{"as_of":"2026-08-10T04:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0ddaa8ccb376b8c497c6178b83216f818ecdb736a02872136163bd5c87b366fd","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:50:33.795622Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T00:30:47.084809Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.20868","snapshot_observed_at":"2026-07-14T23:44:17.042117Z","title":"Strobl, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.10289","last_updated":"2026-06-03T02:01:12Z","snapshot_observed_at":"2026-08-06T19:46:18.775467Z","submitted_at":"2026-03-11T00:15:56Z","title":"Quantum entanglement provides a competitive advantage in adversarial games","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-14T23:44:17.042117Z"},"links":{"cited_paper":"/paper/2508.20868","citing_paper":"/paper/2603.10289"},"observation_digest":"sha256:5e8e3f1f99142f1fb8df5096fe017c2f036afe93604e176fdb20b3c4493f0b2d","observation_id":"afa5e1d3-d039-433c-b2a0-40e86d3928ba","resolution":{"observed_at":"2026-07-14T23:44:17.042117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.20868","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-08T03:34:15.326354Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2508.20868","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:3d4ce4524508b1eccfd849583ff0168b5b32b2181f9822d54f7e67e196f497f3","observation_id":"647c495c-936c-443e-9692-944ff05ab014","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"cited_work":{"arxiv_id":"2508.20868","doi":"10.48550/arxiv.2508.20868","metadata_source":"arxiv_reference","pith_arxiv_id":"2508.20868","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Emre Sahin, Lucas van der Horst, et al","venue":"ArXiv.org","work_id":"c35e5f51-b92c-412c-815f-4482608e3d4f","year":2025},"citing_paper":{"arxiv_id":"2605.04945","last_updated":"2026-05-06T14:13:44Z","snapshot_observed_at":"2026-07-06T23:17:38.226365Z","submitted_at":"2026-05-06T14:13:44Z","title":"Beyond Gates: Pulse Level Quantum Fourier Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-08T16:21:01.344890Z"},"links":{"cited_paper":"/paper/2508.20868","citing_paper":"/paper/2605.04945"},"observation_digest":"sha256:c41500e2bba5a785905761e1ce6893e45d77827fa08c4eb6a175a4b3c5505cfe","observation_id":"dbd4a192-5442-462e-8e4a-6eb5a257b5b5","resolution":{"observed_at":"2026-05-08T20:24:09.327295Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"cited_work":{"arxiv_id":"2508.20868","doi":"10.48550/arxiv.2508.20868","metadata_source":"arxiv_reference","pith_arxiv_id":"2508.20868","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Emre Sahin, Lucas van der Horst, et al","venue":"ArXiv.org","work_id":"c35e5f51-b92c-412c-815f-4482608e3d4f","year":2025},"citing_paper":{"arxiv_id":"2605.31248","last_updated":"2026-05-29T12:47:50Z","snapshot_observed_at":"2026-08-02T17:43:20.028850Z","submitted_at":"2026-05-29T12:47:50Z","title":"Trainable Quantum Spectral Models for Partial Differential Equations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T22:25:56.227151Z"},"links":{"cited_paper":"/paper/2508.20868","citing_paper":"/paper/2605.31248"},"observation_digest":"sha256:09497981e232108e8a0f97d0e7dd5b21dd379c85e0c92065d4120d7322e19008","observation_id":"72b4a086-958e-4eeb-a6f5-7b8f5b9ae29a","resolution":{"observed_at":"2026-06-28T22:32:44.597467Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.20868","snapshot_observed_at":"2026-08-02T03:39:48.281250Z","title":"Strobl, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.13827","last_updated":"2026-07-16T20:46:00Z","snapshot_observed_at":"2026-08-08T10:11:18.806904Z","submitted_at":"2026-07-15T13:33:32Z","title":"Inherent interpretability provides inherent value in quantum machine learning","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-02T03:39:48.281250Z"},"links":{"cited_paper":"/paper/2508.20868","citing_paper":"/paper/2607.13827"},"observation_digest":"sha256:3be6ad44b9942a05a9f691dadafd2c83f00039865d200bd20ccfc2d375a029b2","observation_id":"b192be58-1aba-445b-b542-e537b03f84d2","resolution":{"observed_at":"2026-08-02T03:39:48.281250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.20868","snapshot_observed_at":"2026-08-06T00:30:47.084809Z","title":"Strobl, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01194","last_updated":"2026-08-02T12:21:00Z","snapshot_observed_at":"2026-08-09T20:56:02.742691Z","submitted_at":"2026-08-02T12:21:00Z","title":"Hybrid Quantum Neural Networks: Theory, Implementations, and Applications","version":1},"reference_index":120,"source":"pdf_text","source_observed_at":"2026-08-06T00:30:47.084809Z"},"links":{"cited_paper":"/paper/2508.20868","citing_paper":"/paper/2608.01194"},"observation_digest":"sha256:16ebd3982c3077f330e2c05ac29e5d8aac0e344522c3d9df2416d90b24306e9a","observation_id":"3db6ea55-658a-4cf6-a8ba-95ef0838f7b0","resolution":{"observed_at":"2026-08-06T00:30:47.084809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2508.20868/citation-record","integrity":"/paper/2508.20868/integrity","json":"/paper/2508.20868/citation-record.json","paper":"/paper/2508.20868"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:33.594999Z","title":"Parameterized quantum circuits as machine learning models","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.594999Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:ce716130a8949e7bf88034beab6301e57ec354aaadc4af29e58cb17aae23cdbc","observation_id":"50f36602-c03b-4909-a895-1d8ec0782813","resolution":{"observed_at":"2026-08-05T14:50:33.594999Z","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":"1459.1928","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.590036Z","title":"The Standard Deviation of the Correlation Coefficient","venue":null,"work_id":"b4b69e54-a0ac-42c3-9721-d9b3a344f7b5","year":1928},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.601033Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:772746169f7093e6bcb863279406b75cc09aedd2ce5410b0cc7dd37f8dc6be69","observation_id":"0670831c-fc4b-448c-b156-4c4ad53824a6","resolution":{"observed_at":"2026-08-05T14:50:34.664580Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.868601Z","title":"Fitting the multitemporal curve: A Fourier series approach to the missing data problem in remote sensing analysis","venue":null,"work_id":"77c0fab6-d358-4b80-8908-0526949c9af7","year":2012},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.607430Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:27d0292203fb173b72743211e36e454dec20f551475e692e2e66192a9fe8f048","observation_id":"da5dfb73-2aed-46b1-ac5b-d2d587f5987d","resolution":{"observed_at":"2026-08-05T14:50:34.874093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.851004Z","title":"Challenges and opportunities in quantum machine learning","venue":null,"work_id":"95e3ecc9-e724-44b8-820c-2a317dfcd1ac","year":2022},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.612790Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:a5d776820fd664a55415b7240399451ba8cedd05445a7cd687cf8800979718e5","observation_id":"24a13e17-384f-49b3-b2fd-d56f9351ed98","resolution":{"observed_at":"2026-08-05T14:50:34.855915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.833244Z","title":"Quantum ensemble learning with a programmable superconducting processor","venue":null,"work_id":"6191b746-26b7-4348-b951-5200f074daa3","year":2025},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.618740Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:a3277206e94a4194fca50296507f6fdbb5f768e94326144878a4e6861b53e932","observation_id":"aba32edb-3c67-42b3-acfe-33b40fb30cd0","resolution":{"observed_at":"2026-08-05T14:50:34.838412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:33.623885Z","title":"An Algorithm for the Machine Calculation of Complex Fourier Series","venue":null,"work_id":null,"year":1965},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.623885Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:741219c2e3af615bda8a6cc36fe533b23349b64171e3bfceae24804e9e2dd42b","observation_id":"44cd5b46-1471-44ea-baf7-143980aafffc","resolution":{"observed_at":"2026-08-05T14:50:33.623885Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:33.630935Z","title":"Spectral Bias in Variational Quantum Machine Learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.630935Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:e16408f8e734f8ba18b1cd4b7ecfbb965c05c14bade9c6d9ef69d01396aa8233","observation_id":"a253dae2-3866-4bb6-bc31-32bf08ff2f6d","resolution":{"observed_at":"2026-08-05T14:50:33.630935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.09527","last_updated":"2025-08-26T12:50:19Z","snapshot_observed_at":"2026-08-09T00:18:34.432633Z","submitted_at":"2025-06-11T08:52:31Z","title":"Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.09527","snapshot_observed_at":"2026-08-05T14:50:33.635893Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.635893Z"},"links":{"cited_paper":"/paper/2506.09527","citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:5da8fec51ff09ecb4d50d98e360520bd8823e88b6c6f5006a885f888d97d10c4","observation_id":"f4d137b5-b94d-4fa9-91f6-646dd38c78ee","resolution":{"observed_at":"2026-08-05T14:50:33.635893Z","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":"2026-08-05T14:50:34.816589Z","title":"Statistics (international student edition)","venue":null,"work_id":"df7d9ead-6879-4167-ab68-3aa0742a8632","year":2007},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.641484Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:5803c06b818215c28ff18eaa705d952ae44040c9992a270a4115593b63b6af26","observation_id":"0b26d0d1-a410-4163-aac9-e8593423adc9","resolution":{"observed_at":"2026-08-05T14:50:34.821924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/2632-2153/abc17d","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.101231Z","title":"Quantum machine learning in high energy physics","venue":null,"work_id":"d45d654e-8b3c-4d37-88b3-3f0311697b39","year":2021},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.646296Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:86ff3ecb700d64ae2329775b25da2dd937e637d07096082c55d6d856f1a5116e","observation_id":"703ce496-8e6e-4bfb-b9a2-ccd8fbf53a3d","resolution":{"observed_at":"2026-08-05T14:50:34.107350Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.801322Z","title":"Robust Fourier and polynomial curve fitting","venue":null,"work_id":"a3eb0095-c12d-4368-bc11-643cf7de5b2b","year":2016},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.651523Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:0fd96fbe9479ffeccb33081b29b0e1d91baa63b0fca086e4cecdd8ec106959b3","observation_id":"11db3174-c3a4-4fa0-87b9-a6de84adaa5d","resolution":{"observed_at":"2026-08-05T14:50:34.806096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.15689696","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.081835Z","title":"Zenodo, June 2025.doi: 10.5281/zenodo.15689696","venue":null,"work_id":"c707e7e7-9644-48a9-9c76-c8a516f6e6df","year":2025},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.656585Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:1b6cea990cfd53280611a23e5cf2d8295f6579fd20bbb6298eb4bf0853374291","observation_id":"11f16175-2753-4e83-8fbc-64643fc262e8","resolution":{"observed_at":"2026-08-05T14:50:34.088552Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.783630Z","title":"Supervised learning with quantum- enhanced feature spaces","venue":null,"work_id":"e857e721-eba6-45fe-923d-d8b4c932a264","year":2019},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.661723Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:07037e42ea6ae493dbc1eabe0202fe3953d4d4ab26066247082dcb5a2dba2142","observation_id":"c1f9136d-045c-4262-85e7-273541e4eb14","resolution":{"observed_at":"2026-08-05T14:50:34.789651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.766608Z","title":"Potential of quantum scientific machine learning applied to weather modeling","venue":null,"work_id":"08372918-de83-4e4c-a874-2b2199c28609","year":2024},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.666797Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:1d0f32c9ed5419d5ee3e8a8dbd03acf8cdd2969b2a1b4eb2ee4389116bca25eb","observation_id":"3006a554-495a-4036-a3e9-ef00da6ab02f","resolution":{"observed_at":"2026-08-05T14:50:34.771705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.03279","last_updated":"2024-04-22T15:55:41Z","snapshot_observed_at":"2026-07-31T23:20:30.639169Z","submitted_at":"2023-09-06T18:00:07Z","title":"Let Quantum Neural Networks Choose Their Own Frequencies","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.03279","snapshot_observed_at":"2026-08-05T14:50:33.672885Z","title":"Gentile, Youssef Achari Berrada, et al.Let Quantum Neural Networks Choose Their Own Frequencies","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.672885Z"},"links":{"cited_paper":"/paper/2309.03279","citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:4a203db1b79e55ef7e4db6e7fbcf4737b5ae7b0291ed2c8a0f52be3df1fbf21b","observation_id":"9ce0f08c-bcad-4a70-a8e8-ba14d64f6628","resolution":{"observed_at":"2026-08-05T14:50:33.672885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T14:50:33.679190Z","title":"Kingma and Jimmy Ba.Adam: A Method for Stochastic Optimization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.679190Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:db771018a3770aeaf8ddb47591912bd903341e873e483da5f3fafdac6c333e67","observation_id":"5685a824-630d-4d8e-98b4-251fa20422ac","resolution":{"observed_at":"2026-08-05T14:50:33.679190Z","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":"2026-08-05T14:50:33.685245Z","title":"On Information and Sufficiency","venue":null,"work_id":null,"year":1951},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.685245Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:ea55dacd47a67cbb76c848439677641e086b50aff5bb5b7521feb5fde05eda76","observation_id":"2c83c0ba-d949-4a5c-80b3-1e84eb881a3b","resolution":{"observed_at":"2026-08-05T14:50:33.685245Z","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":"2026-08-05T14:50:34.749600Z","title":"Protocols for trainable and differentiable quantum generative modeling","venue":null,"work_id":"98b58f3e-052a-4074-91f2-31ec3b6a15fd","year":2024},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.691526Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:61897e4a3d908cc431afb1d04b017dc43405115c3f3b72fbe21b13c24853598d","observation_id":"c9dfd345-6bef-46e0-b1b9-b5bdf1831aa9","resolution":{"observed_at":"2026-08-05T14:50:34.755071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.03622","last_updated":"2020-02-10T14:11:29Z","snapshot_observed_at":"2026-08-07T18:38:17.780526Z","submitted_at":"2020-01-10T19:00:01Z","title":"Quantum embeddings for machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T14:50:33.696694Z","title":"Quantum embeddings for machine learning","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.696694Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:4a24e9c3fb8051a7b79d67890f0ee3e5c1278c7f1b74c053691e5b79b1eb44d7","observation_id":"17bec832-9dd1-4f5c-940c-9704556b2d08","resolution":{"observed_at":"2026-08-05T14:50:33.696694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09417","last_updated":"2025-09-02T09:12:37Z","snapshot_observed_at":"2026-07-06T17:44:37.894023Z","submitted_at":"2024-03-14T14:05:24Z","title":"Constrained and Vanishing Expressivity of Quantum Fourier Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09417","snapshot_observed_at":"2026-08-05T14:50:33.702664Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.702664Z"},"links":{"cited_paper":"/paper/2403.09417","citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:8feafbaae10bfbb5127813e731901dbd4e5a1712be1f5338bdd4e0f23d28ca51","observation_id":"c70971d8-cffa-40df-a982-6be507d17e7f","resolution":{"observed_at":"2026-08-05T14:50:33.702664Z","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":"2026-08-05T14:50:34.733822Z","title":"Quantum circuit learning","venue":null,"work_id":"c02ed0fb-249e-4771-96f7-b12ae801005c","year":2018},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.709721Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:96bd00e3cbf0be34c9f4c77a48bf88a22bd7817cbfb4d610d6e1f7e716802fc6","observation_id":"4c77a16e-8e1c-4784-9352-07cbd384f5e2","resolution":{"observed_at":"2026-08-05T14:50:34.738350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.717755Z","title":"Quantum data learning for quantum simulations in high-energy physics","venue":null,"work_id":"af2581c9-ba0a-4f76-a55c-21844ea55ff9","year":2023},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.716030Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:c60259720f47ef635fc694a8b05c96cc9882b76d4dccc391aef9c08e5a137d0e","observation_id":"3658bca6-431e-402a-a551-c3ad5044a011","resolution":{"observed_at":"2026-08-05T14:50:34.723157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:33.722517Z","title":"Fourier expansion in variational quantum algorithms","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.722517Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:f9587dc0f47b2d2b6f8ca582d598810d18f22be010195b0db1eff2fa6ed92cef","observation_id":"40761b21-4dfe-4cf6-8239-e440da8a264e","resolution":{"observed_at":"2026-08-05T14:50:33.722517Z","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":"2026-08-05T14:50:34.699435Z","title":"Experimental quantum end-to-end learning on a superconducting processor","venue":null,"work_id":"9f73206f-b8dc-4329-9f96-f113a0dc83c6","year":2023},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.728752Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:4f3aa6177392f5cdd42585f7afb29d6717573711ca1a289b3582a8d60d1b0691","observation_id":"14cace88-970c-4d6a-8ec7-e4a561259689","resolution":{"observed_at":"2026-08-05T14:50:34.704178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.05523","last_updated":"2023-12-15T00:43:11Z","snapshot_observed_at":"2026-07-06T13:51:23.904805Z","submitted_at":"2022-09-12T18:08:45Z","title":"Generalization despite overfitting in quantum machine learning models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.05523","snapshot_observed_at":"2026-08-05T14:50:33.734623Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.734623Z"},"links":{"cited_paper":"/paper/2209.05523","citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:a3ac4c34d58e3bb67823fb8bb1b4ccf10213c16953d0ae2361d36ab6cf7d27d0","observation_id":"f4737e91-a4ee-42c8-b312-da80e84ceac0","resolution":{"observed_at":"2026-08-05T14:50:33.734623Z","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":"2026-08-05T14:50:33.740779Z","title":"A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.740779Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:395ca2585face213493187d10cd16bff63300e4a985942b276e2b3c51e6991c6","observation_id":"19f906a0-8feb-4fc5-bd1c-5d7ff2ae0085","resolution":{"observed_at":"2026-08-05T14:50:33.740779Z","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":"2026-08-05T14:50:33.746475Z","title":"Circuit-centric quantum classifiers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.746475Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:324dd19cef76cc279d35d0b6ff9903aa7c48df7fde171e336052314a24a24efe","observation_id":"25881282-33ea-452d-abee-1cb8de193ad2","resolution":{"observed_at":"2026-08-05T14:50:33.746475Z","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":"2026-08-05T14:50:33.752721Z","title":"The 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":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.752721Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:24b5711966afcc40216466edb62b21338620ed81505adeff77b41b9904d18435","observation_id":"14f901c6-d880-469e-ad6b-e3a83425d2da","resolution":{"observed_at":"2026-08-05T14:50:33.752721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.13249","last_updated":"2023-03-23T13:29:20Z","snapshot_observed_at":"2026-07-06T15:07:07.796255Z","submitted_at":"2023-03-23T13:29:20Z","title":"Particle track reconstruction with noisy intermediate-scale quantum computers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.13249","snapshot_observed_at":"2026-08-05T14:50:33.758794Z","title":"Particle track reconstruction with noisy intermediate-scale quantum computers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.758794Z"},"links":{"cited_paper":"/paper/2303.13249","citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:a39124f9ee5bf1a1d4ba629df5df0ec15ea7b068bda419289e9883c832601e0e","observation_id":"9e0fc0a3-7f37-47f4-a573-d1b49726140c","resolution":{"observed_at":"2026-08-05T14:50:33.758794Z","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":"1949.23296","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.286396Z","title":"Communication in the Presence of Noise","venue":null,"work_id":"6c614056-ebfb-463f-8047-19786fa7ad8b","year":1949},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.764173Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:1aa2ba177575df69008f790cab5232e5e92eb0e63ef24518b24a6f489d1e344a","observation_id":"b50afb52-d11a-4496-8be4-9c4a0a214e4f","resolution":{"observed_at":"2026-08-05T14:50:34.295351Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:34.676340Z","title":"Exponential data encoding for quantum supervised learning","venue":null,"work_id":"b709ee90-03a0-4a87-bb6f-3a5a8542a4b7","year":2023},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.769284Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:64cf1d797ebd49e0396003dfff3afd0818f94b815f540aed5436b3a051b17113","observation_id":"34de0ca2-511e-4c69-8627-0d3ba68e911f","resolution":{"observed_at":"2026-08-05T14:50:34.681525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:50:33.774139Z","title":"Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum-Classical Algorithms","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.774139Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:cb7de6a1b4fd052c156da550b4a8523a8e5f01ca7b40f1171e7751cc25ab8236","observation_id":"bf2dd2cd-d03d-4d90-a1ec-e52591cd0d43","resolution":{"observed_at":"2026-08-05T14:50:33.774139Z","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":"2026-08-05T14:50:33.779111Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.779111Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:ea7848237869f1297cea1e6d52bcbb31b8de5202ebdd9bc594605cd87de5c07e","observation_id":"8ede98fd-5d79-466f-8f79-0d1f0a50db1e","resolution":{"observed_at":"2026-08-05T14:50:33.779111Z","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":"2026-08-05T14:50:33.784032Z","title":"Potential and limitations of random Fourier features for dequantizing quantum machine learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.784032Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:40510c02a779b45929f9e16aeace3d79e0d3a895322461ebc37d5099fb21070f","observation_id":"5046354f-7c1d-4901-8bc6-5c82878ad0e7","resolution":{"observed_at":"2026-08-05T14:50:33.784032Z","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":"2026-08-05T14:50:33.789784Z","title":"Particle Track Reconstruction with Quantum Algorithms","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.789784Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:f5f40ed64b1d3b6d67bfb0621383a1f87bf33cd068027d947053d4c5887c7d06","observation_id":"5c8ec4c8-cdae-43f8-b995-1f57ef633bda","resolution":{"observed_at":"2026-08-05T14:50:33.789784Z","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":"2026-08-05T14:50:33.795622Z","title":"Scherer.Fourier Analysis of Vari- ational Quantum Circuits for Supervised Learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T14:50:33.795622Z"},"links":{"citing_paper":"/paper/2508.20868"},"observation_digest":"sha256:ce62d979864d4ca91263e5b72af6f96862e42f14176f34c54b1302c37a376564","observation_id":"36cd5a1e-230d-4c4e-a399-3fe3b1c64cab","resolution":{"observed_at":"2026-08-05T14:50:33.795622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-08T00:17:50.884640Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":19,"verified_exact":3,"verified_fuzzy":12},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 6 inbound Pith citation observations for arXiv:2508.20868."}