{"as_of":"2026-08-14T09:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:110bc2808f79bb87664e7b7d6c633ae368cb61ccba8258e00e3bd765166c9140","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T13:46:18.478733Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2501.16243/citation-record","integrity":"/paper/2501.16243/integrity","json":"/paper/2501.16243/citation-record.json","paper":"/paper/2501.16243"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:46:18.215759Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.215759Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:f38e3517770366847856cd769736f250ff7dab1a7a367480d1729a6278927d01","observation_id":"d5c47d6e-8313-453a-b8bb-eeec269c7b5b","resolution":{"observed_at":"2026-08-10T13:46:18.215759Z","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":"2026-08-10T13:46:19.205384Z","title":"M., Lee, J","venue":null,"work_id":"82cb40f1-38c4-4c01-bfce-cc4936c78878","year":2020},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.224875Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:67290a211b28a73396c42ea9d19669997fb17b2aae94734f56119dabc1a9e3a7","observation_id":"b3bac0a4-8e15-4bbc-afcb-0e318e538da9","resolution":{"observed_at":"2026-08-10T13:46:19.210346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.229752Z","title":"M., Lee, J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.229752Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:94f71927bef0e53e66729c4c10c63666991e3f136fadfb3c11721af1fbfba03a","observation_id":"d5644cc9-c969-4dfc-b088-f3ea306a04b7","resolution":{"observed_at":"2026-08-10T13:46:18.229752Z","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":"2026-08-10T13:46:19.175068Z","title":"O., Ghosh, A., and Aggarwal, V","venue":null,"work_id":"84774cd4-d25d-41ea-9d6d-329b297de45e","year":2019},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.239319Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:ab321f0c5f2d98c00b40080b04836a362b467e034f6294fa35bed563500d4cb2","observation_id":"404c4286-9241-4961-9680-cb5a3068ef76","resolution":{"observed_at":"2026-08-10T13:46:19.181149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.155717Z","title":"S., and Aggarwal, V","venue":null,"work_id":"cfa4ea17-3c9d-4486-9afd-6a207cb9995f","year":2023},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.246837Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:37ffd61857b792112310e60b6a8268fc11134be72fddf0dcb1639d470b4964d9","observation_id":"5e3f83a2-33c7-4a4b-a3f6-d465de853708","resolution":{"observed_at":"2026-08-10T13:46:19.164653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.139593Z","title":"and Bartlett, P","venue":null,"work_id":"0e135c31-7c4b-4474-a1ec-b260768978d5","year":2001},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.252922Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:1f0a916420e43c9d182dd7535ec0344997b9f8dee69102c9dfe54ff194c49dbe","observation_id":"eae5daed-6d60-45a3-a5b5-47cce0fe7420","resolution":{"observed_at":"2026-08-10T13:46:19.144290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.122415Z","title":"Quantum amplitude amplification and estimation","venue":null,"work_id":"7aea1823-1e27-48bd-a6da-9ef561974d59","year":2002},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.259481Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:fb323cd93902ae2b7649029bfa194812eb8b50f659ebd60e20f59f0e07164d12","observation_id":"295ac583-0f95-4ba7-a756-9157879c044b","resolution":{"observed_at":"2026-08-10T13:46:19.128801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.107824Z","title":"Quantum bandits","venue":null,"work_id":"25d187f0-b750-43f9-9b39-7033bb975c4e","year":2020},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.268260Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:d6329332bf0208bb6c061a949694ee59a3287656a1710e47e996daa844ab12bb","observation_id":"8ff98503-963e-451f-a515-6cddcc0f908b","resolution":{"observed_at":"2026-08-10T13:46:19.112194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.094471Z","title":"Near-optimal quantum algorithms for multivariate mean estimation","venue":null,"work_id":"45f60c8f-1606-432d-879d-5be2cfb6d80e","year":2022},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.272436Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:2f789f00e16f702a26834e29bd680c336663b041730729819e275efb2c7a6eeb","observation_id":"1c6a3bc6-aa7c-421c-b385-1d94ec1b3ea6","resolution":{"observed_at":"2026-08-10T13:46:19.098582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.074306Z","title":"Quantum reinforcement learning","venue":null,"work_id":"6c973f76-ae4d-49d6-8c60-aa7b1968dc2f","year":2008},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.278160Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:3d69987a039d54e3c2e7b77316fab78ddc569e4e4cf3a39cbeb664d5466a3550","observation_id":"42d025c6-641b-4aab-88d8-8ce07d15268f","resolution":{"observed_at":"2026-08-10T13:46:19.081128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.060900Z","title":"M., and Briegel, H","venue":null,"work_id":"f09fbaf1-b8bf-49de-b58e-b3a1d371e5ea","year":2017},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.284978Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:31e09b92a6b67191dc5f5c1ec25253cec37ce4ca63c83be6933645b2a9c74ce7","observation_id":"29ae8188-d93b-4a9d-9aa9-99d01a78ee95","resolution":{"observed_at":"2026-08-10T13:46:19.065463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.047286Z","title":"U., and Aggarwal, V","venue":null,"work_id":"54b768b0-6573-45be-b9ea-e0088cd5c681","year":2025},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.293604Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:238a2263916f3feb5c4709ec5910df9f76af93bc17f73b893f16130d52849bee","observation_id":"2804b376-a235-49a1-8611-766ad7ef6cfd","resolution":{"observed_at":"2026-08-10T13:46:19.051579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11684","last_updated":"2025-05-26T21:50:05Z","snapshot_observed_at":"2026-08-13T05:47:10.272053Z","submitted_at":"2023-10-18T03:17:51Z","title":"Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes","version":4},"cited_work":{"arxiv_id":"2310.11684","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.11684","snapshot_observed_at":"2026-08-10T13:46:18.604898Z","title":"Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes","venue":"cs.LG","work_id":"5cff588c-8de1-4a28-81f7-f2c816fc4154","year":2023},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.300757Z"},"links":{"cited_paper":"/paper/2310.11684","citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:be6760238b04fdf4e26b9389fcbd52e99b7b785dd42ce87158de43bae7b489a8","observation_id":"7908f74e-771a-434b-b495-62b6e1fea24f","resolution":{"observed_at":"2026-08-10T13:46:18.612116Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01843","last_updated":"2024-12-09T06:38:53Z","snapshot_observed_at":"2026-08-13T00:15:41.632861Z","submitted_at":"2024-05-03T04:26:03Z","title":"Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization","version":5},"cited_work":{"arxiv_id":"2405.01843","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.01843","snapshot_observed_at":"2026-08-10T13:46:18.575630Z","title":"Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization","venue":"cs.LG","work_id":"8f9d646f-00b8-4b45-8e2b-d3466203fda2","year":2024},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.307029Z"},"links":{"cited_paper":"/paper/2405.01843","citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:102436b7ec94db9369e4c74039fd7df3e594c70028dd0c1b030f50474dc191b1","observation_id":"fbfe318f-bf57-4fa4-83c3-f38695fd0e7e","resolution":{"observed_at":"2026-08-10T13:46:18.585626Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.033681Z","title":"W., McKee, J., Hager, G., Aggarwal, V., Xue, Y., et al","venue":null,"work_id":"4211cd5a-c066-4c02-a736-f2dc457ee4a2","year":2023},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.312183Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:9d65c041575e5193118c4bc9843c911412349c916a7814bd7fa2e79aeea3516e","observation_id":"e809ed3c-b9df-43c0-8d24-7115510e088e","resolution":{"observed_at":"2026-08-10T13:46:19.038666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"quant-ph/0208112","last_updated":"2002-08-15T23:00:04Z","snapshot_observed_at":"2026-08-11T09:53:13.384216Z","submitted_at":"2002-08-15T23:00:04Z","title":"Creating superpositions that correspond to efficiently integrable probability distributions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"quant-ph/0208112","snapshot_observed_at":"2026-08-10T13:46:18.316908Z","title":"and Rudolph, T","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.316908Z"},"links":{"cited_paper":"/paper/quant-ph/0208112","citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:e302e290021dfb92d702a1dfe238f4e47253edb49d2b8605fe75569a0b9eefd3","observation_id":"0ca16b0f-4ca2-4bca-80c6-b72b11b7450a","resolution":{"observed_at":"2026-08-10T13:46:18.316908Z","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":"2026-08-10T13:46:19.020539Z","title":null,"venue":null,"work_id":"fd4812b3-113e-4da4-8247-a97b7f1e5dba","year":1996},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.322596Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:42402022f9bd0e453a20f9f2067662a3d0510dcb7f8e7e7f7f04709b57544ed7","observation_id":"3f0248a9-9bd2-4ccd-a55e-be0dd5d58721","resolution":{"observed_at":"2026-08-10T13:46:19.024795Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:19.006626Z","title":"Quantum sub-gaussian mean estimator","venue":null,"work_id":"e073247a-bbeb-434d-80ed-f2402abd3e1c","year":2021},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.327652Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:ce4b8afa257c497f4609bfaad0ad6d3beddca3d46cd5f2d8eb7e4b32441962fc","observation_id":"bb14b24d-8db1-4452-b270-4cda4d1ff195","resolution":{"observed_at":"2026-08-10T13:46:19.011973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.990774Z","title":"M., Nautrup, H","venue":null,"work_id":"25195794-ae78-4459-973d-ffa4cca8fab0","year":2021},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.335320Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:98171b5d27a18700eb4070100a5ffcc1e379df23eaa060cf88eefc558170e5de","observation_id":"f4d6e5a6-e21d-4b91-bc81-50bef315d43e","resolution":{"observed_at":"2026-08-10T13:46:18.995776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.971897Z","title":"Quantum policy gradient algorithms","venue":null,"work_id":"f1d776ec-83fc-42e6-bc0d-ca2e00dcba1d","year":2023},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.343763Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:6694de24d23ad4eed3cc78f8853bfa2c73628ef8f8c17b7c5bfb58513adae0ff","observation_id":"5bd51ae5-de70-4a37-ae1e-7f14e0c2afcf","resolution":{"observed_at":"2026-08-10T13:46:18.977086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.953439Z","title":"An improved analysis of (variance-reduced) policy gradient and natural policy gradient methods","venue":null,"work_id":"26428c1d-0c66-4866-8be1-470e6abe694f","year":2020},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.348891Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:c2940a732f9be770e86e60a06cbc53f1026f3cd0f9a487c94526e1192d081102","observation_id":"c638e435-f701-432f-82f8-c55855952424","resolution":{"observed_at":"2026-08-10T13:46:18.958390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.938914Z","title":null,"venue":null,"work_id":"133ce704-767f-4584-847d-9e2796f0be36","year":2024},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.353524Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:b634a0eed390d5ef0e4accf0a4f178c16258c0a3dda9df27cf727296b0fa250e","observation_id":"616994db-99d3-4fee-937c-7bb69aa6d41c","resolution":{"observed_at":"2026-08-10T13:46:18.943746Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.923328Z","title":null,"venue":null,"work_id":"fe81844b-b998-42f2-af6e-6436ca012a51","year":2024},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.358403Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:84fd3bba5f02316e02c162a2a90cb5124492c028152f3db00ab8782af7f3595f","observation_id":"5aa06c5d-564d-43e8-98a4-9d9e0293dbd1","resolution":{"observed_at":"2026-08-10T13:46:18.928785Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.905772Z","title":"Quantum speedup of monte carlo methods","venue":null,"work_id":"4dcc70d0-00f6-4720-bab4-283899a0d782","year":2015},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.363493Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:4abb426f7dad2ffdfbfe94531747ef468b3d239ba6f0b51ea996cabdac8651e6","observation_id":"464cca96-2515-465b-a734-b42accbcff75","resolution":{"observed_at":"2026-08-10T13:46:18.912762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.890330Z","title":null,"venue":null,"work_id":"0f9f786d-8761-47c8-a941-a8b4333de853","year":2010},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.372034Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:7fd94943c0e72051485c46c112e7c5557ddd04de70df15408eaa34974c659d07","observation_id":"dc005ce3-fa30-45ea-9713-160264ed25b5","resolution":{"observed_at":"2026-08-10T13:46:18.895794Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.865260Z","title":"D., Dunjko, V., Makmal, A., Martin-Delgado, M","venue":null,"work_id":"3a47fa32-8f9e-4be2-8e6e-61e4b0143909","year":2014},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.377440Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:40c6cd814dec634833143f8a7f116966a8c7910aac91a8d444417e63aab9d646","observation_id":"fd4dcedf-32e2-4882-9d3b-2bc03a2e6e66","resolution":{"observed_at":"2026-08-10T13:46:18.875527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.848518Z","title":"and Schaal, S","venue":null,"work_id":"b6ba3626-a860-480f-991a-ed97e8c0bdb0","year":2008},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.383233Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:9b49cdaa57814c8ccc5b14570e479ee169e363fa64c7474a31528a28607e9c51","observation_id":"37b96099-8973-4966-bb60-033671711ab3","resolution":{"observed_at":"2026-08-10T13:46:18.854424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.831684Z","title":"and Zhang, C","venue":null,"work_id":"0b4c7f36-095b-474e-aa2c-9c308f0d19ce","year":2024},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.391575Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:c6cb2b74529d161b0011d7b403e15550793e6806759079598e6d2dd7032f2be8","observation_id":"03cdfe46-0ac6-442a-a251-6626bbf78ce6","resolution":{"observed_at":"2026-08-10T13:46:18.836909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.402908Z","title":"S., McAllester, D., Singh, S., and Mansour, Y","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.402908Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:bc20c2c3b71e78ed16aa1dcee9693a9f01dd7f3ad90b5b9789930083d5778b65","observation_id":"dc57a5e3-fb26-4deb-a1d8-291de3636411","resolution":{"observed_at":"2026-08-10T13:46:18.402908Z","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":"2026-08-10T13:46:18.802543Z","title":"P., Yu, D., and Aggarwal, V","venue":null,"work_id":"89fd6b82-ff63-4827-9890-8394db841f22","year":2024},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.414680Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:0521f85594374f9a973e12670c1267aa128a6fffcefac9aea209826067f7b133","observation_id":"57bb01d1-fb2a-4db3-955a-19f79b5b57eb","resolution":{"observed_at":"2026-08-10T13:46:18.806835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.774408Z","title":"Quantum multi-armed bandits and stochastic linear bandits enjoy logarithmic regrets","venue":null,"work_id":"b5fd0964-ee7b-4aca-9a2a-680514ea25a8","year":2023},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.420274Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:cddf9d24b94a286dbc1afdd1a9b944dd59589ba5ed4b93c9fba0215552393ae6","observation_id":"b4f3d51a-cdd0-4a4a-bfff-d2d85d6c700e","resolution":{"observed_at":"2026-08-10T13:46:18.782423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.757281Z","title":"Quantum algorithms for reinforcement learning with a generative model","venue":null,"work_id":"5fa2dcaf-29c5-46ae-a362-0a635999e13d","year":2021},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.426122Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:17af3c520bce3a84bb509ca7f386f2bdb3c6ef22e69fd649ecc6b14ad5b54de8","observation_id":"43d65252-0114-4900-8a7c-40cfe863b8df","resolution":{"observed_at":"2026-08-10T13:46:18.762175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.738159Z","title":null,"venue":null,"work_id":"202d2d9f-abbc-403f-a930-8fd5fc5fc765","year":2021},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.431041Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:7df920441da005f0ccd3658df69b8d9b674d3b3a6b0c034bebfeee3964c07cbe","observation_id":"10245b49-5810-47be-bc17-fb94c94ce411","resolution":{"observed_at":"2026-08-10T13:46:18.742582Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.722560Z","title":null,"venue":null,"work_id":"91922282-782d-49c4-b6e6-05deaa92216c","year":2023},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.438795Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:c13a01a14a26a6ad663e1d8ff0a6b3200dee842ac3a935cde751d7cda25d7fcc","observation_id":"d52d2c68-c7ed-466a-a406-97e7aab9e1c3","resolution":{"observed_at":"2026-08-10T13:46:18.728065Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.708470Z","title":"Neural policy gradient methods: Global optimality and rates of convergence","venue":null,"work_id":"6dd6df7f-667b-4954-a769-681e86a45ce1","year":2019},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.451578Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:443e8387591f1f540daa8c7c0ff5d5cdb92245acf275e015f9c46896ec4b567d","observation_id":"7ebd41d6-923e-4a01-80a2-be19c57d5aeb","resolution":{"observed_at":"2026-08-10T13:46:18.712904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.688671Z","title":null,"venue":null,"work_id":"8f46bebe-202b-4827-8957-d7f64b5baf73","year":2022},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.456742Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:eb4ab8500e6aa882b74de570955d9f8456d84cda760617de9a41499a64686318","observation_id":"482320dd-70dc-4598-a2ab-a5a0f36a0a8b","resolution":{"observed_at":"2026-08-10T13:46:18.696134Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.09680","last_updated":"2023-01-23T19:23:10Z","snapshot_observed_at":"2026-08-13T12:59:23.735616Z","submitted_at":"2023-01-23T19:23:10Z","title":"Quantum Heavy-tailed Bandits","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.09680","snapshot_observed_at":"2026-08-10T13:46:18.462321Z","title":"Quantum heavy-tailed bandits","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.462321Z"},"links":{"cited_paper":"/paper/2301.09680","citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:6363d3ffad3a27f57242467b5b8d5d843e321999aa0495541a74a3764b00f92e","observation_id":"91445b63-2d3b-43f9-9f36-fff9470504b6","resolution":{"observed_at":"2026-08-10T13:46:18.462321Z","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":"2026-08-10T13:46:18.667072Z","title":"Sample efficient policy gradient methods with recursive variance reduction","venue":null,"work_id":"15611d55-dbf6-4708-b69f-7282134b8baf","year":2019},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.468856Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:0723edc38ab0ce3e95e55eb95ca4aca3076acaa825abfd59025ca9b9eeb929ae","observation_id":"d48dea98-520a-49a5-863c-f933ca4cd7a1","resolution":{"observed_at":"2026-08-10T13:46:18.674584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T13:46:18.473418Z","title":"Global convergence of policy gradient methods to (almost) locally optimal policies","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.473418Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:aa13e03bac3f6f68bc79e01ae443bc7c0e6756b9dbd2e30aa85042ac8ec1f7cf","observation_id":"4f87009a-74de-4ab1-904f-e5df910b84e8","resolution":{"observed_at":"2026-08-10T13:46:18.473418Z","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":"2026-08-10T13:46:18.625962Z","title":"Provably efficient exploration in quantum reinforcement learning with logarithmic worst-case regret","venue":null,"work_id":"553a3b1c-e851-41cc-8526-8eeeb86cc953","year":2024},"citing_paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T13:46:18.478733Z"},"links":{"citing_paper":"/paper/2501.16243"},"observation_digest":"sha256:32095fb62c804ad7a57b0f9f1b866791b34b41ef2432e1661eb2fb75408528f4","observation_id":"28716c83-1b52-46a4-ad54-ee3718a94147","resolution":{"observed_at":"2026-08-10T13:46:18.635523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.16243","last_updated":"2025-06-30T23:59:00Z","latest_version":3,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-10T13:34:10.753188Z","submitted_at":"2025-01-27T17:38:30Z","title":"Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":13,"verified_exact":1,"verified_fuzzy":25},"total_outbound_references":40},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2501.16243."}