{"id":"W3166259024","doi":"10.1038/s41467-022-33923-4","title":"Single-shot quantum error correction with the three-dimensional subsystem toric code","year":2022,"lang":"en","type":"preprint","venue":"Nature Communications","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Government of Canada; Ministry of Colleges and Universities; Innovation, Science and Economic Development Canada; Institut Périmètre de physique théorique; Simons Foundation","keywords":"Error detection and correction; Lattice (music); Computer science; Code (set theory); Toric code; Parity (physics); Quantum; Algorithm; Quantum error correction; Generalization; Parity bit; Quantum computer; Arithmetic; Topology (electrical circuits); Mathematics; Physics; Combinatorics; Quantum mechanics; Mathematical analysis; Set (abstract data type)","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":["metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007638641,0.0004188581,0.0003822714,0.0002054655,0.001860743,0.0003721392,0.007951506,0.0004250555,0.0000121405],"category_scores_gemma":[0.00007926708,0.0002891283,0.0002024489,0.0009778233,0.0001987446,0.0001077916,0.007785666,0.006526359,0.000009584252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002807192,"about_ca_system_score_gemma":0.0004159634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001620601,"about_ca_topic_score_gemma":0.002830426,"domain_scores_codex":[0.9969558,0.0007042385,0.0003907849,0.0007729477,0.0008013066,0.0003749437],"domain_scores_gemma":[0.9915592,0.001050406,0.0005377377,0.006416023,0.0003370785,0.00009955146],"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":[0.0001374595,0.002434596,0.002581226,0.0002915502,0.001071122,0.00008062012,0.007377707,0.5937581,0.0004518202,0.1439723,0.1840761,0.06376749],"study_design_scores_gemma":[0.0001730508,0.000132496,0.002419632,0.0001418371,0.00004802295,0.0001377717,0.00008165964,0.8957649,0.00002460561,0.001095672,0.09954386,0.0004364295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1614086,0.0915951,0.4006084,0.2633848,0.05686574,0.007761635,0.0006695908,0.006621638,0.01108459],"genre_scores_gemma":[0.9693733,0.00003602432,0.02877872,0.0008128665,0.0002684828,0.0002146836,0.0002166172,0.00004824104,0.0002510281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8079647,"threshold_uncertainty_score":0.9999561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04013611466188823,"score_gpt":0.2888612632282098,"score_spread":0.2487251485663216,"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."}}