{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004681837,0.0001873239,0.0004503236,0.00004338792,0.0004107255,0.0000565806,0.00113205,0.000009933953,0.00002710945],"category_scores_gemma":[0.00002872494,0.0001580487,0.0001868693,0.0006433785,0.00003187214,0.00005311782,0.0009340549,0.000424332,0.00008991002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003448817,"about_ca_system_score_gemma":0.00005290355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000438578,"about_ca_topic_score_gemma":6.928595e-8,"domain_scores_codex":[0.9983176,0.0001328816,0.0002463667,0.00049406,0.0004674861,0.0003416316],"domain_scores_gemma":[0.9989541,0.000173853,0.0001347768,0.0006067848,0.0000227782,0.0001076778],"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":[0.000007956094,0.0001701532,0.000002314032,0.0002671226,0.00002151311,0.000009893837,0.0001814525,0.004667626,0.001433807,0.8101671,0.0009814921,0.1820896],"study_design_scores_gemma":[0.000489744,0.00006419754,0.00004246434,0.0001431532,0.000023526,0.00001313346,0.00001116945,0.8685963,0.0003287483,0.08097702,0.04893662,0.0003739286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2874195,0.04840554,0.6329582,0.01476984,0.001474441,0.002911657,0.00002921145,0.002522156,0.009509438],"genre_scores_gemma":[0.9773197,0.0003845991,0.0181502,0.003612551,0.0002627105,0.0002256405,0.000009044852,0.00002195735,0.00001360791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8639287,"threshold_uncertainty_score":0.6445038,"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."}}