{"id":"W4353007475","doi":"10.48550/arxiv.2303.11207","title":"Investigating Topological Order using Recurrent Neural Networks","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Cold Atom Physics and Bose-Einstein Condensates","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Perimeter Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Colleges and Universities; Canadian Institute for Advanced Research; Institut Périmètre de physique théorique; Compute Canada; Mitacs; Government of Canada","keywords":"Recurrent neural network; Quantum entanglement; Toric code; Quantum; Topological entropy in physics; Physics; Topology (electrical circuits); Lattice (music); Topological defect; Symmetry protected topological order; Topological order; Theoretical physics; Computer science; Quantum mechanics; Artificial neural network; Mathematics; Artificial intelligence; Topological quantum number","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004178601,0.0002027784,0.0001948562,0.0003951282,0.0001642868,0.0004859295,0.0003534112,0.0003674978,0.0007220188],"category_scores_gemma":[0.002096732,0.00016319,0.0001544745,0.0002499886,0.0005462241,0.0009320249,0.000393581,0.000373087,0.00006049619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004365926,"about_ca_system_score_gemma":0.0002110148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001805324,"about_ca_topic_score_gemma":0.002216708,"domain_scores_codex":[0.9998796,0.00005019196,0.000004912758,0.0000199096,0.00002527667,0.00002007635],"domain_scores_gemma":[0.9993648,0.0003547648,0.0001242602,0.00005563585,0.00006611509,0.00003440522],"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":[0.0001946608,0.00009247205,0.01047871,0.00009770833,0.00008307993,0.0002269264,0.0002015988,0.844422,0.04453234,0.0793239,0.0007452362,0.01960129],"study_design_scores_gemma":[0.000002477382,0.000008847842,0.0004586581,0.000001322622,0.000001888086,0.000005864045,0.00001193464,0.9931097,0.001362268,0.004963619,0.000070054,0.000003357273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.916637,0.0001064086,0.07974754,0.00020244,0.00001428635,0.00001461651,0.0001028374,0.0001828286,0.002992045],"genre_scores_gemma":[0.9932809,0.00003491808,0.006279254,0.00001552992,0.000004275346,0.000008713027,0.00004702785,0.00001156027,0.0003178195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001805324,"threshold_uncertainty_score":0.003589571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1505918736288977,"score_gpt":0.2297929159645704,"score_spread":0.07920104233567271,"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."}}