{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QC32DAOJMKMUDS3AIS5KMKVJQH","short_pith_number":"pith:QC32DAOJ","schema_version":"1.0","canonical_sha256":"80b7a181c9629941cb6044baa62aa981c12c4203ff9a3c078971ac4789401953","source":{"kind":"arxiv","id":"1806.08815","version":3},"attestation_state":"computed","paper":{"title":"Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","cs.DM"],"primary_cat":"physics.comp-ph","authors_text":"Elisabetta Valiante, Gili Rosenberg, Helmut G. Katzgraber, Hirotaka Tamura, Maliheh Aramon, Toshiyuki Miyazawa","submitted_at":"2018-06-22T18:38:41Z","abstract_excerpt":"The Fujitsu Digital Annealer (DA) is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS hardware and currently solves problems of up to 1024 variables. The DA's algorithm is currently based on simulated annealing; however, it differs from it in its utilization of an efficient parallel-trial scheme and a dynamic escape mechanism. In addition, the DA exploits the massive parallelization that custom application-specific CMOS hardware allows. We compare the performance of the DA to simulated annealing and pa"},"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":"1806.08815","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-06-22T18:38:41Z","cross_cats_sorted":["cond-mat.stat-mech","cs.DM"],"title_canon_sha256":"b5942255b67d2e28c1ecb98933ef48b7ed3bc04fe8ba48f4d9f590411ebd262c","abstract_canon_sha256":"97f689ba7d72adbdb00d0689b55ba08f80eb18add6ad1567992f50d0c3a5740b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:11.616194Z","signature_b64":"/tFv7NoboiAHllmsvR1/Lr9OvztTlfkNMJfVS3ZMbABk9W4P3wqCs/tn+Gzy5GszMDevjqn3AXkNz0YZYdySCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80b7a181c9629941cb6044baa62aa981c12c4203ff9a3c078971ac4789401953","last_reissued_at":"2026-05-17T23:49:11.615502Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:11.615502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","cs.DM"],"primary_cat":"physics.comp-ph","authors_text":"Elisabetta Valiante, Gili Rosenberg, Helmut G. Katzgraber, Hirotaka Tamura, Maliheh Aramon, Toshiyuki Miyazawa","submitted_at":"2018-06-22T18:38:41Z","abstract_excerpt":"The Fujitsu Digital Annealer (DA) is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS hardware and currently solves problems of up to 1024 variables. The DA's algorithm is currently based on simulated annealing; however, it differs from it in its utilization of an efficient parallel-trial scheme and a dynamic escape mechanism. In addition, the DA exploits the massive parallelization that custom application-specific CMOS hardware allows. We compare the performance of the DA to simulated annealing and pa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.08815","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":""},"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":"1806.08815","created_at":"2026-05-17T23:49:11.615610+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.08815v3","created_at":"2026-05-17T23:49:11.615610+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.08815","created_at":"2026-05-17T23:49:11.615610+00:00"},{"alias_kind":"pith_short_12","alias_value":"QC32DAOJMKMU","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QC32DAOJMKMUDS3A","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QC32DAOJ","created_at":"2026-05-18T12:32:46.962924+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/QC32DAOJMKMUDS3AIS5KMKVJQH","json":"https://pith.science/pith/QC32DAOJMKMUDS3AIS5KMKVJQH.json","graph_json":"https://pith.science/api/pith-number/QC32DAOJMKMUDS3AIS5KMKVJQH/graph.json","events_json":"https://pith.science/api/pith-number/QC32DAOJMKMUDS3AIS5KMKVJQH/events.json","paper":"https://pith.science/paper/QC32DAOJ"},"agent_actions":{"view_html":"https://pith.science/pith/QC32DAOJMKMUDS3AIS5KMKVJQH","download_json":"https://pith.science/pith/QC32DAOJMKMUDS3AIS5KMKVJQH.json","view_paper":"https://pith.science/paper/QC32DAOJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.08815&json=true","fetch_graph":"https://pith.science/api/pith-number/QC32DAOJMKMUDS3AIS5KMKVJQH/graph.json","fetch_events":"https://pith.science/api/pith-number/QC32DAOJMKMUDS3AIS5KMKVJQH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QC32DAOJMKMUDS3AIS5KMKVJQH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QC32DAOJMKMUDS3AIS5KMKVJQH/action/storage_attestation","attest_author":"https://pith.science/pith/QC32DAOJMKMUDS3AIS5KMKVJQH/action/author_attestation","sign_citation":"https://pith.science/pith/QC32DAOJMKMUDS3AIS5KMKVJQH/action/citation_signature","submit_replication":"https://pith.science/pith/QC32DAOJMKMUDS3AIS5KMKVJQH/action/replication_record"}},"created_at":"2026-05-17T23:49:11.615610+00:00","updated_at":"2026-05-17T23:49:11.615610+00:00"}