{"id":"https://openalex.org/W7162032849","doi":"https://doi.org/10.48550/arxiv.2605.21164","title":"Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection","display_name":"Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7162032849","doi":"https://doi.org/10.48550/arxiv.2605.21164"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21164","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.21164","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120840753","display_name":"Adam Innan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Innan, Adam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136726068","display_name":"Mansour El Alami","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alami, Mansour El","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136643667","display_name":"Nouhaila Innan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Innan, Nouhaila","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136724729","display_name":"Muhammad Shafique","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shafique, Muhammad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136661774","display_name":"Mohamed Bennai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bennai, Mohamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.4496999979019165,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.4496999979019165,"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/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.21770000457763672,"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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.026100000366568565,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/downstream","display_name":"Downstream (manufacturing)","score":0.6924999952316284},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.6114000082015991},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.570900022983551},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.474700003862381},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.40070000290870667},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.38929998874664307},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.3546999990940094},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.35269999504089355}],"concepts":[{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.6924999952316284},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6801999807357788},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.6114000082015991},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.570900022983551},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.474700003862381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4212000072002411},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.40070000290870667},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38280001282691956},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32670000195503235},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21164","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.21164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21164","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5505620837211609,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Credit":[0],"card":[1],"fraud":[2,74,89,95,190],"detection":[3],"is":[4,70,80],"fundamentally":[5],"challenged":[6],"by":[7,118],"extreme":[8],"class":[9],"imbalance,":[10],"where":[11],"fraudulent":[12],"transactions":[13],"are":[14,102],"rare":[15],"yet":[16],"operationally":[17],"critical.":[18],"This":[19,40],"imbalance":[20],"often":[21],"biases":[22],"supervised":[23],"learners":[24],"toward":[25],"the":[26,58,67,131,154,159,163,182],"legitimate":[27],"class,":[28],"leading":[29],"to":[30,87,140],"high":[31],"overall":[32],"accuracy":[33],"but":[34],"weaker":[35],"fraud-class":[36],"recall":[37],"and":[38,60,79,91,112,120,127,158,178],"F1-score.":[39],"paper":[41],"introduces":[42],"Q-SYNTH,":[43],"a":[44,52,61,141,172],"hybrid":[45,185],"classical--quantum":[46],"generative":[47],"adversarial":[48],"framework":[49],"in":[50,76,167],"which":[51],"parameterized":[53],"quantum":[54,126,186],"circuit":[55],"serves":[56,65],"as":[57,66],"generator":[59],"classical":[62,128,142,160],"neural":[63],"network":[64],"discriminator.":[68],"Q-SYNTH":[69,134,170],"designed":[71],"for":[72,94,188],"minority-class":[73],"synthesis":[75],"tabular":[77],"data":[78],"evaluated":[81],"along":[82],"two":[83],"dimensions:":[84],"statistical":[85],"fidelity":[86,177],"real":[88],"samples":[90,101],"downstream":[92,121,148,165,179],"performance":[93,123,166],"detection.":[96,191],"To":[97],"this":[98],"end,":[99],"generated":[100],"assessed":[103],"using":[104],"distributional":[105,176],"similarity":[106,157],"measures":[107],"based":[108],"on":[109],"Kolmogorov-Smirnov":[110],"statistics":[111],"Wasserstein":[113],"distances,":[114],"real-vs-synthetic":[115],"detectability":[116],"measured":[117],"AUC-ROC,":[119],"classification":[122],"across":[124],"both":[125],"classifiers.":[129],"Under":[130],"reported":[132],"protocol,":[133],"reduces":[135],"marginal":[136],"distribution":[137],"mismatch":[138],"relative":[139],"GAN":[143,161],"baseline":[144],"while":[145],"maintaining":[146],"competitive":[147],"fraud-detection":[149],"performance.":[150],"Although":[151],"SMOTE":[152],"achieves":[153],"strongest":[155],"feature-wise":[156],"attains":[162],"highest":[164],"several":[168],"settings,":[169],"offers":[171],"favorable":[173],"compromise":[174],"between":[175],"performance,":[180],"supporting":[181],"feasibility":[183],"of":[184],"augmentation":[187],"imbalanced":[189]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-22T00:00:00"}
