{"id":"W7114820610","doi":"","title":"Migrating QAOA from Qiskit 1.x to 2.x: An experience report","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mitacs","keywords":"Software; State (computer science); Quantum; Sampling (signal processing); Affect (linguistics); Optimization algorithm; Root (linguistics)","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":[0.005487224,0.0005485312,0.0003388005,0.0004349629,0.0007519503,0.001563404,0.002160131,0.0007075462,0.004797639],"category_scores_gemma":[0.01383091,0.0005910683,0.000651113,0.0006048736,0.001249324,0.0030037,0.00216744,0.002716999,0.00171072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028346,"about_ca_system_score_gemma":0.001421286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003992745,"about_ca_topic_score_gemma":0.002641134,"domain_scores_codex":[0.9978843,0.0007562306,0.0001058068,0.0002660857,0.0007693844,0.0002181493],"domain_scores_gemma":[0.9956095,0.001682129,0.0001204416,0.001463577,0.0008794868,0.0002448604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001656107,0.001251229,0.01583572,0.0008756631,0.0002577825,0.0008000256,0.008927906,0.04392054,0.0576377,0.0606386,0.062432,0.7457668],"study_design_scores_gemma":[0.0007538327,0.00386391,0.01965612,0.0004544129,0.0002327273,0.00176406,0.002455083,0.3753356,0.174015,0.04342882,0.3776513,0.0003892624],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.5673762,0.002094323,0.340509,0.003119835,0.000584251,0.0004628649,0.0006372572,0.04543135,0.03978498],"genre_scores_gemma":[0.6387083,0.0009310003,0.3338821,0.001168899,0.0000606896,0.0002265998,0.00116982,0.01201978,0.01183284],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.005487224,"threshold_uncertainty_score":0.02901953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02556713489135078,"score_gpt":0.2985613040860577,"score_spread":0.272994169194707,"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."}}