{"id":"W1989864319","doi":"10.1103/physreva.76.062314","title":"Optimal bounded-error strategies for projective measurements in nonorthogonal-state discrimination","year":2007,"lang":"en","type":"article","venue":"Physical Review A","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bounded function; Range (aeronautics); Projective test; POVM; Word error rate; Operator (biology); Mathematics; Bounded operator; State (computer science); Applied mathematics; Computer science; Algorithm; Discrete mathematics; Mathematical optimization; Pure mathematics; Quantum; Mathematical analysis; Artificial intelligence; Quantum mechanics; Physics","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":[0.0006741731,0.0001182684,0.000198652,0.00009468698,0.0000585215,0.00009630475,0.0002876116,0.00001237161,0.000001494508],"category_scores_gemma":[0.00004910597,0.00009588205,0.0001311458,0.0005191102,0.00003129423,0.001058941,0.00004219748,0.00008196499,0.00001631142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000444334,"about_ca_system_score_gemma":0.00009069127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009432426,"about_ca_topic_score_gemma":0.00003829666,"domain_scores_codex":[0.9989291,0.00003405916,0.0002923001,0.0001954367,0.0003004787,0.0002485937],"domain_scores_gemma":[0.9994239,0.00006194075,0.0001227435,0.0001808187,0.0001535419,0.00005705879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004235389,0.0005476974,0.0005470901,0.001697251,0.00003357319,0.000001881272,0.004312478,0.0001287507,0.000370254,0.8073955,0.0003977896,0.1845254],"study_design_scores_gemma":[0.004674534,0.001789029,0.1249664,0.00586576,0.0001546785,0.00001858196,0.001639473,0.2306668,0.006157015,0.5779473,0.04361974,0.002500594],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09591746,0.0009925761,0.8963735,0.0004506949,0.00009145289,0.001189416,0.000003054601,0.00007417052,0.004907723],"genre_scores_gemma":[0.9865912,0.0001794455,0.01226939,0.0007279986,0.00003250702,0.0001778454,0.000007784342,0.000006024292,0.000007780904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8906738,"threshold_uncertainty_score":0.3909957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0613610327367844,"score_gpt":0.3666270839694704,"score_spread":0.305266051232686,"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."}}