{"paper":{"title":"MPE-Adam: Multi-Population Evolutionary Optimization with Adam Refinement for QAOA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.ET","authors_text":"Chi Quan Luu, John Le, Thai T. Vu","submitted_at":"2026-06-25T07:03:22Z","abstract_excerpt":"Parameter optimization is a central bottleneck in variational quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA). The classical optimizer must navigate a high-dimensional, non-convex parameter space under measurement noise. From a quantum software perspective, this process forms a multi-stage workflow: global exploration of the parameter space followed by local refinement within the hybrid quantum-classical loop. Most existing approaches, however, employ single-stage optimizers that do not separate these roles, which limits the use of complementary strategies. We "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26670","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.26670/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}