{"id":"W2111033402","doi":"10.1109/qels.2003.237940","title":"Quantum algorithms in the presence of decoherence: optical experiments","year":2003,"lang":"en","type":"article","venue":"","topic":"Semiconductor Lasers and Optical Devices","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Quantum decoherence; Decoherence-free subspaces; Computer science; Quantum computer; Noise (video); Linear subspace; Algorithm; Quantum algorithm; Quantum; Quantum error correction; Quantum mechanics; Physics; Mathematics; Artificial intelligence","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":[0.0001188495,0.00006376134,0.00008651001,0.00002060237,0.00000871109,0.0000128097,0.000141077,0.00003671656,0.0002363609],"category_scores_gemma":[0.00004778592,0.00004000538,0.00002097529,0.0001212965,0.00003449408,0.00006508896,0.000006917075,0.00007486817,0.00001580253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007499904,"about_ca_system_score_gemma":0.000005807232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001859763,"about_ca_topic_score_gemma":0.000006147029,"domain_scores_codex":[0.9995076,0.00001871882,0.0001373722,0.0000759199,0.0001198698,0.0001404858],"domain_scores_gemma":[0.9996958,0.0001201605,0.000006422566,0.0001382661,0.00001046129,0.00002889836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001978989,0.0007047167,0.02066934,0.0002556395,0.0001163967,0.0000803953,0.006211203,0.008784014,0.2096119,0.7398306,0.006494206,0.007221817],"study_design_scores_gemma":[0.001132944,0.0001803551,0.01174913,0.0000986327,0.000019521,0.00002098346,0.008560077,0.1851613,0.7798635,0.006170129,0.006449107,0.0005942176],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9529266,0.0004376755,0.0007991648,0.00001516735,0.0001586144,0.00009534534,7.27568e-7,0.00002666084,0.04554],"genre_scores_gemma":[0.9974418,0.000024782,0.002437311,0.00003310128,0.000008570715,0.00001211898,4.005034e-7,0.000005451059,0.00003651396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7336605,"threshold_uncertainty_score":0.2587986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02696783311163,"score_gpt":0.2710454128903317,"score_spread":0.2440775797787017,"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."}}