Photonic indistinguishability characterization and optimization for cavity-based single-photon source

Indistinguishability of single photons from independent sources is critically important for scalable quantum technologies. We provide a comprehensive comparison of single-photon indistinguishability of different kinds of cavity quantum electrodynamics (CQEDs) systems by numerically simulating Hong–O...

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Main Authors: Miao Cai, Mingyuan Chen, Jiangshan Tang, Keyu Xia
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
Online Access:https://doi.org/10.1088/2632-2153/adf53c
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author Miao Cai
Mingyuan Chen
Jiangshan Tang
Keyu Xia
author_facet Miao Cai
Mingyuan Chen
Jiangshan Tang
Keyu Xia
author_sort Miao Cai
collection DOAJ
description Indistinguishability of single photons from independent sources is critically important for scalable quantum technologies. We provide a comprehensive comparison of single-photon indistinguishability of different kinds of cavity quantum electrodynamics (CQEDs) systems by numerically simulating Hong–Ou–Mandel two-photon interference. We find that the CQED system using natural atoms exhibits superiority in indistinguishability, benefiting from the inherently identical features. Moreover, a $\Lambda-$ type three-level atom shows essential robustness against variation of various system parameters because it exploits the two ground states with considerably smaller decay rates for single-photon generation. Furthermore, a machine learning-based framework is proposed to significantly and robustly improve single-photon indistinguishability for two non-identical CQED systems. This work may pave the way for designing and engineering reliable and scalable photon-based quantum technologies.
format Article
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institution Kabale University
issn 2632-2153
language English
publishDate 2025-01-01
publisher IOP Publishing
record_format Article
series Machine Learning: Science and Technology
spelling doaj-art-2912385d32f94939b77a2f836e6334bb2025-08-20T03:45:14ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303502610.1088/2632-2153/adf53cPhotonic indistinguishability characterization and optimization for cavity-based single-photon sourceMiao Cai0https://orcid.org/0000-0001-9701-8669Mingyuan Chen1https://orcid.org/0000-0002-2633-9841Jiangshan Tang2Keyu Xia3College of Engineering and Applied Sciences, Nanjing University , Nanjing 210023, People’s Republic of China; National Laboratory of Solid State Microstructures, Nanjing University , Nanjing 210093, People’s Republic of ChinaCollege of Engineering and Applied Sciences, Nanjing University , Nanjing 210023, People’s Republic of ChinaCollege of Engineering and Applied Sciences, Nanjing University , Nanjing 210023, People’s Republic of ChinaCollege of Engineering and Applied Sciences, Nanjing University , Nanjing 210023, People’s Republic of China; National Laboratory of Solid State Microstructures, Nanjing University , Nanjing 210093, People’s Republic of China; Shishan Laboratory, Suzhou Campus of Nanjing University , Suzhou 215000, People’s Republic of ChinaIndistinguishability of single photons from independent sources is critically important for scalable quantum technologies. We provide a comprehensive comparison of single-photon indistinguishability of different kinds of cavity quantum electrodynamics (CQEDs) systems by numerically simulating Hong–Ou–Mandel two-photon interference. We find that the CQED system using natural atoms exhibits superiority in indistinguishability, benefiting from the inherently identical features. Moreover, a $\Lambda-$ type three-level atom shows essential robustness against variation of various system parameters because it exploits the two ground states with considerably smaller decay rates for single-photon generation. Furthermore, a machine learning-based framework is proposed to significantly and robustly improve single-photon indistinguishability for two non-identical CQED systems. This work may pave the way for designing and engineering reliable and scalable photon-based quantum technologies.https://doi.org/10.1088/2632-2153/adf53csingle-photon indistinguishabilitycavity quantum electrodynamicssingle-photon sourcemachine learningcavity-atom system
spellingShingle Miao Cai
Mingyuan Chen
Jiangshan Tang
Keyu Xia
Photonic indistinguishability characterization and optimization for cavity-based single-photon source
Machine Learning: Science and Technology
single-photon indistinguishability
cavity quantum electrodynamics
single-photon source
machine learning
cavity-atom system
title Photonic indistinguishability characterization and optimization for cavity-based single-photon source
title_full Photonic indistinguishability characterization and optimization for cavity-based single-photon source
title_fullStr Photonic indistinguishability characterization and optimization for cavity-based single-photon source
title_full_unstemmed Photonic indistinguishability characterization and optimization for cavity-based single-photon source
title_short Photonic indistinguishability characterization and optimization for cavity-based single-photon source
title_sort photonic indistinguishability characterization and optimization for cavity based single photon source
topic single-photon indistinguishability
cavity quantum electrodynamics
single-photon source
machine learning
cavity-atom system
url https://doi.org/10.1088/2632-2153/adf53c
work_keys_str_mv AT miaocai photonicindistinguishabilitycharacterizationandoptimizationforcavitybasedsinglephotonsource
AT mingyuanchen photonicindistinguishabilitycharacterizationandoptimizationforcavitybasedsinglephotonsource
AT jiangshantang photonicindistinguishabilitycharacterizationandoptimizationforcavitybasedsinglephotonsource
AT keyuxia photonicindistinguishabilitycharacterizationandoptimizationforcavitybasedsinglephotonsource