{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:WWJIIWEGBAYH6RXATLCAAXG53C","short_pith_number":"pith:WWJIIWEG","schema_version":"1.0","canonical_sha256":"b59284588608307f46e09ac4005cddd8b3fa9be4267506eb1f6566a5ef3a51c2","source":{"kind":"arxiv","id":"2106.01388","version":1},"attestation_state":"computed","paper":{"title":"Single-component gradient rules for variational quantum algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Frederik Wilde, Jens Eisert, Shozab Qasim, Thomas Hubregtsen","submitted_at":"2021-06-02T18:00:10Z","abstract_excerpt":"Many near-term quantum computing algorithms are conceived as variational quantum algorithms, in which parameterized quantum circuits are optimized in a hybrid quantum-classical setup. Examples are variational quantum eigensolvers, quantum approximate optimization algorithms as well as various algorithms in the context of quantum-assisted machine learning. A common bottleneck of any such algorithm is constituted by the optimization of the variational parameters. A popular set of optimization methods work on the estimate of the gradient, obtained by means of circuit evaluations. We will refer to"},"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":"2106.01388","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-06-02T18:00:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d02a9eb6a51834f9f70cd7c29f0a2e56695004cacf7c10e321f5eb4eb2093919","abstract_canon_sha256":"27b6f79174236f9a30e4f89d13110ddab2225e37125acfc08323b8fd3a1b9977"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:23:04.378547Z","signature_b64":"7g8USsvx8JBpbjPdhFNinJGeVK9lRyyBl5pUfTvK9ewhtjVrQukTrmFli3KRgypb7jOBa//0cDx3vmp8TgxPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b59284588608307f46e09ac4005cddd8b3fa9be4267506eb1f6566a5ef3a51c2","last_reissued_at":"2026-07-05T04:23:04.378042Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:23:04.378042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Single-component gradient rules for variational quantum algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Frederik Wilde, Jens Eisert, Shozab Qasim, Thomas Hubregtsen","submitted_at":"2021-06-02T18:00:10Z","abstract_excerpt":"Many near-term quantum computing algorithms are conceived as variational quantum algorithms, in which parameterized quantum circuits are optimized in a hybrid quantum-classical setup. Examples are variational quantum eigensolvers, quantum approximate optimization algorithms as well as various algorithms in the context of quantum-assisted machine learning. A common bottleneck of any such algorithm is constituted by the optimization of the variational parameters. A popular set of optimization methods work on the estimate of the gradient, obtained by means of circuit evaluations. We will refer to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.01388","kind":"arxiv","version":1},"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/2106.01388/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":"2106.01388","created_at":"2026-07-05T04:23:04.378101+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.01388v1","created_at":"2026-07-05T04:23:04.378101+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.01388","created_at":"2026-07-05T04:23:04.378101+00:00"},{"alias_kind":"pith_short_12","alias_value":"WWJIIWEGBAYH","created_at":"2026-07-05T04:23:04.378101+00:00"},{"alias_kind":"pith_short_16","alias_value":"WWJIIWEGBAYH6RXA","created_at":"2026-07-05T04:23:04.378101+00:00"},{"alias_kind":"pith_short_8","alias_value":"WWJIIWEG","created_at":"2026-07-05T04:23:04.378101+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/WWJIIWEGBAYH6RXATLCAAXG53C","json":"https://pith.science/pith/WWJIIWEGBAYH6RXATLCAAXG53C.json","graph_json":"https://pith.science/api/pith-number/WWJIIWEGBAYH6RXATLCAAXG53C/graph.json","events_json":"https://pith.science/api/pith-number/WWJIIWEGBAYH6RXATLCAAXG53C/events.json","paper":"https://pith.science/paper/WWJIIWEG"},"agent_actions":{"view_html":"https://pith.science/pith/WWJIIWEGBAYH6RXATLCAAXG53C","download_json":"https://pith.science/pith/WWJIIWEGBAYH6RXATLCAAXG53C.json","view_paper":"https://pith.science/paper/WWJIIWEG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.01388&json=true","fetch_graph":"https://pith.science/api/pith-number/WWJIIWEGBAYH6RXATLCAAXG53C/graph.json","fetch_events":"https://pith.science/api/pith-number/WWJIIWEGBAYH6RXATLCAAXG53C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WWJIIWEGBAYH6RXATLCAAXG53C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WWJIIWEGBAYH6RXATLCAAXG53C/action/storage_attestation","attest_author":"https://pith.science/pith/WWJIIWEGBAYH6RXATLCAAXG53C/action/author_attestation","sign_citation":"https://pith.science/pith/WWJIIWEGBAYH6RXATLCAAXG53C/action/citation_signature","submit_replication":"https://pith.science/pith/WWJIIWEGBAYH6RXATLCAAXG53C/action/replication_record"}},"created_at":"2026-07-05T04:23:04.378101+00:00","updated_at":"2026-07-05T04:23:04.378101+00:00"}