{"id":"W2804898954","doi":"10.1103/physrevlett.121.190501","title":"Quantum Error Correction Decoheres Noise","year":2018,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Intelligence Advanced Research Projects Activity; Industry Canada; Canada First Research Excellence Fund; Government of Ontario; Government of Canada; Office of the Director of National Intelligence; Canadian Institute for Advanced Research","keywords":"Fidelity; Quantum error correction; Pauli exclusion principle; Probabilistic logic; Noise (video); Computer science; Error detection and correction; Algorithm; Quantum; Encoding (memory); Quantum noise; Quantum computer; Physics; Quantum mechanics; Telecommunications; Artificial intelligence","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":[0.00155689,0.0006808508,0.0007396604,0.0003899517,0.0006386014,0.001340383,0.001029116,0.001183562,0.002731376],"category_scores_gemma":[0.00999075,0.0002559557,0.0003746624,0.0003655862,0.003122061,0.00245029,0.001594463,0.001946179,0.0005055376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000841936,"about_ca_system_score_gemma":0.001022492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005423139,"about_ca_topic_score_gemma":0.0003229786,"domain_scores_codex":[0.9980053,0.0004855893,0.00007444464,0.0004540871,0.00081006,0.0001704926],"domain_scores_gemma":[0.9949406,0.002824373,0.0004782487,0.001240199,0.0004053326,0.0001113333],"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":[0.00004803493,0.00003705376,0.0005840674,0.00008927543,0.00002318349,0.00009828298,0.00008798787,0.04405919,0.01389137,0.9342505,0.0005574465,0.00627369],"study_design_scores_gemma":[0.00003813302,0.0002174281,0.0007506471,0.00006353052,0.00003342961,0.0003253304,0.00006804179,0.4746445,0.03531381,0.4826083,0.005891689,0.00004520317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1203796,0.0006943134,0.8366035,0.001552085,0.0002135956,0.00008353867,0.0001639437,0.0004675841,0.03984183],"genre_scores_gemma":[0.9572624,0.0005164781,0.03701753,0.0004540768,0.0001001743,0.00008682769,0.00006308535,0.0001127296,0.004386626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002731376,"threshold_uncertainty_score":0.009137392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01555423193603558,"score_gpt":0.2900743188083186,"score_spread":0.274520086872283,"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."}}