{"id":"W4312704343","doi":"10.1109/qce53715.2022.00133","title":"OptiPauli: An algorithm to find a near-optimal Pauli Feature Map for Quantum Support Vector Classifiers","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Quantum Computing and Engineering (QCE)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Earl Haig Secondary School","funders":"","keywords":"Pauli exclusion principle; Kernel (algebra); Feature (linguistics); Feature vector; Algorithm; Quantum state; Computer science; Dimension (graph theory); Optimization problem; Support vector machine; Pattern recognition (psychology); Quantum; Mathematics; Artificial intelligence; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007860229,0.0004818675,0.0004413307,0.000367028,0.0007791515,0.0007174158,0.001586645,0.000109217,0.00006257159],"category_scores_gemma":[0.00007518689,0.0005107138,0.0001702828,0.0004028681,0.00004910066,0.000214994,0.0006229948,0.000933303,0.000015085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001733802,"about_ca_system_score_gemma":0.0002023988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003487511,"about_ca_topic_score_gemma":0.000001701822,"domain_scores_codex":[0.9967635,0.0001053369,0.0004524412,0.001116498,0.0008032646,0.0007589118],"domain_scores_gemma":[0.9984338,0.0002793611,0.0001808893,0.0005251511,0.0001892835,0.0003914876],"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":[0.0001001938,0.0002407663,0.00002881271,0.0000637391,0.0001606819,0.0001352184,0.003672234,0.8503282,0.003482021,0.06067311,0.007767425,0.07334758],"study_design_scores_gemma":[0.0006071646,0.001338784,0.0006105491,0.00007267755,0.00001078847,0.0001585311,0.0002110013,0.9784295,0.0001262521,0.0004707003,0.01734241,0.000621661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2516222,0.00004892869,0.7357141,0.005332475,0.006118328,0.0004022137,0.0002170766,0.0004906742,0.00005405477],"genre_scores_gemma":[0.8501577,0.00000722553,0.1475669,0.0008500435,0.0007165989,0.00008040134,0.0001335799,0.00007069873,0.000416886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5985355,"threshold_uncertainty_score":0.9997345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0241929139138715,"score_gpt":0.2750829854881907,"score_spread":0.2508900715743192,"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."}}