{"as_of":"2026-08-11T15:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e9eeb110cdd521d8bcf182b706c8dada424bf85f4781d3816347aab338035ee7","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T03:46:46.026308Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2607.11701/citation-record","integrity":"/paper/2607.11701/integrity","json":"/paper/2607.11701/citation-record.json","paper":"/paper/2607.11701"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T03:46:46.026308Z","title":"Drug design by machine learning: Support vector machines for pharmaceutical data analysis,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:48594132e99cf2e71d374b1cc979fceb6c938adb20f1cf06694165d36ff23394","observation_id":"8bb03488-bf05-497e-b152-554ebf5595af","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Q 2SAR: A Quantum Multi- ple Kernel Learning Approach for Drug Discovery,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:e8d39ecffa38332c9996cadc50894a0954468244a5269b10d63ed52f510aecb4","observation_id":"caac28fd-e397-44fd-b32f-6eec490b6e87","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Quantum support vector machine for big data classification,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:fd70215be3ace8602805de00b35c040d822e7cc26313a5d7d1eb50692972b77b","observation_id":"631e284d-c49d-4b70-b279-75b8636eff21","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Power of data in quantum machine learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:ce2201789f63a54aa7b583ae19bae9c7edc8479259e73a8c246118063670e161","observation_id":"dd35ffbd-eb81-423f-af9b-5d3de7bec527","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:2160383cce22e2e5388c78d0cfa41dc35d0a99d6c043c650edb77257f9f338b9","observation_id":"38a1c6db-687f-4bc3-8365-0bde9be27cc7","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.09694","last_updated":"2020-11-19T07:19:41Z","snapshot_observed_at":"2026-08-05T04:25:32.247113Z","submitted_at":"2020-11-19T07:19:41Z","title":"Quantum Multiple Kernel Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.09694","snapshot_observed_at":"2026-07-14T03:46:46.026308Z","title":"Quantum Multiple Kernel Learning,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"cited_paper":"/paper/2011.09694","citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:e373aaa35301900b1506766adcb4745167ea7c236b7ab20aa9e184cb1b1cf747","observation_id":"dab947af-66ee-4dd9-b0b6-d57bd95dfd0c","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Gaultonet al.,The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods, Nucleic Acids Research, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:7cb9a0a9fe44efe9e84ff7a9830f48e95a72aeb22d04a83b2e1b604067b0a2c2","observation_id":"53a95731-7bd9-4bba-b98d-8e2c607484cf","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01748","last_updated":"2025-03-03T17:15:23Z","snapshot_observed_at":"2026-08-11T06:22:18.474611Z","submitted_at":"2025-03-03T17:15:23Z","title":"Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.01748","snapshot_observed_at":"2026-07-14T03:46:46.026308Z","title":"Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm:HoLCUsQAOA,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"cited_paper":"/paper/2503.01748","citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:26689801a6b75c34c2e33793d60bdd9c8c4366be7d03d601009ae9fa2bd68df8","observation_id":"335d6a08-95f2-4fc8-8b5c-5c27912256ae","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Data Complexity: a threshold between Classical and Quantum Machine Learning - Part I,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:bdc1aac106605240fc62278954cba051dfad51fd31a6310bfd4baf179b81094a","observation_id":"94617f2d-53bb-4949-bb36-1229597a0c45","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Supervised learning with quantum- enhanced feature spaces,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:c3e11f226c38940bd1920bad08c95060daba74ea6b1cdb5ad2f1ea2a8ad65aa5","observation_id":"e1a770fe-ec39-436d-8632-a75a10ccc920","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Quantum machine learning in feature Hilbert spaces,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:8871452defdd5ff01090dab5adfb6bac05344da0fcde24b12f9b7d8bd9f69db1","observation_id":"f00987b5-993b-4f3f-b90a-ba2be4b53211","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Exponential concentration in quantum kernel methods,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:216faedd5fab6cd2c17dbb5614bf875f5c9adaa7c2eaf6429b0095e5637f3b73","observation_id":"88f47ac1-4d9a-4b98-b7d1-9d652996e2d1","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13395","last_updated":"2025-01-23T05:39:08Z","snapshot_observed_at":"2026-08-10T15:57:36.852676Z","submitted_at":"2025-01-23T05:39:08Z","title":"Enhancing Drug Discovery: Quantum Machine Learning for QSAR Prediction with Incomplete Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.13395","snapshot_observed_at":"2026-07-14T03:46:46.026308Z","title":"Enhancing drug discovery: Quantum machine learning for qsar prediction with incomplete data,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"cited_paper":"/paper/2501.13395","citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:c9c9246c7e464ce98d4142f2c3180c58330916c3be055764fef26627584b39a6","observation_id":"9dc803b8-7d56-4c94-84bb-0ad701ecb559","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs),","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:8ac50985e70c607e36bc1a95b93db8c417027974c28eb27a99015000eef0da87","observation_id":"20accb01-a247-48b0-bb9d-68b88757ce12","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Pesticide effect on earthworm lethality via interpretable machine learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:2481908e906fdf150cb0e19045230b7b4ffc100be4ab0c5532f086a103789ab5","observation_id":"8c13de1d-c02b-4347-8d4d-8b096e849716","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","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":"2026-07-14T03:46:46.026308Z","title":"Logistic classification models for pH- permeability profile: Predicting permeability classes for the biopharma- ceutical classification system,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T03:46:46.026308Z"},"links":{"citing_paper":"/paper/2607.11701"},"observation_digest":"sha256:5418536f2d36ac741c14e82526de71335d312d2476943e7b782d6edec51f2796","observation_id":"f78cd21b-83fe-44fa-833c-3d1ff1276aab","resolution":{"observed_at":"2026-07-14T03:46:46.026308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.11701","last_updated":"2026-07-13T15:33:05Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-09T16:31:36.600123Z","submitted_at":"2026-07-13T15:33:05Z","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":16},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2607.11701."}