{"id":"W2533940598","doi":"10.1103/physrevlett.119.030501","title":"Neural Decoder for Topological Codes","year":2017,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":192,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Institut Périmètre de physique théorique; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Ontario Trillium Foundation; National Science Foundation","keywords":"Boltzmann machine; Computer science; Artificial neural network; Decoding methods; Toric code; Code (set theory); Restricted Boltzmann machine; Topology (electrical circuits); Variety (cybernetics); Algorithm; Artificial intelligence; Theoretical computer science; Mathematics; Physics; Topological order; Quantum","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":[0.0001651741,0.0001393588,0.0002985827,0.00001167874,0.0003939447,0.0001994179,0.001325392,0.0000078031,0.000002459755],"category_scores_gemma":[0.0001627639,0.00009571941,0.0002243827,0.00003859948,0.0001042311,0.000161451,0.0003003423,0.0001375795,0.00002105054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008631894,"about_ca_system_score_gemma":0.00000857847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004817254,"about_ca_topic_score_gemma":4.601299e-7,"domain_scores_codex":[0.9990314,0.00004551217,0.000135638,0.0003492747,0.0001489485,0.000289222],"domain_scores_gemma":[0.9988276,0.0001939302,0.0001209634,0.000747541,0.00002596924,0.00008399313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001179263,0.0003157138,0.0004684258,0.001332166,0.00008201849,0.00006631646,0.0003036338,0.002107909,0.005681689,0.09040561,0.03408946,0.8651353],"study_design_scores_gemma":[0.0002360387,0.00009443067,0.00332642,0.000351232,0.00002120137,0.00001523371,4.194337e-7,0.9651529,0.0001856232,0.007520704,0.02280308,0.0002927421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.286446,0.002804526,0.5272225,0.1817485,0.0006859243,0.0006451232,0.000006389032,0.0002308568,0.0002102686],"genre_scores_gemma":[0.9253655,0.0002155737,0.03313433,0.04060451,0.0006063247,0.00004922176,0.000001939638,0.00001045384,0.00001212948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9630449,"threshold_uncertainty_score":0.3903324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03074162236479804,"score_gpt":0.3318810635453132,"score_spread":0.3011394411805151,"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."}}