{"id":"W4321444449","doi":"10.22331/q-2023-02-21-929","title":"Efficient color code decoders in <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mi>d</mml:mi><mml:mo>&amp;#x2265;</mml:mo><mml:mn>2</mml:mn></mml:math> dimensions from toric code decoders","year":2023,"lang":"en","type":"article","venue":"Quantum","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Government of Canada; Institut Périmètre de physique théorique; Industry Canada; Simons Foundation","keywords":"Decoding methods; Code (set theory); Computer science; Approx; Algorithm; Lattice (music); Square (algebra); Mathematics; Physics; Geometry","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001262167,0.0005956137,0.0003873008,0.0004403221,0.001141348,0.0006619352,0.001922029,0.0007020365,0.0001809159],"category_scores_gemma":[0.0005936148,0.0007963406,0.0006337397,0.001528928,0.0004527596,0.0003165496,0.001645398,0.0010642,0.00186603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004328116,"about_ca_system_score_gemma":0.0006321101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001187965,"about_ca_topic_score_gemma":0.001111369,"domain_scores_codex":[0.9941145,0.0002736535,0.001052661,0.001544753,0.001374469,0.001639966],"domain_scores_gemma":[0.9956795,0.001463995,0.0006549627,0.001522135,0.00009985657,0.0005795631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001299465,0.0002439454,0.0000180161,0.0001283855,0.0002226635,0.0004717823,0.003632627,0.06163003,0.001169624,0.9149501,0.01504086,0.002362055],"study_design_scores_gemma":[0.0009060882,0.000490144,0.0001749672,0.0004296928,0.0001246264,0.0001864104,0.0005660066,0.9756187,0.01000334,0.004379347,0.006309718,0.0008109827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975117,0.0004044911,0.01839844,0.001015547,0.002931157,0.00009015173,0.0002126808,0.0006771337,0.001153427],"genre_scores_gemma":[0.982702,0.0001692636,0.0148478,0.0009904173,0.0005329194,0.00022525,0.0002415275,0.000208971,0.00008184477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9139886,"threshold_uncertainty_score":0.9994488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051097418491303,"score_gpt":0.2495002887244011,"score_spread":0.2289893145394881,"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."}}