{"id":"https://openalex.org/W7220985901","doi":"https://doi.org/10.48550/arxiv.2610.09141","title":"A Cognitive-Aware QML-CRL Framework for Detecting Affinity and Romance-Investment Fraud","display_name":"A Cognitive-Aware QML-CRL Framework for Detecting Affinity and Romance-Investment Fraud","publication_year":2026,"publication_date":"2026-10-06","ids":{"openalex":"https://openalex.org/W7220985901","doi":"https://doi.org/10.48550/arxiv.2610.09141"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.09141","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.09141","pdf_url":"https://arxiv.org/pdf/2610.09141","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.09141","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061466367","display_name":"Bibhas Adhikari","orcid":"https://orcid.org/0000-0002-9739-0329","observed_orcids":["https://orcid.org/0000-0002-9739-0329"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adhikari, Bibhas","raw_affiliation_strings":["Fujitsu Research of America, Inc. , Santa Clara , CA , USA,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research of America, Inc. , Santa Clara , CA , USA,","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103983864","display_name":"Ramya Srinivasan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210094759","display_name":"Fujitsu (United States)","ror":"https://ror.org/0073whr05","country_code":"US","type":"company","lineage":["https://openalex.org/I2252096349","https://openalex.org/I4210094759"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Srinivasan, Ramya","raw_affiliation_strings":["Inc","Security Science Lab , Fujitsu Research of America , Santa Clara , CA 95054"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inc","institution_ids":[]},{"raw_affiliation_string":"Security Science Lab , Fujitsu Research of America , Santa Clara , CA 95054","institution_ids":["https://openalex.org/I4210094759"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9667999744415283,"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":0.9667999744415283,"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":0.0038999998942017555,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.0032999999821186066,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/cognitive-bias","display_name":"cognitive biases","score":0.6869999766349792},{"id":"https://openalex.org/keywords/quantum-classical-hybrid-models","display_name":"quantum-classical hybrid models","score":0.7379999756813049},{"id":"https://openalex.org/keywords/reframing","display_name":"reframing","score":0.550000011920929},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"reinforcement learning","score":0.6589999794960022},{"id":"https://openalex.org/keywords/optimal-stopping","display_name":"optimal stopping","score":0.8769999742507935},{"id":"https://openalex.org/keywords/fraud-detection","display_name":"fraud detection","score":0.5849999785423279}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6703000068664551},{"id":"https://openalex.org/C203087015","wikidata":"https://www.wikidata.org/wiki/Q378201","display_name":"Qubit","level":3,"score":0.6467000246047974},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6420000195503235},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.5875999927520752},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5616999864578247},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.49869999289512634},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4514999985694885},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3824999928474426},{"id":"https://openalex.org/C121040770","wikidata":"https://www.wikidata.org/wiki/Q215675","display_name":"Quantum entanglement","level":3,"score":0.37610000371932983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36230000853538513},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28859999775886536},{"id":"https://openalex.org/C161407221","wikidata":"https://www.wikidata.org/wiki/Q4382939","display_name":"Cognitive model","level":3,"score":0.25699999928474426},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C58053490","wikidata":"https://www.wikidata.org/wiki/Q176555","display_name":"Quantum computer","level":3,"score":0.25609999895095825}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.09141","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.09141","pdf_url":"https://arxiv.org/pdf/2610.09141","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.09141","doi":"https://doi.org/10.48550/arxiv.2610.09141","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.09141","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,"listed_in":[],"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-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.09141","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.09141","pdf_url":"https://arxiv.org/pdf/2610.09141","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7220985901.pdf","grobid_xml":"https://content.openalex.org/works/W7220985901.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7134244915","https://openalex.org/W4409019859","https://openalex.org/W4416332498","https://openalex.org/W7116877011","https://openalex.org/W7203583571","https://openalex.org/W7170765933","https://openalex.org/W6910625724","https://openalex.org/W7222198251","https://openalex.org/W7134867593","https://openalex.org/W7162032849"],"abstract_inverted_index":{"We":[0,98],"present":[1],"a":[2,18,38,45,60,67,78],"hybrid":[3],"quantum-classical":[4],"framework":[5],"that":[6,63,82,106],"detects":[7],"affinity":[8],"and":[9,59,113],"romance-investment":[10],"fraud":[11,30],"by":[12,34,85],"modelling":[13],"the":[14,49,53,90,100],"cognitive":[15,25],"biases":[16,26],"in":[17,37],"manipulative":[19,57],"conversation.":[20],"In":[21],"our":[22],"proposed":[23],"framework,":[24],"central":[27],"to":[28,52,88],"this":[29],"class":[31],"are":[32,74],"carried":[33],"dedicated":[35],"qubits":[36],"structured":[39],"parameterized":[40],"quantum":[41],"circuit,":[42],"together":[43],"with":[44,77],"frame":[46],"qubit":[47,62],"makes":[48],"encoding":[50],"sensitive":[51],"temporal":[54],"order":[55],"of":[56],"reframing,":[58],"narrative":[61],"aggregates":[64],"co-occurrence":[65],"through":[66],"trainable":[68],"entanglement":[69],"layer.":[70],"The":[71],"circuit":[72],"parameters":[73],"trained":[75],"jointly":[76],"classical":[79],"reinforcement-learning":[80],"agent":[81],"decides,":[83],"turn":[84],"turn,":[86],"whether":[87],"flag":[89],"conversation,":[91],"modeled":[92],"as":[93],"an":[94],"optimal":[95],"stopping":[96],"problem.":[97],"evaluate":[99],"model's":[101],"performance":[102],"on":[103],"synthetic":[104],"conversations":[105],"include":[107],"hard":[108],"negatives,":[109],"legitimate":[110,114],"but":[111,115],"urgent,":[112],"pushy":[116],"sales":[117],"conversations.":[118]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-10-09T00:00:00"}