{"id":"W3165245770","doi":"","title":"Financial Portfolio Identification Using Graph Algorithms on a Quantum Annealern (Presentation Slides)","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Quantum annealing; Computer science; Algorithm; Finance; Quantum computer; Portfolio; Quantum algorithm; Quantum; Curse of dimensionality; Speedup; Graph; Theoretical computer science; Artificial intelligence; Parallel computing; Economics","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":[0.0009069118,0.000429873,0.0005662561,0.0007709578,0.000777109,0.001462096,0.001013675,0.0009270161,0.007241596],"category_scores_gemma":[0.004037654,0.0003720836,0.0006764337,0.001063791,0.0010234,0.002285052,0.001141231,0.001222569,0.0008584539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001280252,"about_ca_system_score_gemma":0.001159905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004892571,"about_ca_topic_score_gemma":0.005932596,"domain_scores_codex":[0.9995596,0.0001789071,0.00001877047,0.00009892425,0.0001001456,0.00004364978],"domain_scores_gemma":[0.9984632,0.000894554,0.00008901151,0.000305026,0.00017823,0.00007001695],"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":[0.0001810054,0.0001715328,0.001710779,0.0001038477,0.00007265637,0.0001021513,0.0001480337,0.6185787,0.004853874,0.1895769,0.007116191,0.1773843],"study_design_scores_gemma":[0.00001541175,0.00002675238,0.0002473107,0.000007005944,0.000006215327,0.00001839543,0.0000229394,0.9219673,0.0008139397,0.07554422,0.00132351,0.000007110247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.09490091,0.0002933202,0.8918138,0.001294719,0.0001141245,0.0001014914,0.0001668706,0.001222265,0.01009247],"genre_scores_gemma":[0.4255393,0.0002450078,0.567535,0.0002446282,0.00005133709,0.0001283949,0.0002339925,0.0001993787,0.005823],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.007241596,"threshold_uncertainty_score":0.02422559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266603351616254,"score_gpt":0.268119116407843,"score_spread":0.2554530828916805,"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."}}