{"id":"W1995703916","doi":"10.1103/physrevlett.113.030501","title":"Tensor Networks and Quantum Error Correction","year":2014,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Decoding methods; Quantum error correction; Quantum; Error detection and correction; Equivalence (formal languages); Quantum convolutional code; Computer science; Quantum computer; Code (set theory); Tensor (intrinsic definition); Algorithm; Physics; Discrete mathematics; Mathematics; Quantum mechanics; Pure mathematics","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":[0.0002138082,0.0001412429,0.0002601969,0.00002299361,0.0001044651,0.00006358175,0.0002968437,0.000008153694,0.000001117532],"category_scores_gemma":[0.00005990435,0.0001060041,0.00008805561,0.0002186316,0.00004774435,0.00009713889,0.0001319411,0.0002170758,0.00002226851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008560552,"about_ca_system_score_gemma":0.000004494966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007193372,"about_ca_topic_score_gemma":3.899478e-7,"domain_scores_codex":[0.9989977,0.0001351527,0.0001391963,0.0003401371,0.0001524597,0.0002353761],"domain_scores_gemma":[0.9992865,0.0002026503,0.00007527864,0.0003233495,0.00001927238,0.00009298697],"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":[0.000003289932,0.0001039521,0.0002095003,0.0005287073,0.00002867063,0.000009237578,0.0001634971,0.02843638,0.001171715,0.01043591,0.03330805,0.9256011],"study_design_scores_gemma":[0.00006745006,0.00004804862,0.00166126,0.0004039444,0.00001066708,0.00001767247,4.331493e-7,0.9840212,0.00001131675,0.0003818182,0.01323302,0.000143114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1120747,0.00247487,0.8663757,0.01775151,0.0008746524,0.0001827361,2.294327e-7,0.0002118591,0.00005373897],"genre_scores_gemma":[0.9551573,0.000758539,0.004968686,0.0382854,0.0007861187,0.00001455221,0.000001743251,0.00001547664,0.00001212036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9555849,"threshold_uncertainty_score":0.4322723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009583106663965446,"score_gpt":0.2537221614841419,"score_spread":0.2441390548201764,"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."}}