{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:A556H2ZNINAUAILCBL46NRURVT","short_pith_number":"pith:A556H2ZN","schema_version":"1.0","canonical_sha256":"077be3eb2d43414021620af9e6c691acda061c33b35e7d933bca5558ca6d94fb","source":{"kind":"arxiv","id":"1907.05124","version":2},"attestation_state":"computed","paper":{"title":"Highly parallel algorithm for the Ising ground state searching problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","stat.ML"],"primary_cat":"quant-ph","authors_text":"A.N. Rubtsov, A. Yavorsky, E.A. Polyakov, L.A. Markovich","submitted_at":"2019-07-11T11:50:21Z","abstract_excerpt":"Finding an energy minimum in the Ising model is an exemplar objective, associated with many combinatorial optimization problems, that is computationally hard in general, but occurs in all areas of modern science. There are several numerical methods, providing solution for the medium size Ising spin systems. However, they are either computationally slow and badly parallelized, or do not give sufficiently good results for the large systems. In this paper, we present a highly parallel algorithm, called Mean-field Annealing from a Random State (MARS), incorporating the best features of the classic"},"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":"1907.05124","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2019-07-11T11:50:21Z","cross_cats_sorted":["cond-mat.stat-mech","stat.ML"],"title_canon_sha256":"71db30d91a831bd2a82f358b4cc1cbe0da1c7d0def5eb35b551ba3aa26e1f35d","abstract_canon_sha256":"6801c65db7c82ef7f370afc060149e0ae471c4b2f5dda0e085ce47a7c9c75413"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:29.595480Z","signature_b64":"e0J/FmOLkFXcPSrDd8dxS3dOk5zV495yDAEpsGIFfuQ3JKv1pIVx3nptYGZEJrJmjD9rdJd2JWxPp1KKiSZABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"077be3eb2d43414021620af9e6c691acda061c33b35e7d933bca5558ca6d94fb","last_reissued_at":"2026-05-17T23:40:29.593682Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:29.593682Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Highly parallel algorithm for the Ising ground state searching problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","stat.ML"],"primary_cat":"quant-ph","authors_text":"A.N. Rubtsov, A. Yavorsky, E.A. Polyakov, L.A. Markovich","submitted_at":"2019-07-11T11:50:21Z","abstract_excerpt":"Finding an energy minimum in the Ising model is an exemplar objective, associated with many combinatorial optimization problems, that is computationally hard in general, but occurs in all areas of modern science. There are several numerical methods, providing solution for the medium size Ising spin systems. However, they are either computationally slow and badly parallelized, or do not give sufficiently good results for the large systems. In this paper, we present a highly parallel algorithm, called Mean-field Annealing from a Random State (MARS), incorporating the best features of the classic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.05124","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":""},"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":"1907.05124","created_at":"2026-05-17T23:40:29.593773+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.05124v2","created_at":"2026-05-17T23:40:29.593773+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.05124","created_at":"2026-05-17T23:40:29.593773+00:00"},{"alias_kind":"pith_short_12","alias_value":"A556H2ZNINAU","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"A556H2ZNINAUAILC","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"A556H2ZN","created_at":"2026-05-18T12:33:12.712433+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/A556H2ZNINAUAILCBL46NRURVT","json":"https://pith.science/pith/A556H2ZNINAUAILCBL46NRURVT.json","graph_json":"https://pith.science/api/pith-number/A556H2ZNINAUAILCBL46NRURVT/graph.json","events_json":"https://pith.science/api/pith-number/A556H2ZNINAUAILCBL46NRURVT/events.json","paper":"https://pith.science/paper/A556H2ZN"},"agent_actions":{"view_html":"https://pith.science/pith/A556H2ZNINAUAILCBL46NRURVT","download_json":"https://pith.science/pith/A556H2ZNINAUAILCBL46NRURVT.json","view_paper":"https://pith.science/paper/A556H2ZN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.05124&json=true","fetch_graph":"https://pith.science/api/pith-number/A556H2ZNINAUAILCBL46NRURVT/graph.json","fetch_events":"https://pith.science/api/pith-number/A556H2ZNINAUAILCBL46NRURVT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A556H2ZNINAUAILCBL46NRURVT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A556H2ZNINAUAILCBL46NRURVT/action/storage_attestation","attest_author":"https://pith.science/pith/A556H2ZNINAUAILCBL46NRURVT/action/author_attestation","sign_citation":"https://pith.science/pith/A556H2ZNINAUAILCBL46NRURVT/action/citation_signature","submit_replication":"https://pith.science/pith/A556H2ZNINAUAILCBL46NRURVT/action/replication_record"}},"created_at":"2026-05-17T23:40:29.593773+00:00","updated_at":"2026-05-17T23:40:29.593773+00:00"}