{"as_of":"2026-08-10T19:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bd7c81f2f83f8e35c318e54778837ed342232b5f66da5d435e4e610884dcb486","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-13T10:08:12.401078Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T13:06:59.339061Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2109.11676","last_updated":"2021-09-23T22:39:48Z","snapshot_observed_at":"2026-07-06T11:50:50.694694Z","submitted_at":"2021-09-23T22:39:48Z","title":"Theory of overparametrization in quantum neural networks","version":1},"cited_work":{"arxiv_id":"2109.11676","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2109.11676","snapshot_observed_at":"2026-07-02T13:06:59.339061Z","title":"Larocca, N","venue":null,"work_id":"8e586ab7-ddab-46b1-8418-fcd98fbb5c47","year":2023},"citing_paper":{"arxiv_id":"2602.16141","last_updated":"2026-04-27T23:49:28Z","snapshot_observed_at":"2026-08-05T17:15:48.174081Z","submitted_at":"2026-02-18T02:20:42Z","title":"Reductions of QAOA Induced by Classical Symmetries: Theoretical Insights and Practical Implications","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-15T21:55:23.886019Z"},"links":{"cited_paper":"/paper/2109.11676","citing_paper":"/paper/2602.16141"},"observation_digest":"sha256:897160a9e7290a81c4cddb76d471f1e6616583fbe89be9732ffda89eef1b3028","observation_id":"77141939-8fd3-4959-990b-7699777fb663","resolution":{"observed_at":"2026-05-15T21:56:40.676480Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.11676","last_updated":"2021-09-23T22:39:48Z","snapshot_observed_at":"2026-07-06T11:50:50.694694Z","submitted_at":"2021-09-23T22:39:48Z","title":"Theory of overparametrization in quantum neural networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.11676","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Theory of overparametrization in quantum neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-08T03:34:15.326354Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2109.11676","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:d9cf32c34b859a7899a2996be94e94b83b7601509f24dc0d47b541df4fc21c75","observation_id":"bf1fde90-d97e-4be6-80ee-16cbb1984d5f","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.11676","last_updated":"2021-09-23T22:39:48Z","snapshot_observed_at":"2026-07-06T11:50:50.694694Z","submitted_at":"2021-09-23T22:39:48Z","title":"Theory of overparametrization in quantum neural networks","version":1},"cited_work":{"arxiv_id":"2109.11676","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2109.11676","snapshot_observed_at":"2026-07-02T13:06:59.339061Z","title":"Larocca, N","venue":null,"work_id":"8e586ab7-ddab-46b1-8418-fcd98fbb5c47","year":2023},"citing_paper":{"arxiv_id":"2604.17202","last_updated":"2026-04-19T02:09:37Z","snapshot_observed_at":"2026-07-06T23:04:23.730478Z","submitted_at":"2026-04-19T02:09:37Z","title":"Double Descent in Quantum Kernel Ridge Regression","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T06:42:30.100063Z"},"links":{"cited_paper":"/paper/2109.11676","citing_paper":"/paper/2604.17202"},"observation_digest":"sha256:1200e9a091e93d9134298f2b1047b626c9973c4046f1f8b3c69f882dc39797eb","observation_id":"000f380f-6034-4670-a9b7-e6692c490541","resolution":{"observed_at":"2026-05-10T06:46:37.369727Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.11676","last_updated":"2021-09-23T22:39:48Z","snapshot_observed_at":"2026-07-06T11:50:50.694694Z","submitted_at":"2021-09-23T22:39:48Z","title":"Theory of overparametrization in quantum neural networks","version":1},"cited_work":{"arxiv_id":"2109.11676","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2109.11676","snapshot_observed_at":"2026-07-02T13:06:59.339061Z","title":"Larocca, N","venue":null,"work_id":"8e586ab7-ddab-46b1-8418-fcd98fbb5c47","year":2023},"citing_paper":{"arxiv_id":"2605.05942","last_updated":"2026-05-07T09:52:45Z","snapshot_observed_at":"2026-07-06T23:18:31.432422Z","submitted_at":"2026-05-07T09:52:45Z","title":"Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-08T11:40:19.511088Z"},"links":{"cited_paper":"/paper/2109.11676","citing_paper":"/paper/2605.05942"},"observation_digest":"sha256:3ada88a436f4ba2c41dca67311c52ad106054d3f360607f5a6e45f3798c62324","observation_id":"8ab7c72f-da84-4b6b-92fe-568a7c94f7f9","resolution":{"observed_at":"2026-05-11T19:31:11.272382Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.11676","last_updated":"2021-09-23T22:39:48Z","snapshot_observed_at":"2026-07-06T11:50:50.694694Z","submitted_at":"2021-09-23T22:39:48Z","title":"Theory of overparametrization in quantum neural networks","version":1},"cited_work":{"arxiv_id":"2109.11676","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2109.11676","snapshot_observed_at":"2026-07-02T13:06:59.339061Z","title":"Larocca, N","venue":null,"work_id":"8e586ab7-ddab-46b1-8418-fcd98fbb5c47","year":2023},"citing_paper":{"arxiv_id":"2606.05719","last_updated":"2026-06-04T05:23:17Z","snapshot_observed_at":"2026-07-06T23:45:42.379051Z","submitted_at":"2026-06-04T05:23:17Z","title":"Symmetries and overparametrization properties of Hamiltonian variational ansatzes for the $(1+1)$d $\\mathbb{Z}_2$ lattice gauge theory","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T01:34:02.026364Z"},"links":{"cited_paper":"/paper/2109.11676","citing_paper":"/paper/2606.05719"},"observation_digest":"sha256:2fb48ec09315802bdc262d2eedad79de85510cd75fb6d15bb8cb836a4a64765f","observation_id":"7339f1f0-f9b0-41e2-a62a-57e8405e561e","resolution":{"observed_at":"2026-07-02T13:06:59.340466Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2109.11676/citation-record","integrity":"/paper/2109.11676/integrity","json":"/paper/2109.11676/citation-record.json","paper":"/paper/2109.11676"},"outbound":[],"paper":{"arxiv_id":"2109.11676","last_updated":"2021-09-23T22:39:48Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-07-06T11:50:50.694694Z","submitted_at":"2021-09-23T22:39:48Z","title":"Theory of overparametrization in quantum neural networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2109.11676."}