{"id":"W4393191351","doi":"10.1103/physrevresearch.6.013326","title":"Efficient tensor network simulation of IBM's largest quantum processors","year":2024,"lang":"en","type":"article","venue":"Physical Review Research","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Compute Canada","funders":"Institute for Advanced Studies in Basic Sciences; Eusko Jaurlaritza; Egg Industry Center","keywords":"Qubit; Quantum computer; IBM; Quantum; Computer science; Superconducting quantum computing; Physics; Quantum mechanics; Topology (electrical circuits); Theoretical computer science; Mathematics; Combinatorics; Optics","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":[0.001852729,0.0001411832,0.0003371307,0.0001042237,0.0001538236,0.0001234652,0.0007916776,0.00002703625,0.000007638013],"category_scores_gemma":[0.0004352142,0.00009832571,0.0001654803,0.002391339,0.0001061901,0.00006344049,0.0004222828,0.0005777379,0.0001397301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003023505,"about_ca_system_score_gemma":0.0001469556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001070836,"about_ca_topic_score_gemma":3.343266e-7,"domain_scores_codex":[0.9972623,0.0003510546,0.0002882604,0.000515091,0.001040162,0.0005431183],"domain_scores_gemma":[0.9976391,0.001362531,0.00004744685,0.0005118963,0.0003150894,0.0001239974],"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":[0.000004905035,0.0002634603,0.00002009545,0.005346749,0.00002924152,0.00003065765,0.0003530821,0.7085865,0.0002348616,0.09982395,0.001840529,0.183466],"study_design_scores_gemma":[0.000040649,0.0001207932,0.0001707918,0.003476764,0.000006780286,0.000002801725,0.000001334572,0.9736692,0.00006476678,0.01175045,0.01059012,0.0001055439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3165401,0.2734236,0.3914681,0.01189157,0.001013801,0.002731645,0.00001606258,0.0009332112,0.001981938],"genre_scores_gemma":[0.9970731,0.0007984038,0.001486958,0.00007927421,0.0004652957,0.00002624656,0.000001957863,0.00001606022,0.0000527176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.680533,"threshold_uncertainty_score":0.4009606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05217312381632778,"score_gpt":0.4144442179835854,"score_spread":0.3622710941672577,"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."}}