{"as_of":"2026-08-22T17:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b794b7b67414ad5791aefcfe37c0ba55e315867a6eaf73bb6dd88131ea13a523","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T10:30:01.477176Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2606.22551/citation-record","integrity":"/paper/2606.22551/integrity","json":"/paper/2606.22551/citation-record.json","paper":"/paper/2606.22551"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:30:01.477176Z","title":"Biamonte, P","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:1c49ef9e1259b658dfbf0fb800f9937b52329cfaf3187a835a8b857f58b19fb3","observation_id":"ab9aad25-50c1-40e1-89e0-32eb61a6bfc6","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Benedetti, E","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:688f799ae743a3396a1e5968e95df53a9fa8b53d418efad2c107581cdf0eafc4","observation_id":"a4b4abe4-029f-4012-a263-cd1f3b876a87","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Abbas, D","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:f6bd662c29f510b92502e4a40cd9eb442bd3d82a9d8f3681e64f014d02b44f9d","observation_id":"454454af-6088-4365-aef9-257fe1a5116a","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:607baab522ac1980586459eaa799671729aea1b96192992565ba5e287a657f05","observation_id":"ab6835db-65ea-4e0e-a91b-58d1fdcd4ef1","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Cerezo, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:abeb77863365701bf25c8e63d5d42d6083629b930c179d722e63281d6c131dea","observation_id":"b00071f0-b053-4784-bd89-f4feb44a1a18","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:ace589d07d14f88558167f3f4bfe35e5867c3734915d0149ce77e5db3f1f2751","observation_id":"fbf7f55c-b53a-46aa-b601-51930b468ed3","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Hubregtsen, J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:32bfbefc00f842c68d729b01232149831bbf595125f9047cb92b5f42c8317617","observation_id":"eff9a493-e185-47a1-8dc4-4f64e2a6c58b","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Goodfellow, Y","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:88f2ed0d3e94f7c06dd05c361ca5d097edcb7d9f69e320bd2dc0e409fbf29eb4","observation_id":"97fc0a72-ad78-41cb-9685-f7ef7e67f08c","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:5f65e0467089ba75fc6a6a7f4decca2de608f0e72f6eed59beca6c75f8d5c41c","observation_id":"cab7ac5a-8898-419e-b43e-4e07bac9351e","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Holmes, K","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:8cf7fb266ac499c0c1542814ed059053dcc49a0c5ef29c4198128b08b194013e","observation_id":"a231ccd2-8968-483c-9fdb-53b6c78d2ff4","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Ortiz Marrero, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:27891623953615482d45d6198c51277634039da982bab3cda0b0c33a98cb4a00","observation_id":"d0fc7921-f47a-47d1-9cc4-8a49f88468eb","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Heyraud, Z","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:c7ad99fbc785c34df5ee9525c5bf804909b642410fe2114897ea3496ea8aec71","observation_id":"87ebeac2-4a82-4e12-93e8-cbd5cac53098","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Grant, L","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:e3e27dfc50657f4d7f8b3f4ea6c25811f0a72af5617cdda84789de7855e77b66","observation_id":"810f6646-0d37-45fe-b25e-5e2f7b1e2674","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:25b5c98c0ffa59b7987cee8f3f62869dc0223d23929a07b83bad9a7add16183a","observation_id":"7eea1ab4-8cd2-4ffc-ae9a-83fcb726b417","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:b95ef38205257e96d5529885864e0f06ac289e319e42e299a9130736f7aa796b","observation_id":"5e7349cf-a08e-4f5c-a911-f4f22c0cc0f0","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Ragone, B","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:740dcda90cb17221ca6e6306783d4237efcdfb6f08c256366a2aaa33f151cfe4","observation_id":"29a81ed0-632b-4855-af9c-4f029d384b44","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Thanasilp, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:ba1936c7464f3583c7659afff3aa153633d149bbc5d09b14ae2246dcfe013165","observation_id":"07582cbe-5b45-4762-8add-89f8f51ccf90","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Schuld, R","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:38476c3a0c43994f809918997902d2feb4dc0c8107d594dd162a3569437724f3","observation_id":"a87d75f4-da6f-4783-8014-abf6d00e436e","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Berberich, D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:cad31457ef33d6802a7a277ec2f3c80223e504414be813226f10822ed1ad9710","observation_id":"928e0baf-6a57-4d9a-b042-4b49b79c9302","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Kashif, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:aee846d0e425b840daf5a46304db51a3075b4dce20dee8a6148fd0599c300aca","observation_id":"4095d82f-6a01-4a9c-bc5f-08dc73eb268e","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Volkoff, P","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:2cb8a94be7e039957f370d1fa8a14026d9ec9b4dd7a74e930b3752be10fc171d","observation_id":"50ca03a9-5128-4f7e-881f-7f0241ffda4d","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Sharma, M","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:8f1c8666398adc70c044b525b771237ec727a4d7a72f864657a25f5bf11148e4","observation_id":"5fd9c330-21cc-461d-b19b-e13724801d64","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:fc90eaaa33bb96bc5c431205e8cd1094e3f1e926387e5a58c27de76cc732538d","observation_id":"9226a612-7b79-437d-81f0-7c0490d66da0","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Mahendra, V","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:33c449885f6c93e1bc1195220a74d165d178055552d0f969457ddb55d937fe41","observation_id":"5802ab3e-876a-4183-944e-ee1f6eb3058f","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Huggins, P","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:d5bdf3879cdca9c5e76af1256fd24fbbb750550ddfae32171401d9ae1da23333","observation_id":"05163630-dc35-4764-8608-1bb20a4b98d4","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Rieser, F","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:02bb57dfb3801214b6838c51c973b703dab5d5b5c96d73b3103755bdea4a9f52","observation_id":"99246caf-a393-4483-b465-a92c81780409","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:d4feefb74a25929e63fa9030c78b8a7a09a982504bfa9b842f251dd0d030ee8d","observation_id":"4ed11a31-c738-4263-a207-3467c3a418b1","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.11020","last_updated":"2021-04-17T07:29:07Z","snapshot_observed_at":"2026-08-16T18:49:49.818172Z","submitted_at":"2021-01-26T19:00:04Z","title":"Supervised quantum machine learning models are kernel methods","version":2},"cited_work":{"arxiv_id":"2101.11020","doi":"10.48550/arxiv.2101.11020","metadata_source":"pith","pith_arxiv_id":"2101.11020","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Schuld (2021), arXiv:2101.11020 [quant-ph]","venue":"quant-ph","work_id":"acdc3f96-5ba5-4738-bbe4-c726dc8c44ba","year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"cited_paper":"/paper/2101.11020","citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:d1146c8a9dff9845ed13365314bd9f19bbfa2e68c573be01d7957fce7e35c537","observation_id":"fb5ce4c7-ca40-4005-9907-ec21ee3d0c5c","resolution":{"observed_at":"2026-07-04T09:09:43.027792Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-26T10:30:01.477176Z","title":"Huang, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:ff8813079eed1c5a77b2b8375e55e2ad1e9f7a373ae13a0026765c08dd034478","observation_id":"2eda01e8-c47a-4740-8018-d6b67a320130","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2506.20355","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T09:09:43.028756Z","title":"Lozano-Cruz, A","venue":null,"work_id":"9fc33303-38e9-4410-8f01-189a5ef1b621","year":2025},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:66f8183241ddccdc11ea23e5342113fcae13d94a5424f4ebd97b9cf6204152af","observation_id":"fb6f0255-1a3e-4b23-9802-3376b6de8390","resolution":{"observed_at":"2026-07-04T09:09:43.029999Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:dbdd4c8ed7f705f92f0a83687f02151c182409a680d2895ba232676bdc8f584f","observation_id":"2c453797-6a1a-4ef8-96c7-fb5a21276ac8","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Pesah, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:58e9616e10cc49f9dd17a8530e36b67a1eebe8f8132e4023e6b3763269afa901","observation_id":"cd3a34d8-f4c4-421d-ba61-f95cf6b7b165","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:2701f59e58ceda0560779479000d4c9883238238ff150c294938d8dc6c4a6dbe","observation_id":"0ac47f74-e79f-45f0-acc0-c1ac6d602b67","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Mehrtash, W","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:77a8b1bfc8bd02e81f962491437bd1ff812312156cb4931ba1ffed3ffd099905","observation_id":"1d8e8908-294b-4ddc-9263-7bf8e8a2c4da","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:65ce2e64ce2ce7970ebdf2515eb7b51477332b8a25a13ad54ea065f4cce476e0","observation_id":"746c2944-3a90-44c9-b17f-f7a10929551e","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"10.18293/seke2023-177","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering","work_id":"fb3f776a-aa9e-491a-aa53-5bad8d466aac","year":2023},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:07876e522107b6bdc4212823de45da676415da74be66795b3d13bddf2554ded9","observation_id":"ea3c83f9-ba50-4270-9793-cce793a13af6","resolution":{"observed_at":"2026-06-26T10:39:19.065065Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-26T10:30:01.477176Z","title":"Jaderberg, L","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:1a290a6e6827888b7f5c47286d1efee6abc0d23d4cb1762b59e1fa4f775ff33b","observation_id":"e0d7fd5b-1ac6-48ff-b6d4-070b80698ec1","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:92d02e18c77ca63ca4b9939be0a46c4b9ea2122efa0150b12120a5414ac8f256","observation_id":"ded5d8da-f699-4afe-88ed-e56b8025bc12","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Truckenbrodt, M","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:1105be3e4c068a6c295cc9be7c40d2b9993fc7b0f9f1361de476b141403c7fa5","observation_id":"6d7da2bf-70a2-4548-b239-5fdf84038090","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Truckenbrodt, C","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:8aa3ce5333d5dcbd40b1fa68cdbe7b11c0e59daa436c200ae3c6db9026f25b75","observation_id":"43891326-065b-4618-92b1-1f63434288bf","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:85fd8e2f9e97961d4b71961ec0fe994a2d8d9cbeee3a1c3e18f926f2b3fbec1b","observation_id":"e68098b5-8196-4ec7-a17f-5c64674157c4","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:951dde6dd4fec83a93d8821416f2f9cbb30984fecd8b36bdb63ba48ddda73a13","observation_id":"e2952e68-5932-4c1b-9516-af656a222f65","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:96b6c26af86f270d7e0ffa131e70f5993e36cafa2e541c519286b84b3420af44","observation_id":"7c4f3eed-631d-4548-8ac6-d52d0c4e2d71","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Pérez-Salinas, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:c67110b27671201c0bda222c22181400856fbdbc8e4c1e6d59f86a381794c8c7","observation_id":"879fa1e3-a853-4088-8452-dfb1f93c2f3d","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Pascanu, T","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:8896ce90fadc315c2856ea8074c9081a5fd690ea6f02b0d09ce42ba3c47a801d","observation_id":"fd52f6fc-89d5-4c82-aed2-efef45aa9d36","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","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":"2026-06-26T10:30:01.477176Z","title":"Skolik, J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-26T10:30:01.477176Z"},"links":{"citing_paper":"/paper/2606.22551"},"observation_digest":"sha256:952a6d0d4fc397b5f6f2044ef3288c80b3b0f5febd3cde2b42e7ad3337672c1b","observation_id":"c41fa075-7efa-4192-99e1-086d55cda5fe","resolution":{"observed_at":"2026-06-26T10:30:01.477176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.22551","last_updated":"2026-06-21T15:10:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T08:15:57.053473Z","submitted_at":"2026-06-21T15:10:51Z","title":"Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":43,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":46},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2606.22551."}