{"id":"W1583704075","doi":"10.1364/josab.33.001256","title":"Error-compensation measurements on polarization qubits","year":2016,"lang":"en","type":"article","venue":"Journal of the Optical Society of America B","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"Deutsche Forschungsgemeinschaft; Ontario Ministry of Research, Innovation and Science; National Natural Science Foundation of China; Government of Canada","keywords":"Qubit; Systematic error; Computer science; Error detection and correction; Compensation (psychology); Observational error; Optics; Quantum; Polarization (electrochemistry); Electronic engineering; Physics; Algorithm; Mathematics; Engineering; Quantum 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":[0.0007463145,0.0004452583,0.000391445,0.0004441096,0.0005526654,0.0005195648,0.0008449072,0.0005160935,0.001560573],"category_scores_gemma":[0.003369345,0.0002507862,0.000206237,0.0005639645,0.001140552,0.001433519,0.001252011,0.0009449499,0.000453774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005168841,"about_ca_system_score_gemma":0.000462139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002347492,"about_ca_topic_score_gemma":0.0002632386,"domain_scores_codex":[0.9988275,0.0002523535,0.00005638549,0.0002375779,0.000518913,0.0001072348],"domain_scores_gemma":[0.9985499,0.0003608351,0.0002441237,0.0004564782,0.0003181626,0.00007042834],"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":[0.0004733167,0.0001440105,0.003162702,0.0003045982,0.00006527501,0.0002625708,0.0003462406,0.06299906,0.5151176,0.2557626,0.002001342,0.1593606],"study_design_scores_gemma":[0.00003878282,0.000273761,0.001428366,0.00004712693,0.00003001587,0.0002847981,0.00007367095,0.3515492,0.5879951,0.0479438,0.01025446,0.00008094621],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2485631,0.0007166539,0.7351773,0.0005664857,0.0002905534,0.0001014056,0.0001901979,0.0007361499,0.01365819],"genre_scores_gemma":[0.8656564,0.0003788705,0.1303699,0.0001711314,0.00007346855,0.00009846388,0.0001118526,0.00008672081,0.00305322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001560573,"threshold_uncertainty_score":0.005220652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645016340238882,"score_gpt":0.2516903928107451,"score_spread":0.2252402294083563,"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."}}