{"id":"W3134144170","doi":"10.1103/physrevapplied.17.044005","title":"Customized Quantum Annealing Schedules","year":2022,"lang":"en","type":"article","venue":"Physical Review Applied","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Army Research Laboratory; Intelligence Advanced Research Projects Activity; University of Southern California; Defense Advanced Research Projects Agency; Office of the Director of National Intelligence; National Science Foundation","keywords":"Quantum annealing; Annealing (glass); Ising model; Qubit; Simulated annealing; Ground state; Hamiltonian (control theory); Computer science; Quantum; Physics; Quantum computer; Statistical physics; Quantum mechanics; Mathematics; Algorithm; Mathematical optimization; Thermodynamics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004713318,0.0003665157,0.0003515044,0.0003379082,0.0004649473,0.0005795758,0.0009353858,0.0004481678,0.0079941],"category_scores_gemma":[0.002314846,0.0002882097,0.000214353,0.0003451558,0.0004320185,0.0009750688,0.000621897,0.0006122033,0.0009657876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008903616,"about_ca_system_score_gemma":0.0005165982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005284451,"about_ca_topic_score_gemma":0.001511178,"domain_scores_codex":[0.999706,0.00005100284,0.00002810794,0.00009521082,0.00007656287,0.00004315489],"domain_scores_gemma":[0.9993067,0.000246127,0.0000772157,0.0001928894,0.0001377514,0.00003940457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004991708,0.0002377343,0.00189993,0.0004193865,0.00006440635,0.0002345366,0.0004084341,0.2433741,0.4390913,0.1976041,0.004555818,0.1116111],"study_design_scores_gemma":[0.0001742813,0.0004400866,0.001632782,0.00004053035,0.00007493949,0.0002098956,0.0001244208,0.6478723,0.2440601,0.06634871,0.03893264,0.00008930572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1963055,0.0004668363,0.7703087,0.0004814112,0.0002582563,0.0004845243,0.0003482987,0.002039503,0.02930709],"genre_scores_gemma":[0.8568588,0.0001980201,0.1377202,0.000112046,0.000028043,0.0003441351,0.0001845057,0.000410427,0.004143737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0079941,"threshold_uncertainty_score":0.02674288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142318102586591,"score_gpt":0.267990267066891,"score_spread":0.2565670860410251,"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."}}