{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:PZRRGQOEYW4LE2PJGOELU2IUWP","short_pith_number":"pith:PZRRGQOE","schema_version":"1.0","canonical_sha256":"7e631341c4c5b8b269e93388ba6914b3de9886defe4560414b954cc170365219","source":{"kind":"arxiv","id":"2108.03185","version":2},"attestation_state":"computed","paper":{"title":"Genetic optimization of quantum annealing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Annarita Scocco, Gianluca Passarelli, Pratibha Raghupati Hegde, Procolo Lucignano","submitted_at":"2021-08-06T15:59:23Z","abstract_excerpt":"The study of optimal control of quantum annealing by modulating the pace of evolution and by introducing a counterdiabatic potential has gained significant attention in recent times. In this work, we present a numerical approach based on genetic algorithms to improve the performance of quantum annealing, which evades the Landau-Zener transitions to navigate to the ground state of the final Hamiltonian with high probability. We optimize the annealing schedules starting from polynomial ansatz by treating their coefficients as chromosomes of the genetic algorithm. We also explore shortcuts to adi"},"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":"2108.03185","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2021-08-06T15:59:23Z","cross_cats_sorted":[],"title_canon_sha256":"e1482a0e569283a795d093c169185399412c7e936aa370741ded67f7996187e0","abstract_canon_sha256":"f842f16aacee47be6a2e7b638f1467132359e14cfe33838468157932378b74a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:52:13.884545Z","signature_b64":"ZGmIiK7cQiziNY/RpA1Suy4shNmYdY9zwWjZnjUy0csUG8LkkqdLq5QhQrIlGcEXUdTQmNhmNCe6WL82eUV+Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e631341c4c5b8b269e93388ba6914b3de9886defe4560414b954cc170365219","last_reissued_at":"2026-07-05T03:52:13.884073Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:52:13.884073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Genetic optimization of quantum annealing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Annarita Scocco, Gianluca Passarelli, Pratibha Raghupati Hegde, Procolo Lucignano","submitted_at":"2021-08-06T15:59:23Z","abstract_excerpt":"The study of optimal control of quantum annealing by modulating the pace of evolution and by introducing a counterdiabatic potential has gained significant attention in recent times. In this work, we present a numerical approach based on genetic algorithms to improve the performance of quantum annealing, which evades the Landau-Zener transitions to navigate to the ground state of the final Hamiltonian with high probability. We optimize the annealing schedules starting from polynomial ansatz by treating their coefficients as chromosomes of the genetic algorithm. We also explore shortcuts to adi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.03185","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2108.03185/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":"2108.03185","created_at":"2026-07-05T03:52:13.884129+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.03185v2","created_at":"2026-07-05T03:52:13.884129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.03185","created_at":"2026-07-05T03:52:13.884129+00:00"},{"alias_kind":"pith_short_12","alias_value":"PZRRGQOEYW4L","created_at":"2026-07-05T03:52:13.884129+00:00"},{"alias_kind":"pith_short_16","alias_value":"PZRRGQOEYW4LE2PJ","created_at":"2026-07-05T03:52:13.884129+00:00"},{"alias_kind":"pith_short_8","alias_value":"PZRRGQOE","created_at":"2026-07-05T03:52:13.884129+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/PZRRGQOEYW4LE2PJGOELU2IUWP","json":"https://pith.science/pith/PZRRGQOEYW4LE2PJGOELU2IUWP.json","graph_json":"https://pith.science/api/pith-number/PZRRGQOEYW4LE2PJGOELU2IUWP/graph.json","events_json":"https://pith.science/api/pith-number/PZRRGQOEYW4LE2PJGOELU2IUWP/events.json","paper":"https://pith.science/paper/PZRRGQOE"},"agent_actions":{"view_html":"https://pith.science/pith/PZRRGQOEYW4LE2PJGOELU2IUWP","download_json":"https://pith.science/pith/PZRRGQOEYW4LE2PJGOELU2IUWP.json","view_paper":"https://pith.science/paper/PZRRGQOE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.03185&json=true","fetch_graph":"https://pith.science/api/pith-number/PZRRGQOEYW4LE2PJGOELU2IUWP/graph.json","fetch_events":"https://pith.science/api/pith-number/PZRRGQOEYW4LE2PJGOELU2IUWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PZRRGQOEYW4LE2PJGOELU2IUWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PZRRGQOEYW4LE2PJGOELU2IUWP/action/storage_attestation","attest_author":"https://pith.science/pith/PZRRGQOEYW4LE2PJGOELU2IUWP/action/author_attestation","sign_citation":"https://pith.science/pith/PZRRGQOEYW4LE2PJGOELU2IUWP/action/citation_signature","submit_replication":"https://pith.science/pith/PZRRGQOEYW4LE2PJGOELU2IUWP/action/replication_record"}},"created_at":"2026-07-05T03:52:13.884129+00:00","updated_at":"2026-07-05T03:52:13.884129+00:00"}