{"id":"W4406604134","doi":"10.48550/arxiv.2501.10077","title":"Double descent in quantum kernel methods","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Volkswagen Aktiengesellschaft; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Natural Sciences and Engineering Research Council of Canada; European Commission; Bundesministerium für Bildung und Forschung; Compute Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Quantum; Computer science; Descent (aeronautics); Quantum machine learning; Artificial intelligence; Physics; Quantum mechanics; Quantum algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002306118,0.0004666501,0.000799282,0.0005434681,0.0005753946,0.001402942,0.001077725,0.001149261,0.002490382],"category_scores_gemma":[0.01096229,0.0004512428,0.0004850051,0.0005187957,0.002067919,0.002414881,0.001874185,0.001690306,0.000585823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009132134,"about_ca_system_score_gemma":0.0009249066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001834157,"about_ca_topic_score_gemma":0.001602986,"domain_scores_codex":[0.9986493,0.0007227876,0.0000507533,0.0001397057,0.0003245157,0.000113043],"domain_scores_gemma":[0.9969478,0.001764172,0.0001985392,0.0006093042,0.000341484,0.0001387094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009949179,0.00005040806,0.001093285,0.0001052913,0.00005151353,0.0001265075,0.0001416412,0.2813071,0.002613478,0.6805398,0.002629413,0.03124199],"study_design_scores_gemma":[0.000006708016,0.00001636123,0.0001015814,0.000008067965,0.000002571741,0.00001871982,0.00000891264,0.8828592,0.0004064317,0.1158942,0.0006702025,0.000007039124],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03765672,0.0004892665,0.9553785,0.0009265701,0.00009180285,0.00002422153,0.00003982526,0.0003139922,0.005079149],"genre_scores_gemma":[0.8317925,0.000469115,0.1607106,0.0004213094,0.0001149086,0.0001186875,0.00009796349,0.000306471,0.005968311],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002490382,"threshold_uncertainty_score":0.01219606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05400424538659947,"score_gpt":0.3453270724016386,"score_spread":0.2913228270150391,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}