{"id":"W4394710090","doi":"10.1088/2058-9565/ad3d7f","title":"Efficient quantum algorithm for all quantum wavelet transforms","year":2024,"lang":"en","type":"article","venue":"Quantum Science and Technology","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Defense Advanced Research Projects Agency","keywords":"Quantum; Quantum algorithm; Wavelet; Algorithm; Quantum Fourier transform; Computer science; Quantum error correction; Physics; Quantum mechanics; Artificial intelligence","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":[0.001764745,0.0004158135,0.000430881,0.00154936,0.0007889057,0.0006893508,0.002141993,0.0002604657,0.000003980153],"category_scores_gemma":[0.000142761,0.0003188628,0.0001267749,0.004010798,0.001435137,0.0003266511,0.000513678,0.000556267,0.00003876786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009906335,"about_ca_system_score_gemma":0.0005599849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000223892,"about_ca_topic_score_gemma":0.000002565637,"domain_scores_codex":[0.9957527,0.00002690684,0.0004962179,0.001620164,0.0007465414,0.001357452],"domain_scores_gemma":[0.9982731,0.0002363887,0.00008591361,0.0008358007,0.000312111,0.0002567395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002204206,0.00005714704,0.000002464415,0.00004719062,0.00001774193,0.00005177474,0.0005667132,0.000100396,0.004723677,0.5588965,0.0002414321,0.4352927],"study_design_scores_gemma":[0.0002843173,0.0005677424,0.00002137613,0.00008869279,0.00001433869,0.0003629525,0.0001479668,0.8621179,0.003645726,0.113604,0.01872874,0.0004161518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2062415,0.00206967,0.7731114,0.01418979,0.001958159,0.0005870244,0.00002717419,0.001732577,0.00008271924],"genre_scores_gemma":[0.9190535,0.0001035406,0.0800741,0.0004135871,0.0001611979,0.0001010992,0.000004006909,0.00003938575,0.00004957558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8620176,"threshold_uncertainty_score":0.9999263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00957090269982635,"score_gpt":0.255904255365631,"score_spread":0.2463333526658046,"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."}}