{"id":"W3159267549","doi":"10.22331/q-2022-05-30-727","title":"Quantum Machine Learning with SQUID","year":2022,"lang":"en","type":"article","venue":"Quantum","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Pacific Northwest National Laboratory; Office of Science; University of Washington; Washington Research Foundation; Battelle; Laboratory Directed Research and Development; U.S. Department of Energy","keywords":"MNIST database; Quantum; Scalability; Squid; Variety (cybernetics); Binary number; Quantum computer; Quantum algorithm","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":[0.002084492,0.000538637,0.0008384123,0.0006729862,0.0009252606,0.001582075,0.001775369,0.001140834,0.006874177],"category_scores_gemma":[0.007023478,0.0004068654,0.0005573282,0.0008146701,0.001717978,0.00395732,0.003985593,0.002213164,0.001263158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348985,"about_ca_system_score_gemma":0.001588546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002727655,"about_ca_topic_score_gemma":0.003986293,"domain_scores_codex":[0.9987206,0.0004653139,0.00004957197,0.0002458316,0.0004394093,0.00007929326],"domain_scores_gemma":[0.9985195,0.0005546444,0.00009668663,0.0005033299,0.0002399775,0.00008598978],"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":[0.0003079936,0.0001723642,0.001781327,0.0002509667,0.0001124923,0.0001040539,0.0001932629,0.247126,0.006152621,0.563885,0.01332806,0.1665858],"study_design_scores_gemma":[0.00002270253,0.00004360122,0.0001287924,0.00001379675,0.000007256268,0.00002921661,0.00001223981,0.8465444,0.001890927,0.1469947,0.004298981,0.00001333867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01891855,0.0004812803,0.9686221,0.001001099,0.0001696517,0.00007474402,0.0002142009,0.003397729,0.007120581],"genre_scores_gemma":[0.5200177,0.0003378233,0.4707595,0.0008070132,0.0002144622,0.0002917782,0.0007054223,0.0006212341,0.006245052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006874177,"threshold_uncertainty_score":0.02299643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008859587795351396,"score_gpt":0.2129566588955208,"score_spread":0.2040970711001694,"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."}}