{"id":"W4389348821","doi":"10.1103/physrevlett.131.230601","title":"Superposed Quantum Error Mitigation","year":2023,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Engineering and Physical Sciences Research Council; Canada First Research Excellence Fund; Austrian Science Fund; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Noise (video); Probabilistic logic; Superposition principle; Quantum computer; Computation; Fidelity; IBM; Unitary state; Computer engineering; Quantum noise; Quantum; Algorithm; Physics; Quantum mechanics; Telecommunications; Artificial intelligence","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":[0.0002242197,0.0001481256,0.0002508958,0.00005617257,0.0001068959,0.00006161643,0.0006142497,0.000006928071,0.000003486046],"category_scores_gemma":[0.0000557012,0.0001178865,0.0001742995,0.0009102011,0.00004014615,0.0001528243,0.0001772619,0.0001752701,0.0006529706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001515991,"about_ca_system_score_gemma":0.00001769138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004632275,"about_ca_topic_score_gemma":2.252054e-7,"domain_scores_codex":[0.998709,0.0001103079,0.0001776509,0.0003652847,0.0003158894,0.0003218596],"domain_scores_gemma":[0.9992135,0.000161175,0.00005849354,0.0004491674,0.00002430355,0.00009338134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006002172,0.0003714184,0.0003498415,0.004558384,0.0001430456,0.0003457146,0.002820915,0.01616042,0.1290779,0.08890826,0.2352487,0.5220094],"study_design_scores_gemma":[0.0001048047,0.00003248398,0.002384834,0.000615576,0.00001043307,0.000006817908,0.000002024689,0.9815122,0.0002858195,0.002896115,0.01192961,0.0002192512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8648478,0.001223604,0.04465979,0.08742249,0.0004943105,0.000336245,0.000002920591,0.000949948,0.00006285815],"genre_scores_gemma":[0.9529507,0.0007921368,0.004460034,0.04111815,0.0005650348,0.00004507283,0.00002134874,0.0000265679,0.00002091208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9653518,"threshold_uncertainty_score":0.8392833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01838621201854497,"score_gpt":0.2882338472608237,"score_spread":0.2698476352422787,"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."}}