{"id":"W4388500029","doi":"10.22331/q-2023-11-08-1175","title":"Automated Generation of Shuttling Sequences for a Linear Segmented Ion Trap Quantum Computer","year":2023,"lang":"en","type":"article","venue":"Quantum","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Army Research Office; Bundesministerium für Bildung und Forschung; Office of the Director of National Intelligence; Intelligence Advanced Research Projects Activity; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Qubit; Toffoli gate; Quantum computer; Computer science; Overhead (engineering); Quantum circuit; Trapped ion quantum computer; Quantum; Quantum gate; Algorithm; Topology (electrical circuits); Physics; Quantum error correction; Quantum mechanics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003361297,0.0003461478,0.000266421,0.0002711818,0.0003267724,0.0003620317,0.0006147063,0.0003435772,0.002321929],"category_scores_gemma":[0.001610387,0.0001926589,0.0001935275,0.0002264359,0.0004169768,0.0005315443,0.0004015968,0.0003949107,0.0003100123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005551169,"about_ca_system_score_gemma":0.00089906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00103706,"about_ca_topic_score_gemma":0.001942318,"domain_scores_codex":[0.9997697,0.00005709917,0.00001637009,0.00005618756,0.00006495904,0.00003569752],"domain_scores_gemma":[0.9991041,0.0004462794,0.000118047,0.0001656027,0.0001175406,0.00004845701],"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":[0.0009192874,0.0002619823,0.00212433,0.0003429471,0.00004697679,0.000240416,0.0003681926,0.4595875,0.2109381,0.03740067,0.002792117,0.2849775],"study_design_scores_gemma":[0.00006717316,0.0002207756,0.0002756165,0.000008544296,0.00001114332,0.00006100534,0.00003161971,0.9222661,0.06837849,0.006873676,0.00179097,0.00001481614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2626378,0.00007724675,0.7314609,0.0001341565,0.00002299337,0.0001067461,0.0001408015,0.003269438,0.002149872],"genre_scores_gemma":[0.6357744,0.00004236406,0.3625668,0.00003594722,0.000007572832,0.00009915875,0.0002318943,0.0001528979,0.001089023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002321929,"threshold_uncertainty_score":0.007767618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04316642080551727,"score_gpt":0.2940677572313932,"score_spread":0.250901336425876,"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."}}