{"id":"W2891691304","doi":"10.1109/phosst.2018.8456754","title":"Reinforcement Learning for Quantum Metrology via Quantum Control","year":2018,"lang":"en","type":"article","venue":"","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reinforcement learning; Quantum metrology; Quantum; Metrology; Computer science; Reinforcement; Limit (mathematics); Quantum limit; Quantum sensor; Artificial intelligence; Quantum mechanics; Quantum technology; Open quantum system; Physics; Mathematics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001557673,0.0005971802,0.0007413292,0.0003612123,0.0005021352,0.00108654,0.001110629,0.0009799976,0.002625809],"category_scores_gemma":[0.006204358,0.0002423946,0.0005053744,0.0003486411,0.002673181,0.001785705,0.001790461,0.002101246,0.0002749342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00151437,"about_ca_system_score_gemma":0.001431251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002065664,"about_ca_topic_score_gemma":0.001627307,"domain_scores_codex":[0.999139,0.0003465669,0.0000320725,0.0001221455,0.0002582851,0.000101936],"domain_scores_gemma":[0.9980597,0.001159724,0.0002427854,0.0001931452,0.0002147226,0.0001298286],"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":[0.00004853515,0.00005434207,0.0002823528,0.00006205509,0.00002498456,0.00006370535,0.00007966815,0.4284849,0.001823521,0.5501076,0.0009364714,0.01803185],"study_design_scores_gemma":[0.00001377125,0.00002598111,0.00003954286,0.000008201606,0.000003714508,0.000008244619,0.00000485278,0.841127,0.0003560199,0.1577187,0.0006858883,0.000008020499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01200356,0.0002800645,0.9796057,0.0009168435,0.00008996407,0.00003134186,0.00001969841,0.0001095007,0.006943238],"genre_scores_gemma":[0.8863021,0.0004893434,0.1077171,0.0003355866,0.0001607045,0.0001497991,0.00003065075,0.00005314777,0.004761521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002625809,"threshold_uncertainty_score":0.01098764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293186103867275,"score_gpt":0.2512114069932544,"score_spread":0.2382795459545816,"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."}}