{"id":"W2301999234","doi":"10.1103/physrevlett.116.230502","title":"Quantum Metrology Enhanced by Repetitive Quantum Error Correction","year":2016,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Diamond and Carbon-based Materials Research","field":"Materials Science","cited_by":163,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Israel Science Foundation; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Volkswagen Foundation; Alexander von Humboldt-Stiftung; Defense Advanced Research Projects Agency; Canadian Urological Association; Blanche Moore Foundation; European Research Council; National Science Foundation","keywords":"Quantum decoherence; Quantum sensor; Quantum error correction; Physics; Quantum metrology; Spin (aerodynamics); Quantum; Quantum technology; Noise (video); Quantum mechanics; Quantum information; Computer science; Open quantum system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005586706,0.0002223753,0.0005403495,0.00004146398,0.00008085117,0.00004194199,0.0003192738,0.00002221137,0.0008466686],"category_scores_gemma":[0.0004401116,0.000137986,0.0001655525,0.0001799587,0.0002448731,0.000213143,0.0000824644,0.0001044331,0.001776379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009358429,"about_ca_system_score_gemma":0.00003883282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005817213,"about_ca_topic_score_gemma":0.000002957942,"domain_scores_codex":[0.9976891,0.0004696078,0.0003214776,0.0005519402,0.0004213541,0.0005464865],"domain_scores_gemma":[0.9987996,0.0004151094,0.000162125,0.000393379,0.00007792449,0.0001518746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004322284,0.0000826812,0.000006756163,0.0001881135,0.000007704366,0.000003504781,0.00001336654,8.610475e-7,0.9614058,0.0004031906,0.03503044,0.002814357],"study_design_scores_gemma":[0.0003408965,0.0002112534,0.00008147873,0.0009016857,0.0000432305,0.000002436384,0.000008577973,0.0001038817,0.9827232,0.000414356,0.01488652,0.0002824778],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984522,0.001260672,0.002628809,0.009579568,0.00121012,0.0003579762,0.00005179353,0.0001174087,0.0002716503],"genre_scores_gemma":[0.9893317,0.002424393,0.00002169281,0.007557337,0.000295764,0.0002010778,0.00001182442,0.00002815611,0.0001281142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02131741,"threshold_uncertainty_score":0.9990008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629238366019477,"score_gpt":0.3113606414876764,"score_spread":0.2950682578274816,"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."}}