{"id":"W4385958524","doi":"10.48550/arxiv.2308.01068","title":"Neural network encoded variational quantum algorithms","year":2023,"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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Parameterized complexity; Artificial neural network; Hamiltonian (control theory); Computer science; Quantum computer; Quantum; Quantum circuit; Ansatz; Ground state; Quantum machine learning; Algorithm; Quantum algorithm; Bethe ansatz; Artificial intelligence; Mathematical optimization; Quantum network; Mathematics; Quantum mechanics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005099652,0.0004749841,0.0004544967,0.0002725415,0.0004152967,0.0002835166,0.002816915,0.0003566381,0.00001748653],"category_scores_gemma":[0.00004644497,0.0005254252,0.0003575443,0.001183277,0.00009957518,0.0002205022,0.003968911,0.001167534,0.0001832842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001300141,"about_ca_system_score_gemma":0.0002899829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000191917,"about_ca_topic_score_gemma":0.00002058858,"domain_scores_codex":[0.9967988,0.0002671684,0.0003228481,0.001644524,0.0002225844,0.0007441086],"domain_scores_gemma":[0.9975172,0.0003632107,0.0003404112,0.001361817,0.0001705258,0.0002468583],"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":[0.000006810747,0.00003634643,0.0003252476,0.00002493226,0.00007489778,0.0003949777,0.0001356005,0.8730281,0.000002148246,0.1234287,0.001359878,0.001182335],"study_design_scores_gemma":[0.0002556543,0.00004479456,0.002181772,0.00006611392,0.0000278057,0.00001186717,0.000008264525,0.8070519,0.000002806425,0.1895465,0.0003359994,0.0004665556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06371767,0.00006387343,0.9293344,0.000671098,0.004438783,0.0002659969,0.0000303019,0.001241486,0.0002363725],"genre_scores_gemma":[0.9676826,0.00005585411,0.02925665,0.0002455202,0.001221631,0.000001921303,0.00006803592,0.00005313202,0.001414638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9039649,"threshold_uncertainty_score":0.9997197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06197315344912986,"score_gpt":0.1979388471108701,"score_spread":0.1359656936617403,"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."}}