{"id":"W1608621648","doi":"10.1109/tit.2009.2018339","title":"Quantum Serial Turbo Codes","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; Université de Sherbrooke","funders":"","keywords":"Turbo code; Serial concatenated convolutional codes; Quantum convolutional code; Convolutional code; Concatenated error correction code; Low-density parity-check code; Algorithm; Computer science; Block code; Linear code; Theoretical computer science; BCJR algorithm; Decoding methods","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005241437,0.0003958438,0.0005028867,0.0007341516,0.0009446839,0.0009300613,0.0008191491,0.001114817,0.00482996],"category_scores_gemma":[0.001929774,0.0002729794,0.0006122593,0.0006432848,0.001418895,0.001312279,0.0008352857,0.000748274,0.001169481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008947389,"about_ca_system_score_gemma":0.001059164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001177151,"about_ca_topic_score_gemma":0.000907752,"domain_scores_codex":[0.9994401,0.0001463534,0.00003128574,0.00006500788,0.0002427049,0.00007445958],"domain_scores_gemma":[0.9990228,0.0003073101,0.00009690619,0.0002082996,0.0003070508,0.0000575905],"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":[0.00003513697,0.0000217828,0.000284884,0.00005898562,0.00001214527,0.0001125039,0.00008361478,0.07463139,0.003114624,0.9108036,0.001232315,0.009609045],"study_design_scores_gemma":[0.00002688405,0.00007826044,0.0002379955,0.0000410412,0.00001728134,0.0003666771,0.00004189651,0.7039805,0.007016356,0.2770752,0.01107209,0.00004579534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07732542,0.0006165877,0.8738598,0.0003717392,0.000184775,0.0001240853,0.0001921425,0.0004774393,0.04684804],"genre_scores_gemma":[0.785638,0.0007910567,0.1922546,0.000380698,0.0001312649,0.0002270727,0.0002824777,0.0001960136,0.02009859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00482996,"threshold_uncertainty_score":0.01615787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006329227655511901,"score_gpt":0.2174012273477657,"score_spread":0.2110719996922538,"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."}}