{"as_of":"2026-08-08T08:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7df85b427dd40ffb47c1892bbb76e1d0c5e5165c578ad77b76f627ff754baa49","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:15:37.597202Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T01:10:12.428509Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T01:10:12.644797Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"cited_work":{"arxiv_id":"2505.22502","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.22502","snapshot_observed_at":"2026-08-07T01:10:12.644797Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","venue":"quant-ph","work_id":"8e60fe87-7c15-45ca-b9c7-13bf4f2fdc22","year":2025},"citing_paper":{"arxiv_id":"2506.11994","last_updated":"2025-06-13T17:49:25Z","snapshot_observed_at":"2026-08-08T05:24:13.660390Z","submitted_at":"2025-06-13T17:49:25Z","title":"Spectral Estimation with Free Decompression","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T01:10:12.428509Z"},"links":{"cited_paper":"/paper/2505.22502","citing_paper":"/paper/2506.11994"},"observation_digest":"sha256:f70859b8b99c08971c9d6209742275a75f3a58fa5cd4b7404d6fe5c4a9d4ed41","observation_id":"78c1fe1e-426a-43cc-aedf-b521e2046091","resolution":{"observed_at":"2026-08-07T01:10:12.650902Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.22502/citation-record","integrity":"/paper/2505.22502/integrity","json":"/paper/2505.22502/citation-record.json","paper":"/paper/2505.22502"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:15:56.422822Z","title":"Quantum machine learning","venue":null,"work_id":"610f30ee-ab81-45d6-8e4f-b2ab82e1fcdc","year":2017},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:32.392873Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:0dc0b06f7c51ac8ec4e1272f19e2aa0c9292179c9563e2aae4911072628f5341","observation_id":"1dab3da8-7032-4256-9b3f-b79d88605dce","resolution":{"observed_at":"2026-08-07T13:15:56.505433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:56.292547Z","title":"Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, and Patrick J","venue":null,"work_id":"5672cf5a-351f-4c97-804f-67a234163b04","year":2022},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:32.446160Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:3b438b01c86676226e3ca98aa65b9b8e9128e4fcb99a5e79e6dd3aa3e2053353","observation_id":"3b5f0d62-9a10-4309-aeb3-95e2f455d106","resolution":{"observed_at":"2026-08-07T13:15:56.362925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:56.163162Z","title":"Quantum machine learning: a classical perspective","venue":null,"work_id":"d69c81c6-03ca-404e-8b91-6504933a8b8c","year":2018},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:32.533267Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:a919057e0265cca7ee88f2accbb1998d2393709ba926e539b7dd471d7b2b7b69","observation_id":"6a3de4f6-6dee-4ef5-adc0-acf79146f88e","resolution":{"observed_at":"2026-08-07T13:15:56.218363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1307.0411","last_updated":"2013-11-04T22:51:55Z","snapshot_observed_at":"2026-07-06T03:17:09.663659Z","submitted_at":"2013-07-01T15:38:12Z","title":"Quantum algorithms for supervised and unsupervised machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1307.0411","snapshot_observed_at":"2026-08-07T13:15:32.658759Z","title":"Quantum algorithms for su- pervised and unsupervised machine learning, November 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:32.658759Z"},"links":{"cited_paper":"/paper/1307.0411","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:41503041666ebbb5147f9cbe4e273cc0f84d072daeff5035849d173d8d8cd6ea","observation_id":"f8a928d9-3cc7-4fd4-a9ac-fdbc49cb4ea4","resolution":{"observed_at":"2026-08-07T13:15:32.658759Z","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":"2026-08-07T13:15:56.034164Z","title":"Quantum principal compo- nent analysis","venue":null,"work_id":"0fcbb220-22da-4a28-912d-a9fe19ee187c","year":2014},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:32.790094Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:6dd286b25b106735cbdfbedb3df79b9b9dec0e3cd776561c3061e2d4bec09cc1","observation_id":"58d8b3cb-2943-4b33-918c-bb3b8fb2788d","resolution":{"observed_at":"2026-08-07T13:15:56.103783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:55.876772Z","title":"Quantum Recommendation Systems","venue":null,"work_id":"824f76f9-00ee-4167-b4c6-3cfc9f6443ef","year":2017},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:32.915730Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:9166492232bc64f8b0c088e65e8aa9981c05d8b492098081ca6596b715e29700","observation_id":"b45f5b9c-2c32-48b1-8d57-25b39683d237","resolution":{"observed_at":"2026-08-07T13:15:55.960941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"0811.3171","last_updated":"2009-09-30T15:24:42Z","snapshot_observed_at":"2026-08-07T10:21:06.556588Z","submitted_at":"2008-11-19T20:36:41Z","title":"Quantum algorithm for solving linear systems of equations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0811.3171","snapshot_observed_at":"2026-08-07T13:15:33.003099Z","title":"Harrow, Avinatan Hassidim, and Seth Lloyd","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.003099Z"},"links":{"cited_paper":"/paper/0811.3171","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:e94aff431bf3c9022970b43e3fd60f9dfd230a9590a83c0d57b8006c61f08085","observation_id":"e877ed34-19d8-4b12-a991-01b8249f412c","resolution":{"observed_at":"2026-08-07T13:15:33.003099Z","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":"2026-08-07T13:15:55.704740Z","title":null,"venue":null,"work_id":"1e1efab0-bf2e-4abd-a832-c4090e2cc434","year":2023},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.093523Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:c32f7ca5f8470fac07b1371d4f66fe98f0eeca419f3a8609649ae7788d3dc563","observation_id":"a4895fd3-5fcf-496d-aa41-eac68330a33b","resolution":{"observed_at":"2026-08-07T13:15:55.791015Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:55.572990Z","title":"A quantum-inspired classical algorithm for recommendation systems, May","venue":null,"work_id":"0936c1f0-8619-49b9-9624-cbe480a638cd","year":null},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.170753Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:e76fb28f932821df10ec12d7fa93c59ae1c9a16cfb5c2ae7efbe0244949bd40b","observation_id":"cd1de0c6-c26a-48c2-81db-1a626ac5c75c","resolution":{"observed_at":"2026-08-07T13:15:55.629996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.00414","last_updated":"2021-08-06T20:42:26Z","snapshot_observed_at":"2026-07-06T07:12:02.877773Z","submitted_at":"2018-10-31T03:23:52Z","title":"Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions","version":3},"cited_work":{"arxiv_id":"1811.00414","doi":null,"metadata_source":"pith","pith_arxiv_id":"1811.00414","snapshot_observed_at":"2026-08-07T13:15:38.578377Z","title":"Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions","venue":"cs.DS","work_id":"42692832-8a5d-401b-b54e-fdf1f636fced","year":2018},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.252747Z"},"links":{"cited_paper":"/paper/1811.00414","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:cf461c1ee2e0899941764a10ff9d98f77a10f130b4f75b50a8678a8004fb6811","observation_id":"7f9ee1ad-3a97-4736-bb82-283608332630","resolution":{"observed_at":"2026-08-07T13:15:38.679043Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:55.415186Z","title":null,"venue":null,"work_id":"f4ed7299-3b36-4c36-9ece-39f53061f1e2","year":2005},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.336712Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:ce199e16fd2960a21655e62e8f6d76cce5c2b063f6fb7290d9c1232459d5730e","observation_id":"9cfc6851-24ba-45ad-9278-5c9348fefd9b","resolution":{"observed_at":"2026-08-07T13:15:55.509066Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:55.274473Z","title":"Fitzsimons, and Joseph F","venue":null,"work_id":"c0f4cd2e-1f1b-43b5-9d2d-2dc982048940","year":2019},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.430443Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:b48b59b8494b902c94e3c6cecaaba712852603fd08e15b18e741461dd05041d0","observation_id":"c06d3364-e46c-4467-a4c4-3b896b17a351","resolution":{"observed_at":"2026-08-07T13:15:55.329589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:55.089598Z","title":"Quantum algorithm for Gaussian process regression","venue":null,"work_id":"948085fe-cf1d-4650-bc54-6db8dc097499","year":2022},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.536069Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:7daf440d52c3f6ce99212fd169512e61f0d9201445f3e760dc05f698f66ffc55","observation_id":"4c16720a-91a3-4448-a75f-24632945e934","resolution":{"observed_at":"2026-08-07T13:15:55.214514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:54.958553Z","title":"Galvis-Florez, and Simo S¨ arkk¨ a","venue":null,"work_id":"49e25ae9-1ba3-4985-b5c6-84a9ecd0c53e","year":2024},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.664745Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:4bb9987bcc2ef6aa43c5231ceba31166bf509dd991f637fd5ff3200cf8371d92","observation_id":"a2a1b793-e797-4a65-a6e1-d9170514d3e6","resolution":{"observed_at":"2026-08-07T13:15:55.017638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:54.843984Z","title":"An introduction to the conjugate gradient method with- out the agonizing pain, 1994","venue":null,"work_id":"df7071bf-2b5b-4a43-8d0e-2130080afb42","year":1994},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.790529Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:9b0be6903ebe418d7cc51385acc3eba60c53c1ecf72e55b69eb9ebdc7935fede","observation_id":"4bd7830a-a592-4cf0-b433-096171f656c3","resolution":{"observed_at":"2026-08-07T13:15:54.903888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.10309","last_updated":"2023-04-15T21:53:08Z","snapshot_observed_at":"2026-07-06T10:51:12.871009Z","submitted_at":"2021-03-18T15:12:44Z","title":"Faster quantum-inspired algorithms for solving linear systems","version":2},"cited_work":{"arxiv_id":"2103.10309","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.10309","snapshot_observed_at":"2026-08-07T13:15:38.322521Z","title":"Faster quantum-inspired algorithms for solving linear systems","venue":"quant-ph","work_id":"357c39db-a691-4d34-a95d-232fa52fa9b5","year":2021},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.890396Z"},"links":{"cited_paper":"/paper/2103.10309","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:bcdf363a14a4917d7e60e8fbba4069e2770eca09dc830e34dacb19d632cc478d","observation_id":"2e72ca44-6e94-4b7b-8685-36ea35d846e4","resolution":{"observed_at":"2026-08-07T13:15:38.441052Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:54.604188Z","title":null,"venue":null,"work_id":"9f49bb0a-1385-4667-94c8-5598b8d5e6d7","year":2021},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:33.978545Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:f1081102604a6a742fbb6d23cc8b802bb4ef51d592a024359e25e09b6c538c5d","observation_id":"8bf17589-bc23-4491-92aa-5322077e9c2f","resolution":{"observed_at":"2026-08-07T13:15:54.713500Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:54.424259Z","title":"Eigenvalues and Condition Numbers of Random Matrices","venue":null,"work_id":"f6dce79e-cd00-481a-9f11-cb435f94312a","year":1988},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:34.106892Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:d3f57cce5924e1ac3c219a897a09043a56ed3860e1a2c059f047ad0e90d4461c","observation_id":"7e1b8820-c0c2-43b4-983b-2e5c161c3506","resolution":{"observed_at":"2026-08-07T13:15:54.546347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"math/0703307","last_updated":"2007-03-11T17:17:08Z","snapshot_observed_at":"2026-07-07T06:05:20.752389Z","submitted_at":"2007-03-11T17:17:08Z","title":"The condition number of a randomly perturbed matrix","version":1},"cited_work":{"arxiv_id":"math/0703307","doi":null,"metadata_source":"pith","pith_arxiv_id":"math/0703307","snapshot_observed_at":"2026-08-07T13:15:38.026857Z","title":"The condition number of a randomly perturbed matrix","venue":"math.PR","work_id":"bbe7127f-a6d7-4f44-93af-b547d4acdae9","year":2007},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:34.225207Z"},"links":{"cited_paper":"/paper/math/0703307","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:2e5bf7a9a2c0a81b0d1f283efa4908c38811e794f974355e1adf52084ff22afc","observation_id":"cf0933b0-9402-46c6-b904-f4c707b6a4e7","resolution":{"observed_at":"2026-08-07T13:15:38.198483Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:54.288486Z","title":null,"venue":null,"work_id":"04b7366b-3466-4e8c-af12-3961680ab528","year":1989},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:34.355329Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:0ae880fd161cd0f2259e99da77455789f05d6196d03211aceaff6255fc86e7dd","observation_id":"3cf7434e-30b4-44c8-a7ff-305e0c4d64d2","resolution":{"observed_at":"2026-08-07T13:15:54.349758Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:54.139001Z","title":"Zimmermann","venue":null,"work_id":"37d71a9b-dc62-4f7a-a303-950acb2379cf","year":2015},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:34.462034Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:969a4aa0c0c2dee20a55b109d9bb78712a2764e9b31f0bc67b5277920a11df58","observation_id":"bb5dde40-225d-4153-9da6-7d3d73b204ec","resolution":{"observed_at":"2026-08-07T13:15:54.209848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:54.054356Z","title":"Chaitin-Chatelin","venue":null,"work_id":"8fd1b8d1-1512-4cac-9e87-9c453ec47b1e","year":1983},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:34.637766Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:342efbdaf8c0802d7ac859c610f8825c4ed31997ae83195dab6edb6e7076af1e","observation_id":"e971260f-b4a1-4d29-a48c-095bbca88904","resolution":{"observed_at":"2026-08-07T13:15:54.078803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:53.978653Z","title":"Dunford and J.T","venue":null,"work_id":"1aa2b2c1-dadd-498c-aa70-77ddb1f52da5","year":1988},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:34.733778Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:4b76d02f1a360db0934bb0148a82165a8afa03113008b85fc65d64302c025d6e","observation_id":"8783eb95-7da5-416e-b0e3-b54ff6cfe292","resolution":{"observed_at":"2026-08-07T13:15:54.014217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:53.909355Z","title":null,"venue":null,"work_id":"07dbcc25-927d-4410-941f-94488342b240","year":1955},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:34.862165Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:e058dfde0da8436ea86f5179ca5391e2f1f97103325786f8c53393ef76651a75","observation_id":"d4852ed5-4ace-4834-8ba6-ae97cb3cc004","resolution":{"observed_at":"2026-08-07T13:15:53.919349Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.11835","last_updated":"2020-02-04T13:08:07Z","snapshot_observed_at":"2026-07-06T07:42:13.977835Z","submitted_at":"2019-03-28T08:44:21Z","title":"A Survey on Graph Kernels","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.11835","snapshot_observed_at":"2026-08-07T13:15:35.001868Z","title":"Kriege, Fredrik D","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.001868Z"},"links":{"cited_paper":"/paper/1903.11835","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:1fe8426dbcfbf6478dcba2e069636262706f1d171f23e894bd964ebfb203ab30","observation_id":"5025e20e-a758-4a24-b2ad-b033b60c848e","resolution":{"observed_at":"2026-08-07T13:15:35.001868Z","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":"2026-08-07T13:15:41.044121Z","title":"String kernels construction and fusion: a survey with bioinformatics application","venue":null,"work_id":"be3f0ce3-85ce-4ef7-b545-25d82dc0d43a","year":2022},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.106547Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:33307003d9cfb5d90209b701e19fe310f3e72a2117ad2abd04e1a3babeb2c466","observation_id":"652f0c8b-7994-4fdd-b094-5eb25a042288","resolution":{"observed_at":"2026-08-07T13:15:53.875649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:41.037148Z","title":null,"venue":null,"work_id":"ae9b8e9b-ee1d-4358-aa11-d722b54787a1","year":1997},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.227625Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:21d8b44f90ec1d15472bf674b733fdcd4fd3056893466a6385c45fc1695a1383","observation_id":"36bfbc07-5182-459f-8f3c-472f805d1ec7","resolution":{"observed_at":"2026-08-07T13:15:41.039720Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:35.383328Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.383328Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:6e5d899aec4f2ebab55cd7ca126c2408cdcfd5eaa103ecb23d70cf1b6fb687ff","observation_id":"d63df0d0-b3c3-420c-8f21-20910cee24db","resolution":{"observed_at":"2026-08-07T13:15:35.383328Z","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":"2026-08-07T13:15:40.886316Z","title":"Hilbert space methods for reduced-rank Gaussian process regression","venue":null,"work_id":"02567b33-52b7-4022-8ab8-4ba114be59d4","year":2020},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.482894Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:b1defef9cc600d67f680fa76769d830ec49437798629bc8dae8ebddc2e5a878a","observation_id":"7382bb3e-cee2-45a7-ab1d-07b58a198e9e","resolution":{"observed_at":"2026-08-07T13:15:40.969468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.01065","last_updated":"2019-04-09T12:58:08Z","snapshot_observed_at":"2026-08-01T14:56:19.358319Z","submitted_at":"2018-07-03T10:19:25Z","title":"When Gaussian Process Meets Big Data: A Review of Scalable GPs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.01065","snapshot_observed_at":"2026-08-07T13:15:35.562363Z","title":"When Gaussian Process Meets Big Data: A Review of Scalable GPs, April 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.562363Z"},"links":{"cited_paper":"/paper/1807.01065","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:3fbb172a3302177f387a463468aa442448eb280d2ff498292284d5c5abcc8a0e","observation_id":"7a4a012f-ca3b-4ad6-a888-983b9c00b295","resolution":{"observed_at":"2026-08-07T13:15:35.562363Z","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":"2026-08-07T13:15:40.746293Z","title":"Qubit-Efficient Randomized Quantum Algorithms for Linear Algebra","venue":null,"work_id":"5d70e0c9-1f5c-46c3-bb8e-5fa37cfcc29a","year":2024},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.657013Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:f6179aee8f8abfec794faec011918315abfdbc403782c0ab3b467f3449d85a6a","observation_id":"109052b6-5143-41c0-b5d0-ca15917c1e47","resolution":{"observed_at":"2026-08-07T13:15:40.804351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:40.549585Z","title":null,"venue":null,"work_id":"38adafad-c178-4acb-9906-0491e51519cc","year":2013},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.774727Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:f89f13285cc819845300648c2d4f22719dd0cec0a0dede4821053e71ab77f9e7","observation_id":"96d92622-f47a-48e1-aa68-b461bc30815c","resolution":{"observed_at":"2026-08-07T13:15:40.660615Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:40.386740Z","title":"Quantum circulant preconditioner for a linear system of equations","venue":null,"work_id":"510540bc-9ec5-4ccc-9904-662b9e90b8c1","year":2018},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.899527Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:021175dc97dd5047210ac2e484f50b76548e2af6f9eb09e3a60acb63006e0f75","observation_id":"33aa58a7-9c52-42a5-bfc0-80c5250b5bc5","resolution":{"observed_at":"2026-08-07T13:15:40.446432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.00243","last_updated":"2022-06-18T21:38:55Z","snapshot_observed_at":"2026-07-06T11:24:53.122802Z","submitted_at":"2021-07-01T06:43:11Z","title":"Preconditioning for Scalable Gaussian Process Hyperparameter Optimization","version":5},"cited_work":{"arxiv_id":"2107.00243","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.00243","snapshot_observed_at":"2026-08-07T13:15:37.747282Z","title":"Preconditioning for Scalable Gaussian Process Hyperparameter Optimization","venue":"cs.LG","work_id":"6c903c24-02d8-43d1-a6db-7b8c8a953303","year":2021},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:35.987555Z"},"links":{"cited_paper":"/paper/2107.00243","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:ff65574bfeece218d706e3bec043637e1604cb31da2a6056800176fb76b2ceb2","observation_id":"29a206cc-39be-4393-8492-dfa1f39e7359","resolution":{"observed_at":"2026-08-07T13:15:37.821065Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:40.259364Z","title":"Quantum Support Vector Ma- chine for Big Data Classification","venue":null,"work_id":"646c2a56-0333-47f1-8992-e0ab648b93c3","year":null},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:36.043478Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:793e07dc8495a23cda78d7a1c24282006020b5259403c0bfac30c8239d6b0ede","observation_id":"1fc79f8c-42e2-4006-9c4c-78dbb2a8dbb7","resolution":{"observed_at":"2026-08-07T13:15:40.316533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:39.924319Z","title":"C´ orcoles, Kristan Temme, Aram W","venue":null,"work_id":"9bb5443c-f43d-4be9-bd7a-10fd315f1f70","year":2019},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:36.255067Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:a6b307762fe8fd71c460608d768790947e2b5805344cbe688cc0add570074d2f","observation_id":"e629e42a-754f-4f2f-9c8c-92db3f499e1f","resolution":{"observed_at":"2026-08-07T13:15:39.995273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:39.757896Z","title":"Quantum Machine Learning in Feature Hilbert Spaces","venue":null,"work_id":"8d6919c4-b987-4745-8db9-d14b3a6ccb5c","year":2019},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:36.379306Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:77cb86c5daeed3fa45d9148f09f05fe4e2fdaa437a10c8450c5240ab21055041","observation_id":"48ae4340-2f89-4145-b832-5aceb8728624","resolution":{"observed_at":"2026-08-07T13:15:39.837269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02174","last_updated":"2020-11-30T18:39:39Z","snapshot_observed_at":"2026-08-05T10:29:08.573439Z","submitted_at":"2020-10-05T17:22:22Z","title":"A rigorous and robust quantum speed-up in supervised machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02174","snapshot_observed_at":"2026-08-07T13:15:36.525386Z","title":"A rigorous and robust quantum speed-up in supervised machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:36.525386Z"},"links":{"cited_paper":"/paper/2010.02174","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:d28e35c73a53ae417b595f4536a20a81f893d341f44f59985af4c1168adc30ac","observation_id":"3fcddc55-da66-4c9c-a005-ad1ed1e35e23","resolution":{"observed_at":"2026-08-07T13:15:36.525386Z","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":"2026-08-07T13:15:39.518336Z","title":null,"venue":null,"work_id":"0f9e7a75-94eb-45fc-8218-33e2f4056603","year":2021},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:36.657209Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:14bab597ae3348953f55a7897d88396ca04be448016118a3516c84a06ba742f5","observation_id":"00a856c8-0409-4e37-b874-1a3ae67b3bfa","resolution":{"observed_at":"2026-08-07T13:15:39.689093Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-07T13:15:36.812005Z","title":"Neural Tangent Kernel: Con- vergence and Generalization in Neural Networks, February 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:36.812005Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:5c5e290caaff3ffc40fbefda9a63461e2d2358199f4e1ad269b2144dd893e03f","observation_id":"26098587-6591-4937-b6dc-06953e5fa1f2","resolution":{"observed_at":"2026-08-07T13:15:36.812005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.06720","last_updated":"2019-12-08T06:02:33Z","snapshot_observed_at":"2026-07-06T07:33:52.991927Z","submitted_at":"2019-02-18T18:37:49Z","title":"Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.06720","snapshot_observed_at":"2026-08-07T13:15:36.947914Z","title":"Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:36.947914Z"},"links":{"cited_paper":"/paper/1902.06720","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:222761f844afee339f81751bcb10a04bd95d7b38a4fb0a33859728f7af7bf956","observation_id":"211ee424-5b2c-40b0-a697-b066df62bbae","resolution":{"observed_at":"2026-08-07T13:15:36.947914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.02054","last_updated":"2019-02-05T01:59:59Z","snapshot_observed_at":"2026-07-06T07:05:59.910542Z","submitted_at":"2018-10-04T04:47:47Z","title":"Gradient Descent Provably Optimizes Over-parameterized Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.02054","snapshot_observed_at":"2026-08-07T13:15:37.111510Z","title":"Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:37.111510Z"},"links":{"cited_paper":"/paper/1810.02054","citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:b83480962b97a8f0e95992127c2f613d5691ab9ff01997148a2d468540aa7f26","observation_id":"595179f1-af74-4138-aa6d-58e71585a684","resolution":{"observed_at":"2026-08-07T13:15:37.111510Z","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":"2026-08-07T13:15:39.293060Z","title":null,"venue":null,"work_id":"a350ca2e-32f3-4c3c-8318-a36810be2ee0","year":1978},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:37.286914Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:d846430c0c9e23865e581ee304b6b30187ad263980380cdbb91541f704c440c9","observation_id":"622d69f6-0cc2-4b5e-ad92-5c650fec9015","resolution":{"observed_at":"2026-08-07T13:15:39.405150Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:39.059129Z","title":"Random matrix approximation of spectra of integral operators","venue":null,"work_id":"1d00132f-f049-4d1b-b565-5c76d83ac2ec","year":2000},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:37.460960Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:2b908e8c5935753e57a8fd8e58b895f3bb7ffeb2a88d026229f0924b55b7f5df","observation_id":"df29c00c-ec11-4df7-8936-f5ccf8d15f02","resolution":{"observed_at":"2026-08-07T13:15:39.180740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:38.824411Z","title":"A very short proof of cauchy’s interlace theorem for eigenvalues of hermitian matrices, 2005","venue":null,"work_id":"f26d233c-eed9-40b2-b59a-c4742e4837e5","year":2005},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:37.597202Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:833971e54d660b7fb48387782b029b11fd02cf6b659f7c1b23c0c23e1988930e","observation_id":"35acd12a-fa66-40a5-a87c-0306fb331df1","resolution":{"observed_at":"2026-08-07T13:15:38.926372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:15:40.062529Z","title":null,"venue":null,"work_id":"bd074e09-a423-4d54-92b4-ca29693e3f45","year":null},"citing_paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:36.128455Z"},"links":{"citing_paper":"/paper/2505.22502"},"observation_digest":"sha256:f8799ecc91f36970c9849c310f0bd20de8e0d9a8eaf9495810984ef1991652d7","observation_id":"c9fec235-a543-44a6-a97c-bcb95f6bf1a1","resolution":{"observed_at":"2026-08-07T13:15:40.138855Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.22502","last_updated":"2025-07-03T11:27:35Z","latest_version":2,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-07T13:03:16.820031Z","submitted_at":"2025-05-28T15:50:56Z","title":"Assessing Quantum Advantage for Gaussian Process Regression"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":19,"verified_exact":3,"verified_fuzzy":23},"total_outbound_references":46},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2505.22502."}