{"id":"W3214129522","doi":"10.1103/physrevlett.129.240501","title":"Nearly Optimal Quantum Algorithm for Estimating Multiple Expectation Values","year":2022,"lang":"en","type":"preprint","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Beijing Academy of Quantum Information Sciences; U.S. Department of Energy; Kavli Institute for Theoretical Physics, University of California, Santa Barbara; Office of Science; National Science Foundation","keywords":"Observable; Scaling; Logarithm; Quantum; Mathematics; Flexibility (engineering); Value (mathematics); Algorithm; Quantum algorithm; Applied mathematics; Mathematical optimization; Computer science; Statistical physics; Statistics; Physics; Quantum mechanics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"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"],"consensus_categories":[],"category_scores_codex":[0.0005406585,0.0005117304,0.0008812862,0.00009732177,0.0004082146,0.0002966395,0.001787981,0.00003070302,0.000007029169],"category_scores_gemma":[0.000229451,0.0004752461,0.0007183818,0.0003134192,0.00006547349,0.0001810491,0.00188301,0.0009836322,0.0000139562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001149239,"about_ca_system_score_gemma":0.0001020886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000332381,"about_ca_topic_score_gemma":1.220143e-7,"domain_scores_codex":[0.9967035,0.0002968706,0.0005530901,0.001218962,0.0006652211,0.0005623552],"domain_scores_gemma":[0.9973833,0.0008511419,0.0005288087,0.001006081,0.00008990052,0.0001407583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003591652,0.0002131426,0.000006536672,0.002835538,0.0001204785,0.000026842,0.002027876,0.3839208,0.0003592366,0.000514054,0.005649733,0.6043222],"study_design_scores_gemma":[0.0002057322,0.0001012964,0.00004799668,0.001391146,0.00006225089,0.000007292431,0.00000808029,0.9942907,0.00003740413,0.00178823,0.00152367,0.0005361759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02376008,0.003529079,0.9633925,0.005901785,0.001646814,0.001307898,0.00005006709,0.0004049296,0.000006868959],"genre_scores_gemma":[0.007568099,0.0002078429,0.9838555,0.00587072,0.001466215,0.0008152666,0.0001460577,0.00006368518,0.000006613392],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6103699,"threshold_uncertainty_score":0.9997699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02289260286265953,"score_gpt":0.309280676663546,"score_spread":0.2863880738008865,"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."}}