{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:H4TSJ7RDBBPDNGXYYG5TK33DMN","short_pith_number":"pith:H4TSJ7RD","schema_version":"1.0","canonical_sha256":"3f2724fe23085e369af8c1bb356f63635f50be232c370ee48066b5318db04b40","source":{"kind":"arxiv","id":"2211.15209","version":3},"attestation_state":"computed","paper":{"title":"Deep learning optimal quantum annealing schedules for random Ising models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Gianluca Passarelli, Giovanni Cantele, Pratibha Raghupati Hegde, Procolo Lucignano","submitted_at":"2022-11-28T10:36:37Z","abstract_excerpt":"A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory (LSTM) neural networks to automate the search for optimal annealing schedules for random Ising models on regular graphs. By training our network using locally-adiabatic annealing paths, we are able to predict optimal annealing schedules for unseen instances and even larger graphs than those used for training."},"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":"2211.15209","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2022-11-28T10:36:37Z","cross_cats_sorted":[],"title_canon_sha256":"dfe1668dca66a3300c1eaafdf2c46db6f74a29c675cf6a082b666c60198acb8c","abstract_canon_sha256":"ca7b0b40dfc952d32d9bf1724e54b561e1c1179cefdecaceca313de815fd2d63"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:38:32.311858Z","signature_b64":"tSLjYciUBsoJbhA9mxyyRXZEUY19GHdBRnx/C/+7NdeJuvQXULRFqOBdu4woukozxP+u2Zn6D2Y5gBhNmX26BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f2724fe23085e369af8c1bb356f63635f50be232c370ee48066b5318db04b40","last_reissued_at":"2026-07-05T06:38:32.311359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:38:32.311359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep learning optimal quantum annealing schedules for random Ising models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Gianluca Passarelli, Giovanni Cantele, Pratibha Raghupati Hegde, Procolo Lucignano","submitted_at":"2022-11-28T10:36:37Z","abstract_excerpt":"A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory (LSTM) neural networks to automate the search for optimal annealing schedules for random Ising models on regular graphs. By training our network using locally-adiabatic annealing paths, we are able to predict optimal annealing schedules for unseen instances and even larger graphs than those used for training."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.15209","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/2211.15209/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":"2211.15209","created_at":"2026-07-05T06:38:32.311419+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.15209v3","created_at":"2026-07-05T06:38:32.311419+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.15209","created_at":"2026-07-05T06:38:32.311419+00:00"},{"alias_kind":"pith_short_12","alias_value":"H4TSJ7RDBBPD","created_at":"2026-07-05T06:38:32.311419+00:00"},{"alias_kind":"pith_short_16","alias_value":"H4TSJ7RDBBPDNGXY","created_at":"2026-07-05T06:38:32.311419+00:00"},{"alias_kind":"pith_short_8","alias_value":"H4TSJ7RD","created_at":"2026-07-05T06:38:32.311419+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/H4TSJ7RDBBPDNGXYYG5TK33DMN","json":"https://pith.science/pith/H4TSJ7RDBBPDNGXYYG5TK33DMN.json","graph_json":"https://pith.science/api/pith-number/H4TSJ7RDBBPDNGXYYG5TK33DMN/graph.json","events_json":"https://pith.science/api/pith-number/H4TSJ7RDBBPDNGXYYG5TK33DMN/events.json","paper":"https://pith.science/paper/H4TSJ7RD"},"agent_actions":{"view_html":"https://pith.science/pith/H4TSJ7RDBBPDNGXYYG5TK33DMN","download_json":"https://pith.science/pith/H4TSJ7RDBBPDNGXYYG5TK33DMN.json","view_paper":"https://pith.science/paper/H4TSJ7RD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.15209&json=true","fetch_graph":"https://pith.science/api/pith-number/H4TSJ7RDBBPDNGXYYG5TK33DMN/graph.json","fetch_events":"https://pith.science/api/pith-number/H4TSJ7RDBBPDNGXYYG5TK33DMN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H4TSJ7RDBBPDNGXYYG5TK33DMN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H4TSJ7RDBBPDNGXYYG5TK33DMN/action/storage_attestation","attest_author":"https://pith.science/pith/H4TSJ7RDBBPDNGXYYG5TK33DMN/action/author_attestation","sign_citation":"https://pith.science/pith/H4TSJ7RDBBPDNGXYYG5TK33DMN/action/citation_signature","submit_replication":"https://pith.science/pith/H4TSJ7RDBBPDNGXYYG5TK33DMN/action/replication_record"}},"created_at":"2026-07-05T06:38:32.311419+00:00","updated_at":"2026-07-05T06:38:32.311419+00:00"}