{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003671845,0.0001489561,0.0001250229,0.000223293,0.0003192646,0.0002209066,0.0004561363,0.0000724523,0.00003997255],"category_scores_gemma":[0.000006775467,0.0001287468,0.00009760531,0.0003140445,0.00003354623,0.001165421,0.000001699109,0.0002527917,0.0002386121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002923262,"about_ca_system_score_gemma":0.00004777836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003148363,"about_ca_topic_score_gemma":4.333302e-7,"domain_scores_codex":[0.9990175,0.000086252,0.0002906065,0.0001365689,0.0002425444,0.0002264653],"domain_scores_gemma":[0.9992757,0.0001132956,0.00009261652,0.0003725585,0.00006334238,0.00008248023],"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":[0.0000539272,0.00006811701,1.287802e-7,0.000007200646,0.00001433549,0.000001388083,0.002405861,0.1335884,0.0001483582,0.1096183,0.0002464638,0.7538475],"study_design_scores_gemma":[0.0006758937,0.0004919697,0.0001469217,0.00004607923,0.00001073334,0.00006169025,0.000117467,0.8809379,0.02204633,0.09148586,0.003578773,0.0004003223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01212369,0.000007723604,0.9836619,0.0006942024,0.001208882,0.0001263462,0.00001061356,0.0004443915,0.001722219],"genre_scores_gemma":[0.988261,0.000006334371,0.009824309,0.001750263,0.0000673558,0.000006371026,0.000002756862,0.000003845008,0.00007779807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9761373,"threshold_uncertainty_score":0.5250141,"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."}}