{"as_of":"2026-08-09T23:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:576cd7c8b261978982491ad860729df91b023f83edbe94a2f3130bd82ff898ba","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T12:56:07.813926Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2607.10284/citation-record","integrity":"/paper/2607.10284/integrity","json":"/paper/2607.10284/citation-record.json","paper":"/paper/2607.10284"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Estimation of the size of drug-like chemical space based on GDB-17 data,","venue":null,"work_id":null,"year":2013},"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":1,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:a797d8ae2a1d02e081a6e2d6e26cb6b8ca5b7e90639a5db81261faee721f45a6","observation_id":"0f24c67e-c735-4f78-9c0e-7cf4b9790fc3","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Automatic chemical design using a data-driven continuous representation of molecules,","venue":null,"work_id":null,"year":2018},"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":2,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:c6157d6d60af897b8e24733e77c1104f45ede324186e99454b08b53a962f1bc0","observation_id":"285f463e-6701-4d13-8096-20fee2a3f7d2","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Junction tree variational autoen- coder for molecular graph generation,","venue":null,"work_id":null,"year":2018},"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":3,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:9855ee4be8ed779532d4b34bdcc5ea24d8114f696987af3f866ca12b26369dd1","observation_id":"4e6238f4-8090-4ce8-b976-4112ca38b6f6","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Generating focused molecule libraries for drug discovery with recurrent neural networks,","venue":null,"work_id":null,"year":2018},"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":4,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:fab2151120223e3a59bcfca00da2c7f5be50c2c862c1e81743ce82a81808a687","observation_id":"fa72821b-b573-4d1e-bbf1-60cd54300fb9","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":"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":"2026-07-14T12:56:07.813926Z","title":"MolGAN: An implicit generative model for small molecular graphs,","venue":null,"work_id":null,"year":2018},"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":5,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"cited_paper":"/paper/1805.11973","citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:78d65af8cf8494bda8666c2bea7962c9fa6adf3a8c73f1f2753cbc963d7a07e2","observation_id":"bb6772d0-a2ca-4bd9-9e6d-04631553e0fb","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Quantum machine learning,","venue":null,"work_id":null,"year":2017},"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":6,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:6dc86ff9da2c55e18f252b79afe466cfd055d4eabae2f912746597f393b67504","observation_id":"ace40801-188f-4838-b968-999b817ee5ff","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Variational quantum algorithms,","venue":null,"work_id":null,"year":2021},"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":7,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:2557c481b140cd7be18f34e98d0be42d1fd001906b9c3576d54f798e89f76e0d","observation_id":"5a95c47f-c69d-4f79-95ad-225e988e4861","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Quantum support vector machine for big data classification,","venue":null,"work_id":null,"year":2014},"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":8,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:2132e224ccff5e92393941b497e3a09c2888ff0a285e55fbc310e857cd1cd7ac","observation_id":"1366aa5b-7913-4e01-b4db-9eab97658091","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Supervised learning with quantum- enhanced feature spaces,","venue":null,"work_id":null,"year":2019},"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":9,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:1dde2cbd572aa3672c86b6da4bc6f6aa4e85890b3364f0c4ae555f36f607ceee","observation_id":"c885d771-0964-490a-bf01-29cf571cd199","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Classification of tumor metastasis data by using quantum kernel-based algorithms,","venue":null,"work_id":null,"year":2022},"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":10,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:955fd377dacd8cc47c006306a681196db55afe95165dc84d42d2ad4dc92de828","observation_id":"e1cf1a6a-698b-4596-a94a-d581aad7ea49","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Quantum pointwise convolution: A flexible and scalable approach for neural network enhancement,","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":11,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:5422242721f67daae7a5018e9df5759bb7f596993848edad310dcb6903f26b3e","observation_id":"92f6f526-ee31-4664-b99e-0713df6d8215","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":"2507.11217","last_updated":"2025-07-15T11:40:37Z","snapshot_observed_at":"2026-08-09T20:36:04.130979Z","submitted_at":"2025-07-15T11:40:37Z","title":"Quantum Adaptive Excitation Network with Variational Quantum Circuits for Channel Attention","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.11217","snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Quantum adaptive excitation network with variational quantum circuits for channel atten- tion,","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":12,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"cited_paper":"/paper/2507.11217","citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:e55f0cd9a0e78178bea7924472ca2e26872d1060f50c266a5a983ed863191ded","observation_id":"dae66eb3-d83b-40d7-b71d-296bcd4ca174","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":"2605.27420","last_updated":"2026-05-19T09:35:09Z","snapshot_observed_at":"2026-08-01T19:29:39.113911Z","submitted_at":"2026-05-19T09:35:09Z","title":"Hybrid Classical-Quantum Neural Networks for Multi-Characteristic Co-Optimization of Recessed-Gate AlGaN/GaN MIS-HEMTs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.27420","snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Hybrid classical-quantum neural networks for multi-characteristic co- optimization of recessed-gate AlGaN/GaN MIS-HEMTs,","venue":null,"work_id":null,"year":2026},"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":13,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"cited_paper":"/paper/2605.27420","citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:cc73b65df476a0d8c5354c126ab2e0a396ffe7611004584a5a3e72449e996198","observation_id":"2196bfe4-9ef7-4db9-a8ce-e9d2f60726a6","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"MPM- QIR: Measurement-probability matching for quantum image represen- tation and compression via variational quantum circuit,","venue":null,"work_id":null,"year":2026},"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":14,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:5a13f692bb07136e14bd361b032b23d0b40cc4e32f055cc600bfda6a3cb68749","observation_id":"6a3d3eeb-bf89-4d77-b06e-29039f5ae39f","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Validating large-scale quantum ma- chine learning: Efficient simulation of quantum support vector machines using tensor networks,","venue":null,"work_id":null,"year":2024},"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":15,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:f8e027d8db2b2f44f84d65918413d46720fadf795308b9f60cee3d936c7d2819","observation_id":"3035a95e-60e9-4ed2-b8a4-e40c96373b74","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Scalable tensor network simulation for quantum-classical dual kernel,","venue":null,"work_id":null,"year":2026},"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":16,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:127f1e63159ceeb4979ee8fab345b28691609c5c5d7c8fbc9fc2cdbd5612de75","observation_id":"fda5e4f6-3931-4963-910a-25a10ca1082c","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Iterative matrix product state simu- lation for scalable grover’s algorithm,","venue":null,"work_id":null,"year":2026},"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":17,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:2cb3426b717daabd6354323b587d3a132a528c6395b920e9cb8cbfd5edd76f82","observation_id":"3886ec31-3411-45a6-8ecc-2fd7778e7308","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Quantum generative adversarial networks,","venue":null,"work_id":null,"year":2018},"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":18,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:c1c66a700c1aa7c598066a53ad04e483748b51363dbd344a68d449e67315d312","observation_id":"bdfcf4d8-86d9-432b-b6bf-f40661494f69","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Differentiable learning of quantum circuit Born machines,","venue":null,"work_id":null,"year":2018},"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":19,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:383eeb20714d9a386faeeddbcbe53a61b111eeea59cbf1d3b3c9eb9f703d803f","observation_id":"5da969c6-b95d-43ba-ba8d-350546587d96","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":"2104.00746","last_updated":"2021-04-01T19:53:06Z","snapshot_observed_at":"2026-08-07T07:39:39.292562Z","submitted_at":"2021-04-01T19:53:06Z","title":"Drug Discovery Approaches using Quantum Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.00746","snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Drug discovery approaches using quantum machine learning,","venue":null,"work_id":null,"year":2021},"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":20,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"cited_paper":"/paper/2104.00746","citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:c10c2c28e4da844d2386400648de9222b12b2efca61ce25aeaaa84ac4e4870bc","observation_id":"9aaf3fe4-a228-4fae-9bfa-78b91be62ce5","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":"2026-07-14T12:56:07.813926Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Exploring chemical space with chemistry-inspired dynamic quantum circuits in the nisq era,","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":22,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:28cb64dc540c1cbe78f47833ff956f48f864d4464b0d055563b85d15d3ff1057","observation_id":"f35a7cfd-b630-4e20-97a5-88edb9bd1a4e","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Practical bayesian optimiza- tion of machine learning algorithms,","venue":null,"work_id":null,"year":2012},"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":23,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:cc66fad2671bbeda90b9a9cf912600eadd023893f60ca03a4146c02f074dd819","observation_id":"a558b3fa-980d-41d7-a6c7-f074c47e05aa","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"On the distribution of points in a cube and the approx- imate evaluation of integrals,","venue":null,"work_id":null,"year":1967},"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":24,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:2cabff7efc206e3cc1eebbadfdd18e0375a0654ab010714f41599c8eb3243be5","observation_id":"86308a7c-2344-437e-a024-83f854e7e3ec","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Randomly permuted(t, m, s)-nets and(t, s)-sequences,","venue":null,"work_id":null,"year":1995},"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":25,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:72d3cedb4ab4ace70edcc38d7f3c4f89ee1ad70e7f94ec2d4708fa22600ac52a","observation_id":"3412f478-43fd-4b52-bc39-68858c044527","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Particle swarm optimization with particles having quantum behavior,","venue":null,"work_id":null,"year":2004},"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":26,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:a645681c32445145c3dcb5965786d823fe176e7413f24b760b672e3225bdfb09","observation_id":"51cc1768-35ef-4e6e-b75a-81f144721a28","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T12:56:07.813926Z","title":"Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection,","venue":null,"work_id":null,"year":2012},"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":27,"source":"pdf_text","source_observed_at":"2026-07-14T12:56:07.813926Z"},"links":{"citing_paper":"/paper/2607.10284"},"observation_digest":"sha256:dffb371630f853988f3300e9a46a017163041ad33c77ca03e26f6fcbbc608f73","observation_id":"305e23ca-9a96-4c5f-8ff5-49c290e34a2e","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"}}],"paper":{"arxiv_id":"2607.10284","last_updated":"2026-07-11T12:30:28Z","latest_version":1,"primary_category":"quant-ph","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"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":27,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":27},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.10284."}