{"id":"W3016409050","doi":"10.1038/s41598-021-81138-2","title":"Cellular automaton decoders for topological quantum codes with noisy measurements and beyond","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Engineering and Physical Sciences Research Council; Government of Canada; Ministry of Colleges and Universities; Innovation, Science and Economic Development Canada; Institut Périmètre de physique théorique; Simons Foundation","keywords":"Computer science; Algorithm; Toric code; Cellular automaton; Quantum; Theoretical computer science; Qubit; Quantum error correction; Low-density parity-check code; Lattice (music); Decoding methods; Topology (electrical circuits); Quantum computer; Mathematics; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006226432,0.0002851931,0.0005568226,0.0004001931,0.0004872913,0.0009155043,0.0009841796,0.0008119948,0.001499481],"category_scores_gemma":[0.00540997,0.0001619249,0.0003304103,0.0004105782,0.001074196,0.0008310197,0.0009859087,0.0009555389,0.0004235188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007121932,"about_ca_system_score_gemma":0.0008660764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001470772,"about_ca_topic_score_gemma":0.002081679,"domain_scores_codex":[0.9994152,0.000171267,0.00003369882,0.00007550904,0.0002335373,0.0000708638],"domain_scores_gemma":[0.9977913,0.001179534,0.0001651083,0.0004437891,0.0003453867,0.00007481053],"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":[0.0001712607,0.00006669189,0.001482946,0.00009944714,0.00003756156,0.0002037167,0.0001952996,0.4997948,0.02106262,0.4346603,0.001156793,0.04106861],"study_design_scores_gemma":[0.00001244258,0.00003283984,0.00008511657,0.00000825564,0.000004937101,0.00003039797,0.00001237657,0.9540654,0.007435733,0.0375854,0.0007148072,0.00001216096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1900819,0.0002826861,0.8006725,0.0004492376,0.0001205456,0.00004463107,0.0001133377,0.0007670355,0.007468263],"genre_scores_gemma":[0.9013301,0.0001436202,0.09540187,0.00009619693,0.00002175248,0.0000838144,0.00008557368,0.00007888576,0.002758321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001499481,"threshold_uncertainty_score":0.005167305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002239646396994,"score_gpt":0.2443044144656336,"score_spread":0.2242820180016637,"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."}}