{"id":"W4388981622","doi":"10.1007/978-3-031-37966-6_5","title":"Structure-Aware Minor-Embedding for Machine Learning in Quantum Annealing Processors","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Quantum annealing; Qubit; Computer science; Quantum; Embedding; Minor (academic); Quantum computer; Simulated annealing; Probabilistic logic; Boltzmann machine; Theoretical computer science; Algorithm; Deep learning; Artificial intelligence; Quantum mechanics; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004074503,0.0005997326,0.0006621493,0.0006361142,0.0002982066,0.0002731447,0.001209407,0.0003847893,0.00002564796],"category_scores_gemma":[0.0000927601,0.0005311642,0.0002276599,0.0002151785,0.00004425919,0.000146366,0.0005648129,0.001133285,0.00002250384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007118998,"about_ca_system_score_gemma":0.0001553271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000672926,"about_ca_topic_score_gemma":0.0001598301,"domain_scores_codex":[0.997105,0.0000287472,0.0006073399,0.001140867,0.0004378617,0.0006802037],"domain_scores_gemma":[0.99846,0.0004497574,0.0003421691,0.0004886708,0.0001287294,0.0001306563],"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":[0.00003369336,0.00002246236,0.0001348435,0.001174964,0.0001525106,0.0002449998,0.002369115,0.4658664,0.00007193066,0.3492043,0.0008852532,0.1798395],"study_design_scores_gemma":[0.0003179832,0.0001197139,0.00001652969,0.000485875,0.00001113217,0.00002681178,0.00001375436,0.9315962,0.00004730577,0.05649672,0.01023702,0.0006309511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.003582484,0.00153961,0.9569975,0.002324179,0.003995606,0.002434449,0.0002389711,0.00482602,0.02406119],"genre_scores_gemma":[0.1070575,0.0001857111,0.1531472,0.0008320653,0.002165281,0.00008163272,0.0005586422,0.0007742877,0.7351976],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8038503,"threshold_uncertainty_score":0.999714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207828965451506,"score_gpt":0.260523983628007,"score_spread":0.2397410870828565,"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."}}