{"id":"W4386044496","doi":"10.48550/arxiv.2308.08815","title":"Quantum Process Learning Through Neural Emulation","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Canada; Croucher Foundation; National Natural Science Foundation of China; Basic and Applied Basic Research Foundation of Guangdong Province; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada; John Templeton Foundation","keywords":"Emulation; Computer science; Artificial neural network; Quantum; Process (computing); Quantum state; Representation (politics); Action (physics); Artificial intelligence; Theoretical computer science; Physics; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000967846,0.0004963524,0.0006353608,0.0003819755,0.0003565067,0.0008051152,0.001278205,0.001079393,0.001694734],"category_scores_gemma":[0.004206378,0.0003861291,0.0004839881,0.0003661514,0.00135317,0.001955053,0.001318149,0.00165531,0.0002764155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038424,"about_ca_system_score_gemma":0.0006905951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002599922,"about_ca_topic_score_gemma":0.00242794,"domain_scores_codex":[0.999624,0.0001562644,0.00001458163,0.00007257833,0.00009403977,0.00003854713],"domain_scores_gemma":[0.9989007,0.0006737189,0.00009556878,0.0001735227,0.0001194845,0.00003704524],"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":[0.00003753863,0.00002588307,0.0003068678,0.00002534032,0.00002171956,0.00002438573,0.00002451191,0.9425313,0.001251235,0.04232424,0.0003391661,0.01308785],"study_design_scores_gemma":[0.000001745756,0.000002649446,0.00001737543,0.000001316265,8.225733e-7,0.000002368664,0.000001007953,0.9905897,0.0002691433,0.009028974,0.0000835476,0.0000013004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03686656,0.0001969743,0.9582931,0.0005026874,0.00004073525,0.00003679832,0.00006507613,0.0004083477,0.0035898],"genre_scores_gemma":[0.8690573,0.0002761879,0.1260121,0.0002009971,0.00005780171,0.0001702778,0.0001722311,0.00009330987,0.003959831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002599922,"threshold_uncertainty_score":0.007534266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07862816418234994,"score_gpt":0.2210356981626524,"score_spread":0.1424075339803025,"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."}}