Kedziora, DJ
dc.contributor.author	Musial, A
dc.contributor.author	Rudno-Rudzinski, W
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.date.accessioned	2024-01-18T23:09:11Z
dc.date.available	2024-01-18T23:09:11Z
dc.date.issued	2023-12-01
dc.identifier.citation	MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2023, 4, (4)
dc.identifier.issn	2632-2153
dc.identifier.issn	2632-2153
dc.identifier.uri	http://hdl.handle.net/10453/174782
dc.description.abstract	<jats:title>Abstract</jats:title> <jats:p>Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry. However, the frequent lack of uncertainty discussions and reproducible details raises concerns about their reliability. This study investigates the use of data augmentation, a machine learning technique, to supplement experimental data with bootstrapped samples and quantify the uncertainty of such estimates. Eight datasets obtained from measurements involving a single InGaAs/GaAs epitaxial quantum dot serve as a proof-of-principle example. Analysis of one of the SPS quality metrics derived from efficient histogram fitting of the synthetic samples, i.e. the probability of multi-photon emission events, reveals significant uncertainty contributed by stochastic variability in the Poisson processes that describe detection rates. Ignoring this source of error risks severe overconfidence in both early quality estimates and claims for state-of-the-art SPS devices. Additionally, this study finds that standard least-squares fitting is comparable to using a Poisson likelihood, and expanding averages show some promise for early estimation. Also, reducing background counts improves fitting accuracy but does not address the Poisson-process variability. Ultimately, data augmentation demonstrates its value in supplementing physical experiments; its benefit here is to emphasise the need for a cautious assessment of SPS quality.</jats:p>
dc.language	English
dc.publisher	IOP Publishing Ltd
dc.relation.ispartof	MACHINE LEARNING-SCIENCE AND TECHNOLOGY
dc.relation.isbasedon	10.1088/2632-2153/ad0d11
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4601 Applied computing
dc.subject.classification	4611 Machine learning
dc.title	Harnessing data augmentation to quantify uncertainty in the early estimation of single-photon source quality
dc.type	Journal Article
utslib.citation.volume	4
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 - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-18T23:09:10Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	4

Abstract:

Abstract Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry. However, the frequent lack of uncertainty discussions and reproducible details raises concerns about their reliability. This study investigates the use of data augmentation, a machine learning technique, to supplement experimental data with bootstrapped samples and quantify the uncertainty of such estimates. Eight datasets obtained from measurements involving a single InGaAs/GaAs epitaxial quantum dot serve as a proof-of-principle example. Analysis of one of the SPS quality metrics derived from efficient histogram fitting of the synthetic samples, i.e. the probability of multi-photon emission events, reveals significant uncertainty contributed by stochastic variability in the Poisson processes that describe detection rates. Ignoring this source of error risks severe overconfidence in both early quality estimates and claims for state-of-the-art SPS devices. Additionally, this study finds that standard least-squares fitting is comparable to using a Poisson likelihood, and expanding averages show some promise for early estimation. Also, reducing background counts improves fitting accuracy but does not address the Poisson-process variability. Ultimately, data augmentation demonstrates its value in supplementing physical experiments; its benefit here is to emphasise the need for a cautious assessment of SPS quality.

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http://hdl.handle.net/10453/174782