{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001157501,0.0004071799,0.0003874497,0.0005623268,0.0003318742,0.00106052,0.001011572,0.0006389914,0.001689207],"category_scores_gemma":[0.003947525,0.0002449831,0.0002823055,0.0005476364,0.002424564,0.001874681,0.001428274,0.001173116,0.0002272248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008718632,"about_ca_system_score_gemma":0.0004612289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004291348,"about_ca_topic_score_gemma":0.00022688,"domain_scores_codex":[0.9993656,0.0002770556,0.00001615371,0.00009017784,0.0002091561,0.00004175851],"domain_scores_gemma":[0.9985266,0.0008592281,0.0001441265,0.0003333857,0.00008706943,0.00004956177],"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":[0.0002050471,0.00004955074,0.0005787423,0.0001398945,0.0000273569,0.00009847015,0.0001286495,0.1623168,0.03501695,0.7688344,0.0005815793,0.03202264],"study_design_scores_gemma":[0.00002018955,0.00006209386,0.0002078279,0.00001243332,0.000005976923,0.00006537156,0.00001446167,0.8566756,0.0168459,0.1247428,0.001322503,0.00002470557],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03542108,0.000197645,0.9611051,0.0002243463,0.00003460799,0.00002127376,0.00007302391,0.0002071476,0.002715745],"genre_scores_gemma":[0.7675391,0.0002235864,0.2300735,0.0001013289,0.00004050799,0.00006569919,0.00009703352,0.00007037209,0.001788918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001689207,"threshold_uncertainty_score":0.006325841,"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."}}