{"id":"W4388184023","doi":"10.48550/arxiv.2310.19953","title":"A Hybrid Quantum Algorithm for Load Flow","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Scalability; Convergence (economics); Quantum; Algorithm; Flow (mathematics); Computer science; Mathematical optimization; Quantum algorithm; Newton's method; Mathematics; Physics; Nonlinear system; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007090586,0.0003988443,0.0006281114,0.0005818282,0.0008246602,0.001064896,0.001062585,0.001035974,0.008309717],"category_scores_gemma":[0.001827358,0.0002598847,0.0004635619,0.0008683826,0.0007761957,0.001551484,0.001278524,0.0008092514,0.001086048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005152,"about_ca_system_score_gemma":0.00132781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003524214,"about_ca_topic_score_gemma":0.003576462,"domain_scores_codex":[0.9996439,0.00009610015,0.00001442713,0.00006270136,0.0001448272,0.00003800854],"domain_scores_gemma":[0.9995375,0.000223585,0.00002694084,0.00007616271,0.0001101584,0.0000256074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001781266,0.00008177997,0.0005318092,0.00006972587,0.00004055017,0.00008460817,0.0001372606,0.609882,0.005280344,0.211541,0.005519277,0.1666534],"study_design_scores_gemma":[0.00001734528,0.00001062287,0.0000319389,0.000002211716,0.000002114796,0.00001234965,0.000005044046,0.9829437,0.0003775839,0.01537656,0.001216436,0.000004183958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008071424,0.00007345857,0.9869475,0.0001902255,0.00003616863,0.00004429867,0.00004692099,0.0003724022,0.004217476],"genre_scores_gemma":[0.2827645,0.00013887,0.7055964,0.0001819999,0.00005945525,0.0002856015,0.0001702904,0.0001986377,0.01060428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008309717,"threshold_uncertainty_score":0.02779877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05445647217159943,"score_gpt":0.1986523696118346,"score_spread":0.1441958974402352,"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."}}