{"id":"W4378718360","doi":"10.48550/arxiv.2305.16992","title":"Probing scrambling and operator size distributions using random mixed states and local measurements","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum many-body systems","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Scrambling; Operator (biology); Statistical physics; Observable; Computer science; Quantum state; Algorithm; Quantum; Theoretical computer science; Physics; Quantum mechanics","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":[0.001802024,0.0004682754,0.0004498014,0.0006384976,0.0005769146,0.001267393,0.001116665,0.0007184743,0.001991587],"category_scores_gemma":[0.007276371,0.0003870846,0.0003114847,0.0004808348,0.002596181,0.002926349,0.002012647,0.001516908,0.0003779821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006355064,"about_ca_system_score_gemma":0.000506065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000309266,"about_ca_topic_score_gemma":0.0003765763,"domain_scores_codex":[0.9987422,0.0005208994,0.00004591171,0.0002644242,0.0003335734,0.00009294537],"domain_scores_gemma":[0.9952194,0.002638181,0.0007517581,0.0009587259,0.000217832,0.0002141144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007138982,0.000341704,0.003396107,0.0002415518,0.00009904837,0.0003567213,0.0005463822,0.06300047,0.2128011,0.6763977,0.001158106,0.0409473],"study_design_scores_gemma":[0.00007703497,0.0003309517,0.001201794,0.00003062317,0.00002457826,0.0001851833,0.0001418534,0.696455,0.1354693,0.1639412,0.002029444,0.0001129611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3586172,0.0002112253,0.6329017,0.0005362457,0.00007133624,0.0001093392,0.0001762806,0.0006040859,0.006772652],"genre_scores_gemma":[0.9231203,0.0001374804,0.07479338,0.0001264336,0.00003077784,0.0001458256,0.00007185098,0.00009762559,0.001476233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001991587,"threshold_uncertainty_score":0.009530127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1162622764197132,"score_gpt":0.2193332339326979,"score_spread":0.1030709575129846,"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."}}