{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CD6NDB3Z3WN6X7CDEFG7VDMNXF","short_pith_number":"pith:CD6NDB3Z","schema_version":"1.0","canonical_sha256":"10fcd18779dd9bebfc43214dfa8d8db95514863b79c5af08662143eb642c3cb9","source":{"kind":"arxiv","id":"2104.14543","version":3},"attestation_state":"computed","paper":{"title":"Optimal training of variational quantum algorithms without barren plateaus","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"quant-ph","authors_text":"M.S. Kim, Tobias Haug","submitted_at":"2021-04-29T17:54:59Z","abstract_excerpt":"Variational quantum algorithms (VQAs) promise efficient use of near-term quantum computers. However, training VQAs often requires an extensive amount of time and suffers from the barren plateau problem where the magnitude of the gradients vanishes with increasing number of qubits. Here, we show how to optimally train VQAs for learning quantum states. Parameterized quantum circuits can form Gaussian kernels, which we use to derive adaptive learning rates for gradient ascent. We introduce the generalized quantum natural gradient that features stability and optimized movement in parameter space. "},"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":"2104.14543","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2021-04-29T17:54:59Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"688b7ea7d642b7115d70983a087ed23f17608eeb6c84650e55430daec82b6244","abstract_canon_sha256":"fa71f4871bba4bdcf8408592c2b1d6f6ea780c041204c0bbd57a131698097846"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:51:40.178203Z","signature_b64":"u9/8DQAEjOi9YdPmboS3XVaOLGsPkeTVFIkjjCk5/2PVPgHeLQlpei+AayNXZxJtUFpVVn2+uclkaeZEJrGWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10fcd18779dd9bebfc43214dfa8d8db95514863b79c5af08662143eb642c3cb9","last_reissued_at":"2026-07-05T02:51:40.177696Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:51:40.177696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal training of variational quantum algorithms without barren plateaus","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"quant-ph","authors_text":"M.S. Kim, Tobias Haug","submitted_at":"2021-04-29T17:54:59Z","abstract_excerpt":"Variational quantum algorithms (VQAs) promise efficient use of near-term quantum computers. However, training VQAs often requires an extensive amount of time and suffers from the barren plateau problem where the magnitude of the gradients vanishes with increasing number of qubits. Here, we show how to optimally train VQAs for learning quantum states. Parameterized quantum circuits can form Gaussian kernels, which we use to derive adaptive learning rates for gradient ascent. We introduce the generalized quantum natural gradient that features stability and optimized movement in parameter space. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.14543","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/2104.14543/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":"2104.14543","created_at":"2026-07-05T02:51:40.177755+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.14543v3","created_at":"2026-07-05T02:51:40.177755+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.14543","created_at":"2026-07-05T02:51:40.177755+00:00"},{"alias_kind":"pith_short_12","alias_value":"CD6NDB3Z3WN6","created_at":"2026-07-05T02:51:40.177755+00:00"},{"alias_kind":"pith_short_16","alias_value":"CD6NDB3Z3WN6X7CD","created_at":"2026-07-05T02:51:40.177755+00:00"},{"alias_kind":"pith_short_8","alias_value":"CD6NDB3Z","created_at":"2026-07-05T02:51:40.177755+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02569","citing_title":"RFOX (Rotated-Field Oscillatory eXchange) quantum algorithm: Towards Parameter-Free Quantum Optimizers","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02569","citing_title":"RFOX (Rotated-Field Oscillatory eXchange) quantum algorithm: Towards Parameter-Free Quantum Optimizers","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21266","citing_title":"On the importance of hyperparameters in initializing parameterized quantum circuits","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CD6NDB3Z3WN6X7CDEFG7VDMNXF","json":"https://pith.science/pith/CD6NDB3Z3WN6X7CDEFG7VDMNXF.json","graph_json":"https://pith.science/api/pith-number/CD6NDB3Z3WN6X7CDEFG7VDMNXF/graph.json","events_json":"https://pith.science/api/pith-number/CD6NDB3Z3WN6X7CDEFG7VDMNXF/events.json","paper":"https://pith.science/paper/CD6NDB3Z"},"agent_actions":{"view_html":"https://pith.science/pith/CD6NDB3Z3WN6X7CDEFG7VDMNXF","download_json":"https://pith.science/pith/CD6NDB3Z3WN6X7CDEFG7VDMNXF.json","view_paper":"https://pith.science/paper/CD6NDB3Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.14543&json=true","fetch_graph":"https://pith.science/api/pith-number/CD6NDB3Z3WN6X7CDEFG7VDMNXF/graph.json","fetch_events":"https://pith.science/api/pith-number/CD6NDB3Z3WN6X7CDEFG7VDMNXF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CD6NDB3Z3WN6X7CDEFG7VDMNXF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CD6NDB3Z3WN6X7CDEFG7VDMNXF/action/storage_attestation","attest_author":"https://pith.science/pith/CD6NDB3Z3WN6X7CDEFG7VDMNXF/action/author_attestation","sign_citation":"https://pith.science/pith/CD6NDB3Z3WN6X7CDEFG7VDMNXF/action/citation_signature","submit_replication":"https://pith.science/pith/CD6NDB3Z3WN6X7CDEFG7VDMNXF/action/replication_record"}},"created_at":"2026-07-05T02:51:40.177755+00:00","updated_at":"2026-07-05T02:51:40.177755+00:00"}