{"id":"https://openalex.org/W4414197162","doi":"https://doi.org/10.1109/dac63849.2025.11133272","title":"Towards Training Robustness Against Dynamic Errors in Quantum Machine Learning","display_name":"Towards Training Robustness Against Dynamic Errors in Quantum Machine Learning","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4414197162","doi":"https://doi.org/10.1109/dac63849.2025.11133272"},"language":"en","primary_location":{"id":"doi:10.1109/dac63849.2025.11133272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac63849.2025.11133272","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 62nd ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"},"type":"article","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/A5023765976","display_name":"Shijin Duan","orcid":"https://orcid.org/0000-0002-4317-1489"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Shijin Duan","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038197511","display_name":"Gaowen Liu","orcid":"https://orcid.org/0000-0003-0236-5831"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gaowen Liu","raw_affiliation_strings":["Cisco Research"],"affiliations":[{"raw_affiliation_string":"Cisco Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050613322","display_name":"Charles B. Fleming","orcid":"https://orcid.org/0000-0002-1357-5706"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Charles Fleming","raw_affiliation_strings":["Cisco Research"],"affiliations":[{"raw_affiliation_string":"Cisco Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071146373","display_name":"Ramana Rao Kompella","orcid":"https://orcid.org/0000-0002-7559-8997"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramana Kompella","raw_affiliation_strings":["Cisco Research"],"affiliations":[{"raw_affiliation_string":"Cisco Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041836190","display_name":"Xiaolin Xu","orcid":"https://orcid.org/0000-0002-8203-9878"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Xiaolin Xu","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000834239","display_name":"Shaolei Ren","orcid":"https://orcid.org/0000-0001-9003-4324"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaolei Ren","raw_affiliation_strings":["University of California,Riverside"],"affiliations":[{"raw_affiliation_string":"University of California,Riverside","institution_ids":["https://openalex.org/I103635307"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5023765976"],"corresponding_institution_ids":["https://openalex.org/I87182695"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13432435,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9638000130653381,"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/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9638000130653381,"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/T10320","display_name":"Neural Networks and Applications","score":0.923799991607666,"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":0.9204000234603882,"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/robustness","display_name":"Robustness (evolution)","score":0.6987000107765198},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.5464000105857849},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5059000253677368},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.47530001401901245},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.4729999899864197},{"id":"https://openalex.org/keywords/error-detection-and-correction","display_name":"Error detection and correction","score":0.45980000495910645},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.38589999079704285}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7188000082969666},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6987000107765198},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5059000253677368},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.47530001401901245},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4729999899864197},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.45980000495910645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45840001106262207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42989999055862427},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4075999855995178},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C3018824978","wikidata":"https://www.wikidata.org/wiki/Q2894891","display_name":"Error analysis","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C51003876","wikidata":"https://www.wikidata.org/wiki/Q1536431","display_name":"Quantum error correction","level":4,"score":0.3100000023841858},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C137019171","wikidata":"https://www.wikidata.org/wiki/Q2623817","display_name":"Quantum algorithm","level":3,"score":0.27070000767707825},{"id":"https://openalex.org/C58053490","wikidata":"https://www.wikidata.org/wiki/Q176555","display_name":"Quantum computer","level":3,"score":0.26249998807907104}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac63849.2025.11133272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac63849.2025.11133272","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 62nd ACM/IEEE Design Automation Conference (DAC)","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":25,"referenced_works":["https://openalex.org/W1631356911","https://openalex.org/W1632114991","https://openalex.org/W2055269795","https://openalex.org/W2112796928","https://openalex.org/W2195333992","https://openalex.org/W2561610832","https://openalex.org/W2562526363","https://openalex.org/W2594860211","https://openalex.org/W2781738013","https://openalex.org/W3024586266","https://openalex.org/W3082455048","https://openalex.org/W3094656610","https://openalex.org/W3111162498","https://openalex.org/W3186461170","https://openalex.org/W3207377511","https://openalex.org/W3211386740","https://openalex.org/W3212169067","https://openalex.org/W4249545506","https://openalex.org/W4281632042","https://openalex.org/W4285802213","https://openalex.org/W4288071407","https://openalex.org/W4293025139","https://openalex.org/W4318541662","https://openalex.org/W4321610982","https://openalex.org/W4389672456"],"related_works":[],"abstract_inverted_index":{"Quantum":[0],"machine":[1],"learning,":[2],"crucial":[3],"in":[4,13,26],"the":[5,32],"noisy":[6],"intermediate-scale":[7],"quantum":[8,27,36],"(NISQ)":[9],"era,":[10],"confronts":[11],"challenges":[12],"error":[14,24,42,57,74,101],"mitigation.":[15],"Current":[16],"noise-aware":[17],"training":[18],"(NAT)":[19],"methods":[20,98],"often":[21],"assume":[22],"static":[23],"rates":[25,43,58],"neural":[28],"networks":[29],"(QNNs),":[30],"overlooking":[31],"dynamic":[33],"nature":[34],"of":[35],"noise.":[37],"Our":[38],"work":[39],"highlights":[40],"how":[41],"fluctuate":[44],"over":[45],"time":[46],"and":[47,72],"across":[48,99],"different":[49],"qubits,":[50],"affecting":[51],"QNN":[52],"performance":[53,94],"even":[54],"when":[55],"overall":[56],"are":[59],"similar.":[60],"We":[61],"introduce":[62],"a":[63,77],"novel":[64],"NAT":[65,97],"strategy":[66,88],"that":[67],"dynamically":[68],"adjusts":[69],"to":[70,81],"standard":[71],"fatal":[73,83],"conditions,":[75],"incorporating":[76],"low-complexity":[78],"search":[79],"method":[80],"identify":[82],"errors":[84],"during":[85],"optimization.":[86],"This":[87],"significantly":[89],"improves":[90],"robustness,":[91],"maintaining":[92],"competitive":[93],"with":[95],"leading":[96],"varying":[100],"scenarios.":[102]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
