[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117817-en":3,"doc-seo-117817-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117817,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source Quality","Novel methods for rapidly estimating single-photon source (SPS) quality address the expensive and time-consuming experimental validation required by intensity interferometry. Many approaches lack uncertainty discussions and reproducible details, raising reliability concerns. This study applies data augmentation to create bootstrapped samples that quantify uncertainty in early SPS quality estimates using eight datasets from single InGaAs/GaAs epitaxial quantum dots. Histogram-fitting yields multi-photon emission probability with uncertainty dominated by stochastic Poisson-process variability; least-squares fitting is comparable to Poisson likelihood, and background reduction improves accuracy without removing Poisson variability.","PAPER • OPEN ACCESS  \nHarnessing data augmentation to quantify uncertainty in the early estimation of single-photon source quality  \nTo cite this article: David Jacob Kedziora et al 2023 Mach. Learn. : Sci. Technol. 4 045042  \nView the article online for updates and enhancements.  \nYou may also like  \n-Neutral Hydrogen (H i) 21 cm as a Probe: Investigating Spatial Variations in Interstellar Turbulent Properties  \nAmit Kumar Mittal, Brian L. Babler, Snežana Stanimirovi et al.  \n-Mapping Spatial Variations of H i Turbulent Properties in the Small and Large Magellanic Cloud  \nSamuel Szotkowski, Delano Yoder, Snežana Stanimirovi et al.  \n-Using random forest for brain tissue identification by Raman spectroscopy  \nWeiyi Zhang, Chau Minh Giang, Qingan Cai et al.  \nThis content was downloaded from IP address [121.44.27.134](121.44.27.134) on 18/01/2024 at 23:03  \n Mach. Learn.: Sci. Technol. 4 (2023) 045042 [https://doi.org/10.1088/2632-2153/ad0d11](https://doi.org/10.1088/2632-2153/ad0d11)  \nPAPER  \nHarnessing data augmentation to quantify uncertainty in the early OPEN ACCESS estimation of single-photon source quality  \nRECEIVED  \n24 August 2023 David Jacob Kedziora1, ∗􀁂, Anna Musiał2􀁂, Wojciech Rudno-Rudzin´ski2􀁂 and Bogdan Gabrys1􀁂 REVISED28 October 2023 1 Complex Adaptive Systems Lab, University of Technology Sydney, Sydney, New South Wales 2007, Australia  \n2 Department of Experimental Physics, Wrocław University of Science and Technology, Wybrzee Wyspian´skiego 27, Wrocław 50-370, ACCEPTED FOR PUBLICATION  \nPoland  \n15 November 2023  \n∗ Author to whom any correspondence should be addressed.  \nPUBLISHED  \n4 December 2023 [E-mail: david.kedziora@uts.edu.au](E-mail: david.kedziora@uts.edu.au)  \n   Keywords: quantum dots, quantum communication, emission statistics, bootstrapping, generative models, uncertainty analysis  \nOriginal Content from this work may be used under the terms of the  \nCreative Commons Attribution 4 .0 licence.  \nAny further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nAbstract  \nNovel 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 ofSPS quality.  \n1. Introduction  \nThe world is presently witnessing the advent of the ‘Quantum 2.0’ technological revolution, an era distinguished by the development of devices that, in their operation, involve manipulating quantum states of light and matter [1] . Indeed, these devices seek to exploit purely quantum phenomena, such as superposition or","cbCaipMNlCZqgb20","https://ap.wps.com/l/cbCaipMNlCZqgb20","pdf",1844233,1,18,"English","en",105,"# Introduction\n## Quantum 2.0 and the need for single-photon sources\n## Measuring purity via second-order auto-correlation g(2)(τ)\n## Motivation: rapid estimation and missing uncertainty reporting","[{\"question\":\"Why do current early methods for estimating single-photon source quality raise reliability concerns?\",\"answer\":\"They often omit uncertainty discussions and lack reproducible details, making it difficult to assess how reliable the reported early quality estimates are.\"},{\"question\":\"How does the study use data augmentation to quantify uncertainty?\",\"answer\":\"It supplements experimental data with bootstrapped samples generated via data augmentation, then evaluates uncertainty in early SPS quality metrics derived from histogram fitting.\"},{\"question\":\"What is the dominant source of uncertainty for the SPS quality metric examined?\",\"answer\":\"Uncertainty is significantly driven by stochastic variability in the Poisson processes that describe detection-rate fluctuations; ignoring this can lead to overconfident claims.\"}]","Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source Quality | PDF",1785679736,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"harnessing-data-augmentation-to-quantify-uncertainty-in-the-early-estimation-of-single-photon-source-quality","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/harnessing-data-augmentation-to-quantify-uncertainty-in-the-early-estimation-of-single-photon-source-quality/117817/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do current early methods for estimating single-photon source quality raise reliability concerns?","Question",{"text":75,"@type":76},"They often omit uncertainty discussions and lack reproducible details, making it difficult to assess how reliable the reported early quality estimates are.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study use data augmentation to quantify uncertainty?",{"text":80,"@type":76},"It supplements experimental data with bootstrapped samples generated via data augmentation, then evaluates uncertainty in early SPS quality metrics derived from histogram fitting.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the dominant source of uncertainty for the SPS quality metric examined?",{"text":84,"@type":76},"Uncertainty is significantly driven by stochastic variability in the Poisson processes that describe detection-rate fluctuations; 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