{"id":"W4306925060","doi":"10.1038/s41467-022-33928-z","title":"Flexible learning of quantum states with generative query neural networks","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Quantum many-body systems","field":"Physics and Astronomy","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"Government of Canada; Croucher Foundation; National Natural Science Foundation of China; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada; John Templeton Foundation","keywords":"Computer science; Quantum state; Artificial neural network; Quantum; Set (abstract data type); Generative model; Cluster analysis; Representation (politics); Artificial intelligence; Machine learning; Theoretical computer science; Physics; Generative grammar; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002254017,0.0001172041,0.000184076,0.00006842199,0.0006926586,0.00002917951,0.0007315777,0.00003709511,0.0001098901],"category_scores_gemma":[0.000006109851,0.0001068868,0.00006084634,0.0003972288,0.0001065597,0.00009176371,0.0004342002,0.001475545,0.000002080385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003125652,"about_ca_system_score_gemma":0.00005775426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00028053,"about_ca_topic_score_gemma":0.00002810387,"domain_scores_codex":[0.9988694,0.0004193069,0.0002187273,0.0001472102,0.0001759546,0.0001694521],"domain_scores_gemma":[0.9984364,0.0002475977,0.0002397911,0.0009163045,0.0001224965,0.0000374428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002988002,0.0002094685,0.07705191,0.000006483152,0.0001548967,5.153438e-7,0.001388624,0.2876667,0.000245062,0.6286924,0.003678335,0.0008756967],"study_design_scores_gemma":[0.0002880718,0.0001373843,0.001965925,0.00001243941,0.00003063673,0.00000224498,0.004068963,0.9612818,0.0001554724,0.0007399904,0.03114189,0.0001752111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8594796,0.01554012,0.09520643,0.006067996,0.0009213075,0.001597731,0.0003457707,0.0004541694,0.02038685],"genre_scores_gemma":[0.9981512,0.00001156498,0.0006668509,0.00006458657,0.00007969084,0.0001412116,0.0004973799,0.00002366998,0.0003638072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6736151,"threshold_uncertainty_score":0.6410593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424381309415654,"score_gpt":0.2757450552285343,"score_spread":0.2615012421343777,"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."}}