{"id":"W2884922008","doi":"10.1038/s41534-019-0222-3","title":"Local-measurement-based quantum state tomography via neural networks","year":2019,"lang":"en","type":"article","venue":"npj Quantum Information","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Waterloo","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; Science, Technology and Innovation Commission of Shenzhen Municipality; National Natural Science Foundation of China; Basic and Applied Basic Research Foundation of Guangdong Province; Canadian Institute for Advanced Research","keywords":"Quantum tomography; Artificial neural network; Tomography; Quantum; Quantum state; State (computer science); Scalability; Quantum computer","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001015054,0.0003794598,0.0003167954,0.0008630668,0.0002480746,0.0006358627,0.001045314,0.0001592215,0.00009044685],"category_scores_gemma":[0.00002343069,0.0003536557,0.0002961001,0.001714499,0.00008950197,0.007371431,0.0001246716,0.0003732067,0.001072147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001036583,"about_ca_system_score_gemma":0.0001139946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005296504,"about_ca_topic_score_gemma":0.000008795355,"domain_scores_codex":[0.9965415,0.0001056932,0.00105998,0.0002747742,0.001306593,0.0007114781],"domain_scores_gemma":[0.9975603,0.00006235659,0.0005816029,0.0009468495,0.0006048779,0.0002440642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003919208,0.0002813996,0.008300905,0.0004585007,0.0001542563,0.000007754404,0.003114999,0.3605956,0.0002662296,0.283252,0.008653543,0.334523],"study_design_scores_gemma":[0.001105652,0.0002642687,0.005616998,0.00003964897,0.000007741175,0.0000115654,0.000123381,0.9773022,0.000265161,0.001857383,0.01293882,0.0004671842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04580091,0.00008378943,0.9488941,0.0004533502,0.001463027,0.0006062326,0.000006224251,0.0006880214,0.00200436],"genre_scores_gemma":[0.9951376,0.00001634496,0.001068863,0.003600172,0.0000266993,0.00004633737,0.00008426078,0.00001443089,0.000005277641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9493367,"threshold_uncertainty_score":0.9998915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008852010217680626,"score_gpt":0.1990043965714152,"score_spread":0.1901523863537345,"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."}}