{"id":"W4404402986","doi":"10.48550/arxiv.2411.07144","title":"Autoregressive neural quantum states of Fermi Hubbard models","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Science; International Max Planck Research School for Quantum Science and Technology; International Max Planck Research School for Advanced Methods in Process and Systems Engineering; Government of Canada; Deutsche Forschungsgemeinschaft; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; San José State University; Ministero dello Sviluppo Economico; U.S. Department of Energy; Natural Sciences and Engineering Research Council of Canada; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada; National Science Foundation","keywords":"Hubbard model; Autoregressive model; Quantum; Physics; Statistical physics; Quantum mechanics; Mathematics; Econometrics; Superconductivity","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000883603,0.0002560262,0.0005788107,0.0003168013,0.0003037954,0.0008127081,0.001115592,0.0009491561,0.001710918],"category_scores_gemma":[0.002696512,0.0002701006,0.0003713724,0.0003217744,0.001099882,0.001458931,0.0006802451,0.0008748156,0.0001484808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000741596,"about_ca_system_score_gemma":0.0005898601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004145074,"about_ca_topic_score_gemma":0.004836609,"domain_scores_codex":[0.9997303,0.000139111,0.000009811601,0.00003186991,0.00005002377,0.00003884584],"domain_scores_gemma":[0.9993289,0.0003865487,0.00007609353,0.00008163987,0.00007779084,0.00004911225],"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":[0.0000932248,0.00004777704,0.0008120114,0.0000534726,0.00003016739,0.00009789591,0.00008047984,0.7225708,0.003168647,0.2667537,0.0007065789,0.005585231],"study_design_scores_gemma":[0.000002975906,0.000003821831,0.00006440453,0.000002091289,0.000001688929,0.000003607893,0.000004707079,0.9794322,0.0001463318,0.02028168,0.00005326806,0.000003284353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.67016,0.0005641017,0.3146417,0.001492528,0.00009078268,0.00003630572,0.0001982021,0.000430355,0.012386],"genre_scores_gemma":[0.9885526,0.0001426456,0.009031427,0.00006925794,0.00001513714,0.00002242422,0.00004874239,0.00002545834,0.002092467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004145074,"threshold_uncertainty_score":0.008241892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07876597132097105,"score_gpt":0.1975683582734752,"score_spread":0.1188023869525041,"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."}}