{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RW2RVIAO252BNFVS25D6MHQX42","short_pith_number":"pith:RW2RVIAO","schema_version":"1.0","canonical_sha256":"8db51aa00ed7741696b2d747e61e17e6af28606802d676121625257bbb91cfc5","source":{"kind":"arxiv","id":"2405.09169","version":3},"attestation_state":"computed","paper":{"title":"Towards a Linear-Ramp QAOA protocol: Evidence of a scaling advantage in solving some combinatorial optimization problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"quant-ph","authors_text":"J. A. Montanez-Barrera, Kristel Michielsen","submitted_at":"2024-05-15T08:07:52Z","abstract_excerpt":"The Quantum Approximate Optimization Algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters $\\{\\gamma_i, \\beta_i\\}_{i=0}^{p-1}$. While most prior work has focused on classically optimizing these parameters, we demonstrate that fixed linear ramp schedules, linear ramp QAOA (LR-QAOA), can efficiently approximate optimal solutions across diverse COPs. Simulations with up to $N_q=42$ qubits and $p=400$ layers suggest that the success probability scales as $P(x^*) \\approx 2^{-\\eta(p) N_q + C}$, where $\\et"},"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":"2405.09169","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2024-05-15T08:07:52Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"7b77b3c3c95a1073a8350cd6cd1bd269ce962e30fdb1c70140928683036d2633","abstract_canon_sha256":"75d2dd7c2fd8357fdc057576d58ff7996b5cc238887126f5968b511de281cd2d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:03.511167Z","signature_b64":"cs8DFeaXXasDvE+zNIrXHTg9ne3h5Qv85xn8mH+iY6XCTsNKWqJAWOT1NF23X3H/3THmssOWnUGRCqTDMUpJAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8db51aa00ed7741696b2d747e61e17e6af28606802d676121625257bbb91cfc5","last_reissued_at":"2026-07-05T11:49:03.510627Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:03.510627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards a Linear-Ramp QAOA protocol: Evidence of a scaling advantage in solving some combinatorial optimization problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"quant-ph","authors_text":"J. A. Montanez-Barrera, Kristel Michielsen","submitted_at":"2024-05-15T08:07:52Z","abstract_excerpt":"The Quantum Approximate Optimization Algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters $\\{\\gamma_i, \\beta_i\\}_{i=0}^{p-1}$. While most prior work has focused on classically optimizing these parameters, we demonstrate that fixed linear ramp schedules, linear ramp QAOA (LR-QAOA), can efficiently approximate optimal solutions across diverse COPs. Simulations with up to $N_q=42$ qubits and $p=400$ layers suggest that the success probability scales as $P(x^*) \\approx 2^{-\\eta(p) N_q + C}$, where $\\et"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.09169","kind":"arxiv","version":3},"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/2405.09169/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":"2405.09169","created_at":"2026-07-05T11:49:03.510688+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.09169v3","created_at":"2026-07-05T11:49:03.510688+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.09169","created_at":"2026-07-05T11:49:03.510688+00:00"},{"alias_kind":"pith_short_12","alias_value":"RW2RVIAO252B","created_at":"2026-07-05T11:49:03.510688+00:00"},{"alias_kind":"pith_short_16","alias_value":"RW2RVIAO252BNFVS","created_at":"2026-07-05T11:49:03.510688+00:00"},{"alias_kind":"pith_short_8","alias_value":"RW2RVIAO","created_at":"2026-07-05T11:49:03.510688+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01037","citing_title":"Quantum-Informed Portfolio Selection: An End-to-End Pipeline Validated on Trapped-Ion Hardware with Real Market Data","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2508.02590","citing_title":"Partitioned-Constraint QAOA (PC-QAOA): Structural State Preparation and Penalty Enforcement for Quantum Optimization","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2509.13528","citing_title":"Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2511.17259","citing_title":"Fundamental Limitations of QAOA on Constrained Problems and a Route to Exponential Enhancement","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2603.01809","citing_title":"Finite-Depth, Finite-Shot Guarantees for Constrained Quantum Optimization via Fej\\'er Filtering","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25275","citing_title":"Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2405.08810","citing_title":"Quantum computing with Qiskit","ref_index":71,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08367","citing_title":"Per-Shot Evaluation of QAOA on Max-Cut: A Black-Box Implementation Comparison with Goemans-Williamson","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RW2RVIAO252BNFVS25D6MHQX42","json":"https://pith.science/pith/RW2RVIAO252BNFVS25D6MHQX42.json","graph_json":"https://pith.science/api/pith-number/RW2RVIAO252BNFVS25D6MHQX42/graph.json","events_json":"https://pith.science/api/pith-number/RW2RVIAO252BNFVS25D6MHQX42/events.json","paper":"https://pith.science/paper/RW2RVIAO"},"agent_actions":{"view_html":"https://pith.science/pith/RW2RVIAO252BNFVS25D6MHQX42","download_json":"https://pith.science/pith/RW2RVIAO252BNFVS25D6MHQX42.json","view_paper":"https://pith.science/paper/RW2RVIAO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.09169&json=true","fetch_graph":"https://pith.science/api/pith-number/RW2RVIAO252BNFVS25D6MHQX42/graph.json","fetch_events":"https://pith.science/api/pith-number/RW2RVIAO252BNFVS25D6MHQX42/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RW2RVIAO252BNFVS25D6MHQX42/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RW2RVIAO252BNFVS25D6MHQX42/action/storage_attestation","attest_author":"https://pith.science/pith/RW2RVIAO252BNFVS25D6MHQX42/action/author_attestation","sign_citation":"https://pith.science/pith/RW2RVIAO252BNFVS25D6MHQX42/action/citation_signature","submit_replication":"https://pith.science/pith/RW2RVIAO252BNFVS25D6MHQX42/action/replication_record"}},"created_at":"2026-07-05T11:49:03.510688+00:00","updated_at":"2026-07-05T11:49:03.510688+00:00"}