{"id":"W2253343217","doi":"10.1103/physreva.94.042313","title":"Quantifying the coherence of pure quantum states","year":2016,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University; University of Guelph; Mount Allison University","funders":"Simons Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; U.S. Department of Defense; National Science Foundation","keywords":"Trace distance; Coherence (philosophical gambling strategy); Quantum entanglement; Measure (data warehouse); TRACE (psycholinguistics); Quantum state; Kullback–Leibler divergence; Quantum; Quantum discord; State (computer science); Coherence theory; Coherent states; Quantum mechanics; Statistical physics; Physics; Mathematics; Coherence length; Computer science; Statistics; Algorithm","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":[0.003329096,0.0004991856,0.0006867036,0.003149878,0.0007618436,0.002887354,0.0008982987,0.001212404,0.001674852],"category_scores_gemma":[0.01152766,0.000338471,0.0003818723,0.002553335,0.005116473,0.005928545,0.002348436,0.00108637,0.0001983715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001790808,"about_ca_system_score_gemma":0.0008423786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007042137,"about_ca_topic_score_gemma":0.0005102786,"domain_scores_codex":[0.9975176,0.0009859764,0.0001471513,0.0004370877,0.0007633708,0.0001488203],"domain_scores_gemma":[0.9933599,0.004107972,0.0009711382,0.0005574774,0.0008138518,0.0001897221],"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":[0.00004093754,0.00002585537,0.001406534,0.0002140858,0.0000658131,0.00003963778,0.0002469683,0.01037137,0.0054488,0.9565365,0.0004598662,0.02514352],"study_design_scores_gemma":[0.00001258031,0.00009858125,0.001972467,0.00008063297,0.00003223411,0.0001319698,0.0002489616,0.04117358,0.00474984,0.9443958,0.007028451,0.0000749051],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2372122,0.02079577,0.7115664,0.003224271,0.0001795727,0.00008140871,0.0003558846,0.0002206971,0.02636378],"genre_scores_gemma":[0.934085,0.005076071,0.05871482,0.0002538384,0.0002566159,0.0000831431,0.0001688002,0.00005992686,0.001301853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003329096,"threshold_uncertainty_score":0.01760614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03185113348492923,"score_gpt":0.3564712197128553,"score_spread":0.3246200862279261,"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."}}