{"id":"W4393159670","doi":"10.1609/aaai.v38i10.28963","title":"Generating Universal Adversarial Perturbations for Quantum Classifiers","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Adversarial system; Quantum; Computer science; Theoretical computer science; Artificial intelligence; Quantum mechanics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003223828,0.0007635705,0.0008899049,0.0005703315,0.0005569237,0.001158949,0.001389597,0.001507041,0.001782017],"category_scores_gemma":[0.01398779,0.000444241,0.0006612618,0.0003886444,0.002405403,0.002361183,0.003242146,0.002863382,0.0003494269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00143785,"about_ca_system_score_gemma":0.001009943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005229576,"about_ca_topic_score_gemma":0.0005939535,"domain_scores_codex":[0.9975031,0.001072777,0.00008192733,0.0003721741,0.000770708,0.000199371],"domain_scores_gemma":[0.9940778,0.004030088,0.0005138274,0.0009069277,0.0003041033,0.0001672245],"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":[0.0001515157,0.00006750793,0.001077214,0.00008483791,0.00005423519,0.0001509787,0.0001287918,0.6779045,0.007314455,0.261396,0.002111021,0.04955885],"study_design_scores_gemma":[0.000006542853,0.00002264116,0.0000618916,0.000007433778,0.000004446571,0.00002871581,0.000006327354,0.951884,0.001963301,0.04555257,0.0004556349,0.000006470957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02552621,0.0001297365,0.9710633,0.0004972725,0.00004160755,0.00007440857,0.00005442502,0.0003680203,0.002245061],"genre_scores_gemma":[0.8780802,0.0001877453,0.118283,0.0004616589,0.00007677485,0.0001910371,0.0001460294,0.0001344209,0.002439258],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003223828,"threshold_uncertainty_score":0.01704937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06538845442596776,"score_gpt":0.2893372435940109,"score_spread":0.2239487891680431,"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."}}