Hybrid Classical-Quantum Autoencoder for Anomaly Detection

We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE) and a parametrized quantum circuit (PQC) that is inserted into its bottleneck. The PQC augments the latent space, on which a standard outlier detection method is applied to search for an...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org
Hauptverfasser: Sakhnenko, Alona, O'Meara, Corey, Ghosh, Kumar J B, Mendl, Christian B, Cortiana, Giorgio, Bernabé-Moreno, Juan
Format: Paper
Sprache:Englisch
Veröffentlicht: Ithaca Cornell University Library, arXiv.org 16.12.2021
Schlagworte:
Anomalies
Circuits
Data analysis
Data points
Data search
Datasets
Outliers (statistics)
Power plants
Predictive maintenance
ISSN:2331-8422
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE) and a parametrized quantum circuit (PQC) that is inserted into its bottleneck. The PQC augments the latent space, on which a standard outlier detection method is applied to search for anomalous data points within a classical dataset. Using this model and applying it to both standard benchmarking datasets, and a specific use-case dataset which relates to predictive maintenance of gas power plants, we show that the addition of the PQC leads to a performance enhancement in terms of precision, recall, and F1 score. Furthermore, we probe different PQC Ans\"atze and analyse which PQC features make them effective for this task.
AbstractList We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE) and a parametrized quantum circuit (PQC) that is inserted into its bottleneck. The PQC augments the latent space, on which a standard outlier detection method is applied to search for anomalous data points within a classical dataset. Using this model and applying it to both standard benchmarking datasets, and a specific use-case dataset which relates to predictive maintenance of gas power plants, we show that the addition of the PQC leads to a performance enhancement in terms of precision, recall, and F1 score. Furthermore, we probe different PQC Ans\"atze and analyse which PQC features make them effective for this task.
Author Sakhnenko, Alona
Cortiana, Giorgio
Mendl, Christian B
Bernabé-Moreno, Juan
Ghosh, Kumar J B
O'Meara, Corey
Author_xml – sequence: 1
  givenname: Alona
  surname: Sakhnenko
  fullname: Sakhnenko, Alona
– sequence: 2
  givenname: Corey
  surname: O'Meara
  fullname: O'Meara, Corey
– sequence: 3
  givenname: Kumar
  surname: Ghosh
  middlename: J B
  fullname: Ghosh, Kumar J B
– sequence: 4
  givenname: Christian
  surname: Mendl
  middlename: B
  fullname: Mendl, Christian B
– sequence: 5
  givenname: Giorgio
  surname: Cortiana
  fullname: Cortiana, Giorgio
– sequence: 6
  givenname: Juan
  surname: Bernabé-Moreno
  fullname: Bernabé-Moreno, Juan
BookMark eNotjVtLwzAYQIM4cG77Ab4VfG7NlzSX-lbqZcJAhL2PL2kCHV2iSSvu3zvQpwPn4Zxbch1icITcAa1qLQR9wPQzfFcMgFVUa9lckSXjHEpdM3ZDNjkfKaVMKiYEX5LH7dmkoS-6EXMeLI7lx4xhmk9FO0_RBRt7lwofU9GGeMLxXDy5ydlpiGFNFh7H7Db_XJH9y_O-25a799e3rt2VKJgqOaPcouh1gwpMLUBY01uppHdQX5xn6EGiQO-1NgacqE3jQAnoUXMDfEXu_7KfKX7NLk-HY5xTuBwPTAJQoBoU_wVHx0qs
ContentType Paper
Copyright 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID 8FE
8FG
ABJCF
ABUWG
AFKRA
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
HCIFZ
L6V
M7S
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
DOI 10.48550/arxiv.2112.08869
DatabaseName ProQuest SciTech Collection
ProQuest Technology Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni Edition)
ProQuest Central UK/Ireland
ProQuest Central Essentials
ProQuest Central
ProQuest Technology Collection
ProQuest One Community College
ProQuest Central Korea
SciTech Premium Collection
ProQuest Engineering Collection
Engineering Database
ProQuest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
Engineering Collection
DatabaseTitle Publicly Available Content Database
Engineering Database
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest Engineering Collection
ProQuest One Academic UKI Edition
ProQuest Central Korea
Materials Science & Engineering Collection
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
Engineering Collection
DatabaseTitleList Publicly Available Content Database
Database_xml – sequence: 1
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Physics
EISSN 2331-8422
Genre Working Paper/Pre-Print
GroupedDBID 8FE
8FG
ABJCF
ABUWG
AFKRA
ALMA_UNASSIGNED_HOLDINGS
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
FRJ
HCIFZ
L6V
M7S
M~E
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
ID FETCH-LOGICAL-a527-3203ca5d89a71b4515cbdc676fe149a7f2af16a5aff88bb1e54b9e1751da83b13
IEDL.DBID BENPR
IngestDate Mon Jun 30 09:37:28 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a527-3203ca5d89a71b4515cbdc676fe149a7f2af16a5aff88bb1e54b9e1751da83b13
Notes SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
OpenAccessLink https://www.proquest.com/docview/2611010817?pq-origsite=%requestingapplication%
PQID 2611010817
PQPubID 2050157
ParticipantIDs proquest_journals_2611010817
PublicationCentury 2000
PublicationDate 20211216
PublicationDateYYYYMMDD 2021-12-16
PublicationDate_xml – month: 12
  year: 2021
  text: 20211216
  day: 16
PublicationDecade 2020
PublicationPlace Ithaca
PublicationPlace_xml – name: Ithaca
PublicationTitle arXiv.org
PublicationYear 2021
Publisher Cornell University Library, arXiv.org
Publisher_xml – name: Cornell University Library, arXiv.org
SSID ssj0002672553
Score 1.7794076
SecondaryResourceType preprint
Snippet We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE) and a parametrized quantum circuit (PQC) that...
SourceID proquest
SourceType Aggregation Database
SubjectTerms Anomalies
Circuits
Data analysis
Data points
Data search
Datasets
Outliers (statistics)
Power plants
Predictive maintenance
Title Hybrid Classical-Quantum Autoencoder for Anomaly Detection
URI https://www.proquest.com/docview/2611010817
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwELagBYmJt3hWGVjdNi_HZkEFWpWBKkCHMlV-SpVoUvKo6L_nHFIYkFgYPFj2YN0558_nL_chdKU8AKHCJ9h0qcKBkC6GxjEgB6MYkZz6qhKbiEYjOpmwuE645TWtch0Tq0CtUmlz5B1A-rB74ACLbhbv2KpG2dfVWkJjEzVtpbKggZq3_VH8_J1l8UgEmNn_es6sind1ePYxW7ZhuteGL4ywX0G4OlkGu_9d0x5qxnyhs320oZMDtF0xOmV-iK6HK_szllOpXlpP4KcSrFjOnV5ZpLZ8pdKZA5DV6SXpnL-tnHtdVLys5AiNB_3x3RDXQgmYhx7ECK_rSx4qynjkigAQihRKkogYDfcfHhmPG5fwkBtDqRCuDgPBNOAGV4ErhOsfo0aSJvoEOQqMxbtdEWhFA64NDbUQPpOEBcwYFp2ii7UlpvVmz6c_Zjj7e_gc7XiWEuJ62CUXqFFkpb5EW3JZzPKsVfuuZemXL9CLHx7j10-FZ6fx
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LTwIxEJ4gavTkO77dgx6LbHe325oYY0QCUQgmHPBE-kxIZMEFVH6U_9HZFfRg4s2Dh542aboz38x80047AKeGIglVASOuzA0JlfYJDkmQOTgjmJY8MHmzibjZ5J2OaBXgfX4XJiurnPvE3FGbgc72yM-R6SN6MIDFV8NnknWNyk5X5y00PmFxZ6evmLKNLusV1O8ZpdXb9k2NzLoKEBlRNChaDrSMDBcy9lWI4Vwro1nMnMVkQcaOSuczGUnnOFfKt1GohMUg6xtct_IDnHYBFkPEOi_CYqveaD1-bepQFiNFDz5PT_O3ws5l-tZ7KWGaRUto0Ez88Pl5IKuu_TMRrOOvy6FNN6Bgk01YzutV9WgLLmrT7KqZl_f0zHBGHiaIkUnfu56MB9njnMamHhJy7zoZ9OXT1KvYcV51lmxD-y9WuwPFZJDYXfAM6kaWyyq0hofSOh5ZpQKhmQiFcyLeg8O54LszUx51v6W-__vnE1iptRv33ft68-4AVmlW_OJT4rNDKI7TiT2CJf0y7o3S4xlsPOj-sZY-AFE9Al4
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=Hybrid+Classical-Quantum+Autoencoder+for+Anomaly+Detection&rft.jtitle=arXiv.org&rft.au=Sakhnenko%2C+Alona&rft.au=O%27Meara%2C+Corey&rft.au=Ghosh%2C+Kumar+J+B&rft.au=Mendl%2C+Christian+B&rft.date=2021-12-16&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422&rft_id=info:doi/10.48550%2Farxiv.2112.08869