{"as_of":"2026-08-09T01:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cb8b45f42623162dce53787c00464d6c2b1ad91fd672b3d4a96ad5ec7a5afc3b","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:36:30.352557Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:35:22.069237Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T19:17:31.676486Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14789","snapshot_observed_at":"2026-08-06T19:35:22.069237Z","title":"Solving mnist with a globally trained mixture of quantum experts,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.05190","last_updated":"2025-07-07T16:49:07Z","snapshot_observed_at":"2026-08-08T23:53:25.072497Z","submitted_at":"2025-07-07T16:49:07Z","title":"QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T19:35:22.069237Z"},"links":{"cited_paper":"/paper/2505.14789","citing_paper":"/paper/2507.05190"},"observation_digest":"sha256:96ce452c93d9161b7eba237d40d14f47f6ee2cd0a9ecdc653daa7b87bb0c6470","observation_id":"99dfdcff-7b19-4414-b1f5-400693d1e892","resolution":{"observed_at":"2026-08-06T19:35:22.069237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14789","snapshot_observed_at":"2026-08-04T12:39:10.879272Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.03389","last_updated":"2026-06-05T21:50:39Z","snapshot_observed_at":"2026-08-08T09:25:30.580420Z","submitted_at":"2025-10-03T18:00:00Z","title":"Quantum feature-map learning with reduced resource overhead","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-04T12:39:10.879272Z"},"links":{"cited_paper":"/paper/2505.14789","citing_paper":"/paper/2510.03389"},"observation_digest":"sha256:16c0d9c157b8f23348b84dbe5cfdc2dfe65248da8124317870598bf8553b9d10","observation_id":"0b01cedb-7947-4138-a283-6a2bffeb9ea3","resolution":{"observed_at":"2026-08-04T12:39:10.879272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"cited_work":{"arxiv_id":"2505.14789","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.14789","snapshot_observed_at":"2026-07-10T19:17:31.676486Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","venue":"quant-ph","work_id":"44acc8d1-6f99-4966-8879-85142bcbdaf7","year":2025},"citing_paper":{"arxiv_id":"2607.07754","last_updated":"2026-08-04T09:26:25Z","snapshot_observed_at":"2026-08-08T09:25:06.550732Z","submitted_at":"2026-07-08T13:41:31Z","title":"Image classification via a quantum-inspired strategy involving a mixture of experts","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-10T19:09:03.928334Z"},"links":{"cited_paper":"/paper/2505.14789","citing_paper":"/paper/2607.07754"},"observation_digest":"sha256:eb8362c384e78a803e4eb1b887391ac7627f87a8336ba22f3c005f4afb392d74","observation_id":"d57a357d-2753-49ff-b3ca-4ea1d99d423d","resolution":{"observed_at":"2026-07-10T19:17:31.678013Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.14789/citation-record","integrity":"/paper/2505.14789/integrity","json":"/paper/2505.14789/citation-record.json","paper":"/paper/2505.14789"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:34.101947Z","title":null,"venue":null,"work_id":"40d39bf9-707c-48b5-b46b-cb5c7af198d6","year":null},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:25.993612Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:7c4d257e28086210adf0d1158ebaceca39b16f010653822648d2a8a2a119ff21","observation_id":"1ab29fad-9fb6-4ff6-ba9a-5cab29c1488b","resolution":{"observed_at":"2026-08-07T15:36:34.155920Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:33.973813Z","title":null,"venue":null,"work_id":"e4874eca-07dc-4136-a5ea-a0ddda20f481","year":null},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.040939Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:8ca4c0708e5d9efe2fa719c9f94fe9747c2ad378e31dd3b7951c534afed0f828","observation_id":"34b1a759-355b-4387-9ec9-1ab4559860cb","resolution":{"observed_at":"2026-08-07T15:36:34.026223Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:33.872748Z","title":null,"venue":null,"work_id":"9053c0a2-960b-4d7d-9fb8-00a5b11b5de7","year":null},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.107598Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:f380e44fa4387a1f3d5fb2cc48d2dc9703a81436e882a0d2c58f5762d9be46b4","observation_id":"26060341-4f9e-4412-ba89-087acfad5062","resolution":{"observed_at":"2026-08-07T15:36:33.910303Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:33.755186Z","title":"understanding the LEarning process of QUantum Neural networks (LeQun)","venue":null,"work_id":"b1dd03f0-6447-4873-a63c-f2509daf1d48","year":2022},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.159584Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:51b247542a4f60ef13970c0501f184f1d7aa69fc97a74b7373f821885557b605","observation_id":"fac48c53-5d51-4e9a-9b4a-1120f0369a96","resolution":{"observed_at":"2026-08-07T15:36:33.811529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:26.238635Z","title":"Preskill, Quantum 2, 79 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.238635Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:e9431b267bde7c496ab76e8f8a8b6473cbc11f0d7b7fcac9a0d837b2c9859c51","observation_id":"3c604476-f101-4639-887b-687168142c65","resolution":{"observed_at":"2026-08-07T15:36:26.238635Z","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":"2026-08-07T15:36:26.332224Z","title":"Biamonte, P","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.332224Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:f710941e5f9a9df4232c3abdd639346e521183d7e2f90df9c1e90a73be3344ca","observation_id":"5a698884-cae1-4b8d-8a9c-e07eb99c84b6","resolution":{"observed_at":"2026-08-07T15:36:26.332224Z","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":"2026-08-07T15:36:26.428944Z","title":"Cerezo, G","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.428944Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:61e95435254665e0921db70743e9e9eae3efbdad6d5600f06c319b585c7c3e59","observation_id":"f511d9c5-2816-4edd-9cab-70536b72c36b","resolution":{"observed_at":"2026-08-07T15:36:26.428944Z","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":"2026-08-07T15:36:33.580691Z","title":"Aaronson, Nature Phys","venue":null,"work_id":"36f7655e-bcaa-496f-8ca0-741e1182bb46","year":2015},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.457699Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:4ec039b5a4580d4af33577532189ce1cf63088610178db6a2a93718eaaebf202","observation_id":"0e69e1ed-cdca-44f7-81f9-324d51fc3ff0","resolution":{"observed_at":"2026-08-07T15:36:33.632702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:33.393514Z","title":"Schuld and N","venue":null,"work_id":"f611003c-7170-4540-abc4-ca9fa846d803","year":2022},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.512962Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:b7e524ae8ef636d3ed6142058f9968a0f8aece7c683de4a5ccbbb45719b850e8","observation_id":"7aa63316-57b9-4984-bd92-7531c8bab4df","resolution":{"observed_at":"2026-08-07T15:36:33.463630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:26.590611Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.590611Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:48a92ede16fb51ba811427dd8f4def2d6eb997009e4c50afcb600fddc54f4a19","observation_id":"7a84badc-6940-44a6-b0ae-ab59008a1f8e","resolution":{"observed_at":"2026-08-07T15:36:26.590611Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:33.214952Z","title":"Gily´ en, Y","venue":null,"work_id":"e85abdba-4189-4501-9e7e-e8af3dcbd399","year":2019},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.647108Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:fd493ec392dd1fe6185c59f043347c9621a8eb0324f2de70daa96376e1578aee","observation_id":"b557d9c2-77a8-4a55-b9f9-70797c76d638","resolution":{"observed_at":"2026-08-07T15:36:33.294987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13937","last_updated":"2025-04-23T01:56:25Z","snapshot_observed_at":"2026-07-06T19:35:35.229351Z","submitted_at":"2024-10-17T18:00:03Z","title":"Quantum computational complexity of matrix functions","version":2},"cited_work":{"arxiv_id":"2410.13937","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.13937","snapshot_observed_at":"2026-08-07T15:36:31.662751Z","title":"Quantum computational complexity of matrix functions","venue":"quant-ph","work_id":"960948d3-51ad-420f-b562-46c9cdf82d6a","year":2024},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.716032Z"},"links":{"cited_paper":"/paper/2410.13937","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:e3cac3691f74d6a81b7248037bb836f3153872491854f586648d51b766422ea5","observation_id":"37337c7e-1a3d-4afe-b0b0-fd1fe288af4e","resolution":{"observed_at":"2026-08-07T15:36:31.696143Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:26.760364Z","title":"Cerezo, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.760364Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:64ab8660acebed6208ed7f427e9d70a1fbc9ecf527dd4b704fb25814078bb156","observation_id":"4b2b70cc-5a69-4853-a6a1-dbf520650115","resolution":{"observed_at":"2026-08-07T15:36:26.760364Z","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":"2026-08-07T15:36:26.868759Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.868759Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:21887359026180f8c3d1df1f135097bd12a535001d37c1c051a70f41b718b261","observation_id":"8edf0711-a0fa-4f45-bbfa-e624202c0393","resolution":{"observed_at":"2026-08-07T15:36:26.868759Z","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":"2026-08-07T15:36:26.989599Z","title":"Cerezo, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:26.989599Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:cb852262786abe02aa220fe45f37de8eda718b1481281070d92dccb3671ff24b","observation_id":"27111e82-c069-4722-9e21-8eb9f295f537","resolution":{"observed_at":"2026-08-07T15:36:26.989599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06174","last_updated":"2022-03-11T18:58:12Z","snapshot_observed_at":"2026-08-07T06:38:25.147683Z","submitted_at":"2022-03-11T18:58:12Z","title":"Quantifying the barren plateau phenomenon for a model of unstructured variational ans\\\"{a}tze","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06174","snapshot_observed_at":"2026-08-07T15:36:27.074150Z","title":"Quantifying the barren plateau phenomenon for a model of unstructured variational ans¨ atze,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.074150Z"},"links":{"cited_paper":"/paper/2203.06174","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:7bba02c974f5de0b74bbd87e037865395eb4628622405c48c6f54c492fd419c2","observation_id":"ad11b2d1-4b9a-4db3-86a0-604888cbd387","resolution":{"observed_at":"2026-08-07T15:36:27.074150Z","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":"2026-08-07T15:36:27.217774Z","title":"Holmes, K","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.217774Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:4a372a04e008f7c97246c69ff2087c060ae22af7f93d71ee7c7abb7d845e9c4e","observation_id":"cba4b76f-45ba-4d14-9e64-d4feedd77b51","resolution":{"observed_at":"2026-08-07T15:36:27.217774Z","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":"2026-08-07T15:36:27.331783Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.331783Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:dc182c3fd1bf812606849ecd95af19f6821b560a1fa1bbb9dc36aea78a0103d8","observation_id":"81ec59f3-974b-4434-b85f-b7d7ad842989","resolution":{"observed_at":"2026-08-07T15:36:27.331783Z","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":"2026-08-07T15:36:33.032747Z","title":"Grant, L","venue":null,"work_id":"714f0ba3-daf6-4aaf-b3a3-ceccf15ba42e","year":2019},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.436094Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:4108c70365b72da47033881cc501275efd966956e20bb9939ead675a9c02a2f8","observation_id":"d7dd9ebe-56e9-4a87-b621-bedc5750ed06","resolution":{"observed_at":"2026-08-07T15:36:33.095019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:27.576727Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.576727Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:77f1cfff775388c5d977f2bfde5dbb901939a07e88b0ea377b27c0aca23e45d9","observation_id":"733c524f-655f-44b5-a5e9-4bd4e62c9b2f","resolution":{"observed_at":"2026-08-07T15:36:27.576727Z","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":"2026-08-07T15:36:32.805981Z","title":"Liu, T.-P","venue":null,"work_id":"c6142f85-fa59-4614-be54-3b1d67a1fa27","year":2023},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.687233Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:a738ebc1c28820a862acd6429e2cf616585f14458647bc7b0b7ec249e98058f7","observation_id":"1bec3a9a-a81a-40cb-89b9-d77946b6396e","resolution":{"observed_at":"2026-08-07T15:36:32.909649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:27.751342Z","title":"LeCun, Y","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.751342Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:4809be110122ba3649742f936c882a68e0c309aa4d36d5f8768a7d33b95dcefc","observation_id":"2a9c1c7c-07c7-4db5-b85a-e64eb1c8790b","resolution":{"observed_at":"2026-08-07T15:36:27.751342Z","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":"2026-08-07T15:36:32.606346Z","title":null,"venue":null,"work_id":"835fc472-6e83-43d5-8b71-182c7c63769e","year":2020},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.827696Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:a747ce0ff1e6e0f9a9d9978c6603e5f845b538c2cb1070a63270614df42d9851","observation_id":"cf07d82a-359c-49ad-99ee-20ef4ba032c3","resolution":{"observed_at":"2026-08-07T15:36:32.673983Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:32.470406Z","title":"Abbas, R","venue":null,"work_id":"9043a88b-7ae7-4344-a0f0-21aaf940a947","year":2023},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.895514Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:85998d96e73d2a7d433c82af5ef406cfa16e8b5847c387168c8896361d3d4d6f","observation_id":"63401d32-d63c-4ba9-af2e-aa7155ba1f91","resolution":{"observed_at":"2026-08-07T15:36:32.525041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:32.313685Z","title":"Gily´ en, S","venue":null,"work_id":"5cc41af7-54c8-48b9-9c11-5f0049e10f11","year":2019},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:27.947183Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:392dead2a9c086067897981868c42de011db0efe227d6ce2bc34d285856651d0","observation_id":"ad6151cf-b5ad-4b70-8647-3e2a59d2408d","resolution":{"observed_at":"2026-08-07T15:36:32.363008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:28.046721Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:28.046721Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:cc72fc766fcd39cae4a5e2338b6addd01905163f5d65cee230e371cb658d37e0","observation_id":"3b364656-1b46-480c-8e80-35ef62537fd9","resolution":{"observed_at":"2026-08-07T15:36:28.046721Z","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":"2026-08-07T15:36:28.102810Z","title":"Kerenidis and A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:28.102810Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:dbac9d6db7ce94c52032f2b89f6e3a913fc0e09bce97376b59ddf4e150100f9b","observation_id":"2e6b7598-5a41-4cfa-ae37-b288b9fa80e1","resolution":{"observed_at":"2026-08-07T15:36:28.102810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.08321","last_updated":"2019-11-20T22:24:09Z","snapshot_observed_at":"2026-08-09T00:39:53.984382Z","submitted_at":"2018-06-21T17:02:29Z","title":"Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.08321","snapshot_observed_at":"2026-08-07T15:36:28.307099Z","title":"Quantum kitchen sinks: An algorithm for machine learning on near-term quantum computers,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:28.307099Z"},"links":{"cited_paper":"/paper/1806.08321","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:d881d6df5a91a28f1b4bfa0cbe2f438d442505ba0e10b97e87c4464cd2196dc4","observation_id":"9814be90-08f2-4c8d-b7c8-b8f3dfe55c8c","resolution":{"observed_at":"2026-08-07T15:36:28.307099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.10560","last_updated":"2024-04-05T08:38:13Z","snapshot_observed_at":"2026-07-06T15:56:15.361460Z","submitted_at":"2023-07-20T03:55:53Z","title":"Post-variational quantum neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.10560","snapshot_observed_at":"2026-08-07T15:36:28.406341Z","title":"Post-variational quan- tum neural networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:28.406341Z"},"links":{"cited_paper":"/paper/2307.10560","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:b74c846153fa3b124d7341fa1c5b73b2df1fd653ec7b2a442752c08c8b31010b","observation_id":"5f174c53-c235-4981-8790-923ae1bf93aa","resolution":{"observed_at":"2026-08-07T15:36:28.406341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.06002","last_updated":"2018-08-30T20:23:02Z","snapshot_observed_at":"2026-08-04T12:41:10.101872Z","submitted_at":"2018-02-16T16:09:34Z","title":"Classification with Quantum Neural Networks on Near Term Processors","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.06002","snapshot_observed_at":"2026-08-07T15:36:28.536992Z","title":"Classification with quantum neural networks on near term processors,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:28.536992Z"},"links":{"cited_paper":"/paper/1802.06002","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:20803f75fff7164145b7f619643824565605f173622f948d7d803f2528d9ca15","observation_id":"69c2c7a4-89c6-44ee-948d-5727d417900e","resolution":{"observed_at":"2026-08-07T15:36:28.536992Z","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":"2026-08-07T15:36:28.651576Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:28.651576Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:2840d561c5dc955468951599e9fe0c9afaf65276775ddf9b90617ff3db3bf9dd","observation_id":"a857b03c-0eb8-4308-a8e1-438ad8f0f418","resolution":{"observed_at":"2026-08-07T15:36:28.651576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.09423","last_updated":"2020-09-20T12:29:05Z","snapshot_observed_at":"2026-07-06T09:57:09.838532Z","submitted_at":"2020-09-20T12:29:05Z","title":"A Tutorial on Quantum Convolutional Neural Networks (QCNN)","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.09423","snapshot_observed_at":"2026-08-07T15:36:28.743866Z","title":"A tutorial on quan- tum convolutional neural networks (qcnn),","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:28.743866Z"},"links":{"cited_paper":"/paper/2009.09423","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:a969f17e1a99f9c7b0079e7bd2b94882a0ea418eeb790677a9652be7b3eb5ec8","observation_id":"86a25445-e39e-423b-a3e0-9be471cc6988","resolution":{"observed_at":"2026-08-07T15:36:28.743866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"quant-ph/0510031","last_updated":"2005-10-04T14:54:22Z","snapshot_observed_at":"2026-07-07T07:20:30.967305Z","submitted_at":"2005-10-04T14:54:22Z","title":"Image compression and entanglement","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"quant-ph/0510031","snapshot_observed_at":"2026-08-07T15:36:28.843928Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:28.843928Z"},"links":{"cited_paper":"/paper/quant-ph/0510031","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:158607e9f64ed32d3f5909b6ace9d5e613b70de8144bba1f87df644d4d4aa889","observation_id":"a769d864-a96b-404e-a4f2-fa046a0e9ff9","resolution":{"observed_at":"2026-08-07T15:36:28.843928Z","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":"2026-08-07T15:36:32.110930Z","title":"Cramer, M","venue":null,"work_id":"e8f3d609-5ccb-48b6-9f37-7786dd6c8c6f","year":2010},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:28.971622Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:c851eac9d4572c28fb48f95b1f72dc6e3bea9d5aafadf1d0090309da1684f1d8","observation_id":"7bc13619-1995-4a49-8e1c-151b4b77b8bb","resolution":{"observed_at":"2026-08-07T15:36:32.176873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06204","last_updated":"2025-04-09T13:54:59Z","snapshot_observed_at":"2026-08-07T19:39:57.333135Z","submitted_at":"2024-06-26T16:34:33Z","title":"A Survey on Mixture of Experts in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06204","snapshot_observed_at":"2026-08-07T15:36:29.097336Z","title":"A survey on mixture of experts,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:29.097336Z"},"links":{"cited_paper":"/paper/2407.06204","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:b652e1dd41b50c3a8847bf438ab21470f2b4b1f5cec87f5848383d845229ced5","observation_id":"e9012843-1326-4841-a193-f207d0f0c456","resolution":{"observed_at":"2026-08-07T15:36:29.097336Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-07T15:36:29.243106Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:29.243106Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:823e09d9d9c1d08ac79bcde425e99c29457ea954822100bdbdc1bd8d8359f0dc","observation_id":"ac121243-916b-4ecd-b70b-35d84baa3831","resolution":{"observed_at":"2026-08-07T15:36:29.243106Z","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":"2026-08-07T15:36:31.944749Z","title":"Parameter-shift Rule,","venue":null,"work_id":"f14d5833-f965-4664-9bf9-1e195660e348","year":2025},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:29.372132Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:66f3394613863f3468378e865f5ca5792041279985ad52cf40f9f86b48ffef5b","observation_id":"26803ab5-0623-4178-8290-b0e3bc51bdce","resolution":{"observed_at":"2026-08-07T15:36:32.016810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00220-025-05238-0","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T00:03:45.533247Z","title":"Girardi and G","venue":"Communications in Mathematical Physics","work_id":"0f710719-6494-4e38-a1b3-822b6e2fe231","year":2025},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:29.445210Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:4d3c15e9624cb48747ff8a86747ece08fcef2a8c2c62c6ea9b60f03f66afed00","observation_id":"e90be548-3c9d-419a-ac96-2ac4ebae3e37","resolution":{"observed_at":"2026-08-07T15:36:30.560407Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2412.03182","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:31.182804Z","title":"Quantitative convergence of trained quan- tum neural networks to a gaussian process,","venue":null,"work_id":"bc705892-ee74-4125-b18a-9977371d6cf0","year":2024},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:29.561131Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:f45b3918e92afe1d0937318f3d2c07fa0fa83f79658a18948e37b15b3401321f","observation_id":"9e88b91c-a808-4b11-a0a5-ae9f8ddb6f2e","resolution":{"observed_at":"2026-08-07T15:36:31.254764Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:36:31.804385Z","title":"Abedi, S","venue":null,"work_id":"ee936e76-b170-40a7-a7d7-a57566d2607b","year":2023},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:29.634640Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:ad3c4ec2839b4e4ff57e9776f68f026ca3f6f43a5e5a7c5309ef3d872123ec8d","observation_id":"03a16699-292b-41a4-833c-caa84421cbae","resolution":{"observed_at":"2026-08-07T15:36:31.866413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.04968","last_updated":"2022-07-29T22:39:54Z","snapshot_observed_at":"2026-07-06T07:14:13.912107Z","submitted_at":"2018-11-12T19:18:57Z","title":"PennyLane: Automatic differentiation of hybrid quantum-classical computations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.04968","snapshot_observed_at":"2026-08-07T15:36:29.722964Z","title":"Pennylane: Automatic dif- ferentiation of hybrid quantum-classical computations,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:29.722964Z"},"links":{"cited_paper":"/paper/1811.04968","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:b511120d317b831fb4231e84d8b6dfc3358d9fd217816aa02a2462a15cf5e46d","observation_id":"cfbd90bb-3f18-47b5-88df-9643d04cd48d","resolution":{"observed_at":"2026-08-07T15:36:29.722964Z","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":"2026-08-07T15:36:29.842525Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:29.842525Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:8e4b3cce3c81d690806104734c902ce0c7425790c449b5ab155765002c8e5283","observation_id":"42dd25f6-9c99-454c-b4a3-4b5ae154b396","resolution":{"observed_at":"2026-08-07T15:36:29.842525Z","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":"2026-08-07T15:36:29.964194Z","title":"P´ erez-Salinas, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:29.964194Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:f9317368bdae17d4bc04ef24c7a907112264b83374d5bf418900dbc3cdb64ff6","observation_id":"de3cde22-4234-4754-b70c-81e42128b629","resolution":{"observed_at":"2026-08-07T15:36:29.964194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1202.2745","last_updated":"2012-02-13T14:35:41Z","snapshot_observed_at":"2026-07-06T02:42:47.649624Z","submitted_at":"2012-02-13T14:35:41Z","title":"Multi-column Deep Neural Networks for Image Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1202.2745","snapshot_observed_at":"2026-08-07T15:36:30.099892Z","title":"Cire¸ san, U","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:30.099892Z"},"links":{"cited_paper":"/paper/1202.2745","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:18e91a99712a71cd7dd62f79d86991044ab6b5efbe1b96e6586692701e98907b","observation_id":"d0cacd88-216f-4220-8d75-1d6df294b79c","resolution":{"observed_at":"2026-08-07T15:36:30.099892Z","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":"2026-08-07T15:36:30.196700Z","title":"Deng, IEEE Signal Processing Magazine 29, 141 (2012)","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:30.196700Z"},"links":{"citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:aad55ecb6a2321e46e249ae638c17380dcdac07a10e8f8845758a54171133856","observation_id":"8e5b8eae-dfa8-470f-a748-8b1d66b11e93","resolution":{"observed_at":"2026-08-07T15:36:30.196700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.10400","last_updated":"2020-10-05T03:49:48Z","snapshot_observed_at":"2026-08-06T02:35:15.497394Z","submitted_at":"2020-08-12T09:27:05Z","title":"An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.10400","snapshot_observed_at":"2026-08-07T15:36:30.288587Z","title":"An ensemble of simple convolutional neural network models for mnist digit recognition,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:30.288587Z"},"links":{"cited_paper":"/paper/2008.10400","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:ebcefb7da625774ef455ce7797366118096ccdcc2be33cdd2d873acb15578269","observation_id":"a3f900be-559b-43db-ac63-31120b7a52b2","resolution":{"observed_at":"2026-08-07T15:36:30.288587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1608.06037","last_updated":"2023-04-27T16:20:03Z","snapshot_observed_at":"2026-07-06T05:07:42.723236Z","submitted_at":"2016-08-22T02:50:57Z","title":"Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.06037","snapshot_observed_at":"2026-08-07T15:36:30.352557Z","title":"Lets keep it simple, using simple archi- tectures to outperform deeper and more complex archi- tectures,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:30.352557Z"},"links":{"cited_paper":"/paper/1608.06037","citing_paper":"/paper/2505.14789"},"observation_digest":"sha256:d5aa26b3aef74c9e6f1cb58e4885d5c77ec15612daf83905f5ce06db0fbcfa89","observation_id":"eb6f297c-b343-4ca0-94a5-3f0c57eea97a","resolution":{"observed_at":"2026-08-07T15:36:30.352557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.14789","last_updated":"2025-05-20T18:00:23Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-08T18:31:24.557427Z","submitted_at":"2025-05-20T18:00:23Z","title":"Solving MNIST with a globally trained Mixture of Quantum Experts"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":3,"verified_fuzzy":11},"total_outbound_references":47},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 3 inbound Pith citation observations for arXiv:2505.14789."}