{"id":"W2707034626","doi":"10.1103/physrevlett.120.090501","title":"Quantitative Tomography for Continuous Variable Quantum Systems","year":2018,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Multidisciplinary University Research Initiative","keywords":"Equidistant; Quantum tomography; Interpolation (computer graphics); Function (biology); Measure (data warehouse); Matrix (chemical analysis); Variable (mathematics); Density matrix; Wigner distribution function; Mathematics; Polynomial; Lagrange polynomial; Probability density function; State variable; Mathematical analysis; Quantum state; Applied mathematics; Algorithm; Quantum; Physics; Quantum mechanics; Computer science; Geometry; Classical mechanics; Statistics","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":[0.0003976835,0.000187958,0.0004221236,0.0001022266,0.0001589027,0.0001658289,0.0006820533,0.00001185049,0.000007056472],"category_scores_gemma":[0.00007281776,0.0001510637,0.0002741926,0.0009124731,0.0001380931,0.0006324501,0.00006723492,0.00009270419,0.000202119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001363316,"about_ca_system_score_gemma":0.0000231506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001564415,"about_ca_topic_score_gemma":2.826931e-7,"domain_scores_codex":[0.9985995,0.00009054821,0.0003565009,0.0003063243,0.0002870779,0.0003600996],"domain_scores_gemma":[0.9987087,0.0002395781,0.0002115461,0.0004944023,0.0002309346,0.0001148985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006387087,0.0000702195,0.00001953445,0.0006749913,0.00004370482,5.672439e-7,0.0001782514,0.000009107974,0.001359159,0.9503615,0.04643287,0.0008436369],"study_design_scores_gemma":[0.0008632253,0.001144418,0.0002201048,0.002559123,0.000114255,0.00001334032,0.00006966384,0.4583742,0.0003842212,0.01478855,0.5205427,0.0009261834],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008619533,0.003460417,0.9816193,0.003328659,0.0006355596,0.0009375768,0.0000137952,0.0002315161,0.001153667],"genre_scores_gemma":[0.8470811,0.001234483,0.0550319,0.09475547,0.00084338,0.000947007,0.00003545381,0.00004733406,0.00002382811],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.935573,"threshold_uncertainty_score":0.6160199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974801995202694,"score_gpt":0.2933137409027934,"score_spread":0.2735657209507665,"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."}}