{"id":"W4406276777","doi":"10.1109/qce60285.2024.00149","title":"Towards a Cryogenic CMOS-Memristor Neural Decoder for Quantum Error Correction","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique","funders":"Fonds de recherche du Québec – Nature et technologies; Government of Canada; Ministry of Colleges and Universities; Institut National des Sciences Appliquées de Lyon; Université de Sherbrooke; CMC Microsystems; Indian National Science Academy; Natural Sciences and Engineering Research Council of Canada; Institut Périmètre de physique théorique; Centre National de la Recherche Scientifique; Innovation, Science and Economic Development Canada; École Centrale de Lyon; National Science Foundation","keywords":"CMOS; Memristor; Computer science; Electronic engineering; Error detection and correction; Quantum; Decoding methods; Optoelectronics; Electrical engineering; Physics; Telecommunications; Engineering; Algorithm; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006332243,0.0001325443,0.0001140654,0.00006036587,0.00006909967,0.00003893039,0.00006733063,0.00004972707,0.00007126892],"category_scores_gemma":[0.00002701078,0.0001193332,0.0001071699,0.0001390214,0.00001030523,0.0001703043,0.00001568691,0.0001449022,0.0000392267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006745626,"about_ca_system_score_gemma":0.00001250381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003681929,"about_ca_topic_score_gemma":0.00002020162,"domain_scores_codex":[0.9993562,0.00000719489,0.0001551846,0.0001866617,0.00006411257,0.0002306706],"domain_scores_gemma":[0.9997293,0.00009046088,0.000008148346,0.00009865543,0.00001904147,0.00005442277],"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":[0.00009500569,0.00002966755,0.00002970544,0.0008742811,0.0001291813,0.00005102314,0.0005769624,0.401481,0.1304456,0.002506132,0.07879932,0.3849821],"study_design_scores_gemma":[0.0001055149,0.00005338577,0.00004394165,0.00002432936,0.0000163431,0.00003953158,0.00004721662,0.9417696,0.03005644,0.000424019,0.02725314,0.0001665142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.413633,0.001477089,0.5687675,0.0001417957,0.01174746,0.0002966453,0.000006756426,0.002333286,0.001596439],"genre_scores_gemma":[0.9956594,0.00001219261,0.001580226,0.00008083144,0.0003854466,0.00003294445,0.000006581654,0.00004691804,0.002195442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5820264,"threshold_uncertainty_score":0.4866265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02742387096245544,"score_gpt":0.2848080242883656,"score_spread":0.2573841533259101,"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."}}