{"id":"W2962961223","doi":"10.1103/physrevlett.107.233601","title":"Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes","year":2011,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates - Technology Futures; Natural Sciences and Engineering Research Council of Canada; Humboldt-Universität zu Berlin; Canadian Institute for Advanced Research","keywords":"Metrology; Computer science; Quantum decoherence; Quantum metrology; Algorithm; Quantum; Limit (mathematics); Quantum limit; SQL; Computer engineering; Quantum computer; Physics; Mathematics; Quantum mechanics; Quantum network","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":[0.001277759,0.0009971458,0.0009987801,0.0005753233,0.0004920562,0.0008322856,0.00121506,0.001257403,0.002909977],"category_scores_gemma":[0.003842808,0.000413608,0.0004386073,0.0006146656,0.000715795,0.0008149733,0.001101234,0.001088583,0.0006191824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016968,"about_ca_system_score_gemma":0.001869463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002605844,"about_ca_topic_score_gemma":0.00269012,"domain_scores_codex":[0.9994726,0.000138858,0.00003394656,0.0001051917,0.0001875903,0.00006188206],"domain_scores_gemma":[0.9989427,0.000593957,0.0001019967,0.00009527931,0.0002319622,0.0000340802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005761309,0.00003537186,0.0003503592,0.00004487862,0.0000242784,0.00002343018,0.00003968066,0.9062827,0.001799932,0.01292619,0.0009478445,0.07746765],"study_design_scores_gemma":[0.00001444685,0.0000140763,0.00003411663,0.000002346946,0.000002392859,0.000004272364,0.000002481925,0.9959751,0.0003028612,0.003412477,0.0002329739,0.00000246505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005697033,0.00005967405,0.9923682,0.00009182184,0.00001897652,0.00004970675,0.00002017564,0.0002946094,0.001399773],"genre_scores_gemma":[0.3188655,0.0001021561,0.677903,0.0001078593,0.00004458805,0.0004906155,0.0001518738,0.0001109306,0.00222351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002909977,"threshold_uncertainty_score":0.009734869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03645070856777657,"score_gpt":0.2776241565238244,"score_spread":0.2411734479560478,"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."}}