{"id":"https://openalex.org/W7158819527","doi":"https://doi.org/10.48550/arxiv.2604.26435","title":"QYOLO: Lightweight Object Detection via Quantum Inspired Shared Channel Mixing","display_name":"QYOLO: Lightweight Object Detection via Quantum Inspired Shared Channel Mixing","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7158819527","doi":"https://doi.org/10.48550/arxiv.2604.26435"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.26435","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26435","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"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.2604.26435","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134879691","display_name":"Garvit Kumar Mittal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mittal, Garvit Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134893349","display_name":"Sahil Tomar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tomar, Sahil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134892051","display_name":"Sandeep Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Sandeep","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/T10036","display_name":"Advanced Neural Network Applications","score":0.5968000292778015,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.5968000292778015,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.08429999649524689,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.05079999938607216,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.6541000008583069},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5378999710083008},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5131999850273132},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5098999738693237},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.506600022315979},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.486299991607666},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.48539999127388},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.45829999446868896},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.4399999976158142},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4246000051498413}],"concepts":[{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.6541000008583069},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6503000259399414},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5637000203132629},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5378999710083008},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5131999850273132},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5098999738693237},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.506600022315979},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.486299991607666},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.48539999127388},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.4399999976158142},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4246000051498413},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.36910000443458557},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.3589000105857849},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C48105269","wikidata":"https://www.wikidata.org/wiki/Q1141160","display_name":"Header","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C2776196297","wikidata":"https://www.wikidata.org/wiki/Q17138781","display_name":"Twist","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C199668693","wikidata":"https://www.wikidata.org/wiki/Q1550329","display_name":"Collision detection","level":3,"score":0.2662000060081482},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2619999945163727},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C39394851","wikidata":"https://www.wikidata.org/wiki/Q921594","display_name":"Inter frame","level":4,"score":0.2603999972343445},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C123745756","wikidata":"https://www.wikidata.org/wiki/Q1665949","display_name":"Interconnection","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C62064638","wikidata":"https://www.wikidata.org/wiki/Q553878","display_name":"Design for manufacturability","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C49289754","wikidata":"https://www.wikidata.org/wiki/Q2267081","display_name":"Side channel attack","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.26435","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26435","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.26435","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26435","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"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":{"The":[0,90,120],"rapid":[1],"advancement":[2],"of":[3,23,47,195],"object":[4],"detection":[5,123],"architectures":[6],"has":[7],"positioned":[8],"single":[9],"stage":[10],"detectors":[11],"as":[12],"the":[13,31,72,132,193,200],"dominant":[14],"solution":[15],"for":[16],"real-time":[17],"visual":[18],"perception.":[19],"A":[20],"primary":[21],"source":[22],"computational":[24],"overhead":[25],"in":[26,30,142],"these":[27],"models":[28],"lies":[29],"deep":[32],"backbone":[33,75,108,183],"stages,":[34,109],"where":[35],"C2f":[36,76],"bottleneck":[37],"modules":[38,77],"at":[39,78,176,192],"high":[40],"stride":[41],"levels":[42],"accumulate":[43],"a":[44,60,87,98,139],"disproportionate":[45],"share":[46],"parameters":[48,105],"due":[49],"to":[50,146,179,189],"quadratic":[51],"scaling":[52],"with":[53,86,102,152,162,168],"channel":[54,62,95,112],"width.":[55],"This":[56],"work":[57],"introduces":[58],"QYOLO,":[59],"quantum-inspired":[61],"mixing":[63,100],"framework":[64],"that":[65,136],"achieves":[66,138,159],"genuine":[67],"architectural":[68],"compression":[69],"by":[70],"replacing":[71],"two":[73],"deepest":[74],"P4/16":[79],"(512":[80],"channels)":[81,85],"and":[82,122,128,148],"P5/32":[83],"(1024":[84],"compact":[88],"QMixBlock.":[89],"proposed":[91],"block":[92],"performs":[93],"global":[94],"recalibration":[96],"through":[97],"sinusoidal":[99],"mechanism":[101],"shared":[103],"learnable":[104],"across":[106],"both":[107],"enforcing":[110],"consistent":[111],"importance":[113],"without":[114],"requiring":[115],"independent":[116],"per-stage":[117],"parameter":[118,143],"sets.":[119],"neck":[121,185],"head":[124],"remain":[125],"fully":[126],"classical":[127],"unchanged.":[129],"Evaluation":[130],"on":[131],"VisDrone2019":[133],"benchmark":[134],"demonstrates":[135],"QYOLOv8n":[137],"20.2%":[140],"reduction":[141,151,161,191],"count":[144],"(3.01M":[145],"2.40M)":[147],"12.3%":[149],"GFLOPs":[150],"only":[153],"0.4":[154],"pp":[155,164],"mAP@50":[156],"degradation.":[157,165],"QYOLOv8s":[158],"21.8%":[160],"0.1":[163],"When":[166],"combined":[167],"knowledge":[169],"distillation,":[170],"full":[171],"accuracy":[172,197],"parity":[173],"is":[174],"recovered":[175],"no":[177],"cost":[178,194],"compression.":[180],"An":[181],"expanded":[182],"plus":[184],"variant":[186],"achieved":[187],"38":[188],"41%":[190],"greater":[196],"degradation,":[198],"motivating":[199],"backbone-only":[201],"final":[202],"design.":[203]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-01T00:00:00"}
