{"id":"https://openalex.org/W4416875340","doi":"https://doi.org/10.1109/qce65121.2025.10305","title":"HCVS-Net: Hybrid Continuous-Variable Squeezing Network","display_name":"HCVS-Net: Hybrid Continuous-Variable Squeezing Network","publication_year":2025,"publication_date":"2025-08-30","ids":{"openalex":"https://openalex.org/W4416875340","doi":"https://doi.org/10.1109/qce65121.2025.10305"},"language":null,"primary_location":{"id":"doi:10.1109/qce65121.2025.10305","is_oa":false,"landing_page_url":"https://doi.org/10.1109/qce65121.2025.10305","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Quantum Computing and Engineering (QCE)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100370274","display_name":"Xinyi Li","orcid":"https://orcid.org/0000-0002-3522-9249"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyi Li","raw_affiliation_strings":["Stevens Institute of Technology,Hoboken,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Hoboken,USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101882137","display_name":"Yifeng Peng","orcid":"https://orcid.org/0009-0007-3306-9417"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yifeng Peng","raw_affiliation_strings":["Stevens Institute of Technology,Hoboken,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Hoboken,USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075347539","display_name":"Juntao Chen","orcid":"https://orcid.org/0009-0001-3883-548X"},"institutions":[{"id":"https://openalex.org/I164389053","display_name":"Fordham University","ror":"https://ror.org/03qnxaf80","country_code":"US","type":"education","lineage":["https://openalex.org/I164389053"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juntao Chen","raw_affiliation_strings":["Fordham University,New York,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fordham University,New York,USA","institution_ids":["https://openalex.org/I164389053"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115602710","display_name":"Ying Wang","orcid":"https://orcid.org/0000-0001-6638-8026"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["Stevens Institute of Technology,Hoboken,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Hoboken,USA","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35273055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"115","last_page":"120"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9555000066757202,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9555000066757202,"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":0.011099999770522118,"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":0.009100000374019146,"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/photonics","display_name":"Photonics","score":0.5590000152587891},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.534600019454956},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5002999901771545},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4814999997615814},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.45339998602867126},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4381999969482422},{"id":"https://openalex.org/keywords/brightness","display_name":"Brightness","score":0.43470001220703125},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.43389999866485596},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4311000108718872}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5705999732017517},{"id":"https://openalex.org/C20788544","wikidata":"https://www.wikidata.org/wiki/Q467054","display_name":"Photonics","level":2,"score":0.5590000152587891},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.534600019454956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5310999751091003},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5002999901771545},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4814999997615814},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.45339998602867126},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4381999969482422},{"id":"https://openalex.org/C125245961","wikidata":"https://www.wikidata.org/wiki/Q221656","display_name":"Brightness","level":2,"score":0.43470001220703125},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.43389999866485596},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4311000108718872},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38830000162124634},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.3747999966144562},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.35839998722076416},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3517000079154968},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34929999709129333},{"id":"https://openalex.org/C62869609","wikidata":"https://www.wikidata.org/wiki/Q28137","display_name":"Quadrature (astronomy)","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2969000041484833},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.28769999742507935},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28119999170303345},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C186468114","wikidata":"https://www.wikidata.org/wiki/Q836478","display_name":"Quantum network","level":4,"score":0.259799987077713},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.25679999589920044},{"id":"https://openalex.org/C812465","wikidata":"https://www.wikidata.org/wiki/Q5058375","display_name":"Cellular neural network","level":3,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/qce65121.2025.10305","is_oa":false,"landing_page_url":"https://doi.org/10.1109/qce65121.2025.10305","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Quantum Computing and Engineering (QCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1970948459","https://openalex.org/W2015811642","https://openalex.org/W2112796928","https://openalex.org/W2142739935","https://openalex.org/W2211778065","https://openalex.org/W2808979145","https://openalex.org/W2990961515","https://openalex.org/W3150000293","https://openalex.org/W3211028885","https://openalex.org/W4403420421","https://openalex.org/W4406262055","https://openalex.org/W4406262096","https://openalex.org/W4409363517"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2],"quantum-inspired":[3,66],"machine":[4],"learning":[5,52],"have":[6],"highlighted":[7],"the":[8,44,137,149],"potential":[9],"of":[10,135,145],"using":[11],"non-classical":[12],"effects":[13],"to":[14,128,163],"enhance":[15],"feature":[16,104],"extraction":[17],"and":[18,34,49,125,159],"improve":[19],"robustness.":[20],"In":[21],"particular,":[22],"continuous-variable":[23],"(CV)":[24],"systems":[25],"on":[26,112],"photonic":[27],"platforms":[28],"offer":[29],"operations":[30,68],"such":[31],"as":[32],"single-mode":[33],"two-mode":[35],"squeezing":[36,94,161],"that":[37,63,115,144],"naturally":[38],"encode":[39],"quantum":[40],"correlations.":[41],"Motivated":[42],"by":[43,75],"need":[45],"for":[46],"increasingly":[47],"resilient":[48],"expressive":[50],"deep":[51],"architectures,":[53],"we":[54],"propose":[55],"a":[56,78],"novel":[57],"Hybrid":[58],"Continuous-Variable":[59],"Squeezing":[60],"Network":[61],"(HCVS-Net)":[62],"unifies":[64],"these":[65],"CV":[67,90],"with":[69],"classical":[70,119,157],"convolutional":[71],"neural":[72],"networks":[73],"(CNNs)":[74],"skip-connection.":[76],"Specifically,":[77],"conventional":[79],"CNN":[80],"first":[81],"extracts":[82],"preliminary":[83],"features,":[84],"which":[85],"are":[86],"subsequently":[87],"embedded":[88],"into":[89],"modes":[91],"undergoing":[92],"controllable":[93],"gates,":[95],"emulating":[96],"spontaneous":[97],"parametric":[98],"down-conversion.":[99],"This":[100],"process":[101],"yields":[102],"enriched":[103],"representations":[105],"through":[106],"quadrature":[107],"measurements.":[108],"Our":[109],"experimental":[110],"results":[111],"MNIST":[113],"confirm":[114],"HCVS-Net":[116],"surpasses":[117],"purely":[118],"baselines,":[120],"achieving":[121],"both":[122,156],"competitive":[123],"accuracy":[124,138],"heightened":[126],"resilience":[127],"brightness":[129,133],"distortions.":[130],"Under":[131],"low":[132],"conditions":[134],"0.1,":[136],"is":[139,152],"about":[140],"3%":[141],"higher":[142],"than":[143],"pure":[146],"CNN.":[147],"Furthermore,":[148],"entire":[150],"model":[151],"end-to-end":[153],"differentiable,":[154],"allowing":[155],"parameters":[158,162],"quantum-like":[160],"be":[164],"trained":[165],"jointly":[166],"via":[167],"backpropagation.":[168]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-12-01T00:00:00"}