{"id":"W1981783889","doi":"10.1103/physrevlett.109.050505","title":"Quantum Algorithm for Data Fitting","year":2012,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":535,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Defense Advanced Research Projects Agency; National Science Foundation","keywords":"Quantum phase estimation algorithm; Quantum algorithm; Algorithm; Computer science; Quantum; Function (biology); Quantum computer; Quantum algorithm for linear systems of equations; Quantum state; Quantum error correction; Quantum process; Physics; Quantum mechanics; Quantum dynamics","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":[],"consensus_categories":[],"category_scores_codex":[0.0006301666,0.0001826628,0.0003273586,0.00002602487,0.0001385535,0.00006624806,0.001642003,0.000007961753,0.00000186729],"category_scores_gemma":[0.0001076154,0.0001420306,0.0001422432,0.0002588638,0.00003333664,0.0004835688,0.0006852277,0.0001796967,0.00005607601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000145294,"about_ca_system_score_gemma":0.0000157519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005112398,"about_ca_topic_score_gemma":4.556545e-8,"domain_scores_codex":[0.9984627,0.00008195742,0.00022254,0.0004358634,0.0002487591,0.0005481376],"domain_scores_gemma":[0.9981678,0.0003800536,0.0001181399,0.001152611,0.0000260672,0.0001553284],"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":[3.290297e-7,0.0001010086,0.00001892486,0.0003421006,0.00002298186,0.000001720554,0.0001354477,0.00003838114,0.0004425511,0.002839169,0.02053631,0.9755211],"study_design_scores_gemma":[0.00009435607,0.00001993317,0.00008585049,0.0003898424,0.00002249367,0.00001022191,7.966325e-7,0.9194957,0.00005748909,0.0003658818,0.0792574,0.0002000086],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003902758,0.00824218,0.9743621,0.01242556,0.0005181617,0.0003464842,0.0000147623,0.0001651672,0.00002281341],"genre_scores_gemma":[0.07886302,0.0008785523,0.8483316,0.06718297,0.004496385,0.00008753913,0.00009282302,0.00005526092,0.00001181553],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9753211,"threshold_uncertainty_score":0.579184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03921103619299199,"score_gpt":0.3208609344253031,"score_spread":0.2816498982323111,"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."}}