{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005241985,0.000303337,0.0003834804,0.0005856259,0.0004496726,0.0006928008,0.0008058463,0.0009798561,0.004229571],"category_scores_gemma":[0.004733926,0.0001615335,0.0002853506,0.0003755027,0.001028196,0.001228534,0.001278659,0.001167022,0.001191466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008089052,"about_ca_system_score_gemma":0.0009371159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009037438,"about_ca_topic_score_gemma":0.00114145,"domain_scores_codex":[0.9996513,0.00009718713,0.00001629475,0.00003939494,0.0001511772,0.00004454068],"domain_scores_gemma":[0.9992622,0.0003015208,0.00004883883,0.0001181031,0.0002273549,0.00004202356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009312278,0.00002817032,0.0003622621,0.00008176998,0.00001541241,0.0001032818,0.00009590488,0.1797766,0.006135817,0.7456242,0.004136117,0.06354722],"study_design_scores_gemma":[0.00001394972,0.00001988505,0.00005632532,0.00001304464,0.000003887485,0.00006466118,0.0000127218,0.8200604,0.00358031,0.1733648,0.002797411,0.00001252337],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03438322,0.0001799106,0.9484551,0.0008298825,0.0001910955,0.00005438265,0.000179531,0.0007491181,0.01497774],"genre_scores_gemma":[0.6751772,0.0003056978,0.3059553,0.000453008,0.000135602,0.0002135346,0.0003038553,0.0004172623,0.0170386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004229571,"threshold_uncertainty_score":0.01414937,"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."}}