{"id":"W2948835082","doi":"10.1364/prj.7.000a27","title":"High-dimension experimental tomography of a path-encoded photon quantum state","year":2019,"lang":"en","type":"article","venue":"Photonics Research","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Superposition principle; Photon; Optics; Computer science; Quantum tomography; Tomography; Quantum optics; Physics; Path (computing); Quantum; Quantum information; Tomographic reconstruction; Optical path; Dimension (graph theory); Quantum cryptography; Quantum state; Mathematics; Quantum mechanics","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":[0.001610755,0.0001807516,0.0003163241,0.0003571815,0.0003074089,0.0001378535,0.001579216,0.00007838112,0.00005721275],"category_scores_gemma":[0.00002204289,0.0001499303,0.0001482348,0.00145145,0.0001165364,0.0003005183,0.003015652,0.0005794365,0.00006849381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006856098,"about_ca_system_score_gemma":0.0001368822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004705199,"about_ca_topic_score_gemma":0.000006990598,"domain_scores_codex":[0.9963713,0.0003285549,0.0003894527,0.0006605854,0.001435709,0.000814443],"domain_scores_gemma":[0.9980028,0.0003317918,0.0001165574,0.001033571,0.0003313758,0.00018389],"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":[0.0001688934,0.0005820983,0.001024679,0.000127016,0.00005537219,0.00009125839,0.002105063,0.006072697,0.9642434,0.02251043,0.00119562,0.001823458],"study_design_scores_gemma":[0.0005382392,0.0005584438,0.0001231633,0.00009096589,8.578144e-7,0.000007247415,0.00006670217,0.6101359,0.3841585,0.003653675,0.000501319,0.0001649738],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956497,0.0009063059,0.001474254,0.0001485166,0.0005942368,0.0006167378,0.000003572123,0.00008501659,0.0005216903],"genre_scores_gemma":[0.9922127,0.0001066289,0.007398178,0.00005047734,0.00001914017,0.00002213245,0.000003252723,0.00002070145,0.0001668422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6040632,"threshold_uncertainty_score":0.611398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03462383435711709,"score_gpt":0.3196920467421805,"score_spread":0.2850682123850635,"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."}}