{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GMFH3URPEJ2KMCGLVTU5QZ6DYT","short_pith_number":"pith:GMFH3URP","schema_version":"1.0","canonical_sha256":"330a7dd22f2274a608cbace9d867c3c4d43f5b83e5faf3ea062d92a5badd88a0","source":{"kind":"arxiv","id":"2502.13947","version":1},"attestation_state":"computed","paper":{"title":"IC-D2S: A Hybrid Ising-Classical-Machines Data-Driven QUBO Solver Method","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Armin Abdollahi, Massoud Pedram, Mehdi Kamal","submitted_at":"2025-02-19T18:42:50Z","abstract_excerpt":"We present a heuristic algorithm designed to solve Quadratic Unconstrained Binary Optimization (QUBO) problems efficiently. The algorithm, referred to as IC-D2S, leverages a hybrid approach using Ising and classical machines to address very large problem sizes. Considering the practical limitation on the size of the Ising machine(IM), our algorithm partitions the QUBO problem into a collection of QUBO subproblems (called subQUBOs) and utilizes the IM to solve each subQUBO. Our proposed heuristic algorithm uses a set of control parameters to generate the subQUBOs and explore the search space. A"},"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":"2502.13947","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-02-19T18:42:50Z","cross_cats_sorted":[],"title_canon_sha256":"84a3ace7a98d0e6d1b21774bab976e7dc02830056da51801721011fd48f9e38f","abstract_canon_sha256":"bea5d98a1f729fe49bcd3ac24556d0549ffb0254d49a565f63351957e281543c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:02.845447Z","signature_b64":"uuE7ehxuKP9Rc3EanZlJmRN6wG7PXmQeoSUfEIBQ/3+5m2jC1LkzyoU74I6vALnMzgsHaE2fxY9Orupvgp+lBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"330a7dd22f2274a608cbace9d867c3c4d43f5b83e5faf3ea062d92a5badd88a0","last_reissued_at":"2026-07-05T10:17:02.845032Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:02.845032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IC-D2S: A Hybrid Ising-Classical-Machines Data-Driven QUBO Solver Method","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Armin Abdollahi, Massoud Pedram, Mehdi Kamal","submitted_at":"2025-02-19T18:42:50Z","abstract_excerpt":"We present a heuristic algorithm designed to solve Quadratic Unconstrained Binary Optimization (QUBO) problems efficiently. The algorithm, referred to as IC-D2S, leverages a hybrid approach using Ising and classical machines to address very large problem sizes. Considering the practical limitation on the size of the Ising machine(IM), our algorithm partitions the QUBO problem into a collection of QUBO subproblems (called subQUBOs) and utilizes the IM to solve each subQUBO. Our proposed heuristic algorithm uses a set of control parameters to generate the subQUBOs and explore the search space. A"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.13947","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/2502.13947/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":"2502.13947","created_at":"2026-07-05T10:17:02.845088+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.13947v1","created_at":"2026-07-05T10:17:02.845088+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.13947","created_at":"2026-07-05T10:17:02.845088+00:00"},{"alias_kind":"pith_short_12","alias_value":"GMFH3URPEJ2K","created_at":"2026-07-05T10:17:02.845088+00:00"},{"alias_kind":"pith_short_16","alias_value":"GMFH3URPEJ2KMCGL","created_at":"2026-07-05T10:17:02.845088+00:00"},{"alias_kind":"pith_short_8","alias_value":"GMFH3URP","created_at":"2026-07-05T10:17:02.845088+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GMFH3URPEJ2KMCGLVTU5QZ6DYT","json":"https://pith.science/pith/GMFH3URPEJ2KMCGLVTU5QZ6DYT.json","graph_json":"https://pith.science/api/pith-number/GMFH3URPEJ2KMCGLVTU5QZ6DYT/graph.json","events_json":"https://pith.science/api/pith-number/GMFH3URPEJ2KMCGLVTU5QZ6DYT/events.json","paper":"https://pith.science/paper/GMFH3URP"},"agent_actions":{"view_html":"https://pith.science/pith/GMFH3URPEJ2KMCGLVTU5QZ6DYT","download_json":"https://pith.science/pith/GMFH3URPEJ2KMCGLVTU5QZ6DYT.json","view_paper":"https://pith.science/paper/GMFH3URP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.13947&json=true","fetch_graph":"https://pith.science/api/pith-number/GMFH3URPEJ2KMCGLVTU5QZ6DYT/graph.json","fetch_events":"https://pith.science/api/pith-number/GMFH3URPEJ2KMCGLVTU5QZ6DYT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GMFH3URPEJ2KMCGLVTU5QZ6DYT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GMFH3URPEJ2KMCGLVTU5QZ6DYT/action/storage_attestation","attest_author":"https://pith.science/pith/GMFH3URPEJ2KMCGLVTU5QZ6DYT/action/author_attestation","sign_citation":"https://pith.science/pith/GMFH3URPEJ2KMCGLVTU5QZ6DYT/action/citation_signature","submit_replication":"https://pith.science/pith/GMFH3URPEJ2KMCGLVTU5QZ6DYT/action/replication_record"}},"created_at":"2026-07-05T10:17:02.845088+00:00","updated_at":"2026-07-05T10:17:02.845088+00:00"}