{"as_of":"2026-08-09T04:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6a7ad5a87354c10edf3685ad8e3b6f8559bb56e43b04540887c84b84efd569d0","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:51:35.428551Z","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":54,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.16089","last_updated":"2021-03-30T05:46:21Z","snapshot_observed_at":"2026-08-08T01:55:58.769219Z","submitted_at":"2021-03-30T05:46:21Z","title":"Reinforcement learning for optimization of variational quantum circuit architectures","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16089","snapshot_observed_at":"2026-08-07T13:51:35.428551Z","title":"Reinforcement learning for optimization of variational quantum circuit architectures,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.20863","last_updated":"2025-07-23T13:04:46Z","snapshot_observed_at":"2026-08-08T09:25:09.476459Z","submitted_at":"2025-05-27T08:14:58Z","title":"Leveraging Diffusion Models for Parameterized Quantum Circuit Generation","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:51:35.428551Z"},"links":{"cited_paper":"/paper/2103.16089","citing_paper":"/paper/2505.20863"},"observation_digest":"sha256:ac01294665c3962722337a469d2a0425b04fbabbffb9e9f9d34ed7387089c000","observation_id":"b979d731-5329-420b-977d-611dff7cc9ae","resolution":{"observed_at":"2026-08-07T13:51:35.428551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16089","last_updated":"2021-03-30T05:46:21Z","snapshot_observed_at":"2026-08-08T01:55:58.769219Z","submitted_at":"2021-03-30T05:46:21Z","title":"Reinforcement learning for optimization of variational quantum circuit architectures","version":1},"cited_work":{"arxiv_id":"2103.16089","doi":"10.48550/arxiv.2103.16089","metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16089","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Reinforcement learning for optimization of variational quantum circuit architectures","venue":"arXiv (Cornell University)","work_id":"9ab51d39-00ad-4ffa-ae2e-5108df85e7f8","year":2021},"citing_paper":{"arxiv_id":"2506.01666","last_updated":"2026-04-07T13:07:51Z","snapshot_observed_at":"2026-08-03T19:19:58.935952Z","submitted_at":"2025-06-02T13:35:33Z","title":"Synthesis of discrete-continuous quantum circuits with multimodal diffusion models","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-19T11:39:14.375702Z"},"links":{"cited_paper":"/paper/2103.16089","citing_paper":"/paper/2506.01666"},"observation_digest":"sha256:f9d85f57564a70311a65a4dc35c853d746dad7c591686ed66e923b5548b40d0d","observation_id":"b86c8854-5b70-46ba-80e6-9ce06910c8aa","resolution":{"observed_at":"2026-05-19T11:42:15.834397Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2103.16089/citation-record","integrity":"/paper/2103.16089/integrity","json":"/paper/2103.16089/citation-record.json","paper":"/paper/2103.16089"},"outbound":[],"paper":{"arxiv_id":"2103.16089","last_updated":"2021-03-30T05:46:21Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-08T01:55:58.769219Z","submitted_at":"2021-03-30T05:46:21Z","title":"Reinforcement learning for optimization of variational quantum circuit architectures"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2103.16089."}