{"as_of":"2026-08-10T17:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e22cabee4008172d4e5ee67268605608ef08abcfbb5dcafcecb52fe019f13e0a","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":"2026-08-07T20:55:26.431592Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":85,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1712.05304","last_updated":"2019-08-10T11:39:15Z","snapshot_observed_at":"2026-08-03T08:24:00.976940Z","submitted_at":"2017-12-14T15:57:56Z","title":"A quantum algorithm to train neural networks using low-depth circuits","version":2},"cited_work":{"arxiv_id":"1712.05304","doi":"10.48550/arxiv.1712.05304","metadata_source":"arxiv_reference","pith_arxiv_id":"1712.05304","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Verdon, M","venue":"arXiv (Cornell University)","work_id":"8238f942-ae39-4987-b493-7b639102a4ca","year":2017},"citing_paper":{"arxiv_id":"1811.04968","last_updated":"2022-07-29T22:39:54Z","snapshot_observed_at":"2026-07-06T07:14:13.912107Z","submitted_at":"2018-11-12T19:18:57Z","title":"PennyLane: Automatic differentiation of hybrid quantum-classical computations","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T15:14:44.070015Z"},"links":{"cited_paper":"/paper/1712.05304","citing_paper":"/paper/1811.04968"},"observation_digest":"sha256:abc1f9359bf605f561d009f4c34d3092102240e3a2a1fb6b8900d12c31845758","observation_id":"57f086e3-99ec-4be0-abae-d9f85dc44c7e","resolution":{"observed_at":"2026-05-10T15:14:44.127663Z","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":"1712.05304","last_updated":"2019-08-10T11:39:15Z","snapshot_observed_at":"2026-08-03T08:24:00.976940Z","submitted_at":"2017-12-14T15:57:56Z","title":"A quantum algorithm to train neural networks using low-depth circuits","version":2},"cited_work":{"arxiv_id":"1712.05304","doi":"10.48550/arxiv.1712.05304","metadata_source":"arxiv_reference","pith_arxiv_id":"1712.05304","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Verdon, M","venue":"arXiv (Cornell University)","work_id":"8238f942-ae39-4987-b493-7b639102a4ca","year":2017},"citing_paper":{"arxiv_id":"1907.05415","last_updated":"2019-07-11T17:57:56Z","snapshot_observed_at":"2026-07-06T08:07:12.362127Z","submitted_at":"2019-07-11T17:57:56Z","title":"Learning to learn with quantum neural networks via classical neural networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-24T22:59:51.101448Z"},"links":{"cited_paper":"/paper/1712.05304","citing_paper":"/paper/1907.05415"},"observation_digest":"sha256:5eb265c12c9bffcff4b19d86719f00d7cf510905e462542fe74b29d257aa3aec","observation_id":"3715dc0e-f600-4f8a-b69c-565a872ef341","resolution":{"observed_at":"2026-05-24T23:00:03.232589Z","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":"1712.05304","last_updated":"2019-08-10T11:39:15Z","snapshot_observed_at":"2026-08-03T08:24:00.976940Z","submitted_at":"2017-12-14T15:57:56Z","title":"A quantum algorithm to train neural networks using low-depth circuits","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05304","snapshot_observed_at":"2026-08-07T20:55:26.431592Z","title":"Verdon, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.09698","last_updated":"2025-03-27T13:23:15Z","snapshot_observed_at":"2026-08-09T04:15:44.193981Z","submitted_at":"2025-02-13T19:00:00Z","title":"Variational quantum thermalizers based on weakly-symmetric nonunitary multi-qubit operations","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T20:55:26.431592Z"},"links":{"cited_paper":"/paper/1712.05304","citing_paper":"/paper/2502.09698"},"observation_digest":"sha256:0ed55f3dd402da53b3edf8874569e1bc1a170dde8898fbadf870d8da9ea5664f","observation_id":"85756700-f88a-45c0-a45f-9607a111b4d7","resolution":{"observed_at":"2026-08-07T20:55:26.431592Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05304","last_updated":"2019-08-10T11:39:15Z","snapshot_observed_at":"2026-08-03T08:24:00.976940Z","submitted_at":"2017-12-14T15:57:56Z","title":"A quantum algorithm to train neural networks using low-depth circuits","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05304","snapshot_observed_at":"2026-08-06T20:54:57.828844Z","title":"Verdon, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.02043","last_updated":"2025-07-21T09:11:41Z","snapshot_observed_at":"2026-08-10T16:17:16.199105Z","submitted_at":"2025-07-02T18:00:01Z","title":"Scaling Quantum Algorithms via Dissipation: Avoiding Barren Plateaus","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:57.828844Z"},"links":{"cited_paper":"/paper/1712.05304","citing_paper":"/paper/2507.02043"},"observation_digest":"sha256:01495507497e47b18c6ec05f3ffff0d5ce1892a42e75a0c275e2def944158fe9","observation_id":"f5149eb3-f601-4448-b1b4-1d5b203d3d64","resolution":{"observed_at":"2026-08-06T20:54:57.828844Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05304","last_updated":"2019-08-10T11:39:15Z","snapshot_observed_at":"2026-08-03T08:24:00.976940Z","submitted_at":"2017-12-14T15:57:56Z","title":"A quantum algorithm to train neural networks using low-depth circuits","version":2},"cited_work":{"arxiv_id":"1712.05304","doi":"10.48550/arxiv.1712.05304","metadata_source":"arxiv_reference","pith_arxiv_id":"1712.05304","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Verdon, M","venue":"arXiv (Cornell University)","work_id":"8238f942-ae39-4987-b493-7b639102a4ca","year":2017},"citing_paper":{"arxiv_id":"2510.14099","last_updated":"2026-04-09T20:41:37Z","snapshot_observed_at":"2026-07-06T22:32:47.087967Z","submitted_at":"2025-10-15T21:15:23Z","title":"A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-18T06:41:57.161564Z"},"links":{"cited_paper":"/paper/1712.05304","citing_paper":"/paper/2510.14099"},"observation_digest":"sha256:f83d4c42543cdef75654bc5b52ab5cb4211e2c9f0159063e7b9a87fe760d9c3a","observation_id":"55f1989c-5042-4535-a2b7-2384d1c8219e","resolution":{"observed_at":"2026-05-18T06:42:26.349411Z","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/1712.05304/citation-record","integrity":"/paper/1712.05304/integrity","json":"/paper/1712.05304/citation-record.json","paper":"/paper/1712.05304"},"outbound":[],"paper":{"arxiv_id":"1712.05304","last_updated":"2019-08-10T11:39:15Z","latest_version":2,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-03T08:24:00.976940Z","submitted_at":"2017-12-14T15:57:56Z","title":"A quantum algorithm to train neural networks using low-depth circuits"},"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:1712.05304."}