{"as_of":"2026-08-17T02:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f406c53a69e3708e5086e991cad79331990372925da317b79541b51836934a28","coverage":[{"denominator":71,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":71,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:17:38.732866Z","state":"measured"},{"denominator":71,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":71,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2608.11373/citation-record","integrity":"/paper/2608.11373/integrity","json":"/paper/2608.11373/citation-record.json","paper":"/paper/2608.11373"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1073/pnas.0403822101","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:39.224650Z","title":"Cross-scale interactions, nonlinearities, and forecasting catastrophic events,","venue":null,"work_id":"31cc5e7e-653c-46d3-aaab-ebd23258c3cc","year":2004},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.276409Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:846ec67a90cfa966b4d25cc10e8398ee69c0d1b0c306dcb6ba7cd161e64b8122","observation_id":"1a893cc5-8961-4216-ba51-e104974a5f16","resolution":{"observed_at":"2026-08-15T14:17:39.231392Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.641243Z","title":"Towards foundation models that learn across biological scales,","venue":null,"work_id":"1ba8f4b4-a89f-4319-b8c3-090efb6e2593","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.287220Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:e6cb31860ef884c0368d944a141dc19c1dc6299b995d2eabadcd357a915cb8e1","observation_id":"dabe135a-0350-4c00-ac9f-e0e31be1c8f6","resolution":{"observed_at":"2026-08-15T14:17:40.646657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.621882Z","title":"Chapter 2 - types of omics data: Genomics, metagenomics, epige- nomics, transcriptomics, proteomics, metabolomics, and phenomics,","venue":null,"work_id":"7219fafd-857f-46b6-bcc3-f89566250651","year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.294100Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:6fade73cc4ba31e6d8f2583e1d1814dfb0da99a7fd9e4812a302e8b757de46bf","observation_id":"3961bbc8-99c2-4ade-ab0d-62fa0248d363","resolution":{"observed_at":"2026-08-15T14:17:40.629107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/bfgp/elae013","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:39.203781Z","title":"A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology,","venue":null,"work_id":"2e7c6857-5603-4d17-9d53-a20c05d76446","year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.299905Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:dadeda4af163b7142a69590f2f1a0049aafe55853f4805cec06edf1b713d8aee","observation_id":"f3315ec8-35a2-4c4c-b608-96ae59b2173c","resolution":{"observed_at":"2026-08-15T14:17:39.211111Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.592496Z","title":"Evaluation of prognostic and predictive models in the oncology clinic,","venue":null,"work_id":"b9f3e487-1d6c-4c7d-9893-12c86f53ec5d","year":2022},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.308612Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:4fae0de0158e09c8011f11e70b0f437a7b782604a0904e3bf941552b520abc58","observation_id":"a71d2f48-3865-41ab-86c9-da999f3e7a90","resolution":{"observed_at":"2026-08-15T14:17:40.598090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15489","last_updated":"2025-01-26T11:32:43Z","snapshot_observed_at":"2026-08-16T17:25:02.062408Z","submitted_at":"2025-01-26T11:32:43Z","title":"AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications","version":1},"cited_work":{"arxiv_id":"2501.15489","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.15489","snapshot_observed_at":"2026-08-15T14:17:40.009163Z","title":"AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications","venue":"cs.AI","work_id":"d9fce8fb-fea3-4f46-b02c-0af8eb403097","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.314020Z"},"links":{"cited_paper":"/paper/2501.15489","citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:3ad3ec26064b1eaf52a9c32be30c477f7bdf1d4fa1c5bc11494ac807049fa713","observation_id":"5f3e4d98-feeb-4d03-afc8-ce9a22cf45bf","resolution":{"observed_at":"2026-08-15T14:17:40.019533Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1158/2159-8290.cd-23-1199","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:39.176616Z","title":"Artificial intelligence in oncology: Current landscape, challenges, and future directions,","venue":null,"work_id":"444f6a4a-1f7b-4c2a-a950-d288a672f4e5","year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.322058Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:574543fde13b0fb2ecdf7b3cd13728042b5d4f83cc70f3f02f4f2260f2c9d8cf","observation_id":"1fa885a9-3cc5-4aa3-abaa-b538fed3119b","resolution":{"observed_at":"2026-08-15T14:17:39.183423Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.568305Z","title":"Bias and class imbalance in oncologic data—towards inclusive and transferrable ai in large scale oncology data sets,","venue":null,"work_id":"9492c61f-1105-43d7-9c57-5a0e6d5362b5","year":2022},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.329595Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:3c8d9e792f342f7f7eed5c9548ea6a82ed961da45d7fe0f3c609b0a53d9ee525","observation_id":"7d82f521-2d53-46c5-a35e-b725c937a218","resolution":{"observed_at":"2026-08-15T14:17:40.574853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.335022Z","title":"Missing data in multi-omics integration: Recent advances through artificial intelligence,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.335022Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:e81dc44f1bcc5609d5cf5b8bb74b29c4c51b7551f1ce94f556b1d8f9d4236425","observation_id":"556b7bcb-9e4b-4976-b640-c29141cc1257","resolution":{"observed_at":"2026-08-15T14:17:38.335022Z","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":"2026-08-15T14:17:40.551147Z","title":"Learning from noisy labels with deep neural networks: A survey,","venue":null,"work_id":"f10e0e8a-be1a-43f3-b127-b71176d144b8","year":2023},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.341222Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:c039746424f285be7e68297a7fa9f32a66c54dee83a4192f754a35b8dead0840","observation_id":"e07723dc-c856-40fe-b633-3c05a8088c81","resolution":{"observed_at":"2026-08-15T14:17:40.556595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.529929Z","title":"High-dimensional data analysis: The curses and blessings of dimensionality,","venue":null,"work_id":"72773e3d-6527-4e02-b42d-5b905e277849","year":2000},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.347125Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:91bd9b5b61296ba3f856ce6099c0526a81b3e6f4f444def7906ef43e260ec2a0","observation_id":"1dc5cb0a-f9c4-4996-9292-7228c33dbc92","resolution":{"observed_at":"2026-08-15T14:17:40.536924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3578580","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:39.150229Z","title":"A universal law of robustness via isoperimetry,","venue":null,"work_id":"241c52c2-88a1-48f6-93cd-a888fd0c08c2","year":2023},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.352490Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:1ca7b872e0425ecda8cf652378caa582c0707c1c9c07eb47ab3dbeb8344c2c1b","observation_id":"a4a5362a-133f-494b-b50b-338cc3c6b502","resolution":{"observed_at":"2026-08-15T14:17:39.158663Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s43018-024-00770-9","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:39.130099Z","title":"Quantum computing for oncology,","venue":null,"work_id":"337e902c-651f-4a74-be10-a3b98872dbe5","year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.359822Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:1b18a262070aa49310d966cd62ee4d445c634debcb38a293162caceea5bd6e47","observation_id":"e61baf36-3675-40ad-b0f5-7625cafca16e","resolution":{"observed_at":"2026-08-15T14:17:39.136430Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.20658","last_updated":"2025-07-14T18:34:04Z","snapshot_observed_at":"2026-08-14T10:59:58.784887Z","submitted_at":"2025-06-25T17:53:35Z","title":"A Framework for Quantum Advantage","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.20658","snapshot_observed_at":"2026-08-15T14:17:38.365688Z","title":"A framework for quantum advantage,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.365688Z"},"links":{"cited_paper":"/paper/2506.20658","citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:98bb8207f2f9c3bccc79f56ec72b63b04a7e5b455bf84910f49f74ecf3a5e4e4","observation_id":"87b846bb-677a-47ca-b056-26c55d9252be","resolution":{"observed_at":"2026-08-15T14:17:38.365688Z","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":"2026-08-15T14:17:38.371355Z","title":"Challenges and opportunities in quantum machine learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.371355Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:eac11d23c7298d884f2aa6cff542630d90d77d56da2f490b8a9d256f22d4624d","observation_id":"dab2c2ee-23b5-4b35-805f-714a6e9c7256","resolution":{"observed_at":"2026-08-15T14:17:38.371355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07059","last_updated":"2024-03-14T11:02:26Z","snapshot_observed_at":"2026-08-16T14:10:51.128439Z","submitted_at":"2024-03-11T18:00:06Z","title":"Better than classical? The subtle art of benchmarking quantum machine learning models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07059","snapshot_observed_at":"2026-08-15T14:17:38.377749Z","title":"Better than classical? the subtle art of benchmarking quantum machine learning models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.377749Z"},"links":{"cited_paper":"/paper/2403.07059","citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:4bd6a0634b84c429ec0c327dbc69796feefb947006a0bdc9fc8088b2376eb7da","observation_id":"456f77b5-7e79-477d-93ca-a5f8db5aa487","resolution":{"observed_at":"2026-08-15T14:17:38.377749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01146","last_updated":"2025-02-03T08:33:44Z","snapshot_observed_at":"2026-08-16T17:22:31.392984Z","submitted_at":"2025-02-03T08:33:44Z","title":"Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01146","snapshot_observed_at":"2026-08-15T14:17:38.382925Z","title":"Quantum machine learning: A hands-on tutorial for machine learning practitioners and researchers,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.382925Z"},"links":{"cited_paper":"/paper/2502.01146","citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:f2e25f9a8a5cd7834b247bb67e378f39e2ed716086227f8c8af60734cf0cbf1f","observation_id":"6a6e3b1f-428d-4823-ba7c-6920058c6d81","resolution":{"observed_at":"2026-08-15T14:17:38.382925Z","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":"2026-08-15T14:17:38.388936Z","title":"How quantum computing can enhance biomarker discovery,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.388936Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:78d15b9d2e4be2c21790677447f89746441071b9a386cf31d5a762e89edb5f0d","observation_id":"d27b0610-3675-4e2f-bfd4-7c221fb1a05d","resolution":{"observed_at":"2026-08-15T14:17:38.388936Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-15T14:17:40.510115Z","title":"Generalization in quantum machine learning from few training data,","venue":null,"work_id":"95de8977-1783-432c-b164-ee9b1b120305","year":2022},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.394557Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:021fc8ed68d290edb4c52394c2326a69a7132dc2e4c0019328d4ed659ec20006","observation_id":"6ff95257-6bcc-442b-b03c-81a2cdbec78d","resolution":{"observed_at":"2026-08-15T14:17:40.516315Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.408585Z","title":"The power of quantum neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.408585Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:2ef475cef19bd6bde1563f695dac0c7010ce38d0266f0be85ec7753fb78e000e","observation_id":"3a4c4df5-1224-4fac-9df6-bb75cf887060","resolution":{"observed_at":"2026-08-15T14:17:38.408585Z","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":"2026-08-15T14:17:38.416122Z","title":"Quantum machine learning advantages beyond hardness of evaluation,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.416122Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:446acad59828e2e7937b18b5c3033b4557f6d30154fe74992e106f71eb49fe63","observation_id":"0282bf62-b5d4-4dc4-a9b9-9b45baa5424f","resolution":{"observed_at":"2026-08-15T14:17:38.416122Z","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":"2026-08-15T14:17:40.491880Z","title":"Is quantum advantage the right goal for quantum machine learning?","venue":null,"work_id":"d1cf0a9c-9096-482f-90fe-7b942d532ce9","year":2022},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.422817Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:19ccfcc1b4a6d3e46d91f1c590865d800cc359b7aa4d73d09f0c529c0ec916c0","observation_id":"c179ca89-1375-4f62-8e1a-ff6e05863e3a","resolution":{"observed_at":"2026-08-15T14:17:40.497137Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.431354Z","title":"Power of data in quantum machine learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.431354Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:b7238da00b8f95b9daeda991ab463cece6fdc11f0d978dcd2e8a61d2aa965050","observation_id":"5739f948-e4ac-4538-ab2b-ddeae7671fa6","resolution":{"observed_at":"2026-08-15T14:17:38.431354Z","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":"2026-08-15T14:17:38.437101Z","title":"Quantum computing for genomics: conceptual challenges and practical perspectives,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.437101Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:add0344318c90058d81cf179126aa650c4843131af633a8f70db7ade3feec0e0","observation_id":"d931d917-e6c7-4035-8530-58822f9e5fa6","resolution":{"observed_at":"2026-08-15T14:17:38.437101Z","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":"2026-08-15T14:17:38.444110Z","title":"A systematic review of quantum machine learning for digital health,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.444110Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:f34e408fa70e1cee2314cb53a8c40cd02960eb5fa1252161295a804fa7e9699a","observation_id":"b33c2be4-c71b-4173-bf8a-2a636e3c0388","resolution":{"observed_at":"2026-08-15T14:17:38.444110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.02148","last_updated":"2025-01-08T06:12:44Z","snapshot_observed_at":"2026-08-16T12:59:45.024889Z","submitted_at":"2025-01-04T00:35:14Z","title":"Bit-bit encoding, optimizer-free training and sub-net initialization: techniques for scalable quantum machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.02148","snapshot_observed_at":"2026-08-15T14:17:38.451063Z","title":"Bit-bit encoding, optimizer-free training and sub-net initialization: techniques for scalable quantum machine learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.451063Z"},"links":{"cited_paper":"/paper/2501.02148","citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:0e1b7e1e5c4a24b70dc5641f540299cbb3c482583a462c49f8fa70e8c05f7d9b","observation_id":"74328f53-c095-42f0-b8cb-2524ae177c30","resolution":{"observed_at":"2026-08-15T14:17:38.451063Z","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":"2508.20992","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:39.571759Z","title":"How many qubits does a machine learning problem require?","venue":null,"work_id":"3955a4a0-1e5d-4fae-a5de-dbdc17044191","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.456875Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:e5628cb56199fa2a9ee55630c79464545f40be56bca899ad960ed5fecf02aa0a","observation_id":"fedf654c-204d-47d2-8047-87bd72b4e001","resolution":{"observed_at":"2026-08-15T14:17:39.581562Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-981-99-8413-8","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:39.048630Z","title":"Ribeiro, A","venue":null,"work_id":"3438ada6-4091-4444-849e-7082dcb80198","year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.463296Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:a10c8a6fdbba82c1ba61f642ccb778dc7f462b902974104c2174f6b7e9cecfd3","observation_id":"fb1b759c-a746-4d8b-a0cc-cfb5d4443c6c","resolution":{"observed_at":"2026-08-15T14:17:39.053777Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.473230Z","title":"Breast Cancer Wisconsin (Diagnostic),","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.473230Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:b83785880a3c206664898a0ef895c16ad863006fca2b0c7c550e6d9e748fc5dc","observation_id":"5f9ef7b8-7a00-4d91-b19a-fac51fdb5039","resolution":{"observed_at":"2026-08-15T14:17:38.473230Z","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":"2026-08-15T14:17:40.476033Z","title":"Nuclear feature extraction for breast tumor diagnosis,","venue":null,"work_id":"1913f275-8b86-44be-bf50-647c92c6e11f","year":1993},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.480764Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:3bd8ef03d7861f7f357fa329050800da9a825cd7e8ac15a029c0d059ac734bfc","observation_id":"5ab4dfc1-2b63-4a5c-ab78-fa4dd1b16dfb","resolution":{"observed_at":"2026-08-15T14:17:40.480863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.459261Z","title":"Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis,","venue":null,"work_id":"cd9a9c47-b003-45f2-9343-d05a130a4f5f","year":null},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.485223Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:3515ee1830bd513a4cb47e2aa7e543d09570e9b8795c3b2e2857abbd72bc3c96","observation_id":"03cfe39a-82e2-4041-97f8-2f2ff944ecea","resolution":{"observed_at":"2026-08-15T14:17:40.465106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.495450Z","title":"Clinical data classification with noisy intermediate scale quantum computers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.495450Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:9f1e447553392c345c3df3c6ef0f80ca7bc6bccf8361527636b16d2d1f7a211e","observation_id":"ec360573-0108-4197-a828-5212b884a1ca","resolution":{"observed_at":"2026-08-15T14:17:38.495450Z","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":"10.21203/rs.3.rs-1434074/v1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:38.974099Z","title":"Demonstration of breast cancer detection using qsvm on ibm quantum processors,","venue":null,"work_id":"3d8debd3-5940-4ff6-b562-b7876e6c41f2","year":2022},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.501981Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:e2805192ba82798e3f7c6f7dc8e6062b993c12f5555a8a3b0a98c1fb0213c439","observation_id":"ee4e2ef8-6d70-4a7d-a49f-9d3b2368216b","resolution":{"observed_at":"2026-08-15T14:17:38.981309Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.441657Z","title":"Quantum machine learning for breast cancer detection: a comparative study with conven- tional machine learning methods,","venue":null,"work_id":"0683a6f0-a87f-450e-b7ad-ab2408efd0ba","year":2023},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.510311Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:908e91020dda4f4b9dc79fc0ec997ebe43d60f2fff70a4763ff55f6a76a47632","observation_id":"c8032755-c38a-4380-adc3-fc0a8da0906b","resolution":{"observed_at":"2026-08-15T14:17:40.448524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.516468Z","title":"A novel feature selection method based on quantum support vector machine,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.516468Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:8d34a60f214923bb52460a71ec5821bb234b5b52a3b134a5ffa92632d828eac5","observation_id":"4e2c99d7-a0bc-431b-9107-48e9a22ee4c5","resolution":{"observed_at":"2026-08-15T14:17:38.516468Z","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":"2026-08-15T14:17:40.419001Z","title":"Comparison of machine learning and quantum machine learning for breast cancer detection,","venue":null,"work_id":"0515dd2b-53c8-4dc9-b3a5-1e2666b82f2c","year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.525210Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:9834956328eb37961707e5f39e4fa77847aad567f86d437204f7588e2a336a96","observation_id":"5ba5ca2f-619a-4bd5-826c-0af869afeac6","resolution":{"observed_at":"2026-08-15T14:17:40.424144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.394814Z","title":"Integrating xai with quan- tum machine learning models for interpretable breast cancer clas- sification,","venue":null,"work_id":"7dc1f278-8cc5-4554-a663-98140c929c3a","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.530498Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:0393eaaec2fad2059b10bfced1af444dbb0600d6f9b4c022f1e3c26e36de1b3e","observation_id":"1df9f6f4-da16-427f-9962-f2ee9326aa04","resolution":{"observed_at":"2026-08-15T14:17:40.402017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.371982Z","title":"Harnessing quantum-classical techniques for improved breast cancer prediction,","venue":null,"work_id":"ee5ff10a-7ac5-4621-91ce-3b909c5363b1","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.535373Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:80c5d0df276e51120e04abffc0b06305ebd8466541c1dd85618c4f95671bc4d8","observation_id":"983bbe27-d9e6-4562-9174-460c0946c828","resolution":{"observed_at":"2026-08-15T14:17:40.380383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.351050Z","title":"Quantum processor-inspired machine learning in the biomedical sciences,","venue":null,"work_id":"aeabd43e-0266-4b52-8048-dd95a327b7da","year":null},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.543202Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:6001085200618605abd07218608500b85288d5c4eb64a2c67d4c10554dd28e9b","observation_id":"36e52373-caf7-45c8-87c7-bac6115ad676","resolution":{"observed_at":"2026-08-15T14:17:40.356872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s42452-024-06220-6","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:38.935594Z","title":"Potential of quantum machine learning for solving the real-world problem of cancer classification,","venue":null,"work_id":"8bda618e-3e79-47cf-8c77-692308fe4f01","year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.553201Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:537099e0913551f692532216593d0ae6bb88e636bbbd5ba444a586bb25b62b2d","observation_id":"a251e230-0f1f-4637-bf0d-8a54d835dfb1","resolution":{"observed_at":"2026-08-15T14:17:38.944157Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.547665Z","title":"Available: https://doi.org/10.1016/j.patter.2021.100246","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.547665Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:1ba8cd36c948f6c9fd74cea8639dee85a1950f7db91b63cdd1ea46efdbb35854","observation_id":"e980146b-faa7-486f-af65-34297b90ae1e","resolution":{"observed_at":"2026-08-15T14:17:38.547665Z","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":"2026-08-15T14:17:40.331838Z","title":"Investigating the application of quantum machine learning in breast cancer: A systematic review: Quantum machine learning in bc,","venue":null,"work_id":"20389da5-87cc-490c-af9b-4acbb47afb75","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.564447Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:9cb2891cb2a9a4b654fbd0a3f6a48356ba2d20e3b32da137cb47e9fc32dc6444","observation_id":"920bf000-5b97-4f99-acb3-5572a10a719a","resolution":{"observed_at":"2026-08-15T14:17:40.337559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s12859-024-05755-0","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:38.918125Z","title":"Biomarker discovery with quantum neural networks: a case-study in ctla4-activation pathways,","venue":null,"work_id":"233bc210-6cfe-4526-806c-ef060d07d459","year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.559642Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:25bc301592360b412d5ce35a1002e3de606c9f4e99db9a73b4cb590c61a15edd","observation_id":"e67ca0ed-ed43-4c57-9dcc-a1d49abc1e5f","resolution":{"observed_at":"2026-08-15T14:17:38.923320Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41597-025-05235-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:38.899595Z","title":"Mlomics: Cancer multi-omics database for machine learning,","venue":null,"work_id":"2d817597-7ea8-454c-b2d4-a720a9649388","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.574471Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:970dd9dd6b025320f734cfbac135c3a13fb91330e2458c95d2e32ff15a2225a1","observation_id":"36d7a13b-93e9-4575-8ebc-804d1cb4bcac","resolution":{"observed_at":"2026-08-15T14:17:38.904433Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.300618Z","title":"Multi-omic and quantum machine learning integration for lung subtypes classification,","venue":null,"work_id":"5770e1e9-f71c-4844-a4bc-ad5276145e02","year":2026},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.569804Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:f0ff4747886bcb8e6a1c3771bad6d7462df176c86d8d258e0c0888f628f9e4dc","observation_id":"9c667476-5563-45d0-a47f-59a73401adbd","resolution":{"observed_at":"2026-08-15T14:17:40.311309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.254848Z","title":"Quantum machine and deep learning for medical image classification: A systematic review of trends, methodologies, and future directions,","venue":null,"work_id":"4a7a5d7a-f5ba-478a-811e-f7c763515cac","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.584445Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:0ef07e2adc4fb07a41d30ee4827a3007f8a49e84670750255c7dfe0a82fdd3aa","observation_id":"c8884167-3f96-4ec4-8ded-272fe6b6e023","resolution":{"observed_at":"2026-08-15T14:17:40.259929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.278465Z","title":"Quantum machine learning in medical image analysis: A survey,","venue":null,"work_id":"ba12ae72-066e-4f52-850a-472fe65d8584","year":2023},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.580097Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:90aa1970380c8305f163f8a6c93124dc132c9ca50fed9bfed2b1c0b2587d3664","observation_id":"b097b851-427b-4feb-ae78-150c81b2e528","resolution":{"observed_at":"2026-08-15T14:17:40.285629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/2632-2153/acffa3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:38.869664Z","title":"Universal adversarial perturbations for multiple classification tasks with quantum classifiers,","venue":null,"work_id":"85b88fd1-ecbc-4868-90ce-c03cd9ef5e50","year":2023},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.593973Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:89d5971374c5f342c1449b1e4743ab494e810f58b5545693889ecb6682164eb8","observation_id":"6ceb1f77-9d25-451d-89c6-9d1adf32d078","resolution":{"observed_at":"2026-08-15T14:17:38.877386Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.589248Z","title":"Medmnist v2 - a large-scale lightweight benchmark for 2d and 3d biomedical image classification,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.589248Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:15ce36c4eb146855cfb8bf453c53ae35f08edd560eee0684dcf0cf52fa921174","observation_id":"e36f08b6-a4fb-45d6-bcb3-f3fae9cf0abd","resolution":{"observed_at":"2026-08-15T14:17:38.589248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.10510","last_updated":"2025-03-13T16:14:08Z","snapshot_observed_at":"2026-08-17T02:11:25.348031Z","submitted_at":"2025-03-13T16:14:08Z","title":"Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification","version":1},"cited_work":{"arxiv_id":"2503.10510","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.10510","snapshot_observed_at":"2026-08-15T14:17:39.301574Z","title":"Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification","venue":"q-bio.QM","work_id":"9aa92a91-4451-46a0-889c-5abaae4ce3cb","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.604385Z"},"links":{"cited_paper":"/paper/2503.10510","citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:b689d9f8217e849ac7403ee2736eafebbcb5a35cc73cde6edb002c552c04b435","observation_id":"198600eb-2471-43c2-a56b-dc5a48e9fcd6","resolution":{"observed_at":"2026-08-15T14:17:39.307279Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.235948Z","title":"Quantum Methods for Neural Networks and Application to Medical Image Classification,","venue":null,"work_id":"c6062fc1-2008-43f6-9ac1-cec6545fe933","year":2022},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.599676Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:9f4489140dfe15d6a159dc6bb97e2575917fcf2e43e3223f1bc31bf1c04a82f6","observation_id":"df1219fd-1f2f-44fb-bd00-75dfc232fa4a","resolution":{"observed_at":"2026-08-15T14:17:40.241678Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.194236Z","title":"Quantum machine learning approaches for high-dimensional cancer genomics data analysis,","venue":null,"work_id":"0f1efcfc-baf1-4136-8917-42ef0da82d6f","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.615867Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:61e3bd6129c6a5a7f2478da31e7fc3db622eb42de03cdca736047d1d9c180e86","observation_id":"40036dc9-9c67-4be7-827d-29b7efceed08","resolution":{"observed_at":"2026-08-15T14:17:40.204054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.217550Z","title":"Hybrid quantum-classical neural network for breast cancer detection,","venue":null,"work_id":"2c6a4c33-1282-49d9-8751-fd6eac40f4d7","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.610521Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:397995ffce169df4fef6a332a7d1d231038095da908620b13601401b4b8134d1","observation_id":"7ec9114e-2c25-4c8b-bfd5-8bd1d81ef2e5","resolution":{"observed_at":"2026-08-15T14:17:40.222870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.627360Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.627360Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:f89135b751651790fb61ed9a0df035e05c0f540159e708ac785cc997424233b8","observation_id":"84777311-d522-4bad-86ea-0d6dc0e01614","resolution":{"observed_at":"2026-08-15T14:17:38.627360Z","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":"2026-08-15T14:17:38.621637Z","title":"Scikit-learn: Machine learning in Python,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.621637Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:80da87965db409d46b20e096586a654990bbdd97937a64851264b1b881e5fbd6","observation_id":"ef1db750-d11c-441d-9d3d-05ca45c97c5e","resolution":{"observed_at":"2026-08-15T14:17:38.621637Z","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":"2026-08-15T14:17:40.127898Z","title":"A systematic review of quantum image processing: Representation, applications and future perspectives,","venue":null,"work_id":"18ee3dba-25b4-4dfe-86f6-186e8b7f445e","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.638969Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:1ef5e0cd2dd8d47034c97d059106f423b197b1de5eb55c4844100b27e38e321b","observation_id":"40eb49ac-21fa-4134-9983-7d851ed233aa","resolution":{"observed_at":"2026-08-15T14:17:40.138174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.632846Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.632846Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:6e36fce7037cf1b89bdd5b11a373f6889cce2aa59171f5b2201b6ae063a609e9","observation_id":"9e1863cd-e00e-4554-9208-b52735af7566","resolution":{"observed_at":"2026-08-15T14:17:38.632846Z","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":"2026-08-15T14:17:38.650841Z","title":"Effect of data encoding on the expressive power of variational quantum-machine-learning models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.650841Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:9e467ab66b12b7af173a03bd62d3c41971068bc310300e74949389360016cfa3","observation_id":"5fd8c77f-2d44-47c3-bc10-c7cd019f13a3","resolution":{"observed_at":"2026-08-15T14:17:38.650841Z","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":"2026-08-15T14:17:38.643867Z","title":"Encoding patterns for quantum algorithms,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.643867Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:ee1dc28844e6e04500038008c4faa15b57c0da5a37d7acb8065d0ec4b7a71058","observation_id":"975ec3a9-8ba4-4a83-add9-801de3a27986","resolution":{"observed_at":"2026-08-15T14:17:38.643867Z","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":"2026-08-15T14:17:38.665695Z","title":"Computational power of random quantum circuits in arbitrary geometries,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.665695Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:5ace296ff3c7a66f0655fd76aa0b8ce56adcb0cc64b4c13316400c5d3b7aa57b","observation_id":"6f262d71-93d3-4fa5-b3d9-10577de61900","resolution":{"observed_at":"2026-08-15T14:17:38.665695Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01545","last_updated":"2025-03-03T13:52:47Z","snapshot_observed_at":"2026-08-17T00:42:16.305675Z","submitted_at":"2025-03-03T13:52:47Z","title":"Limitations of Amplitude Encoding on Quantum Classification","version":1},"cited_work":{"arxiv_id":"2503.01545","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.01545","snapshot_observed_at":"2026-08-15T14:17:39.274555Z","title":"Limitations of Amplitude Encoding on Quantum Classification","venue":"quant-ph","work_id":"8337b257-1da9-4391-acf0-b1c94d48b8e0","year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.657952Z"},"links":{"cited_paper":"/paper/2503.01545","citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:c3b5d5a994861422f8b277adcae416a258251c14589c74f032aba0932e43eb11","observation_id":"74f7c248-871a-4867-b2ff-f4bbdf76c820","resolution":{"observed_at":"2026-08-15T14:17:39.280479Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:38.683170Z","title":"Does provable absence of barren plateaus imply classical simulability?","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.683170Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:d9368755c37f73cbd54ae99b5f6df15057c2fe5a07a6a803b1b14d0b69b4ad9d","observation_id":"b12ddb66-ddf2-4cb4-b4a8-2a09b3d67f90","resolution":{"observed_at":"2026-08-15T14:17:38.683170Z","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":"2026-08-15T14:17:38.673679Z","title":"Barren plateaus in quantum neural network training landscapes,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.673679Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:ebfdf84f9c07fece4d8dff2789ffb6e9adadde51f0fd81735eadb94c8d14b564","observation_id":"47d5631b-8043-4f14-946a-d6293256335e","resolution":{"observed_at":"2026-08-15T14:17:38.673679Z","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":"2026-08-15T14:17:38.696642Z","title":"Hyperparameter importance and optimization of quantum neural networks across small datasets,","venue":null,"work_id":null,"year":1941},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.696642Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:b690d61f554ddebe29a8bd9de92d6253e967ca76e4ed6e7a2914e9674bdd1e9f","observation_id":"c696abad-0ce4-4340-ad91-16cfc9f0070a","resolution":{"observed_at":"2026-08-15T14:17:38.696642Z","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":"2026-08-15T14:17:38.689469Z","title":"Variational quantum algorithms,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.689469Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:2e73faf580aa16c4d713c00e3cbfef2c00d8f1e15681a4bdd23b78a1d6d03b18","observation_id":"97b2fdbd-2034-4586-bdd9-07d46f23ea3b","resolution":{"observed_at":"2026-08-15T14:17:38.689469Z","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":"2026-08-15T14:17:40.066876Z","title":"Shot optimization in quantum machine learning architectures to accelerate training,","venue":null,"work_id":"5546711c-a73c-4322-ac54-34cb1ca09837","year":2023},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.721224Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:ad9dad69ea6764f21dc868956745cccd6521b4d95479286e499735946e46096d","observation_id":"cc15c851-6249-4694-9b4a-01fbfa75bc1c","resolution":{"observed_at":"2026-08-15T14:17:40.074091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:17:40.090784Z","title":"Adaptive shot allocation for fast convergence in variational quantum algorithms,","venue":null,"work_id":"a0dd9b15-2d05-419a-bc08-dd3a39bf5e68","year":null},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.705893Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:3dfd8945937d1e0d3d864bc9650f1a8e65358ec760c17059a0a42e2d81fc07c0","observation_id":"04952cd7-ef75-419a-b75f-5a94d0ecbb52","resolution":{"observed_at":"2026-08-15T14:17:40.097263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.10434","last_updated":"2021-08-23T22:29:44Z","snapshot_observed_at":"2026-08-16T18:01:26.683180Z","submitted_at":"2021-08-23T22:29:44Z","title":"Adaptive shot allocation for fast convergence in variational quantum algorithms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.10434","snapshot_observed_at":"2026-08-15T14:17:38.713913Z","title":"Available: https://arxiv.org/abs/2108.10434","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.713913Z"},"links":{"cited_paper":"/paper/2108.10434","citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:8ab90b21c4d80eaeeea6c4258ac469db0f358e9ce8a0a6a48427402d21c66b82","observation_id":"dc85ae0d-7bb7-4712-b30d-32214c7af6c1","resolution":{"observed_at":"2026-08-15T14:17:38.713913Z","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":"2026-08-15T14:17:38.727379Z","title":"Challenges of variational quantum optimization with measurement shot noise,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.727379Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:a3912238d2875a4e5f0cd536303c19048a99426c1fba446c3c8f6c084de15884","observation_id":"bc725ee2-ea18-409a-9e5b-3e795bc575bd","resolution":{"observed_at":"2026-08-15T14:17:38.727379Z","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":"2026-08-15T14:17:40.037820Z","title":"Accurate cancer classification using expressions of very few genes,","venue":null,"work_id":"3695ba7d-e266-44a5-828e-6009aa9d6112","year":2007},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.732866Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:b8161fd2682b43ba61866bc6285e452427a132a60a18326a5c62ebd5659c941e","observation_id":"89e8f4a2-46c2-4e0e-89ec-de1ea484a58e","resolution":{"observed_at":"2026-08-15T14:17:40.046637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1515/jisys-2020-0089","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:17:39.015633Z","title":"Available: https://doi.org/10.1515/jisys-2020-0089","venue":null,"work_id":"5317f40a-9deb-4382-933d-a70acf13ae6d","year":2020},"citing_paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T14:17:38.490585Z"},"links":{"citing_paper":"/paper/2608.11373"},"observation_digest":"sha256:c44fd8a93a5cec0b1f8b60a8fdda794e8e08b236fcb6d9c7a11e298ff09f095d","observation_id":"f96e3ca3-4a63-4040-9d5b-551c30cb8683","resolution":{"observed_at":"2026-08-15T14:17:39.021803Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.11373","last_updated":"2026-08-11T19:28:22Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-15T23:10:03.501440Z","submitted_at":"2026-08-11T19:28:22Z","title":"Benchmarking Quantum and Classical Machine Learning Models on Oncological Data"},"reference_resolution":{"displayed":71,"state_counts":{"malformed_identifier":4,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":28,"verified_exact":16,"verified_fuzzy":23},"total_outbound_references":71},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2608.11373."}