{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TANPRPY2HZ5R7ZL6IGOGWAL2SN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7ff84f3392eed9410c5ded34b1c292cd947e77a119313044b5706f2ec61ba1ee","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-13T06:43:10Z","title_canon_sha256":"4c40e405ade12da2fc9d1803dc0db31df2c6c37a513d5bcbd44f261989db1b24"},"schema_version":"1.0","source":{"id":"2605.13072","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13072","created_at":"2026-05-18T03:08:58Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13072v1","created_at":"2026-05-18T03:08:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13072","created_at":"2026-05-18T03:08:58Z"},{"alias_kind":"pith_short_12","alias_value":"TANPRPY2HZ5R","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"TANPRPY2HZ5R7ZL6","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"TANPRPY2","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:85b4293d3e5d4753d8278ccd13bfde287354bfd69dbfa2a9f02dbecfdedeb187","target":"graph","created_at":"2026-05-18T03:08:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"our gradient-driven approach broadly outperforms heuristic baselines, ranking first on 101 instances. It exhibits zero-shot generalization across out-of-distribution graph topologies and scales."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The differentiable quantum evaluator acts as a high-fidelity performance surrogate that supplies accurate gradient guidance for the joint generator."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A differentiable generative evaluative network jointly learns graph partitions and QAOA parameter initializations, outperforming heuristic baselines on 101 of 183 tested QUBO, Ising, and MaxCut instances with zero-shot generalization."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A neural generator learns graph partitions and QAOA starting parameters together by back-propagating through a differentiable quantum evaluator."}],"snapshot_sha256":"070918e6bfe9c44be266967f6f9526e24190f10f404a0e5bb62efa21f0685ebf"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The quantum approximate optimization algorithm (QAOA) holds promise for combinatorial optimization but is constrained by limited qubits. While divide-and-conquer frameworks like QAOA$^{2}$ address scalability by partitioning graphs into subgraphs, existing methods suffer from two fundamental limitations: i) misalignment between heuristic partitioning metrics and quantum optimization goals, and ii) topology-blind parameter initialization that leads to optimization cold starts. To bridge these gaps, we propose Neural QAOA$^{2}$, an end-to-end differentiable framework that jointly generates graph","authors_text":"Jiahao Wu, Shengcai Liu, Zubin Zheng","cross_cats":["cs.AI"],"headline":"A neural generator learns graph partitions and QAOA starting parameters together by back-propagating through a differentiable quantum evaluator.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-13T06:43:10Z","title":"Neural QAOA$^{2}$: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimization"},"references":{"count":17,"internal_anchors":0,"resolved_work":17,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"We collect all applicants for partitionjinto a candidate setU j","work_id":"d61c6981-f3bc-4f51-b3ae-10fb785aa32f","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"• If|U j| ≤C (j) remain, all candidates are accepted","work_id":"326d9711-342a-4561-8a4f-143781d3b266","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"weakly-coupled","work_id":"a24ebc77-03ce-4f15-aac5-0d3e611f5133","year":2013},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"These features are standardized (zero mean, unit variance) within each graph instance to handle variations in graph scale","work_id":"991ce594-5d46-4cfd-ae9f-e3f510ff4cb5","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Edge weight normalization: Edge weights are normalized by the maximum absolute weight in the graph, ensuring wij ∈[−1,1]. Construction of the Offline Dataset (Doffline).To pre-train the quantum evalua","work_id":"fd5b4e8e-2caf-459f-bdd2-a5c869819e1f","year":null}],"snapshot_sha256":"0b1edc318022d2de88369e80a3d946820751f2ef49d049b425c7e066f2712ec8"},"source":{"id":"2605.13072","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T19:07:48.107641Z","id":"5f25995d-9dd4-4ecc-a0ed-b454f6c98a41","model_set":{"reader":"grok-4.3"},"one_line_summary":"A differentiable generative evaluative network jointly learns graph partitions and QAOA parameter initializations, outperforming heuristic baselines on 101 of 183 tested QUBO, Ising, and MaxCut instances with zero-shot generalization.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A neural generator learns graph partitions and QAOA starting parameters together by back-propagating through a differentiable quantum evaluator.","strongest_claim":"our gradient-driven approach broadly outperforms heuristic baselines, ranking first on 101 instances. It exhibits zero-shot generalization across out-of-distribution graph topologies and scales.","weakest_assumption":"The differentiable quantum evaluator acts as a high-fidelity performance surrogate that supplies accurate gradient guidance for the joint generator."}},"verdict_id":"5f25995d-9dd4-4ecc-a0ed-b454f6c98a41"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e19fd82c1bebb88ba0065814320968719c4682c454986fedf4936e05ae62b39c","target":"record","created_at":"2026-05-18T03:08:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7ff84f3392eed9410c5ded34b1c292cd947e77a119313044b5706f2ec61ba1ee","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-13T06:43:10Z","title_canon_sha256":"4c40e405ade12da2fc9d1803dc0db31df2c6c37a513d5bcbd44f261989db1b24"},"schema_version":"1.0","source":{"id":"2605.13072","kind":"arxiv","version":1}},"canonical_sha256":"981af8bf1a3e7b1fe57e419c6b017a937a24fc61483a24caa4a1ffd72d15901c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"981af8bf1a3e7b1fe57e419c6b017a937a24fc61483a24caa4a1ffd72d15901c","first_computed_at":"2026-05-18T03:08:58.844246Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:08:58.844246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5eD5jXKSBwFu7+IB/BG0vCDUy0RNo/tIRssynui9zqEhFOE2YeBaq0MoqtaJbdx+Is4TgLSkLnYVSD0CAx8bCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:08:58.844921Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13072","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e19fd82c1bebb88ba0065814320968719c4682c454986fedf4936e05ae62b39c","sha256:85b4293d3e5d4753d8278ccd13bfde287354bfd69dbfa2a9f02dbecfdedeb187"],"state_sha256":"3918353736df78eed2a4b1ec728cf393a039b607e0273c4d3f2b4d139110fee0"}