{"id":"W2339618058","doi":"10.1103/physrevlett.117.243602","title":"Spectrally Engineering Photonic Entanglement with a Time Lens","year":2016,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Industry Canada; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Photon; Lens (geology); Photonics; Quantum entanglement; Optics; Photon entanglement; Physics; Ultrashort pulse; Quantum optics; Dispersion (optics); Parametric statistics; Spontaneous parametric down-conversion; Quantum; Quantum mechanics; Laser","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":[0.00009954532,0.0001580306,0.000242729,0.00002029118,0.00003930864,0.00003952343,0.0005247597,0.000004018525,0.00001510107],"category_scores_gemma":[0.000007649714,0.00008192368,0.0001109909,0.0002249568,0.00002002939,0.0002388684,0.0001570845,0.00009077954,0.0001627624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004722659,"about_ca_system_score_gemma":0.00001201472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001310038,"about_ca_topic_score_gemma":2.145432e-7,"domain_scores_codex":[0.9988839,0.00003173117,0.0001462595,0.0003227462,0.0002572177,0.0003581313],"domain_scores_gemma":[0.9993654,0.0001053844,0.00006197693,0.0003850125,0.00001604469,0.0000662133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001645089,0.0003618394,0.0002646311,0.001367356,0.0002762971,0.0004849294,0.0001861203,0.004625321,0.8315492,0.01069352,0.03855634,0.111618],"study_design_scores_gemma":[0.003224049,0.001417816,0.002699073,0.03849417,0.0002384132,0.0003059244,0.000001354052,0.6174138,0.03524503,0.0004477887,0.2967996,0.003712994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4434699,0.006418435,0.3852942,0.1617285,0.0003533963,0.001342109,0.000002774895,0.0007973627,0.0005933422],"genre_scores_gemma":[0.9474277,0.002481019,0.01613752,0.03316437,0.0006224465,0.00005476403,0.000001211538,0.00004069251,0.00007026802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7963041,"threshold_uncertainty_score":0.3340751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008499628243270475,"score_gpt":0.2198760135733238,"score_spread":0.2113763853300533,"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."}}