{"id":"https://openalex.org/W4415449377","doi":"https://doi.org/10.1088/2632-2153/ae1655","title":"Neural network enhanced cross entropy benchmark for monitored circuits","display_name":"Neural network enhanced cross entropy benchmark for monitored circuits","publication_year":2025,"publication_date":"2025-10-22","ids":{"openalex":"https://openalex.org/W4415449377","doi":"https://doi.org/10.1088/2632-2153/ae1655"},"language":"en","primary_location":{"id":"doi:10.1088/2632-2153/ae1655","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ae1655","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ae1655/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ae1655/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068345398","display_name":"Yangrui Hu","orcid":"https://orcid.org/0000-0002-3548-8574"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]},{"id":"https://openalex.org/I4210157032","display_name":"Institute of Mathematical Sciences","ror":"https://ror.org/05e9bn444","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210157032"]}],"countries":["CA","ES"],"is_corresponding":true,"raw_author_name":"Yangrui Hu","raw_affiliation_strings":["Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, People\u2019s Republic of China","Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada"],"raw_orcid":"https://orcid.org/0000-0002-3548-8574","affiliations":[{"raw_affiliation_string":"Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, People\u2019s Republic of China","institution_ids":["https://openalex.org/I4210157032"]},{"raw_affiliation_string":"Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042683074","display_name":"Yi Hong Teoh","orcid":"https://orcid.org/0000-0003-0692-3327"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yi Hong Teoh","raw_affiliation_strings":["Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada"],"raw_orcid":"https://orcid.org/0000-0003-0692-3327","affiliations":[{"raw_affiliation_string":"Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016488881","display_name":"William Witczak\u2010Krempa","orcid":"https://orcid.org/0000-0003-2350-2583"},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"William Witczak-Krempa","raw_affiliation_strings":["Centre de Recherches Math\u00e9matiques, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC H3C 3J7, Canada","D\u00e9partement de Physique, Universit\u00e9 de Montreal, Montreal, QC H3C 3J7, Canada","Institut Courtois, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC H2V 0B3, Canada"],"raw_orcid":"https://orcid.org/0000-0003-2350-2583","affiliations":[{"raw_affiliation_string":"Centre de Recherches Math\u00e9matiques, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC H3C 3J7, Canada","institution_ids":["https://openalex.org/I70931966"]},{"raw_affiliation_string":"D\u00e9partement de Physique, Universit\u00e9 de Montreal, Montreal, QC H3C 3J7, Canada","institution_ids":["https://openalex.org/I70931966"]},{"raw_affiliation_string":"Institut Courtois, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC H2V 0B3, Canada","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091069084","display_name":"Roger G. Melko","orcid":"https://orcid.org/0000-0002-5505-8176"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]},{"id":"https://openalex.org/I183974707","display_name":"Perimeter Institute","ror":"https://ror.org/013m0ej23","country_code":"CA","type":"facility","lineage":["https://openalex.org/I183974707"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Roger G Melko","raw_affiliation_strings":["Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada","Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada"],"raw_orcid":"https://orcid.org/0000-0002-5505-8176","affiliations":[{"raw_affiliation_string":"Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada","institution_ids":["https://openalex.org/I183974707"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5068345398"],"corresponding_institution_ids":["https://openalex.org/I151746483","https://openalex.org/I4210157032"],"apc_list":{"value":1600,"currency":"GBP","value_usd":1962},"apc_paid":{"value":1600,"currency":"GBP","value_usd":1962},"fwci":1.0546,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8226953,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"6","issue":"4","first_page":"045030","last_page":"045030"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9423999786376953,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9423999786376953,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10320","display_name":"Neural Networks and Applications","score":0.9311000108718872,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7182000279426575},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.5411999821662903},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.529699981212616},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5157999992370605},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.48179998993873596},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.46070000529289246},{"id":"https://openalex.org/keywords/electronic-circuit","display_name":"Electronic circuit","score":0.4339999854564667},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.4251999855041504}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7182000279426575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6468999981880188},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.5411999821662903},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.529699981212616},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.48179998993873596},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.4339999854564667},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.4251999855041504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41819998621940613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4065999984741211},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3978999853134155},{"id":"https://openalex.org/C124148022","wikidata":"https://www.wikidata.org/wiki/Q2122210","display_name":"Quantum circuit","level":5,"score":0.3774999976158142},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3707999885082245},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.335999995470047},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33340001106262207},{"id":"https://openalex.org/C58053490","wikidata":"https://www.wikidata.org/wiki/Q176555","display_name":"Quantum computer","level":3,"score":0.32580000162124634},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C192576344","wikidata":"https://www.wikidata.org/wiki/Q194706","display_name":"Boltzmann machine","level":3,"score":0.29030001163482666},{"id":"https://openalex.org/C169699857","wikidata":"https://www.wikidata.org/wiki/Q2122243","display_name":"Quantum information","level":3,"score":0.28949999809265137},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.2822999954223633},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1088/2632-2153/ae1655","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ae1655","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ae1655/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:bca7443198794284bbcb4f8bf0dc535f","is_oa":true,"landing_page_url":"https://doaj.org/article/bca7443198794284bbcb4f8bf0dc535f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning: Science and Technology, Vol 6, Iss 4, p 045030 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1088/2632-2153/ae1655","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ae1655","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ae1655/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320319880","display_name":"Government of Canada","ror":"https://ror.org/010q4q527"},{"id":"https://openalex.org/F4320321716","display_name":"Industry Canada","ror":"https://ror.org/03zp01h17"},{"id":"https://openalex.org/F4320331257","display_name":"Alliance de recherche num\u00e9rique du Canada","ror":"https://ror.org/010r6td27"},{"id":"https://openalex.org/F4320331462","display_name":"Institut P\u00e9rim\u00e8tre de physique th\u00e9orique","ror":"https://ror.org/013m0ej23"},{"id":"https://openalex.org/F4320331472","display_name":"Courtois Foundation","ror":null},{"id":"https://openalex.org/F4320331473","display_name":"Ministry of Colleges and Universities","ror":null},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4415449377.pdf"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W2032610839","https://openalex.org/W2038591556","https://openalex.org/W2091465285","https://openalex.org/W2170858670","https://openalex.org/W2336871513","https://openalex.org/W2415710521","https://openalex.org/W2886483748","https://openalex.org/W2886712022","https://openalex.org/W2888053895","https://openalex.org/W2896854735","https://openalex.org/W2898121159","https://openalex.org/W2913031193","https://openalex.org/W2944084836","https://openalex.org/W2945488351","https://openalex.org/W2949253647","https://openalex.org/W2964199361","https://openalex.org/W2967300259","https://openalex.org/W2969500900","https://openalex.org/W2969894439","https://openalex.org/W2986209554","https://openalex.org/W2994877072","https://openalex.org/W2997957343","https://openalex.org/W3013685828","https://openalex.org/W3016939811","https://openalex.org/W3021177691","https://openalex.org/W3033312235","https://openalex.org/W3035785882","https://openalex.org/W3043723517","https://openalex.org/W3050945359","https://openalex.org/W3097070339","https://openalex.org/W3106722198","https://openalex.org/W3124127657","https://openalex.org/W3134336374","https://openalex.org/W3142599207","https://openalex.org/W3147660836","https://openalex.org/W3165381633","https://openalex.org/W3171663017","https://openalex.org/W3186107346","https://openalex.org/W3199634743","https://openalex.org/W3208869263","https://openalex.org/W3216562638","https://openalex.org/W4211090409","https://openalex.org/W4226066309","https://openalex.org/W4226338262","https://openalex.org/W4281651499","https://openalex.org/W4293171638","https://openalex.org/W4319601507","https://openalex.org/W4321210970","https://openalex.org/W4321491582","https://openalex.org/W4377291932","https://openalex.org/W4379015738","https://openalex.org/W4379878597","https://openalex.org/W4385954927","https://openalex.org/W4391105010","https://openalex.org/W4392682060","https://openalex.org/W4403423003","https://openalex.org/W4404041122"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"We":[1],"explore":[2],"the":[3,35,54,62,118,136,144,149],"interplay":[4],"of":[5,64,117,138,151],"quantum":[6,22,153,164],"computing":[7,154],"and":[8,33,126,155],"machine":[9,100,156],"learning":[10,96,101,157],"to":[11,113,141,158],"advance":[12],"experimental":[13],"protocols":[14],"for":[15,95,121],"observing":[16],"measurement-induced":[17],"phase":[18],"transitions":[19],"(MIPTs)":[20],"in":[21,79,162],"devices.":[23,81],"In":[24,104],"particular,":[25],"we":[26,60,107],"focus":[27],"on":[28],"trapped":[29],"ion":[30],"monitored":[31],"circuits":[32],"apply":[34],"cross":[36,145],"entropy":[37],"benchmark":[38],"recently":[39],"introduced":[40],"by":[41],"Li":[42],"et":[43],"al":[44],"(2023":[45],"Phys.":[46],"Rev.":[47],"Lett.":[48],"130":[49],"220404),":[50],"which":[51,73],"can":[52,133],"mitigate":[53],"post-selection":[55],"problem.":[56],"By":[57],"doing":[58],"so,":[59],"reduce":[61,135],"number":[63,137],"projective":[65,84],"measurements\u2014the":[66],"sample":[67],"complexity\u2014required":[68],"per":[69],"random":[70],"circuit":[71],"realization,":[72],"is":[74],"a":[75,88,98,109,115,122],"critical":[76],"limiting":[77],"resource":[78],"real":[80],"Since":[82],"these":[83],"measurement":[85,119],"outcomes":[86],"form":[87],"classical":[89],"probability":[90],"distribution,":[91],"they":[92],"are":[93],"suitable":[94],"with":[97],"standard":[99],"generative":[102,131],"model.":[103],"this":[105,130],"paper,":[106],"use":[108],"recurrent":[110],"neural":[111],"network":[112],"learn":[114],"representation":[116],"record":[120],"native":[123],"trapped-ion":[124],"MIPT,":[125],"show":[127],"that":[128],"using":[129],"model":[132],"substantially":[134],"measurements":[139],"required":[140],"accurately":[142],"estimate":[143],"entropy.":[146],"This":[147],"illustrates":[148],"potential":[150],"combining":[152],"overcome":[159],"practical":[160],"challenges":[161],"realizing":[163],"experiments.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-24T00:00:00"}