{"id":"W1999637717","doi":"10.1103/physrevlett.112.014102","title":"Information Gain in Tomography–A Quantum Signature of Chaos","year":2014,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum chaos and dynamical systems","field":"Physics and Astronomy","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Economic Development and Innovation; Ministero dello Sviluppo Economico; National Science Foundation","keywords":"Quantum chaos; Physics; Quantum tomography; Tomography; Statistical physics; Quantum information; Quantum; Random matrix; Information gain; Quantum state; Quantum mechanics; Algorithm; Computer science; Artificial intelligence; Optics; Quantum dynamics; Eigenvalues and eigenvectors","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":[0.0008981242,0.0002269499,0.0003117239,0.0007733963,0.0002788191,0.000647576,0.0004533717,0.0004860353,0.001158275],"category_scores_gemma":[0.006881897,0.0001625623,0.0001711758,0.0004795598,0.002024692,0.001802556,0.001060456,0.0005689673,0.00008338727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004450122,"about_ca_system_score_gemma":0.0002336225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002258602,"about_ca_topic_score_gemma":0.0001839846,"domain_scores_codex":[0.9995384,0.0001521009,0.00001897354,0.00005990293,0.0001676354,0.00006301724],"domain_scores_gemma":[0.9965179,0.001858906,0.0006648709,0.0005261613,0.0002635966,0.0001685581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001248454,0.0001878599,0.01874851,0.0002572825,0.0001223641,0.0006980183,0.001187329,0.1098539,0.4023927,0.4013269,0.0007491314,0.06322768],"study_design_scores_gemma":[0.00005791508,0.0006193294,0.03405343,0.00004787078,0.00004865057,0.001299833,0.000285105,0.4989071,0.2132849,0.2498656,0.001381851,0.0001484187],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9384229,0.0002474007,0.05811884,0.0004097732,0.00001087208,0.00001361606,0.00005310108,0.0001843141,0.002539246],"genre_scores_gemma":[0.9959067,0.00003310692,0.003891715,0.00001058919,0.000004183572,0.00000375013,0.000009720012,0.000009112542,0.0001311828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001158275,"threshold_uncertainty_score":0.004749775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005153324753304066,"score_gpt":0.2400519597799833,"score_spread":0.2348986350266792,"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."}}