{"id":"W2891371211","doi":"10.1103/physrevlett.122.140402","title":"Operational Advantage of Quantum Resources in Subchannel Discrimination","year":2019,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"Air Force Office of Scientific Research; Intelligence Advanced Research Projects Activity; Army Research Office; Templeton Religion Trust; Zhejiang University; H2020 European Research Council; Ontario Ministry of Research, Innovation and Science; Government of Canada; National Science Foundation","keywords":"Computer science; Quantum entanglement; Robustness (evolution); Quantum; Resource (disambiguation); Resource dependence theory; Theoretical computer science; Statistical physics; 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":[0.0001927376,0.00008346311,0.0002008703,0.00008895925,0.00001647907,0.00002492913,0.000352516,0.000005509131,0.00001751024],"category_scores_gemma":[0.00001872086,0.00006743133,0.0001001312,0.0004031717,0.000026481,0.0006620558,0.00005879912,0.00007945004,0.0001039729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001249092,"about_ca_system_score_gemma":0.000009711037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007722327,"about_ca_topic_score_gemma":6.7209e-7,"domain_scores_codex":[0.999128,0.00005953182,0.0002583064,0.0001478309,0.0002853842,0.000120876],"domain_scores_gemma":[0.9995289,0.00006163934,0.0001040041,0.0002422438,0.00003199589,0.00003116984],"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":[0.000008143811,0.0003072251,0.003330882,0.001961594,0.00001405395,0.000002398667,0.002511284,0.000631685,0.01375658,0.9685022,0.002091928,0.006882041],"study_design_scores_gemma":[0.001453448,0.0002881993,0.0668066,0.004401322,0.00002935066,0.000008224478,0.0002380615,0.8898451,0.002935503,0.008648573,0.02444769,0.0008979102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9521605,0.0008913545,0.0373414,0.00835137,0.0001072683,0.0003490685,0.000002681658,0.00003392326,0.0007624203],"genre_scores_gemma":[0.9903225,0.0004299371,0.0004373667,0.008760802,0.00001926342,0.00001621618,0.000007507464,0.0000032067,0.000003154554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9598536,"threshold_uncertainty_score":0.274977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01082431231318544,"score_gpt":0.270183681929755,"score_spread":0.2593593696165696,"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."}}