{"id":"W3091663083","doi":"10.1103/physrevlett.128.090501","title":"Autoregressive Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation","year":2022,"lang":"en","type":"preprint","venue":"Physical Review Letters","topic":"Quantum many-body systems","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Illinois at Urbana-Champaign; Compute Canada; Canadian Institute for Advanced Research; Shared Hierarchical Academic Research Computing Network; U.S. Department of Energy; National Science Foundation","keywords":"POVM; Computer science; Artificial neural network; Quantum; Hilbert space; Probabilistic logic; Curse of dimensionality; Boltzmann machine; Measure (data warehouse); Benchmark (surveying); Statistical physics; Quantum dynamics; Mathematics; Applied mathematics; Artificial intelligence; Quantum operation; Quantum mechanics; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008256648,0.0007088389,0.001824446,0.00004685138,0.0004197737,0.0004335358,0.001361348,0.00003495928,0.00007423526],"category_scores_gemma":[0.00006834036,0.0006402457,0.0007987028,0.0002181795,0.00005086765,0.0002594051,0.00172964,0.0008703273,0.00002584282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002602574,"about_ca_system_score_gemma":0.0001241753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006195239,"about_ca_topic_score_gemma":0.000001367092,"domain_scores_codex":[0.9958615,0.0005964292,0.001120113,0.001114523,0.000552063,0.0007553942],"domain_scores_gemma":[0.9960896,0.0008249081,0.001679264,0.00109859,0.000144863,0.000162759],"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":[0.00003598591,0.0001802171,0.001457495,0.01203831,0.0003867594,0.000004312806,0.0002490568,0.9060881,0.0001185503,0.0655751,0.01193712,0.001929],"study_design_scores_gemma":[0.0003756588,0.00006761828,0.0002113535,0.005359028,0.0004314877,8.941074e-7,0.00001958423,0.9724844,0.000001030355,0.009887573,0.01045545,0.0007059674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7298502,0.02075316,0.1406452,0.008367033,0.01352986,0.08256687,0.001973238,0.0007338615,0.001580545],"genre_scores_gemma":[0.9877896,0.0000141893,0.0002536591,0.001034649,0.004005569,0.005241709,0.001474602,0.0001436894,0.00004235077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2579393,"threshold_uncertainty_score":0.9996049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04162881250266647,"score_gpt":0.3427849671985113,"score_spread":0.3011561546958448,"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."}}