{"id":"https://openalex.org/W7162126453","doi":"https://doi.org/10.48550/arxiv.2605.21916","title":"A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction","display_name":"A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162126453","doi":"https://doi.org/10.48550/arxiv.2605.21916"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21916","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21916","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.2605.21916","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136757971","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/A5136804609","display_name":"M. Murali Karthick","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karthick, M. Murali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136728150","display_name":"Simeon Kandan Sonar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sonar, Simeon Kandan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136793258","display_name":"Vivek Chaturvedi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chaturvedi, Vivek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136804773","display_name":"Muhammad Shafique","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shafique, Muhammad","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/T11273","display_name":"Advanced Graph Neural Networks","score":0.8478999733924866,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.8478999733924866,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.02280000038444996,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"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/T13283","display_name":"Mental Health Research Topics","score":0.017899999395012856,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6933000087738037},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6195999979972839},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5630000233650208},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5254999995231628},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4334000051021576},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4187999963760376},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.39660000801086426},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.38999998569488525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.71670001745224},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6933000087738037},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6195999979972839},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5684999823570251},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5630000233650208},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5254999995231628},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4334000051021576},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4187999963760376},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35530000925064087},{"id":"https://openalex.org/C180205008","wikidata":"https://www.wikidata.org/wiki/Q159190","display_name":"Amplitude","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3312000036239624},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C148067565","wikidata":"https://www.wikidata.org/wiki/Q3323718","display_name":"Moral graph","level":5,"score":0.303600013256073},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.30300000309944153},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C123757187","wikidata":"https://www.wikidata.org/wiki/Q9195957","display_name":"Network dynamics","level":2,"score":0.26179999113082886}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21916","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21916","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.2605.21916","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21916","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Dynamic":[0],"link":[1,229],"prediction":[2,230],"is":[3,162,197],"important":[4],"for":[5,227],"modeling":[6],"evolving":[7,31],"interactions":[8,33],"in":[9,34],"social,":[10],"communication,":[11],"financial,":[12],"and":[13,81,107,131,151,185],"transportation":[14],"networks.":[15],"Classical":[16],"temporal":[17,58,105,225],"graph":[18],"models":[19],"capture":[20],"changes":[21],"over":[22,182,190],"time,":[23],"but":[24],"they":[25],"may":[26],"struggle":[27],"to":[28,88,98,129,139,164],"represent":[29],"rapidly":[30],"node-edge":[32],"large":[35],"dynamic":[36,228],"graphs.":[37],"We":[38],"propose":[39],"A2QTGN":[40,118],"(Adaptive":[41],"Amplitude":[42],"Quantum-Integrated":[43],"Temporal":[44,63,114],"Graph":[45,64,115],"Network),":[46],"a":[47,57,62,169,211],"hybrid":[48],"quantum-classical":[49],"framework":[50,97],"that":[51,157,218],"introduces":[52],"adaptive":[53,159,219],"amplitude":[54,85],"encoding":[55],"as":[56],"embedding":[59,161],"layer":[60],"within":[61],"Network.":[65],"Unlike":[66],"fixed":[67],"quantum":[68,79,110,160,207,220,236],"embeddings,":[69],"the":[70,89,96,123,142,146,158,165],"proposed":[71],"module":[72],"maps":[73],"temporally":[74],"varying":[75],"node":[76,101],"features":[77],"into":[78],"states":[80],"selectively":[82],"refreshes":[83],"their":[84],"representations":[86,226],"according":[87],"magnitude":[90],"of":[91,127,137,172],"feature":[92],"change.":[93],"This":[94],"allows":[95],"preserve":[99],"stable":[100],"information,":[102],"emphasize":[103],"meaningful":[104],"variations,":[106],"reduce":[108],"redundant":[109],"re-encoding.":[111],"Across":[112],"five":[113],"Benchmark":[116],"datasets,":[117],"achieves":[119],"test":[120,176],"area":[121],"under":[122,234],"curve":[124],"(AUC)":[125],"values":[126,136],"up":[128,138],"0.9957":[130],"mean":[132],"reciprocal":[133],"rank":[134],"(MRR)":[135],"0.7832,":[140],"including":[141],"highest":[143],"MRR":[144],"among":[145],"compared":[147],"baselines":[148],"on":[149,168,204],"tgbl-review":[150],"tgbl-flight.":[152],"Ablation":[153],"results":[154,216],"further":[155],"show":[156,217],"central":[163],"model":[166,196],"performance:":[167],"25k-event":[170],"subset":[171],"tgbl-wiki,":[173],"it":[174],"improves":[175],"accuracy":[177],"by":[178,186,210],"13.36":[179],"percentage":[180,188],"points":[181,189],"always":[183],"updating":[184],"22.44":[187],"using":[191,200],"no":[192],"updates.":[193],"The":[194,215],"trained":[195],"also":[198],"evaluated":[199],"noisy":[201],"simulations":[202],"based":[203],"an":[205],"IBM":[206],"device,":[208],"followed":[209],"smaller":[212],"real-device":[213],"experiment.":[214],"embeddings":[221],"can":[222],"provide":[223],"effective":[224],"while":[231],"remaining":[232],"executable":[233],"current":[235],"hardware":[237],"constraints.":[238]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-05-23T00:00:00"}
