{"id":"W2997289813","doi":"10.1103/physreva.104.032610","title":"Probabilistic simulation of quantum circuits using a deep-learning architecture","year":2021,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; MaRS; Vector Institute","funders":"Kavli Institute for Theoretical Physics, University of California, Santa Barbara; Natural Sciences and Engineering Research Council of Canada; Institut Périmètre de physique théorique; Instituto Nacional de Ciência e Tecnologia de Informação Quântica; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Instituto Serrapilheira; University of Illinois at Urbana-Champaign; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; National Science Foundation; Compute Canada; Innovation, Science and Economic Development Canada; Canadian Institute for Advanced Research; U.S. Department of Energy; Government of Canada","keywords":"Quantum algorithm; Quantum computer; Quantum machine learning; Quantum circuit; Quantum simulator; Computer science; Quantum network; Qubit; Quantum; Ansatz; Theoretical computer science; Open quantum system; Quantum dynamics; Statistical physics; 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":[0.0004841944,0.000361298,0.0004343729,0.0002485695,0.0003784264,0.0005429902,0.001312675,0.0008541535,0.002149408],"category_scores_gemma":[0.001463984,0.000265402,0.0004537292,0.0002759992,0.0008875267,0.001174083,0.0007083936,0.001158168,0.000155661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233344,"about_ca_system_score_gemma":0.0009639978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006104657,"about_ca_topic_score_gemma":0.007721196,"domain_scores_codex":[0.999861,0.00004639437,0.000005259536,0.00002674224,0.00003486545,0.00002566479],"domain_scores_gemma":[0.9995642,0.0002544181,0.00002573643,0.00005876728,0.00006233733,0.00003456477],"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":[0.00003073491,0.00003504244,0.0003076082,0.00002038393,0.00001713161,0.0000282797,0.00002789705,0.9653805,0.001687418,0.02329162,0.0002569415,0.008916405],"study_design_scores_gemma":[0.000001637183,0.000003182921,0.00001472195,5.029714e-7,6.448459e-7,0.000001324327,0.000001200591,0.9962524,0.0001916161,0.00348796,0.00004424963,7.405749e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.188673,0.000151261,0.8025776,0.0007790307,0.00006787368,0.00006435025,0.0001084995,0.0008235598,0.006754804],"genre_scores_gemma":[0.8949584,0.00007527957,0.1024195,0.0001018577,0.00001657588,0.0000798212,0.00009314386,0.00004377797,0.002211695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006104657,"threshold_uncertainty_score":0.01213825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133268467775311,"score_gpt":0.3433576732251804,"score_spread":0.3220249885474273,"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."}}