{"id":"W2067739665","doi":"10.1103/physrevlett.96.050501","title":"Method to Find Quantum Noiseless Subsystems","year":2006,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Waterloo; University of Toronto","funders":"","keywords":"Linear subspace; Decoherence-free subspaces; Formalism (music); Quantum; Quantum decoherence; Computer science; Quantum error correction; Realization (probability); Hilbert space; Algebraic number; Algebraic structure; Quantum operation; Quantum computer; Algebra over a field; Quantum mechanics; Mathematics; Open quantum system; Pure mathematics; Physics; Mathematical analysis","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":[0.0004135719,0.000227663,0.0004729988,0.00005692752,0.0001030965,0.0001088421,0.000935088,0.000009673302,0.000002492728],"category_scores_gemma":[0.00001660178,0.0001769023,0.0002152974,0.0006792339,0.00002043509,0.0001071525,0.0002193882,0.0002039871,0.0002262184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002903387,"about_ca_system_score_gemma":0.00001898955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008810752,"about_ca_topic_score_gemma":0.000001033104,"domain_scores_codex":[0.9981053,0.0002401149,0.0002950444,0.0005487789,0.0003891822,0.0004215765],"domain_scores_gemma":[0.9988979,0.0002305051,0.0001029326,0.0005934876,0.00003988016,0.0001352869],"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":[0.000008277977,0.0006718665,0.0002016126,0.003475109,0.0000910324,0.0001964208,0.0008115323,0.06711396,0.126644,0.1900414,0.1650338,0.445711],"study_design_scores_gemma":[0.0002292514,0.00009611168,0.002902925,0.001980162,0.00003416755,0.0000461819,0.000001441438,0.8780684,0.001165945,0.003376922,0.1113481,0.0007504214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07451379,0.001998217,0.8956505,0.02685709,0.0002237285,0.0003765914,0.00000277138,0.0002040739,0.0001732414],"genre_scores_gemma":[0.6510861,0.0001073799,0.2699196,0.07626564,0.002345987,0.0001220558,0.000009921355,0.000061502,0.00008183373],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8109545,"threshold_uncertainty_score":0.7213868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133614846101209,"score_gpt":0.2895565373759855,"score_spread":0.2782203889149734,"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."}}