{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005350064,0.0003353548,0.0003445475,0.0005133845,0.00035432,0.0001594421,0.001622417,0.0001704577,0.00002607951],"category_scores_gemma":[0.00003565934,0.0004084541,0.0001718456,0.00103997,0.00003762445,0.0003006735,0.003947434,0.001503845,0.000006395413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002590753,"about_ca_system_score_gemma":0.0001530618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007057145,"about_ca_topic_score_gemma":0.00004635309,"domain_scores_codex":[0.9974506,0.0004388408,0.0002810328,0.001225731,0.0001560812,0.0004477422],"domain_scores_gemma":[0.9986428,0.0001142009,0.0003196244,0.0007538621,0.00006183433,0.0001076972],"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":[0.000007526571,0.000043888,0.00297567,0.00003637762,0.0000181152,0.0002473281,0.0006115587,0.9170616,0.0000155462,0.07790035,0.00001146951,0.001070536],"study_design_scores_gemma":[0.0003352946,0.00004791805,0.0004425143,0.0000708796,0.00001160593,0.000006132444,0.00003236786,0.9480711,0.00001059858,0.05022728,0.0003247923,0.0004194753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4444743,0.000147748,0.554197,0.00006256329,0.0004288499,0.0001343635,0.000005389694,0.0002487223,0.0003011045],"genre_scores_gemma":[0.9940225,0.0001525017,0.005156186,0.00009701146,0.00007435268,0.000001220256,0.00004389628,0.00002967679,0.0004226007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5495483,"threshold_uncertainty_score":0.9998367,"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."}}