{"id":"https://openalex.org/W3201525770","doi":"https://doi.org/10.1109/iccad51958.2021.9643516","title":"Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)","display_name":"Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W3201525770","doi":"https://doi.org/10.1109/iccad51958.2021.9643516","mag":"3201525770"},"language":"en","primary_location":{"id":"doi:10.1109/iccad51958.2021.9643516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643516","pdf_url":null,"source":{"id":"https://openalex.org/S4363608354","display_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006138748","display_name":"Mahabubul Alam","orcid":"https://orcid.org/0000-0003-1441-2623"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mahabubul Alam","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, Penn State University,University Park","School of Electrical Engineering and Computer Science, Penn State University, University Park"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Penn State University,University Park","institution_ids":["https://openalex.org/I130769515"]},{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Penn State University, University Park","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013754781","display_name":"Satwik Kundu","orcid":"https://orcid.org/0000-0002-2140-6486"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Satwik Kundu","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, Penn State University,University Park","School of Electrical Engineering and Computer Science, Penn State University, University Park"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Penn State University,University Park","institution_ids":["https://openalex.org/I130769515"]},{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Penn State University, University Park","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084170591","display_name":"Rasit Onur Topaloglu","orcid":"https://orcid.org/0000-0001-8759-6959"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rasit Onur Topaloglu","raw_affiliation_strings":["IBM Corporation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Corporation","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085567454","display_name":"Swaroop Ghosh","orcid":"https://orcid.org/0000-0001-8753-490X"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Swaroop Ghosh","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, Penn State University,University Park","School of Electrical Engineering and Computer Science, Penn State University, University Park"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Penn State University,University Park","institution_ids":["https://openalex.org/I130769515"]},{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Penn State University, University Park","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5006138748"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":3.2989,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.93770795,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.991100013256073,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10020","display_name":"Quantum Information and Cryptography","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7137678265571594},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.688088059425354},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6836874485015869},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6796890497207642},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6155925989151001},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5822607278823853},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.532572329044342},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48250746726989746},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46237680315971375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4342954158782959},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4167347550392151},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.25038760900497437}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7137678265571594},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.688088059425354},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6836874485015869},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6796890497207642},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6155925989151001},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5822607278823853},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.532572329044342},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48250746726989746},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46237680315971375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4342954158782959},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4167347550392151},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25038760900497437}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccad51958.2021.9643516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643516","pdf_url":null,"source":{"id":"https://openalex.org/S4363608354","display_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G8815606771","display_name":null,"funder_award_id":"CNS-1722557,CCF-1718474,OIA-2040667,DGE-1723687,DGE-1821766","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1522301498","https://openalex.org/W1542886316","https://openalex.org/W1631356911","https://openalex.org/W2007339694","https://openalex.org/W2096733369","https://openalex.org/W2136655611","https://openalex.org/W2145287260","https://openalex.org/W2146502635","https://openalex.org/W2211722331","https://openalex.org/W2257937122","https://openalex.org/W2555618208","https://openalex.org/W2581082771","https://openalex.org/W2618530766","https://openalex.org/W2666784499","https://openalex.org/W2750384547","https://openalex.org/W2755255888","https://openalex.org/W2764347725","https://openalex.org/W2788945937","https://openalex.org/W2792946961","https://openalex.org/W2796293949","https://openalex.org/W2797105324","https://openalex.org/W2798434869","https://openalex.org/W2887925010","https://openalex.org/W2896712926","https://openalex.org/W2901859177","https://openalex.org/W2903221501","https://openalex.org/W2945680873","https://openalex.org/W2953384591","https://openalex.org/W2954939433","https://openalex.org/W2970971581","https://openalex.org/W2971805687","https://openalex.org/W2982169647","https://openalex.org/W2988221611","https://openalex.org/W2995742898","https://openalex.org/W2998175747","https://openalex.org/W2998932335","https://openalex.org/W3007475506","https://openalex.org/W3015189332","https://openalex.org/W3027746348","https://openalex.org/W3037607219","https://openalex.org/W3075559820","https://openalex.org/W3098400458","https://openalex.org/W3098599423","https://openalex.org/W3098662938","https://openalex.org/W3099206234","https://openalex.org/W3101427288","https://openalex.org/W3101479050","https://openalex.org/W3105380624","https://openalex.org/W3108277684","https://openalex.org/W3124579796","https://openalex.org/W3125913056","https://openalex.org/W3129458892","https://openalex.org/W3132743969","https://openalex.org/W3146428203","https://openalex.org/W3152025936","https://openalex.org/W3164032799","https://openalex.org/W3173713405","https://openalex.org/W3200507677","https://openalex.org/W4287905360","https://openalex.org/W4289606390","https://openalex.org/W4295312788","https://openalex.org/W4300988299","https://openalex.org/W6681435938","https://openalex.org/W6713134421","https://openalex.org/W6790242622"],"related_works":["https://openalex.org/W281791438","https://openalex.org/W2911497689","https://openalex.org/W2952813363","https://openalex.org/W2963346891","https://openalex.org/W4360783045","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W3010730661","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Image":[0],"classification":[1,150],"is":[2],"a":[3,137,159,168,207,218],"major":[4],"application":[5],"domain":[6],"for":[7,105,148,170,179],"conventional":[8],"deep":[9],"learning":[10,14],"(DL).":[11],"Quantum":[12],"machine":[13],"(QML)":[15],"has":[16],"the":[17,39,48,76,79,132],"potential":[18,197],"to":[19,35,46,99,135,210],"revolutionize":[20],"image":[21,27,40,106,112,149,180],"classification.":[22,107],"In":[23],"any":[24],"typical":[25],"DL-based":[26],"classification,":[28],"we":[29,109,166],"use":[30,131],"convolutional":[31],"neural":[32,83],"network":[33,44,84],"(CNN)":[34],"extract":[36,72,111],"features":[37,74,113,134],"from":[38,75],"and":[41,94,130,155,175,199,212],"multi-layer":[42],"perceptron":[43],"(MLP)":[45],"create":[47,88,100,211],"actual":[49],"decision":[50,90],"boundaries.":[51,91],"QML":[52,103],"models":[53,86,147,216],"can":[54,71,87,96,110],"be":[55,97],"useful":[56],"in":[57,173],"both":[58],"of":[59,183,201,220],"these":[60,202,214],"tasks.":[61],"On":[62,78],"one":[63],"hand,":[64,81],"convolution":[65],"with":[66,217],"parameterized":[67],"quantum":[68,82],"circuits":[69],"(Quanvolution)":[70],"rich":[73],"images.":[77],"other":[80],"(QNN)":[85],"complex":[89],"Therefore,":[92],"Quanvolution":[93,174],"QNN":[95],"used":[98],"an":[101],"end-to-end":[102],"model":[104],"Alternatively,":[108],"separately":[114],"using":[115,158,186],"classical":[116,160],"dimension":[117,156,184],"reduction":[118,157,185],"techniques":[119],"such":[120,189],"as,":[121,190],"Principal":[122],"Components":[123],"Analysis":[124],"(PCA)":[125],"or":[126],"Convolutional":[127],"Autoen-coder":[128],"(CAE)":[129],"extracted":[133],"train":[136],"QNN.":[138,164],"We":[139,192,204],"review":[140],"two":[141],"proposals":[142],"on":[143],"quantum-classical":[144],"hybrid":[145,215],"ML":[146],"namely,":[151],"Quanvolutional":[152],"Neural":[153],"Network":[154],"algorithm":[161],"followed":[162],"by":[163],"Particularly,":[165],"make":[167],"case":[169],"trainable":[171],"filters":[172],"CAE-based":[176],"feature":[177],"extraction":[178],"datasets":[181],"(instead":[182],"linear":[187],"transformations":[188],"PCA).":[191],"discuss":[193],"various":[194],"design":[195,221],"choices,":[196],"opportunities,":[198],"drawbacks":[200],"models.":[203],"also":[205],"release":[206],"Python-based":[208],"framework":[209],"explore":[213],"variety":[219],"choices.":[222]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4}],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-10T00:00:00"}