{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RRYVO5B5NKEZGGT2GMLYLWS4AN","short_pith_number":"pith:RRYVO5B5","schema_version":"1.0","canonical_sha256":"8c7157743d6a89931a7a331785da5c03558b5db17e3b5a078b8a3717a36060fb","source":{"kind":"arxiv","id":"2107.02789","version":3},"attestation_state":"computed","paper":{"title":"Digitized-counterdiabatic quantum approximate optimization algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"A. del Campo, E. Solano, F. Albarr\\'an-Arriagada, K. Paul, N. N. Hegade, P. Chandarana, Xi Chen","submitted_at":"2021-07-06T17:57:32Z","abstract_excerpt":"The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since QAOA is an ansatz-dependent algorithm, there is always a need to design ansatz for better optimization. To this end, we propose a digitized version of QAOA enhanced via the use of shortcuts to adiabaticity. Specifically, we use a counterdiabatic (CD) driving term to design a better ansatz, along with the Hamiltonian and mixing terms, enhancing th"},"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":"2107.02789","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2021-07-06T17:57:32Z","cross_cats_sorted":[],"title_canon_sha256":"7e2f9449c931bacce252c4e1579c93b2be863a2756287190e8b31e6ac48eca9c","abstract_canon_sha256":"211e197d03a5b39275d6f68e1cef031ea032d903e9e6653849450460a0611597"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:02:17.432120Z","signature_b64":"I4BomYm4M4yw+NXe9uHc3o2FwUQS2UsRSKvYyuJqAl/vNmqAEEVRAj8/vZQDfQ081Zj5NOLEcAgpX8IDj2YXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c7157743d6a89931a7a331785da5c03558b5db17e3b5a078b8a3717a36060fb","last_reissued_at":"2026-07-05T04:02:17.431717Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:02:17.431717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Digitized-counterdiabatic quantum approximate optimization algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"A. del Campo, E. Solano, F. Albarr\\'an-Arriagada, K. Paul, N. N. Hegade, P. Chandarana, Xi Chen","submitted_at":"2021-07-06T17:57:32Z","abstract_excerpt":"The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since QAOA is an ansatz-dependent algorithm, there is always a need to design ansatz for better optimization. To this end, we propose a digitized version of QAOA enhanced via the use of shortcuts to adiabaticity. Specifically, we use a counterdiabatic (CD) driving term to design a better ansatz, along with the Hamiltonian and mixing terms, enhancing th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.02789","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/2107.02789/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":"2107.02789","created_at":"2026-07-05T04:02:17.431776+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.02789v3","created_at":"2026-07-05T04:02:17.431776+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.02789","created_at":"2026-07-05T04:02:17.431776+00:00"},{"alias_kind":"pith_short_12","alias_value":"RRYVO5B5NKEZ","created_at":"2026-07-05T04:02:17.431776+00:00"},{"alias_kind":"pith_short_16","alias_value":"RRYVO5B5NKEZGGT2","created_at":"2026-07-05T04:02:17.431776+00:00"},{"alias_kind":"pith_short_8","alias_value":"RRYVO5B5","created_at":"2026-07-05T04:02:17.431776+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.22924","citing_title":"Optimizing QUBO on a quantum computer by mimicking imaginary time evolution","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RRYVO5B5NKEZGGT2GMLYLWS4AN","json":"https://pith.science/pith/RRYVO5B5NKEZGGT2GMLYLWS4AN.json","graph_json":"https://pith.science/api/pith-number/RRYVO5B5NKEZGGT2GMLYLWS4AN/graph.json","events_json":"https://pith.science/api/pith-number/RRYVO5B5NKEZGGT2GMLYLWS4AN/events.json","paper":"https://pith.science/paper/RRYVO5B5"},"agent_actions":{"view_html":"https://pith.science/pith/RRYVO5B5NKEZGGT2GMLYLWS4AN","download_json":"https://pith.science/pith/RRYVO5B5NKEZGGT2GMLYLWS4AN.json","view_paper":"https://pith.science/paper/RRYVO5B5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.02789&json=true","fetch_graph":"https://pith.science/api/pith-number/RRYVO5B5NKEZGGT2GMLYLWS4AN/graph.json","fetch_events":"https://pith.science/api/pith-number/RRYVO5B5NKEZGGT2GMLYLWS4AN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RRYVO5B5NKEZGGT2GMLYLWS4AN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RRYVO5B5NKEZGGT2GMLYLWS4AN/action/storage_attestation","attest_author":"https://pith.science/pith/RRYVO5B5NKEZGGT2GMLYLWS4AN/action/author_attestation","sign_citation":"https://pith.science/pith/RRYVO5B5NKEZGGT2GMLYLWS4AN/action/citation_signature","submit_replication":"https://pith.science/pith/RRYVO5B5NKEZGGT2GMLYLWS4AN/action/replication_record"}},"created_at":"2026-07-05T04:02:17.431776+00:00","updated_at":"2026-07-05T04:02:17.431776+00:00"}