{"as_of":"2026-08-08T05:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ad6be70984f05acad924a4b44f1834a7ce58204b37b7fb8c623f7f847300118d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":35,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:03:04.877002Z","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":71,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2501.14660","last_updated":"2026-04-30T17:29:40Z","snapshot_observed_at":"2026-08-02T17:44:18.014747Z","submitted_at":"2025-01-24T17:29:41Z","title":"Mean-field limit from general mixtures of experts to quantum neural networks","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-23T05:32:20.919412Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2501.14660"},"observation_digest":"sha256:78ec9dd12807bf4bf8bcbe1b151e1daa8da0a30a20adb36b49212d21791f6f26","observation_id":"342d04a7-21d1-4f47-aebd-0b8e10ff4478","resolution":{"observed_at":"2026-05-23T05:32:36.192201Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2506.01432","last_updated":"2026-04-27T09:33:29Z","snapshot_observed_at":"2026-07-06T21:34:53.033109Z","submitted_at":"2025-06-02T08:43:58Z","title":"New aspects of quantum topological data analysis: Betti number estimation, and testing and tracking of homology and cohomology classes","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-19T12:06:38.050214Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2506.01432"},"observation_digest":"sha256:8e34c81e8af14d0d4a7eec9bee097df6a2c13c656320d2d45ff597124f27249c","observation_id":"c8a49519-2e1e-4dff-bf1f-9536bc26c4e1","resolution":{"observed_at":"2026-05-19T12:07:20.830465Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T11:03:04.877002Z","title":"Quantum embeddings for machine learning","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.03779","last_updated":"2026-05-28T19:34:03Z","snapshot_observed_at":"2026-08-08T04:39:39.225064Z","submitted_at":"2025-06-04T09:40:48Z","title":"Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-07T11:03:04.877002Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2506.03779"},"observation_digest":"sha256:ddce2532208929355b0ccff18fdbe270ae8744e08b740995d2af59485fe8cdff","observation_id":"07cf3dc9-1883-4be2-b1ad-a53c83eabc13","resolution":{"observed_at":"2026-08-07T11:03:04.877002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2506.21161","last_updated":"2026-04-18T09:42:24Z","snapshot_observed_at":"2026-07-29T23:50:38.487434Z","submitted_at":"2025-06-26T11:38:54Z","title":"Hardware-Aware Quantum Kernel Design Based on Graph Neural Networks","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-19T07:59:06.404337Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2506.21161"},"observation_digest":"sha256:7d853fb33f373ef5f93c4c3a008202808960e73ffab0a6ab57d6b7269d1da93c","observation_id":"0e49750b-29bd-4b5c-a83b-87cd36291794","resolution":{"observed_at":"2026-05-19T08:02:10.682852Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T22:22:09.368321Z","title":"Quantum embeddings for machine learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.21842","last_updated":"2025-06-27T01:19:49Z","snapshot_observed_at":"2026-08-06T22:15:31.115417Z","submitted_at":"2025-06-27T01:19:49Z","title":"Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:22:09.368321Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2506.21842"},"observation_digest":"sha256:e4a4b500ae9de9489bd4298418ead9bdec14b5acb3cf47ba4144265f7603e90c","observation_id":"9351f494-baec-4d62-81ff-c4c69ae67c5a","resolution":{"observed_at":"2026-08-06T22:22:09.368321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T22:12:37.758196Z","title":"& Killoran, N","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22340","last_updated":"2025-06-27T15:51:19Z","snapshot_observed_at":"2026-08-07T13:09:05.993764Z","submitted_at":"2025-06-27T15:51:19Z","title":"QuKAN: A Quantum Circuit Born Machine approach to Quantum Kolmogorov Arnold Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T22:12:37.758196Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2506.22340"},"observation_digest":"sha256:ea98653475e3ff1671678fd7fcebad37f44c11e949502e6905a0c5a6a834b94f","observation_id":"fd2025e1-90f0-4503-9f1d-9096295d84c6","resolution":{"observed_at":"2026-08-06T22:12:37.758196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T18:45:54.731274Z","title":"Lloyd, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.07457","last_updated":"2025-07-10T06:18:58Z","snapshot_observed_at":"2026-08-06T18:38:01.721530Z","submitted_at":"2025-07-10T06:18:58Z","title":"Enhanced Quantum behavior on frustrated Ising model: Quantum Approximate Optimization Algorithm study","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T18:45:54.731274Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2507.07457"},"observation_digest":"sha256:ca7194f101b59445adcbb6d0fab52cc4bb3caf3fadebe59849b9aa5cddaff5d8","observation_id":"92d23d08-cbf6-4ca0-b4ee-d55bb5446a30","resolution":{"observed_at":"2026-08-06T18:45:54.731274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2508.04498","last_updated":"2026-05-21T09:41:52Z","snapshot_observed_at":"2026-07-06T22:08:50.319355Z","submitted_at":"2025-08-06T14:48:01Z","title":"Efficient classical computation of the neural tangent kernel of quantum neural networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-22T13:43:57.680890Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2508.04498"},"observation_digest":"sha256:5f803a685899870163722c46b348fa90b82c42fdbed417ffad6d48c1bb1cb1ff","observation_id":"bb08001b-18e0-415c-9fd2-d98b30535a7e","resolution":{"observed_at":"2026-05-22T13:44:52.897205Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:38:26.490727Z","title":"Lloyd, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.18161","last_updated":"2025-08-25T16:12:18Z","snapshot_observed_at":"2026-08-06T18:17:08.535217Z","submitted_at":"2025-08-25T16:12:18Z","title":"Hybrid Quantum-Classical Learning for Multiclass Image Classification","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T16:38:26.490727Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2508.18161"},"observation_digest":"sha256:1efd41df2d13189a482881296bf8300e460b80835ccece7411f40a9f57bc21e7","observation_id":"dadfe139-a2b7-4aa2-b2f0-dde542031c64","resolution":{"observed_at":"2026-08-05T16:38:26.490727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-05T15:51:00.208344Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19394","last_updated":"2025-08-28T03:34:01Z","snapshot_observed_at":"2026-08-08T00:16:59.197686Z","submitted_at":"2025-08-26T19:39:33Z","title":"Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-05T15:51:00.208344Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2508.19394"},"observation_digest":"sha256:6fc2a46d957cc5d1d6c61ff2dcc62d95f473cf844d9eff51e5b7d168acb6df78","observation_id":"b679a258-df13-4ac8-8dba-a23fd633bc9c","resolution":{"observed_at":"2026-08-05T15:51:00.208344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"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":"2026-08-04T22:01:57.966056Z","title":"Quantum embeddings for machine learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.07633","last_updated":"2025-09-09T12:00:33Z","snapshot_observed_at":"2026-08-08T00:17:48.922976Z","submitted_at":"2025-09-09T12:00:33Z","title":"Variational Quantum Circuits in Offline Contextual Bandit Problems","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T22:01:57.966056Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2509.07633"},"observation_digest":"sha256:8d7db5c598490483e9b8c9a91e09ff08ea0234d28369aede5db65584edfe3810","observation_id":"b2379e6d-073f-4886-864e-3c3b8246de73","resolution":{"observed_at":"2026-08-04T22:01:57.966056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-04T17:14:40.325984Z","title":"https://arxiv.org/abs/2001.03622","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.11046","last_updated":"2025-09-14T02:20:21Z","snapshot_observed_at":"2026-08-06T12:40:55.836621Z","submitted_at":"2025-09-14T02:20:21Z","title":"Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Prediction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T17:14:40.325984Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2509.11046"},"observation_digest":"sha256:72ea0c4b5b347115a939159b5956b2c9b4635fb813880fc83b2efbb19e9757f1","observation_id":"76b56b43-7469-4e74-88da-2fe5e1bf59d3","resolution":{"observed_at":"2026-08-04T17:14:40.325984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-04T12:48:33.107217Z","title":"Quantum embeddings for machine learning.arXiv preprint arXiv:2001.03622,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2510.02497","last_updated":"2026-05-25T17:40:10Z","snapshot_observed_at":"2026-08-07T10:54:59.418793Z","submitted_at":"2025-10-02T19:00:41Z","title":"HattriQ: Designing Integrated Gradients for Feature Attribution in Quantum Machine Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T12:48:33.107217Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2510.02497"},"observation_digest":"sha256:c9dccca5f9dc7b6c45d790826a87ccbdc2cb55d42c586b5591fe23a5d890f48a","observation_id":"aa59a1b4-c032-4e8e-b70e-07eb48c47f70","resolution":{"observed_at":"2026-08-04T12:48:33.107217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-04T12:39:05.945891Z","title":"Lloyd, M","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2510.03389","last_updated":"2026-06-05T21:50:39Z","snapshot_observed_at":"2026-08-06T22:10:48.257052Z","submitted_at":"2025-10-03T18:00:00Z","title":"Quantum feature-map learning with reduced resource overhead","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T12:39:05.945891Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2510.03389"},"observation_digest":"sha256:6da206eaff48a2c40c4f491a8ecde0a23c619cf5ffa240f736d61c0a35c17fa5","observation_id":"a8a16817-49f4-4795-a08d-1fcbdf369624","resolution":{"observed_at":"2026-08-04T12:39:05.945891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2511.14989","last_updated":"2026-05-21T23:18:26Z","snapshot_observed_at":"2026-08-02T23:14:07.717727Z","submitted_at":"2025-11-19T00:13:17Z","title":"SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-21T18:42:35.602685Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2511.14989"},"observation_digest":"sha256:95f3bba8b297062de00a69781ec0fdcfef60271c85dac765f1a389320ffc0fe5","observation_id":"a84b8dfe-6fe1-42d1-8baa-4c299c250bfb","resolution":{"observed_at":"2026-05-21T18:44:18.908948Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2511.14989","last_updated":"2026-05-21T23:18:26Z","snapshot_observed_at":"2026-08-02T23:14:07.717727Z","submitted_at":"2025-11-19T00:13:17Z","title":"SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness","version":4},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-25T07:38:56.051810Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2511.14989"},"observation_digest":"sha256:1c0c4db6af007bf711b274234bdcef245359a542b54c231370410fcc376937bf","observation_id":"c4713096-c061-4ff3-b13b-6629f6f4c54a","resolution":{"observed_at":"2026-05-25T07:40:28.783780Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2603.09789","last_updated":"2026-07-06T16:00:19Z","snapshot_observed_at":"2026-07-15T00:00:49.008942Z","submitted_at":"2026-03-10T15:23:41Z","title":"A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-15T13:19:59.354430Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2603.09789"},"observation_digest":"sha256:c5ef21b91c719731930484852edfe8fa4f2dd3449da1ed0ae12d26230a19a2de","observation_id":"abee5eaa-61a2-4d9e-b4ac-1c6108695e01","resolution":{"observed_at":"2026-05-15T13:20:01.927748Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-15T00:00:49.391141Z","title":"Quantum embed- dings for machine learning.arXiv preprint arXiv:2001.03622, 2020","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2603.09789","last_updated":"2026-07-06T16:00:19Z","snapshot_observed_at":"2026-07-15T00:00:49.008942Z","submitted_at":"2026-03-10T15:23:41Z","title":"A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-15T00:00:49.391141Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2603.09789"},"observation_digest":"sha256:bd671fccc48d1faa620278aa666924c53c7b58b2483e168fcf03baebbffa6d42","observation_id":"be8bb719-d1e1-4746-ae5d-ee8f812db96c","resolution":{"observed_at":"2026-07-15T00:00:49.391141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2604.05881","last_updated":"2026-04-07T13:44:53Z","snapshot_observed_at":"2026-07-06T22:54:30.567988Z","submitted_at":"2026-04-07T13:44:53Z","title":"Hybrid Quantum-Classical Algorithm for Hamiltonian Simulation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T20:20:04.389235Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2604.05881"},"observation_digest":"sha256:87b08e98fefb5e4e5fb73a53512c5864ad01bb72fef4efd8c2095fac73ce53bb","observation_id":"d5101541-9e79-42eb-b73b-e626730c7f2e","resolution":{"observed_at":"2026-05-10T22:00:49.203366Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2604.22877","last_updated":"2026-04-24T02:20:21Z","snapshot_observed_at":"2026-07-06T23:09:10.050398Z","submitted_at":"2026-04-24T02:20:21Z","title":"A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-08T12:22:16.357542Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2604.22877"},"observation_digest":"sha256:0b8b7e0a750496a7c40564b2f2aa3ec749253e74062c52899b5adf09d5666148","observation_id":"bfae9a1a-d2f6-4b96-888d-39f9017f047b","resolution":{"observed_at":"2026-05-08T21:39:23.093303Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2605.21916","last_updated":"2026-07-26T17:26:27Z","snapshot_observed_at":"2026-08-02T13:29:59.875463Z","submitted_at":"2026-05-21T02:38:29Z","title":"A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-22T06:33:41.569991Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2605.21916"},"observation_digest":"sha256:5e61665f97a1330cdb7044f906c6d1b2eab3bc6d70ed0bfee29c4eb244fb1e97","observation_id":"fba33126-063f-4655-87e4-ac7bc49a3c49","resolution":{"observed_at":"2026-05-22T06:34:40.789228Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-02T13:30:01.661653Z","title":"Quantum embeddings for machine learning,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2605.21916","last_updated":"2026-07-26T17:26:27Z","snapshot_observed_at":"2026-08-02T13:29:59.875463Z","submitted_at":"2026-05-21T02:38:29Z","title":"A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T13:30:01.661653Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2605.21916"},"observation_digest":"sha256:19c460c6f9b0cc4081de1022f1e47f27e332ec6310485b94a2763035fd135d7e","observation_id":"087bfb6f-6b3e-4e4a-82a3-ab5cd088da82","resolution":{"observed_at":"2026-08-02T13:30:01.661653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2605.28927","last_updated":"2026-05-27T18:00:00Z","snapshot_observed_at":"2026-08-02T14:15:16.962790Z","submitted_at":"2026-05-27T18:00:00Z","title":"Quantum encodings that preserve persistent homology","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-06-29T11:45:53.392594Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2605.28927"},"observation_digest":"sha256:20e32ed3abec692583bba698e906482cf99a866354f5a036a5e705cfa9b0179a","observation_id":"943e2b7b-cf58-402c-acba-919b102c0c26","resolution":{"observed_at":"2026-06-29T11:53:24.434475Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2605.30866","last_updated":"2026-05-29T05:48:04Z","snapshot_observed_at":"2026-08-02T13:49:00.094909Z","submitted_at":"2026-05-29T05:48:04Z","title":"Generative Quantum Data Embeddings for Supervised Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T22:10:38.902738Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2605.30866"},"observation_digest":"sha256:618e6371791329d743bf01503d917fd3b302c5eb9151a2a1d93435e22c8d1834","observation_id":"508c6e66-9dc8-4551-a6ca-62d6ec694942","resolution":{"observed_at":"2026-07-01T19:36:09.448541Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2606.05387","last_updated":"2026-07-28T07:18:03Z","snapshot_observed_at":"2026-08-07T09:05:12.897199Z","submitted_at":"2026-06-03T19:46:35Z","title":"Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-28T05:33:39.259676Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2606.05387"},"observation_digest":"sha256:621d290fdbe45b17c59a0dcca72946c986ce0d299079c822913ca0b716b857e9","observation_id":"9dccb1cd-1e5c-443c-8418-be4255cc66f9","resolution":{"observed_at":"2026-07-02T09:16:49.448187Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-02T12:26:02.930554Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05387","last_updated":"2026-07-28T07:18:03Z","snapshot_observed_at":"2026-08-07T09:05:12.897199Z","submitted_at":"2026-06-03T19:46:35Z","title":"Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T12:26:02.930554Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2606.05387"},"observation_digest":"sha256:98fc81c38b9f8f99e06766ca8a239e9d48009a1997220cc457d6289d2a13d9df","observation_id":"20b25dfe-5da6-498e-9a56-3a88a66e9e03","resolution":{"observed_at":"2026-08-02T12:26:02.930554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2606.21721","last_updated":"2026-06-19T20:10:41Z","snapshot_observed_at":"2026-08-08T01:58:44.614758Z","submitted_at":"2026-06-19T20:10:41Z","title":"On a Central Limit Theorem and Sanov's principle for quantum neural networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T13:41:07.026350Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2606.21721"},"observation_digest":"sha256:435b882f2ec9ccb02b1f8128c939c1209c846f4b86dd367a0e077801889c8545","observation_id":"ddea1770-bc56-4ab4-bbc1-433cec2942b6","resolution":{"observed_at":"2026-07-04T07:09:38.169604Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2606.26312","last_updated":"2026-06-24T18:53:41Z","snapshot_observed_at":"2026-08-01T18:24:24.269376Z","submitted_at":"2026-06-24T18:53:41Z","title":"Tailor Made Embeddings for Quantum Machine Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T01:29:32.201460Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2606.26312"},"observation_digest":"sha256:1cbff7acb06ae1cdbad35ca828a4f05caccd2675e23a84609f325e31de25c9ef","observation_id":"af1da2d6-057c-4282-92c5-939ba7676834","resolution":{"observed_at":"2026-07-04T15:39:57.030439Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2606.27815","last_updated":"2026-06-26T07:58:37Z","snapshot_observed_at":"2026-07-07T00:01:59.695342Z","submitted_at":"2026-06-26T07:58:37Z","title":"Quantum Dynamic Time Warping for Multivariate Time Series Classification","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T04:55:53.739596Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2606.27815"},"observation_digest":"sha256:e849fcccfcd7245f01b3396c1e128507fd86062facb6b9bea77368a2d6e5211b","observation_id":"04def910-febc-4aaf-8594-92ed49a49202","resolution":{"observed_at":"2026-06-29T19:03:52.343020Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2001.03622","doi":"10.48550/arxiv.2001.03622","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.03622","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Quantum embeddings for machine learning","venue":"arXiv (Cornell University)","work_id":"902a0702-d84b-4ef8-a3f3-84f2dc6cbb7b","year":2020},"citing_paper":{"arxiv_id":"2607.01564","last_updated":"2026-07-02T00:45:51Z","snapshot_observed_at":"2026-08-07T15:23:15.696182Z","submitted_at":"2026-07-02T00:45:51Z","title":"An Information-Theoretic Principle for Optimal Quantum Encoding: Tight Frames and Equiangular Ensembles","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-03T00:27:03.652589Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2607.01564"},"observation_digest":"sha256:dbe7ceb4d9899779d2ea59d48c1bfda8c3d8a60140fb5899e5598bb37fd13a0e","observation_id":"7e00ae59-5ebb-4dfe-9d06-b1fcfb201364","resolution":{"observed_at":"2026-07-03T00:27:29.149225Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-02T03:38:01.101125Z","title":"Lloyd, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.13847","last_updated":"2026-07-15T13:53:48Z","snapshot_observed_at":"2026-08-04T15:35:01.942521Z","submitted_at":"2026-07-15T13:53:48Z","title":"Quantum Topological Data Encoding","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T03:38:01.101125Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2607.13847"},"observation_digest":"sha256:393a3f152f26ebb9501061651f377debe3133557a371ce9f7cbd0a788f0f6e2a","observation_id":"9333ba55-baaa-4ff3-972e-2098d0a27bb2","resolution":{"observed_at":"2026-08-02T03:38:01.101125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-01T11:48:21.708319Z","title":"Quantum embeddings for machine learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.19782","last_updated":"2026-07-22T06:03:20Z","snapshot_observed_at":"2026-08-08T00:17:20.864538Z","submitted_at":"2026-07-22T06:03:20Z","title":"A Multiclass Quantum Aligned Centroid Kernel","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T11:48:21.708319Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2607.19782"},"observation_digest":"sha256:1903ea96b57065330986661a55cb6dec7b9ea37c8b76cfdb7099b7eacc9c80e7","observation_id":"e0d56487-7ffb-4683-be25-94b1041fdd97","resolution":{"observed_at":"2026-08-01T11:48:21.708319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-01T04:37:05.451285Z","title":"Quantum embeddings for machine learning, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22516","last_updated":"2026-07-24T17:39:14Z","snapshot_observed_at":"2026-08-06T18:06:46.492984Z","submitted_at":"2026-07-24T17:39:14Z","title":"Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T04:37:05.451285Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2607.22516"},"observation_digest":"sha256:3578ec570b19bf8e79fb703c8058c5a1757e4f9edf042a1d3882e2220d1f5af6","observation_id":"d7427c49-0544-4c9f-aeff-581d39813cc2","resolution":{"observed_at":"2026-08-01T04:37:05.451285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-03T00:27:29.505599Z","title":"arXiv preprint arXiv:2001.03622 , year=","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.28795","last_updated":"2026-07-30T19:43:04Z","snapshot_observed_at":"2026-08-06T06:09:30.413730Z","submitted_at":"2026-07-30T19:43:04Z","title":"High-rate qLDPC processors","version":1},"reference_index":121,"source":"arxiv_source","source_observed_at":"2026-08-03T00:27:29.505599Z"},"links":{"cited_paper":"/paper/2001.03622","citing_paper":"/paper/2607.28795"},"observation_digest":"sha256:e4c5e1315a3eedcaf6f41b0a61c4fabacfab123e99b4fd322292e837ccab2ca6","observation_id":"ab7a9229-87d7-4fd6-b3fe-e2ba6792dfb1","resolution":{"observed_at":"2026-08-03T00:27:29.505599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2001.03622/citation-record","integrity":"/paper/2001.03622/integrity","json":"/paper/2001.03622/citation-record.json","paper":"/paper/2001.03622"},"outbound":[],"paper":{"arxiv_id":"2001.03622","last_updated":"2020-02-10T14:11:29Z","latest_version":2,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-07T18:38:17.780526Z","submitted_at":"2020-01-10T19:00:01Z","title":"Quantum embeddings for machine learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 35 inbound Pith citation observations for arXiv:2001.03622."}