{"id":"https://openalex.org/W7148448592","doi":"https://doi.org/10.48550/arxiv.2604.00919","title":"Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors","display_name":"Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148448592","doi":"https://doi.org/10.48550/arxiv.2604.00919"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00919","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00919","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.2604.00919","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132746600","display_name":"Gilhan Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Gilhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132818279","display_name":"Daniel K. Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Daniel K.","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/T11804","display_name":"Quantum many-body systems","score":0.45559999346733093,"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"}},"topics":[{"id":"https://openalex.org/T11804","display_name":"Quantum many-body systems","score":0.45559999346733093,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2224999964237213,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.07829999923706055,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.8181999921798706},{"id":"https://openalex.org/keywords/boltzmann-machine","display_name":"Boltzmann machine","score":0.607699990272522},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5163999795913696},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4934999942779541},{"id":"https://openalex.org/keywords/quantum-annealing","display_name":"Quantum annealing","score":0.4090999960899353},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4074000120162964},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.40220001339912415},{"id":"https://openalex.org/keywords/qubit","display_name":"Qubit","score":0.399399995803833},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.39160001277923584}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.8181999921798706},{"id":"https://openalex.org/C192576344","wikidata":"https://www.wikidata.org/wiki/Q194706","display_name":"Boltzmann machine","level":3,"score":0.607699990272522},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5163999795913696},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4934999942779541},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.491100013256073},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48890000581741333},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42419999837875366},{"id":"https://openalex.org/C90408235","wikidata":"https://www.wikidata.org/wiki/Q938141","display_name":"Quantum annealing","level":4,"score":0.4090999960899353},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4074000120162964},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C203087015","wikidata":"https://www.wikidata.org/wiki/Q378201","display_name":"Qubit","level":3,"score":0.399399995803833},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.39160001277923584},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3901999890804291},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.38769999146461487},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.3792000114917755},{"id":"https://openalex.org/C35304006","wikidata":"https://www.wikidata.org/wiki/Q5962","display_name":"Boltzmann constant","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.357699990272522},{"id":"https://openalex.org/C193999330","wikidata":"https://www.wikidata.org/wiki/Q834200","display_name":"Boltzmann distribution","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3361999988555908},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.3158000111579895},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.30730000138282776},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.30320000648498535},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C187192777","wikidata":"https://www.wikidata.org/wiki/Q381699","display_name":"Rejection sampling","level":5,"score":0.28209999203681946},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2567000091075897},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00919","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00919","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.2604.00919","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00919","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":[{"score":0.8976141214370728,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Energy-based":[0],"models":[1,28],"provide":[2],"a":[3,22,60,136,160,218],"natural":[4],"bridge":[5],"between":[6],"statistical":[7],"physics":[8],"and":[9,84,110,127,144,150,155,201,220,228],"machine":[10],"learning":[11,42,83,227],"by":[12,46],"representing":[13],"data":[14],"through":[15],"structured":[16],"energy":[17,119,172],"landscapes.":[18],"Boltzmann":[19,55,69,91],"machines":[20],"are":[21],"particularly":[23],"compelling":[24],"class":[25],"of":[26,50,82,103,205,234],"such":[27],"for":[29,93,107,124,224],"capturing":[30],"complex":[31],"interactions":[32],"among":[33],"latent":[34],"variables,":[35],"but":[36],"their":[37],"use":[38],"in":[39,197,209],"modern":[40],"generative":[41,229],"has":[43],"been":[44],"limited":[45],"the":[47,104,117,164,170,203,232],"classical":[48,236],"intractability":[49],"sampling":[51],"from":[52,193],"general":[53,68],"(non-restricted)":[54],"distributions.":[56],"Here":[57],"we":[58,140],"develop":[59],"quantum-annealing-based":[61],"framework":[62,72],"that":[63,178,187],"enables":[64],"variational":[65],"autoencoders":[66],"with":[67,113,163],"priors.":[70],"The":[71],"employs":[73],"three":[74],"complementary":[75],"annealing":[76,88,97,112,216],"modes":[77],"tailored":[78],"to":[79,132],"different":[80],"stages":[81],"deployment:":[85],"diabatic":[86],"quantum":[87,215],"provides":[89,174],"unbiased":[90],"samples":[92,99,192],"efficient":[94],"training,":[95],"slower":[96],"concentrates":[98],"near":[100],"low-energy":[101],"configurations":[102],"learned":[105,118,171],"prior":[106],"unconditional":[108],"generation,":[109,169],"conditional":[111,125],"external":[114],"fields":[115],"steers":[116],"landscape":[120],"toward":[121],"attribute-specific":[122],"regions":[123],"generation":[126,146],"semantic":[128],"editing.":[129],"Using":[130],"up":[131],"2000":[133],"qubits":[134],"on":[135,147],"D-Wave":[137],"Advantage2":[138],"processor,":[139],"demonstrate":[141,186],"stable":[142],"training":[143],"high-quality":[145],"MNIST,":[148],"Fashion-MNIST,":[149],"CelebA,":[151],"achieving":[152],"faster":[153],"convergence":[154],"lower":[156],"reconstruction":[157,183],"loss":[158],"than":[159],"Gaussian-prior":[161],"VAE":[162],"same":[165],"encoder-decoder":[166],"architecture.":[167],"Beyond":[168],"function":[173],"out-of-distribution":[175],"detection":[176,204],"signals":[177],"add":[179],"discriminative":[180],"power":[181],"beyond":[182,231],"loss.":[184],"We":[185],"these":[188],"scores":[189],"separate":[190],"in-distribution":[191],"held-out":[194],"digit":[195],"classes":[196],"one-class":[198],"MNIST":[199],"experiments":[200],"improve":[202],"market":[206],"regime":[207],"shifts":[208],"financial":[210],"data.":[211],"These":[212],"results":[213],"establish":[214],"as":[217],"practical":[219],"controllable":[221],"physical":[222],"mechanism":[223],"energy-based":[225],"representation":[226],"modeling":[230],"reach":[233],"tractable":[235],"approaches.":[237]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-03T00:00:00"}