{"id":"W3210159903","doi":"10.1103/physrevlett.128.220504","title":"Negative Quasiprobabilities Enhance Phase Estimation in Quantum-Optics Experiment","year":2022,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Smithsonian Astrophysical Observatory; Stiftelsen Lars Hiertas Minne; Foundational Questions Institute; Canadian Institute for Advanced Research; Silicon Valley Community Foundation; Harvard University; Girton College, University of Cambridge; National Science Foundation","keywords":"Quantum metrology; Physics; Photon; Operator (biology); Measure (data warehouse); Metrology; Quantum; Birefringence; Detector; Noise (video); Interferometry; Phase (matter); Quantum noise; Quantum imaging; Optics; Statistical physics; Quantum error correction; Quantum mechanics; Quantum information; Computer science; Quantum network; Data mining","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":[0.003373221,0.0004756595,0.0006118781,0.0005864511,0.001024622,0.002131005,0.001140718,0.001336766,0.00544905],"category_scores_gemma":[0.009906955,0.0005663669,0.000364,0.0004075362,0.005817711,0.005650512,0.003336293,0.002707214,0.0006092039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007593391,"about_ca_system_score_gemma":0.0006410515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001998005,"about_ca_topic_score_gemma":0.0002033855,"domain_scores_codex":[0.9983974,0.0005662992,0.00005922544,0.0002335345,0.0005992669,0.0001441887],"domain_scores_gemma":[0.9907174,0.006279109,0.000717331,0.00159875,0.0003877509,0.0002997358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001037118,0.00005549906,0.0005990967,0.00007830549,0.0000107147,0.00012734,0.0002371217,0.004414704,0.01348845,0.9724129,0.0006002949,0.007871703],"study_design_scores_gemma":[0.00004826428,0.0001521647,0.001177419,0.00004789102,0.00001423021,0.0002235531,0.00008642575,0.06817498,0.01301077,0.9133466,0.003659436,0.00005827505],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3143643,0.001731671,0.598707,0.006187041,0.0005254039,0.0001507472,0.0002069827,0.0007430654,0.07738391],"genre_scores_gemma":[0.9551507,0.0004322605,0.04161757,0.0003248328,0.0001446877,0.00007413882,0.00002290739,0.00007004884,0.002162923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00544905,"threshold_uncertainty_score":0.01822889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01829258916440905,"score_gpt":0.322190496482428,"score_spread":0.303897907318019,"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."}}