{"as_of":"2026-08-10T20:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:75c972629080f7e75ba5725609490340607f2efdcd7ca76bf73f4d2e3d610974","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T05:42:33.224597Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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":"2026-08-02T03:54:30.070539Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.05051","snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"QCA-MolGAN: Quantum circuit associative molecular GAN with multi-agent reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10284","last_updated":"2026-07-11T12:30:28Z","snapshot_observed_at":"2026-08-08T20:47:34.201933Z","submitted_at":"2026-07-11T12:30:28Z","title":"Rank-Refined Quantum-Behaved Particle Swarm Optimization for Quantum Molecular Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"cited_paper":"/paper/2509.05051","citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:8b255cdc145ea16f8e538d62a7a2ee5a5a6a5a4c9e1a760fd17ad60b132d7b34","observation_id":"0ad6f8f1-0981-43b6-a29a-7aa4a19035cc","resolution":{"observed_at":"2026-07-14T12:56:07.813926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.05051","snapshot_observed_at":"2026-08-02T03:54:30.070539Z","title":"Qca-molgan: Quantum circuit associative molecular gan with multi- agent reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.13737","last_updated":"2026-07-26T19:25:58Z","snapshot_observed_at":"2026-08-08T23:17:51.288445Z","submitted_at":"2026-07-15T11:54:02Z","title":"Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T03:54:30.070539Z"},"links":{"cited_paper":"/paper/2509.05051","citing_paper":"/paper/2607.13737"},"observation_digest":"sha256:433222fda0038b55319256e4474a7e56b82ba01cff880824fc20c7f09cda0150","observation_id":"f82fad6f-9395-4ba4-869c-a71bcb2600b3","resolution":{"observed_at":"2026-08-02T03:54:30.070539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2509.05051/citation-record","integrity":"/paper/2509.05051/integrity","json":"/paper/2509.05051/citation-record.json","paper":"/paper/2509.05051"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.546610Z","title":"Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound dis- position and safety,","venue":null,"work_id":"24123f3f-3e0f-4030-8600-4f9fc05c55e7","year":2011},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.895330Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:2fbc1a7ebc6708839d6f38fdb3f0f72156a277897152904f2c645e8302560bb6","observation_id":"f779a847-b8c4-4457-9235-443fce089ba3","resolution":{"observed_at":"2026-08-05T05:42:34.552319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.518452Z","title":"Deep reinforcement learning for de novo drug design,","venue":null,"work_id":"f43da069-58d4-483c-9b52-38ad107f6915","year":2018},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.905570Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:0781ecfeb35c99d9f88440302038cb27ba895c23a59b288c6afc5a5e34906986","observation_id":"4eba4142-37ff-4468-9413-2b19d28ff2db","resolution":{"observed_at":"2026-08-05T05:42:34.526085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.492270Z","title":"Inverse design in search of materials with target function- alities,","venue":null,"work_id":"76dbd50f-b51e-4d9f-b69d-d95e59aa5249","year":2018},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.913993Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:c9ca5b469c9641c303554f8e5fb90be1979ee15123030dfb9e75af5ed5ae651c","observation_id":"cba15930-9d85-4395-af6c-fe8047f29031","resolution":{"observed_at":"2026-08-05T05:42:34.499141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.465314Z","title":"Virtual compound libraries in computer-assisted drug discovery,","venue":null,"work_id":"a60e87be-cdca-44d5-907a-c3a6a0924c88","year":2019},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.920216Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:ee54ba4d9f1d097ee22464c269fdc450b8868f4f3ad9e0bb3eb6f7f3dc367472","observation_id":"ecbb6298-2dd3-49bf-bed0-124607db9a30","resolution":{"observed_at":"2026-08-05T05:42:34.473968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.437170Z","title":"Principles of early drug discovery,","venue":null,"work_id":"c6b0c2af-89fb-4b0c-a895-2a7f71da83d2","year":2011},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.926636Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:af30a2f8cfe0a8d3ca74a1115cad41e0f908b58f735817d8a0e6044ff88ae7dd","observation_id":"1aeba0c5-f355-4a5c-8004-2225caba1c3e","resolution":{"observed_at":"2026-08-05T05:42:34.443991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.409641Z","title":"Applications of machine learning in drug discovery and development,","venue":null,"work_id":"8f21ac99-156d-4bf8-9e63-de1503a35aae","year":2019},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.933020Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:e4390512f5b6c9688ebd7f3b49870b81ac2521f44c6bac3f338259194c385e06","observation_id":"1f9d7730-3820-44c3-80aa-3ebcfdf4c7e0","resolution":{"observed_at":"2026-08-05T05:42:34.419679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.387708Z","title":"Machine learning in drug discovery: a review,","venue":null,"work_id":"c6979641-281c-4e14-bf3f-1a2935de4191","year":1947},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.940532Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:a0c858a3912f1fb5adb9b39b50992c3258467f914f3ef2ec796138a972f59ad9","observation_id":"18d997ab-210a-466f-9972-6ddabc1129c6","resolution":{"observed_at":"2026-08-05T05:42:34.395764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:32.946926Z","title":"Generative adversarial nets,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.946926Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:d93ee020073c4bf86ad87b6e4350e1e0fbf8cfc32ced6ff858d5830b1ac792e2","observation_id":"3f620db6-e1c1-45bd-a022-ada02e964206","resolution":{"observed_at":"2026-08-05T05:42:32.946926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-05T05:42:32.955573Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.955573Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:71d8eb85f5726384789f4499001cc0651959ee90a725450319e63da7b35857fd","observation_id":"abffe472-5c60-42c8-9d3a-e87975c7c4d0","resolution":{"observed_at":"2026-08-05T05:42:32.955573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.338500Z","title":"Recurrent neural networks,","venue":null,"work_id":"6397ff15-8f3b-49fa-a566-ee5f63648fb7","year":2001},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.962830Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:709723b0fbdf3e0c09f3ecdc01b82356603c730b1701425f2b91f391923a6ec5","observation_id":"3c22411e-be75-4b8c-9851-c62e0c7c3817","resolution":{"observed_at":"2026-08-05T05:42:34.351130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:32.968786Z","title":"Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.968786Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:f2c502df23ab36f566444c04b1d487dd632fead4ae86c3ab573d9a78f86de452","observation_id":"8d2c7a3f-10c2-4921-be06-acb2dc5d02a6","resolution":{"observed_at":"2026-08-05T05:42:32.968786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.272338Z","title":"Quantitative structure- activity relationship methods: Perspectives on drug discovery and tox- icology,","venue":null,"work_id":"f70243fa-cc7b-435b-8381-baa653e44e09","year":2003},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.974722Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:1b2ba034d7d659132d5e3df06a25d385b5d04527a754885f024212f97c3ff2b0","observation_id":"7440cb77-3736-4012-bbdc-c73e24e01da5","resolution":{"observed_at":"2026-08-05T05:42:34.281797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1805.11973","last_updated":"2022-09-27T10:04:29Z","snapshot_observed_at":"2026-08-07T21:51:58.025112Z","submitted_at":"2018-05-30T13:56:06Z","title":"MolGAN: An implicit generative model for small molecular graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.11973","snapshot_observed_at":"2026-08-05T05:42:32.981349Z","title":"Molgan: An implicit generative model for small molecular graphs,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.981349Z"},"links":{"cited_paper":"/paper/1805.11973","citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:dafb0569adad26ff30d7f7a96af2c1315323e6543e66e5aa869d6d296ffcbdc1","observation_id":"1f588d98-230b-41f6-9b1d-ddff6fed42e1","resolution":{"observed_at":"2026-08-05T05:42:32.981349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.233007Z","title":"Temporally unstructured quantum computation,","venue":null,"work_id":"aa57c0a7-6c84-4e3b-ad2c-5f82c311c1d7","year":2009},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.988543Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:78fd44029bff16ddd810ff8f0c9cd48c85fb151aa0e21945a9684d6c60bb9873","observation_id":"1da13f00-a0af-4e91-8112-4553dbe4df3f","resolution":{"observed_at":"2026-08-05T05:42:34.244278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:32.995184Z","title":"Quantum computing in the nisq era and beyond,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:32.995184Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:595eb2960e3b783819128dd339894edc33b76dc65ecef1e2abfd0df67c34ad15","observation_id":"b12e5195-447f-4837-9b3f-2f0a4b73489e","resolution":{"observed_at":"2026-08-05T05:42:32.995184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.012359Z","title":"Quantum machine learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.012359Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:9e735d79eb98e7098ed6632e50a3c64583426488e90f0bb06c4159f0417db9f9","observation_id":"6b42b190-519f-4211-97e6-3cd69e350199","resolution":{"observed_at":"2026-08-05T05:42:33.012359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.168306Z","title":"Quantum computational advantage with a programmable photonic processor,","venue":null,"work_id":"a53d4b4b-2699-4f93-b071-97cfb05042b2","year":2022},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.030768Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:810e67e55e08af999d3ac404c9595ba8796c920541e59752df2ad63197a9b1dc","observation_id":"3def14fb-6372-4800-91a9-9976f1f57c0f","resolution":{"observed_at":"2026-08-05T05:42:34.176714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.142939Z","title":"Quantum generative models for small molecule drug discovery,","venue":null,"work_id":"b68f7a08-7f15-4c38-bfda-220f31114864","year":2021},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.037728Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:d9fc093843115f86f0a332fa277918ce7c90aa83d4b35a792eb5b88283e99cdc","observation_id":"d79fbf1d-ff85-4043-85ea-8738b7491ca8","resolution":{"observed_at":"2026-08-05T05:42:34.149972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.114016Z","title":"Hybrid quantum-classical machine learning for generative chemistry and drug design,","venue":null,"work_id":"d54cd9a2-3b00-4227-9a55-50caa048e5fd","year":2023},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.042847Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:9a14ec6e99647db696884478d5f8a2d4b5a8764ac0de26e86f34266af68d526d","observation_id":"130b5147-3310-44bf-a783-dfbb3ea71145","resolution":{"observed_at":"2026-08-05T05:42:34.124265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.073255Z","title":"Ex- ploring the advantages of quantum generative adversarial networks in generative chemistry,","venue":null,"work_id":"2018755c-beea-4965-a391-65bfcc442a32","year":2023},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.048919Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:f386c7d14f267a17b51fd45baea3a01b6207a0ff84d30d77610c47f1b660b079","observation_id":"efb628e5-8eca-47fe-b3cd-53caa5570a4a","resolution":{"observed_at":"2026-08-05T05:42:34.093794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.043068Z","title":"Hybrid quantum cycle generative ad- versarial network for small molecule generation,","venue":null,"work_id":"04562091-d135-43fb-9e1a-210a9d05c6e5","year":2024},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.056279Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:2988876b56e0aefa836213a1502b14f2d5857d0859e9839e3171e789a4f7b241","observation_id":"ec140c11-6e85-4527-a417-f73a6d5fbfa8","resolution":{"observed_at":"2026-08-05T05:42:34.051154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2105.03684","last_updated":"2021-05-12T07:52:07Z","snapshot_observed_at":"2026-08-08T16:26:12.860025Z","submitted_at":"2021-05-08T12:11:44Z","title":"Quantum Machine Learning For Classical Data","version":2},"cited_work":{"arxiv_id":"2105.03684","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.03684","snapshot_observed_at":"2026-08-05T05:42:33.431433Z","title":"Quantum Machine Learning For Classical Data","venue":"quant-ph","work_id":"3c0520f9-98f0-45ed-b234-8846800f3b1e","year":2021},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.064346Z"},"links":{"cited_paper":"/paper/2105.03684","citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:cb9c8cd195ab485f641a8661bb667b120fb464341adeac7d6475d0d7e039729e","observation_id":"f36d150f-a559-4700-9f42-454fb1ebff45","resolution":{"observed_at":"2026-08-05T05:42:33.439027Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:34.013867Z","title":"A review on mode collapse reducing gans with gan’s algorithm and theory,","venue":null,"work_id":"eebdb82a-f35e-4cd7-bbdc-46013b7f552d","year":2023},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.069685Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:9720b406dd9f1af9166d907b60e1d27f0473f43aa527de2a957b8342bb3b17a6","observation_id":"eb348898-6aed-43b1-8654-00895b70c8c6","resolution":{"observed_at":"2026-08-05T05:42:34.025376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.985681Z","title":"L- molgan: An improved implicit generative model for large molecular graphs,","venue":null,"work_id":"8f56a888-1e32-472a-8355-9176179e67c8","year":2021},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.074903Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:60a623d6758b860a33ca4b146c9270fa0d06e55f1370369a858e26d3cd7a88ab","observation_id":"72157ca7-064f-4293-92dc-b510a809230f","resolution":{"observed_at":"2026-08-05T05:42:33.994052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.079600Z","title":"A comprehensive survey on graph neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.079600Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:fd7f8919cb765b9666aeca0d619b4436727a35dc847daf88f7bdd37ff37994ce","observation_id":"8df6e664-2ef4-4388-9037-db57703e6998","resolution":{"observed_at":"2026-08-05T05:42:33.079600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.947683Z","title":"Graph neural networks: A review of methods and applications,","venue":null,"work_id":"32ebaacc-e906-441c-8979-911e91654791","year":2020},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.086962Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:82464c7a699ed3b4820da9ea9b2c8e0a99f1b09203720fc9d02b1e962b69c576","observation_id":"cae9eb50-4728-4cbf-bdae-76210ebe4ce1","resolution":{"observed_at":"2026-08-05T05:42:33.956385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.911788Z","title":"Modeling relational data with graph convolutional networks,","venue":null,"work_id":"9ac7c6ef-6605-41b7-8f1f-e3a8c80b6611","year":2018},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.093113Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:7942bb32b4f02dcc66f9bd3e2c987059f0a5ea8a01c83857381c8cf51d6bf0e8","observation_id":"9ecf480c-3eff-4182-aebc-06fd7dd3816b","resolution":{"observed_at":"2026-08-05T05:42:33.920048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.885837Z","title":"Games of gans: Game-theoretical models for generative adversarial networks,","venue":null,"work_id":"bf78669d-36b8-4783-a325-fcf89733f18b","year":2023},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.101116Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:9a38256a4ea09b87f8952d655146f336b250e303077c27b58b49b46daff51f9e","observation_id":"e34360ec-48c4-41e7-8688-b9816466e85d","resolution":{"observed_at":"2026-08-05T05:42:33.892996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.861536Z","title":"Exploration in deep rein- forcement learning: A survey,","venue":null,"work_id":"94dafd28-9942-4a02-832d-f613d4d94c56","year":2022},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.110163Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:eb8b9f09ae88b0db5fb0757410119e20dc781cbfa00ec49c7d4ecfb6e25289cc","observation_id":"331bb897-8e1b-4d79-a9cb-c91808eb583c","resolution":{"observed_at":"2026-08-05T05:42:33.868144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.836778Z","title":"Deterministic policy gradient algorithms,","venue":null,"work_id":"fe7388f7-04ef-4cc5-a837-8e3ba5478960","year":2014},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.116764Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:23836cebbffee48d387724426e26d2b064f418863561662ff7bd9b759a83ca00","observation_id":"47b20c4c-8d45-48f9-8003-11309d615db5","resolution":{"observed_at":"2026-08-05T05:42:33.843982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1509.02971","last_updated":"2019-07-05T10:47:27Z","snapshot_observed_at":"2026-07-06T04:29:24.362640Z","submitted_at":"2015-09-09T23:01:36Z","title":"Continuous control with deep reinforcement learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.02971","snapshot_observed_at":"2026-08-05T05:42:33.124546Z","title":"Continuous control with deep reinforcement learning,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.124546Z"},"links":{"cited_paper":"/paper/1509.02971","citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:aa6ba51bc89de91c7a26382a8730b6eee6303a5458fd0cbec97694283c1902a7","observation_id":"7ba3a75a-4dc8-45d5-b7ee-8af458c0d728","resolution":{"observed_at":"2026-08-05T05:42:33.124546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.809908Z","title":"Quantifying the chemical beauty of drugs,","venue":null,"work_id":"3447fdb6-19f9-4fb9-b3b5-474ff5a9de2d","year":2012},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.132275Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:6cb4d53ee6577bc7b9eafa12bb1569c96207ed8d8b980fe41b63ba17cf52d18a","observation_id":"120cd722-7f70-43f5-b040-46fd8e131036","resolution":{"observed_at":"2026-08-05T05:42:33.815900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.778842Z","title":"Lipophilicity profiles: theory and measurement,","venue":null,"work_id":"2bfb5fc6-a4fc-4c84-87ed-771f43c0d271","year":2001},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.141413Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:a930e9ba1fce116cedc580d5cb49ef00efb1b4f9c6d8b3590bbc7af30c2fd59e","observation_id":"f3d40711-3b9c-43ff-a69a-15cb7644680b","resolution":{"observed_at":"2026-08-05T05:42:33.789587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.754499Z","title":"Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions,","venue":null,"work_id":"70bb6c83-a42f-4fbb-b553-90fd495c2210","year":2009},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.151092Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:d40d6ac6b2b05cb57301a7573dc9e9a24ab92ec18b50493c896a5be6187f2745","observation_id":"7eb6adb1-4be9-4b95-999c-2ca4af9daeb3","resolution":{"observed_at":"2026-08-05T05:42:33.760236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1611.06953","last_updated":"2016-11-18T02:11:40Z","snapshot_observed_at":"2026-08-07T22:28:23.589996Z","submitted_at":"2016-11-18T02:11:40Z","title":"Associative Adversarial Networks","version":1},"cited_work":{"arxiv_id":"1611.06953","doi":null,"metadata_source":"pith","pith_arxiv_id":"1611.06953","snapshot_observed_at":"2026-08-05T05:42:33.341019Z","title":"Associative Adversarial Networks","venue":"cs.LG","work_id":"a46d46a9-b9b7-4e7c-b075-493c005194a4","year":2016},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.157160Z"},"links":{"cited_paper":"/paper/1611.06953","citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:0e8cb3e39523779bd2bd26e64fa61c301381e8e2e63cc9735f54c4ee842e5d72","observation_id":"428cc005-ed7e-412e-9df2-631814af10f4","resolution":{"observed_at":"2026-08-05T05:42:33.350361Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.724698Z","title":"A learning algorithm for boltzmann machines,","venue":null,"work_id":"f7cfbaeb-d9c3-4900-ae4e-5921e146a7ae","year":1985},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.163576Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:651dc73a931f70872bcd36d4cc411d652b1cc2423b87c3add4444bb8b7797b7c","observation_id":"0903eef0-7781-49d4-a0a7-5c371a5a2f9e","resolution":{"observed_at":"2026-08-05T05:42:33.731396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.699113Z","title":"Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning,","venue":null,"work_id":"68b4ccd1-4d34-47cc-81b2-c3e51e6ad510","year":2021},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.172860Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:8732b4a503acf3d5f63bb79b8441ef03eed91d23102b2f73b384c885727e009e","observation_id":"448e8e13-c000-4310-a066-0bb085959ddf","resolution":{"observed_at":"2026-08-05T05:42:33.705676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.672912Z","title":"Generation of high-resolution handwritten digits with an ion-trap quantum computer,","venue":null,"work_id":"2cafd79c-f1dd-441f-a9d5-f9e352b12bc2","year":2022},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.179312Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:3f4d2d53e46f18c3137e92d97e98f5cb833314c0f4adc556324455952a45ac7d","observation_id":"d7f7d1af-9c56-409c-b48c-814f4edd80b0","resolution":{"observed_at":"2026-08-05T05:42:33.679186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.632769Z","title":"Comparing the effects of boltzmann machines as associative memory in generative adversarial networks between classical and quantum samplings,","venue":null,"work_id":"24927f94-4349-4dd6-b20b-9dbe21b25239","year":2022},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.185382Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:bd4268edc19841d03697f15c057136eaa96618acb15cfc328f59e699c704cd6a","observation_id":"877c6457-f9b0-43f2-ae34-8fee237c81af","resolution":{"observed_at":"2026-08-05T05:42:33.653479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.195019Z","title":"Improved training of wasserstein gans,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.195019Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:9e9fb0c96d5dbabe6b64d5fded2fb2af187b337cbee943ac35c336f1b25deabc","observation_id":"8fa0173d-e169-4061-be49-428dfb1e355d","resolution":{"observed_at":"2026-08-05T05:42:33.195019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00081","last_updated":"2025-09-08T10:22:05Z","snapshot_observed_at":"2026-07-06T17:53:15.011246Z","submitted_at":"2024-03-29T08:55:39Z","title":"Molecular Generative Adversarial Network with Multi-Property Optimization","version":2},"cited_work":{"arxiv_id":"2404.00081","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.00081","snapshot_observed_at":"2026-08-05T05:42:33.290510Z","title":"Molecular Generative Adversarial Network with Multi-Property Optimization","venue":"q-bio.BM","work_id":"27925589-ea11-4bd2-97ea-31e76fbe22eb","year":2024},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.200942Z"},"links":{"cited_paper":"/paper/2404.00081","citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:45e93fcb22058c55032b6b83ed229aa63d542ded35269a055df8388d10ccbf96","observation_id":"e323f19f-a80c-4a51-8891-d12e6744a373","resolution":{"observed_at":"2026-08-05T05:42:33.300855Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.208363Z","title":"Quantum chemistry structures and properties of 134 kilo molecules,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.208363Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:01d2d64ae1ba0d59eac5a3c7fb78e8866b1318620d76a83f5dba8988e83951e2","observation_id":"a94c309d-a06d-4ae2-a6a1-586d4bdd51cb","resolution":{"observed_at":"2026-08-05T05:42:33.208363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.561430Z","title":"Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17,","venue":null,"work_id":"c80ad1bf-3709-4d54-b4fd-cd3d7f440f6f","year":2012},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.218031Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:aace4c86c0749cf4b67cc0f742381839e361417b2139fb3daa2a7e5992870a8e","observation_id":"4028195d-9748-48af-ad59-f1f7e457b743","resolution":{"observed_at":"2026-08-05T05:42:33.571409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:42:33.531924Z","title":"Efficient global optimization using spsa,","venue":null,"work_id":"b8ba41bf-4086-4d73-9a64-947ff8c2c805","year":1999},"citing_paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T05:42:33.224597Z"},"links":{"citing_paper":"/paper/2509.05051"},"observation_digest":"sha256:0c0a442223547cdd5ae587320d9434a46591c10068222f7e46717ef60d9942cb","observation_id":"8a2e1cb0-ac32-4291-9d7d-9dd8c3624474","resolution":{"observed_at":"2026-08-05T05:42:33.540636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2509.05051","last_updated":"2025-09-05T12:31:58Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-09T11:38:40.895655Z","submitted_at":"2025-09-05T12:31:58Z","title":"QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":3,"verified_fuzzy":31},"total_outbound_references":44},"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 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2509.05051."}