{"as_of":"2026-08-13T05:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:45d96080bd45b060dfaba73c9d2354e1ff6496eb7842540abcae67c4109bc8df","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T13:03:25.534956Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.16560/citation-record","integrity":"/paper/2411.16560/integrity","json":"/paper/2411.16560/citation-record.json","paper":"/paper/2411.16560"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:03:26.576288Z","title":"Farhi, J","venue":null,"work_id":"63a646f3-8394-4b6c-810a-87c205552461","year":null},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:24.887752Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:fb967bb56fff968df9c4b0533f74916173c57002e670945f7a5cca42c4122bc3","observation_id":"7fbf6a70-eac9-4bb6-a990-62fade34296b","resolution":{"observed_at":"2026-08-12T13:03:26.615941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.05415","last_updated":"2019-07-11T17:57:56Z","snapshot_observed_at":"2026-07-06T08:07:12.362127Z","submitted_at":"2019-07-11T17:57:56Z","title":"Learning to learn with quantum neural networks via classical neural networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.05415","snapshot_observed_at":"2026-08-12T13:03:24.973849Z","title":"Verdon et al., Learning to learn with quan- tum neural networks via classical neural net- works, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:24.973849Z"},"links":{"cited_paper":"/paper/1907.05415","citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:ca1e5ac4994da9d9b3830fb8b24840fc79fe8ce72dc1a1ad43ebfdcb924948ec","observation_id":"04b0bacc-70e3-42cc-8d8d-a3a04e40d31a","resolution":{"observed_at":"2026-08-12T13:03:24.973849Z","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":"2026-08-12T13:03:24.979031Z","title":"From the quantum approximate optimization algorithm to a quantum alternating operator ansatz,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:24.979031Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:b03467a04b9a40df036f701a2a7d3d3f515812cb18d653103edf1812f0c64e85","observation_id":"92b3fe32-8a8e-4069-85b0-b127d275ae2b","resolution":{"observed_at":"2026-08-12T13:03:24.979031Z","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":"2026-08-12T13:03:26.560196Z","title":"Quantum approximate optimization al- gorithm for maxcut: A fermionic view,","venue":null,"work_id":"8871c6ab-0c06-4881-9983-f53c5bf0cae4","year":2018},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:24.984528Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:6d98d60b35b15c82c8acb985cc8b1ef0656c14a2dc374ed443072ae41c155b6a","observation_id":"dc26dd8f-6240-4d8c-a3a3-fa1cda32157a","resolution":{"observed_at":"2026-08-12T13:03:26.565401Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:03:24.989988Z","title":"Training the quantum approximate optimization algorithm without access to a quantum processing unit,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:24.989988Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:69214d6e07d75d5e5b513f65934301aff48b5b8ad7fc65ee3bb18f0233f97556","observation_id":"ec33515b-ad90-4ed6-9f20-37327d03885b","resolution":{"observed_at":"2026-08-12T13:03:24.989988Z","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":"2026-08-12T13:03:26.503930Z","title":"Kyriienko, A","venue":null,"work_id":"4856d9bb-26b0-40ec-b569-3b16cb635f6f","year":2021},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:24.995163Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:b27b5feadf7222063e0e7b4efcf8a9494e67d4dbaacbb9fb34d3af213b443ddd","observation_id":"107118c6-c56e-4124-9536-13ea5f67e922","resolution":{"observed_at":"2026-08-12T13:03:26.548104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:03:25.048819Z","title":"Quantum Computing in the NISQ era and beyond,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.048819Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:9d5ed0f784680d9ff9b2f9a5e276f81d4c5e1dda9cd4fb9921b16adfb8a1531e","observation_id":"6bf4f376-f242-45b0-9462-baa6f2fcc5df","resolution":{"observed_at":"2026-08-12T13:03:25.048819Z","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":"2026-08-12T13:03:26.397650Z","title":"Barren plateaus in quantum neural network training landscapes,","venue":null,"work_id":"bf03f9bb-e1b6-43db-ba74-d0b034ca677b","year":2018},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.141743Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:6008e3d2bcf489871abe3f782c9c78ba4c119fa51c7d3c8a9c6a06cfb27f9ad6","observation_id":"c9cfeff0-229d-4274-91be-bf7ac584581d","resolution":{"observed_at":"2026-08-12T13:03:26.441254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:03:26.381073Z","title":"Grant, L","venue":null,"work_id":"8f892b48-eee9-4cad-9150-fef15679ad25","year":2019},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.172395Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:0556eeeb463dbe4fef3cd0d957e14a6824f165ce83cd8b68fcdc371e78254768","observation_id":"16dd9ae4-7615-4be2-8557-151708dd43dd","resolution":{"observed_at":"2026-08-12T13:03:26.387238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.09376","last_updated":"2025-02-19T06:34:55Z","snapshot_observed_at":"2026-08-06T06:25:50.538954Z","submitted_at":"2022-03-17T15:06:40Z","title":"Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.09376","snapshot_observed_at":"2026-08-12T13:03:25.177771Z","title":"Zhang, L","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.177771Z"},"links":{"cited_paper":"/paper/2203.09376","citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:23c6992450b8675b42a97cd386df2c56392340e61534d851dda2ab17a891124c","observation_id":"2b4d0c0d-e9c7-4722-a550-53c414086618","resolution":{"observed_at":"2026-08-12T13:03:25.177771Z","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":"2026-08-12T13:03:26.365013Z","title":"Connecting ansatz expressibility to gra- dient magnitudes and barren plateaus,","venue":null,"work_id":"e8d5466d-adb5-445a-8104-7cbba820b8ae","year":2022},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.183370Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:c23903cda8027bcce7dbf719cfe7f5a3415f32fd52143ace767b8a586d268925","observation_id":"8765d170-757e-42d2-92f0-0ac5e3f976c9","resolution":{"observed_at":"2026-08-12T13:03:26.369900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.05125","last_updated":"2022-06-07T15:29:01Z","snapshot_observed_at":"2026-08-07T08:07:45.534113Z","submitted_at":"2022-01-13T18:30:18Z","title":"GradMax: Growing Neural Networks using Gradient Information","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.05125","snapshot_observed_at":"2026-08-12T13:03:25.302280Z","title":"Gradmax: Growing neural networks using gradient infor- mation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.302280Z"},"links":{"cited_paper":"/paper/2201.05125","citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:20900aec42c0bfc1b061d30c232e75357db2456c5b9d6c509db57d2dc1542128","observation_id":"aca75750-aed9-479b-90a7-812a7563cf8d","resolution":{"observed_at":"2026-08-12T13:03:25.302280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.14115","last_updated":"2022-06-28T16:23:24Z","snapshot_observed_at":"2026-07-06T13:25:35.920469Z","submitted_at":"2022-06-28T16:23:24Z","title":"Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.14115","snapshot_observed_at":"2026-08-12T13:03:25.310836Z","title":"Duong, S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.310836Z"},"links":{"cited_paper":"/paper/2206.14115","citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:d1fa36c8659f9d79fbb50baf7114b5bb889e05cc17c7a04652215c928836fe62","observation_id":"4bd131b9-50fb-4a36-87b0-a4a0537ded53","resolution":{"observed_at":"2026-08-12T13:03:25.310836Z","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":"2026-08-12T13:03:26.200521Z","title":"Quantum circuit architecture search for variational quantum algorithms,","venue":null,"work_id":"10cc1286-8bd6-4b89-9a83-49df25b355e3","year":2022},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.316302Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:ba1dcd0a591adedfd72bbb4a0c30f6a093e2bf708d9552e2d7103bce7f45bf37","observation_id":"817415d7-9eb2-4015-b781-72497629267b","resolution":{"observed_at":"2026-08-12T13:03:26.206876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:03:25.327213Z","title":"Hierarchical quan- tum circuit representations for neural architec- ture search,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.327213Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:04a877857c67c0fc158479ce19bf0a8201333d3d922974abf49a93b337e17ef0","observation_id":"fd5903b3-b01f-45c3-b30c-52745f2152ad","resolution":{"observed_at":"2026-08-12T13:03:25.327213Z","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":"2026-08-12T13:03:26.077126Z","title":"Qubit-adapt-vqe: An adaptive algorithm for constructing hardware- efficient ans¨ atze on a quantum processor,","venue":null,"work_id":"cb30833b-02e7-4b7c-9cf5-90108dbdbf33","year":2021},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.357395Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:f5ee276360d2634d0acb33eaf097d43a2780ff296e362ec84a5e2b146e05c288","observation_id":"cc5d016e-7228-4fc3-8edc-e117f038b683","resolution":{"observed_at":"2026-08-12T13:03:26.121443Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:03:25.982123Z","title":"Ef- fect of data encoding on the expressive power of variational quantum-machine-learning mod- els,","venue":null,"work_id":"06b808b2-94e9-429a-92ef-38641c76b1a6","year":2021},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.420582Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:4ce29c31e04b664dac44ab115184ff06a7c493f238d7bcf6d3d444b17debe101","observation_id":"8318e2ef-78e6-40f3-85f9-236247cecde2","resolution":{"observed_at":"2026-08-12T13:03:26.022898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s42484-020-00036-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:03:25.661970Z","title":"Layerwise learning for quantum neural networks,","venue":null,"work_id":"7bb35b47-ef09-4cb8-8455-613cc8818f3f","year":2021},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.446936Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:ac40108477167e3e213ddf474b1b12fae4213e3b5335921767d3d12078b38500","observation_id":"10904cdd-d0d8-41fc-b48b-c7d7a0c368f9","resolution":{"observed_at":"2026-08-12T13:03:25.735682Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:03:25.953453Z","title":"Robust data encod- ings for quantum classifiers,","venue":null,"work_id":"aaf3f801-fc17-4705-83f7-7e8f0222fe8a","year":2020},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.457615Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:e34dc9c1986aa8e99d4df1c24600dd6bd46ad81bd4ba36b7d47963cd1cf24667","observation_id":"704ef7b5-97b2-4cc8-9f96-a17c985f1490","resolution":{"observed_at":"2026-08-12T13:03:25.965926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:03:25.461887Z","title":"Let quan- tum neural networks choose their own frequen- cies,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.461887Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:0338ad41b52baefc0f11b0d51bf0b13c0c9441ee5880a15dcd260c478e68e362","observation_id":"7c192a5f-34f5-4a58-8ba8-6b988732a0d5","resolution":{"observed_at":"2026-08-12T13:03:25.461887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.22331/q-2019-12-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:03:25.620008Z","title":"An initialization strategy for ad- dressing barren plateaus in parametrized quan- tum circuits,","venue":null,"work_id":"8e6d84f3-e679-4a68-bdc9-cbed009c9e68","year":2019},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.467373Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:2effa0074a413d33d2b8ee9cadd9bf073af818868bb71f064b6d8e86ce4f5663","observation_id":"098c473d-09f4-4e52-ba26-2ac0421e9bbe","resolution":{"observed_at":"2026-08-12T13:03:25.626645Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:03:25.452748Z","title":"Available: http://dx.doi.org/ 10.1007/s42484-020-00036-4","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.452748Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:336a9a7d4f578dd94ef5973f8ce336e46f9b418d26d72fbb885f1aca18a2fdaa","observation_id":"c0a17929-0ab6-4ea6-8965-4f0b8f58d22e","resolution":{"observed_at":"2026-08-12T13:03:25.452748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.15188","last_updated":"2024-03-05T04:42:21Z","snapshot_observed_at":"2026-08-11T12:33:45.571598Z","submitted_at":"2022-11-28T09:57:15Z","title":"Incremental Spatial and Spectral Learning of Neural Operators for Solving Large-Scale PDEs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.15188","snapshot_observed_at":"2026-08-12T13:03:25.534956Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.534956Z"},"links":{"cited_paper":"/paper/2211.15188","citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:daa2eb2a808e7e6f929cbd9ff2528eac09ab5a79114f86ea6cd71e916c732b11","observation_id":"39aba0f6-b3e6-4cb8-b3b1-4936e0ba1fe5","resolution":{"observed_at":"2026-08-12T13:03:25.534956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.02424","last_updated":"2022-09-28T23:27:41Z","snapshot_observed_at":"2026-08-13T01:42:37.231447Z","submitted_at":"2021-10-06T00:16:10Z","title":"Spectral Bias in Practice: The Role of Function Frequency in Generalization","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.02424","snapshot_observed_at":"2026-08-12T13:03:25.472350Z","title":"Fridovich-Keil, R","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.472350Z"},"links":{"cited_paper":"/paper/2110.02424","citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:13cbdd19f10369bf294161e42eb2d01e0564eec8771283a8d2106358de7c8390","observation_id":"1c1c80e7-34c6-42bd-9f75-42ccbfcb278d","resolution":{"observed_at":"2026-08-12T13:03:25.472350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1411.4028","last_updated":"2014-11-14T19:57:57Z","snapshot_observed_at":"2026-07-06T04:00:38.324755Z","submitted_at":"2014-11-14T19:57:57Z","title":"A Quantum Approximate Optimization Algorithm","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1411.4028","snapshot_observed_at":"2026-08-12T13:03:24.924736Z","title":"[Online]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:24.924736Z"},"links":{"cited_paper":"/paper/1411.4028","citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:d68309463100ea5d4cee8cecb595e8bf175d823fa468cea0cd79d2edaa09b05c","observation_id":"c9159e87-c2c3-4f77-a656-6731135c270b","resolution":{"observed_at":"2026-08-12T13:03:24.924736Z","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":"2026-08-12T13:03:26.288036Z","title":"1103 / prxquantum","venue":null,"work_id":"aa81802c-fa9c-49df-8085-cd62dcd27d08","year":null},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":3399,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.277554Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:f55f36ef4768dcdf881040de18233b7e28c392ce0f7e0c55c35bf8b86f42c31a","observation_id":"b7796aa7-41c4-4fb0-9798-c53dd55453c3","resolution":{"observed_at":"2026-08-12T13:03:26.350596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:03:26.147506Z","title":"1038 / s41534 - 022 - 00570 - y","venue":null,"work_id":"fc3df35f-ba61-4140-99f5-eb0a361a350c","year":null},"citing_paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures","version":1},"reference_index":6387,"source":"pdf_text","source_observed_at":"2026-08-12T13:03:25.321355Z"},"links":{"citing_paper":"/paper/2411.16560"},"observation_digest":"sha256:2e8b0bb141a20781638197e31f5300fe37f7c65daa0588aa6d10bc9f18721bd8","observation_id":"a618f044-926b-447a-874b-140ed89a989f","resolution":{"observed_at":"2026-08-12T13:03:26.186608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.16560","last_updated":"2024-11-25T16:46:22Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-13T04:07:15.939318Z","submitted_at":"2024-11-25T16:46:22Z","title":"Quantum Circuit Training with Growth-Based Architectures"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":4,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":10},"total_outbound_references":27},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2411.16560."}