{"as_of":"2026-08-22T07:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e86904bc17a5de61a761c413fa797605a0025c112bc8c1c15c82b10c6406ad2f","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:27:20.057704Z","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":"2026-07-04T08:09:41.964053Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.12884","last_updated":"2025-03-17T07:29:05Z","snapshot_observed_at":"2026-08-16T12:49:28.001272Z","submitted_at":"2025-03-17T07:29:05Z","title":"Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.12884","snapshot_observed_at":"2026-08-06T23:27:20.057704Z","title":"arXiv preprint arXiv:2503.12884 (2025) https://doi.org/10.48550/arXiv.2503.12884","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18002","last_updated":"2025-06-22T11:45:27Z","snapshot_observed_at":"2026-08-18T08:38:16.643918Z","submitted_at":"2025-06-22T11:45:27Z","title":"A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:20.057704Z"},"links":{"cited_paper":"/paper/2503.12884","citing_paper":"/paper/2506.18002"},"observation_digest":"sha256:c45be70429de648e1721607051039e44c01188ecabd74cd5b3e50ea0d0a50261","observation_id":"8956002b-b3f2-4f84-8833-3195b0f40e90","resolution":{"observed_at":"2026-08-06T23:27:20.057704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.12884","last_updated":"2025-03-17T07:29:05Z","snapshot_observed_at":"2026-08-16T12:49:28.001272Z","submitted_at":"2025-03-17T07:29:05Z","title":"Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models","version":1},"cited_work":{"arxiv_id":"2503.12884","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.12884","snapshot_observed_at":"2026-07-04T08:09:41.964053Z","title":"Optimizing ansatz design in quantum generative adversarial networks using large language models","venue":null,"work_id":"7289d546-8595-41a6-a29f-60227181a6ee","year":2025},"citing_paper":{"arxiv_id":"2509.08351","last_updated":"2026-05-10T16:10:11Z","snapshot_observed_at":"2026-08-14T06:48:56.924094Z","submitted_at":"2025-09-10T07:41:55Z","title":"Generative quantum eigensolver with constrained circuit-cutting overhead","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-18T18:30:59.240631Z"},"links":{"cited_paper":"/paper/2503.12884","citing_paper":"/paper/2509.08351"},"observation_digest":"sha256:5ce1a38be2cb8673de9f40ff61a0af6c7d6d1224c98534cdb5776e8046ce3d9a","observation_id":"0fd489dc-2c7c-4957-9058-66f787815d3b","resolution":{"observed_at":"2026-05-18T18:31:44.090986Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.12884","last_updated":"2025-03-17T07:29:05Z","snapshot_observed_at":"2026-08-16T12:49:28.001272Z","submitted_at":"2025-03-17T07:29:05Z","title":"Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.12884","snapshot_observed_at":"2026-08-04T20:43:40.035638Z","title":"”Optimizing Ansatz Design in Quan- tum Generative Adversarial Networks Using Large Language Models.” arXiv preprint arXiv:2503.12884 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08385","last_updated":"2025-09-10T08:23:58Z","snapshot_observed_at":"2026-08-13T01:15:22.515234Z","submitted_at":"2025-09-10T08:23:58Z","title":"LLM-Guided Ans\\\"atze Design for Quantum Circuit Born Machines in Financial Generative Modeling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T20:43:40.035638Z"},"links":{"cited_paper":"/paper/2503.12884","citing_paper":"/paper/2509.08385"},"observation_digest":"sha256:8fc85c7f591934987fd06fff528d2e96da42718af659c9ee2d29ca391f088e96","observation_id":"c0975778-15c1-4fcc-a492-02234221ba8d","resolution":{"observed_at":"2026-08-04T20:43:40.035638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.12884","last_updated":"2025-03-17T07:29:05Z","snapshot_observed_at":"2026-08-16T12:49:28.001272Z","submitted_at":"2025-03-17T07:29:05Z","title":"Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.12884","snapshot_observed_at":"2026-08-03T05:02:35.573238Z","title":"Ueda and A","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.03466","last_updated":"2026-07-07T22:50:14Z","snapshot_observed_at":"2026-08-15T11:19:58.871141Z","submitted_at":"2026-02-03T12:41:25Z","title":"Quantum Circuit Generation via test-time learning with large language models","version":5},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T05:02:35.573238Z"},"links":{"cited_paper":"/paper/2503.12884","citing_paper":"/paper/2602.03466"},"observation_digest":"sha256:4042d58ecfe4f0e360e9815b770092975fe47edf544820f3acabf6ffda0e3e5f","observation_id":"63ae0563-fa86-4307-8617-eaf7f822d1b9","resolution":{"observed_at":"2026-08-03T05:02:35.573238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.12884","last_updated":"2025-03-17T07:29:05Z","snapshot_observed_at":"2026-08-16T12:49:28.001272Z","submitted_at":"2025-03-17T07:29:05Z","title":"Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models","version":1},"cited_work":{"arxiv_id":"2503.12884","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.12884","snapshot_observed_at":"2026-07-04T08:09:41.964053Z","title":"Optimizing ansatz design in quantum generative adversarial networks using large language models","venue":null,"work_id":"7289d546-8595-41a6-a29f-60227181a6ee","year":2025},"citing_paper":{"arxiv_id":"2606.21974","last_updated":"2026-06-20T10:06:29Z","snapshot_observed_at":"2026-08-16T20:57:34.315165Z","submitted_at":"2026-06-20T10:06:29Z","title":"Fine-Tuning Large Language Models for Quantum Reasoning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T12:02:14.680564Z"},"links":{"cited_paper":"/paper/2503.12884","citing_paper":"/paper/2606.21974"},"observation_digest":"sha256:5d4e49f03c93c00f2d7d41ccdd7d634ed6a23e3e90a5af9ebab8fdeca277d198","observation_id":"eb7c45ee-d1e3-43f9-ac75-218f1cea2b8f","resolution":{"observed_at":"2026-07-04T08:09:41.965742Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.12884","last_updated":"2025-03-17T07:29:05Z","snapshot_observed_at":"2026-08-16T12:49:28.001272Z","submitted_at":"2025-03-17T07:29:05Z","title":"Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.12884","snapshot_observed_at":"2026-08-02T03:58:57.446427Z","title":"https://doi.org/10.48550/arXiv","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.13722","last_updated":"2026-07-15T11:37:58Z","snapshot_observed_at":"2026-08-15T07:35:16.285396Z","submitted_at":"2026-07-15T11:37:58Z","title":"Towards quantum machine learning for assessing the resilience of post-quantum cryptography","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T03:58:57.446427Z"},"links":{"cited_paper":"/paper/2503.12884","citing_paper":"/paper/2607.13722"},"observation_digest":"sha256:2a94b76c89e6f8c901ea5a7edae75b975b06e4043c237c14a8cd563be67e2e02","observation_id":"ee948edc-8118-4f71-8c82-35dbce98d1f2","resolution":{"observed_at":"2026-08-02T03:58:57.446427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2503.12884/citation-record","integrity":"/paper/2503.12884/integrity","json":"/paper/2503.12884/citation-record.json","paper":"/paper/2503.12884"},"outbound":[],"paper":{"arxiv_id":"2503.12884","last_updated":"2025-03-17T07:29:05Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-16T12:49:28.001272Z","submitted_at":"2025-03-17T07:29:05Z","title":"Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models"},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2503.12884."}