{"id":"W3005437076","doi":"10.1073/pnas.2006337117","title":"Generation of thermofield double states and critical ground states with a quantum computer","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the National Academy of Sciences","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"Basic Energy Sciences; Division of Physics; Air Force Office of Scientific Research; Intelligence Advanced Research Projects Activity; High Energy Physics; U.S. Army; Office of the Director of National Intelligence","keywords":"Physics; Quantum entanglement; Quantum simulator; Quantum mechanics; Quantum; Ising model; Statistical physics; Quantum computer; Open quantum system; Quantum information; Quantum state; Quantum network; Theoretical physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000285935,0.0002184696,0.0002470606,0.0002509115,0.0004145658,0.0006312504,0.0004973569,0.0004145105,0.001929425],"category_scores_gemma":[0.0009061599,0.0001542788,0.0001682448,0.0002187694,0.0008873774,0.0008295781,0.0007112686,0.0006103727,0.00009549802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005077462,"about_ca_system_score_gemma":0.0004326618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004789623,"about_ca_topic_score_gemma":0.000851426,"domain_scores_codex":[0.9998811,0.00003004865,0.000006821506,0.00002515848,0.00003065446,0.00002618324],"domain_scores_gemma":[0.9996424,0.0001702626,0.00004149628,0.00007409797,0.00003653679,0.0000352348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005943075,0.0004165821,0.00256553,0.0001799618,0.00006569905,0.0002315883,0.0003188209,0.3289711,0.1874285,0.4428774,0.001283126,0.03506733],"study_design_scores_gemma":[0.00005150967,0.0001243345,0.0001730771,0.000008213221,0.00001020579,0.0000293713,0.00003581528,0.9185238,0.04321337,0.03719756,0.0006204954,0.00001230701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9189558,0.00009493243,0.07433181,0.0002490319,0.00002895389,0.0000441024,0.00003815165,0.000305923,0.005951296],"genre_scores_gemma":[0.9804833,0.00002270759,0.0189261,0.00002391639,0.000003310863,0.00002491595,0.00001755132,0.00001662312,0.0004817055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001929425,"threshold_uncertainty_score":0.006454587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06467249614952789,"score_gpt":0.3100669000831405,"score_spread":0.2453944039336126,"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."}}