{"id":"W4308109489","doi":"10.21203/rs.3.rs-2180857/v1","title":"Quantum computing reduces systemic risk in financial networks","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Systemic risk; Cascading failure; Computer science; Scalability; Context (archaeology); Convergence (economics); Cascade; Quantum; Mathematical optimization; Financial institution; Finance; Financial crisis; Mathematics; Business; Engineering; Economics","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":[0.001145315,0.0005350329,0.000748412,0.0003860662,0.0007517039,0.001025438,0.0008236517,0.0007036817,0.002673367],"category_scores_gemma":[0.003652945,0.0003258233,0.0003988597,0.0005332807,0.001381192,0.001601227,0.001287866,0.001085394,0.000153763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001405166,"about_ca_system_score_gemma":0.001217698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003917454,"about_ca_topic_score_gemma":0.003395538,"domain_scores_codex":[0.9993894,0.0002556517,0.00001603939,0.0000840452,0.000138212,0.0001166206],"domain_scores_gemma":[0.9976838,0.001590423,0.000203574,0.0002098486,0.0001992586,0.000113125],"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":[0.00005213221,0.0000372886,0.0002852876,0.00002124486,0.00001324431,0.00003344442,0.00002312762,0.9598566,0.001385174,0.03014246,0.0003339653,0.007816135],"study_design_scores_gemma":[0.000006103281,0.00001568383,0.0000552518,0.000002084165,0.000002828397,0.000004053737,0.000006291311,0.9860715,0.0003704743,0.01333625,0.0001275251,0.000001912351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2454364,0.0002917023,0.7426122,0.000924718,0.00006633873,0.00008086541,0.00005625639,0.000314138,0.01021734],"genre_scores_gemma":[0.9614145,0.00009434592,0.0370243,0.00007629095,0.00001380786,0.00003203891,0.00002198537,0.00002552884,0.001297321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003917454,"threshold_uncertainty_score":0.01019526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04195497758999018,"score_gpt":0.342483067748776,"score_spread":0.3005280901587858,"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."}}