{"id":"W4389681670","doi":"10.1002/ail2.89","title":"On a quantum inspired approach to train machine learning models","year":2023,"lang":"en","type":"article","venue":"Applied AI Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Computer science; Quantum machine learning; Quantum; Context (archaeology); Artificial intelligence; Field (mathematics); Machine learning; Quantum computer; 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":[0.0009648716,0.0002311882,0.0005166164,0.0004501214,0.0004659785,0.0006781465,0.001143484,0.00102848,0.004115975],"category_scores_gemma":[0.003519391,0.0002958248,0.0004855953,0.0004520749,0.00111128,0.001249856,0.0008895039,0.00166877,0.0004568898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008316684,"about_ca_system_score_gemma":0.0006532067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002160277,"about_ca_topic_score_gemma":0.002215685,"domain_scores_codex":[0.9995869,0.0002278785,0.00001462551,0.00004013224,0.0001033172,0.00002717984],"domain_scores_gemma":[0.9986784,0.0009811881,0.0000542445,0.0001490672,0.00009987134,0.00003722503],"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":[0.00003394119,0.0000493495,0.0002719298,0.00004669996,0.00002973144,0.00003310089,0.00004307062,0.7370867,0.001987869,0.2418262,0.0008962544,0.01769516],"study_design_scores_gemma":[0.000003507733,0.000005951907,0.00001767875,0.000003302011,0.000001373873,0.000003179971,0.000001525528,0.9817612,0.0002608178,0.01756174,0.0003776653,0.000002190069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01722881,0.0002078114,0.9757971,0.0007003508,0.00006113723,0.00004036664,0.00003944513,0.0002876793,0.005637261],"genre_scores_gemma":[0.5896937,0.0004535273,0.4035098,0.0004897707,0.000144726,0.0002215202,0.0001289087,0.0001349607,0.005223035],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004115975,"threshold_uncertainty_score":0.01376927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017566435521722,"score_gpt":0.2203118351828928,"score_spread":0.2001361708276756,"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."}}