{"id":"W2793050864","doi":"10.1364/oe.26.007528","title":"Resolution limits of quantum ghost imaging","year":2018,"lang":"en","type":"article","venue":"Optics Express","topic":"Random lasers and scattering media","field":"Physics and Astronomy","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"H2020 Marie Skłodowska-Curie Actions; H2020 European Research Council; Engineering and Physical Sciences Research Council; Canada Excellence Research Chairs, Government of Canada; European Commission; QuantIC","keywords":"Ghost imaging; Point spread function; Optics; Quantum imaging; Physics; Image resolution; Resolution (logic); Photon; Detector; Optical transfer function; Image quality; Photon counting; Quantum; Computer science; Image (mathematics); Quantum information; Computer vision; Artificial intelligence; Quantum network","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":[0.00009009201,0.00007628225,0.0001182937,0.00002947237,0.00006316767,0.00002245661,0.0001177062,0.00001440757,0.0001251291],"category_scores_gemma":[0.000006706137,0.00007087362,0.00004812004,0.0000522613,0.00009868052,0.00007385791,0.00003331733,0.00005514379,0.00003180142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004544297,"about_ca_system_score_gemma":0.0000142877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005927648,"about_ca_topic_score_gemma":4.753978e-7,"domain_scores_codex":[0.9994481,0.00001706676,0.0001489013,0.0001221642,0.0001011963,0.0001626026],"domain_scores_gemma":[0.9995524,0.00003577048,0.00007285038,0.00020986,0.00008078338,0.00004837796],"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":[0.0004550211,0.0007018635,0.09945484,0.0001276507,0.0002277632,0.00001043627,0.006390517,0.0006931833,0.6174442,0.201412,0.03712937,0.03595317],"study_design_scores_gemma":[0.006207304,0.0003349533,0.01154871,0.0005405301,0.0001591793,0.000004123615,0.001865407,0.2168466,0.7008896,0.0177847,0.04269497,0.001123856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8997128,0.00004233412,0.06214591,0.0001354782,0.000550843,0.000103868,0.00002975267,0.00002117226,0.03725787],"genre_scores_gemma":[0.9972718,0.000001986212,0.001900962,0.00001774857,0.0004934262,0.000005781619,0.00001091898,0.00001124679,0.0002861535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2161534,"threshold_uncertainty_score":0.2890142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137078447497224,"score_gpt":0.2491906801057195,"score_spread":0.2354828353559971,"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."}}