{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006290437,0.00008570046,0.0002959697,0.0009667054,0.0001505426,0.00003819113,0.005365102,0.0001665098,0.0000216894],"category_scores_gemma":[0.001473279,0.00004852261,0.0001036548,0.006408048,0.0002267194,0.0004002209,0.002896917,0.0001579687,0.000002764919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001252465,"about_ca_system_score_gemma":0.00001846588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003159865,"about_ca_topic_score_gemma":0.00008219942,"domain_scores_codex":[0.9989437,0.00009035769,0.0002656566,0.000376892,0.0001665261,0.0001568051],"domain_scores_gemma":[0.9926689,0.002609752,0.0001416024,0.004333737,0.0001745254,0.00007142813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000004103856,0.0002195777,0.01141769,0.000003334856,0.0007840996,4.286932e-7,0.0000212367,0.000001965542,0.0001083663,0.7446575,0.002479988,0.2403017],"study_design_scores_gemma":[0.001004387,0.0003185597,0.4188088,0.00001583037,0.00233534,0.000007017092,0.00007113581,0.2220488,0.0001762359,0.03794437,0.3166698,0.0005998212],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003011759,0.00786296,0.9705747,0.0172343,0.00005417223,0.0001246465,0.0008888365,0.00006215769,0.0001865046],"genre_scores_gemma":[0.8404094,0.001595289,0.1575636,0.0001734956,0.00001073792,0.00001162676,0.0001778569,0.000001894783,0.00005606086],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8373977,"threshold_uncertainty_score":0.9969783,"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."}}