{"id":"W4303986923","doi":"10.1038/s41598-022-20375-5","title":"An optimizing method for performance and resource utilization in quantum machine learning circuits","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Electronic circuit; Resource (disambiguation); Quantum; Machine learning; Artificial intelligence; Computer engineering; Computer network; Electrical engineering; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00443264,0.0001170179,0.0001535803,0.000311688,0.001265071,0.0003950474,0.0003456256,0.00002462261,0.000005498612],"category_scores_gemma":[0.00008127703,0.0001172435,0.00003564712,0.0008383096,0.00004340834,0.0002698484,0.0003334859,0.0002615034,2.19752e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003867595,"about_ca_system_score_gemma":0.00007577919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001981777,"about_ca_topic_score_gemma":0.000003936734,"domain_scores_codex":[0.9978519,0.0002192777,0.0003407312,0.0008881512,0.000380015,0.0003199168],"domain_scores_gemma":[0.9990296,0.00008656065,0.0002201425,0.0005340848,0.00004819209,0.00008137564],"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":[0.000003829369,0.000057653,0.002500965,0.00003034034,0.000003196776,0.00006704988,0.003248572,0.7529302,0.003018611,0.0005705272,0.0001542944,0.2374148],"study_design_scores_gemma":[0.0001233118,0.000131658,0.0006268283,0.0000145785,0.000002167727,0.0003868927,0.00009192662,0.9727997,0.0006221192,0.001887776,0.02316599,0.0001470685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4325871,0.000190869,0.565284,0.0001417416,0.001329262,0.0002439246,0.000001017562,0.0001372356,0.00008483109],"genre_scores_gemma":[0.9102603,0.000001772481,0.08931309,0.00004929513,0.00003130491,0.00003382335,0.00003384433,0.00001349971,0.0002631333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4776732,"threshold_uncertainty_score":0.973003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02366737128254578,"score_gpt":0.2768634904240757,"score_spread":0.25319611914153,"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."}}