{"id":"W4226055566","doi":"10.48550/arxiv.2112.08409","title":"Quantum Model Learning Agent: characterisation of quantum systems through machine learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; A priori and a posteriori; Quantum; Artificial intelligence; Hamiltonian (control theory); Machine learning; Protocol (science); Physical system; Theoretical computer science; Mathematics; Physics; Mathematical optimization; 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":[0.0004620703,0.0003633447,0.0006976164,0.0002234496,0.0003194444,0.000278448,0.001076781,0.0002728222,0.00002680115],"category_scores_gemma":[0.00007735441,0.0004167849,0.0004022941,0.0007147743,0.00007206947,0.0007968785,0.001831042,0.0009926358,0.00001463871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001530404,"about_ca_system_score_gemma":0.0001619386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001101489,"about_ca_topic_score_gemma":0.00001837052,"domain_scores_codex":[0.9975084,0.0003438824,0.0004873394,0.001083157,0.0001942226,0.0003830208],"domain_scores_gemma":[0.9976229,0.0000830963,0.001054596,0.0008120608,0.0003266082,0.0001007363],"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":[0.00001219076,0.00004532003,0.002045601,0.0001841947,0.0001428829,0.00007253672,0.001417663,0.9056587,0.0002893381,0.08990356,0.000004594622,0.0002234807],"study_design_scores_gemma":[0.0001951361,0.00005837119,0.0002602106,0.000256217,0.0001134047,0.000005842392,0.0007034155,0.9962962,0.00007018398,0.001458929,0.000192742,0.0003894066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.370028,0.0002685329,0.6287092,0.00001974979,0.0002807404,0.0001034255,0.000005471956,0.0001317037,0.0004531553],"genre_scores_gemma":[0.9966663,0.0006266328,0.001087586,0.00001133128,0.0000568365,9.221002e-7,0.0001170825,0.00002819786,0.001405186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6276216,"threshold_uncertainty_score":0.9998284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08873922865682089,"score_gpt":0.1900521218438109,"score_spread":0.10131289318699,"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."}}