{"id":"W2064067957","doi":"10.1109/isit.2014.6874999","title":"Branching MERA codes: A natural extension of classical and quantum polar codes","year":2014,"lang":"en","type":"article","venue":"","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Institut de Ciències Fotòniques; Compute Canada","keywords":"Quantum convolutional code; Computer science; Decoding methods; List decoding; Berlekamp–Welch algorithm; Theoretical computer science; Algorithm; Linear code; Concatenated error correction code; Discrete mathematics; Mathematics; Block code","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":[0.00037118,0.0002896263,0.0002886565,0.0005138203,0.0006131261,0.0009837961,0.0008135856,0.0006646227,0.003301519],"category_scores_gemma":[0.00140206,0.0001665294,0.0002902679,0.0005006844,0.001866288,0.001364524,0.0008899744,0.001564043,0.0004934458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004880823,"about_ca_system_score_gemma":0.0006692752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005136271,"about_ca_topic_score_gemma":0.0005812204,"domain_scores_codex":[0.9996887,0.00006591036,0.00001085541,0.00005431554,0.0001373353,0.00004286644],"domain_scores_gemma":[0.9993911,0.0002182281,0.0000685101,0.0001391354,0.0001032514,0.00007976734],"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":[0.00002461734,0.00001384682,0.0001114803,0.0000209801,0.000002154904,0.0000601365,0.00006153699,0.009111715,0.004552507,0.9791849,0.0003511218,0.006504975],"study_design_scores_gemma":[0.000034089,0.00006749809,0.0001531521,0.00002502367,0.000006546517,0.0003791226,0.00003792624,0.1809456,0.008246837,0.7946594,0.01541824,0.00002648396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.130611,0.0006658984,0.8067295,0.0008353201,0.0001982202,0.0001489536,0.0002685353,0.000558966,0.05998373],"genre_scores_gemma":[0.8039787,0.0005436316,0.1846753,0.0003411304,0.0001242792,0.0001412787,0.0001456348,0.0001334552,0.00991663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003301519,"threshold_uncertainty_score":0.01104474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007101051191386065,"score_gpt":0.2292656965864647,"score_spread":0.2221646453950786,"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."}}