{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:IPFRH6AZZQ5LJDHJTUUURRQN72","short_pith_number":"pith:IPFRH6AZ","schema_version":"1.0","canonical_sha256":"43cb13f819cc3ab48ce99d2948c60dfeb3557755ccc7fe044b0d3635346b77eb","source":{"kind":"arxiv","id":"2207.06294","version":2},"attestation_state":"computed","paper":{"title":"Reinforcement Learning Assisted Recursive QAOA","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"quant-ph","authors_text":"Sofiene Jerbi, Thomas B\\\"ack, Vedran Dunjko, Yash J. Patel","submitted_at":"2022-07-13T15:46:58Z","abstract_excerpt":"Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what famil"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2207.06294","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-07-13T15:46:58Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"2127c28b9f05d5918e1faa28598e130e17011b453007b44d52878cfba9178854","abstract_canon_sha256":"5542660471ec681eda8905e4d0bbf1338c138c54f57cbdc7ebdc9c83158a217e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:41:36.776911Z","signature_b64":"yOvBvL1/01h6BMcQgHyaNbHABI6FvvgQCkFNPwQUPoWfKAXG+mzMTfLckV1A1uxZbGo1rDvwITVago19th2nBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43cb13f819cc3ab48ce99d2948c60dfeb3557755ccc7fe044b0d3635346b77eb","last_reissued_at":"2026-07-05T07:41:36.776398Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:41:36.776398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reinforcement Learning Assisted Recursive QAOA","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"quant-ph","authors_text":"Sofiene Jerbi, Thomas B\\\"ack, Vedran Dunjko, Yash J. Patel","submitted_at":"2022-07-13T15:46:58Z","abstract_excerpt":"Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what famil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.06294","kind":"arxiv","version":2},"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/2207.06294/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2207.06294","created_at":"2026-07-05T07:41:36.776462+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.06294v2","created_at":"2026-07-05T07:41:36.776462+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.06294","created_at":"2026-07-05T07:41:36.776462+00:00"},{"alias_kind":"pith_short_12","alias_value":"IPFRH6AZZQ5L","created_at":"2026-07-05T07:41:36.776462+00:00"},{"alias_kind":"pith_short_16","alias_value":"IPFRH6AZZQ5LJDHJ","created_at":"2026-07-05T07:41:36.776462+00:00"},{"alias_kind":"pith_short_8","alias_value":"IPFRH6AZ","created_at":"2026-07-05T07:41:36.776462+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12310","citing_title":"Partitioned Iterative Quantum Scheduling of Satellites for Urgent Disaster Response: Case study of Wildfire","ref_index":53,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IPFRH6AZZQ5LJDHJTUUURRQN72","json":"https://pith.science/pith/IPFRH6AZZQ5LJDHJTUUURRQN72.json","graph_json":"https://pith.science/api/pith-number/IPFRH6AZZQ5LJDHJTUUURRQN72/graph.json","events_json":"https://pith.science/api/pith-number/IPFRH6AZZQ5LJDHJTUUURRQN72/events.json","paper":"https://pith.science/paper/IPFRH6AZ"},"agent_actions":{"view_html":"https://pith.science/pith/IPFRH6AZZQ5LJDHJTUUURRQN72","download_json":"https://pith.science/pith/IPFRH6AZZQ5LJDHJTUUURRQN72.json","view_paper":"https://pith.science/paper/IPFRH6AZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.06294&json=true","fetch_graph":"https://pith.science/api/pith-number/IPFRH6AZZQ5LJDHJTUUURRQN72/graph.json","fetch_events":"https://pith.science/api/pith-number/IPFRH6AZZQ5LJDHJTUUURRQN72/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IPFRH6AZZQ5LJDHJTUUURRQN72/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IPFRH6AZZQ5LJDHJTUUURRQN72/action/storage_attestation","attest_author":"https://pith.science/pith/IPFRH6AZZQ5LJDHJTUUURRQN72/action/author_attestation","sign_citation":"https://pith.science/pith/IPFRH6AZZQ5LJDHJTUUURRQN72/action/citation_signature","submit_replication":"https://pith.science/pith/IPFRH6AZZQ5LJDHJTUUURRQN72/action/replication_record"}},"created_at":"2026-07-05T07:41:36.776462+00:00","updated_at":"2026-07-05T07:41:36.776462+00:00"}