{"id":"https://openalex.org/W7125825957","doi":"https://doi.org/10.48550/arxiv.2601.18047","title":"Laser interferometry as a robust neuromorphic platform for machine learning","display_name":"Laser interferometry as a robust neuromorphic platform for machine learning","publication_year":2026,"publication_date":"2026-01-26","ids":{"openalex":"https://openalex.org/W7125825957","doi":"https://doi.org/10.48550/arxiv.2601.18047"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.18047","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.18047","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.2601.18047","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059093715","display_name":"Amanuel Anteneh","orcid":"https://orcid.org/0000-0003-3715-3783"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anteneh, Amanuel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123929168","display_name":"Kyungeun Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Kyungeun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123964013","display_name":"J. M. Schwarz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schwarz, J. M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091791454","display_name":"Israel Klich","orcid":"https://orcid.org/0000-0002-8979-0170"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klich, Israel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5000891817","display_name":"Olivier Pfister","orcid":"https://orcid.org/0000-0003-3386-9661"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pfister, Olivier","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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9952999949455261,"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":0.9952999949455261,"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/T11804","display_name":"Quantum many-body systems","score":0.0006000000284984708,"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/T10320","display_name":"Neural Networks and Applications","score":0.00039999998989515007,"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/backpropagation","display_name":"Backpropagation","score":0.7113000154495239},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7084000110626221},{"id":"https://openalex.org/keywords/interferometry","display_name":"Interferometry","score":0.6420999765396118},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.5666999816894531},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4625000059604645},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.45159998536109924},{"id":"https://openalex.org/keywords/displacement","display_name":"Displacement (psychology)","score":0.44350001215934753}],"concepts":[{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.7113000154495239},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7084000110626221},{"id":"https://openalex.org/C166689943","wikidata":"https://www.wikidata.org/wiki/Q850283","display_name":"Interferometry","level":2,"score":0.6420999765396118},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5903000235557556},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.5666999816894531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5187000036239624},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4625000059604645},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C107551265","wikidata":"https://www.wikidata.org/wiki/Q1458245","display_name":"Displacement (psychology)","level":2,"score":0.44350001215934753},{"id":"https://openalex.org/C520434653","wikidata":"https://www.wikidata.org/wiki/Q38867","display_name":"Laser","level":2,"score":0.4368000030517578},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.40070000290870667},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.382999986410141},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.33149999380111694},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2745000123977661}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.18047","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.18047","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.2601.18047","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.18047","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,96],"present":[1],"a":[2,32],"method":[3,69],"for":[4,29,47],"implementing":[5],"an":[6,38],"optical":[7,13,94],"neural":[8,33],"network":[9,34],"using":[10],"only":[11],"linear":[12,93],"resources,":[14],"namely":[15],"field":[16],"displacement":[17],"and":[18,58,104],"interferometry":[19],"applied":[20],"to":[21,54,85,108,112],"coherent":[22],"states":[23],"of":[24,40,91,101],"light.":[25],"The":[26],"nonlinearity":[27],"required":[28],"learning":[30],"in":[31],"is":[35],"realized":[36],"via":[37],"encoding":[39],"the":[41,68,92,99,106],"input":[42],"into":[43],"phase":[44],"shifts":[45],"allowing":[46],"far":[48],"more":[49],"straightforward":[50],"experimental":[51],"implementation":[52],"compared":[53],"previous":[55],"proposals":[56],"for,":[57],"demonstrations":[59],"of,":[60],"$\\textit{in":[61,65,71],"situ}$":[62,66,72],"inference.":[63],"Beyond":[64],"inference,":[67],"enables":[70],"training":[73],"by":[74],"utilizing":[75],"established":[76],"techniques":[77],"like":[78],"parameter":[79],"shift":[80],"methods":[81],"or":[82],"physical":[83],"backpropagation":[84],"extract":[86],"gradients":[87],"directly":[88],"from":[89],"measurements":[90],"circuit.":[95],"also":[97],"investigate":[98],"effect":[100],"photon":[102],"losses":[103],"find":[105],"model":[107],"be":[109],"very":[110],"resilient":[111],"these.":[113]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-01-28T00:00:00"}