Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication

End-to-end radio communication needs to be optimized against noisy channel conditions and other distortion effects. This paper presents a novel concept, a set of hybrid quantum-classical autoencoder architectures with a comprehensive feasibility study using standard encoded radio signals, to evaluat...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access Jg. 13; S. 82181 - 82192
Hauptverfasser:	Tabi, Zsolt I., Bako, Bence, Nagy, Daniel T. R., Vaderna, Peter, Kallus, Zsofia, Haga, Peter, Zimboras, Zoltan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Autoencoders Coding Computer architecture Encoding Feasibility studies Machine learning Network design Neural networks Quadrature amplitude modulation quantum autoencoder Quantum circuit Quantum machine learning Radio communication Radio communications Radio signals Random noise Receivers Receivers & amplifiers Symbols Transceivers
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	End-to-end radio communication needs to be optimized against noisy channel conditions and other distortion effects. This paper presents a novel concept, a set of hybrid quantum-classical autoencoder architectures with a comprehensive feasibility study using standard encoded radio signals, to evaluate quantum neural network design requirements for the radio context. The hybrid scenarios include single-sided, i.e., quantum encoder (transmitter) or quantum decoder (receiver), as well as fully quantum radio channel autoencoder (transmitter-receiver) systems. We provide detailed formulas for each scenario and validate our model through an extensive set of simulations. Our results demonstrate model robustness and adaptability. Supporting experiments are conducted utilizing 4 and 16 Quadrature Amplitude Modulation schemes and we expect that the model is adaptable to more general encoding schemes. We explore model performance against both additive white Gaussian noise and Rayleigh fading models. Our numerical findings highlight the importance of designing efficient quantum neural network architectures for meeting application performance constraints - including data re-uploading methods, encoding schemes, and core layer structures.
AbstractList	End-to-end radio communication needs to be optimized against noisy channel conditions and other distortion effects. This paper presents a novel concept, a set of hybrid quantum-classical autoencoder architectures with a comprehensive feasibility study using standard encoded radio signals, to evaluate quantum neural network design requirements for the radio context. The hybrid scenarios include single-sided, i.e., quantum encoder (transmitter) or quantum decoder (receiver), as well as fully quantum radio channel autoencoder (transmitter-receiver) systems. We provide detailed formulas for each scenario and validate our model through an extensive set of simulations. Our results demonstrate model robustness and adaptability. Supporting experiments are conducted utilizing 4 and 16 Quadrature Amplitude Modulation schemes and we expect that the model is adaptable to more general encoding schemes. We explore model performance against both additive white Gaussian noise and Rayleigh fading models. Our numerical findings highlight the importance of designing efficient quantum neural network architectures for meeting application performance constraints – including data re-uploading methods, encoding schemes, and core layer structures.
Author	Tabi, Zsolt I. Haga, Peter Bako, Bence Kallus, Zsofia Vaderna, Peter Zimboras, Zoltan Nagy, Daniel T. R.
Author_xml	– sequence: 1 givenname: Zsolt I. orcidid: 0000-0003-2070-7452 surname: Tabi fullname: Tabi, Zsolt I. email: zsolttabi@ik.elte.hu organization: Faculty of Informatics, Eötvös Loránd University, Budapest, Hungary – sequence: 2 givenname: Bence orcidid: 0009-0004-8756-7890 surname: Bako fullname: Bako, Bence organization: Faculty of Informatics, Eötvös Loránd University, Budapest, Hungary – sequence: 3 givenname: Daniel T. R. surname: Nagy fullname: Nagy, Daniel T. R. organization: Quantum Computing and Information Group, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary – sequence: 4 givenname: Peter orcidid: 0000-0003-1813-1562 surname: Vaderna fullname: Vaderna, Peter organization: Ericsson Research, Budapest, Hungary – sequence: 5 givenname: Zsofia orcidid: 0000-0002-5270-1891 surname: Kallus fullname: Kallus, Zsofia organization: Ericsson Research, Budapest, Hungary – sequence: 6 givenname: Peter surname: Haga fullname: Haga, Peter email: peter.haga@ericsson.com organization: Ericsson Research, Budapest, Hungary – sequence: 7 givenname: Zoltan surname: Zimboras fullname: Zimboras, Zoltan email: zimboras.zoltan@wigner.hu organization: Faculty of Informatics, Eötvös Loránd University, Budapest, Hungary
BookMark	eNqFkU1P3DAQhi0EEpTyC9pDpJ6z9dj5sI-raFuQUBGFni1nMmm9ysbUdg799_UShFAvncuMR_O8Y837jp3OfibGPgDfAHD9edt1u4eHjeCi3si6aQRvT9iFgEaXspbN6Zv6nF3FuOc5VG7V7QX7dr_YOS2HsptsjA7tVGyX5GlGP1AotgF_uUSYlkCxGH0odvNQJl_mVHy3g_NF5w-HZc5kcn5-z85GO0W6esmX7MeX3WN3Xd7efb3ptrclVgCpVHZEJalt-6YWgEQ0tFz1iLwahaibno-Q31r1kkuFAKg1VG0zaugVKZCX7GbVHbzdm6fgDjb8Md4689zw4aexITmcyGiyYHlFHEWWoErbXqKEipRALiqetT6tWk_B_14oJrP3S5jz940UvGqVVuI4JdcpDD7GQOPrVuDm6INZfTBHH8yLD5nS_1Do0vOlUrBu-g_7cWVdvs-bbVpBWyv5FyvBluE
CODEN	IAECCG
CitedBy_id	crossref_primary_10_1109_ACCESS_2025_3584000
Cites_doi	10.1109/MCOM.001.2000804 10.1109/ACCESS.2020.3025597 10.1103/physrevresearch.1.033063 10.1007/978-3-030-83098-4 10.1007/s11128-014-0809-8 10.1109/ACCESS.2021.3114543 10.1103/physrevlett.121.040502 10.1109/JSTSP.2017.2784180 10.1109/MCOM.001.2200362 10.1103/physrevlett.124.130502 10.1109/SPAWC48557.2020.9154335 10.1016/j.phycom.2024.102583 10.1007/s42484-022-00075-z 10.1109/TII.2022.3146552 10.1145/3341302.3342072 10.1002/0471200573 10.1038/s43588-021-00084-1 10.1109/comst.2024.3502762 10.1109/OJCOMS.2023.3309268 10.1103/physreva.101.032308 10.1109/ALLERTON.2017.8262721 10.22331/q-2022-03-30-677 10.1109/ICASSP43922.2022.9747369 10.1103/physreva.103.032430 10.1109/TCCN.2019.2919300 10.1109/COMSNETS59351.2024.10427450 10.1109/TAP.2004.835272 10.1017/9781009489843 10.1088/2632-2153/ac4559 10.1109/ISSPIT.2016.7886039 10.22331/q-2020-02-06-226 10.1126/science.1127647 10.1016/j.comcom.2021.09.033 10.36244/ICJ.2021.1.2 10.1109/TCCN.2017.2758370 10.1088/2058-9565/aa8072 10.1103/physreva.102.032412 10.1109/SEC54971.2022.00071 10.1007/s43673-021-00030-3
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA
DOI	10.1109/ACCESS.2025.3566207
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Open Access Full Text
DatabaseTitle	CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional
DatabaseTitleList	Materials Research Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2169-3536
EndPage	82192
ExternalDocumentID	oai_doaj_org_article_9ea1a04e0c2147e49ab3c314e82c0240 10_1109_ACCESS_2025_3566207 10981758
Genre	orig-research
GrantInformation_xml	– fundername: Quantum Information National Laboratory of Hungary grantid: 2022-2.1.1-NL-2022-00004 – fundername: QuantERA II Project Hybrid Quantum Classical Computation grantid: (HQCC)-101017733 – fundername: Hungarian National Research, Development and Innovation Office (NKFIH) grantid: KDP-2021; KDP-2023; TKP-2021-NVA-04; FK 135220 funderid: 10.13039/501100018818 – fundername: European Union within the Horizon Europe Program HORIZONCL4-2022-QUANTUM-01-Specific Grant Agreement (SGA) Project grantid: 101113946 OpenSuperQPlus100
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c411t-8afc83e77b6521ceeed708bcc04f2256b0f108b98b3038c11c991476f91b8e813
IEDL.DBID	DOA
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001489664500010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2169-3536
IngestDate	Fri Oct 03 12:18:51 EDT 2025 Sat Nov 01 15:53:20 EDT 2025 Sat Nov 29 07:53:55 EST 2025 Tue Nov 18 20:47:28 EST 2025 Wed Aug 27 02:01:58 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c411t-8afc83e77b6521ceeed708bcc04f2256b0f108b98b3038c11c991476f91b8e813
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-2070-7452 0000-0003-1813-1562 0009-0004-8756-7890 0000-0002-5270-1891
OpenAccessLink	https://doaj.org/article/9ea1a04e0c2147e49ab3c314e82c0240
PQID	3204789820
PQPubID	4845423
PageCount	12
ParticipantIDs	crossref_primary_10_1109_ACCESS_2025_3566207 ieee_primary_10981758 doaj_primary_oai_doaj_org_article_9ea1a04e0c2147e49ab3c314e82c0240 proquest_journals_3204789820 crossref_citationtrail_10_1109_ACCESS_2025_3566207
PublicationCentury	2000
PublicationDate	20250000 2025-00-00 20250101 2025-01-01
PublicationDateYYYYMMDD	2025-01-01
PublicationDate_xml	– year: 2025 text: 20250000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2025
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	O’Shea (ref43) 2017 ref12 Goodfellow (ref41) 2016 ref15 ref14 ref11 ref10 García (ref24) 2022; 181 ref16 ref19 O’Malley (ref49) 2019 Bergholm (ref46) 2018 (ref17) 2022 Schuld (ref13) 2021 ref42 ref44 ref8 ref7 ref9 Ng (ref39) 2011; 72 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref38 Farhi (ref45) 2014 Kingma (ref48) 2014 ref23 ref26 Abadi (ref47) 2015 ref25 ref20 ref22 ref21 ref28 ref27 ref29 (ref18) 2023
References_xml	– ident: ref23 doi: 10.1109/MCOM.001.2000804 – ident: ref25 doi: 10.1109/ACCESS.2020.3025597 – ident: ref4 doi: 10.1103/physrevresearch.1.033063 – volume-title: Machine Learning With Quantum Computers year: 2021 ident: ref13 doi: 10.1007/978-3-030-83098-4 – ident: ref3 doi: 10.1007/s11128-014-0809-8 – ident: ref30 doi: 10.1109/ACCESS.2021.3114543 – ident: ref8 doi: 10.1103/physrevlett.121.040502 – ident: ref21 doi: 10.1109/JSTSP.2017.2784180 – ident: ref33 doi: 10.1109/MCOM.001.2200362 – ident: ref12 doi: 10.1103/physrevlett.124.130502 – volume-title: 6G-Connecting a Cyber-physical World—A Research Outlook Toward 2030 year: 2022 ident: ref17 – ident: ref26 doi: 10.1109/SPAWC48557.2020.9154335 – ident: ref27 doi: 10.1016/j.phycom.2024.102583 – ident: ref14 doi: 10.1007/s42484-022-00075-z – volume-title: Deep Learning year: 2016 ident: ref41 – ident: ref1 doi: 10.1109/TII.2022.3146552 – ident: ref28 doi: 10.1145/3341302.3342072 – ident: ref16 doi: 10.1002/0471200573 – ident: ref2 doi: 10.1038/s43588-021-00084-1 – volume-title: TensorFlow: Large-scale Machine Learning on Heterogeneous Systems year: 2015 ident: ref47 – ident: ref32 doi: 10.1109/comst.2024.3502762 – ident: ref31 doi: 10.1109/OJCOMS.2023.3309268 – ident: ref38 doi: 10.1103/physreva.101.032308 – ident: ref22 doi: 10.1109/ALLERTON.2017.8262721 – ident: ref5 doi: 10.22331/q-2022-03-30-677 – ident: ref9 doi: 10.1109/ICASSP43922.2022.9747369 – year: 2014 ident: ref48 article-title: Adam: A method for stochastic optimization publication-title: arXiv:1412.6980 – ident: ref37 doi: 10.1103/physreva.103.032430 – ident: ref44 doi: 10.1109/TCCN.2019.2919300 – ident: ref35 doi: 10.1109/COMSNETS59351.2024.10427450 – ident: ref42 doi: 10.1109/TAP.2004.835272 – ident: ref15 doi: 10.1017/9781009489843 – volume-title: Ai-driven Communication & Computation Co-design: Final Solutions year: 2023 ident: ref18 – volume-title: Kerastuner year: 2019 ident: ref49 – year: 2018 ident: ref46 article-title: PennyLane: Automatic differentiation of hybrid quantum-classical computations publication-title: arXiv:1811.04968 – ident: ref6 doi: 10.1088/2632-2153/ac4559 – ident: ref19 doi: 10.1109/ISSPIT.2016.7886039 – year: 2014 ident: ref45 article-title: A quantum approximate optimization algorithm publication-title: arXiv:1411.4028 – ident: ref36 doi: 10.22331/q-2020-02-06-226 – ident: ref40 doi: 10.1126/science.1127647 – volume: 181 start-page: 192 year: 2022 ident: ref24 article-title: Model-free machine learning of wireless SISO/MIMO communications publication-title: Comput. Commun. doi: 10.1016/j.comcom.2021.09.033 – ident: ref29 doi: 10.36244/ICJ.2021.1.2 – volume: 72 start-page: 1 issue: 2011 year: 2011 ident: ref39 article-title: Sparse autoencoder publication-title: CS294A Lect. Notes – ident: ref20 doi: 10.1109/TCCN.2017.2758370 – ident: ref11 doi: 10.1088/2058-9565/aa8072 – ident: ref10 doi: 10.1103/physreva.102.032412 – ident: ref34 doi: 10.1109/SEC54971.2022.00071 – year: 2017 ident: ref43 article-title: Deep learning based MIMO communications publication-title: arXiv:1707.07980 – ident: ref7 doi: 10.1007/s43673-021-00030-3
SSID	ssj0000816957
Score	2.3474853
Snippet	End-to-end radio communication needs to be optimized against noisy channel conditions and other distortion effects. This paper presents a novel concept, a set...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	82181
SubjectTerms	Adaptation models Autoencoders Coding Computer architecture Encoding Feasibility studies Machine learning Network design Neural networks Quadrature amplitude modulation quantum autoencoder Quantum circuit Quantum machine learning Radio communication Radio communications Radio signals Random noise Receivers Receivers & amplifiers Symbols Transceivers
SummonAdditionalLinks	– databaseName: IEEE Xplore dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Na9wwEB2a0ENySL8SuklafMgxSi1La0nH7bJLT0tbEshNWNIYAu267Nr9_R3JyrIhtNCLvxhjWU_yjCTPewBXRmBjao6MtzwwabxkjeC0kaEW3CmFQSaxCbVa6ft78zUnq6dcGERMP5_hTTxMa_mh80OcKqMebjS5O30AB0qpMVlrN6ESFSTMVGVmITL9NJvP6SVoDFhNbwSFLVXUjN3zPomkP6uqPPsUJ_-yfPWfJXsNJzmQLGYj8m_gBa7fwvEeveA7WH0bqN6GnywpX0Y0itnQd5G6MuCmmO2tIWwLCl6LxTqwvmO0K7434aErnqSPnMLdcnE7_8KyfgLzkvOe6ab1WqBSriYnTd4Qgyq1876ULXXj2pUtp3OjHfkx7Tn3FCxKVbeGO42aizM4XHdrfA8FVXcwZOKmQclGBMd1q7E2UktfBiwnUD3Wq_WZXDxqXPywaZBRGjuCYSMYNoMxgevdTb9Gbo1_m3-OgO1MIzF2ukBI2NzPrMGGN6XE0kcBJpSmccJT40Nd-UjnNoHTiN7e80bgJnD5iL_NvXhrRRW5iwwFSed_ue0CjmIRxzmZSzjsNwN-gJf-d_-w3XxMDfQPi6LidA priority: 102 providerName: IEEE
Title	Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication
URI	https://ieeexplore.ieee.org/document/10981758 https://www.proquest.com/docview/3204789820 https://doaj.org/article/9ea1a04e0c2147e49ab3c314e82c0240
Volume	13
WOSCitedRecordID	wos001489664500010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NS8QwEA0iHvQgfuLqKj14NJo02SY51mXFi4uKgrfQfBQE7cpu69Hf7iTtSkXQi5eUlrRpXiaZSdq8h9CpYr5QGfWYltRhrizHBaOQcJcxaoTwjkexCTGdyqcndduT-gr_hLX0wC1wF8oXtCDcExsUdTxXhWEWnuZlagM_Vxh9IerpTabiGCxppkaioxmiRF3k4zHUCCaE6eicQQyTBgHZniuKjP2dxMqPcTk6m6sttNlFiUnevt02WvHVDtrocQfuouldA6A0rzjKWgaok7ypZ4GX0vl5kvc-ECwSiEyTSeVwPcNwSO4L9zxLvu0N2UOPV5OH8TXuxBGw5ZTWWBallcwLYTLwwODqvBNEGmsJL6GPZoaUFM6VNOCkpKXUQiTIRVYqaqSXlO2j1WpW-QOUCCGcgixm5AQvmDNUltJniktuifNkgNIlTtp2zOFBwOJFxxkEUboFVwdwdQfuAJ193fTWEmf8nv0yNMBX1sB6HS-ALejOFvRftjBAe6H5euUpCeGRHKDhsj1110UXmqWBmEhBBHT4H2UfofVQn3Z1ZohW63njj9Gafa-fF_OTaJ2Q3nxMTuIew0_79uZh
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB1BQaIc-GzFlgI5cMQljr2xfdyuWhVRVoCK1JsV2xOpUtmg3YTfz9hxV4sQlXrJlyaK42dnxnbmPYD3RmBjao6MtzwwabxkjeC0kaEW3CmFQSaxCbVY6MtL8zUnq6dcGERMP5_hUTxMa_mh80OcKqMebjS5O30fHkylrPiYrrWZUokaEmaqMrcQGX-czef0GjQKrKZHggKXKqrGbvmfRNOfdVX--RgnD3P69I5lewZPcihZzEbsn8M9XL6Ax1sEgy9h8W2gmht-sqR9GfEoZkPfRfLKgKtitrWKsC4ofC1OloH1HaNd8b0JV13xVwLJHvw4PbmYn7GsoMC85Lxnumm9FqiUq8lNkz_EoErtvC9lSx25dmXL6dxoR55Me849hYtS1a3hTqPmYh92lt0SX0GhlAqGTNw0KNmI4LhuNdZGaunLgOUEqpt6tT7Ti0eVi2ubhhmlsSMYNoJhMxgT-LC56dfIrnG7-XEEbGMaqbHTBULC5p5mDTa8KSWWPkowoTSNE56aH-rKR0K3CexF9LaeNwI3gcMb_G3ux2srqsheZChMOvjPbe_g0dnFl3N7_mnx-TXsxuKOMzSHsNOvBnwDD_3v_mq9epsa6x95CeW7
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Quantum-Classical+Autoencoder+Architectures+for+End-to-End+Radio+Communication&rft.jtitle=IEEE+access&rft.au=Tabi%2C+Zsolt+I.&rft.au=Bak%C3%B3%2C+Bence&rft.au=Nagy%2C+D%C3%A1niel+T.+R.&rft.au=Vaderna%2C+P%C3%A9ter&rft.date=2025&rft.issn=2169-3536&rft.eissn=2169-3536&rft.volume=13&rft.spage=82181&rft.epage=82192&rft_id=info:doi/10.1109%2FACCESS.2025.3566207&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_ACCESS_2025_3566207
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon