{"id":"W4306808597","doi":"10.4230/lipics.itcs.2024.97","title":"Commuting Local Hamiltonians Beyond 2D","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; National Science Foundation","keywords":"Lemma (botany); Event (particle physics); Bounded function; Weak measurement; Mathematics; Discrete mathematics; Type (biology); Set (abstract data type); Quantum; State (computer science); Quantum state; Random access; Sequence (biology); Square root; Combinatorics; Algorithm; Computer science; Physics; Quantum mechanics; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004389166,0.0003289588,0.000340486,0.0002527896,0.0005729351,0.0001666803,0.003159018,0.000183883,0.0002112784],"category_scores_gemma":[0.00002602225,0.0004033842,0.0002481938,0.0006272242,0.0001300877,0.0001966857,0.006568356,0.001869675,0.0001030887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002728354,"about_ca_system_score_gemma":0.0002116137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006592776,"about_ca_topic_score_gemma":0.00002749295,"domain_scores_codex":[0.9977003,0.0003531555,0.0001986461,0.001130955,0.0001554435,0.0004615003],"domain_scores_gemma":[0.9978096,0.000149839,0.0002417285,0.001555555,0.00006698165,0.0001762859],"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":[0.000008778912,0.0001070214,0.00442507,0.00005979777,0.00008315803,0.0009197799,0.000790292,0.8263977,0.00000462434,0.1555444,0.0007320734,0.01092728],"study_design_scores_gemma":[0.0003068462,0.000061094,0.001117059,0.00003232839,0.0000314461,0.00001319149,0.0002107507,0.9719765,0.000009502228,0.01786908,0.007885441,0.0004868072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05687797,0.00008167193,0.9203483,0.0003156294,0.001132908,0.0001595102,0.00001603998,0.0005917939,0.02047621],"genre_scores_gemma":[0.9890624,0.00003889232,0.004257685,0.0003038116,0.00009892395,0.000001032797,0.00002903368,0.000024741,0.006183425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9321845,"threshold_uncertainty_score":0.9998418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0403656324946088,"score_gpt":0.1892337081471688,"score_spread":0.14886807565256,"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."}}