{"id":"W4304140399","doi":"10.21203/rs.3.rs-2136572/v1","title":"Variational Quantum Approximate Support Vector Machine With Inference Transfer","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Samsung","keywords":"Quantum machine learning; Support vector machine; Qubit; Computer science; MNIST database; Parameterized complexity; Quantum; Quantum circuit; Quadratic equation; Inference; Quadratic unconstrained binary optimization; Algorithm; Quantum computer; Mathematics; Artificial intelligence; Artificial neural network; Quantum error correction; Quantum mechanics","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":[0.001419571,0.0005207376,0.001224183,0.0005262927,0.000552086,0.001137418,0.001930016,0.001333087,0.003933279],"category_scores_gemma":[0.006869781,0.0003806166,0.0005934573,0.0009164006,0.001052975,0.001855154,0.001478206,0.001322396,0.0006213301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00115264,"about_ca_system_score_gemma":0.001387391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003876311,"about_ca_topic_score_gemma":0.002472066,"domain_scores_codex":[0.9990243,0.0003692906,0.0000567225,0.0001864135,0.0002548053,0.0001085132],"domain_scores_gemma":[0.9980372,0.001036921,0.0001124853,0.0003123341,0.0004208674,0.00008034563],"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":[0.0001729564,0.0000928895,0.0009395026,0.0000944225,0.000064254,0.000087919,0.00007264231,0.7589596,0.002634607,0.07186969,0.00349754,0.161514],"study_design_scores_gemma":[0.000004571489,0.000006445523,0.00002293892,0.000001161174,0.000001165257,0.000003815133,0.000001954347,0.9923636,0.0002347071,0.007265993,0.00009230008,0.000001463993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03430458,0.0002195656,0.961834,0.0005534037,0.00005375332,0.00006735616,0.00009004547,0.0009160346,0.001961388],"genre_scores_gemma":[0.7491356,0.00009609719,0.247051,0.0002988157,0.00008255868,0.000162303,0.0002989353,0.0001333796,0.002741406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003933279,"threshold_uncertainty_score":0.01315814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03798680379483731,"score_gpt":0.3381174474329685,"score_spread":0.3001306436381311,"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."}}