{"as_of":"2026-08-16T08:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0f7fa7ba7e2c87d572c6d88e8d8ffe27b6ee7f4e92ebb42390b57adfcd26cc84","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:20:18.133146Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2608.11396/citation-record","integrity":"/paper/2608.11396/integrity","json":"/paper/2608.11396/citation-record.json","paper":"/paper/2608.11396"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:17.856788Z","title":"A variational eigenvalue solver on a photonic quantum processor","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.856788Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:4c236be4249fb29fb1221499a2ff31fbe63c9dcc00ee9a93ac69335acb13790b","observation_id":"aecb6ed7-3a9f-4970-9b5c-a5a009a4280e","resolution":{"observed_at":"2026-08-15T14:20:17.856788Z","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":"2026-08-15T14:20:17.862192Z","title":"Quantum computing in the NISQ era and beyond","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.862192Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:1a5168a11c2dac69c8f47243661f57319aeeb41981cf08eb8ca9858f925d7b97","observation_id":"27fc392e-d935-41de-ba7c-1c8103ce24fd","resolution":{"observed_at":"2026-08-15T14:20:17.862192Z","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":"2026-08-15T14:20:17.867049Z","title":"The variational quantum eigensolver: A review of methods and best practices","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.867049Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:b7b555bc9dc60e2f591659b9fef7cd3a2d00452ec6803550308bdf78be8af652","observation_id":"db8e2914-4173-4954-bf84-ac676c976d13","resolution":{"observed_at":"2026-08-15T14:20:17.867049Z","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":"2026-08-15T14:20:17.872852Z","title":"Quantum Measurement for Quantum Chemistry on a Quantum Computer","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.872852Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:4224a174eccd4872e3b6f53346612d672afc2fb3a2ceead2944923aa08d8aac0","observation_id":"b8451c96-c61d-463b-9e01-8176690e2ed6","resolution":{"observed_at":"2026-08-15T14:20:17.872852Z","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":"2026-08-15T14:20:17.877828Z","title":"Learning many-body Hamiltonians with Heisenberg-limited scaling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.877828Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:9b16afae39f87140975547d1d60a00f00e266e9b99a8b990b47796e25ca38b7a","observation_id":"2c655757-25cf-415f-aa3d-287bd03bed7a","resolution":{"observed_at":"2026-08-15T14:20:17.877828Z","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":"2026-08-15T14:20:17.882665Z","title":"Predicting many properties of a quantum system from very few measurements","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.882665Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:385c2197f7d1ed08389f29278f0297768fa6ce8c8958fc4435bd76871ad66311","observation_id":"82398365-146f-40ac-8f0d-6f9a457ddecf","resolution":{"observed_at":"2026-08-15T14:20:17.882665Z","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":"2026-08-15T14:20:17.887875Z","title":"Theoretical and experimental perspectives of quantum verification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.887875Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:f551bf4d6457da518951994261739522c3e5f1e2eb73663c43d6ef96546f0b72","observation_id":"8c1e97ba-8627-4347-8adf-dcd5c70417c4","resolution":{"observed_at":"2026-08-15T14:20:17.887875Z","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":"2026-08-15T14:20:17.893274Z","title":"Progress towards practical quantum variational algorithms","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.893274Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:70112d292472b441279562c4af2519311441cff1291cc9f874bd569dfbe39ef7","observation_id":"3f2d2d88-15a3-4301-8746-2e776db27940","resolution":{"observed_at":"2026-08-15T14:20:17.893274Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:17.897710Z","title":"Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.897710Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:ee3679477ad5b242ba1fa98a1845373911fdf7a54e93c35ebc0554e70b6eadf4","observation_id":"94b96e77-eb6c-4dcf-99fa-b43df5669208","resolution":{"observed_at":"2026-08-15T14:20:17.897710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1103/physrevx.10.031064","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:18.563414Z","title":"Nearly optimal measurement scheduling for partial tomography of quantum states","venue":null,"work_id":"178a2a5f-34d4-4416-bfbc-5b797d94e436","year":2020},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.902827Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:e055e49c13d0f61cd2ed6edb85b2a11fd4d41e0650200342e1679426e0c6ccc5","observation_id":"b02321f7-c409-44d0-b9fb-d8a46c5208c1","resolution":{"observed_at":"2026-08-15T14:20:18.568399Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00220-022-04343-8","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:18.549012Z","title":"Measurements of quantum Hamiltonians with locally-biased classical shadows","venue":null,"work_id":"4d811745-7590-47d7-b585-341466405287","year":2022},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.907371Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:b9707f851c8b49b132f9d94a2c8c31f78b8c7feb680aed6f401df6ec606c091f","observation_id":"87005a41-3196-446b-84fc-424096f8acd5","resolution":{"observed_at":"2026-08-15T14:20:18.553649Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.239666Z","title":"Efficient estimation of Pauli observables by derandomization","venue":null,"work_id":"4ab71dcc-a30f-4600-89ef-c09c1aeea223","year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.912391Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:97816612f40b29eb4e1405f189328c10f53674f0b0e28cac2dcda4a52eb5fc45","observation_id":"52507e2c-232d-4f42-b85e-72b5a95bfcbd","resolution":{"observed_at":"2026-08-15T14:20:19.244304Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.22331/q-2023-01-13-896","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:18.533370Z","title":"Overlapped grouping measurement: A unified framework for measuring quantum states","venue":null,"work_id":"6a577de3-7c38-4799-be6d-0f14a7e109f2","year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.916909Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:01adb1ba58a8c909e675754aa9d8ced3537ae5a43bc390464ea0a12a31f93485","observation_id":"42463ee1-3889-4d20-96e6-1039692d0676","resolution":{"observed_at":"2026-08-15T14:20:18.538859Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:17.922577Z","title":"Measurement optimization in the variational quantum eigensolver using a minimum clique cover","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.922577Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:412f697dc4e5639975c226472e23dc3f984c3e4134bcf6e9e8cca0cf18b292c6","observation_id":"1b83b0ef-9624-4079-9b3b-b13a81186922","resolution":{"observed_at":"2026-08-15T14:20:17.922577Z","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":"2026-08-15T14:20:17.927105Z","title":"Unitary partitioning approach to the measurement problem in the variational quantum eigensolver method","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.927105Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:4348d9fde4e1c6ca88cb69c3612447f837c1425ee62b752415e93165810613f6","observation_id":"0d5d3092-5611-4296-a486-396a356a9a48","resolution":{"observed_at":"2026-08-15T14:20:17.927105Z","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":"2026-08-15T14:20:17.931529Z","title":"Measuring all compatible operators in one series of single-qubit measurements using unitary transformations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.931529Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:d933a37e0993cd52e4d76e89c2af81ceea4783ee5f76abcf9406a918f69444e7","observation_id":"743f8f55-37d9-4f39-b1e1-4c5fe1610a3c","resolution":{"observed_at":"2026-08-15T14:20:17.931529Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:17.936853Z","title":"Measurement reduction in variational quantum algorithms","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.936853Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:3e6332562481f6e02afe2fa0ce1987a3f0c45a7b0862716dcf5e169a97e8c784","observation_id":"d67e8006-dc7c-4929-b74d-6ac80b641947","resolution":{"observed_at":"2026-08-15T14:20:17.936853Z","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":"2026-08-15T14:20:17.941968Z","title":"Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.941968Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:fc84e2a14b56a799cca6105b0ae80b4be449b6c1de13046e982f0c79ab29047f","observation_id":"c1cc8050-571b-4ee7-a619-6233a9cb5397","resolution":{"observed_at":"2026-08-15T14:20:17.941968Z","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":"2026-08-15T14:20:17.946857Z","title":"Cartan subalgebra approach to efficient measurements of quantum observables","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.946857Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:35f2fcf5f762041edce64cbdc543e864e4389e1a25d68fc0294d68bb665bf038","observation_id":"34d7a0fe-4a29-4e2e-9406-8e1c68e14683","resolution":{"observed_at":"2026-08-15T14:20:17.946857Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:17.951915Z","title":"Efficient quantum measurement of Pauli operators in the presence of finite sampling error","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.951915Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:086c41864f873da52c5f68abd6dc5caba671fc5c759ac20cd018cee7e94d37ce","observation_id":"c6424574-cfab-42ca-af82-5fa567109995","resolution":{"observed_at":"2026-08-15T14:20:17.951915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"quant-ph/9807006","last_updated":"1998-07-01T19:34:39Z","snapshot_observed_at":"2026-08-13T13:22:45.686549Z","submitted_at":"1998-07-01T19:34:39Z","title":"The Heisenberg Representation of Quantum Computers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"quant-ph/9807006","snapshot_observed_at":"2026-08-15T14:20:17.956762Z","title":"1998.doi: 10","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.956762Z"},"links":{"cited_paper":"/paper/quant-ph/9807006","citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:a65da2269a92e8e8aa1bce43decfa31e59a162e6b8c054723fd613087a9da403","observation_id":"8e4633d5-2a37-4dc1-81e8-42bd4179cf44","resolution":{"observed_at":"2026-08-15T14:20:17.956762Z","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":"2026-08-15T14:20:17.962193Z","title":"Improved simulation of stabilizer circuits","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.962193Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:6cb17da43bce229d7309a413a906686407310d857fa2c63be94fe9194d91cb5a","observation_id":"349c47d6-d72c-4739-9924-d09ad887338d","resolution":{"observed_at":"2026-08-15T14:20:17.962193Z","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":"2021.30814","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:18.893538Z","title":"Hadamard-free circuits expose the structure of the Clifford group","venue":null,"work_id":"5b74003f-91d8-44a1-8b28-605f6fe87c27","year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.966762Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:9e7d3ebf3d4383321adc8af7676a407c15c2f1b479612d73b5a6b2410c998241","observation_id":"56fc3c86-0398-44ad-b759-dbb6e91de064","resolution":{"observed_at":"2026-08-15T14:20:18.901169Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:17.971083Z","title":"The randomized measurement toolbox","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.971083Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:13d95d1a7526290ce54fff51279fa4534beb2afa2b15bc3ad4711992cac30832","observation_id":"fc635875-4456-4c6d-874a-0ed6026f4531","resolution":{"observed_at":"2026-08-15T14:20:17.971083Z","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":"2026-08-15T14:20:17.975538Z","title":"Learning to measure: Adaptive informationally complete generalized measurements for quantum algorithms","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.975538Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:312b76c0bd24e4951fe372492c14aded3b541b538e001d55f2f110bdbd0b503a","observation_id":"b8d1ecd3-febd-4a3c-8a84-36794a923d2b","resolution":{"observed_at":"2026-08-15T14:20:17.975538Z","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":"2026-08-15T14:20:19.225094Z","title":"Fermionic partial tomography via classical shadows","venue":null,"work_id":"12d3f04c-e711-4e5e-bae0-40f3c8f8e4f4","year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.980641Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:f61993364f7948f0fa476705ca64a5d94f1723e9fd04b36c2d59a18722d90dda","observation_id":"dfcd4cee-ea06-4fca-ad3d-6e45f624cd50","resolution":{"observed_at":"2026-08-15T14:20:19.230023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.12924","last_updated":"2024-12-20T10:17:36Z","snapshot_observed_at":"2026-08-13T16:55:30.125313Z","submitted_at":"2022-09-26T18:01:19Z","title":"Shallow shadows: Expectation estimation using low-depth random Clifford circuits","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.12924","snapshot_observed_at":"2026-08-15T14:20:17.984594Z","title":"Shallow shadows: Expectation estimation using low-depth random Clifford circuits","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.984594Z"},"links":{"cited_paper":"/paper/2209.12924","citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:a90f1629d7415e8e7b4fa9abb1ee19c1396a9a4ede748fb9a41d4a3f82638b23","observation_id":"78a4f974-e89b-4692-b941-7a064f90eac6","resolution":{"observed_at":"2026-08-15T14:20:17.984594Z","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":"2026-08-15T14:20:17.989502Z","title":"Operator relaxation and the optimal depth of classical shadows","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.989502Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:8f1045ff670cc582ad8e761a45759c2e683e872a44f103c117e625df2a2cc96c","observation_id":"bd753223-3480-4681-a002-5750987a7098","resolution":{"observed_at":"2026-08-15T14:20:17.989502Z","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":"2026-08-15T14:20:17.994406Z","title":"Classical shadow tomography with locally scrambled quantum dynamics","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.994406Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:5dfaa7b021735e39eaae5673c03a9c4b015915a8b5ddb711eb8a1210327434b5","observation_id":"45611d22-fc83-475f-b666-aaebc150030f","resolution":{"observed_at":"2026-08-15T14:20:17.994406Z","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":"2026-08-15T14:20:17.998684Z","title":"Scalable and flexible classical shadow tomography with tensor networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:17.998684Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:b515739c836c4ebcc19804d94826c796dc03e489e162d4f9e1191a7d1827569e","observation_id":"e68885fa-3e6f-40b2-934e-1fcea78f0a34","resolution":{"observed_at":"2026-08-15T14:20:17.998684Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:18.003117Z","title":"Demonstration of robust and efficient quantum property learning with shallow shadows","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.003117Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:4765eefc10b3aabb2556eae2a91cb973123ee5f97703995ab76656841a569f53","observation_id":"8227ad5f-f1ab-4fd4-8e16-ca7fb4d2a217","resolution":{"observed_at":"2026-08-15T14:20:18.003117Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.18973","last_updated":"2024-12-25T19:23:29Z","snapshot_observed_at":"2026-08-11T00:55:38.305038Z","submitted_at":"2024-12-25T19:23:29Z","title":"Derandomized shallow shadows: Efficient Pauli learning with bounded-depth circuits","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.18973","snapshot_observed_at":"2026-08-15T14:20:18.007635Z","title":"2024.doi:10.48550/arXiv.2412.18973","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.007635Z"},"links":{"cited_paper":"/paper/2412.18973","citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:34e02768704f0b59384306e879cae12f1b22734abfed3c6db688a6f1da46b01e","observation_id":"b30f1b1e-64d5-4d42-9a5b-0a5fe5c7a3eb","resolution":{"observed_at":"2026-08-15T14:20:18.007635Z","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":"2026-08-15T14:20:19.209477Z","title":"Flow network based generative models for non-iterative diverse candidate generation","venue":null,"work_id":"814ed06d-70c0-4a72-82dd-fd7231dff943","year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.013459Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:ef5a4372b6287ba060fc2d112b1f6f49893357e6a19bc3ff9696a6051285c593","observation_id":"013cc913-75a5-4b79-83f1-9024cba724f0","resolution":{"observed_at":"2026-08-15T14:20:19.214405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.195085Z","title":"Trajectory balance: Improved credit assignment in GFlowNets","venue":null,"work_id":"0921c795-c935-4bdd-9580-b3586a266b90","year":2022},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.017856Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:b7baaa51f7a496db60d1f854b307a1d80052ecc8d9797c20fa4ed54179e7b7f7","observation_id":"cbe18354-2a52-4805-962e-a0aa05b534ad","resolution":{"observed_at":"2026-08-15T14:20:19.199717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.181126Z","title":"GFlowNet foundations","venue":null,"work_id":"e144be37-970f-44e5-a567-82906e49d2cc","year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.022469Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:cb156fee2ed2d477b535439c19f69a84be78b99495c0ae42e9fd1e0a50f1ddce","observation_id":"018c4559-f311-4e61-8262-a61914d8e430","resolution":{"observed_at":"2026-08-15T14:20:19.185621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.166640Z","title":"Sutton and Andrew G","venue":null,"work_id":"1255ddb0-687c-4cf4-aee5-a74a21315da8","year":2018},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.027515Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:69e094416a95c9be3348e1967076dc7157f06135a456d5153351a0a1c83e5363","observation_id":"cb657890-ea27-44bb-9c42-c9fd1e7a1af0","resolution":{"observed_at":"2026-08-15T14:20:19.171564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.152533Z","title":"Biological sequence design with GFlowNets","venue":null,"work_id":"936bb266-997d-472e-93a8-7ec0d33f5208","year":2022},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.032062Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:32794400ec944cc3b859d451cd9bd02b0a94ebf8002e857db2b1aef651d8baf0","observation_id":"1c5a061a-0bb0-489e-bbf2-e896a2663077","resolution":{"observed_at":"2026-08-15T14:20:19.157167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.137996Z","title":"Multi-objective GFlowNets","venue":null,"work_id":"bde0e102-a1ab-480e-9041-6f18ba4533d1","year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.036644Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:005cc8ff3000d7504b6835e1993360de5f3aafaa8d060660f4f8aad37900905c","observation_id":"5e0fd4cf-e067-430b-9499-e4e54918b95e","resolution":{"observed_at":"2026-08-15T14:20:19.142624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.122847Z","title":"Let the flows tell: Solving graph combinatorial problems with GFlowNets","venue":null,"work_id":"0052129f-207f-4761-a0d2-daf624ade275","year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.041497Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:a77d5a9d9ca9747895db19a094fef485e6c4883191a12d72eb9581c434e37de4","observation_id":"c12b80b7-30a0-42e7-a0eb-3afa32cc729c","resolution":{"observed_at":"2026-08-15T14:20:19.127785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.05446","last_updated":"2023-02-14T10:19:50Z","snapshot_observed_at":"2026-08-13T13:02:59.423476Z","submitted_at":"2023-01-17T18:59:15Z","title":"Robust Scheduling with GFlowNets","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.05446","snapshot_observed_at":"2026-08-15T14:20:18.046416Z","title":"Robust scheduling with GFlowNets","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.046416Z"},"links":{"cited_paper":"/paper/2302.05446","citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:90a3d14deef33d3c5d82db629aafbe3bfcc7b474af201e78c49f3da9eabf76ee","observation_id":"8ee362c0-684b-477a-823b-9355f92aba87","resolution":{"observed_at":"2026-08-15T14:20:18.046416Z","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":"2026-08-15T14:20:19.108053Z","title":"Bayesian structure learning with generative flow networks","venue":null,"work_id":"c25a3da1-bca4-4adf-a0db-01bd1fc3e656","year":2022},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.051670Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:b9f88a358acb925b5cf9d22ff9391dedc8afa28f7b5f9e69420985fd1b9a4cc3","observation_id":"3d868dac-ad89-4bcf-b031-cbe8e14ba0ef","resolution":{"observed_at":"2026-08-15T14:20:19.113069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.092263Z","title":"Generative flow networks for discrete probabilistic modeling","venue":null,"work_id":"bce0f4ef-0ced-436c-a986-eed57f31af0f","year":2022},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.056270Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:2c19442136fd5a17497b72475c0e075071c9536acbeb26e124a69f28ad0a5717","observation_id":"fb0ef88a-3936-41c3-a077-ae3c05bbe1d6","resolution":{"observed_at":"2026-08-15T14:20:19.097982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.16041","last_updated":"2024-10-21T14:14:29Z","snapshot_observed_at":"2026-08-14T22:18:11.590322Z","submitted_at":"2024-10-21T14:14:29Z","title":"GFlowNets for Hamiltonian decomposition in groups of compatible operators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.16041","snapshot_observed_at":"2026-08-15T14:20:18.060773Z","title":"Huidobro-Meezs et al.GFlowNets for Hamiltonian decomposition in groups of compatible operators","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.060773Z"},"links":{"cited_paper":"/paper/2410.16041","citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:decc28313923203c2d4ea23e20647270e3dc18f5058ce6743d454da6f7e9d5b9","observation_id":"70b2fbac-3c03-4a62-a3a2-2e44faa57d94","resolution":{"observed_at":"2026-08-15T14:20:18.060773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.48550/arxiv.2510.26688","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:18.338671Z","title":"2025.doi: 10","venue":null,"work_id":"f5ecfe11-c2de-4cd8-a5ef-ef5aa1e19a76","year":2025},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.066372Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:441689c5fddaf6f1034de640224ab751b703dbc401f4070410210f1f7d424732","observation_id":"0a2fe433-3aba-438d-8a68-983ad1e2d851","resolution":{"observed_at":"2026-08-15T14:20:18.346870Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:18.070648Z","title":"¨Uber das Paulische ¨Aquivalenzverbot","venue":null,"work_id":null,"year":1928},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.070648Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:90a037de73e8c19da9a3600bd283a955179589353299ee57fb9f1fcc08350cce","observation_id":"469b8f5c-42aa-4e6c-b646-d3982af58a0c","resolution":{"observed_at":"2026-08-15T14:20:18.070648Z","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":"2026-08-15T14:20:19.076909Z","title":"https://github","venue":null,"work_id":"fa7949f8-d1f2-48cb-85ec-1589a08c4058","year":2026},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.075856Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:d949d1bfb3b2886c868b1b2fbb665b3b761527838f4878124575433e080ff23f","observation_id":"ea3faec8-e1f7-48e3-b851-1352f7b8addb","resolution":{"observed_at":"2026-08-15T14:20:19.081920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:18.080322Z","title":"Compact fermion to qubit mappings","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.080322Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:6a40e6617757e6d3039060983c17434ab3bb9f72bad98e45a87184001dd4a2f1","observation_id":"c035da63-c469-4fd4-836a-0f683fc598a6","resolution":{"observed_at":"2026-08-15T14:20:18.080322Z","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":"2026-08-15T14:20:18.086058Z","title":"Scalable simulation of fermionic encoding performance on noisy quantum computers","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.086058Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:157a52aaea41197b026436e7c3ed97425f773c740fa373193a104e0177de86b4","observation_id":"35bcde4e-3a6b-4275-8f27-e7a3dbab00a3","resolution":{"observed_at":"2026-08-15T14:20:18.086058Z","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":"2026-08-15T14:20:18.090756Z","title":"Density matrix formulation for quantum renormalization groups","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.090756Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:e30bfd58784f59419a9d6373d9a97159e4f3da9f73aa38b82ff4220a4c5ed884","observation_id":"40c29cc9-d26b-4963-ba08-c911ffb7ae8e","resolution":{"observed_at":"2026-08-15T14:20:18.090756Z","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":"2026-08-15T14:20:18.096412Z","title":"The density-matrix renormalization group in the age of matrix product states","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.096412Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:87346343a22b60a5b233dfcf8ebbfb53adca8a222ed51fd36de62bb9effb18a0","observation_id":"955f136b-1fb6-4e24-8e32-400fdfb95b20","resolution":{"observed_at":"2026-08-15T14:20:18.096412Z","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":"2026-08-15T14:20:18.101130Z","title":"On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection","venue":null,"work_id":null,"year":1934},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.101130Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:d2ddbf1c508cac9e43631578d4ea9aef8e5e77474039565e0a97e7ac57fab386","observation_id":"f1219343-744e-4139-9caf-8093ae5c1785","resolution":{"observed_at":"2026-08-15T14:20:18.101130Z","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":"2026-08-15T14:20:18.105995Z","title":"Improving quantum measurements by introducing “ghost","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.105995Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:048d8fec951f85f83dac332a96ee223699fc295554af006ebe17b0de2a383728","observation_id":"35fc58b6-c38a-445e-af22-d5454e36fef4","resolution":{"observed_at":"2026-08-15T14:20:18.105995Z","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":"2026-08-15T14:20:18.110613Z","title":"Deterministic improvements of quantum measurements with grouping of compatible operators, non-local transformations, and covariance estimates","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.110613Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:f6e16f9b80f56ba9e54eaada6962c7d7c405db1cc250e0d80a07412807e10193","observation_id":"11937246-f17d-4298-8269-ecaf51953216","resolution":{"observed_at":"2026-08-15T14:20:18.110613Z","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":"2026-08-15T14:20:19.062007Z","title":null,"venue":null,"work_id":"3843c1d3-e665-42ee-94e2-04189c455e67","year":null},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.115109Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:d2041eb62350aa2dab4397808c009cfbea64cd06efd67114c5771d92815e9935","observation_id":"b0efe369-612c-499b-a30d-a73ac5f65643","resolution":{"observed_at":"2026-08-15T14:20:19.066577Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.047027Z","title":"Learning GFlowNets from partial episodes for improved convergence and stability","venue":null,"work_id":"52f36614-75d2-46dd-8458-fb2d81a26e9b","year":2023},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.124204Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:10c295e7d631c1f649cfcdab61383a557b90eb70ef02c7edb843c8f1f5071b9e","observation_id":"70e4ef3f-c9c2-4ac4-913f-978a58ec402b","resolution":{"observed_at":"2026-08-15T14:20:19.051919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.031627Z","title":"PyTorch: An imperative style, high-performance deep learning library","venue":null,"work_id":"91db9293-cb7d-4d6a-8232-a97c55af1e2e","year":2019},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.128673Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:4a216528d7292b4d9d354038f1b193bef55c5738024e1ea0c357a4e15ce7d7c2","observation_id":"3ed79d3e-d7a1-4331-8578-ba9d950ebe83","resolution":{"observed_at":"2026-08-15T14:20:19.036517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T14:20:19.015550Z","title":"CuPy: A NumPy-compatible library for NVIDIA GPU calculations","venue":null,"work_id":"d13aef17-94c0-488c-ba77-d54e820014d2","year":2017},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.133146Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:0ac48f14ea21fcdf9edbb6415b8e332dca7da18346ef6ab2eb4bc25bda0342d6","observation_id":"c83ed003-69c3-489f-8525-bd550b9a8874","resolution":{"observed_at":"2026-08-15T14:20:19.021299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.12782","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:20:18.716330Z","title":null,"venue":null,"work_id":"583f0bf7-9979-4f09-83da-984b9fe218f2","year":null},"citing_paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T14:20:18.119839Z"},"links":{"citing_paper":"/paper/2608.11396"},"observation_digest":"sha256:88d1f47500c664c551a64cfea1058f2918e52a0f55ccb5b5117b737d683a2254","observation_id":"ace2da30-ff9f-40e9-860c-ac6ca628b223","resolution":{"observed_at":"2026-08-15T14:20:18.724897Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.11396","last_updated":"2026-08-11T20:02:41Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-08-15T23:10:04.881123Z","submitted_at":"2026-08-11T20:02:41Z","title":"Generative Learning for Quantum Measurement Design"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":7,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":31,"verified_exact":5,"verified_fuzzy":14},"total_outbound_references":58},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2608.11396."}