{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:KX3TWETTNJ5TPD5CMKXANRAHRQ","short_pith_number":"pith:KX3TWETT","schema_version":"1.0","canonical_sha256":"55f73b12736a7b378fa262ae06c4078c31e24bc4e44b6d102d47c3fa43c84c09","source":{"kind":"arxiv","id":"2111.13454","version":3},"attestation_state":"computed","paper":{"title":"Performance comparison of optimization methods on variational quantum algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Bruno Senjean, Charles Moussa, Diederick Vermetten, Hao Wang, Thomas B\\\"ack, Thomas E. O'Brien, Vedran Dunjko, Xavier Bonet-Monroig","submitted_at":"2021-11-26T12:13:20Z","abstract_excerpt":"Variational quantum algorithms (VQAs) offer a promising path toward using near-term quantum hardware for applications in academic and industrial research. These algorithms aim to find approximate solutions to quantum problems by optimizing a parametrized quantum circuit using a classical optimization algorithm. A successful VQA requires fast and reliable classical optimization algorithms. Understanding and optimizing how off-the-shelf optimization methods perform in this context is important for the future of the field. In this work, we study the performance of four commonly used gradient-free"},"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":"2111.13454","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2021-11-26T12:13:20Z","cross_cats_sorted":[],"title_canon_sha256":"b123996608cb2089396893818402958ccf3065389e0a4f08ff4c4208f966c58e","abstract_canon_sha256":"5ec2ee31134b6d1452de06f0885dbcd2b764530e675c2280b4842957cf57620e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:12.616111Z","signature_b64":"QywKAyHoVzDS1nXGcRwAXQ+8rPzy55YDrcw+T59fY15/8FrVv9OScqr0Lq02Zp/Kd1Qr5aj/XekWs3flauREAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55f73b12736a7b378fa262ae06c4078c31e24bc4e44b6d102d47c3fa43c84c09","last_reissued_at":"2026-07-05T05:49:12.615632Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:12.615632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Performance comparison of optimization methods on variational quantum algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Bruno Senjean, Charles Moussa, Diederick Vermetten, Hao Wang, Thomas B\\\"ack, Thomas E. O'Brien, Vedran Dunjko, Xavier Bonet-Monroig","submitted_at":"2021-11-26T12:13:20Z","abstract_excerpt":"Variational quantum algorithms (VQAs) offer a promising path toward using near-term quantum hardware for applications in academic and industrial research. These algorithms aim to find approximate solutions to quantum problems by optimizing a parametrized quantum circuit using a classical optimization algorithm. A successful VQA requires fast and reliable classical optimization algorithms. Understanding and optimizing how off-the-shelf optimization methods perform in this context is important for the future of the field. In this work, we study the performance of four commonly used gradient-free"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.13454","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/2111.13454/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":"2111.13454","created_at":"2026-07-05T05:49:12.615706+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.13454v3","created_at":"2026-07-05T05:49:12.615706+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.13454","created_at":"2026-07-05T05:49:12.615706+00:00"},{"alias_kind":"pith_short_12","alias_value":"KX3TWETTNJ5T","created_at":"2026-07-05T05:49:12.615706+00:00"},{"alias_kind":"pith_short_16","alias_value":"KX3TWETTNJ5TPD5C","created_at":"2026-07-05T05:49:12.615706+00:00"},{"alias_kind":"pith_short_8","alias_value":"KX3TWETT","created_at":"2026-07-05T05:49:12.615706+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09734","citing_title":"Adaptive directional gradients for parameterised quantum circuits","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KX3TWETTNJ5TPD5CMKXANRAHRQ","json":"https://pith.science/pith/KX3TWETTNJ5TPD5CMKXANRAHRQ.json","graph_json":"https://pith.science/api/pith-number/KX3TWETTNJ5TPD5CMKXANRAHRQ/graph.json","events_json":"https://pith.science/api/pith-number/KX3TWETTNJ5TPD5CMKXANRAHRQ/events.json","paper":"https://pith.science/paper/KX3TWETT"},"agent_actions":{"view_html":"https://pith.science/pith/KX3TWETTNJ5TPD5CMKXANRAHRQ","download_json":"https://pith.science/pith/KX3TWETTNJ5TPD5CMKXANRAHRQ.json","view_paper":"https://pith.science/paper/KX3TWETT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.13454&json=true","fetch_graph":"https://pith.science/api/pith-number/KX3TWETTNJ5TPD5CMKXANRAHRQ/graph.json","fetch_events":"https://pith.science/api/pith-number/KX3TWETTNJ5TPD5CMKXANRAHRQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KX3TWETTNJ5TPD5CMKXANRAHRQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KX3TWETTNJ5TPD5CMKXANRAHRQ/action/storage_attestation","attest_author":"https://pith.science/pith/KX3TWETTNJ5TPD5CMKXANRAHRQ/action/author_attestation","sign_citation":"https://pith.science/pith/KX3TWETTNJ5TPD5CMKXANRAHRQ/action/citation_signature","submit_replication":"https://pith.science/pith/KX3TWETTNJ5TPD5CMKXANRAHRQ/action/replication_record"}},"created_at":"2026-07-05T05:49:12.615706+00:00","updated_at":"2026-07-05T05:49:12.615706+00:00"}