{"id":"W2951629661","doi":"10.48550/arxiv.1006.4147","title":"Investigating the Performance of an Adiabatic Quantum Optimization Processor","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"D-Wave Systems (Canada)","funders":"","keywords":"Adiabatic process; Adiabatic quantum computation; Quantum computer; Qubit; Quantum annealing; Quantum; Statistical physics; Quadratic unconstrained binary optimization; Physics; Computation; Spin (aerodynamics); Computer science; Quantum mechanics; Mathematics; Algorithm; 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":[0.0003925808,0.0002406932,0.0002453,0.0001470199,0.0002845707,0.0001137699,0.002270112,0.0002021063,0.000004784613],"category_scores_gemma":[0.0000645225,0.0002002994,0.00009198445,0.0005868239,0.000205106,0.0003069909,0.001125683,0.0009288567,0.0000036709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002730741,"about_ca_system_score_gemma":0.0002719582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005924877,"about_ca_topic_score_gemma":0.00001067768,"domain_scores_codex":[0.9985838,0.000133537,0.0002268544,0.0006717196,0.0001209129,0.000263201],"domain_scores_gemma":[0.9980405,0.0001112249,0.0004532989,0.001088281,0.0001906518,0.000116028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002049992,0.0000340473,0.0004571207,0.0001193717,0.00001570089,0.000005374062,0.0006071018,0.989466,0.00005160852,0.00784968,0.000004168743,0.001387851],"study_design_scores_gemma":[0.0001407937,0.00009206136,0.0007120814,0.0001480444,0.00002440749,0.000006183114,0.00002901519,0.9928634,0.0003866117,0.005346179,0.00001146463,0.0002397803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7123604,0.00001554326,0.2868612,0.00008907692,0.0003087136,0.0001760452,0.000003178498,0.0001181965,0.00006761407],"genre_scores_gemma":[0.970103,0.00001834429,0.02965187,0.0000644939,0.00008984904,8.651194e-7,0.000009265301,0.00001486375,0.00004744982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2577426,"threshold_uncertainty_score":0.8167972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03158273989195935,"score_gpt":0.1822790658511738,"score_spread":0.1506963259592145,"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."}}