{"id":"W4297820909","doi":"10.48550/arxiv.2209.05523","title":"Generalization despite overfitting in quantum machine learning models","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Xanadu Quantum Technologies (Canada); Perimeter Institute; University of Waterloo","funders":"","keywords":"Overfitting; Artificial intelligence; Quantum; Generalization; Computer science; Machine learning; Artificial neural network; Mathematics; Physics; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004300529,0.0007444205,0.001163024,0.0009501703,0.0008409419,0.001449034,0.001426484,0.001511159,0.001559741],"category_scores_gemma":[0.02713867,0.0007582848,0.001284106,0.0006376909,0.004075424,0.003923167,0.00303349,0.003711928,0.0002186901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00160678,"about_ca_system_score_gemma":0.0008561641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003586289,"about_ca_topic_score_gemma":0.002280306,"domain_scores_codex":[0.9979546,0.0009787086,0.00009600401,0.000338307,0.0004388801,0.0001934853],"domain_scores_gemma":[0.9908859,0.004851982,0.0008804211,0.002546427,0.0004877452,0.0003475696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009533489,0.00005638503,0.003366143,0.0001228831,0.0001005605,0.0002419064,0.0005262752,0.5069993,0.003143883,0.471061,0.002143106,0.01214327],"study_design_scores_gemma":[0.000007351466,0.0000204291,0.0004668514,0.00001795843,0.000008969403,0.00004670295,0.00002455909,0.7404624,0.000429417,0.2581652,0.0003371107,0.00001303595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2957639,0.000609019,0.6882671,0.003991792,0.00008313612,0.00006405859,0.0002589548,0.0009683943,0.00999367],"genre_scores_gemma":[0.9751043,0.0003286162,0.02183717,0.0004009267,0.00007492885,0.00007100944,0.0001672554,0.0002327544,0.001783052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004300529,"threshold_uncertainty_score":0.0227437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05877136541337432,"score_gpt":0.1894600810102303,"score_spread":0.1306887155968559,"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."}}