{"id":"W2266138411","doi":"10.1038/ncomms10138","title":"Quantum algorithms for topological and geometric analysis of data","year":2016,"lang":"en","type":"article","venue":"Nature Communications","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":251,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Army Research Office; Multidisciplinary University Research Initiative; Defense Advanced Research Projects Agency; National Science Foundation","keywords":"Topological data analysis; Persistent homology; Betti number; Computer science; Algorithm; Eigenvalues and eigenvectors; Homology (biology); Topology (electrical circuits); Theoretical computer science; Mathematics; Discrete mathematics; Combinatorics; Biology; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004763604,0.001292513,0.001795314,0.005210612,0.001744811,0.004988838,0.00273238,0.001890668,0.005709766],"category_scores_gemma":[0.02539616,0.0008280768,0.002066819,0.004837991,0.004029403,0.008312264,0.005438586,0.004849446,0.001670487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003215746,"about_ca_system_score_gemma":0.002238426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002614019,"about_ca_topic_score_gemma":0.002278873,"domain_scores_codex":[0.9958383,0.001724538,0.0003473891,0.0007720045,0.001145511,0.0001723472],"domain_scores_gemma":[0.9876869,0.008214968,0.0006402294,0.002284567,0.0009192597,0.0002540745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007215412,0.00007286645,0.0008198164,0.0003758885,0.0001452296,0.00007358397,0.0002424262,0.05972001,0.001093362,0.7408286,0.006721266,0.1898348],"study_design_scores_gemma":[0.00001481317,0.00001332178,0.0001173259,0.00002674207,0.00001110505,0.00003674998,0.00003868633,0.2193877,0.0003831933,0.7760928,0.003863188,0.00001445232],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00245315,0.0009968063,0.993355,0.0008769148,0.00008847467,0.00006685995,0.0001346344,0.0004994516,0.001528731],"genre_scores_gemma":[0.08197559,0.001812514,0.9115683,0.0004359836,0.0004057993,0.0004902714,0.0007355936,0.0003017445,0.002274243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005709766,"threshold_uncertainty_score":0.02519262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106837397453084,"score_gpt":0.376496454858394,"score_spread":0.2658127151130856,"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."}}