{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003824487,0.0002539697,0.0002381229,0.0003298423,0.0002794236,0.0001790992,0.0009137309,0.0000570356,0.000001558163],"category_scores_gemma":[0.00001537576,0.0002259787,0.00008526794,0.00114994,0.00003221953,0.00008406662,0.0003247301,0.0005106269,0.0002898297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003106937,"about_ca_system_score_gemma":0.00001958544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002340328,"about_ca_topic_score_gemma":6.43361e-7,"domain_scores_codex":[0.9980049,0.00006080223,0.0002186653,0.0007215125,0.00040999,0.0005840886],"domain_scores_gemma":[0.9991392,0.0001309577,0.00005688142,0.0004981926,0.0000121902,0.0001625217],"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":[0.000009651192,0.00003877348,0.000002546366,0.00001165733,0.00001467954,0.00001550816,0.001977427,0.8217897,0.005243598,0.1400635,0.004436514,0.02639647],"study_design_scores_gemma":[0.0003222525,0.00006068076,0.0000992055,0.00001417763,0.000002489825,0.000006068692,0.00001690884,0.986836,0.0001888094,0.0100758,0.002084699,0.0002929588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1721769,0.000005863391,0.8125273,0.01178969,0.0001747871,0.0002807642,0.000002975677,0.001164352,0.001877442],"genre_scores_gemma":[0.95215,0.000001012134,0.02645232,0.02109056,0.0001246385,0.0000617749,0.00001829554,0.00003593293,0.00006545521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7860749,"threshold_uncertainty_score":0.9215142,"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."}}