{"id":"W3211962989","doi":"10.22323/1.396.0243","title":"Investigating the variance increase of readout error mitigation through classical bit-flip correction on IBM and Rigetti quantum computers","year":2022,"lang":"en","type":"article","venue":"Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"Nuclear Physics; European Regional Development Fund; Office of Science; Ministry of Colleges and Universities; Institut Périmètre de physique théorique; Research and Innovation Foundation; Industry Canada; National Science Foundation; Government of Canada; U.S. Department of Energy","keywords":"Variance (accounting); Pauli exclusion principle; IBM; Observable; Computer science; Quantum; Algorithm; Quantum computer; Electronic engineering; Scale (ratio); Encoding (memory); Computer engineering; Physics; Artificial intelligence; Quantum mechanics; 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":[0.001332875,0.0002732286,0.0002811489,0.0001084914,0.0006425885,0.000167002,0.001727193,0.00008853638,0.00002050175],"category_scores_gemma":[0.0006584933,0.0001939649,0.0001625353,0.0004594708,0.0002937744,0.0003350044,0.001001398,0.000872547,0.000002798484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000897002,"about_ca_system_score_gemma":0.00006224709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009787746,"about_ca_topic_score_gemma":0.000001377664,"domain_scores_codex":[0.9974575,0.0002139863,0.0005680684,0.0005676969,0.0009098014,0.0002829599],"domain_scores_gemma":[0.9965159,0.002028398,0.0007745717,0.0003513023,0.0002513607,0.00007850415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002367271,0.0003051343,0.001485223,0.0000829382,0.0001673964,0.000002567176,0.005439308,0.008079798,0.005884414,0.9695548,0.001621364,0.00714028],"study_design_scores_gemma":[0.0008791835,0.001153054,0.006113262,0.0006319505,0.0000860084,0.0001315105,0.0007457709,0.8545662,0.01623451,0.1168607,0.002155853,0.0004420189],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.931504,0.00005590547,0.01025338,0.04730076,0.004957933,0.0005380181,0.00002596883,0.0001252286,0.005238795],"genre_scores_gemma":[0.9909264,0.000007590781,0.004610615,0.003825184,0.0002818728,0.00003115555,0.000003892098,0.00002175138,0.000291522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8526942,"threshold_uncertainty_score":0.7909659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01072264521118357,"score_gpt":0.2439120149272173,"score_spread":0.2331893697160337,"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."}}