{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001647884,0.00017008,0.0002646027,0.00003676103,0.0001550626,0.00007080741,0.0006329127,0.000008231901,0.00001232531],"category_scores_gemma":[0.00006709931,0.000130592,0.0001588122,0.0004181441,0.00009974825,0.0001606893,0.0001755162,0.0001922569,0.0004368083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002216903,"about_ca_system_score_gemma":0.00002033186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001813621,"about_ca_topic_score_gemma":0.000002248356,"domain_scores_codex":[0.998742,0.0001119062,0.0001815042,0.000406481,0.0002543691,0.0003037629],"domain_scores_gemma":[0.9991187,0.0001219547,0.00009733115,0.0005068274,0.00005154659,0.000103623],"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":[0.000009285718,0.0003340671,0.0002042244,0.0006993722,0.00006519969,0.00003805799,0.0009360415,0.0009861051,0.01848741,0.006655415,0.2367747,0.7348102],"study_design_scores_gemma":[0.0001071258,0.0001389041,0.001372055,0.0008161496,0.00001743694,0.00002513538,0.000001632905,0.9561512,0.0007455669,0.000982597,0.03936484,0.0002773901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5432593,0.003077792,0.4180197,0.03073031,0.003498782,0.0004131484,0.000001525283,0.0006007323,0.0003987215],"genre_scores_gemma":[0.947157,0.0003088987,0.007688029,0.0430468,0.001723761,0.00002131395,0.000002222138,0.00002141092,0.0000305491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.955165,"threshold_uncertainty_score":0.5614433,"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."}}