{"id":"W4404568877","doi":"10.1103/prxquantum.5.040326","title":"Enhancing Dispersive Readout of Superconducting Qubits through Dynamic Control of the Dispersive Shift: Experiment and Theory","year":2024,"lang":"en","type":"article","venue":"PRX Quantum","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Université de Sherbrooke","funders":"Army Research Office; ETH Zürich Foundation; Intelligence Advanced Research Projects Activity; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Natural Sciences and Engineering Research Council of Canada; Office of the Director of National Intelligence","keywords":"Qubit; Physics; Initialization; Resonator; Quantum computer; Fidelity; High fidelity; Quantum entanglement; Quantum mechanics; Electronic engineering; Quantum; Computer science; Optoelectronics; Telecommunications; Engineering; Acoustics","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":[0.0006395546,0.0004341847,0.000337833,0.00033493,0.0002983806,0.0008918123,0.001210802,0.0006040887,0.001575163],"category_scores_gemma":[0.001467819,0.0002812368,0.0001544523,0.0003371781,0.001895568,0.001316741,0.001168568,0.0007903821,0.0003468053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000837362,"about_ca_system_score_gemma":0.0002781471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003763603,"about_ca_topic_score_gemma":0.0004929047,"domain_scores_codex":[0.9995845,0.00008956542,0.00001758854,0.0000818371,0.0001923919,0.00003405477],"domain_scores_gemma":[0.9993622,0.0003351665,0.0001028903,0.0001123659,0.00005001842,0.00003739186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002518013,0.0002386339,0.001074948,0.0003449175,0.00003823822,0.0002145779,0.0002951481,0.02276589,0.7895275,0.1537595,0.0007283289,0.03076048],"study_design_scores_gemma":[0.00008590332,0.0005660375,0.001082285,0.00006884644,0.00003185679,0.0002701639,0.00005263328,0.4924214,0.4614812,0.03532583,0.008492365,0.0001213285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4958819,0.002976717,0.4758706,0.001607185,0.000279798,0.0001577386,0.0001963463,0.0009538435,0.02207588],"genre_scores_gemma":[0.9285604,0.001023161,0.06811077,0.0001241513,0.000103165,0.00008592382,0.00004479633,0.00007325354,0.001874344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001575163,"threshold_uncertainty_score":0.006075442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008554318214806204,"score_gpt":0.2475004984950143,"score_spread":0.2389461802802081,"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."}}