{"id":"W4404450770","doi":"10.48550/arxiv.2411.09131","title":"Artificial Intelligence for Quantum Computing","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Research Executive Agency; Engineering and Physical Sciences Research Council; European Commission; Government of Canada; Ministero dello Sviluppo Economico; Natural Resources Canada; UK Research and Innovation; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Quantum computer; Computer science; Quantum; Data 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":[0.002394134,0.0007579676,0.0008546868,0.001446002,0.001573111,0.004213197,0.001191088,0.003253926,0.01414327],"category_scores_gemma":[0.005113707,0.0003331244,0.0008272569,0.001078024,0.00661539,0.006794133,0.002803311,0.007754279,0.004251752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003189448,"about_ca_system_score_gemma":0.002131023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001781782,"about_ca_topic_score_gemma":0.001307065,"domain_scores_codex":[0.9983896,0.0006739877,0.00006681948,0.0002502966,0.0005008246,0.0001185544],"domain_scores_gemma":[0.9978513,0.001207567,0.00008800573,0.000423188,0.0003220863,0.0001078344],"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":[0.0000034266,0.000006219782,0.00003430175,0.00007291082,0.000005792402,0.0000127789,0.00004214446,0.0005372404,0.0001087194,0.9819931,0.008667678,0.008515655],"study_design_scores_gemma":[0.000002678604,0.00000458866,0.00003799732,0.00005866452,0.000001961953,0.00002197452,0.00001644513,0.00141132,0.00007049807,0.9289513,0.06941685,0.000005880279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.005362345,0.1413529,0.2711802,0.1427444,0.008888504,0.000176855,0.0006873949,0.001043214,0.4285643],"genre_scores_gemma":[0.424154,0.1642785,0.2302845,0.03577323,0.01611106,0.001203827,0.001136588,0.001034662,0.1260236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01414327,"threshold_uncertainty_score":0.04731393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08235524372634985,"score_gpt":0.2227170033704733,"score_spread":0.1403617596441235,"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."}}