{"id":"W7071458364","doi":"","title":"Simulation and optimization of superconducting qubit control and readout","year":2023,"lang":"en","type":"dissertation","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Natural Sciences and Engineering Research Council of Canada; ETH Zürich Foundation; Office of Science; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Canada First Research Excellence Fund; Intelligence Advanced Research Projects Activity; National Center of Competence in Research Quantum Science and Technology; Eidgenössische Technische Hochschule Zürich; National Science Foundation; Government of Canada; Office of the Director of National Intelligence; U.S. Department of Energy","keywords":"Transmon; Qubit; Population; Process (computing); Control (management); Field (mathematics); Electronic circuit; Fidelity; High fidelity","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000675183,0.0003754828,0.0004972073,0.0002914834,0.0003895324,0.0007238297,0.0007318705,0.0007740573,0.003304881],"category_scores_gemma":[0.002888051,0.0002700195,0.0003439253,0.0002741137,0.0008558464,0.0004836898,0.0005804069,0.0007815217,0.0001633061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001108619,"about_ca_system_score_gemma":0.001266498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006294976,"about_ca_topic_score_gemma":0.005471057,"domain_scores_codex":[0.999782,0.00007948455,0.000008731465,0.00002533081,0.00005290997,0.00005158681],"domain_scores_gemma":[0.9984367,0.001123308,0.0001105814,0.00008513002,0.0001752869,0.00006890845],"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":[0.00006708167,0.00004384676,0.001027948,0.000029755,0.00001169764,0.00002722944,0.00003107132,0.9910372,0.001138804,0.004467182,0.0001981206,0.001920048],"study_design_scores_gemma":[0.00001235901,0.00001870977,0.00007731949,0.000002110688,0.000002349138,0.000001733951,0.00000688259,0.9986469,0.0006635286,0.0004611946,0.000105252,0.000001571844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9159798,0.000170435,0.06681133,0.0006559075,0.00007735067,0.000115423,0.0002346172,0.0003786238,0.01557649],"genre_scores_gemma":[0.9849961,0.00003757058,0.01337095,0.00004017902,0.000005064938,0.00006946735,0.0001062183,0.00003362798,0.001340883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006294976,"threshold_uncertainty_score":0.01251668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181407417799668,"score_gpt":0.2200190350325286,"score_spread":0.2082049608545319,"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."}}