{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:SDP742FFYY344VQ3SNX3DHXANL","short_pith_number":"pith:SDP742FF","schema_version":"1.0","canonical_sha256":"90dffe68a5c637ce561b936fb19ee06ae4773c74b97d94b081de0707aa3e1f6e","source":{"kind":"arxiv","id":"2104.02955","version":2},"attestation_state":"computed","paper":{"title":"Avoiding local minima in Variational Quantum Algorithms with Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Antonio Ac\\'in, Javier Rivera-Dean, Joseph Bowles, Patrick Huembeli","submitted_at":"2021-04-07T07:07:28Z","abstract_excerpt":"Variational Quantum Algorithms have emerged as a leading paradigm for near-term quantum computation. In such algorithms, a parameterized quantum circuit is controlled via a classical optimization method that seeks to minimize a problem-dependent cost function. Although such algorithms are powerful in principle, the non-convexity of the associated cost landscapes and the prevalence of local minima means that local optimization methods such as gradient descent typically fail to reach good solutions. In this work we suggest a method to improve gradient-based approaches to variational quantum circ"},"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":"2104.02955","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2021-04-07T07:07:28Z","cross_cats_sorted":[],"title_canon_sha256":"5db6b932ce26b02f69411f7effbb8d8d856278cadf91da8b7be78fb37ff50fb9","abstract_canon_sha256":"fa921e02b64fae753e1fc19e27b82a755e5737eb44e0385108820f4a7c129751"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:23:09.276181Z","signature_b64":"QdRx5QLhfUVkLRH+2AHJA7E+NgKlWMXVNS+xYzPKPFSLH4V+IVrWaYiGuH3OW3o2zIrxOMNmgThGJzBhekbeBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90dffe68a5c637ce561b936fb19ee06ae4773c74b97d94b081de0707aa3e1f6e","last_reissued_at":"2026-07-05T03:23:09.275629Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:23:09.275629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Avoiding local minima in Variational Quantum Algorithms with Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Antonio Ac\\'in, Javier Rivera-Dean, Joseph Bowles, Patrick Huembeli","submitted_at":"2021-04-07T07:07:28Z","abstract_excerpt":"Variational Quantum Algorithms have emerged as a leading paradigm for near-term quantum computation. In such algorithms, a parameterized quantum circuit is controlled via a classical optimization method that seeks to minimize a problem-dependent cost function. Although such algorithms are powerful in principle, the non-convexity of the associated cost landscapes and the prevalence of local minima means that local optimization methods such as gradient descent typically fail to reach good solutions. In this work we suggest a method to improve gradient-based approaches to variational quantum circ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.02955","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/2104.02955/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":"2104.02955","created_at":"2026-07-05T03:23:09.275693+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.02955v2","created_at":"2026-07-05T03:23:09.275693+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.02955","created_at":"2026-07-05T03:23:09.275693+00:00"},{"alias_kind":"pith_short_12","alias_value":"SDP742FFYY34","created_at":"2026-07-05T03:23:09.275693+00:00"},{"alias_kind":"pith_short_16","alias_value":"SDP742FFYY344VQ3","created_at":"2026-07-05T03:23:09.275693+00:00"},{"alias_kind":"pith_short_8","alias_value":"SDP742FF","created_at":"2026-07-05T03:23:09.275693+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05719","citing_title":"Symmetries and overparametrization properties of Hamiltonian variational ansatzes for the $(1+1)$d $\\mathbb{Z}_2$ lattice gauge theory","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SDP742FFYY344VQ3SNX3DHXANL","json":"https://pith.science/pith/SDP742FFYY344VQ3SNX3DHXANL.json","graph_json":"https://pith.science/api/pith-number/SDP742FFYY344VQ3SNX3DHXANL/graph.json","events_json":"https://pith.science/api/pith-number/SDP742FFYY344VQ3SNX3DHXANL/events.json","paper":"https://pith.science/paper/SDP742FF"},"agent_actions":{"view_html":"https://pith.science/pith/SDP742FFYY344VQ3SNX3DHXANL","download_json":"https://pith.science/pith/SDP742FFYY344VQ3SNX3DHXANL.json","view_paper":"https://pith.science/paper/SDP742FF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.02955&json=true","fetch_graph":"https://pith.science/api/pith-number/SDP742FFYY344VQ3SNX3DHXANL/graph.json","fetch_events":"https://pith.science/api/pith-number/SDP742FFYY344VQ3SNX3DHXANL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SDP742FFYY344VQ3SNX3DHXANL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SDP742FFYY344VQ3SNX3DHXANL/action/storage_attestation","attest_author":"https://pith.science/pith/SDP742FFYY344VQ3SNX3DHXANL/action/author_attestation","sign_citation":"https://pith.science/pith/SDP742FFYY344VQ3SNX3DHXANL/action/citation_signature","submit_replication":"https://pith.science/pith/SDP742FFYY344VQ3SNX3DHXANL/action/replication_record"}},"created_at":"2026-07-05T03:23:09.275693+00:00","updated_at":"2026-07-05T03:23:09.275693+00:00"}